We measured circRNA abundance across different human samples and tissues. Our results highlight the potential of this RNA class to act as a specific diagnostic marker in blood and serum, by detection of circRNAs from genes exclusively expressed in the brain. The powerful and scalable tools we present will enable studies of circRNA function and facilitate screening of circRNA as diagnostic biomarkers.Circular RNAs (circRNAs) were recently discovered as a class of widely expressed noncoding RNA and have been implicated in regulation of gene expression. However, the function of the majority of circRNAs remains unknown. Studies of circRNAs have been hampered by a lack of essential approaches for detection, quantification and visualization. We therefore developed a target-enrichment sequencing method suitable for screening of circRNAs and their linear counterparts in large number of samples. We also applied padlock probes and  It is believed that most circRNAs are produced from precursor mRNA through back-splicing of exons, where a downstream (3′) splice donor site is spliced together with an upstream (5′) splice acceptor site. This process mainly occurs at annotated exon boundaries, forming covalently closed single-stranded circular transcripts4. Although circRNAs were originally reported more than two decades ago, only few examples were found, and circRNAs were mostly considered to be splicing noise6. However, with the recent advances in RNA sequencing technologies and newly developed bioinformatics approaches, several reports revealed that circRNA expression is widespread in human tissues and appears to be a general feature in all eukaryotes11. It is estimated that as much as 10% of expressed genes in examined cells and tissues produce circRNAs, often times in a cell or tissue and developmental-stage specific manner14. Even though circRNA are often expressed at low levels, there are at least 100 genes that express circRNAs as the predominant transcript isoform10.Circular RNAs (circRNAs) were recently discovered as a novel class of noncoding RNACDR1as and SRY circRNAs16. Moreover, emerging evidence suggests potential roles for circRNAs in important physiological functions, for example, in the nervous system20. In addition to their role as microRNA sponges, circRNAs are believed to regulate RNA polymerase II transcription, and compete with the canonical splicing machinery21. Recently, several reports provided in vivo and in vitro evidence in multiple metazoan species, including human, that a subset of circRNAs are translated into proteins by a cap-independent translation, thereby adding a new layer to the complexity of eukaryotic gene and protein expression24.Although the function of the vast majority of circRNAs is still unknown, several studies demonstrated that circRNAs could function as competing endogenous RNAs or microRNA sponges by sequestering microRNA and influencing their ability to act on their target genes. The best-studied examples are 25, and have been demonstrated to be involved in the function of the brain25. In addition, circRNAs have been shown to accumulate with age in Drosophila9 and mouse brains26. Recent data suggest that circRNAs may play crucial roles in the development of various diseases, such as neurological disorders and various cancers mainly through differential expression analysis between healthy and disease tissues29. CircRNAs are highly stable as compared to linear RNA transcripts due to their closed structure that prevents their cleavage and degradation by exonucleases, and can be detected in saliva, exosomes and blood33. They are therefore considered as a promising new class of biomarkers34.CircRNAs are extraordinarily enriched in the mammalian brain, particularly in the synapses, and are upregulated during neuronal differentiation36, yet the efficiency of circRNA calls and reproducibility across different approaches remain relatively low. RNAse R treatment, a process that digests all linear RNAs but preserves circRNAs, followed by PCR or RNA-seq has been instrumental for validating circRNAs identified from total RNA-seq. However, this strategy hampers the ability to quantify the abundance of circRNAs in relation to their cognate linear RNA. Although some studies attempted to define the localization of circRNAs in cells and tissues using available in situ methods such as FISH37, it is still challenging to define the localization of circRNAs at the subcellular level. This is mainly due to their generally low expression levels compared to mRNA, relatively short sequence and the sequence similarity with their cognate RNA. Therefore, developing tools to study subcellular localization of circRNAs would provide novel insights to their function. For instance, if circRNAs are enriched in a particular subcellular compartment different from their cognate RNA, the spatial discrepancy between the two molecules could indicate independent functions and regulatory pathways.Although thousands of circRNAs have been identified using total RNA-seq data, the detection and characterization of circRNAs remain challenging. CircRNAs represent a small fraction of the total RNA pool and the majority of circRNAs are expressed at low levels. In addition, circRNAs are made up of the same sequence as their linear counterparts and can only be differentiated by the sequence reads spanning the back-splice junctions. Such back-splice junction reads are rare in RNA-seq data, making up less than 1/1000 reads in a typical sequencing library. A plethora of bioinformatics approaches have been developed to predict circRNAs and measure their expression from RNA-seq datain situ, and to determine their subcellular localization in cells and tissues.Although circRNAs are currently under intensive investigation, there is a lack of approaches to study their function, including high-throughput methods to quantify their abundance in relation to the expression of their cognate linear mRNA. Also there is a lack of methods to define the spatio-temporal expression of circRNAs across tissues, within tissues, and at the subcellular level. To address these methodological issues, we developed a robust target-enrichment sequencing approach to simultaneously measure the abundance of circRNA expression and their cognate linear transcripts across many samples. Furthermore, we adapted the padlock probes to visualize circRNA and their correlated linear transcript in situ sequencing to define the spatio-temporal expression of circRNAs at the subcellular level . A schematic representation of the target enrichment approach for the circRNA and linear RNA is described in Fig. In order to identify targets to include in the AmpliSeq panel enriching for selected circular and linear RNAs, we first performed standard RNA-seq on total RNA extracted from human fetal frontal cortex, fetal liver, fetal heart, and SHSY-5Y cells. To identify circRNAs expressed in these tissues, we then called sequence reads mapping to back-splice junctions from known exon junctions using the circRNA_finder pipeline described in2 = 0.963). We also found the circRNA to linear RNA ratio to be highly correlated between these replicates, highlighting the reproducibility of the circRNA AmpliSeq panel  with no or low coverage in our total RNA-seq and in previously published data11, but that were detected at high levels with the circRNA panel , which had coverage in both total RNA-seq and in the circRNA panel. We used qrtPCR to measure their actual expression levels in the original RNA sample, and found the qrtPCR results to be in agreement and correlate better with the circRNA panel data compared to RNA-seq . To obtain an overview of circRNA expression patterns in the different tissues, we performed hierarchical clustering of the expression counts obtained from the circRNA panel for all the samples Fig. . In agreWe then tested if we are able to enrich for and detect circRNA from specimens with low RNA quality such as formalin-fixed paraffin embedded (FFPE) tissues or low RNA content such as in serum. Total RNA was first extracted from 6 FFPE tissues and 1 serum sample (Supplementary Data file) and then sequenced using the circRNA panel. In this experiment, we also performed RNA sequencing using the circRNA panel following RNAse R treatment from one of the adult frontal cortex (Brain 5) as a control for the panel specificity. The relative circRNA expression (circRNA reads/(LinOut reads + circRNA reads)) in each sample is shown in Fig. DNAJC6 is of particular interest, since only the circRNA isoform is expressed in blood and serum. DNAJC6 encodes the neuronal protein Auxilin, and it is known have a role in clathrin-mediated endocytosis and to be implicated in the pathogenesis of Parkinson disease and intellectual disability39. Previous data from The Genotype-Tissue Expression (GTEx) project shows that DNAJC6 is exclusively expressed in the brain41. The presence of circRNA in serum reflects the high stability of circRNA as compared to linear RNA transcripts. DNAJC6 represent an example of circRNAs that could be detected in blood and serum. Including larger number of serum samples and thorough validation experiments could pave the way to unravel the possibility of using circRNA as biomarkers for a particular biological or pathological condition.Strikingly, we find several circRNAs to be highly expressed in the serum sample. Three examples of such circRNAs are shown in Fig. in situ, we adapted a protocol for padlock probes in combination with rolling circle amplification (RCA)42. Initially, we tested this method in human fixed cells. We performed multiplex detection of three circRNAs  using padlock probes targeting sequences surrounding the back-splice junctions followed by RCA , which was sequenced using the circRNA panel. In this experiment, we designed padlock probes targeting 20 bp sequences overlapping and surrounding the backsplice junction of 8 circRNA targets   to distinguish different targets43  Fig. . We alsos43 Fig. . We usedeus Fig. , while tasm Fig. . The cirTM technology to enrich for circRNAs known to be expressed in human brain and their corresponding mRNA transcripts. Using this panel, we produced an average of 8 million reads/sample which resulted in a coverage over circRNAs several orders of magnitude higher than RNA-seq with 40 million reads/sample. In the second approach, we adapted the padlock technology in combination with RCA and in situ sequencing to detect and identify the spatio-temporal expression of circRNAs at the subcellular level in cells and fresh/frozen tissue sections.Although there is an increasing interest in unraveling the function of circRNAs and understanding their role in human disease, there is a lack of efficient and reliable tools to study their basic characteristics such as their expression levels-especially in relation to their cognate linear mRNA, and their cellular localization. In this study, we developed two approaches to allow an improved understanding of the nature and function of circRNAs. In the first approach, we aimed to develop an efficient and cost effective tool to characterize the expression patterns of circRNAs in different tissues and cells. To achieve this, we designed a pilot target-enrichment sequencing panel using the Ion AmpliSeqThe ability to quantify the abundance of circRNAs in various tissues and to identify how their expression is correlated with their corresponding mRNA is crucial to understand the nature and the function of circRNAs. Using the circRNA panel, we successfully enrich for the circRNAs and their corresponding mRNAs from various input materials. We find the circRNA panel is more efficient at detecting circRNAs than total RNA-seq. Using regular coverage total RNA-seq data (up to 100 million reads) the quantification of circRNA together with their linear counterparts is limited to the most abundantly expressed circles. The target enrichment approach leads to significantly higher sequence coverage over circRNA and mRNA targets as compared to total RNA-seq. This enabled the detection of circRNAs with very low (or no) read coverage in RNA-seq data, leading to improved and more sensitive estimation of circRNA expression, especially for circRNA expressed at low levels or in specimens that contain a low amount of RNA. Several of these targets were detected in RNAse R treated samples, indicating that lack of detection in total RNA was not due to inability to align the sequence reads (data not shown). The target enrichment allowed us to screen multiple samples (n = 19) using one P1 Ion Proton sequencing chip, producing approximately 80 million reads/P1 chip. On the other hand, the total RNA-seq for the samples in our screening was run with 2 