Flaviviridae family. Although research findings for the assembly of HCV particles are accumulating due to development of HCV cell culture system, the mechanism(s) by which the HCV genome becomes encapsidated remains largely unclear. In general, viral RNA represents only a small fraction of the RNA molecules in the cells infected with RNA viruses, but the viral genomic RNA is considered to selectively packaged into virions. It was recently demonstrated that HCV RNAs containing 3′ end of the genome are selectively incorporated into virus particles during the assembly process and the 3′ untranslated region functions as a cis-acting element for RNA packaging. Here, we discuss the molecular basis of RNA encapsidation of HCV and classical flaviviruses, contrast with the packaging mechanism of HIV-1.Hepatitis C virus (HCV), a major etiologic agent of human liver diseases, is a positive-sense single-stranded RNA virus and is classified in the  Hepatitis C virus (HCV) infection is a major cause of chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma worldwide. In spite of the fact that HCV is targeted by innate, cellular and humoral immune mechanisms, its long-standing persistent infection can be established in a majority of the infected individuals. The recent development of direct-acting antivirals, which target specifically HCV non-structural proteins, initiated the era of high efficacy and well-tolerated medications with high cure rates .Hepacivirus genes within the Flaviviridae family, which includes the classical flaviviruses such as yellow fever virus and dengue virus. The viral particle consists of a nucleocapsid, surrounded by a lipid envelope containing two viral glycoproteins, E1 and E2    located at NS5B region, which consists of three conserved domains, 5BSL3.1, 5BSL3.2, and 5BSL3.3, have been demonstrated for their regulatory roles in translation and genome replication via long distance interacting with 5′ UTR, 3′ UTR and SL9033 (a structured RNA element upstream of CRE) (The 5′ UTR is a ∼340-nucleotide (nt) element composed by four highly structured domains and is implicated in almost the whole processing of HCV life cycle, except virions entry. Domain I of the 5′ UTR comprises a single stem-loop. Domains II to IV of the 5′ UTR constitute an internal ribosomal entry site (IRES), which is a prerequisite for cap-independent translation of viral RNA (igure 1) . While iigure 1) . The 3′ igure 1) . Althougigure 1) . Howeverigure 1) . The 3′Xigure 1) . Though  of CRE) . The pol of CRE) .cis-acting RNA structures required for the viral encapsidation  within genomic nucleic acids, termed packaging signal(s), by the viral capsid or coat protein, we hypothesized that the specific packaging of HCV genome is driven by interaction of the viral RNA with capsid protein, Core. Our equences . Notablyin vitro binding assays suggested that 3′ UTR, rather than other RNA elements tested, is potentially implicated in the viral encapsidation, only successful directing of RNA into virions could authentically prove its role as a cis-packaging signal. By utilizing a trans-packaging system  to screen RNA aptamers that bind specifically to Core and identified 8 short RNA motifs within the HCV ORF that are possibly involved in the virion assembly or the late stages of the HCV life cycle , Pegivirus and Pestivirus. Flaviviruses, members of genus Flavivirus, such as the yellow fever virus and dengue virus, contains a range of zoonotic viruses and are largely transmitted to vertebrate hosts by arthropod vectors. The genomic organization of flaviviruses shared great similarity with HCV, and the genome consists of a single large open reading frame flanked by a short 5′ UTR and a short 3′ UTR (7-GpppA-me2), thus translation of the flavivirus genome is Cap-dependent. There are two conserved structural elements in 5′ UTR, a large stem loop (SLA) and a short stem loop (SLB). SLA is likely to act as a promoter, which is essential for viral RNA synthesis. SLB is involved in interactions between the 5′ UTR and 3′ UTR. The 3′ UTRs are typically 0.3–0.5 kb in length and contain several highly conserved structures, some of which have been characterized to be important in facilitating the viral genome replication (cis-acting elements important for RNA packaging have not been identified in the genomic RNA thus far. It has been considered that replication and encapsidation of flaviviruses are functionally coupled, which was evidenced by unable to detect infectious virus particles production with a plasmid DNA encoding replication-deficient full-length Kunji virus cDNA (trans-packaging system with replication-defective subgenome was approximately 10-fold lower compared to that with the replication-competent setting (The t 3′ UTR . Differelication . The intlication . Howeverrus cDNA . As it h setting . One cou setting . The sig setting . It is scis-acting RNA elements in the viral genome and the trans-acting elements in Gag protein (So far, the packaging of genomic RNA of retroviruses has been better elucidated than any other RNA viruses. Herein, we will take HIV-1 as an example to briefly compare the packaging processing with that of HCV. Upon viral entry to the target cells, the RNA genome of HIV is reverse-transcribed into double-stranded DNA, followed by integrating into the cellular genome to form a provirus . The spl protein . The 5′  protein , which c protein . A part  protein . Packagi protein , thus pa protein . Mutatio protein . A recen protein , suggest protein . IntriguThere is accumulating evidence regarding nucleic acids packaged in the virus particles determined by new-generation sequencing techniques. In addition to the viral RNAs, significant amounts of host RNAs are detectable in HIV-1 particles, such as host mRNAs, tRNAs, and 7SL RNA . Recruitcis-acting RNA elements that are crucial for the genome packaging of flaviviruses or other positive-strand RNA viruses.For several positive-strand RNA viruses; not only flavivirus , but polAll authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Bacillus genus, like Bacillus pumilus, a common producer of extracellular RNase binase, can populate the intestinal microbiome as a colonizing organism. Without involving metal ions as cofactors, binase depolymerizes RNA by cleaving the 3′,5′-phosphodiester bond and generates 2′,3′-cyclic guanosine phosphates in the first stage of a catalytic reaction. Maintained in the reaction mixture for more than one hour, such messengers can affect the human intestinal microflora and the human body. In the present study, we found that the rate of 2′,3′-cGMP was growing in the presence of transition metals that stabilized the RNA structure. At the same time, transition metal ions only marginally reduced the amount of 2′,3′-cGMP, blocking binase recognition sites of guanine at N7 of nucleophilic purine bases.Extracellular enzymes of intestinal microbiota are the key agents that affect functional activity of the body as they directly interact with epithelial and immune cells. Several species of the Bacillus secrete RNases that can perform both stages of the catalytic reaction. Specifically, such species are B. pumilus and B. licheniformis that can be isolated from the human gastrointestinal tract, representing resident rather than transient microbiota [ Bacillus spores in the human intestine and transient colonization are part of the life cycle of human related bacilli [ B. pumilus in the biopsies of human rectal epithelium after preoperative bowel cleansing [The T1 family EC 3.1.27.3) ribonucleases (RNases) hydrolize RNA and cleave the 3′,5′-phosphodiester bond between guanosine 3′-phosphate and the 5′-OH group of the adjacent nucleotide, forming a 2′,3′-cyclic guanosine phosphate in the first stage of a catalytic reaction. This stage is reversible and is much faster than the second, in which the cyclic intermediate is hydrolyzed to a corresponding 3′-phosphate [7.3 ribonleansing . Hence, leansing , 7. In aleansing  and, simleansing . Althougleansing , 11.It is known that cyclic phosphates can only be hydrolyzed by RNase after all poly- and oligoribonucleotides have been cleaved . Recentl Escherichia coli BL21, carrying pGEMGX1/ent/Bi plasmid. Binase catalytic activity toward yeast RNA is 14000000 U/mg at pH 8.5 [We used guanyl preferring RNase, binase , isolated as a homogeneous protein with catalytic activity, from a culture fluid of a recombinant strain oft pH 8.5 . Torula yeast, and 3′,5′-cGMP were purchased from Sigma-Aldrich (Germany). Ions of transition  and nontransition  elements were compounded in the reaction mixture in the form of high-purity chlorides .Positional isomers of cyclic nucleotides 3′,5′-cGMP and 2′,3′-cGMP, tRNA from2HPO4, 1.77 мМ NaH2PO4, 140 мМ NaCl, and pH 7.4) was added, and the solution was thoroughly mixed and centrifuged for 2 min at 12000 ×g. The supernatant was decanted into a tube containing 60 mcL EPBS and frozen at −80°C. The obtained 2′,3′-cGMP product was determined by enzyme immunoassay (ELISA), using antibodies against 3′,5′-cGMP.Binase was incubated in a total volume of 200 mcL in 0.5 M Tris-HCl buffer (рH 8.5) with yeast RNA at concentrations, defined for specific experiments, at 37°C for 15 min. The catalytic reaction was stopped in cold. Then, 100 mcL EPBS (8.24 мМ Na−5 М (10 nM/mL and higher) [2SO4 was added and the plate was incubated for 5 min and analyzed on a colorimeter at 450 nm. The colour intensity is inversely proportional to the amount of cyclic nucleotides that bound to antibody. Concentrations of cyclic nucleotides were calculated by using the calibration curve [We used commercial 96-well plates with immobilized goat anti-rabbit antibodies (secondary antibody), primary rabbit anti-3′,5′-cGMP, and the appropriate reagents in accordance with manufacturer's instructions . RNA hydrolysis by guanyl-specific microbial RNases does not lead to the formation of 3′,5′-cGMP . Earlier higher) . In totaon curve .1Н NMR-spectra in an aqueous solution that contained chlorides of transition (Mn2+) and nontransition metals , as well as tRNA that contained Mn2+ in aqueous solution, were recorded on a NMR spectrometer AVANCE III-700 , equipped with a quad cryogenic sensor  CryoProbe. 1Н NMR recording of tRNA spectra in the aqueous solution with ions of nontransition metals  was performed by using an NMR spectrometer AVANCE II-500 .Binase 2H. The 1H-NMR-spectra were recorded using 90° pulses . NMR-spectra were recorded at 298 K, pH 5.5.The spectrometers operated in the mode of internal stabilization at the resonance line 2+ led to a broader signal in the NMR spectrum  was found to be insignificant. Unlike Mn2+, these ions do not have strong paramagnetic properties and, therefore, have no pronounced effect on RNA in the NMR spectrum  and restores the folding [It is difficult to interpret the role of ions, as it is hard to distinguish between the direct catalytic impact of ions and the indirect role of ions in active RNA structure. Recently, it has been found that the transition metals such as Nitructure . Mg2+, Msplicing . Ions of or Мg2+ . For sev folding .μM) and more than 20 weak Mg2+-binding sites [2+ interaction is primarily mediated by direct binding of oxygen atoms in spatially close phosphate groups and, to a lesser extent, by binding of 2′-hydroxyl groups, N7 atom of purine bases, and keto-oxygen atoms (U and G). RNA-Mg2+ binding motifs are represented by (1) the major groove of mixed G•U pair, followed by Y•G pair; (2) shifted G•A pair; (3) magnesium “clamp”; (4) metal ion lightning; (5) E-loop motif; (6) AA platform; and (7) phosphate guanine binding motif [We have shown that transition and nontransition ions have different impact on the tRNA structure and 2′,3′-cGMP  [Despite having similar radius, charge, and RNA site, Mgdentical . Mg2+ anintained . Transit purine) .2+ in the NMR spectroscopy of RNA can be explained by its affinity to N7 purines (2+ ions either limits accessibility for guanyl-specific binase sites in RNA or hinders the recognition of guanine. As a result, we observed no increase in the rate of 2′,3′-cGMP, as a reaction intermediate catalyzed by cyclizing RNases (Therefore, the characteristic behaviour of Mn purines . Guanineg RNases . Based oBinase is a promising therapeutic agent with anticancer –36 and aIt was shown that the rate of 2′,3′-cGMP, produced by binase during RNA cleavage, was increased in the presence of nontransition metals that stabilized the RNA structure. In the case of transition metal ions the amount of 2′,3′-cGMP was reduced because of blocked guanine in binase recognition sites of RNA. As production of RNase by bacilli in the human body is a natural mechanism, the activation of 2′,3′-cGMP by nontransition metals can enhance biological effects of binase.


