During oral reading, the eyes tend to be ahead of the voice . It has been hypothesized that the extent to which this happens depends on the automaticity of reading processes, namely on the speed of print-to-sound conversion. We tested whether EVS is affected by another automaticity component – immunity from interference. To that end, we manipulated word familiarity  and word length as proxies of immunity from interference, and we used linear mixed effects models to measure the effects of both variables on the time interval at which readers do parallel processing by gazing at word N + 1 while not having articulated word N yet (offset EVS). Parallel processing was enhanced by automaticity, as shown by familiarity × length interactions on offset EVS, and it was impeded by lack of automaticity, as shown by the transformation of offset EVS into voice-eye span (voice ahead of the offset of the eyes) in PWs. The relation between parallel processing and automaticity was strengthened by the fact that offset EVS predicted reading velocity. Our findings contribute to understand how the offset EVS, an index that is obtained in oral reading, may tap into different components of automaticity that underlie reading ability, oral or silent. In addition, we compared the duration of the offset EVS with the average reference duration of stages in word production, and we saw that the offset EVS may accommodate for more than the articulatory programming stage of word N. When EVS is defined in terms of time , a distinction is made between the time from the onset of word fixation to the onset of word naming (onset EVS), and the time from the offset of word fixation to the onset of word naming (offset EVS). The temporal onset EVS of word N is equivalent to the naming latency for that word. It encompasses all stages of word processing that take place before articulation, and may thus be referred to as the word’s processing time . The temporal offset EVS of word N refers to a shorter period. During this period, the reader gazes at word N + 1 while not yet having started to articulate N . The temporal offset EVS is a particularly interesting period, in that it seems to signal the reader’s engagement in the parallel processing of N and N + 1, and thus some of her/his reading skills. Offset EVS is the focus of the present study, where we investigate the extent to which it depends on one of the components of automaticity - immunity to interference .Why do readers gaze at a new word without having articulated the previous word? An available explanation for EVS is that the eyes tend to be ahead of the articulatory system because visual processing is faster than articulation . Althougautomaticity in reading , we analyzed the distribution of offset EVS values for each familiarity x length condition, and we located the point at which offset EVS values become negative.A second question pertains the type of relation between automaticity  and offset EVS. Possible evidence that automaticity esources . Two, thWhether automaticity is beneficial or necessary to offset EVS , offset EVS should predict reading velocity, since reading velocity itself depends on automaticity. A critical way of testing this would be examining whether the offset EVS of the experimental task predicts reading velocity in a concurrent task, since this would tap into readers’ automaticity skills in different contexts. Thus, in order to strengthen our analysis, we tested if offset EVS predicted reading velocity in the 3DM reading test .which processing stages of N take place while readers gaze at N + 1. Two different perspectives are found in the literature. While Finally, the notion that parallel processing takes place during offset EVS raises a third question – the question of Our approach is novel in two ways. First, unlike recent studies on EVS for text , we presGaze Time to Processing Time ratio . GT/PT is obtained by dividing the gaze time on a word by the onset EVS (the processing time) of the same word. One advantage of GT/PT is that it is suitable for describing eye-voice span  as well as voice-eye span . A second advantage of GT/PT is that it is a relative measure, describing the weight of different processing stages (gaze-dependent vs. gaze-independent) within the onset EVS (naming latency) period. A relative measure such as this is crucial to validate offset EVS results, since it describes the contribution of parallel processing to the complete processing (naming latency) of a given word, rather than just the duration of the parallel processing stage (offset EVS). Offset EVS (absolute) values may be misleading in the sense that differences in offset EVS between words do not necessarily mean different contributions of parallel processing. For instance, offset EVS values of 300 and 600 ms indicate equivalent contributions of parallel processing if naming latencies  are 600 and 1200 ms, respectively (contribution of 50% in both). Conversely, it is possible that the offset EVS values of two words