samples/chip, producing approximately 40 million reads/sample, highlighting the cost effectiveness of circRNA panel over total RNA-seq. We do note that two linear targets in the panel yielded low or no signal Fig. , indicatUsing the circRNA panel to analyze circRNA and mRNA expression in various tissues we found, in accordance with previous reports, that circRNAs show distinct expression patterns between different tissues. Moreover, the circRNA to mRNA expression ratio exhibited different patterns among the different samples. Detection of circRNA in FFPE samples was particularly inefficient. Although we could still detect a small number of highly expressed circRNAs in FFPE samples , there was a clear reduction of circRNA as compared to their linear counterparts. These results indicate that circRNAs are not present in extracted total RNA from FFPE, either due to degradation or the general challenges associated with RNA purification from FFPE samples. Hence, it is important to note that standard extraction of RNA from FFPE samples will lead to erroneous measures of circRNA in relation to linear RNA, irrespective of how the circRNA screening is performed.DNAJC6, a neuronal protein where the mRNA, in our data and in previous studies, is expressed exclusively in the brain40. Here we show for the first time that its circRNA isoform is expressed at high levels in blood, and also detectable in serum. These results support a possible role of circRNAs as stable tissue-specific biomarkers accessible in serum. The findings also highlight the efficiency and feasibility of our circRNA panel to facilitate the analysis of a large number of such samples to further evaluate and test the hypothesis that circRNA could represent potential biomarkers for diagnosis and treatment.We found several examples where the circRNA isoform is highly expressed, while the linear mRNA isoform is not expressed or expressed at very low levels. One example is 19. In this study, we demonstrate that the padlock approach followed by RCA can be used to visualize circRNA and their corresponding mRNA in cells and tissue sections. The abundance of circRNA obtained from the padlock experiment from the SHSY-5Y cell line correlated well with the results from the circRNA panel. However, it is clear that the method has limited sensitivity as only a few targets per cell are detected. We believe that further optimization is possible to increase the sensitivity for detecting circRNAs expressed at moderate and low levels. When combined with in situ sequencing (sequencing-by-ligation), this approach allowed multiplex detection of circRNA and their corresponding mRNA in human brain at the subcellular level. These results highlight the possibility of using our in situ sequencing strategy to define the subcellular localization of circRNAs and how they co-localize with their corresponding mRNA isoforms. To our knowledge, this strategy is the first of its kind to allow a multiplex detection and subcellular localization data of circRNAs and their corresponding mRNAs in situ at the subcellular level. In addition, it offers an opportunity to resolve the spatio-temporal expression of circRNA in complex tissues such as the brain.Prior to this study, defining the cellular localization of circRNA and their cognate linear mRNA in the same experiment using common approaches, such as FISH, was hampered due to the sequence similarity between circRNA and their corresponding mRNA and because circRNAs are typically composed of short sequences. Similar to other non-coding RNAs, defining the subcellular localization of circRNA could provide valuable insights into their function. For instance, cytoplasmic circRNAs are suggested to regulate gene expression through interfering with miRNA pathways. While nuclear circRNAs, which mostly represent intron-containing circRNAs, are suggested to interact with the splicing machinery and promote transcriptionin vivo. This approach can also be used to define the spatial context of circRNA expression profiles between different cell populations within the same tissue. In conclusion, our approaches represent powerful a toolbox for deep profiling and characterization of circRNA and for unlocking their roles as potential diagnostic biomarkers.Both the circRNA panel and the circRNA padlock visualization approaches presented here will pave the way for an improved hypothesis-driven targeted analysis of circRNA functions in cells and tissues. The enrichment of the circRNA panel allows a cost-effective approach to perform surveys of circRNA expression profiles in large sample sets. The circRNA panel setup offers the possibility to increase the number of targets and to perform custom design for the circRNAs of interest. On the other hand, the padlock strategy offers, for the first time, an experimental approach to perform multiplex detection of circRNA cellular localization Informed consent was obtained from subject or their guardians for the collection of blood and tissue samples. Sample collection, methods and experimental protocols were carried out in accordance with local guidelines and regulations. The use of the samples and experimental protocols were approved by the Uppsala Ethical Review Board (dnr 2010/236 for blood samples and 2012/082 for tissue samples).Total RNA from the fetal frontal lobe, fetal liver and fetal heart was purchased from Capital Biosciences. Total RNA from fetal tissues and SHSY-5Y cells was extracted using RiboPureTM RNA purification kit (Ambion) according to the manufacturer instructions.2, and 1.0 μl (20 U/μl) RNase R enzyme (Epicentre®). The mix was then incubated at 37 C° for 10 minutes. RNase R was then inactivated by adding 1:1 volume of H2O. All samples are listed in Supplementary Materials.Adult brain samples  were obtained from the Harvard Brain Tissue Resource. Total RNA was extracted from 5 adult frontal lobe samples. To obtain total RNA, tissue pieces were first fixed in OCT and two sections were used for total RNA purification. Total RNA purification for these samples and 6 human blood samples was performed using RiboPureTM RNA purification kit (Ambion) according to the manufacturer instructions. Total RNA from fetal and adult liver, fetal and adult heart, and placenta were purchased from Clonetech. Total RNA from the FFPE tissues was extracted using High Pure FFPET RNA isolation kit (Roche) according to the manufacturer instructions. RNA from the serum was purified using Plasma/Serum Circulating and Exosomal RNA purification Kit (Norgen). RNase R treatment of total RNA was performed after ribosomal RNA depletion. Briefly, 2 μg RNA was added to a mixture of 1x RNase R buffer (Epicentre®), 1 mM MgClRNA was treated with the Ribo-Zero Magnetic Gold Kit for human/mouse/Rat (Epicentre®) to remove ribosomal RNA, and purified using Agencourt RNAClean XP Kit (Beckman Coulter). The rRNA depleted RNA was then treated with RNaseIII according to Ion Total RNA-Seq protocol v2 and purified with Magnetic Bead Cleanup Module (Thermo Fisher). Sequencing libraries were prepared using the Ion Total RNA-Seq Kit for the AB Library Builder™ System and quantified using the Fragment analyzer . The quantified libraries were pooled followed by emulsion PCR on the Ion OneTouch 2 system, and sequenced on the Ion Proton System.9 was used. In short, reads were aligned using STAR48. Reads were aligned using the following parameters to identify chimeric transcripts: ––chimSegmentMin 20 ––chimScoreMin1 ––alignIntronMax 1000000 ––outFilterMismatchNmax 4 ––alignTranscriptsPerReadNmax 10000 ––outFilterMultimapNmax 2. The candidate chimeric junction reads were then filtered to only include cases where the read spanned a junction with the splice acceptor on the same chromosome and strand as the splice donor, at most 100,000 bp upstream. In the subsequent analysis only circular junctions matching GT-AG splice sites we included. All the scripts are available at https://github.com/orzechoj/circRNA_finder.git and further details are provided in9.To identify circular RNAs in total RNA data, a computational pipeline adapted fromLoci to be included in the circRNA Ampliseq panel were selected using the following criteria: First, circRNAs with high expression in total RNA-seq data from brain were chosen, so that each circRNA originated from a different mRNA transcript (i.e. a backs-plice site between annotated exons). We required the length of each circRNA (assuming introns are spliced out) was at least 300 nt. Controls measuring expression of linear RNA isoforms (LinOut) were placed on the same transcript 300 nt upstream from the circRNAs if possible, otherwise 300 nt downstream. As negative controls, possible back splice sites with no support from the RNA-seq data were sampled, from transcripts expressed in the brain. The only exception to this scheme was CDR1as, which was added manually to the panel and doesn’t have a control for linear mRNA, as it does not originate from a known mRNA transcript. Design of the circRNA panel was based on the hg19 genome and transcript models from the UCSC genome browser.RNA was reverse transcribed to cDNA, and the acquired cDNA was amplified using the custom made Ion AmpliSeq™ circRNA panel. The sequencing libraries were prepared using the Ion AmpliSeq™ Library Kit 2.0 (Thermo Fisher). Sequencing libraries were purified using the Agencourt AMPure XP reagent (Beckman Coulter) and amplified. The amplicons were quantified using the Fragment Analyzer instrument . Samples were then pooled, followed by emulsion PCR using the Ion Chef System, and sequenced on the Ion Proton System.Analysis data from the circRNA panel was performed using the Coverage Analysis plugin in the Torrent Suite Software. In each of the samples, this analysis counts the number of reads originating for the different targets in the panel . To make the circRNA panel data comparable between samples, a normalization was performed by dividing each read count with the total number of reads generated for the sample.Starting with 1 μg of total, cytoplasmic, nuclear RNA, cDNA was synthesized using the RevertAid First strand cDNA synthesis kit (Fermentas) with random hexamers according to the manufacturer’s recommendations. 1 μl of the resulting cDNA was used for qrtPCR to measure the relative circRNA/linear RNA ratio in each sample. The qrtPCR was performed with Stratagene Mx3000P in 96-well plates. The reactions were carried out with an initial denaturation at 95 °C for 10 min followed by 40 cycles of denaturation at 95 °C for 15 s, primer annealing at 60 °C for 30 s and extension at 72 °C for 30 s. The qrtPCR contained 12.5 ng single stranded cDNA, 0.4 μM for each primer and 12.5 μl Maxima SYBR Green/ROX qPCR Master Mix (Fermentas) in 25 μl reactions. All samples were amplified in triplicate and the mean values were used to calculate the expression level of each target. The circRNA RNA expression level were determined using the ΔΔCT method. CT values were first normalized to the levels of B-actin. Then, expression values for circRNA expression after normalization were calculated as a ratio between the circRNA expression values and the expression values of the negative control. Raw data were analyzed using MxPro Mx3000P software (Stratagene).SHSY-5Y cells were cultured in 1:1 mixture of Ham’s F12 and DMEM (Gibco) medium (Gibco) with L-glutamine and phenol red, supplemented with 2 mM L-glutamine (Sigma), 10% FBS (Sigma) and 1 × PEST (Sigma). All cell lines were incubated at 37 °C, 5% CO2. To prepare cell slides for the padlock experiment, cells with >80% confluence were treated with 0.25% (w/v) trypsin-EDTA (Sigma) and resuspended in culturing medium. Thereafter, cells were seeded on Superfrost Plus slides (ThermoFisher) and placed in a 150 mm × 25 mm Petri dish (Corning), and culturing medium was added to a final volume of 25 ml. Cells were then incubated for 12 h at 37 °C, 5% CO2. Cell fixation was then performed in 3.7% (w/v) paraformaldehyde (Sigma) in DEPC-treated PBS for 20 min at RT. Following the fixation, cell slides were washed twice in DEPC-treated PBS and dehydrated in an ethanol series of 70%, 85% and 100% for 5 min each. Then cell slides were kept at −80 °C until use. Tissue sections (4 μm) from human fresh/frozen frontal lobe  were prepared with microtome and mounted on a Superfrost Plus slides (ThermoFisher). Tissue samples were stored at −80 °C until fixation. Fixation was performed in 3.7% (w/v) paraformaldehyde (Sigma) in DEPC-treated PBS with 0.05% Tween-20 (Sigma) (DEPC-PBS-T) for 45 min at RT and two washes in DEPC-PBS-T. The tissue sections were then permeabilized in 2 mg/ml pepsin (Sigma) in 0.1 M HCl at 37 °C for 2 min. After two washings in DEPC-treated PBS and dehydration in an ethanol series, as