Viruses possessing a non-segmented genome require a specific recognition of their nucleic acid to ensure its protection in a capsid. A similar feature exists for viruses having a segmented genome, usually consisting of viral genomic segments joined together into one viral entity. While this appears as a rule for animal viruses, the majority of segmented plant viruses package their genomic segments individually. To ensure a productive infection, all viral particles and thereby all segments have to be present in the same cell. Progression of the virus within the plant requires as well a concerted genome preservation to avoid loss of function. In this review, we will discuss the “life aspects” of chosen phytoviruses and argue for the existence of RNA-RNA interactions that drive the preservation of viral genome integrity while the virus progresses in the plant. Orthomyxoviridae members where all genomic negative-stranded ribonucleoprotein (RNP) complexes are selectively assembled together within one enveloped viral particle [Bunyaviridae [Reoviridae or the well-known Cystoviridae pseudomonas phage Ø6 that possesses three genomic double-stranded (ds)RNA segments within the same viral structure [Preserving genome integrity is a key challenge for any organism, and viruses with an RNA-based genome are not excluded from this basic rule. Obligate parasites that have a monopartite genome mainly face recombination events that draw viral evolution. While viruses with segmented genomes also encounter similar evolutionary traits, another constraint applies to the maintenance of genome integrity. Indeed, all genomic segments should be available within the same cell and be transmitted from one cell or one organism to another. To do so, all genomic components must be packaged within the same viral particle. This is well exemplified by particle , or Bunyaviridae  where altructure . Leviviridae enterobacteria phage Qß), animals and insects  and also plants . To date, distinguishing features of (+)-strand RNA viruses that infect bacteria, animals or insects reside in the nature of the viral genome that is limited to either one single-stranded (ss)RNA molecule or two genomic RNAs packaged within a unique icosahedral capsid . However, (+)-strand RNA phytoviruses with segmented genomes range between two to five positive-strand RNA molecules and such so-called multipartite viruses possess either an icosahedral (sometimes bacilliform) or helical shape. A large majority of these phytoviruses have distinctive features compared to the aforementioned animal (+)-strand RNA viruses, as each segment can be individually packaged as ribonucleoprotein complexes within a helical structure. Icosahedral phytoviruses with a segmented positive-sense RNA genome either package single segment species in each capsid or a combination of segments within the limits of physical size constraints. A.L.N. Rao has reviewed mechanisms driving genome packaging of spherical plant RNA viruses [Viruses that possess a positive-stranded RNA genome are found in genera that infect bacteria  or RNP complex is still open and it appears closely linked to the ability of the viral RNAs to be “packaged.” Such infection of vascular tissues is not as uniform as those of mesophyll cells where phytoviruses usually begin their journey by moving cell to cell (for review see ). SeveraWhatever the structural shape adopted for plant multipartite RNA viruses, their genomic segments are separated by distinct capsids. This situation raises concerns about the preservation of the viral genome integrity that requires all RNA segments to move from one cell to a neighbor or distant cell in the plant . This enFor some viral species, icosahedral shells are able to assemble in virus-like particles where the genome, usually dsDNA, is incorporated using energy-dependent nanomachines, extensively studied for bacterioviruses T4, P22 or Ø29. The latter uses a non-coding RNA hexamer for DNA entry. Recent insights about the genomic encapsidation of the positive-strand RNA genome of bacterioviruses MS2 revealed the importance of coat protein (CP) dimer interactions, with an estimated number of 60 hairpin RNA structures distributed around the RNA genome , leading to induced-fit RNA-viral protein interactions rather than electrostatic interactions. In this sense, genomic MS2 RNA constitutes a scaffold for assembly initiated or terminated by the assembly protein, following a Hamiltonian path ,9,10.Tombusviridae family is structured by an association of 180 copies of a 38 kDa CP. Its crystallographic structure resembles the tomato bushy stunt virus. TCV viral particles can be dissociated at elevated pH where RNA is attached to six CP subunits that permit reassembly in vitro [Turnip crinkle virus (TCV) from the in vitro . Mappingin vitro , suggestin vitro  as this in vitro ,15.T = 1 virus with a small genomic RNA. Atomic structure revealed 30 stem-loop RNA structures, each associated with a two-fold symmetry axis [T = 1 capsid. This discrepancy indicates a selection of the RNA genome into an icosahedral structure following a Hamiltonian pathway as described for MS2 bacteriophage [Satellite tobacco mosaic virus (STMV) is an icosahedral try axis . Interestry axis  does notriophage . The exiriophage . A systeUmbravirus genus and does not form conventional virions. Indeed, this species does not encode coat proteins. GRV possesses a monopartite genome encoding proteins necessary for its autonomous replication and movement within the plant. Its genome encapsidation occurs during mixed infection with an HV that belongs to the Luteoviridae family, specifically transmitted by aphids and only in the presence of a satellite RNA required for the selective encapsidation of GRV [Groundnut rosette virus (GRV) belongs to the n of GRV . In a hon of GRV . This ex® for nanotechnologies. The combination of genetically modified CP subunits, sometimes together with wild-type CP (both expressed in Escherichia coli) with RNAs containing TMV-OA, led to the construction of nanostructures displaying new chemical reactivity, owing to their usage as nanomaterials [The reconstruction of TMV from purified CP and viral RNA  set up taterials . Nicotiana benthamiana (N. benthamiana) [N. tabacum cv. Samsun plants [Reverse genetics using a full-length infectious clone of TMV demonstrated that the CP is not required for cell-to-cell movement but is needed for the efficient systemic infection of hosts such as hamiana) . Hence, n plants . Such RNn plants .The progression of TMV infection within mesophyll cells is well characterized and occurs thanks to the transport of uncoated viral RNAs and MP RNP complex. However, little is known about the nature of the infectious material that permits viral vascular movement. Disruption of the OA by point mutations that preserve functional CP abolishes TMV systemic movement, suggesting that virions represent the long-distance entities . NeverthAs stated above, in the absence of CP synthesis, TMV is able to move cell-to-cell to access VP but fails to enter CC, suggesting a requirement of the CP to cross the VP/CC boundary . The TMVMasked strain M-TMV shows a delayed systemic spread as compared to the virulent strain U1-TMV. However, comparable replication levels and cell-to-cell movement efficiency in mesophyll cells is described for the inoculated leaves . The delAlthough RNA stabilization by encapsidation may be a prerequisite for TMV to enter VP and CCs, it is not necessary in the SE where no RNase activity was detected in exudates ,33,34. FTobamovirus odontoglossum ringspot virus protein (ORSV) is able to infect such a host but is unable to move long distance in tobacco [Other indirect experiments permit speculation about the possibility of MP to be included as a constituent of the viral entity that moves long distance. In tobacco plants with a reduced synthesis of pectin methylesterase in vascular tissue, a cell-wall enzyme which interacts with MP , TMV is  tobacco . The rep tobacco . The hos tobacco . Dianthovirus genus and Tombusviridae family. This icosahedral virus possesses a bipartite positive-sense single-stranded RNA genome. RNA1 codes for the proteins involved in replication and also for CP subunits through a sgRNA [Red clover necrotic mosaic virus (RCNMV) belongs to the  a sgRNA ,42,43,44 a sgRNA ,46,47. A a sgRNA ,49,50,51 a sgRNA , because a sgRNA . Therefo a sgRNA . N. benthamiana revealed that RNA1 and RNA2 could move long distance in the absence of the CP and thus without the formation of virions [While the CP is not required for cell-to-cell movement, it is considered necessary for the systemic spread of RCNMV . However virions . AlthougT = 3 quasi-icosahedral symmetry [All three components of the tripartite genome of brome mosaic virus (BMV) (~8.2 kbp) are essential for viral replication and for systemic infection of plants. The RNAs are encapsidated separately in 28 nm particles with symmetry . RNA1 ansymmetry . A ~200 symmetry  but not symmetry . Conservsymmetry . Besidessymmetry . In wheasymmetry , suggestsymmetry . During symmetry ,63. Virasymmetry ,65. Agrobacterium tumefaciens-mediated transient expression of BMV genomic RNAs in leaves of N. benthamiana, Gopinath and Kao demonstrated that viral RNAs can also move long distance independently, without the presence of the viral CP [trans. To access distant cells, trans acting proteins or viral messenger RNAs will be needed in huge amount. Taking these requirements into consideration, only segmented DNA viruses—such as geminiviruses or nanoviruses (circular ssDNA genome) that express mRNA thanks to their stabilized replicative dsDNA segments—can fulfill such a constitutive expression. These viral DNAs ensure a continuous expression of proteins in different tissues, and viral products could move from one cell to another as proteins, mRNAs or both. In the case of BMV described above, the use of agroinfection allowed the continuous production of infectious viral RNAs from cDNAs. This situation recapitulates what could happen for segmented DNA phytoviruses described above.Packaging was thought to play an important role in long-distance movement of the virus in the plant. However, using viral CP , probablviral CP . This alRecent advances using RNA sequencing (RNAseq) analyses demonstrated the role of TLS structures in the systemic transport of mRNAs containing such structural motifs . The preIf TLS motifs and/or signal peptides could drive the systemic movement of RNP complexes, the hypothesis of an RNP involving RNA-RNA network nucleating genomic segments and driven by few viral proteins appears easier to draw. To highlight our thoughts and findings, we present our views applied to the benyvirus biology and then extend our approach to pomoviruses.Benyviridae family is composed of the Benyvirus genus of four viral species, namely, beet necrotic yellow vein virus (BNYVV), beet soil-borne mosaic virus (BSBMV), burdock mottle virus and rice stripe necrosis virus, the latter two species having a bipartite genome [The e genome . Benyvire genome . This incis acting sequences located in the first 300 and the last 70 nt of the RNA3 species [The OA for viral particle morphogenesis has only been studied on the RNA3 and RNA4 species. Preliminary experiments indicate the presence of  species ,72. The  species . This 75 species . Mutants species . These d species . If it e species . On the  species . Further species .Chenopodium quinoa, BNYVV produces local lesions within which each RNA species accumulates and encapsidates individually  movement proteins  and post-transcriptional gene silencing suppressor  provided by RNA2 species [N. benthamiana host. As Rep3 is encapsidated, we ruled out the thesis of virions as moving material and we formulated the hypothesis about the existence of RNA-RNA interactions allowing RNA segments to interact with one another to ensure a stabilization of an RNP complex for the long-distance movement of the viral genome in the plant. Such an RNP complex contains CP as this structural protein is required for systemic spread [In systemic hosts such as es hosts ,83,84. H species ,86  according to the genomic ssRNA being packaged [Spongospora subterranean vector, because spontaneous deletions occur in isolates with disrupted transmission, particularly after serial mechanical inoculation [N. benthamiana using a combination of transcripts corresponding to RNA1 and RNA3 [Potato mop top virus (PMTV) belongs to the packaged . The parpackaged . As descculation ,92. Howeculation . The encculation . This paand RNA3  and thusand RNA3 . This doand RNA3 . This reand RNA3  , located within the CP-RT C-terminal coding sequence that could possibly interact with a sequence (5′- 2244CUUGCCUGACGUCGAUCCUG2263 -3′) present in the RNA1 replicase sequence with a 2 nt bulge (underlined). In a model where genome integrity needs to be preserved during the systemic movement by the association of all genomic RNAs in a RNP complex, such 20 nt base pairing, with a predicted variation of the Gibbs free energy of −16.35 Kcal/mol, could participate in an RNA2-RNA1 specific interaction. We did not extend our computational analyses to the RNA3-RNA2 or RNA3-RNA1 interaction domains. However, taking into account the systemic infection mediated by RNA1 and RNA3, such domains should exist [Taking into consideration our preliminary data accumulated on BNYVV, we checked if similar RNA-RNA interactions could exist, using the IntaRNA program (Freiburg RNA Tools) ,98 and lld exist .trans and will guarantee the formation of RNP complexes dedicated to the long-distance movement thanks to the movement proteins produced via subgenomic RNAs. Late in the infection, the accumulation of structural protein would negatively regulate the RNA-RNA interaction, leading to the encapsidation of genomic RNA species ready for their acquisition by the vector thanks to the minor structural protein recognition [The advantage of a regulatory network involving RNA-RNA interactions eliminates the need for a viral protein dedicated to associate RNA species with a correct stoichiometry. At the beginning of a cell infection, translation and replication events are predominantly monopolizing viral RNAs that switch between unstructured and structured forms. Thereby, RNA-RNA interactions could take place in ognition . As for As previously mentioned, to maintain their viral genome as a functional unit, segmented positive-strand viruses require the presence of at least one of each viral RNA segment in the target cell. Such conditions require a high multiplicity of infection (MOI), especially for viruses with four or more genomic entities as demonstrated by theoretical studies . BecauseViral cell-to-cell movement ensures the progression of the infection via plasmodesmata, the connection channels between plant cells , viral eHowever, the viral genome must contend with RNA decay mechanisms whereby RNA species are translated or cleaved by the silencing machinery , step 8.We have reviewed published literature about distinct virus packaging and long-distance movement focusing on some data and arguments in favor of a viral transport as RNP complexes rather than virions. Describing BMV long-distance movement, it appears that the RNA3 species could as well be involved in RNA-RNA interactions with other viral species to nucleate the formation of a RNP complex as postulated for BNYVV and PMTV RNA1-RNA2 species. While one RNA species modulates the recruitment of other species, no information is yet available about the stoichiometry of the genomic components in the moving RNP complex. In order to safeguard genomic integrity, it could be postulated that more than one RNA species should be present to ensure, on the one hand, the expression of essential protein(s) in the distant cell and, on the other hand, the presence of replication-ready genomic components. A notable example of preservation of viral genomic integrity is the superinfection exclusion (SIE) that exists among some phytoviruses and prevents some co-infections from occurring. Described for T-even bacteriophages, SIE consists on the inhibition of viral genome entry in the cytoplasm of an infected cell thanks to the expression of viral proteins . In eukatrans during their amplification by BNYVV RNA1 + RNA2, we previously demonstrated that within local lesions, reporter RNA species exclude one another, leading to sectored lesions as shown in A similar observation was made in sugar beet infected with both BNYVV and BSBMV (two benyviruses). The infection of the plant with one virus limits the possible infection of the same plant with the second virus, if inoculated a few days later , suggest


Antibody heavy chains contain a variable and a constant region. The constant region of the antibody heavy chain is encoded by multiple groups of exons which define the isotype and therefore many functional characteristics of the antibody. We performed both single B cell RNAseq and long read single molecule sequencing of antibody heavy chain transcripts and were able to identify novel exons for IGHA1 and IGHA2 as well as novel isoforms for IGHM antibody heavy chain. Sequencing and annotation of the human immunoglobulin constant regions was largely accomplished in the 1980s and early 1990s. The IGH locus was mapped to the telomeric region of the q arm of chromosome 14 ,3, the sEach isotype  was thought to be expressed as one of two isoforms: secreted or membrane bound which contain S or M exons respectively ,9,19. TrAnalysis of the IGH locus and its transcripts is highly challenging due to the fact that every IGH transcript is the result of a unique somatic recombination event and can be expressed as different isotypes and isoforms. While RNAseq has been proven to be a very powerful tool for the discovery and annotation of transcript isoforms, standard RNAseq protocols rely on RNA pooled from thousands to millions of cells. As large pools of B cells will contain a highly diverse mixture of IGH transcripts standard RNAseq would result in an even more diverse pool of short read fragments that would be impossible to de-convolute. We used two strategies to analyse IGH transcripts in more