differ little , but such differences reflect important contrasts in the contribution of buffer-based processing . In order to control for possible misleading effects of offset EVS (absolute) values, we performed the analysis of familiarity x length effects on both measures, and we compared the effects of both measures on scores of reading velocity.Additionally, we explore a novel approach to offset EVS, involving an EVS-related measure that we named SD = 26 ± 5; Mean years of schooling ±SD = 15 ± 2) were included in the analysis. All had normal or corrected-to-normal vision. None had neurological problems or was taking drugs. Screening tests . We generated a set of 80 PWs (see Appendix). In each familiarity level, there were 40 short  and 40 long items . Short and long HF words did not differ in frequency , neither did LF words . The six familiarity × length levels were balanced for bigram frequency  and neighborhood density . In total, there were 240 (80 × 3) experimental stimuli organized into 30 lists for multiple-item presentation . The items in each list had the same level of familiarity, length and regularity status. Lists of long words or PWs comprised 12 items (3 rows × 4 columns), and lists of short words comprised 15 (3 rows × 5 columns). Filler items were included (filler words in word lists and filler PWs in PW lists), so as to avoid artifacts at critical positions (first column and last word slot of each list), and also to keep the number of items constant across lists. There were 156 filler items, summing up to 396 (240 + 196) stimuli.We selected 80 HF and 80 LF words from CLUL database , which pThe 3DM test, which we used for screening reading disability (see Participants), had a second purpose in our study: we also used it as a measure of individual reading velocity. We wanted to know whether offset EVS values for our experimental stimulus set could predict reading velocity in a concurrent test , so as to strengthen possible evidence that EVS taps into reading velocity (see Introduction). In the 3DM test, participants were presented with 75 LF words, 75 HF words and 75 PWs (none of these included in the eye-tracking experiment) for a fixed time interval. Their task was to name as many words or PWs as possible.Participants were instructed to name the items, in rows, as accurately and fast as possible, while remaining still and avoiding blinking. They were asked to press the space bar of the computer keyboard at the end of each list. The 30 lists were randomly presented across subjects.1. Subjects placed their head on a chin rest and sat 80 cm away from the monitor. At this distance, the minimal inter-word spacing subtended 6.8° of the visual angle and was, thus, larger than parafoveal vision. Vocal responses were recorded with a Logitech webcam, synchronized with the eye-tracker as provided by SMI “Observation package” software. Subjects were first given practice trials. The recording session started with a thirteen-point calibration procedure, and tracking errors larger than 0.5° led to a new calibration.Eye movements were monocularly recorded at 1250 Hz with a tower-mounted SMI hi-speed eye tracking system2. Naming responses were classified for articulation accuracy (correct vs. misarticulated). Eye data per item × subject was scanned for blinks, lack of eye entry in the AOI (skipped item), and accidental eye entries at the onset of the list. Misarticulated items, as well as those containing any type of eye artifact  were removed from the analysis. Since the EVS is often readjusted by means of regressions  were visually inspected for artifacts, and those with more than 25% of signal loss were marked as contaminated trials. Subjects with more than 25% contaminated lists were excluded from the analysis (see Participants). Audio data were analyzed offline with Praat softwareressions , we exclFigure 2) to compute first-pass gaze times and onset EVS per item x subject. Onset EVS was calculated as the interval between the first valid eye-entry on the item’s AOI and the naming (articulatory) onset. Offset EVS per item × subject was obtained by subtracting first-pass gaze time to onset EVS of item N. Positive offset EVS values indicate that the eyes are ahead of the voice , and negative ones indicate the opposite . Finally, Gaze Time to Processing Time ratio (GT/PT) values (first-pass gaze time/onset EVS) were obtained. Values larger than 1 follow positive offset EVSs  and values smaller than 1 follow negative offset EVS values . The distributions of offset EVS or GT/PT showed no marked deviations from normality.Rectangular Areas Of Interest (AOIs) were placed around each word/PW  would emerge. The mean and standard deviation of offset EVS and GT/PT for each stimulus class allowed us to estimate whether one standard deviation away from a positive offset-EVS mean (eye-voice span) would show negative values. This was complemented with percentile analyses, which specified, for each stimulus class, the percentile at which eye-voice