in the cell line, tissue sections on slides were then covered with Secure-Seal hybridization chambers (Invitrogen).2, 0.5 mM NAD and 0.01% Triton X-100), 100 nM of each padlock probe, 50 μM dNTPs, 0.5 U/μl Ampligase, 0.4 U/μl RNase H (Fermentas), 50 mM KCl and 20% formamide was added into the sample reaction chambers on slides. The slide was incubated at 37 °C for 30 min, and 45 °C for 45 min, and then washed twice with 1 × DEPC-PBS-T. For RCA, the reaction chamber was incubated with a RCA mix (1 U/μl phi29 polymerase (Fermentas), 1 × phi29 polymerase buffer, 0.25 mM dNTPs, 0.2 μg/μl BSA and 5% glycerol in DEPC-H2O) for 2.5 h washed twice in DEPC-PBS-T. For tissue slide, RCA was carried out overnight.Cells and tissue samples were first rinsed with DEPC-PBS-T. A reversed transcription mix, containing 5 μM of unmodified random decamers, 0.2 μg/μl BSA (NEB) 500 μM dNTPs (Fermentas), 20 U/μl of TranscriptMe reverse transcriptase (Gdansk), and 1 U/μl RiboLock RNase Inhibitor (Fermentas) in the TranscriptMe reaction buffer, was applied to the slides. The incubation was carried out for 3 h at 37 °C for the cell line and overnight for the tissue sections. Slides were washed twice with DEPC-PBS-T, and followed by a post-fixation step in 3.7% (w/v) paraformaldehyde in DEPC-PBS for 10 min (or 30 min for tissues) at room temperature. After post-fixation, the samples were washed twice in DEPC-PBS-T. Following the reverse transcription, RNA degradation, hybridization and ligation of padlock probe on synthesized cDNA were performed. A mix contained 1 × Ampligase buffer , 0.2 μg/μl BSA, 0.02 U/μl UNG (Fermentas) for 30 min at 37 °C. Slides were washed twice with DEPC-PBS-T, and then washed three times with 65% formamide for 60 s each. In sequencing-by-ligation chemistry, anchor primers and interrogation probes were hybridized separately before the ligation. Briefly, a hybridization mix containing 500 nM of anchor primers in 2 × SSC and 20% formamide were applied to the sample and incubated at RT for 30 min, and followed by DEPC-PBS-T wash twice. A ligation mix containing each interrogation probe, 100 ng/ml of DAPI, 1 mM ATP (Fermentas), 1 × T4 ligase buffer (Fermentas), and 0.1 U/μl of T4 ligase (Fermentas), was added to the samples and incubated for 30 min at RT. Following 3x DEPC-PBS-T wash, the slides were mounted in SlowFade Gold Antifade Mountant (ThermoFisher).An ethanol wash series were firstly used to remove the mounting medium on tissue sections. As previous study43, a stack of images at different focal depths were captured and merged to a single image, and followed by a automatically stitching in the Zeiss AxioVision software. The fully automated sequence decoding was performed similar as previous study43. In short, cell nuclei were separated based on shape descriptors. Definition of cell cytoplasm uses nucleus as a seed and depends on sufficient cytoplasmic auto-fluorescence. The image of the general stain was enhanced by a top-hat filter, and RCPs were separated by watershed segmentation. The images were aligned, and fluorescence intensity from each of the signals representing A, C, T and G were extracted. The optimal transformation between a merged image of all signals (A+C+T+G) and the general stain was determined based on intensity49. Analysis were performed with CellProfiler 50 calling ImageJ plugins from Fiji for image registration. All intensity information was decoded using a script written in Matlab. Briefly, RCP was assigned for the base with the highest intensity for each RCP in all hybridization steps. A quality score was extracted from each base, and the quality of a transcript was defined as the lowest quality of all the bases in the transcript. The quality score ranges from 0.25  to 1 . The frequency of each sequence was extracted after a typical quality threshold of 0.4–0.55 was set.Images were acquired using an AxioplanII epifluorescence microscope (20 × objective). Exposure times for all the experiments are listed in Supplementary Materials. After imaging, the slides were prepared for the next three sequencing cycle by UNG treatment buffer as described above followed by repeating the hybridization, ligation and imaging processes. The image analysis was performed as described inSupplementary FiguresSupplementary Dataset 1


Plant molecular pharming has emerged as a reliable platform for recombinant protein expression providing a safe and low-cost alternative to bacterial and mammalian cells-based systems. Simultaneously, plant viruses have evolved from pathogens to molecular tools for recombinant protein expression, chimaeric viral vaccine production, and lately, as nanoagents for drug delivery. This review summarizes the genesis of viral vectors and agroinfection, the development of non-enveloped viruses for various biotechnological applications, and the on-going research on enveloped plant viruses. Virology began in 1892 with D. Ivanovsky’s paper describing the retention of virulence in leaf sap extracted from Crimean tobacco with mosaic leaf disease . Even thTobamovirus genus with a positive sense, single-stranded genomic RNA (gRNA) with a 7-methylguanosine-5-triphosphate cap at the 5′ terminus  for the management of type 1 Gaucher’s disease produced in carrot cells by Protalix Biotherapeutics Inc.  into which the heterogenous sequence or gene of interest (GOI) was cloned. The first published virus-based vector was a gene-replacement model in which the GOI replaced the CP of brome mosaic virus (BMV)  . Howeverus (BMV) . Still tus (BMV) . A thirdus (BMV) . Other dus (BMV)  or at thus (BMV) . FiguresAgrobacterium delivered T-DNA  (Arabidopsis actin 2 (ACT2) as a promoter together with removal of cryptic thymine-rich intron sites plus selective introduction of introns   and TMV ®) . The mod introns , 2005. M introns .Similarly, a deconstructed virus strategy was developed for the bipartite RNA1/RNA2 CPMV. Early research was based on maintaining an unmodified RNA-1 while introducing the recombinant gene to the RNA-2 construct. Recombinant expression was achieved using this methodology with both full-length and defective versions of RNA-2 coding plasmids . SurprisLoxP recombination system , self-assembled structures devoid of any genomic material  and furt imaging , and ass imaging , as showNon-enveloped plant viruses have been engineered in three different ways: modification of the outer capsid , incorporation of heterologous cargo in the inner cavity, or particle integration into multivalent structures. Production of plant virus based VLPs as vaccines received the attention since the early 1990 . Plant REscherichia coli (E. coli or eukaryotic-based systems (The production of plant virus based VLP(s) were not limited to plants, and a variety of heterologous systems such as insect cells , yeast  or Eschehia coli  have bee systems , and has systems .E. coli (® [for hepatitis B virus (HBV)] (® [for human papillomavirus (HPV)] ] , and Cers (HPV)] , produceHPV)] ]  (in vitro association of antigenic peptides to synthesized phospholipid membranes) , there has been a growing trend toward the use of eVLPs as production platform . In thispeptide)  and breambranes) .Bunyavirales and Mononegavirales, of which the family Rhabdoviridae, and have a negative strand RNA genome (Influenza eVLPs have been produced from the co-expression of the two major antigenic envelope proteins hemagglutinin (HA) and neuraminidase (NA) plus matrix 1 (M1)  or from A genome . ConsideRhabdoviridae family contains an ecologically diverse group of viruses infecting hosts from a wide plethora of aquatic and terrestrial vertebrates and plants , Phosphoprotein (P), Matrix (M), Glycoprotein (G), and Large Polymerase (L) , in addi trailer   . The ribsequence  and the sequence . TranscrLater during the infection, the N-P complex inhibits transcription and switches the function of the L protein from that of transcription to replication . First, Initial attempts to generate rhabdoviruses from cDNA were based on mammalian rhabdoviruses. Research on VSV revealed that genomic RNA must be encapsidated with the viral N protein to be a functional template for RNA-dependent RNA polymerase . This diN. benthamiana cellulose-digested protoplasts were found to be suitable for studying sonchus yellow net virus (SYNV) replication , remains the prototype for reverse engineering and molecular adaptation of rhabdoviruses. Like all other rhabdoviruses it has the canonical N, P, M, G, and L genes in a negative sense single stranded RNA genome . VSV wasThe ability to rescue VSV without its inherent G protein (VSVΔG) gave the opportunity to generate safer non-propagating VSV based vaccines . Moreove1, such as Phase I/II clinical trials for VSV based Ebola virus vaccines (rVSV-ZEBOV)  .Nowadays VSV is commonly considered a very successful and widely used platform for heterologous expression of more complex and interesting pharmaceuticals and biomolecules, and for the generation of (pseudo) typed eVLPs for vaccine purposes. It is expected that the availability and exploitation of enveloped plant viruses as biological particles, in analogy to VSV, will give a boost to the exploitation of plant-based production platforms for more complex (glyco)proteins.Plant molecular pharming has materialized as a reliable and cost-effective platform for the production of pharmaceuticals, vaccines, and biobetter products with a number of plant-produced proteins starting to be commercialized . SimultaConsidering VSV as prototype, the inherent rhabdovirus characteristics of being enveloped, with defined genomic transcription units , genome AI wrote the first draft of the manuscript. AI, VO, and RK contributed to manuscript revision, and also read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The CRISPR system for prokaryotic adaptive immunity provides RNA-mediated protection from viruses and mobile genetic elements. When viral RNA transcripts are detected, type III systems adopt an activated state that licenses DNA interference and synthesis of cyclic oligoadenylate (cOA). cOA activates nucleases and transcription factors that orchestrate the antiviral response. We demonstrate that cOA synthesis is subject to tight temporal control, commencing on target RNA binding, and is deactivated rapidly as target RNA is cleaved and dissociates. Mismatches in the target RNA are well tolerated and still activate the cyclase domain, except when located close to the 3’ end of the target. Phosphorothioate modification reduces target RNA cleavage and stimulates cOA production. The ‘RNA shredding’ activity originally ascribed to type III systems may thus be a reflection of an exquisite mechanism for control of the Cas10 subunit, rather than a direct antiviral defence. The gene editing tool often known simply as CRISPR has become well known in recent years. Its potential applications are wide ranging, including uses in research, healthcare and agriculture. Yet, the CRISPR system originated in microbes where it helps to protect them from viral infections. Viruses infect by inserting their own genes into a host cell, and – almost like a pair of scissors – the CRISPR system can cut up the virus’s DNA to stop infections.CRISPR experts know the popular form of CRISPR as type II, but there are others. Type III CRISPR is less useful as a genetic tool but does also protect microbes from viruses. In addition to targeting DNA, type III CRISPR targets the related RNA molecules from viruses. When it encounters RNA from a virus, the type III CRISPR produces a small molecule called cyclic oligoadenylate (or cOA for short). The cOA molecule activates enzymes known as non-specific ribonucleases, which can destroy all the RNA in the cell. This defence is a less subtle than that provided type II CRISPR and can also damage the cell by destroying other RNA molecules that the microbes use to survive. As such, proper regulation is essential to prevent the type III system from unnecessarily killing the infected cell.Sulfolobus solfataricus, which is found in volcanic springs. This species has been a model for studies of the CRISPR system for many years, in part because its proteins are very stable which makes them easier to work with in the laboratory. The results show that the type III CRISPR makes cOA by combining four molecules of adenosine triphosphate (ATP) into a ring. CRISPR responds immediately to viral RNA in the cell. It also detaches from the RNA as soon as it starts to be destroyed. Rapid activation and silencing of the production cOA ensures that the CRISPR system is tightly controlled.Rouillon et al. studied the control of the type III CRISPR system from the heat-loving microbe These findings reveal that cOA production is tightly linked to