detail than previously possible. First, we used single B cell RNAseq ,18 to reUsing these strategies, we discovered novel exons in the 3’UTR of IGHA1 and IGHA2 transcripts. We also found that in addition to the canonical secreted and membrane isoforms, all IGH isotypes and especially IGHM showed further, less abundant shorter isoforms.We applied single cell RNAseq to human B cells to analyse complete variable and constant regions of IGH transcripts expressed by single B cells .For single B cell RNAseq, we amplified full length cDNA from single B cells using the C1 Autoprep system and used Nextera XT transposase to generate libraries which we sequenced on the Illumina MiSeq and HiSeq Sequencers. When we mapped the resulting RNAseq reads to the IGH locus we identified a B cell expressing high levels of both secreted and membrane bound isoforms of IGHA1. Interestingly, instead of one continuous exon as previously assumed (IGHA1 M), this cell appeared to have two exons (subsequently named IGHA1 M1 and IGHA1 M2) in the membrane isoform, with the second located in the 3′UTR. While the non-coding transcript AL928768.3 containing only IGHA1 M1 and IGHA1 M2 has previously been identified, the expression of both IGHA1 M1 and IGHA1 M2 appeared to be dependent on IGHA1 expression as the exons were absent in a B cells expressing IGHM exclusively . AssemblWhile single B cell RNAseq provided a strong indication about the isoform structure of membrane bound IGHA1 we aimed to validate this result using single molecule long read sequencing,18 Fig.. In contFinally, we also performed PCR on cDNA from an independent individual using primers in the IGHA1 CH3 exon as well as the IGHA1 M2 exons and detected a band of the expected size for the VDJ-CH1-CH2-CH3-M1-M2 isoform .Taken together, we used RNAseq, single molecule read sequencing and cDNA size separation to conclusively show that membrane bound IGHA1 and IGHA2 transcripts undergo a previously unknown splice event. This splicing event creates a non-coding exon in their 3’UTR.Surprisingly, when we gel separated the IGHA1 constant region cDNA, we also detected a shorter band  and SangAs the single molecule long read sequencing data was not limited to IGHA transcripts, we expanded our analysis to IGHM. Indeed, IGHM transcripts showed abundant expression of previously unknown isoforms composed of known exons. In addition to 56 transcripts featuring canonical isoforms, we identified three previously unknown isoforms of the IGHM transcript , which wTo identify a target B cell population expressing the more abundant, novel IGHM isoforms we quantified the rate of hypermutation in the V region of these transcripts. Different population of B cells express distinctly different IGH transcripts. Naïve B cells express predominantly membrane bound IGH transcripts that are non-mutated and of either IGHM or IGHD isotype. Activated B cells express predominantly secreted IGH transcripts that is mutated and of either IGHM, IGHA(1–2), IGHG(1–4), or IGHE isotype.The 3 novel isoforms we observed  all showed levels of somatic hypermutation similar to membrane bound IGHM and IGHD  and far The highly diverse and repetitive nature of the IGH locus and its transcript has made it hard to analyze. Here we employed several strategies to overcome these difficulties. First, instead of analyzing thousands to millions B cells, we used RNAseq to analyze complete IGH transcripts expressed by single B cells. To accomplish this we utilized the C1 Autoprep System which enables the automated amplification of full length cDNA from single cells which is then fragmented and sequenced on an Illumina sequencer. After sequencing, short reads can be computationally assembled into complete transcripts. Second, in addition to short read RNAseq, we sequenced full length IGH cDNA using the single molecule long read PacBio RS II sequencer. The resulting long reads represent complete IGH transcripts. By combining these technologies we were able to discover and validate a previously unknown splicing event in the IGH transcripts of IGHA isotypes. Instead of one continuous membrane exon (IGHA M), IGHA1 and IGHA2 feature two exons specific to their membrane bound isoform (IGHA M1 and IGHA M2) . This brAll study protocols and consent procedures were approved by the Institutional Review Boards at Stanford University. Written consent was obtained from every participant in the study. Blood samples were collected by the Stanford School of Medicine Blood Center. B cell were extracted from Whole Blood using Ficoll  gradients combined with Rosette-Sep B-cell (STEMCELL Technologies) separation kits.Total RNA was extracted from B cells using AllPrep DNA/RNA Mini kits (Qiagen) and cDNA was generated from B-Cell total RNA using SuperscriptIII with oligodT primers containing a tail with a universal priming site. We amplified the cDNA using Hifi Platinum Taq (Life Tech) with a primer for the universal priming site and a primer pool specific for the Leader region of the V segments. Library preparation and sequencing were done following standard Pacific Biosciences (PacBio) protocols. PacBio raw reads processed into circular consensus (CCS) reads using the PacBio pipeline. CCS reads were aligned to the IGH locus using IgBLAST (v.1.2.0) to identB cell suspension was loaded onto the Small RNAseq chip for the C1 system (Fluidigm) and RNAseq libraries were prepared following the standard protocol using Clontech and Nextera Reagents (Illumina). Libraries were sequenced on the HiSeq2000 (Illumina). The resulting reads (∼5 million per cell) were trimmed for high quality bases. Trimmed Reads were aligned to the genome using Tophat2. Reads mIsoforms where amplified from RNA using Superscript III and Hifi Platinum Taq (Life Tech) using primers targeting the respective exons. Bands were extracted from 2% agarose EX eGels (Life Tech) and if necessary reamplified. Sanger sequencing was performed by Sequetech (Mountain View).To generate amplicon for PacBio sequencing RNA was converted to cDNA using primer Oligo_dT_Universal TGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTTTTTTTTTTTTTTTTTTTTTTTV. We amplified cDNA using Universal_Primer TGACTGGAGTTCAGACGTGTGCTCTTCCGATC and V Leader primer pool . For Sanger sequencing validation of IGHA isoforms primers Val_IGA_CH3_F GACGCTGACGTGCCTGGCAC and Val_IGA_M2_R TGGAAGGCATGTGTAGTAAGGGCG were used. For Sanger sequencing validation of IGHM isoforms primers Val_J4_F ACTGGGGCCAAGGGACCCTGGT and Val_IGM_M2_R GAAGCCTGTGTGAGAGCACACAACT were used. For the identification and validation of low abundance isoforms of IGHD, IGHA and IGHG primers Val_J4_F and Val_IGD_M1_R TGATGAAAGTGACAATGCCGCTGTAG, Val_IGG_M1_R GGAGAAGATCCACTTCACCTTGAAGAAGG, and Val_IGA_M1_R CAGGCTCAGCAGGAAGAGGGTG were used.S1 Figa) In an alignment both PacBio read consensus generated from bulk B cell IGH RNA, and single B cell RNAseq read assembly generated of a single IGHA expressing B cells agreed to with the genomic reference in the IGHA1 M1/M2 region. Splice Donor and Splice Acceptor were predicted using SplicePort and agreed with the splice sites determined by PacBio reads and single B cell RNAseq read assembly b) Isoforms were amplified from bulk B cell RNA using primer for exons J4 and IGHA1 M2, gel purified and Sanger sequenced. Sanger sequence and consensus sequence of PacBio reads were aligned to a spliced genomic reference. c) PacBio single molecule sequencing reads mapped to the IGHA2 locus. Reads containing the whole VDJ region as well as either S or M exons were grouped and quantified. As for IGHA1, reads mapping to IGHA2 also showed a splicing separating 2 membrane exons (named IGHA2 M1 and M2)(PDF)Click here for additional data file.S2 FigIsoform transcripts were amplified from bulk B cell RNA using primer for exons J4 and IGHM M2, gel purified and Sanger sequenced. Sanger sequence and consensus sequence of PacBio reads (if isoforms are represented by more than 2 PacBio reads) were aligned to a spliced genomic reference of isoforms VDJ-CH1-M1-M2, VDJ-M1-M2, and VDJ-M2. Sanger and PacBio consensus reads agreed on splice junction in all cases.(PDF)Click here for additional data file.S3 FigIsoform transcripts were amplified from bulk B cell RNA using primer for exons J4 and M1 of the respective isotype. Several new isoforms were detected gel purified and Sanger sequenced. Gel images are shown on the left. Schematic representation of isoform splice structure as validated by Sanger sequencing are shown in the middle. Sanger sequences aligned to a spliced genomic reference of isoforms is shown on the right.(PDF)Click here for additional data file.S4 Figa) PacBio reads of IGHA transcripts and RNAseq read assemblies of single IGHA expressing B cells show that the IGHA1/2 3′UTRs are composed of 2 exons instead of one continuous 3′UTR. b) PacBio reads of IGHM transcripts and Sanger sequencing show several alternative splice variants of the IGHM constant region. If translated, these novel isoforms could serve a unique function on the membrane of likely naive B cells.(PDF)Click here for additional data file.