Epigenetic modifications of DNA and chromatin are long known to control stem cell differentiation and organ function but the role of similar modifications at the level or regulatory RNAs is just beginning to emerge. Over 160 RNA modifications have been identified but their abundance, distribution and functional significance are not known. The few available maps of RNA modifications indicated their dynamic regulation during somatic stem cell differentiation, brain development and function in adulthood suggesting a hitherto unsuspected layer of regulation both at the level of RNA metabolism and post-transcriptional control of gene expression. The advent of programmable, RNA-specific CRISPR-Cas editing platforms together with the identification of RNA modifying enzymes now offers the opportunity to investigate the functional role of these elusive epitranscriptome changes. Here, we discuss recent insights in studying the most abundant modifications in functional mRNAs and lncRNAs, N6-methyladenosine and 5-(hydroxy-)methylcytosine, and their role in regulating somatic stem cell differentiation with particular attention to neural stem cells during mammalian corticogenesis. An outlook on novel CRISPR-Cas based systems that allow stem cell reprogramming by epitranscriptome-editing will also be discussed. During embryonic development, rapid changes in protein expression and their activity are required to initiate and promote the switch from proliferation to differentiation of stem cells. Historically, stem cell research has been primarily focused on understanding the control of gene expression at the transcriptional level by transcription factors or epigenetic modifications of DNA or histones . Mapping of m1A by antibody pull-down and sequencing initially led to the conclusion that this modification is broadly abundant within mRNAs , 5-methylcytosine (5mC), and 5-hydroxymethylcytosine (5hmC). Their functions in different cell types will be discussed with particular attention to neural stem cell differentiation during mammalian corticogenesis and brain function in adulthood.Methylation of adenine at the 6 position (m6A) is commonly found on DNA of prokaryotes but generally rare in eukaryotes and highly debated in mammals  consensus motif  sex determination and neuronal function with impaired locomotion in flies  and its oxidized form 5-hydroxymethylcytosine (5hmC) are widely found in eukaryotic DNA and are associated with transcriptional regulation and DNA stability  to 5hmC  that do not target DNA, synthesis of 5hmC is mediated by the very same TET enzymes that promiscuously target both RNA and DNA (Lian et al., The importance of RNA modifications for developmental processes has just begun to emerge and new studies will soon provide us with additional knowledge about their abundance, specificity and role. As a main limitation in this field, functional characterization of mRNA and lncRNAs modifications are so far restricted to the ablation of the enzymes acting as writers, readers or erasers. This has several intrinsic limitations such as that some of these enzymes are unknown, have overlapping or redundant functions or act on different substrates as shown in the case of TET enzymes. Furthermore, ablation of RNA modifying enzymes would still not resolve site-specific roles of such modifications and their impact on specific transcripts.These limitations can be overcome by the development of site-specific manipulation of RNA modifications as a means to directly prove their functional implications in a way similar to what shown for recent advances in epigenome editing (Thakore et al., These challenges may be overcome by the recently characterized Class 2 subtype VI CRISPR-Cas effector Cas13 that has been used as a programmable endoribonuclease (Abudayyeh et al., While the CRISPR-dCas13 system proved to be very specific, versatile and efficient it could still harbor potential drawbacks that need to be assessed. For example, the RNA secondary structure may alter binding recognition (Smargon et al., Although identified decades ago (Desrosiers et al., Among different cell types and tissues, the developing and adult mammalian brain appears to be the organ system more vulnerable to manipulations of the epitranscriptome. For example, although individuals affected by mutations for epitranscriptome writer or eraser genes showed different defects in various organ systems, they all share deficits in brain function including mental retardation and psychological disorders (Boissel et al., “…the slightest variations; rejecting those that are bad, preserving and adding up all that are good; silently and insensibly working, whenever and wherever opportunity offers, at the improvement of each organic being…”. In light of this, it is not surprising that the mechanisms that allow the better tuning of gene expression by DNA modifications were revealed to be very similar to the ones used to better tune gene translation by RNA modifications. It is unclear whether during evolution the former were subsequently adapted to attain the latter but given life's origins from an “RNA World” the opposite possibility is also worth considering (Forterre and Grosjean, With regard to evolution, in the Origin of Species Charles Darwin wrote that natural selection is constantly working to scrutinize FN and FC contributed equally to the writing of this manuscript.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.


Alfalfa mosaic virus (AMV). Starting infections with different segment combinations, we found that the relative abundance of each segment evolves towards a constant ratio. Population genetic analyses show that the segment ratio at this equilibrium is determined by frequency-dependent selection. Replication of RNAs 1 and 2 was coupled and collaborative, whereas the replication of RNA 3 interfered with the replication of the other two. We found that the equilibrium solution is slightly different for the total amounts of RNA produced and encapsidated, suggesting that competition exists between all RNAs during encapsidation. Finally, we found that the observed equilibrium appears to be host-species dependent.The existence of multipartite viruses is an intriguing mystery in evolutionary virology. Several hypotheses suggest benefits that should outweigh the costs of a reduced transmission efficiency and of segregation of coadapted genes associated with encapsidating each segment into a different particle. Advantages range from increasing genome size despite high mutation rates, faster replication, more efficient selection resulting from reassortment during mixed infections, better regulation of gene expression, or enhanced virion stability and cell-to-cell movement. However, support for these hypotheses is scarce. Here we report experiments testing whether an evolutionary stable equilibrium exists for the three genomic RNAs of  In contrast, bacteria and archaea typically have a single chromosome3. Although many viruses also have a single genome segment, in some species the genome has been partitioned into multiple segments6. Whereas most viruses package multiple segments into a single virus particle , some plant and fungal viruses package each segment into a separate virus particle, a property known as multipartition. By contrast, the only multipartite animal viruses are the single-stranded DNA bidensoviruses infecting silkworms7 and the very recently discovered single-stranded positive-sense RNA virus in mosquitoes related to the Flaviviridae8. In the extreme case, plant nanoviruses have up to eight DNA genome segments plus several satellite-like segments packaged up into different viral particles, although not all segments must enter a cell to cause infection4. For multipartite RNA viruses, the number of segments is typically lower, ranging from two to five. It is thought that all genome segments must enter the same cell to produce all the RNAs and proteins that are required to complete the process of infection and release the progeny that will infect new cells and transmit to new individual hosts6.The highest level of physical organization of the genome is the division of the hereditary material into multiple segments. Genome segmentation is a ubiquitous feature of eukaryotes, with nuclear chromosome numbers covering an enormous range: from 29. All else being equal, an equimolecular composition of particles would maximize the probability of initiating the infection of a host cell successfully. Deviations from this situation would increase the cost of multipartition. Another potential cost of genome segmentation would be the breakage of co-adapted groups of genes during coinfection with several strains of the virus10. Several advantages have been proposed to compensate for these costs: (i) for the high mutation rates of most RNA viruses, smaller segments are more likely to be copied without errors than larger segments11, (ii) smaller genomic segments should be replicated faster12, (iii) segmentation favors genomic reassortment and thus increases genetic variability by rapidly bringing together beneficial mutations that have occurred in different lineages14, minimizing the effect of clonal interference15 and speeding up the rate of adaptation, (iv) encapsidation of smaller genomes results in enhanced capsid stability16, (v) particularly in the case of plant viruses, smaller capsids would facilitate trafficking throughout the size-limiting plasmodesmata17, and (vi) segmentation represents an efficient yet simple way to control gene expression by regulating gene copy numbers18.The evolution of segmented genomes revolves around tradeoffs between potential costs and benefits inherent to different genome architectures. An obvious cost of multipartition is the necessity of coinfecting cells with at least one particle of each kind to ensure the presence of at least one copy of each segment, a cost that increases with the number of segments and particlesBromoviridae family such as Brome mosaic virus (BMV) and Cowpea chlorotic mottle virus (CCMV). Many interesting conclusions were drawn in these studies, but particularly relevant for the problem of the evolution of multipartite virus are: (i) the ratio of RNA segments varies among closely related viral species (BMV and CCMV)19 and even among different BMV isolates20. (ii) The relative abundances of genomic segments for a particular bromovirus species varies among host species22. (iii) Mutations in coding and noncoding sequences of different genomic segments have a profound impact on the accumulation of the other segments27.Starting on the mid-seventies, a number of publications have addressed different aspects of the replication and regulation of gene expression of plant multipartite viruses, especifically for members of the et al. monitored the frequency of the eight single-gene-encoding segments, made of circular DNA, that constitute the genome of the nanovirus Faba bean necrotic stunt virus (FBNSV) during infection of single host plants18. They observed that regardless of the