the abundance of viral RNA, ensuring a proportional and timely response to infection. Using cOA amplifies the cell's response because it allows a single RNA molecule to activate a larger change.Type III CRISPR systems are widespread in nature, and a better understanding of them could improve the yield of products, like yoghurt, that depend on healthy bacteria; currently viruses cause a lot of economic damage in this industry. Further research in this area could also lead to new antibiotics that over-activate type III CRISPR to destroy bacterial cells. Escherichia coli, bind target DNA and then recruit the Cas3 enzyme via an interaction with the Cse1 subunit, resulting in Cas3 mediated DNA degradation (CRISPR systems provide adaptive immunity in prokaryotes against mobile genetic elements (MGE). DNA sequences derived from MGE are incorporated into the host genome separated by short direct repeats, forming the CRISPR locus. This is transcribed and processed to generate CRISPR RNAs (crRNA) that are loaded into effector complexes, programming them to detect and subsequently destroy the cognate MGE when the cell next encounters one. Class two effector complexes include Cas9, which has seen wide application in genome engineering. Class one complexes are more intricate, multisubunit systems that use a backbone built around Cas7 subunits which binds the crRNA. This backbone subunit is present in multiple copies and is conserved across the Class one effectors, which are further differentiated into type I, III and IV systems (reviewed in ). The tyradation . In contradation . The HD-radation . This isradation , providiradation  that allradation . Type IIradation .Most recently a third activity of type III effector complexes has been described. For some time, it was known that many type III system operons encode a protein such as Csm6 or Csx1 that is not part of the effector complex. These genes have been shown to be important for CRISPR-based immunity in vivo . The proSulfolobus solfataricus has been a model for studies of the CRISPR-Cas system for many years (reviewed in  In the discussion, we rephrased the relevant section to read:“These observations are consistent with the hypothesis that target RNA cleavage (and therefore dissociation) by type III systems functions as an “off switch” for the cyclase and HD nuclease active sites . This, rather than the protective effect of direct degradation of viral transcripts, may be the primary role of target RNA cleavage.”B1 and kB2 maybe problematic here. The authors need to provide a detailed description of how the rate constants were calculated. For example, B1 bands accumulated by cleavage at B1 position, but also diminish upon B2 cleavage.- Figure 6: The rate constant measurement for kThis is a good point. Although interconversion of B1 bands to B2 products is limited, we do see a slight decrease in B1 at later time points. The effect is limited, partly because not all the product is cleaved, but also seems to be a general property of these enzymes, as discussed in the Discussion in this section:“It is also consistent with the general observation for type III systems that target RNA cleavage products tend to accumulate under single turnover conditions rather than undergoing processive cleavage to the smallest possible products .”Nevertheless, we agree with the referee that there could be a danger of underestimating the rate of B1 cleavage to some extent due to limited conversion to B2 products. We have therefore determined the rate constants for B1 and B2 cleavage from the early time points and have added the following sentence to the Materials and methods:“For the transcript cleavage, a single experiment was quantified, but replicates were qualitatively similar. […] This appears to be a minor effect however (see Discussion).”We have added further detail and appropriate citations for the methods used to calculate all rate constants:“For kinetic analysis, cleaved and uncleaved species were quantified using the Bio-Formats plugin  of ImageJ  as distributed in the Fiji package  and fitted to a single exponential curve using Kaleidagraph (Synergy Software), as described previously .”As we switched to this software package, which is optimised for the accurate quantification of phosphorimages, we recalculated all rates. This has resulted in small changes to the absolute rates as noted, but no changes to the overall interpretation of the data and conclusions drawn.- How did the authors reach the conclusion that "cleavage up to the centre of the target RNA (site B2) appears sufficient to allow product dissociation"? What is the identity of RNA 5' end + trap DNA in Figure 8A? Can you provide a B2RNA + trap DNA marker?As the referee points out, an important control was missing. We have provided an additional control by synthesising a synthetic RNA oligonucleotide (Target RNA B2 product) and demonstrating that, when hybridised to the DNA trapper, the heteroduplex runs at the same place in the gel as the main trapped product. This is now shown in Figure 9—figure supplement 1 .


Flaviviridae family, including dengue virus (DENV) and yellow fever virus, cause serious disease in humans, whilst maternal infection with Zika virus (ZIKV) can induce microcephaly in newborns. Following infection, flaviviral RNA genomes are translated to produce the viral replication machinery but must then serve as a template for the transcription of new genomes. However, the ribosome and viral polymerase proceed in opposite directions along the RNA, risking collisions and abortive replication. Whilst generally linear, flavivirus genomes can adopt a circular conformation facilitated by long-range RNA–RNA interactions, shown to be essential for replication. Using an in vitro reconstitution approach, we demonstrate that circularization inhibits de novo translation initiation on ZIKV and DENV RNA, whilst the linear conformation is translation-competent. Our results provide a mechanism to clear the viral RNA of ribosomes in order to promote efficient replication and, therefore, define opposing roles for linear and circular conformations of the flavivirus genome.Members of the  Flavivirus genus, including dengue virus (DENV), West Nile virus (WNV) and yellow fever virus pose major risks to public health, particularly in South-East Asia, Africa and Latin America (Zika virus (ZIKV) is a mosquito-borne flavivirus which causes severe congenital birth defects, including neonatal microcephaly, and has been associated with autoimmune Guillain–Barré syndrome ,2. Other America ,4. Coupl America ,6.pol), which inherently progress in opposite directions, presenting the likelihood of a collision and abortive translation or replication products. This has led to the model of a ‘lifestyle switch’ whereby, following initial rounds of viral protein synthesis, translation of the viral RNA genome is inhibited freeing the RNA of ribosomes so that it can efficiently serve as a template for negative strand synthesis. Indeed, it has been demonstrated for poliovirus, a single-stranded positive-sense RNA picornavirus, that translation and replication are mutually exclusive, with cleavage of trans-acting factors required for translation initiation by a virally encoded protease serving as a switch to promote temporal viral genome usage  and classical swine fever virus (CSFV), contain an internal ribosome entry site (IRES) to facilitate translation initiation (iMet), eIF1 and eIF1A , stem loop B (SLB) and a short hairpin in the capsid-coding region (cHP)  Figure . Particuzed form ,30. Conszed form .in vitro NS5pol is more efficiently recruited to the linear form of the viral RNA  to produce a fragment that was inserted between BstBI and PmeI sites. pUC57-ZIKVmini was derived from pUC57-ZIKV-Fluc  and Δ3′ CS mutant DENV1 (KC692517.1) or DENV4 (FJ196850.1) minigenomes, or the first 359 nt of ZIKV PE243 (KX197192.1), with flanking XbaI and EcoRV sites (DENV1/4) or XbaI and HindIII sites (ZIKV5′utr+) and 5′ T7 promotor, were synthesized by IDT and cloned into pUC57. Rocaglamide was purchased from Sigma and hippuristanol was generously shared by Jerry Pelletier.Recombinant human eIF expression plasmids have been described previously: eIF1 and eIF1A , eIF4A a736–1115 , DHX29 (736–1115 . The eIFescribed . The claescribed . A pCC1Bescribed  were kinIKV-Fluc  by site-5′utr+and ZIKVmini), EcoRV (DENV1/4mini), AgeI , EcoRI (CSFV) or BstN1 (tRNAiMet). RNAs were transcribed with recombinant T7 polymerase (50 ng/μl) in buffer containing 40 mM HEPES pH 7.5, 32 mM MgOAc, 40 mM dithiothreitol (DTT), 2 mM Spermidine, 7.5 mM each nucleotide triphosphate (NTP) and 0.2 U/μl RNaseOUT (Invitrogen) for 2 h at 37°C. Scrambled and targeted antisense RNAs were similarly transcribed from T7 promoter-bearing reverse complement DNA oligos annealed to a T7 promoter-containing forward primer. Sequences are shown in fl and ZIKVNluc RNA were DNaseI treated and purified using TRI Reagent (Sigma) before ethanol precipitation. All other transcription reactions were treated with DNaseI and RNA was extracted with acidic phenol/chloroform and ethanol precipitated. Residual nucleotides were removed with Illustra MicroSpin G-50 columns . RNA was capped using the ScriptCap system (CellScript).Plasmids were linearized with HindIII  Star and purified by affinity chromatography on Ni-NTA agarose beads (Qiagen) and polished by FPLC on monoQ or monoS , as previously described  as previously described . Recombiescribed ,43. DHX2escribed .iMet, 4 pmol eIF2, 3 pmol eIF3, 10 pmol eIF4A, 5 pmol eIF4B, 5 pmol eIF4G736-1115, 2.5 pmol eIF4F, 10 pmol eIF1, 10 pmol eIF1A, 0.4 pmol DHX29) at 37°C for 10 min in a reaction volume of 20 μl containing 20 mM Tris pH 7.5, 100 mM KCl, 2.5 mM MgCl2, 2 mM DTT, 0.25 mM spermidine, 1.6 U/μl RNaseOUT (Invitrogen), 0.4 mM guanosine triphosphate (GTP) and 2 mM adenosine triphosphate. Assembled complexes were analysed by primer extension inhibition using 2.5 U avian myeloblastosis virus reverse transcriptase (AMV-RT) (Promega) in the presence of 32P-labelled primer, 8 mM MgCl2 and 0.5 mM dNTPs. cDNA products were phenol/chloroform extracted and ethanol precipitated before being resolved on denaturing 6% polyacrylamide sequencing gels and detected by autoradiography using an FLA7000 Typhoon scanner (GE).48S complexes were assembled as previously described . 0.2 pmoIn vitro transcribed WT and mutant minigenome RNAs (0.5 pmol) were subjected to 4.5% native polyacrylamide gel electrophoresis (PAGE) at 100 V for 2.5 h at 4°C. Antisense or scrambled RNA oligos (2.4 pmol) were annealed to minigenome RNAs (1.2 pmol) by heating to 75°C in the presence of a RNA refolding buffer , then snap cooled on ice. Gels were stained with 1 μg/ml ethidium bromide and visualized using a UV transilluminator.N-methylisatoic anhydride (NMIA) (ThermoFisher) in 10 μl of buffer containing 30 mM Tris pH 7.5, 100 mM KCl and 2.5 mM MgCl2 for 45 min at 37°C. RNA was subsequently phenol/chloroform extracted and ethanol precipitated. Modified bases were detected by inhibition of primer extension using 2.5 U AMV-RT in the presence of 32P-labelled primer and 0.5 mM dNTPs. cDNA products were phenol/chloroform extracted and ethanol precipitated before being resolved on denaturing 6% polyacrylamide sequencing gels and detected by autoradiography using an FLA7000 Typhoon scanner (GE).RNA (50 nM) was incubated in the presence or absence of 10 mM t-test as calculated in GraphPad Prism v7 and displayed in Resolved cDNA products were quantified using ImageQuant TL software (GE). Band intensities from sample lanes not treated with NMIA were subtracted from those treated with NMIA to obtain selective 2′-hydroxyl acylation analysed by primer extension (SHAPE) reactivity. For each experiment the top 2% of SHAPE reactivities obtained for nucleotides 117–160, which were quantifiable for all RNAs examined, were excluded and the top 10% of the remaining SHAPE reactivities averaged. Reactivities at other positions were then normalized to this average with 0 representing unreactive nucleotides and 1 as highly reactive. Data from three experiments was analysed and standard deviations calculated in GraphPad Prism v7. Statistical significance of differences between the SHAPE reactivities of nucleotides corresponding to the 5′ CS sequence was determined from three independent experiments using the unpaired two-tailed Student's in vitro transcribed ZIKVNluc RNA (5 μg) was electroporated into 3 × 106 Vero cells suspended in 100 μl of Opti-MEM (Gibco) using a NEPA21 electroporator (Nepagene). Cells were seeded sub-confluently in 24-well plates and samples were harvested for luciferase assays and RT-qPCR analysis at the indicated time points by washing with phosphate-buffered saline and lysis in passive lysis buffer (Promega).Capped (cap0), Nanoluciferase activity was measured using the Nano-Glo luciferase assay system (Promega) by GloMax (Promega). Luciferase signal was normalized to protein concentration, determined by bicinchoninic acid (BCA) protein assay kit (Pierce).Total RNA was extracted from cell lysates by adding a 2:1 ratio of TRI Reagent (Sigma). Subsequent extraction was achieved following the manufacturer's instructions, except isopropanol precipitation was performed in the presence of 25 ng/μl GlycoBlue (Invitrogen) and the resulting pellet was washed twice with 75% ice cold ethanol to remove contaminating phenol and salts. Pellets were resuspended in water and RNA was quantified by measuring absorbance at 260 nm using a NP80 spectrometer (Implen). The number of viral genome copies in each sample was quantified by RT-qPCR analysis using TaqMan chemistry with a Genesig Standard Zika virus quantification kit (Primer Design) and PrecisionPLUS OneStep 2× RT-qPCR reagent (Primer Design) on a StepOnePlus real-time PCR system (Applied Biosystems). Per reaction, 0.3 μl primer/probe mix, 5 μl of 2× RT-qPCR reagent and 4.7 μl sample were used ensuring that the total amount of RNA in each reaction was <500 ng. Cycling parameters consisted of an RT step , an initial denaturation step  and then 45 cycles of amplification . Ct values for each sample were calculated using fluorescence data and compared to a standard curve derived from dilutions of known ZIKV genome copy number generated for each plate analysed. Amplification of no template controls was not observed.in vitro. This approach has previously been used to successfully probe the mechanism of cellular cap-dependent and viral IRES-dependent translation (in vitro transcribed RNA bearing the ZIKV 5′ UTR and first 252 nt of the capsid protein (ZIKV5′utr+). 