span turned into voice-eye span. Mean offset EVS and GT/PT values were also used to investigate whether offset EVS periods might accommodate for processing stages other than motor programming (150 ms).lme4  criterion. We also followed the principle that, with a large sample size, absolute t-values larger than 2 indicate significant results at the 5% level (Tables 2 and 3).We used R  and lme4lme4  to perfolme4 . P-value5% level , and thiWe used similar procedures to test for offset EVS and GT/PT as predictors of reading velocity in the concurrent 3DM test. We modeled reading velocity with offset EVS or GT/PT as predictors, and compared the two models with the intercept-only model.Figure 3) for all items (M ±SD: 148 ± 187 ms) indicated that the readers’ eyes were, on average, fixating N + 1 (mean first-pass gaze time was 494 ms) when starting to name N. However, descriptive statistics for the six familiarity × length levels (Table 1) indicated a negative mean value for long PWs. Also, for LF long words and all PWs, negative offset EVSs started as soon as one standard deviation below the mean . GT/PT values  showed a similar picture, with a mean GT/PT > 1 for long PWs and GT/PTs > 1 starting one standard deviation above the mean in LF words and all PWs.The mean values of offset EVS , were large enough to accommodate for more than the average time of motor programming (150 ms).The average values of offset EVS for long and short HF words (191 and 259 ms), as well as for short LF words  showed significant effects of familiarity, length, and a significant familiarity × length interaction, indicating that the effects of length increased as familiarity decreased. Concerning random factors, the variance arising from subjects was larger than from items in both cases.For both offset EVS and GT/PT the analysis of fixed factors (2(1) = 8.11, p = 0.004, see Figure 4], and it increased as GT/PT increased .Both offset EVS and GT/PT predicted reading velocity scores. Reading velocity in 3DM increased as offset EVS in the experimental task increased [χCurrent approaches to the dynamics of eye and voice during oral reading suggest that the extent to which the eyes go ahead of the voice depends on the automaticity of the processes involved, automaticity referring to the speed of those processes. We have expanded the work on this hypothesis by focusing on a different component of automaticity – release from attentional control, leading to immunity from interference. This automaticity component is conceptually close to the parallel processing taking place during the offset eye-voice span period, and thus we investigated its role empirically, using word familiarity and its interactions with word length as proxies of immunity from interference. Our goal was threefold. First, we wanted to gather further evidence that automaticity leads to increased offset EVS values. Second, we wanted to determine if extreme decreases in automaticity eliminate parallel processing and transform eye-voice span into voice-eye span, so as to clarify if automaticity is necessary to offset EVS, rather than just beneficial. Third, we wanted to get preliminary information on whether the processing of word N in parallel with word N + 1 is limited to motor programming or, on the contrary, if it encompasses previous stages in the processing chain. We addressed these goals by investigating the effects of word familiarity and length on offset EVS as well as on an EVS-related measure that we named gaze time to processing time ratio (GT/PT).levels of automaticity. From this viewpoint, we concluded that a PW (less familiar) requires, on average, longer offset EVSs than a HF word (more familiar). Nevertheless, gradient effects were also apparent: among HF words, words with the highest frequency values seemed to elicit the longest offset EVSs , so it is highly likely that a continuous approach would also show significant effects. The effects of word length on offset EVS and GT/PT increased as familiarity decreased, signaling the interaction we predicted.Supporting our predictions, automaticity (immunity from interference) lengthened the parallel processing period corresponding to offset EVS. Less familiar words elicited shorter offset eye-voice span values (absolute measure) as well as stronger investment on gaze during word processing , compared to more familiar words. Due to the categorical approach we made, our analyses highlighted the effects of different More than just beneficial, automaticity seems to be necessary to the parallel processing occurring during the offset EVS period. As familiarity decreased and length increased, a true eye-voice span vanished, and the reader started to name word N while still viewing it. Our data included many instances of voice-eye span, most of these found in LF words and PWs. In LF long words, data points that were one standard deviation above the mean represented voice-eye spans instead of eye-voice spans, and, in long PWs, even mean values did the