Importin α1 is involved in nuclear import as a receptor for proteins with a classical nuclear localization signal (cNLS). Here, we report that importin α1 is localized to the cell surface in several cancer cell lines and detected in their cultured medium. We also found that exogenously added importin α1 is associated with the cell membrane via interaction with heparan sulfate. Furthermore, we revealed that the cell surface importin α1 recognizes cNLS-containing substrates. More particularly, importin α1 bound directly to FGF1 and FGF2, secreted cNLS-containing growth factors, and addition of exogenous importin α1 enhanced the activation of ERK1/2, downstream targets of FGF1 signalling, in FGF1-stimulated cancer cells. Additionally, anti-importin α1 antibody treatment suppressed the importin α1−FGF1 complex formation and ERK1/2 activation, resulting in decreased cell growth. This study provides novel evidence that functional importin α1 is located at the cell surface, where it accelerates the proliferation of cancer cells. Nuclear−cytoplasmic transport of karyophilic proteins is a process that is conserved across species. In this process, signal sequences of cargo proteins, including classical nuclear localization signals (cNLSs), are recognized by transport factors to allow the cargo proteins to pass through the nuclear pore complex (NPC) between the cytoplasm and the nucleus12343434In humans, seven subtypes of importin α, which show different tissue-specific expression patterns and distinct cargo specificities, have been identified to date36791011Furthermore, many studies have recently reported that importin α1 is highly expressed in diverse types of cancers, including breast cancer, hepatocellular carcinoma, lung cancer, melanoma, and ovarian cancer131415In this study, using a combination of flow cytometric, biochemical, and confocal microscopic approaches, we show for the first time that importin α1 is localized to the cell surface in several human cancer cell lines. Furthermore, we found that importin α1 at the cell surface is associated with a growth factor, FGF1, thereby enhancing its signalling pathway and accelerating the proliferation of cancer cells. This is the first evidence showing that proteins that ordinarily function within cells can localize to the cell surface where they participate in novel physiological activities.Recently, we performed cell-based proteomic experiments using human vascular endothelial cells to screen for cell surface protein targets that may be involved in systemic sclerosisTo confirm the findings above, we next performed a cell surface biotinylation assay, in which cell surface proteins of HCT116, HepG2, and AGS cells were first biotinylated, and the cell lysates were then incubated with avidin beads to precipitate the cell surface biotinylated proteins, followed by immunoblot analysis. As shown in Furthermore, we attempted to visualize cell surface expression of importin α1 in HCT116 cells using confocal laser scanning microscopy. Intracellular importin α1 showed diffuse localization throughout the cell after detergent-based cell permeabilization after fixation , +TritonTo further confirm that importin α1 is localized to the cell surface, HCT116 cells were transiently transfected with the N-terminal Flag-tagged importin α1 expression vector. Flow cytometric analysis using either anti-Flag antibody , which is recognized by importin α1. Therefore, next, to address whether the cell surface importin α1 is functional, we examined whether it binds to a typical NLS-containing substrate, the SV40 large T antigen NLS-fused GST and GFP (GST-NLS-GFP), which is known to bind to importin α1Furthermore, to examine whether the association of GST-NLS-GFP with HCT116 cells is dependent on importin α1, we manipulated the cell surface expression levels of importin α1 by knockdown or overexpression procedures. Knockdown of importin α1 by siRNA significantly decreased the amount of cell surface importin α1 , leadingIt has previously been reported that importin α1 is detected in the sera of patients with lung cancerNext, to exclude the possibility that importin α1 in cultured medium was enclosed by a lipid layer, e.g., exosomes, we measured free importin α1 in cultured medium by a sandwich ELISA using two different anti-importin α1 antibodies. Under non-denatured conditions, free importin α1 was detected in the supernatant of HepG2 cultures, while the amount of importin α1 in the supernatant of AGS culture was comparable to background levels , indicatThese findings led us to suppose that the importin α1 released into the extracellular space is attached to the cell surface. To verify this, we added recombinant Flag-importin α1 proteins to the cultured medium. As shown in in vitro solution-binding assay, in which recombinant importin α1 was mixed with heparin-conjugated beads. As expected, we found that heparin directly bound to importin α1 , vascular endothelial growth factor (VEGF)-A and hepatocyte growth factor (HGF), have cationic amino-acid clusters212223ortin α1 . To idenortin α1 . In addiortin α1 , indicatortin α1 .To clarify the interaction between HS and importin α1 on the cell surface, HCT116 cells were incubated with recombinant importin α1 in the presence of free heparin. Association of importin α1 with the cells was clearly reduced by the addition of heparin . Moreovein vitro binding assay, we confirmed that recombinant GST-tagged FGF1, FGF2, and IGF-BP5 proteins interacted with importin α1 , are localized to and function in the nucleus, and that most of these proteins have their own cNLSs that are recognized by the importin α family and translocated into the nucleus262728ortin α1 . In thisWe investigated whether the cell surface importin α1 affects FGF1 signalling in cancer cells. In HCT116 cells, the exogenous addition of FGF1 slightly increased cell proliferation . Under tNext, to determine how the cell surface importin α1 is involved in the cellular response to FGF1, we monitored the activation of extracellular signal-regulated kinase 1/2 (ERK1/2), major targets of the FGF signalling pathway3031To confirm the involvement of the cell surface importin α1 in FGF1 signalling, we performed an RNAi study using HepG2 cells. Knockdown of importin α1 resulted in marked down-regulation of the activation of ERK1/2 in HepG2 cells in the presence of FGF1, and this suppressive effect was restored by the addition of exogenous importin α1 under the FGF1 stimulation . These rTo further confirm that importin α1 affects FGF1 signalling, we applied anti-importin α1 monoclonal antibody (mAb) to block the function of the extracellular and cell surface importin α1. We found that anti-importin α1 mAb impaired the interaction of importin α1 with FGF1 , and decFinally, we treated cancer cells with the anti-importin α1 mAb to establish whether the anti-importin α1 mAb can indeed diminish the proliferation of cancer cells. In HCT116 and HepG2 cells, cell proliferation was significantly reduced by anti-importin α1 mAb treatment , while tThis study demonstrates the novel cellular localization and function of importin α1. That is, importin α1 is localized to and functions at the cell surface of some cancer cells. Indeed, we showed that cell surface importin α1 associates with growth factors, such as FGFs (FGF1 and FGF2), and enhances FGF1 signalling. Furthermore, we found that the treatment with an anti-importin α1 antibody impaired FGF1 signalling, resulting in the suppression of cancer cell proliferation.Importin α1 has been regarded as a novel cancer marker in several cancers, because of its overexpression in some tumour tissues that correlates with tumour progression141518Several studies have so far reported that some proteins that were originally identified as having a cytoplasmic or nuclear localization, such as calreticulin , GRP78/BiP (ER), hnRNP-K (nucleus), and nucleolin (nucleus)193435363738It has recently been reported that importin α1 is present in the sera of lung cancer patientsIt is known that FGFs can regulate cellular functions through an evolutionarily conserved signalling module that functions in both invertebrates and vertebratesIt is also known that FGFs have cNLSs and accumulate in the nucleus in an importin α/β-dependent manner. However, it is still unknown how extracellular FGFs traverse the cell membrane to enter the cell. Thus, it is interesting to know whether cell surface importin α1 is also involved in the internalization of extracellular FGFs. Further studies will be required to address these questions.Cell surface proteins specific to cancer cells and soluble proteins in serum that promote tumorigenesis have been considered as potential targets for the treatment of cancers. Therapies targeted at the extracellular importin α1 , as well as those that are targeted at released importin α1-mediated induction of FGF1 signalling, are expected to perturb the proliferation of importin α1-releasing cancer cells, thereby providing a novel option for the treatment of cell surface importin α1-related cancers.The colon cancer line HCT116, the hepatocellular carcinoma lines HepG2, Hep3B, and HLE, the lung cancer lines A549 and PC9, the breast cancer lines MCF-7, and MRK-nu-1, the fibroblast cell line TIG-1, and the gastric cancer lines KATOIII and AGS were obtained from the Japanese Collection of Research Bioresources . The breast cancer lines, SKBr3 and MDA-MB-231, and the normal mammary epithelial cell line MCF-10A, were kindly provided by H. Fujii . HCT116, HepG2, Hep3B, HLE, TIG-1, A549, and SKBr3 were cultured in Dulbecco’s modified Eagle’s medium  supplemented with 10% fetal bovine serum . KATOIII, AGS, PC9, and MDA-MB-231 were cultured in RPMI1640 (Sigma) supplemented with 10% FBS. The microvascular endothelial cells dHMVEC  was cultured in growth factor complete endothelial basal media  supplemented with 5% FBS. MCF-10A was cultured in MEGM BulleKit (Lonza). MCF7 was cultured in MEM (Sigma) supplemented with 0.1 mM NEAA, 1 mM sodium pyruvate, 10 μg/mL insulin, and 10% FBS. The breast cancer line MRK-nu-1 was cultured in DMEM/F12 (Sigma) supplemented with 10% FBS.Recombinant EGF, FGF1, FGF2, IGF-BP3, IGF-BP5, and IFN-γ1 were purchased from Peprotech . pGEX-6P2-3 × Flag-importin α1 (human) was obtained as described previously8The proteins were added to