initial ratio of segments in the inocula, the ratio of segments always evolved towards a constant composition that the authors designated as the “setpoint genome formula” (hereafter referred as SGF), which did not represent an equimolecular mixture of genomic segments. They also found that the SGF corresponds to a state of maximal viral accumulation and of enhanced symptoms, thus suggesting that segmentation has evolved as a mechanism to regulate gene expression. Finally, in agreement with the bromoviruses results, they also found that the exact stoichiometry of the SGF depends on the host plant species. FBNSV, although being a well-suited model system for addressing questions related to genome segmentation and multipartition, is not very representative of most multipartite plant viruses: most are RNA viruses and have a lower number of genome segments.Sicard 27, although it is not known for these viruses whether an SGF exists and whether it is evolutionarily stable. In other words: we still miss experimental evidences to support or reject some of the evolutionary genetic mechanism brought forward to explains the evolution of multipartition in RNA virus populations14. If no stable SGF exists, or if multiple stable equilibria are possible, genome segment ratios could be in a state of perpetual flux, and some hypotheses for the advantages of segmentation would need to be discarded or revisited. Similarly, if different SGF exist in different host species, this would suggest that segmentation can facilitate adaptation by rapidly altering the proportion of RNAs –and possibly their expression– in a mutation-independent manner18.As mentioned above, the molecular biology of multipartite RNA viruses has been extensively studiedSGF also exists for a prototypical multipartite RNA virus. Second, we also set out to determine the effect of host species on the stoichiometry of the SGF. Third, we also propose a novel analytical and computational framework, of universal applicability, to the evolutionary analysis of the abundance of any number of segments in segmented viral genomes. To tackle these questions, we have chosen Alfalfa mosaic virus , whose genome is composed of three single-stranded positive-sense RNA molecules . Briefly, RNA1 and RNA2 encode proteins essential for replication (P1 and P2) while RNA3 encodes for the movement (MP) and coat (CP) proteins, the latter being translated from a subgenomic RNA4 (sgRNA4) produced by transcription of the negative-sense strand of RNA328. Our results show that an evolutionary stable but host-species-dependent SGF occurs for this prototypical plant RNA virus.The aim of our study is threefold. First, we sought to explore whether a SGF exists for AMV, and if so to determine its characteristics. To this end we performed inoculation experiments with a range of RNA1, RNA2 and RNA3 ratios. Nicotiana benthamiana plants were mechanically inoculated with a constant amount of RNA (1 μg) but varying proportions of the three segments as detailed in the Methods section. The seven ratios employed were 1:1:1, 10:1:1, 1:10:1, 1:1:10, 10:10:1, 10:1:10, and 1:10:10. If a SGF exists, then we expect that these different combinations will all evolve towards it as infection progresses. We sampled different tissues , the replicate plants inoculated with each RNA mixture (P) and the different tissues sampled (S). All three factors contribute in a highly significant manner to the observed variability in RNA segments frequency . The resulting relative accumulations of RNA1, RNA2 and RNA3 differ among the seven input mixtures (factor M). Overall, plants inoculated with the same mixture also differ in the estimated output mixtures ). An overall difference also exist among tissue samples from plants inoculated with the same RNA mixture ). Finally, significant differences exist among the four sample types taken from each one of the three different plants inoculated with the same mixture of RNAs (factor (P × S)(M)).We first set out to determine whether an ues Fig.  at diffeP-value tells nothing about the magnitude of the effect that a factor has on the measured variables; a small effect may still be significant from an statistical point of view, while being too small to be biologically relevant. To assess the magnitude of effects we used the P(M) term in equation (P × S)(M) term in equation . Despite these differences, significant effects have been detected for the other factors.However, a significant mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimi.e., tissues; S(M) term in equation (i) different tissues being colonized by different subpopulations of RNAs simply by chance , (ii) different tissues being at different developmental stages may impose differences in susceptibility to AMV infection, (iii) variability in the antiviral defense status of new tissues compared to old ones , and even (iv) genetic differences in the composition of viral populations at different stages in their within-host evolution as a consequence of interplay between mutation and variable selection pressures on different tissues 31. The weakest effect, M term in equation . Figure SGFtotal ratio.The grand mean estimate for the relative frequencies corresponds to a P(M) in equation (P = 0.248). The magnitude of significant effects is SGFencap of 1.07:2.55:1.75 (±1 SEM: 0.07:0.39:0.26), a value that does not differ from the ~1:3:2 reported above for total RNAs, and in both cases shows that RNA2 is the most abundant one. As above, Fig. SGFencap value.Table equation ; P = 0.2i) this equilibrium is driven by frequency-dependent selection (FDS) and (ii) it is stable32.Overall, the results summarized in Fig. SGF. Marginal mean frequency data shown in Fig. 32. For each RNA, we computed the output and input log-relative abundance and plotted them (different symbols). First, the data were fitted to a set of models with increasing number of parameters, but in all cases the linear regression was the best-fitting model (shown as solid lines). In all three cases, statistical significance of the FDS is tested by the deviation of the slope of the linear regression from 1 . All three regression lines in Fig. t5 ≥ 6.964, P ≤ 0.001). The analysis of Fig. i.e., whether the FDS is linear or not, how strong it is, and if an equilibrium point exists. A point of equilibrium occurs if and where the regression line crosses the diagonal32. At such point the focal RNA segment frequency is at the same frequency as expected in the absence of FDS. In all three cases, the slope is less than one, meaning that the equilibrium is evolutionarily stable; the system evolves towards an equilibrium SGFtotal that is stable against random perturbations of any of the three RNA components. For instance, perturbations may be associated with the inoculation process or by bottlenecks inherent to systemic movement and colonization of new growing tissues in the apical meristem29.Figure t5 ≥ 13.610, P ≤ 0.001) and thus the SGFencap also corresponds to an evolutionarily stable equilibrium. Notice that in the case of encapsidated RNAs, the three curves show similar positive slopes while in the case of the total RNAs the curves for RNA1 and RNA2 are similar in slope and both positive wherease the curve for RNA3 is negative.Figure M in equation , plant equation ) and tisequation ). Table SGF. To do so, we inoculated five different susceptible hosts  with a 1:1:1 mixture of the genomic RNA segments and evaluated the output frequency of each segment 7 days post inoculation days (dpi) for N. benthamiana and 12 dpi for the rest of species, following the same sampling scheme than in the experiments previously described. Frequency data are shown in Fig. n = 3 different plants). These data were fitted to equation , a value that sharply contrasts to the above stable equilibrium value found for N. benthamiana (~1:3:2) due to the larger bias in the synthesis of RNA3 that characterizes the infection of C. pepo (1.00:6.70:16.11 (±1 SEM: 0.39:1.86:3.42)), M. sativa (1.00:2.37:12.45 (±1 SEM: 0.27:0.70:1.87)) and C. annuum (1.00:2.61:14.35 (±1 SEM: 0.35:0.96:2.78)).Next, we explored to which extent the host species determines the value of 83 Table . There i83 Table , althougM. sativa and C. annuum were not different from negative controls and thus were not considered for the following analyses. In case of segment frequencies in the encapsidated RNAs, the only not significant factor was the interaction between plant replicate and type of sample (SGFencap across hosts is 1.00:3.24:2.74 (±1 SEM: 0.00:0.71:0.97), which also differs from the stable equilibrium value found for N. benthamiana but to a lesser extent; e.g., for C. pepo 1.00:2.95:4.62 (±1 SEM: 0.12:0.54:0.44). Figure SGF is host-species dependent.Frequency data obtained from encapsidated RNAs are shown in Fig. entclass1pt{minimaequation , althougSGFtotal is maintained by a FDS mechanism, and that the actual value taken by the SGFtotal depends on the host wherein AMV replicates. Has SGFtotal been optimized in each host by natural selection to maximize the total accumulation of the three genomic RNAs? To tackle this question, we have computed a partial correlation coefficient between the total RNA accumulation (summing up the accumulations of the three RNA segments) and the Euclidean distance  from the SGF values obtained for each individual sample  and their corresponding evolutionarily stable SGFtotal estimates using as control variables the host species , thus backing up the hypothesis that RNA accumulation is maximal at the equilibrium SGFtotal. Notice that this test implicitly assumes that no defective genomes are being produced and hence every RNA molecule that has been counted would eventually contribute to successfully complete the infection process. If a fraction of the accounted molecules was defective, then the test would still be valid as far as the number of defective molecules for each of the three RNAs would distribute proportionally to their rate of production. If an RNA segment produces more defective copies than others, the validity of the test would be jeopardized.We have observed that equation ), input equation ), plant equation ) and tisequation ). The raSGFencap and the total amount of the three RNAs encapsidated was not significant , suggesting that the strength of selection for encapsidation has been weaker than for replication or, alternatively, that selective constraints for encapsidation may differ among tissues and that by pooling together data from different tissues we have lost the statistical power to detect a significant correlation.By contrast, despite being negative as expected under the above hypothesis, the correlation observed between the distance to the evolutionary stable value of SGF will be largely perturbed in a non-random manner. This last qualification is important, since we found that SGF represents an evolutionary stable equilibrium and thus small random perturbations will result in the system returning to this equilibrium. To test this hypothesis, we inoculated