same.Finally, what is going on with word N while the eyes are ahead? Our findings are consistent with the possibility that the processing of N in parallel with N + 1 is not restricted to motor programming, the last stage before articulation. For HF (short and long) words and short LF words, we saw mean offset EVS values that accommodate for more than the average duration of articulatory programming, which is about 150 ms . For insIn the comparative analysis of offset EVS with GT/PT, both measures exhibited the expected familiarity x length interactive effects, and both predicted reading velocity in the expected direction (velocity increased with longer offset EVS and decreased with larger GT/PT). Therefore, our results for offset EVS seem valid enough. Although, in our case, offset EVS measures were not misleading since GT/PT indices did not change the picture, the concept of GT/PT expressed the observed negative offset EVS values (voice-eye span) in a simpler, less biased way. GT/PT values larger than 1 indicate that readers spend more time gazing at the word than the time needed to process it (begin its articulation). In contrast, the idea of a negative offset EVS is less transparent. Therefore, GT/PT seems to hold, at least, a conceptual advantage over offset EVS.Our findings contributed to strengthen the link between offset EVS and the automaticity of reading, but the fact that we manipulated automaticity in an indirect manner, that is, using proxies (word familiarity and length), is one limitation. Direct manifestations of automatic processes may be captured with Stroop tasks  when using sentence-like materials that discard block effects and elicit semantic and syntactic integration, should stand as a next step in research. Second, we tried to eliminate parafoveal processing  by contrThe main contribution of our study was to strengthen the relation between offset EVS and automaticity in reading. Although we focused on measures that pertain to oral reading , the results of our study ultimately support the understanding of offset EVS  as an index of automaticity, which underlies both oral and silent reading. Establishing offset EVS or GT/PT as indices of automaticity is an important step in clinical and experimental applications of the double-deficit hypothesis on dyslexia , which pviewed as immunity to interference. Our findings are consistent with increasing evidence that dyslexic individuals – who typically show shorter EVSs – have problems in dealing with multiple presented items such as in RAN tasks (Specifically, our study highlighted the relation between offset EVS and automaticity AN tasks , and theWe wanted to test the effects of immunity to interference on offset EVS, and we used word familiarity as a proxy of immunity to interference. We did that based on In our approach to the offset EVS, we put the emphasis on the extent to which it is a manifestation of parallel timelines of word processing, and many questions remain unanswered concerning these timelines. One question that is raised by our findings is why the eyes remain on the word during articulation in cases of voice-span, instead of moving on to the next word as soon as articulation begins. May articulation itself be dependent on gaze? For which purpose? Under which circumstances? We believe that this and other questions may strongly benefit from using methods of co-registration of eye-tracking and EEG in future research.SS designed the experiment, collected data, analyzed data, and wrote the draft. LC collected data, analyzed data, and wrote the draft. AR and KP designed the experiment and revised the draft. LF designed the experiment, analyzed data and revised the draft. SS, AR, LC, KP, and LF did the final approval of the version to be published and showed their agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.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.


Influenza A virus is a threat for humans due to seasonal epidemics and occasional pandemics. This virus can generate new strains that are dangerous through nucleotide/amino acid changes or through segmental recombination of the viral RNA genome. It is important to gain wider knowledge about influenza virus RNA to create new strategies for drugs that will inhibit its spread. Here, we present the experimentally determined secondary structure of the influenza segment 5 (+)RNA. Two RNAs were studied: the full-length segment 5 (+)RNA and a shorter construct containing only the coding region. Chemical mapping data combined with thermodynamic energy minimization were used in secondary structure prediction. Sequence/structure analysis showed that the determined secondary structure of segment 5 (+)RNA is mostly conserved between influenza virus type A strains. Microarray mapping and RNase H cleavage identified accessible sites for oligonucleotides in the revealed secondary structure of segment 5 (+)RNA. Antisense oligonucleotides were designed based on the secondary structure model and tested against influenza