48S complex assembly was assayed by primer extension inhibition, since assembled complexes arrest reverse transcriptase resulting in a truncated cDNA product 15–17 nt downstream of the initiating codon, as identified by concomitant Sanger sequencing  that supports translation initiation on Type 1 and Type 2 picornaviral IRESs  or a mutant RNA in which the 3′ CS was replaced with the 5′ CS to abrogate base pairing  lacking the capsid duplication or Nluc reporter (in vitro transcribed RNA is modified by incubation with N-methyl isatoic anhydride (NMIA) which reacts with the 2′-hydroxyl group of RNA. This modification is detected as a premature stop in a reverse transcription reaction, with unpaired bases characteristic of unstructured RNA being more reactive than those trapped in secondary structure elements.A recent study has shown by RNA structure mapping that the majority of ZIKV genomic RNA in cells during infection is linear . This prreporter . As thisreporter . In thisfl and ZIKVfl-Δ3′ CS, in which the 3′ CS was replaced with the 5′ CS as in the Nluc reporter RNA  with a decreased linker length of 60 nt between the otherwise unaltered 5′ and 3′ UTRs. Similar constructs were previously used to dissect the role of circularization on flavivirus replication  complementary to the cyclization elements at the 3′ end of the WT ZIKV minigenome. Hybridization with the ASO, but not a scrambled oligo of equal length, retards the migration of the ZIKVS Figure . As showt Figure . Our res108, complex assembly at two upstream near-cognate codons (UUG41 and UUG80) was enhanced  and its membrane-associated cofactor NS2B (hel (in vitro system presented here. Identifying and targeting these factors which influence flavivirus genome circularization, to disrupt the delicate balance between translation and replication, potentially opens up avenues for developing novel anti-viral therapeutics.During infection, flaviviral genomic RNA is recruited to the ER  but the et al.  reportedtor NS2B . DENV NSS2B (hel  and the S2B (hel  have beeS2B (hel ,67 and LS2B (hel  were shoFlavivirus infection induces cellular membrane reorganization into vesicles that provide sites for viral genome replication , from whin vitro on a member of the Flavivirus genus. We found that a 5′ cap and all the canonical cap-dependent eIFs were required for translation initiation on ZIKV RNA (Figure in vitro and in vivo (in vitro reconstitution experiments demonstrate a requirement for the canonical translation factors for efficient ZIKV cap-dependent translation, it remains to be determined if flavivirus RNAs can interact with these factors in a non-canonical fashion.Here, translation initiation has been fully reconstituted A Figure  and D, w in vivo ,72. Inte in vivo , consist in vivo ,75. AlthIn this report we have revealed an unusual mechanism of translational control used by flaviviruses. Our findings fill a major hole in the understanding of the life cycle of this important class of human pathogens and extends the multiple roles performed by dynamic viral RNA structures at different stages of infection.gkz686_Supplemental_FileClick here for additional data file.


We show that, although generically weak, system-level crosstalk patterns (i) are enhanced by transcriptional heterogeneities, (ii) can achieve high-intensity even for RNAs that are not co-regulated, (iii) are robust to variability in transcription rates, and (iv) are significantly non-local, i.e. correlate weakly with miRNA-RNA interaction parameters. Furthermore, RNA levels are generically more stable when crosstalk is strongest. As some of these features appear to be encoded in the network’s topology, crosstalk may functionally be favoured by natural selection. These results suggest that, besides their repressive role, miRNAs mediate a weak but resilient and context-independent network of cross-regulatory interactions that interconnect the transcriptome, stabilize expression levels and support system-level responses.microRNAs (miRNAs) regulate gene expression at post-transcriptional level by repressing target RNA molecules. Competition to bind miRNAs tends in turn to correlate their targets, establishing effective RNA-RNA interactions that can influence expression levels, buffer fluctuations and promote signal propagation. Such a potential has been characterized mathematically for small motifs both at steady state and away from stationarity. Experimental evidence, on the other hand, suggests that competing endogenous RNA (ceRNA) crosstalk is rather weak. Extended miRNA-RNA networks could however favour the integration of many crosstalk interactions, leading to significant large-scale effects in spite of the weakness of individual links. To clarify the extent to which crosstalk is sustained by the miRNA interactome, we have studied its emergent systemic features  in silico using a variety of quantitative measures. Despite their weakness, miRNA-mediated couplings constitute a highly interconnected regulatory layer with robust interaction patterns that contribute to the stabilization of expression levels and allow for tunable system-level responses to specific signals. As some of these features are encoded, to a large degree, in the network’s topology, natural selection appears to have favored the evolution of this “soft mode” of cross-regulation between RNAs.Large regulatory networks integrate a huge number of molecular interactions into robust system-level outcomes. This capability can emerge even when individual interactions are weak and/or strongly heterogeneous. We show this in the context of human post-transcriptional regulation driven by microRNAs (miRNAs). These small non-coding RNAs mediate an extended network of weak cross-regulatory interactions between their targets. We characterize such a network  Competition to bind substrates, enzymes or gene expression machinery is ubiquitous in biological networks and impacts regulatory processes in several ways –12. For In silico studies of small motifs, summarized e.g. in )i is unrepressed  by miRNA species a when a can exert on i. Similar considerations hold for a will be sequestered by RNA species i.Assuming deterministic mass-action kinetics, molecular levels  variation in the transcription rate of species j, quantified by the susceptibility [dj in χij dimensionless.) Note that (i) χij ≥ 0 (i.e. the effective interaction tends to increase or decrease the levels of both RNAs) and (ii) χij and χji are a priori different  pair in any miRNA-RNA network specified by a given set of kinetic parameters  while N × N matrix with elementsi and j. Note that i and j need not be targeted by a common miRNA species in order for χij to be non-zero, as crosstalk can propagate through chains of miRNA-mediated interactions [Starting from ractions , 27 ; (c2) sequence-specific pairings with up to one bulge or mismatch in the seed region ; (c3) a 9 nt stems no-seed interaction allowing for bulged nucleotides in the target (“noseed-9nt”); and (c4) a no-seed interaction with distributed weak pairings (“noseed” for short). Non-canonical pairings are the most abundant in the CLASH interactome, accounting for roughly 77% of all miRNA-RNA interactions [We shall mainly focus on the human miRNA interactome reconstructed in  using thractions   pair, δa = δ for all a and di = d for all i. With this choice, one has, for each miRNA-RNA pair,d and δ compatible with empirical evidence (see βa for miRNAs and bi for RNAs), and (ii) the values of either For sakes of simplicity, we assume ence see , we set bi  and miRNA transcription rates βa  are log-normal i.i.d. random variables with means tr. In particular, we have exploited the log-normality of transcription rates to explore a 20-fold range of values of CVtr, from CVtr = 0.1 to 2.Throughout this study, we assume that both RNA transcription rates es/h see , while wμ0 a constant taken to be equal to 4 molecules  pairs interacting via k-mer pairing (for which we take To appraise how heterogeneities in the miRNA-RNA interaction strengths (binding heterogeneity or BH for short) affect the emergent crosstalk landscape, we consider three variants of the structure of binding affinities encoded in the CLASH interactome see . At the ules see .  link to a miRNA-RNA pair drawn randomly among all possible pairs with equal probability. This type of re-wiring disregards topological correlations of all orders, including node connectivities [We furthermore characterize the extent to which crosstalk patterns are induced by the specific wiring of the CLASH interactome by comparing it to the patterns that arise in randomized versions of the same network. In particular, we study ensembles of networks obtained by re-assigning each  miRNA availability modulates f it see , where mOn the other hand, the picture just derived strongly suggests that the mean susceptibility profile is determined to a large extent by the topology of the network. We shall validate this hypothesis in the following. This conclusion, as well as the overall qualitative crosstalk characteristics illustrated by i ≠ j). At odds with  e.g. in , 27.gi and hj vary between 0 and 1, as do Sin and Sout. A value gi ≃ 0 indicates that a large number of RNA species can almost equally affect the steady state of RNA species i, whereas a value of gi ≃ 1 indicates that RNA i is regulated by a small number of other RNA species. Likewise, when hj ≃ 0 a perturbation of the transcription rate of RNA j affects the steady state of a large number of other RNA species almost equally, while if hj ≃ 1 RNA j only affects a small number of other RNAs. In turn, Sin (respectively Sout) represents the average of the inverse number of RNAs, a perturbation of which can considerably affect the level of a given RNA (resp. whose level can be affected by a perturbation of a given mRNA). We will call Sin the incoming selectivity and Sout the outgoing selectivity, respectively.Along with a higher potential for propagation, increased TH makes crosstalk more selective by systematically involving a smaller number of targets, both in terms of in-coming regulation (i.e. of the number of different transcripts that can regulate a given RNA) and, more significantly, in terms of out-going regulation (i.e. of the number of different transcripts that are regulated by a given RNA). To quantify this aspect, we evaluated the quantitiesBecause the selectivity is ultimately a global property tied to susceptibility distributions, a few characteristics of these curves can be understood from features of the latter. For instance, the high selectivity achieved outside the susceptible region is likely due to the existence of strongly crosstalking pairs, enhanced by transcriptional heterogeneities cf. . Peak inχij are not a priori symmetric, one can also quantify the degree of asymmetry in terms of the mean relative difference between χij and χji. Results  that do not share a miRNA regulator.  