transgenic N. tabacum plants expressing P1, P2 or P1 and P2 (P12 plants)33 with a 1:1:1 input ratio of the three segments. As in all experiments described so far, we quantified the output segments frequencies in both total RNA extracts , which departs from the stable equilibrium described above, yet with a large excess of RNA3. Paying attention to the situation in different transgenic plants, the marginal means are 1.58:4.19:1.00 (±1 SEM: 0.01:0.04:0.03) for P1, 2.68:9.53:1.00 (±1 SEM: 0.01:0.04:0.03) for P2 and 1.00:5.10:39.36 (±1 SEM: 0.01:0.04:0.03) for P12, suggesting large departures from the equilibrium SGFtotal  for P1, 13.60:18.58:1.00 (±1 SEM: 0.01:0.04:0.03) for P2 and 1.00:4.08:10.40 (±1 SEM: 0.01:0.04:0.03) for P12 , and that all three RNAs competed with each other for the CP for encapsidation . With the only exception of RNA2, no positive correlation exists between the accumulation of total and encapsidated RNAs, suggesting that the process of encapsidation is not only determined by the accumulation of the corresponding RNAs. Interestingly, negative correlations exist between the total accumulation of RNA2 and encapsidated RNA3 and vice versa but not with total and encapsidated forms of RNA1. Although these inferences on the interactions between the genome segments are based on a correlation analyses, they do reflect a biologically relevant association between viral traits. However, we have not tested the underlying mechanisms, and do not know whether there is a direct causal link between both traits or the correlation is mediated by a third yet unknown factor. Future work will explore the mechanisms of these correlations.Here, we have explored the within-host evolution of the ratio of the three genomic segments of the multipartite plant virus AMV. We found that regardless the ratio used at inoculation, an evolutionary stable equilibrium is reached in which the three RNA segments are represented in a ~1:3:2 stoichiometric ratio, in SGF appears to be dependent on the host species, suggesting the involvement of host factors that differ among host species play a role in its regulation. In agreement with our findings, Ni et al. also found that the relative abundances of encapsidated and total RNA segments of BMV were also dependent on the host species34. Indeed, when the ratios of the three segments were followed during the progression of infection in two monocot hosts, they converge into stable SGFtotal (1:2:3 for barley and 1:2:2 for wheat). However, at odds with our findings, these authors concluded that no relationship existed between the total RNAs produced and their relative encapsidation. Nonetheless, a significant correlation exists between the relative frequencies of total and encapsidated RNAs in wheat  but not in barley .We found that the 18. As the changes in SGF observed here in alternative host species occurred within a narrow time window, multipartition might be advantageous for rapid adaptation to new hosts in a manner that is largely nucleotide-sequence independent, and therefore also mutation independent. Such an approach to adaptation could be especially advantageous in alternative hosts, where founder numbers may be small due to low infection probabilities and effective population sizes might also not be large initially due to poor replication. The hypothesis of segmentation as a mechanism to regulate gene expression has been proposed for segmented DNA viruses18. Unfortunately, in the case of RNA viruses, it is not trivial to distinguish between RNA molecules that are serving as templates for replication from those that are serving as mRNAs at different stages during infection, therefore, we cannot directly test this hypothesis with our data. Additional experiments quantifying the number of RNA segments that either serve as mRNA, are encapsidated, or simply not used in any way, would be necessary for directly testing this possibility for a segmented RNA virus.Multiple theories have been proposed to explain the existence of multipartite viruses, most commonly found in plants. The most recent and tantalizing proposal is that genome segmentation represents an efficient and rapidly adaptable way of regulating gene expression throughout manipulation of gene copy numbersBromoviridae family, it has been shown that BMV and Cucumber mosaic virus (CMV) 1a protein accumulates to larger amounts than the corresponding 2a protein in purified replication complexes37, in spite that the SGFtotal for BMV was 1:2:3 for barley and 1:2:2 for wheat, suggesting that either trans elements are controlling the translation of viral RNAs or that cis-acting elements may affect the efficiency of each RNA in recruiting ribosomes. In this sense, it has been observed that AMV CP enhances the translational efficiently of viral RNAs in vivo38 via the interaction with the 3′ termini, which adopts two alternative structures for translation (a linear array of hairpins with high affinity for CP) and replication (a pseudo-knotted structure)39. A similar mechanism has been reported as regulator for translation of the replication complex proteins of BMV40. The assumption that the amount of protein expressed is always proportional to the amount of messenger RNA, although appealing, has been proven wrong. For example, during mixed phage infections of bacterial cells, increasing the number of genomic copies results in switches between lytic and lysogenic states and the concomitant production of viral proteins41. Indeed, in such instances the regulation of is an emerging property of the structure of regulatory networks rather than directly resulting from gene copy number41. Translation efficiency and RNA stability are inexorably linked42, further challenging simple interpretations of the effects of observed RNA levels on actual protein expression levels. Indeed, it would not be surprising that RNA stability and translational productivity of the three RNA segments of AMV may be affected by the actual host species.On the other hand, according to this hypothesis, a tight link must exist between the necessity of producing a given protein and the abundance of the RNA segment that encodes for it. At first glance, this hypothesis does not apply to AMV for two reasons. Firstly, as one may imagine that CP, necessary for producing infectious virions and encoded by the RNA3, would be required in larger numbers than the replicase complex, encoded by RNA1 and RNA2. However, it is important to recall at this point that CP is translated from a sgRNA4. We have not quantified the abundance of this subgenomic RNA. Interestingly, however, we observed that the ratio of RNA1 and RNA2 remains more or less constant in all experimental conditions tested (see below). Secondly, our observation of RNA2 accumulating more than RNA1 may suggest that P2 should also accumulate more than P1. Unfortunately, no quantitative data are available on the accumulation of AMV P1 and P2 in virus infected tissues. Comparing with other members of the SGF estimated from total RNA production and from encapsidated RNA. These results may stem in part from different interactions between the different 3′ untranslated regions of the RNA segments and the CP, and their effects on encapsidation43. Furthermore, selection may operate in distinct ways here, eventually resulting in an evolutionary tradeoff. On the one hand, within-cell selection on replication will result in a SGFtotal that maximizes replication of the three RNA segments, likely by producing an optimal combination of RNAs and proteins, thus a segments ratio that would necessarily depart from the 1:1:1 as more proteins encoded by one segment are needed than proteins from other segments. This possibility is clearly supported by the negative correlation that we have observed between the total amount of genomic RNA produced and the distance to the SGFtotal. On the other hand, selection operating at the systemic movement and at the between-host levels will result in a SGFencap that maximizes the probability of successful transmission. The SGFencap values that maximize within-cell replication and transmission might be different, resulting in a tradeoff. However, our observations do not back up this possibility, as we have not observed the predicted negative correlation between total encapsidated RNAs and the distance to the SGFencap.We found significant differences between the SGF remain elusive. Probably the evolutionarily stable SGF results from complex molecular interactions between viral and host components, inextricably intertwined with viral population dynamics. Some evidences available in the literature may help to bring light into this complex question. For instance, results from transient expression experiments of proteins P1 and/or P2 revealed that replication of RNA1 and RNA2 depends on the presence of these proteins in cis and that, within infected cells, the replication of RNA1 and RNA2 is strictly coordinated through the encoded proteins rather than by RNA-RNA interactions44. This coordination may ensure the expression of proteins P1 and P2 in the correct ratio to form the replication complex. However, the replication of RNA3 is not linked to the replication of RNA1 and RNA246. In this sense, these interactions explain the results reported in Table trans and, thus, competes with the replication of RNA1 and RNA2. These observations lead to the prediction that the ratio between RNA1 and RNA2 could be constant (see below) but also suggest that the different RNAs of segmented viruses could be considered as independent molecules unless they replicate coordinately, with cis elements that constraint the accumulation of the corresponding viral RNAs. Independent of whether different viral RNAs are coupled or not, we observed that providing a specific viral protein in trans  alters the AMV SGF, and thereby probably the relationship between accumulation, virulence and transmission. It is tempting to speculate that virus resistance mediated by the expression of viral proteins –normally the CP in transgenic plants– could be at least partially due to a drastic alteration of the genome segment ratio and the negative effects thereof on viral replication.The mechanisms that may determine AMV’s N. benthamiana, first we must acknowledge a limitation of our experimental design: as we did not consider different starting ratios or multiple time points post inoculation, one could question whether virus populations have reached a stable SGF. Conservatively speaking, we can only conclude that at advanced stages of infections in all hosts , the ratio of segments significantly differs among hosts and significantly departs from the one value estimated for N. benthamiana. This being said, we observed that the ratio between RNA1 and RNA2 remains constant (ca. 1 RNA1 molecule per 3 RNA2 molecules) probably due to the coordinated replications between both RNAs, as mentioned in the previous paragraph. The coordinated replication of both RNA1 and RNA2 may determine the ratio of both viral RNAs independently of the host