virus in cell culture. Inhibition of influenza virus proliferation was noticed, identifying good targets for antisense strategies. Effective target sites fall within two domains, which are conserved in sequence/structure indicating their importance to the virus. Influenza is a common disease that spans the globe and is dangerous for human health and life. Even seasonal flu is life threatening for young infants, the elderly, and immunocompromised patients. The most dangerous outbreaks are global pandemics that appear sporadically when immunologically novel influenza strains  gain the ability to infect humans, causing severe illness and complications in healthy populations.2. Knowledge about secondary structure provides insights to better understand virus biology and could be used for designing new therapeutics; however, additional work is needed to experimentally model influenza structure.Influenza is a single-stranded negative sense RNA virus with segmented genome . The vRNA genome of influenza is divided into eight segments, which are independent transcription-replication units. Each step in the influenza life cycle is dependent on RNA. vRNA is used to generate two types of (+)RNA: cRNA (complementary RNA), and mRNAs (messenger RNA). cRNA is used as a template for replicating vRNAs while mRNA encodes viral proteins. More than 8 viral proteins are encoded through alternative splicing and translation initiation. Recent studies are revealing the roles of vRNA and mRNA secondary structure during different steps in the viral life cycle3. Oligonucleotides are a major focus of drug development; numerous clinical trials have been performed and many oligonucleotide therapeutics are in phase III development. Several oligonucleotide therapeutics are FDA approved5. Oligonucleotide strategies offer advantages in comparison to other existing approaches against influenza. In fast evolving viruses, such as influenza, targeting surface proteins is challenging, as each season novel proteins appear in circulating strains. In contrast, the RNA of influenza, due to combination of evolutionary constrains, possess features that remain constant between strains. One such feature is the need of influenza to conserve RNA secondary structure. Therefore, viral RNA secondary structure determination is a key component in the design of effective inhibitory oligonucleotides.Antisense oligonucleotide (ASO) drugs are designed to alter RNA expression and function, thus allowing one to influence pathological processes beyond protein or receptor involvement7. Only several of these motifs have been examined experimentally, and very few have been studied in vivo10. Only for two entire viral genomic segments, 8 and 7, has secondary structure been determined12. The structural information derived for segment 8 was used for designing effecting antisense oligonucleotides (ASOs) that inhibited virus proliferation13. Influenza RNA possesses complex and partially dynamic secondary structure, and the in vitro studies determining the conserved structure of vRNA elements bring us closer to interpreting their functions. In the case of mRNA, conserved structural motifs were discovered using sequence/structure comparisons, an analysis of the constraints of RNA folding on codon evolution, and were confirmed by the study of RNA models in vitro in segment 8, 7 and 515 and experiments in vivo2.Bioinformatics studies showed the possibility of conserved structures both in (+) and in (–) RNA strandsnucleoprotein (NP), which is a structural protein that also takes part in the regulation of transcription and replication16. NP, along with the viral polymerase and vRNAs, create eight viral ribonucleoprotein complexes (vRNPs), which are organized into an active virus particle. The crucial role of NP in virus assembly and the viral replication cycle makes segment 5 an attractive target for screening effective inhibitors. Three phosphorothioate modified antisense oligonucleotides targeting NP gene significantly inhibited H5N1 type virus replication; one oligonucleotide could protect the mice from a lethal dose of influenza virus17. Recently, a 121 nt conserved structural motif from the segment 5 (+)RNA was used to design effective antisense oligonucleotides against a modified, single-cycle infectious influenza A virus strain. This highlights the importance of this motif in virus proliferation and also highlights a new method for the design of ASOs based on target RNA secondary structure8. Segment 5 (+)RNA was also the focus of other studies, where additional predicted motifs in other regions  were suggested to be important18. Therefore, segment 5 (+)RNA, which is rich in predicted structural motifs, was selected for the full-length analyses of (+)RNA structure: to validate/model these motifs in the context of the full-length sequence.In this current study, we focus on the (+)RNA of segment 5. The segment 5 mRNA differs from cRNA by being capped and polyadenylated. Segment 5 mRNA encodes open reading