In brief, as a and RNA i, larger values of Kij imply a stronger crosstalk potential between RNA species i and j based only on the network’s local interaction structure and kinetic parameters. If crosstalk mostly occurs between co-regulated RNAs one should therefore expect the pattern of susceptibilities to match that of Kijs, at least qualitatively. We hence focus on the Pearson correlation coefficient between the Kijs and the susceptibilities χijs, i.e.ρ ≃ 1 when RNA crosstalk occurs mainly between co-regulated RNA species, while it gets smaller as the number of non-neighbouring targets that significantly crosstalk increases. Hence ρ effectively quantifies the degree of non-locality in crosstalk patterns .Increased crosstalk intensity and selectivity are accompanied by the establishment of non-local effects, represented by strong effective interactions coupling RNAs that are separated by more than one miRNA species in the miRNA-RNA network. This phenomenon has been addressed e.g. in , 44 in tThe most marked effect induced by binding heterogeneities consists in an increase of By controlling the availability of their targets, miRNAs effectively process the variability induced by RNA transcription rates. In some cases (e.g. in presence of specific patterns of correlation between transcription rates), fluctuations can be reduced leading to more finely tuned expression levels , 22, 23.In iles see .Recalling the behaviour of the maximal susceptibility ¯max see , one not¯max see . In otheThese results clearly implicate transcriptional heterogeneities as a key determinant of the stability of expression profiles even in presence of crosstalk, in line with previous observations on small networks , 23. It tr (CVtr = 0.1) persist in the top sextile when TH is 20-fold larger (CVtr = 2). Such a fraction is larger than would be expected by chance (about 58%), implying the existence of a significant backbone of RNA-RNA interactions resilient to transcriptional heterogeneity. A similar picture holds for the other sextiles. It is also instructive to inspect robustness specifically for RNA pairs that do not share any miRNA regulators, which amount to roughly 90% of the total. After analysing systemic properties, we now ask to what degree crosstalk patterns are preserved upon increasing the level of transcriptional heterogeneity. A global analysis shows see  that susWeak sensitivity to changes in transcriptional heterogeneity would be expected if crosstalk interactions were functionally significant. Remarkably, this appears to be the case across a broad range of degrees of TH, both for short-range (mediated by a single miRNA species) and long-range (resulting from extended miRNA-mediated chains) crosstalk interactions.To appraise the role of the specific wiring encoded by the CLASH data in determining the scenario described so far, we compared our results against a null model obtained by randomly re-wiring the CLASH interactome. Specifically, we re-assigned each link to a randomly chosen miRNA-RNA pair, thereby preserving only the overall numbers of links and nodes while altering all other topological features like node degrees, degree-degree correlations, etc see . Each inRandomized networks display a much larger (about two-fold) mean susceptibility for crosstalk than the CLASH interactome, possibly due to the fact that miRNA targets are generically closer in the randomized versions. However, the maximum achievable crosstalk strength gion see . Naturalgion see . miRNA-mgion see . This feNote that, by applying a more conservative protocol that reshuffles miRNA-RNA links while preserving node degrees, one retrieves a crosstalk scenario that is essentially identical to that found for the original CLASH interactome  k-mer pairings or, rather, if non-canonical (weaker) binding sites contribute to its establishment. A breakdown of the topology of the subnetworks induced by the different classes of interactions in the CLASH data shows significant similarities see . Based oTo clarify this point, one can repeat the above analysis by successively adding each type of pairings shown in To further validate this picture, we have analyzed the RNA crosstalk scenario in the cancer-specific interactomes reconstructed in . These nd and 1/δ , this choice excludes from our analysis the rich phenomenology observed during transients [The system-level crosstalk scenarios studied here were derived under a few key methodological choices. First, we focused on the steady states of the mass action kinetics of miRNA-RNA interactions, ansients , when cransients . Second,tr). Significant inhomogeneities in the average biosynthesis rates of individual molecular species may affect our results. Yet, the highly interconnected structure of miRNA-RNA networks and the hierarchical organization of miRNA-RNA binding affinities [By assuming constant mean transcription rates we are effectively discarding the possibility that different RNAs or miRNAs are transcribed at very different rates . In such conditions, the typical RNA-to-RNA distance, in terms of number of links of the miRNA-mRNA interaction networks, above which one species can be considered to be insensitive to perturbations carried out on another species becomes comparable to the diameter of the network. A perturbation in the transcription level of one RNA can then be broadcast (via a chain of miRNA-mediated effective interactions) to distant RNA nodes, potentially propagating over the entire network. Such a feature is intrinsically due to competition and renders local kinetic parameters ineffective priors to predict crosstalk interactions. miRNAs therefore appear to manage a system-level regulatory layer where they operate collectively to mediate a complex, heterogeneous and robust network of RNA cross-regulation. More work is however required to fully unravel its functional capabilities, especially concerning the buffering of fluctuations and gene expression noise.The scenario we describe is qualitatively preserved if crosstalk is assumed to be carried by canonical interactions alone. In particular, the latter are highly effective modulators of crosstalk intensity. Non-canonical binding sites however, while substantially ineffective repression-wise, can enhance non-locality thereby extending the crosstalk range. Topological features of the naturally selected interactome were also found to bear a significant effect on crosstalk. Specifically, they lead to a broader susceptible region, higher maximal susceptibility, and more pronounced non-local effects than their randomized counterparts. In this respect, selection appears to have favoured the emergence of such features at system level.It is important to stress that the crosstalk interactions on which we focus are quantified by susceptibilities, Unfortunately, probing the crosstalk scenario we describe in experiments could be challenging essentially due to weakness and non-locality. To validate the picture we describe, both in terms of individual interactions and of global features, one may however resort to transcriptomic data. Recent work has indeed identified a specific group of correlation functions that, under certain conditions, yield excellent approximations for the real susceptibilities . EvaluatBesides their important role as negative controllers of gene expressiom, miRNAs mediate the establishment of extended networks of RNA cross-regulation. Several features of these networks appear to be hard-wired in the topology of the underlying miRNA-RNA interactome, while others are modulated by transcriptional and/or binding heterogeneities. Whereas the typical crosstalk interaction generated by small changes in RNA availability is weak, non-local effects are significant. Crosstalk-based regulation therefore appears essentially as a system-wide phenomenon, enhanced by variability in kinetic parameters. In physiological conditions, such a regulatory layer can potentially contribute to a variety of functions, such as the processing of transcriptional heterogeneities and the coordination of large-scale rearrangements of RNA levels, similar to the responses observed in . The broN = 6, 943 RNA species  and M = 383 miRNA species connected by 17,411 edges carrying different binding strengths. The same pipeline was applied to the tumor-type specific miRNA-RNA networks obtained in [For the CLASH interactome, after parsing the original bipartite network derived in  to removained in  and baseained in , which ahttps://github.com/matmi8/RNAnet. In presence of TH, results were averaged over multiple independent realizations of the vectors tr. The number of realizations was chosen in each case to ensure a stable estimation of different quantities. Details are given in figure captions. All other parameters, both kinetic and topologic, were kept fixed. Likewise, in the case of topological heterogeneity, results were averaged over 100 networks obtained by independently randomizing the original miRNA-RNA network while keeping all other parameters, both transcriptional and kinetic, fixed. 100 independent randomizations of the interactome sufficed to ensure stable averages in each condition.With parameters set as described, S1 TextSupplementary text.(PDF)Click here for additional data file.S1 Fig10χ = 0).Distributions of susceptibilities between different RNA pairs for single realizations of the CLASH interactome with different values of the mean miRNA transcription rate (TIFF)Click here for additional data file.S2 Fig(a) Mean self-susceptibility  as a function of the mean miRNA transcription rate (b) Mean maximal self-susceptibility  as a function of the mean miRNA transcription rate (c) Comparison between maximum self-susceptibility , mean self-susceptibility  and (TIFF)Click here for additional data file.S3 FigFor each susceptibility sextile, we report the fraction of crosstalk interactions between distant RNAs (i.e. RNAs that do not share any miRNA regulator) that are conserved upon increasing the degree of TH. The last column reports the fraction of interactions that are conserved passing from the lowest to the highest degree of TH. Results obtained by averaging over 100 independent realizations of transcriptional heterogeneity in each case, assuming high binding heterogeneity and mean miRNA transcription rate (TIFF)Click here for additional data file.S4 Figtr, assuming high BH and mean miRNA transcription rate Note that involved susceptibilities (given by color code at the bottom) are of the order of the self-susceptibility. Results were obtained by averaging over 100 independent realizations of TH for each value of CV(TIFF)Click here for additional data file.S5 Figtr. Note that involved susceptibilities (given by color code at the bottom) are roughly 2 orders of magnitude larger than the mean susceptibility Click here for additional data file.S6 FigNote that the susceptible regime in the latter is narrower compared to the original CLASH network.(TIFF)Click here for additional data file.S7 Fig(a) Inverse of incoming and (b) outgoing selectivities as functions of (TIFF)Click here for additional data file.S8 FigDegree distributions for RNA nodes (left) and miRNA nodes (right) are displayed for the entire network (top panels) and for the four subnetworks defined by the interaction classes considered in this work Click here for additional data file.S9 FigDegree distributions for RNA nodes (left) and miRNA nodes (right) representing the miRNA-RNA networks for prostate adenocarcinoma (PRAD), ovarian adenocarcinoma (OV), breast adenocarcinoma (BRCA) and glioblastoma (GBM) cells. Data from . Notice (TIFF)Click here for additional data file.S10 Fig(a) Mean susceptibility. (b) Maximal susceptibility. (c) Pearson correlation coefficient between susceptibilities and local kinetic parameters. (d) Coefficient of variation of RNA levels. TH was set at CVtr = 0.4 and the lowest degree of binding heterogeneity was assumed. Averages were performed over 100 realizations of TH in all cases except for panels (d), where 1000 realizations were taken. Results for other cases are qualitatively similar.Different columns represent results obtained for prostate adenocarcinoma (PRAD), ovarian adenocarcinoma (OV), breast adenocarcinoma (BRCA) and glioblastoma (GMB) cells. (TIFF)Click here for additional data file.S1 TableEach subnetwork is obtained by selecting all links associated to the same kind of interaction occurring between the miRNA-RNA couples. If the subnetwork thus obtained is disjoint, the largest connected component was selected. The term ‘CLASH-noseed’ indicates the full CLASH network except for noseed type of interactions.(PDF)Click here for additional data file.