species. In this sense, we observed that the ratio of both viral RNAs oscillated between 1:2–1:3. Even in the highly artificial situation in which one of the replicase components was constitutively provided in trans by the plant but the other necessarily was translated from viral RNAs (P1 or P2 plants), the ratio between both RNA1 and RNA2 was maintained between 1:2 (2:4:1 for P1) and 1:3 (3:9:1 for P2). This suggests that the RNA1, that encodes for the methyltransferase-helicase P1 protein, is actually the most limiting factor in the system. Protein P1 is required to form a complex with the P2 RNA-dependent RNA polymerase protein (encoded by RNA2), acting in trans for replication and transcription of RNA 2, RNA3 and sgRNA4. However, efficient replication of RNA1 may also require translation of RNA1 into P1 protein in cis, indicating a linkage between translation and replication of this segment, as shown for BMV48. Only in the case of P12 plants, the ratio was completely altered between the three RNAs, probably due to the transgenic presence of the full replicase complex in trans (P12 plants) and the incapacity of the virus to modulate the transgenes or its expression. It is interesting to note the low accumulation of RNA3 in P1 and P2 plants but not in P12 plants, which is consistent with the observation that coordinated expression of BMV proteins 1a and 2a from RNA1 and RNA2, respectively, is also required to complete the synthesis of RNA3 in transgenic yeast cells49. Apparently, the system dynamically evolves to maintain the correct ratio between RNA1 and RNA2, to the detriment of the accumulation of the RNA3. Furthermore, the accumulation of RNA3 was significantly altered depending of the host species, indicating that the virus may use this RNA to accommodate its life cycle to the presence/absence of different host factors, for instance, the transcription factor promoting salicylic-dependent defense signaling response recently reported to interact with the AMV CP50.Regarding the results obtained from hosts other than 51. In solution, AMV CP occurs as dimers and these dimers are the building blocks of viral capsids51. N-terminal peptides of CP bind to the 39 nucleotides of the 3′UTR RNAs in a 2:1 stoichiometric ratio52. Binding of the CP to the 3′UTR also enhances translation of viral RNAs by mimicking the function of the host poly(A)-binding protein54. Altogether, these evidences point to the idea of a CP with multiple functions that are critical at different steps of the virus infectious cycle. The results obtained in the present work support the idea that the RNAs are competing for the CP, and it is therefore a limiting factor that could be used for interventions aimed at controlling virus infection. In agreement with this result, it has been recently observed that AMV CP accumulated at the nucleus and nucleolus, an observation interpreted as a mechanism to control virus expression by the cytoplasmic/nuclear balance of CP accumulation55. The sequestering of the CP at the nucleus may represent a defensive strategy to control the virus cycle, but also the accumulation of the viral RNAs containing this open reading frame.All three AMV RNAs contain binding sites for the CP at the 3′UTR and bind it with an equal distribution between all viral RNAsC. annuum L., C. pepo L., N. benthamiana Domin, N. tabacum L. cv. Samsun, and M. sativa L. were all mechanically inoculated with 5 μL of a mixture of 5′-capped transcripts corresponding to AMV strain 425 RNA1, RNA2 and RNA3 plus a few μg of purified AMV CP as described previously56. In addition, transgenic N. tabacum plants that express AMV polymerase proteins P1 (P1 plants) and P2 (P2 plants) or both (P12 plants)33, were also assayed. For the transcription reactions, clones pUT17A, pUT27A and pAL3-NcoP3, containing full-length cDNAs of AMV RNA1, RNA2 and RNA3, respectively, were linearized with appropriate restriction enzymes and transcribed with mMESSAGE mMACHINE® T7 kit . The quantification of the AMV RNAs was performed with a ND-1000 spectrophotometer  and agarose gel eletrophoresis using an RNA ladder  and several dilutions of the transcribed RNAs.Plants from the experimental hosts N. benthamiana plants we selected a final concentration of total RNAs of 40 ng/μL × 5 μL = 0.2 μg each, whereas for the rest of hosts it was necessary to increment the transcripts concentration five times .Before addressing the specific questions of this study, we estimated the minimal amount of AMV transcripts required to initiate an infection in the different hosts by performing serial dilutions of an initial inoculum mixture with a ratio 1:1:1. Henceforth, all ratios of AMV genomic RNA segments are given as RNA1:RNA2:RNA3 (±1 SEMs). For N. benthamiana plants that were also inoculated with ratios: 10:1:1, 1:10:1, 1:1:10, 10:10:1, 10:1:10, and 1:10:10. For each of these experiments, at least three plants were inoculated. All plants were grown in a biosafety level-2 greenhouse at 24/20 °C day/night temperature with 16 h light. After 7 dpi (N. benthamiana) or 12 dpi (rest of species), all inoculated plants were analyzed for the abundance of each RNA segment in both total RNA extraction and virus particle purification from inoculated , 5th and ≥ 8th leaves .All species were inoculated with the AMV RNAs ratio of 1:1:1 (three plants per ratio) except ves Fig.  and from2 to minimize the putative irregular virus distribution in the tissue. Total RNA extraction was performed using 0.1 g of tissue and the Plant RNA Isolation Mini Kit  following the manufacturer’s protocol. All samples were diluted to a final concentration of 50 ng of total RNA/µL. Virus particles purification was performed using 0.5 g of the homogenized tissue, following the protocol previously described57. The fraction of enriched virus particles was resuspended in 100 μL of PE buffer , that was subsequently subjected to RNA extraction using the Plant RNA Isolation Mini Kit . All RNA samples were stored at −80 °C until use.Leaves Fig.  or entir10 (RNA1) or 2 × 109 (RNA2 and RNA3) molecules of the corresponding viral RNA per μL. All dilutions were made in a solution containing 50 ng/μL of total RNA extracted from healthy N. benthamiana plants.The standard curves to quantify the AMV RNA1, RNA2 and RNA3 in the samples by RT-qPCR were prepared using known amounts of DNase-treated transcripts derived from the linearized pUT17A, pUT27A and pAL3-NcoP3 plasmids, respectively. To ensure a correct estimation of the transcripts concentration, all sample were analyzed with a ND-1000 spectrophotometer  and by agarose gel electrophoresis. To construct the standard curve for each RNA, we selected six (RNA2 and RNA3) or seven (RNA1) different viral RNAs concentrations, calculated in terms of molecules/μL (www.endmemo.com/bio/dnacopynum.php), that were generated by 5-fold serial dilutions of a starting solution containing 10® Design Tool version 2.2.3 , selecting the parameters GC% = 40–60%, Tm = 57–60 °C, and size = 100–150 bp. The primers used for the RT-qPCR reactions for AMV RNA1, RNA2 and RNA3 are listed in Supplementary Table R2 > 0.98) and of the slope-derived amplification efficiency (90–110%). For those samples that fell within the dynamic range, the estimated linear regression parameters were used to estimate the unknown concentrations in the virus samples.The primers used for amplifying RNA1, RNA2 and RNA3 were designed using PrimerQuest® 1-step RT-qPCR system (SYBR® Green)  and the StepOnePlus Real-Time PCR System . Each reaction contained 50 ng RNA sample, 5 μL of the 2× master mix, 10 μM of both the forward and reverse primer, 0.2 μL of GoScriptTM RT Enzyme Mix and 0.155 μL of CXR reference Dye (30 μM). The reactions were incubated at 42 °C for 15 min, followed by 95 °C during 10 min and 40 cycles of 95 °C for 10 s, 62 °C for 34 s and 72 °C for 30 s. After the RT-qPCR reaction, the melting curve stage was determined by incubating 95 °C for 15 s, 60 °C for 1 min and 95 °C for 15 s. The quantification of RNAs 1, 2 and 3 copy number was calculated using the StepOne Software v.2.2.2 .Duplicated RT-qPCR reactions were carried out in 10 μL reaction volume using the GoTaqSupplementary File S2 online contains the absolute quantifications of the three RNA segments for all the experimental samples used in this study.i, RNAi, on each sample were transformed into relative frequencies, fi, by dividing them by the sum of the values estimated for every RNA segment on the corresponding sample, averaged across the two technical replicates of RT-qPCR: l ∈ {1,2} of sample Sk (k ∈ {inoculated leaf (3rd true leaf), 4th leaf, 5th leaf, and the rest of the plant }) taken from plant replicate Pj  that was inoculated with a mixture Mi  of RNA segments. Factors P and S, as well as their interaction, were treated as orthogonal, and nested within factor M. SGF. Wilk’s Λ distribution was used for the multivariate tests of each factor in the model.The number of copies of RNA segment To assess the magnitude of effects we used the SGF depends on the host species inoculated with a 1:1:1 mixture, the corresponding frequency data  one or more equilibrium points exist and (ii) their stability. If the relationship between i.e., the equation of slope one and intercept zero). Equilibria stability can be assessed by evaluating the value of the derivative If FDS exists, then it can be evaluated whether .Table S1Supplementary information