frame (ORF) for NP. Here, (+)RNA5 corresponds to the cRNA generated from vRNA as well as the mRNA (minus the cap and poly(A) tail). Influenza generates mRNA through a “cap snatching” mechanism, where fragments of host mRNA 5′ untranslated regions  are stochastically cleaved and extended to contain the viral (+)RNA sequence; the poly-A tail is added during mRNA maturation. (+)RNA5-ORF is the (+)RNA minus flanking viral UTR sequences and was included to assess the impact of removing the partially complementary UTRs, which in the vRNA are used to circularize the molecule (via a vRNA “panhandle” structure20) and in the (+)RNA can form a cRNA panhandle formed by the vRNA reverse complement sequence. Due, however, to vRNA GU pairs being converted to CA mismatches in the cRNA, this cRNA panhandle is structurally distinct. This structural distinction between the vRNA and cRNA panhandle structures is proposed to play a key role in regulating the promoter site used for cRNA, vRNA and mRNA replication21. Based on experimental data and bioinformatics analysis, the secondary structure for entire (+)RNA5 is proposed here. This is the first experimentally informed secondary structure model of (+)RNA5, which presents features likely to be important in segment 5 mRNA or/and cRNA function. This structure model was used to design antiviral oligonucleotides, which were shown to have activity in inhibiting the native influenza virus replication in an MDCK cell line.Herein, two RNA constructs were studied: the full-length segment 5 (+)RNA5 and a shorter construct, (+)RNA5-ORF, containing only the 23 and SHAPE mapping 24 were applied to probe the folding in (+)RNA5 and (+)RNA5-ORF. Before mapping, appropriate folding conditions were found to yield a single RNA conformation, as assessed by non-denaturing agarose gel. Both (+)RNA5 and (+)RNA5-ORF adopt a single conformation in buffer containing 100 mM KCl (or 300 mM) and 5 mM MgCl2. Mapping was carried out at 23 °C and 37 °C to assess the impact of temperature on folding. This range of temperatures was used because there are influenza strains that can effectively replicate in low temperature (e.g. cold adapted viruses) and others in high temperature (e.g. avian strains)26; it is thus valuable to assess how RNA folding may be affected by different temperatures. To read out mapping results, reverse transcription was used with fluorescent labeled primers (Supplementary Table CMCT (modifies N3 of U and N1 of G when unpaired), DMS (methylates N1 of A and N3 of C when unpaired), and kethoxal (modifies G when unpaired)8.To predict the secondary structure of segment 5 (+)RNA based on the experimental probing data, the results of chemical mapping were used to constrain predictions in the RNAstructure 5.5 program. SHAPE data were loaded as pseudoenergy constraints  and DMS, CMCT, and kethoxal modifications were included as chemical mapping constraints (reactive nt are forbidden to be in Watson-Crick base pairs flanked by Watson-Crick base pairs). Additional constraints were added to constrain pairing in previously-modeled conserved structural motifs in regions: 1051–1171 nt (1077–1197 (+)RNA5-ORF) pairs 1081/1191, 1083/1190 and 1085/1189 ((+)RNA5 numbering) were added as base pairing constraints27. There are distinct structural motifs that are well defined by chemical mapping, such as the hairpins in regions: 45–105, 283–334, 388–444, 451–492, 671–743, 1129–1173. SHAPE and chemical mapping show that there are fourteen relatively long flexible regions .The secondary structure of (+)RNA5 appears circular in folding Fig. . SeveralPossible changes of secondary structure at higher temperature (37 °C) were assessed due to fact that influenza can propagate in different temperatures in various species. The model secondary structures of (+)RNA5 probed at 23 °C and 37 °C are almost identical and only minor changes were noticed RNA5 hairpin 577–593 . Similarly, domain 832–931 of (+)RNA5-ORF RNA5 structure with ΔG°37 = −27.0 kcal/mol RNA5-ORF, was determined. The modeled structure was almost identical to that of corresponding coding region of (+)RNA5, indicating that local motifs within the ORF are stable independently of the UTR fold. Only the structures of two regions  were different Figure  has predThe possibility of preferential local folding in segment 5 (+)RNA was also considered for the entire (+)RNA5. For that purpose, beside experimental constraints, a maximum paring distance of 600 nt was set in RNAstructure 5.5 during the prediction of secondary structure: here, the greatest allowable distance between base pairs is 600 nt. All other experimental constraints were also incorporated in this calculation. The consequences of this additional constraint are the disappearance of helix 244–281/1478–1533 and the appearance of several new motifs on the 5′ and 3′ ends RNA5 structure model to identify the level of base pair conservation and identify structurally relevant mutations. Overall, model base pairs are 85% conserved across all analyzed sequences  RNA5 were acquired from the NCBI Influenza Virus Resourcent) Fig. . The mosmutual information (MI), to suggest co-evolution of paired nt in the alignment30. The only extended regions with higher-than-average MI RNA5 to the isoenergetic microarray was conducted at 23 °C and 37 °C in buffer containing 300 mM KCl, 5 mM MgCl2 and 50 mM HEPES pH 7.0. Only strong and medium binding probes were considered in the analysis of structure (Tables (+)RNA5 accessibility to oligonucleotides was studied using isoenergetic microarrays and RNase H cleavage induced by DNA oligonucleotidese Tables .Table 1DRNase H cleavage was used to validate several oligonucleotide probe binding sites Figs . 