Structural information about protein–RNA complexes supports the understanding of crucial recognition processes in the cell, and it can allow the development of high affinity ligands to interfere with these processes. In this respect, the identification of amino acid hotspots is particularly important. In contrast to protein–protein interactions, in silico approaches for protein–RNA interactions lag behind in their development. Herein, we report an analysis of available protein–RNA structures. We assembled a data set of 322 crystal and NMR structures and analyzed them regarding interface properties. In addition, we describe a computational alanine-scanning approach which provides interaction scores for interface amino acids, allowing the identification of potential hotspots in protein–RNA interfaces. We have made the computational approach available as an online tool, which allows interaction scores to be calculated for any structure of a protein–RNA complex by uploading atomic coordinates to the PRI HotScore web server (https://pri-hotscore.labs.vu.nl). The transcriptome encompasses a large variety of different functional RNA classes. As a common feature, most of the RNA functions rely on interactions with proteins. In addition, RNA biosynthesis and regulation is governed via numerous RNA-binding proteins (RBP). As a result, RNA is associated with proteins through most of its life cycle. Initially described RBPs often contained conserved RNA-binding domains . With re─1 , allowing the calculation of interaction scores for any protein–RNA complex for which atomic coordinates are available.Herein, we report an analysis of structures of protein–RNA complexes available via the PDB. Based on these complexes a data set of 322 crystal and NMR structures was assembled which served as basis for our analysis. After the investigation of structural features of these complexes, we describe a computational alanine-scanning approach which provides an “interaction score” for interface amino acids allowing the identification of potential hotspots in the RNA-binding region. We made this approach available via the PRI HotScore web server . In the resulting 1115 structures, we checked proteins in complexes with the same RNA sequence for redundancy, which reduced the number of PDB entries to 544. Among protein–RNA complexes, ribosomal PRIs represent a very particular case, as large RNA structures are permanently associated with embedded, relatively small protein chains. As a consequence, ribosomal RNA exhibits unique properties such as high structural integrity and catalytic activity. For these reasons, we decided to remove complete ribosomes from the data set. Finally, complexes involving RNA sequences with less than 10 nucleotides (nt) and more than 50% modified nucleobases were excluded resulting in the final data set of 49 NMR and 273 crystal structures .Initially, we assembled a data set of protein–RNA structures suitable for the investigation of PRIs. First, the PDB was searched for crystal structures with a resolution of 3 Å and below, and NMR structures with complexes in which the protein component contains greater or equal to 10 amino acids (BSA) providing an average of 3211 Å2 with most of the complexes (80%) ranging between 1400 and 6000 Å2. In contrast, interfaces in PPIs have been reported to range between 600 and 4700 Å2 . A comparison of these groups (BSA = 2377 Å2) and a relatively narrow BSA distribution  followed by dsRNA .For this data set, we first determined the buried surface area . For dsRNA and hpRNA, nucleotides are relatively evenly distributed  in the PRI . On average, 56 amino acids are involved per PRI, with 60% of all structures revealing between 26 and 80 interface amino acids. Analysis of the amino acid composition in the PRI interface shows a clear preference for lysine (K) and arginine (R) with an average occurrence of 6.1 and 6.5 residues per PRI, respectively . Analyzing the four bases separately, we found arginine (R) to be heavily involved in interactions with all bases, contributing with 16%–20% of all contacts while lysine (K) only provides 3%–9%. Other than arginine (R), only phenylalanine (F) and tyrosin (Y) contribute to ≥10% of the protein–nucleobase contacts: U shows a particularly high involvement  followed by A  and G . For the remaining amino acids, we do not observe substantial deviations from overall interface frequency.Overall amino acid frequencies are dominated by interactions between the protein and the RNA backbone B explainTo evaluate the contribution of interface amino acids to binding, we aimed for the development of an in silico alanine scanning workflow enabling the analysis of protein–RNA structures. To ensure accurate predictions as well as short calculation times, we considered the use of knowledge-based potentials which sum over individual pairwise interaction terms of interface residues and have proven useful for in silico alanine scanning in protein–protein complexes . We seleDARS) values, which are a measure for the contribution of each interface amino acid for RNA-binding: Large values indicate a great loss in binding affinity upon alanine substitution. Notably, for 19.6% of the interface amino acids we obtain negative Δln(DARS) scores indicating considerable involvement of these residues in intramolecular contacts (DARS) scores were normalized per interface to provide the final interaction score (IS). As a consequence of the normalization, the average of all IS values per interface equals 1.We determined DARS scores for the 322 starting complexes and for 13,780 complexes resulting from the variation of each interface amino acid in these complexes to alanine. In this analysis, a distance of 7 Å was used as threshold, which was shown to be the optimal cut-off for analyzing protein–nucleic acid interactions with such potentials . Glycinecontacts . For subIS values between 0 and 6.62 (IS > 1). Only 10.3% of considered residues obtain scores that are twofold over average (IS > 2). Since this occurrence is similar to the frequency of hotspots in PPIs (9.5%)  is similar to the one found for PRI hotspots (IS > 2) (IS > 2 (hotspot) but all complexes contain residues with IS > 1 (hotspot and/or warmspot). Compared to hot- and warmspots, the remaining residues  show very different amino acid frequencies  , we defi per PRI B differ  per PRI C. We obs per PRI . Interes(IS > 2) C. Based quencies D, which quencies C but shoExperimental data about the contribution of interface amino acids to the stability of protein–RNA complexes is limited since studies often focus on a variation of the RNA rather than the protein sequence. Among the complexes in our data set, we identified two examples with considerable data on alanine substitution of interface residues. One is the complex of nucleolar essential protein 1  with its target ribosomal RNA , and theSaccharomyces cerevisiae and a fragment of the small subunit rRNA reveals a C2-symmetric Nep1 homodimer bound to two identical 14-mer RNA hairpins  values and the remaining 25 amino acids provide IS values between 0 and 4.32  were tested in a yeast three-hybrid system showing for all variants a complete loss in their ability to bind the target RNA (▾ in IS = 0.24) suggesting only small or no contributions to RNA binding by the glutamate side chain.The cocrystal structure of Nep1 from and 4.32 B. Among and 4.32 C. Prior RNA ▾ in . ImportaRNA ▾ in . StrikinRNA ▾ in B indeed RNA ▾ in . ImportahcnA RNA transcript  values. The interaction score for the remaining 20 residues ranges between 0.04 and 2.86 (A N G G A N) while the other hot- and warmspots have contacts with the RNA backbone  was performed using a phenotypic readout : L2A (73-fold), L4A (62-fold), R44A (150-fold). Importantly, all of these residues are predicted hotspots.In a study that predates the NMR structure, an alanine-scan of the RNA-binding domain of a very closely related CsrA ortholog from  readout . Focusin readout B exhibitons * in B, we obsons * in . These mIS values are normalization per interface, which hampers the comparison of residues of different unrelated complexes. For this reason, we searched for complexes in dbAMEPNI that provide at least five pairs of experimental ΔΔG and IS values. This search yielded seven complexes with five to maximally 20 data pairs . Most notably, for five of these complexes, we obtain a good Pearson correlation coefficient (r = 0.49–0.77). One of the complexes  is part of the original DARS training set, which may explain its good correlation. However, here it is important to note that the generation of the DARS-RNP did not involve information about experimental alanine scanning but only considered structural data , which contains experimental ΔΔG-values resulting from alanine subsitution for 50 protein–RNA complexes . In the ral data .BSA) in PRIs appears to be larger than in PPIs which may account for the generally higher flexibility of RNA-structures , duplexes (19%), hairpins (34%), and more complex folds (27%). Overall, the size of the interaction area  and allows the scoring of individual amino acid side chains regarding their contribution to a PRI. Importantly, PRI HotScore is independent of information regarding the conservation of amino acids, and it is computationally not demanding. However, the latter comes at the cost of simplifications such as the exclusion of water molecules and the consideration of rigid protein and RNA structures. In addition and similar to analogous alanine scanning approaches for PPIs, RNA targeting amino acids that are also involved in intradomain interactions receive a lower score which can lead to false-negative results. However, these simplifications result in a fast algorithm that recapitulates the experimental data for two test complexes very well.To enable the prediction of amino acid hotspots in known protein–RNA structures, a computational workflow was established using the knowledge-based DARS potential. Our approach was implemented into the PRI HotScore web server, which is publicly available (DARS) value] except for glycine, proline, and alanine. IS values are normalized per interface, resulting in a mean IS of 1 when averaging all considered interface residues. Notably, amino acids with an interaction score above average  exhibit a distinct pattern with high frequencies of arginine (R) and a relatively high occurrence of seven additional amino acids . Interestingly, this is very different from PPI hotspots which are mainly composed of tryptophan , arginine , and tyrosine   and warmspots (2 ≥ IS > 1) comprising 10.3% and 19.5% of all alanine-scanned amino acids, respectively. We introduced this separation since interaction scores twofold over average (IS > 2) can be expected to be significantly increased and due to the fact that about 10% of interface residues in PPIs are also considered hotspots  for every interface amino acid which contributes to RNA binding  per interface (j). Subsequently, each Δln(DARS) value was divided by the average score of its interface (IS) asThe Δln are available using the PRI HotScore web server (https://pri-hotscore.labs.vu.nl).Residues with Supplemental material is available for this article.