One of the long-standing principles of molecular biology has been that DNA functions as a template for transcription of messenger RNAs, which are eventually translated into a protein. Thus, proteins were seen as the main mediators of nearly all aspects of cell and tissue function. However, this perception started changing rapidly when high-throughput sequencing platforms became available, unraveling that more than two-thirds of the human genome are transcribed into RNA but only <2% of transcripts encode proteins , 2. ThusThe original classification of lncRNAs is based on a length of at least 200 nt and lack of protein-coding potential. lncRNAs can be spliced, capped, and/or polyadenylated and localize either to the nucleus or the cytoplasm of the cell , 10. IntIn contrast to mRNAs, lncRNAs generally show less primary sequence conservation among species, contain fewer but longer exons, and exhibit an intriguingly cell type-specific expression , 9. In aWith the lncRNA field still being in its infancy, novel lncRNAs are detected in human cells and tissues on a regular basis, resulting in several thousand predicted human lncRNAs to date , 31, 32.In addition to being part of another transcriptional unit, lncRNAs themselves can harbor protein-coding genes or other ncRNAs such as circular RNAs, tRNAs, miRNAs, and snoRNAs , 35–39. Despite their classification as long “non-coding” RNAs, several studies showed that lncRNAs can be translated into small peptides and are associated with ribosomes, further increasing the complexity within lncRNA loci , 42, 43.Diversity within a lncRNA locus is not solely reflected by overlapping or embedded transcripts within a lncRNA but also the transcription initiation and termination sites can vary. Correspondingly, almost two different 3′ ends can be found for each transcriptional start of a given lncRNA , 48. OneBesides alternative polyadenylation, 3′ processing of lncRNAs has been described, further expanding the potential diversity of lncRNA isoforms generated within the cell. As an example, the lncRNAs MALAT1 and NEAT1 feature a tRNA-like structure at their 3′ end, which is subject to RNAse P cleavage, resulting in a stable RNA triple-helix at the 3′ end of both lncRNAs, which serve as compensatory poly-A tails –54.Similar to mRNAs, lncRNAs exploit the usage of alternative transcription start sites , 56. ForBy using both alternative polyadenylation and transcription start sites, a multiplicity of lncRNAs originating from the DM1-AS locus has been reported, which is even further increased through alternative splicing events .Despite functioning in the regulation of RNA splicing, lncRNAs too can be alternatively spliced, which presumably alters their function within the cell , 61. OneAlong these lines, another study focused on the transcriptome of hepatocellular carcinoma (HCC) patients. As a result, Zhang et al. found that in addition to differential expression, lncRNAs also displayed alternative splicing in HCC specimens compared to controls, suggesting a potential role for those splice variants as biomarkers and therapeutic targets for HCC . Taken tGiven the examples above, which illustrate multiple means of RNA isoform generation, we can assume that our current knowledge of diversity within gene loci in general is far from being complete, and current annotations are not always exhaustive. Accordingly, comprehensive analysis of gene loci might in many cases be necessary to enable accurate functional and mechanistic investigation of the resulting isoforms. Below, we will discuss approaches to identify expressed lncRNA isoforms and further explore a given lncRNA locus.The gold standard in elucidating lncRNA expression and isoform discovery on a large scale is RNA-seq. In general, this sensitive method requires significant bioinformatical expertise, especially when investigating lncRNA isoforms or alternative splicing . NeEven though RNA-seq is very sensitive and bioinformatical tools are constantly improving, the experimental validation of potential isoforms employing methods such as northern blot or rapid amplification of cDNA ends (RACE) is still required to complement the bioinformatic predictions. With RACE, the RNA ends can be deciphered; however, most approaches utilize a 3′ poly-A tail, circumventing the detection of non-polyadenylated transcripts . For 5′ Once a pool of potential isoforms has been established, northern blots can be used to verify their presence and predicted length . Moreove6A on position 2,577 of MALAT1 was found to alter its secondary structure, resulting in tighter binding of heterogeneous nuclear ribonucleoprotein C (With new lncRNAs being continuously identified, this exciting research field is rapidly growing. In the hunt for new functions and mechanisms, close attention has to be paid to the wealth of lncRNA isoforms and their potential to being processed to other ncRNAs or translated into small peptides to discover new facets of lncRNAs. Recently, the presence of lncRNA modifications and lncRNA editing has been reported and associated with structural and functional changes, increasing the variety of lncRNAs , 76–78. Within this article, we highlighted the complexity of lncRNA isoform generation and outlined approaches for lncRNA isoform detection and their drawbacks, which should be rather seen as impulses than an exhausting discussion and motivate researchers to move forward into this very intriguing and challenging field of study.CZ and MK drafted and revised the manuscript.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.


High-Throughput called “Microarrays in Non-Coding RNAs Profiling”, which will include the specific usages of microarrays for lncRNAs.Increasing evidence suggests that the numbers of long non-coding RNAs (lncRNAs) are more than those of protein-coding genes in various organisms. Although the detection methods for lncRNAs are being increasingly established, there are advantages and disadvantages that exist for each method. In this opinion article, I highlight the differences between microarrays and RNA sequencing (RNA-seq) for the detection of lncRNAs. Compared to RNA-seq, microarrays are limited to the known sequences. However, the detection method as well as data analysis workflow is more established, which makes it easier to analyze the data for bench scientists without extensive knowledge about computer programming. In order to highlight the usage of microarrays over RNA-seq for the detection of lncRNAs, we are organizing a special issue for  It is now firmly recognized that coding parts of protein-coding genes occupy a very minor part of the mammalian genome, although a vast majority of the mammalian genome is transcribed to RNA ,2,3. CurTo detect lncRNAs, there are two most common methods: microarrays and RNA sequencing (RNA-seq) using next generation sequencers. The former is well-established method, which has a rather long history for the detection of protein-coding genes. Given that probes can be designed as long as sequences are known, it is not surprising that microarrays can be designed to detect lncRNAs. More importantly, the previously built microarrays contain probe sequences that match lncRNAs, although they were initially designed for protein-coding genes. This is due partially to the re-annotation of previously thought protein-coding genes as lncRNAs. Recently, we and others re-annotated the probe sequences of microarrays and found that many lncRNAs can be recorded from Affymetrix-based GeneChips as these types of microarrays contain many probe sequences that do not match exons of protein-coding genes ,28. In tAlthough RNA-seq offers more comprehensive coverage of whole transcriptomes compared to microarrays, there is a question of how deep the RNA-seq reads should be. It is commonly accepted that at least 10–20 million reads are needed to be comparable with the expression profiling performed via microarrays . HoweverHigh-Throughput called “Microarrays in Non-Coding RNAs Profiling”, which will include the specific usages of microarrays for lncRNAs.One additional point about the difference between microarrays and RNA-seq is that, in most cases, microarrays do not involve PCR-based amplification of sample probes , whereas RNA-seq requires multiple cycles of PCR amplifications. Given that PCR amplifications might introduce a certain bias, it may skew the data for GC-rich regions. Taken together, although RNA-seq is more powerful on detecting novel lncRNAs as well as splicing and RNA modifications , if


Recent studies implicate Poly (ADP-ribose) polymerase 1 (PARP1) in alternative splicing regulation, and PARP1 may be an RNA-binding protein. However, detailed knowledge of RNA targets and the RNA-binding region for PARP1 are unknown. Here we report the first global study of PARP1–RNA interactions using PAR–CLIP in HeLa cells. We identified a largely overlapping set of 22 142 PARP1–RNA-binding peaks mapping to mRNAs, with 20 484 sites located in intronic regions. PARP1 preferentially bound RNA containing GC-rich sequences. Using a Bayesian model, we determined positional effects of PARP1 on regulated exon-skipping events: PARP1 binding upstream and downstream of the skipped exons generally promotes exon inclusion, whereas binding within the exon of interest and intronic regions closer to the skipped exon promotes exon skipping. Using truncation mutants, we show that removal of the Zn1Zn2 domain switches PARP1 from a DNA binder to an RNA binder. This study represents a first step into understanding the role of PARP1–RNA interaction. Continued identification and characterization of the functional interplay between PARPs and RNA may provide important insights into the role of PARPs in RNA regulation. The major target for PARylation is PARP1 itself, but a number of other covalently PARylated proteins have been described, including histones, chromatin remodeling proteins, and transcription factors. PARylation influences the activity of target proteins by modulating protein–nucleic acid interactions, enzymatic activity, protein–protein interactions, and/or subcellular localization.Poly (ADP-ribose) polymerase 1 (PARP1) or ADP-ribosyl transferase 1, a multifunctional nuclear protein, belongs to the PARP family of proteins. PARP1 is responsible for initiation, elongation, and branching of ADP-ribose units from donor NADPARP1 was first characterized as a sensor for DNA breaks . Some ofin vivo, supporting the idea of co-transcriptional RNA splicing  / [R][P]nfree. Separating variables and taking logarithms gives:Self-consistent estimates of binding stoichiometry  as a function of ln[P]free has a slope equal to the stoichiometry of the binding step, n. The equilibrium constant is most simply estimated at the midpoint, where ln([PnR]/[R])=0 and lnKn=−n ln[P]free. Because the assessed stoichiometries differ for different complexes, we estimated the equilibrium constants for the overall reactions, Kn (Mn−) and the corresponding monomer-equivalent association constants, K (M−1).For these experiments, [R]For competition assays, equimolar amounts (0.05 μM) of radiolabeled ssRNA and radiolabeled ssDNA of the same nucleotide sequence were mixed in a 20 μl reaction and incubated with increasing concentrations of PARP1-FL or its truncated mutants (0–2.5 μM). The binding reaction was performed as described above. The ratio of binding affinities KDNA/KRNA was determined from the relationship  and [PnR] were calculated according to the relationships [PmD]=([D]0–[D]free)×m and [PnR]=([R]0–[R]free)×n. [D]0 and [R]0 are the initial concentrations of DNA and RNA, respectively, and [D]free and [R]free are the free concentrations of the competitors at each PARP1 concentration in the initial titration experiment. Because all the components of the equation can either be measured or calculated from our experimental data, KDNA/KRNA for the modest values of [PARP1] was calculated from the plots and the ratio of binding affinities estimated from the linear part of the plot by extrapolation to the [PARP1]=0.Where 19-mer: CCGUACGCGGGUUUAAACGA24-mer: CGUACGCGGAAUAGUUUAAACUGU19-mer: UAGGCACCGGCAUCUUGAC20-mer: CCGUACGCGGGUUUAAACGA24-mer: CGUACGCGGAAUAGUUUAAACUGU19-mer: TAGGCACCGGCATCTTGACdsDNA is the 601 widom sequence: 5′-ctggagaatcccggtgccgaggccgctcaattggtcgtagacagctctagcaccgcttaaacgcacgtacgcgctgtcccccgcgttttaaccgccaaggggattactccctagtctccaggcacgtgtcagatatatacatcctgt-3′in vitro transcription of the widom sequence using the MEGAscript T7 Transcritpion kit (AM1334).dsRNA was made from 2, and 1 mM DTT and allowed to incubate for 1 h at 30 °C. Twenty microliters of the NAD+ stock (1 mM) were added to the above tubes for the