19 DNA The probe binding sites: deduced (confirmed by RNase H cleavage) and probable  were marked on the (+)RNA5 secondary structure Fig. . The bin17 and confirmation of determined secondary structure elements. Antisense oligonucleotides (ASOs) were designed to be fully complementary to segment 5 (+)RNA of strain A/California/04/2009 (H1N1) (Table 17 (sequence nonspecific to influenza virus RNA) and Neg 5 (complementary to a region of (+)RNA5 that forms a helix: 1262-1272) (Table The highly conserved (across type A influenza strains) secondary structure of segment 5 (+)RNA presented in this work allow to designate new targets for oligonucleotides. The new target regions, could be alternative to those previously found for the NP (+)RNA8. ASOs 2A, 3A and 2B inhibited influenza virus propagation in both assays, but the level of inhibition presented here was smaller, probably because experiments were conducted on a wild-type, fast replicating influenza virus in natural cells. Four-fold inhibition of viral replication was shown using ASO 400A. ASOs 615A, 760A, 640A and 600A inhibited virus propagation about three-fold. qRT-PCR confirmed the inhibitory potential of selected oligonucleotides. 87%, 79%, 72%, 71% 70%, 64% levels of inhibition were observed for 727A, 3A, 2A, 400A, 615A, 640A, respectively. 600A, 2B and 760A inhibited propagation of virus at the same level – 63% . 9 of 21 tested ASOs showed inhibition of influenza virus replication Fig. . ASO 727Nine effective ASOs  and two negative control oligonucleotides (Neg 5 and N) were tested for cytotoxicity Figure . None ofThe secondary structure of segment 5 (+)RNA based on experimental structure mapping, sequence/structure comparison and free energy minimization revealed structure motifs that are conserved across diverse influenza A strains. These motifs are thermodynamically stable and have high calculated base pair probability. Additionally, the discovered motifs persist at both low and high temperatures. All these results point to these stable and conserved motifs being functionally significant.7.The effect of RNA folding on sequence evolution was most apparent in the UTRs. Elsewhere, however, there were specific pairs that showed greater-than-average MI -mRNA structure. Once in the cytoplasm, additional posttranscriptional control mechanisms work to modulate gene expression. Many of these may also be influenced by RNA folding. The cRNA remains in the nucleus, where it is used to replicate additional vRNAs. In addition to the viral polymerase, cRNA also interacts with multiple copies of NP to form a ribonucleoprotein complex (cRNP). Cryo-electron microscopy, revealed that cRNP exhibits a circular, filamentous double-helical organization with defined termini, containing the viral RNA-dependent RNA polymerase at one end and a loop structure at the other end39. It was also noticed that sufficient flexibility exists within cRNP to form secondary RNA structures and favor suggestion of importance of cRNA secondary structure motifs. Recent work on NP associations in the RNP revealed that specific regions of RNA were dynamically interacting with protein40. Protein-free regions of cRNP can adopt structures that play roles and, vice versa, cRNA structure can affect NP binding dynamics: thus influencing replication rate and RNA stability, for example.The segment 5 (+)RNA construct comprises both cRNA and mRNA; thus the secondary structure studied in silico, based on probing data on “naked” RNA, lacking all interacting proteins, suggests that these motifs can form independently of the cRNP and form in advance of polymerase binding; this supports the idea that cRNA panhandle structure plays important function in the specificity of recognition by the viral polymerase. In addition, the novel hairpin identified nearby to the panhandle is also likely to play important roles, possibly in mRNA5, in the regulation of translation: e.g., the hairpin is directly upstream of the NP start codon, which is itself embedded within a long hairpin RNA5 secondary structure model has circular folding that recapitulates the cRNA panhandle structure that is likely recognized by the viral polymerase, which distinguishes cRNA from vRNA. Interestingly, this interaction, as well as a downstream hairpin that occurs just before