transport buffer  in the presence or absence of anti-importin α1 mAb, and mixed with glutathione-sepharose 4B beads  or heparin-sepharose beads (Sigma). The mixtures were incubated at 4 °C for 1 h and then washed with transport buffer. Bound proteins were eluted with sample buffer for sodium dodecylsulfate-polyacrylamide gel electrophoresis (SDS-PAGE).p3 × Flag-CMV-importin α1 and p3 × Flag-CMV empty vectors were transfected into HCT116 cells with Lipofectamine 3000 . The sequences of the siRNA (chimeric RNA-DNA) duplexes have previously been describedg for 5 min at 4 °C. Cell suspensions were incubated with anti-human importin α1 mouse mAb , Alexa488-conjugated anti-mouse IgGs  in phosphate-buffered saline (PBS) containing 0.1% FBS. Suspensions were incubated for 30 min at 4 °C. Flow cytometric analysis was performed using FACSCantoII . At least three independent experiments were performed.Cells were dissociated by Accutase (Thermo Fisher Scientific), and pelleted by centrifugation at 500 × 2 and 0.5 mM MgCl2 (PBS-CM) at 4 °C, and incubated for 15 min at 4 °C in 1.0 mg/mL of sulfo-N-hydroxysulfosuccinimide (sulfo-NHS)-Biotin (Thermo Fisher Scientific) dissolved in PBS-CM. To quench unreacted biotin, cells were washed three times with PBS-CM plus 50 mM glycine and twice with PBS-CMCells were washed twice with PBS containing 1 mM CaClELISA was performed using MaxiSorp plate (Thermo Fisher Scientific) coated with 0.5 μg/well of recombinant proteins. Candidate proteins or conditioned medium were incubated with plate-bound each protein for 1 h. The antigen-antibody complexes were detected with 1:5,000-diluted HRP-conjugated second antibodies with 3, 3′, 5, 5′-tetramethylbenzidine  as the substrate. OD was read at 450 nm using a Bio-Rad Microplate Reader Model 680 .Cultured cells grown on glass coverslides were fixed in 4% paraformaldehyde for 15 min at room temperature; then, cells were permeabilized with PBS containing 0.1% Triton-X100 and 1 mg/mL bovine serum albumin (BSA) for 5 min. The cells were washed three times with PBS before incubation at 4 °C overnight with anti-importin α1 mouse mAb . After washing three times with PBS, the cells were incubated at room temperature for 30 min with the appropriate Alexa Fluor 488-, or 647-conjugated secondary antibodies and stained with Hoechst 33342 (Thermo Fisher Scientific) for detection of nuclei. Cells were observed by confocal fluorescence microscopy .3 cells/well). After 24, 48, and 72 h of incubation at 37 °C, 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5--2H-tetrazolium  was added to each well. After 1 h of incubation, the water-soluble formazan dye, 1-methoxy-5-methylphenazinium, which is formed upon bio-reduction in the presence of an electron carrier, was measured in a microplate reader (Bio-Rad) at 450 nm. All samples were assayed in triplicate, and the results reported were means of triplicate wells.Cells were cultured in 96-well plates either in media alone, with isotype control IgG or anti-importin α1 mAb  in a total volume of 100 μL .


The pervasive expression of circular RNA is a recently discovered feature of gene expression in highly diverged eukaryotes, but the functions of most circular RNAs are still unknown. Computational methods to discover and quantify circular RNA are essential. Moreover, discovering biological contexts where circular RNAs are regulated will shed light on potential functional roles they may play.We present a new algorithm that increases the sensitivity and specificity of circular RNA detection by discovering and quantifying circular and linear RNA splicing events at both annotated and un-annotated exon boundaries, including intergenic regions of the genome, with high statistical confidence. Unlike approaches that rely on read count and exon homology to determine confidence in prediction of circular RNA expression, our algorithm uses a statistical approach. Using our algorithm, we unveiled striking induction of general and tissue-specific circular RNAs, including in the heart and lung, during human fetal development. We discover regions of the human fetal brain, such as the frontal cortex, with marked enrichment for genes where circular RNA isoforms are dominant.The vast majority of circular RNA production occurs at major spliceosome splice sites; however, we find the first examples of developmentally induced circular RNAs processed by the minor spliceosome, and an enriched propensity of minor spliceosome donors to splice into circular RNA at un-annotated, rather than annotated, exons. Together, these results suggest a potentially significant role for circular RNA in human development.The online version of this article (doi:10.1186/s13059-015-0690-5) contains supplementary material, which is available to authorized users. The pervasive expression of circular RNA from protein- and non-coding loci is a recently discovered feature of highly diverged eukaryotic gene expression programs, conserved from humans to very simple organisms such as fungi –5. IsolaDrosophila, where non-statistical alignment procedures produced millions of candidate circular RNAs which those authors believed to be dominated by artifacts [While isolated examples of circular RNA acting as microRNA sponges have been reported , 7, comprtifacts .High confidence reconstruction of mRNA splicing events in linear RNAs from short read RNA-Seq data remains a challenging and unsolved problem, despite advances in this area. Detection of circular RNA faces the same challenges as well as additional concerns, including systematic biases in many widely used sequencing techniques and algorithms that can lead to characteristic false positive or false negative calls. For example, some approaches will be biased against detecting very small exonic circles because read lengths exceed the length of these circular RNAs. Such an ascertainment bias might negatively impact results from genome-wide approaches to test the hypothesis that circular RNAs containing a single exon tend to be longer than other circularized exons and are therefore more efficiently circularized, as was suggested in .p value for this probability) for each detected circular or linear RNA junction from RNA-Seq experiments. It differs from other published methods in that it calculates a statistical score for each read based on alignment properties, including the number of mismatches and mapping quality. These scores are aggregated for all reads that span a putative junction to assess the strength of evidence that this junction is expressed, a concept which we previously used in a much more simple form to detect circular RNAs only at annotated exonic boundaries [In this paper, we present a new statistical algorithm for quantifying the expression of circular and linear RNA spliced at annotated and un-annotated exon boundaries. This new algorithm assigns confidence  and the offset (the number of nucleotides that overlap the junction boundary) should jointly predict the likelihood that a read is a decoy. Decoy reads, considered as a group, would be predicted to generally have smaller offsets and worse mapping quality and alignment scores compared with other classes of reads. We therefore built a statistical model, described below, to predict whether a read is a decoy based on these variables. Specifically, rather than ignoring decoy reads, we assigned them to a bucket (class 2) and compared them with reads consistent with originating from linear mRNA (class 1). We fit a logistic generalized linear model (GLM) with three predictors — alignment score , mapping quality, and offset position — to the response variable, which is the binary set of class labels (class 1 or 2 above). Because of the systematic differences in biochemistry and read quality between read 1 and read 2 on Illumina platforms, particularly for random-primed libraries, we only considered read 1 in assigning a paired-end read to class 1 or 2 , 18. ThiThe GLM was fit for each dataset, with the larger class downweighted so that each class had equal influence in the model. All reads within a class were initially weighted equally. After fitting this model, the weights of reads with poor fit to the model were downweighted in proportion to their lack of fit, maintaining the constraint that, together, reads in each class had equal total weight , the vast majority of circular junctions (70–80 %) detected in RNA-Seq data have fewer than five aligned reads. This holds even when experimental procedures to enrich for circular RNA, such as RNase-R, are used  + and poly(A)- fractions of multiple cell lines generated by the ENCODE consortium. Although poly(A) + libraries are greatly enriched for RNA with poly(A) tails, a small number of RNAs that lack poly(A) tails, such as small nucleolar RNAs (snoRNAs), are still present. For example, in RNA-Seq data from H9 embryonic stem cells (ESCs), although snoRNA is depleted during poly(A) selection as expected (25 % of snoRNAs have an average coverage of 0.6 or less in poly(A) + libraries compared with an average coverage of 1 or less in poly(A)- and of 2 or less in RNase-R+ libraries), some snoRNA are still readily detected in poly(A) + samples (25 % of snoRNA have an average coverage of 3.9 or more compared with 6.3 or more in poly(A)- and 31.9 or more in RNase-R+ libraries) (see “Assessing circular RNA expression in poly(A)+/− datasets, compared with benchmarking against RNase-R-treated RNA-Seq, allows a completely orthogonal approach to assessing computational methodology. As our previous work  and the Examples of the effectiveness of our approach in identifying false positive junctions with many mapped reads are depicted in Fig. Another example is highlighted in the analysis of our own RNA-Seq data from fetal heart tissue introduced below. In multiple samples, a putative circle formed by splicing MYH6 exon 7 to MYH7 exon 8 appears to be one of the two most highly expressed circular RNAs, and is a putative circle predicted by find_circ . MYH6 anp value for having the observed number of mismatches in the reads aligned to that junction under the null hypothesis that all mismatches are due to sequencing errors [p value represents a junction identified as a false positive while those with high p values represent junctions identified as true positives. As demonstrated by comparing the results of