final 500 μM [NAD+]. Reactions were quenched after 1 min with 5×Laemmli buffer, were immediately boiled for 3 min, and were analyzed by 8% SDS-PAGE. Gels were stained with coomassie. If the protein is active, with NAD+ in the presence of 'activators', it makes higher molecular weight smeared band.PARP1 (constant at 1 μM) and 'activators'  were mixed to a final volume of 20 μl in 50 mM Tris (pH 8), 50 mM NaCl, 10 mM MgClCLIP-seq analyses: Replicate libraries were multiplexed and sequenced on an Illumina Hi-seq 2500 using 100 bp paired-end sequencing. Each library yielded between 0.9 and 97 million unique reads , each of these regions was marked on the hg38 assembly of the human genome using Ensembl genes and transcripts . AnnotatK-mer enrichment motif analysis was carried out calculating 4-mer enrichments by sliding within a 20-nt-long window along PAR-CLIP clusters and using the shuffled (10 000 times) hg38 human protein-coding open reading frames as background sequences.Homo sapiens reference genome assembly hg38 were retrieved from the Ensembl database [Exonic regions on the database . The exodatabase  to randodatabase . With thRNA-seq libraries were constructed using the TruSeq stranded mRNA LT Sample preparation Kit with poly-A enrichment according to the manufacturer's instruction. The libraries corresponded to the three control samples  and three PARP1 knockdown (Dharmacon Inc). These libraries were multiplexed and sequenced on the Illumina NextSeq 500 using the NextSeq 500/550 2×75 cycle High Output Kit v2 (Cat# FC-404-2002).P-value≤0.05 and |FC| Differential gene expression and alternative splicing analysis. RNA-seq reads were mapped to the hg38 reference genome assembly using tophat2 (version 2.0.13) , generatWe next identified PARP1-regulated differential alternative splicing events corresponding to five major types of alternative splicing event patterns by rMATS (v3.2.5) . For eacIn order to determine the binding patterns of PARP within significantly detected skipped exon events, the PAR-CLIP peaks and all detected skipped/retained exon events were used as input into the rMAPS  server. http://bit.ly/2l7f5OY).RNA-seq data are deposited in GEO (GSE91051) along with the PAR-CLIP data (GSE95360). The processed files for the PARCLIP peaks, differential gene expression, and alternative splicing analysis are provided as


A-to-I RNA editing is an important step in RNA processing in which specific adenosines in some RNA molecules are post-transcriptionally modified to inosines. RNA editing has emerged as a widespread mechanism for generating transcriptome diversity. However, there remain significant knowledge gaps about the variation and function of RNA editing.cis genetic polymorphisms. Additionally, we find that a subset of these polymorphisms is linked to genome-wide association study signals of complex traits or diseases. Finally, compared to random cis polymorphisms, polymorphisms associated with RNA editing variation are located closer spatially to their respective editing sites and have a more pronounced impact on RNA secondary structure.In order to determine the influence of genetic variation on A-to-I RNA editing, we integrate genomic and transcriptomic data from 445 human lymphoblastoid cell lines by combining an RNA editing QTL (edQTL) analysis with an allele-specific RNA editing (ASED) analysis. We identify 1054 RNA editing events associated with cis variation in RNA editing among genetically distinct individuals and sheds light on possible phenotypic consequences of such variation on complex traits and diseases.Our study reveals widespread The online version of this article (doi:10.1186/s13059-017-1270-7) contains supplementary material, which is available to authorized users. ADAR family of enzymes [Adar  die embryonically at approximately embryonic day 12.5 [Adarb1  die shortly after birth due to seizures [ADAR enzymes [ADARs may have been to combat viral dsRNAs [ADARs [ADAR1 plays a central role in mammalian innate immunity by down-regulating immune response to endogenous dsRNA [cis variation and phenotypic association of RNA editing in human populations.RNA editing is a prevalent post-transcriptional regulatory process that adds an additional layer of complexity to the transcriptome. In mammals, the most common form of RNA editing is A-to-I RNA editing, in which adenosine is deaminated to inosine by the  enzymes . Mice laday 12.5  while miseizures . Double-l dsRNAs ; howevers [ADARs , which mus dsRNA , 9. Therus dsRNA  and alteus dsRNA . Howeverus dsRNA , miRNA bus dsRNA , and miRus dsRNA  or targeus dsRNA  are someus dsRNA , viral ius dsRNA , and neuus dsRNA . A-to-I us dsRNA –22, withcis-regulatory mechanisms of quantifiable phenotypes such as gene expression (eQTL) [cis-QTLs are defined as significant polymorphisms that are located sufficiently close to the loci while trans-QTLs are defined as polymorphisms located beyond a fixed (often arbitrary) distance or on a separate chromosome. Similar to QTL analysis, allele-specific analysis has been used to investigate cis-regulation of gene expression [Quantitative trait loci (QTL) analysis has been successfully used to identify n (eQTL)  and alten (eQTL) . These ln (eQTL) , chromatn (eQTL) , and tran (eQTL) . For molpression  and RNA pression .cis variation of RNA editing in human populations, here we apply a comprehensive edQTL and ASED analysis to 445 lymphoblastoid cell lines (LCLs) from multiple ethnic groups and identified 1054 RNA editing sites that show significant evidence of population and allelic variation. We find that many of these edQTL and ASED signals are associated with genome-wide association study (GWAS) signals of complex traits and diseases. Lastly, we provide evidence that many cis SNPs associated with changes in RNA editing may regulate editing via effects on RNA secondary structure.To the best of our knowledge, RNA editing quantitative trait loci (edQTL) analysis has only been applied to mouse  and fly cis polymorphisms, we applied a preliminary set of filters to the ~2.6 million annotated RADAR RNA editing sites and collected 9094 candidate sites for downstream analyses. Briefly, we required the sites to have a minimum average coverage of at least two reads supporting the edited version , a minimum average total coverage of ten reads, and a minimum of 10% difference between the editing level of the 90% quantile and the 10% quantile across all 445 individuals. Within these sites, we found that RNA editing can be variable among different individuals . We found that a significant number of RNA editing events were quantitatively associated with genomic polymorphisms  using a generalized linear mixed model GLiMMPS , which asms Fig. . As expesms Fig. . From thing Fig. . The avecis-variation of RNA editing through an ASED analysis  in ZDHHC20 is a rare variant within the CEU population with a minor allele frequency of 1%. However, the effect of cis-regulation of RNA editing site chr13:21948578 can be observed reproducibly with 12 RNA-seq replicates from one individual (GM12878) who is heterozygous for this SNP  and obtained 826 unique ASED RNA editing sites at an FDR of 10%  with GWAS SNPs from the NHGRI GWAS Catalog [cis-regulated RNA editing sites  via their respective edQTL or ASED SNPs. For example, chr11:108237832, an RNA editing site with a significant signal in both the edQTL  was performed to identify comparable regions across multiple IRAlus  were plotted (panels 5 and 6). We noticed a subtle X-shape in the positional distribution of significant SNPs (panels 5 and 6), with one diagonal of the X representing SNPs located on the same Alu as the RNA editing site and the other diagonal of the X representing SNPs located on the opposite Alu to the RNA editing site. These data suggest that genetic variants spatially near the RNA editing site within the IRAlu hairpin are more likely to influence RNA editing. IRAlus can be formed from a tail-to-tail  orientation or a head-to-head  orientation, so we analyzed both types separately. Based on the predicted secondary structure of the IRAlu hairpin, we found that significant edQTL SNPs  tend to be closer to the editing site than random control non-edQTL SNPs   Fig. , when we−3) Fig.  and the −3) Fig.  of the pcis SNPs that potentially influence RNA editing via RNA secondary structure  in the gene NDE1  and it has been suggested that this allows for a rapid post-transcriptional control of gene expression in response to stimuli [ATM gene product and mediating a poor response to metformin for treating type 2 diabetes, although a definitive proof would require additional functional experiments. Recently, interest has also grown in the therapeutic potential for metformin to treat cancer and a number of clinical trials are in progress to determine the efficacy of metformin in cancer treatment [One example of GWAS linked RNA editing events is in the  stimuli . In addi stimuli . Thus, ireatment , 57. Howreatment . Since acis genetic variation could affect RNA editing levels via their effects on RNA secondary structure, extending previous reports using a smaller list of cis-regulated editing sites in mouse [We also found evidence that in mouse  and fly in mouse . SpecifiIn the human genome, Alu elements are the most successful retrotransposon with over a million copies, and a new Alu element is inserted in approximately one in twenty births . Similarcis-regulated RNA editing sites in LCLs of 445 individuals across five populations. Given measurement limitations such as the modest RNA-seq coverage, this number is expected to be an underestimate for cis-regulated RNA editing events in the LCLs. Among these sites, 393 were significantly associated with edQTL SNPs and 826 were significantly associated with ASED SNPs, at an FDR of 10%. Many of these SNPs were in high LD with GWAS signals, which suggests that RNA editing may play a mechanistic role in linking genetic variation to complex traits and diseases. Additionally, we suggest a structural explanation for the causal impact of these genetic variants. Taken together, we show widespread cis variation of RNA editing within Alu elements and suggest that such variation may potentially contribute to phenotypic diversity across human populations.In this work we demonstrate that RNA editing can be variable between individuals within a population and such variability can be genetically controlled. We used two orthogonal approaches (edQTL and ASED) to identify 1054 unique http://www.ebi.ac.uk/Tools/geuvadis-das/) [RNA-seq alignments (hg19) for LCLs were obtained from the Geuvadis RNA-seq Project (is-das/) . Genotypis-das/) . Both RNis-das/)  and the is-das/) . We defiWe required the RNA editing sites to meet the following criteria: a minimum average coverage of at least two reads supporting the edited version, a minimum average total coverage of at least ten reads, and a minimum of 10% difference between the editing levels of the 90% quantile and the 10% quantile across all individuals. To remove potential artifacts, we