In vitro selection with long random RNA libraries has been used as a powerful method to generate novel functional RNAs, although it often requires laborious structural analysis of isolated RNA molecules. Rational RNA design is an attractive alternative to avoid this laborious step, but rational design of catalytic modules is still a challenging task. A hybrid strategy of in vitro selection and rational design has been proposed. With this strategy termed “design and selection,” new ribozymes can be generated through installation of catalytic modules onto RNA scaffolds with defined 3D structures. This approach, the concept of which was inspired by the modular architecture of naturally occurring ribozymes, allows prediction of the overall architectures of the resulting ribozymes, and the structural modularity of the resulting ribozymes allows modification of their structures and functions. In this review, we summarize the design, generation, properties, and engineering of four classes of ligase ribozyme generated by design and selection. The 3D structures of these biopolymers often have a modular organization, in which the whole structure is divided into several structural domains ,2. In thD structuofactors  indicatein vitro selection as this technique enables identification of particular RNA sequences with desired functions from structurally unconstrained RNAs containing random sequences ranging from 30 to 200 nucleotides with 1014–1016 different nucleotide sequences .,20.19,20election , right ,3920,39]14–1016 different sequences, four distinct classes of artificial ribozymes with 3',5'-ligase activity were isolated through the selection procedure developed by Bartel and Szostak . T. Tin vitn system A, left aCoevolution experiments were performed using two distinct classes of ligase ribozymes (the class-DSL ribozyme and the class I ligase ribozyme) capable of undergoing continuous evolution . In the in vitro selection system to isolate novel RNA-RNA interacting modules that functionally substitute the GNRA/receptor module. Ohuchi et al. generated a novel RNA sequence that recognizes the C-loop motif found in ribosomal RNA (cis-DSL-1S in which the L1 GAAA loop and the R(11nt) motif were replaced with the C-loop motif and a random sequence of 19 nucleotides  receptor in P3 ,43. Thisomal RNA B 55]. U. Uin vitleotides B, left. isolated B, right in vitro selection. The design and selection would be applicable not only to generate novel ribozymes but also to construct novel aptamers and riboswitches because naturally occurring riboswitches often possess modular architectures in which RNA structural motifs such as GNRA-receptor interactions are used as key motifs to establish their 3D structures [Four classes of RNA ligase ribozyme have been generated using the design and selection strategy, the concept of which was inspired by the modular architectures of naturally occurring ribozymes and also protein antibodies. Modular architectures of these RNA ligase ribozymes allowed us to engineer their structures and functions more readily and quickly than ribozymes generated by ordinary ructures ,57. This


This method can be used to classify a broad range of ILTV strains to facilitate the classification and genotyping of ILTV and help to further understand recombination in these viruses.Live attenuated vaccines against infectious laryngotracheitis virus (ILTV) are widely used in the poultry industry to control disease and help prevent economic losses. Molecular epidemiological studies of currently circulating strains of ILTV within poultry flocks in Australia have demonstrated the presence of highly virulent viruses generated by genomic recombination events between vaccine strains. In this study, high-resolution melting (HRM) analysis was used to develop a tool to classify ILTV isolates and to investigate ILTV recombination. The assay was applied to plaque-purified progeny viruses generated after co-infection of chicken embryo kidney (CEK) monolayers with the A20 and Serva ILT vaccine strains and also to viruses isolated from field samples. The results showed that the HRM analysis is a suitable tool for the classification of ILTV isolates and can be used to detect recombination between ILTV vaccine strains The online version of this article (10.1007/s00705-018-4086-1) contains supplementary material, which is available to authorized users. Gallid alphaherpesvirus 1), has an impact on the poultry industry worldwide [Infectious laryngotracheitis (ILT), caused by infectious laryngotracheitis virus  analysis is a simple, rapid and cost-efficient method for genotyping of multiple single-nucleotide polymorphisms (SNPs) . This mein vitro conditions.This study aims to develop an accurate and cost-effective HRM analysis that can be applied to the classification of known and emerging ILTV strains, and to study genomic recombination of ILTV vaccine strains under controlled The eight ILTV isolates used in this study are listed in Table in vitro and in ovo experiments were performed on CEK cells or CAM from embryonated SPF hen eggs .CEK cells were prepared using kidneys harvested from 18-day-old chick embryos as described previously . MonolayPrimary CEK monolayers were infected with a mixture of Serva and A20 ILTV strains in 6-well tissue culture plates. After 1 hour of incubation at 37 °C, the inoculum was removed and 3 mL of fresh medium was added. The total multiplicity of infection (MOI) for the coinfection studies was 10. The progeny virus populations were collected by scraping the cells into culture supernatant 48 hours post infection when an extensive cytopathic effect was observed. Samples collected from this experiment were stored at -80 °C prior to virus isolation and plaque purification.in vitro coinfection experiment were prepared and inoculated onto CEK monolayers in 6-well plates as described previously [2, 20 well-isolated plaques were picked using a micropipette guided under an inverted light microscope. Picked plaques were resuspended in 500 μL of DMEM, and each plaque was purified through three rounds of plaque purification before a final propagation step on CEK cells in 24-well plates. One freeze/thaw cycle was performed between rounds of plaque purification.Ten-fold dilutions of progeny viruses from an eviously . Followi® Pro 96 viral RNA/DNA purification kit  and a QIAxtractor automated vacuum system. Purified DNA from 200-μL samples was eluted in 200 μL of pyrogen-free water  and stored at -20 °C until testing. Pyrogen-free water, rather than the elution buffer supplied with the extraction kit, was used to avoid introducing any variation in HRM patterns.Nucleic acid extraction was performed using a PureLinkin silico using uMelt software [Tm) gap in their PCR products. Ultimately, six regions were selected based on their even distribution across the genome (in order to increase the chance of detecting recombinants), the ability to measure differences in their Tm values, and their ability to produce unique patterns for known/tested ILTV strains. Targeted sites included both transition and transversion mutations and resulted in either synonymous or non-synonymous changes . Reaction mixtures (20 μL) were prepared containing 2 mM MgCl2, 200 μM each dNTP, 400 nM each forward and reverse primer, 8 μM SYTO® 9 (Thermo Fisher Scientific), 0.04 U of GoTaq® Flexi DNA Polymerase  per mL, Colorless GoTaq® Flexi Buffer , and 2 μL of DNA template. PCR amplification and the HRM assay were performed on a Rotor-Gene Q 2plex HRM instrument  in 100-tube rings .For the HRM assay, all reactions were prepared using a QIAgilityThe thermal cycling profile included 45 cycles of a denaturation step at 95 °C for 30 seconds, an annealing step at a primer-specific temperature (listed in Table The HRM data were processed using Rotor-Gene Q Series software (version 2.1.0 Build 9) and Rotor-Gene ScreenClust HRM software (version 1.10.1.3)  in superin silico analyses and to examine the potential for a wider application of the HRM-based genotyping to ILTV strains, including strains from other geographical regions. Using the methods previously established with the Australian ILTV isolates, a series of in silico amplifications was performed in Geneious 11.1.4 [in silico PCRs were imported to uMelt Batch software (v. 2.2) [The full genome sequences of five additional ILTV strains were collected from GenBank Table  to enabls 11.1.4  on multis 11.1.4 . The pro(v. 2.2)  to prediAn HRM panel Table  targetinThe HRM panel targeting the same six sites  was applied to DNA extracted from plaque-purified viruses generated after a 1:1 coinfection of CEK monolayers with the A20 and Serva ILT vaccine strains  was applied to extracted DNA from these viruses  were retrieved from GenBank and compared to the Australian ILTV isolates used previously in this study Table . A panelin silico analysis of five US strains  in some regions. Therefore, when designing the second and third set of primers, the regions with a Tm gap of ≥1 °C were preferred. In order to use this system to classify viruses submitted from outbreaks of disease in the field it is likely that viruses from any clinical samples would also require processing (isolation and DNA extraction) using uniform techniques. It is conceivable that future assays could be developed and optimized for direct use on specific clinical samples  after extensive evaluations using DNA extracted directly from clinical specimens. This would have time and cost advantages and represents an important area of future work. The direct use of the HRM assay on clinical samples has been successfully applied to several pathogens in the past [The HRM analysis used in the current study could differentiate field and vaccine strains of ILTV and classify them into distinct genotypes that had previously been determined by PCR-RFLP  and fullet al. . In ordethe past –49. The the past . Currentin vivo coinfection study in SPF chickens using a mixture of CSW-1 and V1-99 ILTV strains delivered intratracheally, where 46.5% (in birds co-inoculated with 103 PFU of each virus) to 79.2% (in birds co-inoculated with 104 PFU of each virus) of isolated progeny viruses were identified as recombinants.The HRM system developed in this study could also be used successfully to detect recombinant viral progeny. This represents the first time that HRM methods have been used to detect recombination in herpesviruses. Previous studies have shown that genomic recombination between Serva and A20 ILTV strains has occurred in the natural host and has resulted in field recombinants with higher levels of virulence . The resIn conclusion, the HRM assay developed in this study was successfully applied as a high-throughput screening tool for recombinant detection and could be used to successfully classify different strains of ILTV. In these applications the assay was shown to be accurate, time-efficient and cost-efficient. The HRM genotyping method promises to be an important tool to facilitate the study of ILTV epidemiology and recombination in both experimental and field settings.Supplementary material 1 (PDF 112 kb)Below is the link to the electronic supplementary material.