applying both methods to the ENCODE poly(A) + and poly(A)- RNA-Seq data for H1 ESCs  + and poly(A)- RNA-Seq data as well as RNase-R-treated RNA-Seq data generated in  with theIn data from HeLa RNA treated with RNase-R to enrich for circular RNA, we detected approximately 30 % more circles than CIRI, and comparable levels in poly(A)- data from BJ cells. However, in the RNase-R- HeLa cells, we detected an additional 984 circular RNAs, 1.6 times the number of circles reported by CIRI. Although these 984 circular RNAs were depleted by RNase-R, it has been demonstrated by PCR that some circular RNAs are particularly sensitive to RNase-R, although certainly much less sensitive than linear mRNA , 8, 25. As reported in , find_ciIn order to assess whether our increased sensitivity is accompanied by an increase in detection of likely false positive circular RNA, we quantified the overlap of circular RNA predicted in poly(A)+, poly(A)-, ribosomal RNA-depleted, and RNase-R-treated libraries generated from HeLa cells Fig. . Our GLMWe also analyzed the human H9 ESC data used by  to benchWe tested whether these improvements in detection sensitivity could have implications for inferences regarding mechanisms underlying species-specific circular RNA production by considering two examples highlighted in : 1) thatPrevious analyses suggest that short exons are less likely to be circularized , 22. We Testing the extent of evolutionary conservation between circular RNA production in orthologous genes has the potential to uncover mechanisms underlying circular RNA biogenesis and to infer function of these molecules , 6, 22. In contrast to this previous report, our algorithm identified expression of circular RNA from Polr2a in mouse ESCs, including in exons lacking flanking inverted repeats  selected RNA . FurtherIn its first step, the algorithm proceeds like many previously published methods: splitting each unaligned read into two parts and aligning each part separately to the genome. The algorithm then selects reads where the two parts aligned to the genome on the same strand, but in a relative orientation inconsistent with a linear RNA splice. Next, the algorithm bins the genome into non-overlapping 50-nucleotide bins and computes a list of all pairs of bins  where two separate fragments of the same read have aligned. For each such pair, the algorithm compiles all sequences which have split reads aligning to the bin pair  and performs a statistical test of whether these reads are consistent with being independent realizations of a single splicing event, producing a score statistic see “”. To redUsing this de novo method, we detected 38 of the junctions missed by our annotation-dependent algorithm when comparing our results with  on theirTo benchmark our algorithm, we examined the global distribution of the dinucleotides flanking the backspliced junctions identified by our de novo pipeline. This distribution is an unbiased assessment of its ability to discover splicing events as, importantly, generation of candidates has no bias to discover splicing events at canonical U2 “GT-AG” boundaries. However, in cases where the breakpoint of a circular junction read was ambiguous, we assigned it to a “GT-AG” boundary  if such existed. We focused our analysis on ENCODE tissue data (ENCODE accession GSE24565), and found a strong enrichment for the consensus motifs that flank canonical introns recognized by the major spliceosome: “GT” at the 5′ end of the intron and “AG” at the 3′ end. After re-alignment to the de novo junctional database see “”, ~70 % Complex alternative splicing is a hallmark of multicellular development. For example, a handful of splicing regulators are known to control essential alternative splicing programs in the brain and heart –31. To tWe used our algorithm’s quantitative estimate of junctional splice expression and a posterior probability of the confidence of this estimate to identify circular and linear RNAs that were expressed with high confidence (see “For each organ and each gene with a circular isoform(s), we derived a z score for each circular splice and each cognate exterior category of linear splices . The ENCODE RNA-Seq data were, like our own, produced from ribosomal-depleted RNA, and allow us to survey a wider array of tissues for evidence of circular RNA induction during fetal development. However, the ENCODE data do not have as much temporal sampling as our data; for most organs, there are only two developmental time points, collected between 2 and 10 weeks apart  in the later developmental time point compared with the earlier time point using binomial confidence intervals for the ratio of C/(E + C), which intrinsically normalizes for sequencing depth  and the maximum expression of any exterior mRNA splice (E). We quantified the number of genes where the ratio of circular RNA expression compared with mRNA was higher . Under the assumption that at least one splice junction is constitutive in each linear transcript, this will be a fair estimate of circular RNA:mRNA expression, if not an underestimate. These estimators revealed hundreds of genes with higher expression of circular RNA compared with linear RNA in many fetal tissues Fig. , includip < 0.001), suggesting a general lack of a predictive relationship between circular and linear expression, as previously reported in cell lines [We also tested for a relationship across all samples between the level of circular:linear isoform expression, which is intrinsically normalized for sequencing depth, and the level of linear isoform expression normalized to the total number of reads mapping to linear splice junctions. Specifically, for the 100 genes having the highest levels of circular RNA expression across all datasets, we computed the fraction of circular counts C/(C + E), and tested for a significant relationship with normalized exterior linear count expression (E) using regression see “”. The mall lines , 5, 6.Even when a regression coefficient is not significant, residuals from regression analysis can be used to assess whether the variation in the dependent variable, here circular RNA expression, is greater than can be explained by “noise” see “”. We useWhile the human fetal brain has the most remarkable level of total circular RNA expression, the single gene with the highest level of developmental induction of a circular RNA and highest expression across all of our RNA-Seq data was the dominant circular isoform of NCX1 in the heart. NCX1 is a sodium/calcium exchanger responsible for transporting calcium out of the cardiomyocyte after contraction . We obseThe dominant circular isoform of NCX1 contains a single exon encompassing the boundary between the 5′ untranslated region (UTR) and translation start site and is expressed at roughly two to five times the level of the linear isoform. Using our de novo algorithm, we also identified a highly expressed circular RNA differing from the dominant isoform by a three nucleotide deletion at the backsplice junction, which is present at roughly 10 % of the level of the major circular isoform and is also induced during development Fig. . We valiHuman tissues are comprised of a mixture of different cell types. The remarkable abundance of the circular isoform of NCX1 in the fetal heart samples suggests that it must be highly expressed in the majority of developing heart cells, and that its induction might be recapitulated by in vitro directed differentiation of human ESCs to cardiomyocytes. We tested this by harvesting human ESCs at sequential stages of differentiation — undifferentiated (day 0), mesoderm (day 2), cardiac progenitor (day 5) and definitive cardiomyocyte (day 14) — although these cells still lack some gene expression characteristics of fetal cardiomyocytes. We performed RNA sequencing in biological triplicate and the same analysis to test for temporal induction of circular RNA as we did for our human fetal samples . These junctions had a similar distribution of expression to those using annotated splice sites  to annotated splice sites, and flanked by canonical U2 dinucleotides. This phenomenon has been reported as a mechanism to expand the proteome through mRNA splicing , but to The U12, or minor, spliceosome is highly evolutionarily diverged from the U2 spliceosome and splices a small minority of human genes . HoweverHowever, analysis of U12 splicing with our de novo pipeline in the ENCODE data revealed a group of 17 predicted U12-dependent circular RNAs that use at least one splice site not currently annotated as exonic. The U12 circular RNAs using un-annotated splice sites were four-fold more highly expressed than U12 circular RNA at annotated exons. In some cases, U12 acceptors found by the de novo pipeline were in very close proximity to the mRNA transcript in the gene, such as in RANBP17 Fig. . ValidatIn this paper we have developed a new statistical algorithm for detecting and quantifying circular RNA and applied this algorithm to discover new biology of circular RNA expression. Our methods constitute a contribution to quantification of splicing events from RNA-Seq data by assigning statistical confidence to the expression of each junctional sequence by which we can identify both false positive junctional expression and improve detection sensitivity. Our approach to de novo detection of circular RNA is conceptually different from previous approaches that treat these events as binary without an associated statistical confidence. While we focus on applying this algorithm to discover circular RNA, we anticipate that it will be widely applicable to a range of experimental questions being addressed by RNA-Seq. The methods are also applicable to linear RNA splicing and we have used them to discover and validate novel linear splicing events (data not shown). We believe further algorithmic development along the conceptual lines we developed in this paper will lead to more powerful inferences from sequencing studies.Given the wide variety of RNA isolation and library preparation techniques used and the biases inherent in each , many RNtrans-acting factor that generally promotes circular RNA expression. Another possible explanation for this phenomenon is that circular RNA degradation is a slow process, and, therefore, as cells become quiescent and post-mitotic, they accumulate