also limited our analysis to annotated RADAR RNA editing sites that did not overlap with annotated SNPs from the 1000 Genomes Project.p values. Using five permutations, we recorded the minimum p value for each site over all cis SNPs in each permutation, and used this set of p values as the empirical null distribution. For a given FDR value f, we defined the p value cutoff z such that P(p0 < z)/P(p1 < z) = f, where P(p0 < z) is the fraction of expected p values from the null distribution less than z and P(p1 < z) is the fraction of observed p values from the real data less than z. For each editing site, the edQTL SNP was defined as the closest SNP with the most significant association. Here we used an FDR threshold of 10%.For each RNA editing site, we applied the GLiMMPS statistical model  to SNPs Allele-specific alignments were obtained by aligning RNA-seq reads using STAR v2.4.2a  to the hΦi1k and Φi2k be the editing levels of site i for allele 1 versus allele 2 in the kth individual. For each RNA editing site in each individual, the editing level Φ of allele 1 or allele 2 can be modeled by the counts of RNA-seq reads corresponding to the edited (I) and unedited (A) sequences via the binomial distributions:We used a paired replicate statistical framework for reliable detection of allele-specific RNA editing signals in population-scale RNA-seq datasets. We treated the two alleles as matched pairs and multiple individuals sharing a given heterozygous SNP as replicates. We modeled and tested for the paired difference between the two alleles. Conceptually, a hierarchical framework was used to simultaneously account for the estimation uncertainty of RNA editing levels in each individual and model for the paired allelic difference in RNA editing levels across replicates. Let logit(Φi1k) and logit(Φi2k) can be modeled by the normal distributions:αik; the parameter δi captures the difference between the logit transformed editing levels between the two alleles; and σi12 and σi22 are the variances of allele 1 or allele 2 across multiple individuals (or replicates). The Benjamini–Hochberg procedure was used to control the FDR at 10%.We used an additive model to account for the allelic difference in RNA editing across multiple individuals. The logit transformed editing levels p values less than 10−3. The liftover tool from the UCSC genome browser [2 > 0.8) with GWAS SNPs. Only the four European populations were used in the LD calculation.We used the NHGRI GWAS Catalog   . Alu seqA panel of 86 LCLs from the HapMap3 project was purchased from the Coriell Institute for Medical Research, Camden, NJ, USA. Three cell lines were selected for each of the three genotypes of a SNP. Genomic DNA was extracted using a Quick-DNA Miniprep Plus Kit .PCR primers were designed to amplify the flanking areas of the target editing sites on the corresponding genomic DNA. Primers are: NDE1_Forward, 5′- CAACCAGGTGGAATCGTCTT-3′; NDE1_Reverse, 5′- ACTCGAACGCACCTCTAGGA-3′; ATM_Forward, 5′-CCAGGACAGCTACAGCATCA-3′; ATM_Reverse, 5′-CTAAGCCCTTCCCTTCCAAC-3′; MDM4_Forward, 5′-GTGATGGGGGATAGGGAGTT-3′; MDM4_Reverse, 5′-GCATTTCATCCCTCCTTTGA-3′; H2AFV_Forward, 5′-AGGCATGAGAATGACGTGAA-3′; H2AFV_Reverse, 5′-CTTCAACCTGGGCAAAAGAG-3′. PCR amplicons were purified by agarose gel electrophoresis and gel extraction using a PureLink® Quick Gel Extraction Kit , followed by Sanger sequencing to confirm the genomic sequence of the editing sites.Additional file 1: Table S1.List of available Geuvadis LCL samples with matched RNA-seq and genotype data. (XLSX 19 kb)Additional file 2: Table S2.List of edQTL RNA editing sites with associated SNPs. (XLSX 28 kb)Additional file 3: Figure S1.blue. (PDF 209 kb)Representative Sanger sequencing chromatograms of the genomic DNAs of four RNA editing sites to confirm absence of unannotated A/G SNPs. The RNA editing site is at the center, highlighted in Additional file 4: Table S3.List of ASED RNA editing sites with associated SNPs. (XLSX 45 kb)Additional file 5: Table S4.List of edQTL/ASED SNPs linked to GWAS SNPs in the NHGRI GWAS Catalog. (XLSX 13 kb)Additional file 6: Figure S2.cis-regulated edQTL RNA editing sites. Light blue, tan, yellow, and red represent A, C, G, and T, respectively. Dark blue represents no aligned sequence (gaps) and white is an artificial spacer placed between the two Alu sequences in IRAlu hairpins. The rows of the heatmap represent individual IRAlu sequences and are sorted by the relative positions of the RNA editing sites (panels 3 and 4). The location of significant cis SNPs within the IRAlu hairpins are indicated (panels 5 and 6). Tail-to-tail  and head-to-head  IRAlus are analyzed separately, and the rows and columns of each group (tail-to-tail or head-to-head) of panels correspond to each other. (PDF 1540 kb)Locations of edQTL RNA editing sites and significant SNPs within IRAlu hairpins. A diagram of the architecture of an Alu repeat is shown at the top. (panels 1 and 2). Heatmap of multiple sequence alignments (MSA) of IRAlu hairpins containing Additional file 7: Figure S3.Allele 1 and the allele with higher RNA editing is labeled as Allele 2. (PDF 104 kb)Histograms of RNA editing levels for heterozygous sites determined from the ASED analysis. Significant RNA editing sites were obtained from the ASED analysis and the average RNA editing level (Φ) of each allele for all heterozygous individuals was calculated. This was done for each population separately and the distribution of Φ values is plotted with respect to each allele. The allele with lower RNA editing is labeled as Additional file 8:In-house python scripts for the ASED analysis. List of command-line commands and python scripts to generate allele-specific alignments along with R code for paired statistical test. (GZ 12 kb)Additional file 9: Table S5.Data sources of 12 RNA-seq replicates of GM12878. (XLSX 11 kb)


Long non-coding RNAs (lncRNAs) regulate critical cellular processes and their dysregulation contributes to multiple diseases. Although only a few lncRNAs have defined mechanisms, many of these characterized lncRNAs interact with transcription factors to regulate gene expression, suggesting a common mechanism of action. Identifying RNA-bound transcription factors is especially challenging due to inefficient RNA immunoprecipitation and low abundance of many transcription factors. Here we describe a highly sensitive, user-friendly, and inexpensive technique called RATA (RNA-associated transcription factor array), which utilizes a MS2-aptamer pulldown strategy coupled with transcription factor activation arrays for identification of transcription factors associated with a nuclear RNA of interest. RATA requires only ~5 million cells and standard molecular biology reagents for multiplexed identification of up to 96 transcription factors in 2–3 d. Thus, RATA offers significant advantages over other technologies for analysis of RNA-transcription factor interactions. Dysregulation of lncRNAs has recently been linked to multiple human diseases, especially cancer . While tin vitro transcribed, biotinylated RNA with cell lysate significantly increases efficiency of RNA purification . ASLNCR1 interactors (Brn3a and AR) [Because our methodology requires overexpression of the tagged RNA of interest, there are important potential caveats to consider when using this approach. First, inclusion of an RNA tag may affect secondary structure of the RNA, and therefore may also affect interactions with partner proteins. If possible, phenotypic or gene expression changes should be quantified following overexpression of either the untagged or tagged RNA to determine if the tag interferes with normal functionality of the RNA (Step 1.2.1). Ideal placement of the RNA tag may need to be determined empirically. Second, overexpression of RNA may increase/induce non-specific binding with proteins that do not interact with the RNA under physiological conditions. Thus, candidate RNA-transcription factor interactions identified through RATA should be validated through orthogonal assays. For example, RIP assays, in which an RNA binding protein of interest is immunoprecipitated and associated RNAs are identified through RT-qPCR, may be used to confirm if a candidate transcription factor interactor identified through RATA maintains its interaction with endogenous RNAs . Indeed, and AR) .® based assays capable of quantifying 40 transcription factors in up to 96 samples  in a single plate. The wide variety of transcription factor arrays available enables great flexibility in assay design. The selection of a transcription factor array depends on the RNA being studied and the type of information desired. For example, if a scientist is interested in testing the hypothesis that a lncRNA binds to transcription factors, Signosis’ Array I or II, which contain 48 or 96 of the most well-studied transcription factor, respectively (many of which have been shown to bind to lncRNAs), may be suitable. Alternatively, if the lncRNA in question is expressed exclusively in cancer and implicated in tumorigenesis, Signosis’ Cancer Stem Cell Array (containing 4 sets of 23 transcription factor specific probes) may be appropriate. For a more comprehensive analysis of associated transcription factors, scientists may choose to screen multiple arrays, or use the 345 probe Panomics Combo Array. It may also be helpful to consider which transcription factors are expressed in the cell line being tested; however, the arrays are very sensitive and are capable of detecting transcription factors even when they are not detected via western blotting.While RATA is limited to the identification of transcription factors included in the arrays, there are multiple versions of commercially available transcription factor activation arrays compatible with this technique. Signosis offers 12 different arrays, each including various combinations of over 150 unique transcription factors. Panomics offers 5 different transcription factor activation arrays , with their largest array (Combo Array) able to quantify 345 transcription factors in a single assay. Collectively, the Panomics and Signosis Arrays cover over 400 unique transcription factor in varying combinations. Importantly, these transcription factors represent the most biologically relevant and well-characterized transcription factors, covering approximately 25% of the 1672 currently identified human transcription factors . AdditioThis report details a novel protocol for the efficient and low cost identification of transcription factors associated with an RNA of interest, including lncRNAs. We have designed an exclusively nuclear MS2 fusion protein and optimized conditions to enable highly efficient isolation of ribonucleoprotein complexes. This easily implemented protocol will enable scientists from a range of fields to more efficiently and comprehensively investigate the role that RNAs play in modulating transcription factor activity and regulating gene expression.Table 2.Potential problems and troubleshooting suggestions are listed in