circular RNA while maintaining relatively constant levels of mRNA. A trivial explanation for our finding of developmental induction of circular RNA could be derived from sample handling, resulting in older samples having a bias towards ascertaining circular RNA at a higher rate than linear RNA. In principle, this could be due to, for example, higher activity of endogenous endonucleases in early fetal samples. However, our orthogonal tests did not support this hypothesis, as we were able to reproduce our findings in the ENCODE datasets and recapitulate induction of circular RNAs such as NCX1 and RHOBTB3 in the heart using an in vitro system where induced pluripotent stem cells are differentiated into cardiomyocytes.Our new methods allowed us to rapidly screen diverse datasets and identify biological contexts where circular RNA is regulated. We discovered global and specific examples of circular RNA variation over human fetal development. The global trend of increasing circular RNA expression during fetal development points to the possibility of up-regulation of a There is increasing evidence that circular RNA is involved in biochemical pathways, including acting as miRNA sponges , 7 and bDrosophila that suggests accumulation of circular RNA during aging [trans-acting factors controlling the production or degradation as an organism ages.Considering our results that circular RNAs are induced during human fetal development, and a recent study in ng aging , developDevelopmental regulation of circular RNA could potentially function to change protein–protein interactions (scaffolded by RNA) or the kinetics of bound protein. Either of these hypothetical functions could be important in development as gene expression programs change rapidly. Our finding of dynamic expression of circular RNA during development is a first step toward experiments that may reveal functions of circular RNA: it has already led us to find in vitro differentiation systems that recapitulate circular RNA expression over time and highlighted circular RNAs which we are now studying in the development of cardiac and neural lineages.Fetal tissue samples were collected under a protocol approved by the UCSD Institutional Review Board. Fetal tissue samples <100 mg in size were placed into DNase- and RNase-free 1.5 ml microfuge tubes containing 1 ml of RNAlater RNA Stabilization Reagent (Qiagen) within 1 h of the pregnancy termination procedure. After storage at room temperature for a period of 24–72 h, excess RNAlater was removed from the microfuge tubes and the samples were placed in the −80 °C freezer for storage until RNA isolation was performed. The tissues were lysed in mirVana (Life Technologies) lysis buffer, using a Mini-Beadbeater-16 (Biospec), with agitation for 1 min in the presence of 1 mm zirconia beads. Samples were then centrifuged at maximum speed in a tabletop microcentrifuge for 1 min and the lysed solution was transferred to a fresh microfuge tube. The remainder of the extraction was per the manufacturer’s protocol for the mirVana kit (Life Technologies). After extraction, RNA was quantified using the Quant-iT RNA BR Assay Kit (Life Technologies). RNA quality was assessed using the Agilent 2100 Bioanalyzer (Agilent) using RNA Nano-chips.Following RNA isolation , the RNA was quantified , and quality controlled . We used 200–1000 ng as input for the Illumina TruSeq Stranded Total RNA with Ribo-Zero Gold sample prep kit  and sequencing libraries were created according to the manufacturer’s protocol. Briefly, first both cytoplasmic and mitochondrial rRNA was removed by selectively hybridizing biotinylated probes to target sequences and using magnetic beads to capture the bound products. Following rRNA removal, the RNA was fragmented and copied into first strand cDNA using random primers and reverse transcriptase. Next, second strand cDNA synthesis was completed using DNA polymerase I and RNase H. The cDNA was then ligated to Illumina supplied adapters and enriched by PCR to create the final cDNA libraries. The libraries were then pooled and sequenced on a HiSeq 2000  instrument as per the manufacturer’s instructions. Sequencing was performed up to 2 × 101 cycles. Data are available under accession GSE64283.cDNA synthesis was done with random hexamers and M-MLV Reverse Transcriptase RNase H Minus Point Mutant (Promega or NEB) with the following program: 25 °C for 10 min, 42 °C for 50 min, 45 °C for 5 min, 50 °C for 5 min, 85 °C for 5 min, 4 °C hold. Pre-treatment with RNase-R followed the protocol in [Raw fastq files available on 8 August 2014 were downloaded from the ENCODE project website. We selected all whole-cell long poly(A)- and poly(A) + reads banked at . At thatRaw fastq files available on 9 October 2014 were downloaded from . We seleRaw fastq files available on 1 November 2014 were downloaded from the Sequence Read Archive (SRA). There were two to four technical replicates per sample and we selected the run that generated the most reads for each sample. All 32 samples available for adrenal, brain, heart, kidney, liver, lung, and muscle, and the 16 available for uterus were downloaded.Raw fastq files for ribo-minus experiments available on 12 December 2014 for ventricle tissues were downloaded from SRA. One sample from each of five time points ranging from postnatal day 1 to postnatal day 90 were available.The raw fastq file for poly(A)- RNA available on 6 December 2014 was downloaded from SRA.The raw fastq file for poly(A)-/RNase-R-treated RNA available on 8 December 2014 was downloaded from SRA.The raw fastq files under SRR1637089 and SRR1637090 were downloaded from SRA on 28 January 2015.Raw fastq files were processed using TrimGalore  version We start with an annotated genome (hg19 UCSC annotation downloaded from ) to creaWe align the mate reads from a paired-end RNA-Seq experiment independently to the genome index, linear junction index, and scrambled junction index as if they were single-end reads and subsequently aggregate the mate information for all diagnostic reads, those where read 1 (R1) aligned across a junctional sequence. To be conservative, diagnostic reads where R1 also aligned to the genome are discarded, and if R1 aligned to both a scrambled junction and a linear junction, it is assigned to the linear junction only. Similarly, if read 2 (R2) aligns to more than one of the indices, we select the one with the highest Bowtie2 alignment score and in the case of ties we give preference to the genomic alignment, then the linear junction, and finally the scrambled junction. Based on the alignment position of its mate (R2), each diagnostic read is categorized as linear, circular, or decoy. Linear reads are those where R1 aligns to the linear junction index and R2 aligns concordantly in the genome or linear junction index to support a linear transcript. All reads where R1 aligns to the scrambled index are categorized as circular if R2 aligns within the genomic region of the presumed circle defined by the junctional exons , or decoy if R2 aligns outside of this region. We allow for the possibility that the presumed circle may include or exclude any introns and exons within the genomic coordinates involved in the junction.In the absence of a ground truth, we proceeded with the premise that the large majority of linear reads most likely represent true alignments and the decoy reads are more likely to be false alignments due to either sequencing or alignment errors. Supporting the validity of this premise, we do observe that the linear reads tend to have higher alignment scores (indicating fewer mismatches) and higher mapping qualities  compared with the decoy reads. We also observed a difference in the distribution of the number of nucleotides in a junction-aligned read that extend across the junction boundary. The distribution of these alignment features for the two categories of reads in a representative sample is shown in Additional file i is a correct alignment (Yi = 1) or an artifact (Yi = 2). Using a binomial distribution with probability wi, is adjusted by an amount proportional to the probability predicted by the GLM in step 1, ith read in the linear category, ith read in the decoy category, We build a statistical model for each sample that combines the information contained in these three alignment features to estimate the probability that each read potentially representing a circular RNA is a true alignment, as opposed to an artifactual alignment, using a logistic GLM. We use this model to achieve an estimated probability that each read The model is then fit using these updated weights and this final model is used to predict To be conservative, we use the lower bound of the 95 % confidence interval on p value for the posterior probability based on the null hypothesis that the probability Pn is a function of:As outlined in the text, we compute a pi for each read as predicted from fitting the GLM, and thereby a p value computed for yn or log(yn) is equivalent to a p value for Pn. We use this transformation so the empirical variance can be obtained in closed form. We compute the null distribution for each ln = log(yn) as follows: a normalized z-score is computed as:E(ln) = n ∗ μ and μ is the empirical average of the random variable ln across all decoys and linear junctions and var(ln) is the empirical variance of ln. Since zn is normally distributed under the null hypothesis, we report a p value based on a one-sided test on this z score that is low if the posterior probability that the junction is truly expressed is higher than expected by chance.where the product is over p value as calculated for linear junctions is overly conservative, since the GLM predicts on data that was not used in fitting the GLM. Thus, for circular junctions, we use a simple model to estimate the p value (and Benjamini-Hochberg corrected FDR) for each junction. Under this model, we assume a null model that if a junction is an artifact, with 95 % probability, a read will have a predicted probability of 0.1, and with 5 % probability (due to chance), it will have a predicted probability of 0.9. Using this model, we compute a z score and p value as described above where:For circular junctions, the p value is conservatively computed using the minimum posterior probability for each junction to be considered in our report processing: 0.9. Using this lower bound for all posterior probabilities allows us to estimate a conservative Benjamini-Hochberg FDR which compares the ith smallest p value to The resulting p value for observing this many or fewer mismatches given the total number of bases in the aligned reads for this junction. This method is used by our pipeline when only single-end RNA-Seq data are available, and for de novo discovery.We model the null hypothesis that all mismatches are due to sequencing error using Poisson(0.01) for Illumina reads. We sum the number of mismatches observed in all reads aligned to a junction and calculate the p and at least n reads were carried forward in the analysis and are reported in Additional file n = 2; for the ENCODE data, n = 2; for cardiomyocyte reports, n = 1; p = 0.9 for all datasets.Junctions with a posterior probability exceeding Reads that failed to align to any reference in the annotated circular RNA pipeline were culled for further analysis. In this paper, we only consider R1 for building the candidate list of novel junctions. To do this, each R1 was aligned to the genome using Bowtie version 0.12.7 in two segments via a ‘split read’ approach: the 5′ end of the read was aligned using the flag --trim5 65 and the 3′ end of the read was aligned using the flag --trim3 65. Each such segment must align uniquely with no more than two mismatches (Bowtie flags -v 2 -m 1).After this step, the algorithm bins the genome into disjoint 50-nucleotide bins . Note that CIRI  circumveNi is the number of such sequences at position i and p is the technical error associated with misreading the wild-type base. For Illumina reads, if p = 0.01 and the sum is taken over roughly 100 nucleotides, the expected score is 1. We do not consider the variance of this score in this paper, but only consider de novo junctions where the score is lower than 5. For each pair of bins, the algorithm returns the consensus assembly of all sequences mapping to the  pair. A name is assigned to each de novo junction consensus sequence based on a heuristic that if the junction falls within 1 kb of an annotated gene, it is named by that gene and “UNAN” (un-annotated) is assigned otherwise. A de novo index is created from these consensus sequences.where the sum is over all nucleotides with more than one overlapping sequence, p values > 0.9 using the naïve algorithm detailed above, decoy/circle ratios < 0.1, and bins with breakpoints greater than 200 nucleotides were carried forward in analysis, and bins were subsequently collapsed to unique chromosomal breakpoints.All unaligned reads from the annotation-dependent pipeline are realigned to the de novo index using Bowtie2.2.2 and the same alignment criteria described above for annotated junction indices. A read was considered a decoy if the mate aligned to a different chromosome, in the same orientation instead of opposite as expected for paired-end reads, or outside the circle defined by the junction boundary . Less stringent lower bounds were imposed for cardiomyocyte data because sampling depth was much more shallow.Linear junctions where both splice sites are interior to any circular RNA expressed at greater than p value and FDR calculation for circular junction posterior probabilities), this results in an estimated FDR of < 0.001. We repeated this analysis without the minimum read count of 10 (data not shown) and also using linear junctions that shared a splice site with a circular RNA , the sufficient statistics (data) for the expression of each junction are the sum of the read counts. Analysis of change of junctional expression over time was computed by z statistics that were functions of these summed counts and defined as:ri are the raw values and ci are the per-sample normalizing constants above.xi} are independent and identically distributed (i.i.d.). Therefore, defining:Under the null hypothesis of no change over time, {yi = xi − μ for any function o with sum ||o||22 = 1 and ||o||1 = 0, define the statistic:xi are i.i.d., the numerator of zi is a linear combination of exchangeable random variables. The mean of the numerator is 0 by construction, so z has mean zero as o is orthogonal to the unit vector. By independence:Under the null hypothesis that ri} are Poisson, the constant cs has small but nonzero covariance with {xi} and the plug in estimator for μ is mean {xi}. We approximate cs and x as being independent. Because ||o||22 = 1 by construction, under the null hypothesis, z is approximately N. We chose oi to be proportional to the sample age in weeks of sample i so that large values of z can be interpreted as corresponding to consistent and large changes over time.under the assumption that {Figure p value and FDR calculation for circular junction posterior probabilities), this results in an estimated FDR of < 0.001. After linear reads are assigned to the exterior category for each gene, the maximum count for any exterior linear junction is taken. For each gene, a pair of numbers, nc (circle count) and nl (maximum linear count), is used in the following analysis. For each tissue with samples from only two time points, both fetal times, the sample from the earliest time point is considered “early” and the sample from the latest time point is considered “late”. For the heart, where there were samples from three time points, we used the two earliest time points as early and late  as follows:we took all reads mapping to a junction and divided by We chose the genes with the 100 most highly expressed circular RNAs (at the level of absolute counts) in the ENCODE fetal dataset and performed the following analysis. First, the fraction of circular reads was calculated where:nc being the circular reads and nl being the exterior linear reads described above. Under the assumption that nc and nl are Poisson, conditioned on n = nl + nc, nc has the binomial distribution with success nl, normalized to the total number of reads. Normalization was performed by dividing the number of reads mapped to each junction by the number of reads mapping to linear junctions. It should be noted that normalization does not impact the relative circular:linear ratio for any gene in a given sample since the circular and linear counts are divided by the same number.estimating To identify significant outliers and test if there were tissue-consistent patterns in such outliers if they existed, we took the residuals from using the normalized linear reads in a linear model for estimating zo by the empirical median estimated standard deviation of zo, sd(zo), always > 1, and referred the values zo/sd(zo) to the t distribution with n-2 degrees of freedom where n = number of data points. We called outliers by transforming zo/sd(zo) to p values and used Benjamini-Hochberg multiple testing correction to control the FDR at level 0.001, identifying samples with circular RNA expression that exceeded what was predicted by the model. Out of 30 genes with more than one outlier, 19 genes were represented by both samples from the same organ .which should be approximately standard normal under the null hypothesis. To account for the overdispersion in the Poisson, we additionally divided RUES2 human ESCs were maintained in mouse embryonic fibroblast-conditioned medium. Standard cardiomyocyte directed differentiation using a monolayer platform was performed with a modified protocol based on previous reports –49. The +; replicate 2, 92 ± 1.2 % cTnT+; replicate 3, 91 ± 0.3 % cTnT+.Wild-type RUES2 cells were labeled for flow cytometry using an anti-cardiac troponin T antibody  or corresponding isotype control to assess the purity of purified cells. Cells were analyzed using a BD FACSCANTO II with FACSDiva software (BD Biosciences). Instrument settings were adjusted to avoid spectral overlap. Data analysis was performed using FlowJo . Statistics were: replicate 1, 93 ± 0.3 % cTnT4 possible pairs of “donors” and “acceptors”. All of these decoy flanking dinucleotides were significantly less, if at all, enriched than “GT/AG”. Although several U12 flanking dinucleotides were identified, as with linear RNA, these account for a small minority of circular RNA splicing.All de novo sequences with score S (described above) < 4 were culled for further analysis. Each junctional read representing the consensus sequence of the de novo junction associated with the bin (see above) was split into all possible sequence fragments of at least 20, modeling each potential breakpoint, and each fragment was separately aligned to the subset of the genome 500 nucleotides upstream and downstream of the 50-nucleotide binned coordinates to reduce complexity of the index, using the Bowtie parameters --all -m 40 -f -v 1 . The flanking dinucleotides were then computed for each potential junction break point. Candidate junctions where a breakpoint was successfully mapped were carried forward. A Bowtie2 index was built with these sequences and reads were realigned using the same parameters initially used to align to the annotation-dependent junction indices. After realignment of all unaligned reads to the index of de novo assembled sequences where breakpoints could be mapped (as above), we computed the total number of reads mapping to these de novo junctions. Note that in some cases there is ambiguity in exact breakpoint for a junction as the consensus at the 5′ splice site is “ag-GT” . Therefore, if the mapped donor and acceptors ever had a flanking GT-AG (or TATCCT one nucleotide downstream of the donor), the read was called canonical U2 (or U12). To test whether this default assignment biased our results, we repeated the di-nucleotide enrichment analysis for all 4RNA-Seq data and processed reports for our fetal samples, as well as processed reports for the ENCODE fetal samples analyzed, are available under accession GSE64283. RNA-Seq data and processed reports for induced cardiomyocytes are available under accession GSE64417.The algorithm for the quantification of splicing events and assigning a confidence to each junction is written in a set of custom bash, Python, Perl, and R scripts. Running the entire pipeline requires invoking two bash commands, described in a README file available with the code. Code is provided to execute the analysis pipeline on a single Linux machine or on a cluster using SLURM. The code is available at .