the NP protein start codon, were some of the best-characterized structures in our model: they had the highest mutual information scores of any base-paired regions (due to the lack of coding constraints) as well as high base pair conservation and partition function probability. The ability to identify these interactions pin Fig. .18. It is possible that, preferentially local folding or circular folding motifs exist in vivo at certain times of the virus life cycle, and are promoted by viral or/and cellular factors. Hypothetically, changes of structure from circular to linear or linear to circular could take part in the life cycle of influenza virus; however, it likely only induces relatively small changes in global folding.Most revealed (+)RNA5 motifs remain the same, independent of the consideration of preferential local folding and also independent of the influence of the UTRs. Interestingly, only when local folding is constrained the structural motif at the 3′ end (nt 1478–1528) is formed RNA5 could be functional, for example in mRNA5  or in cRNA5 on replication . Interestingly, a hairpin in region 577–593 is formed in both the UTR-deleted and temperature elevated models. This hairpin, with a 7-nt hairpin loop RNA panhandle structure, there are two hairpins: one just before the START codon and another that includes the START codon RNA5 START codon in a hairpin structure (nt 47–104) and preceding small hairpin (nt 16–39) could both potentially regulate translation in influenza.A previous bioinformatics analysis showed a possible structured region for coding region 1–160 of (+)RNA5 (region 47–206 using entire (+)RNA5 numbering)33. The accessibility to oligonucleotide binding gives information on regions that are prone to intermolecular interactions, as well as regions that are mostly single stranded or weakly paired. This opens up the possibility for discovering new antiviral targets that can cause misfolding or block active regions of conserved motifs, which could then inhibit virus proliferation.Microarray mapping results show which regions from the full-length (+)RNA5 are accessible. Binding sites for microarray probes are in agreement with the proposed secondary structure model Figs  and 5; h8. Better fitness of the wide type virus and/or different experimental conditions could explain this difference. Several new target regions were also revealed in our current study RNA. ASOs , that target a 121 nt conserved motif of (+)RNA5 and were previously tested on a modified, single-cycle infectious influenza A virus (sciIAV), inhibited the propagation of a wild influenza virus Figs  and 7. Dudy Figs  and 7. Tudy Figs  and 7. A17. NP628 showed the best antiviral effect of three other phosphorothioate oligonucleotides tested by these authors. In addition, liposome mediated introduction of NP628 into mice partially protected them from lethal H5N1 virus challenge and resulted in a lower viral titer. The effectiveness of both oligonucleotides, 640A and NP628, are likely due to the accessibility of the (+)RNA secondary structure in this region.A region that overlapped (was one nt longer) the binding site of ASO 640A (628–645), was previously targeted using an antisense phosphorothioate-modified oligonucleotide (named NP628) specific to the influenza H5N1 virus (strain A/Tiger/Harbin/01/2002 H5N1)8 and the new region spanning nt 554–763. These two domains could take part in RNA-RNA or RNA protein interactions and be important in the influenza virus life cycle.Internal loops or hairpin loops were targeted by ASOs 760A (745–763), 2A (1146–1156) and 3A (1141–1156). Results for ASOs show that two domains are good targets for oligonucleotides – the previously described region formed by nt 1079–119417. NP749 reduced H5N1 virus titer and NP mRNA level. Both results show that targeting this region of different influenza strains is effective and also that this region is functionally important. We could not compare directly the antiviral effect because different conditions and method of analysis used. It is, however, notable that conserved regions for different strains of H5N1 influenza are good targets for antisense oligonucleotides and their effectiveness is correlated to the revealed RNA secondary structure described here. This model will facilitate future research into the functional analysis of influenza RNA as well as its therapeutic targeting.Oligonucleotide 760A partially targets the same region as published antisense oligonucleotide NP749 targeting strain A/Tiger/Harbin/01/2002Plasmid pPol1 contains sequence of segment 5 of influenza virus strain A/Vietnam/1203/2004(H5N1) was received from Professor Beak Kim (University of Rochester).43. Concentrations of all oligonucleotides were measured using a Spectrophotometer UV (NanoDrop2000 Thermo Scientific). Primers for reverse transcription were synthesized with aminolinker on 5′ end and after deprotection and desalting were labeled from the 5′ end with fluorophores: 5-FAM, 5-ROX, 6-TAMRA, and 6-JOE (dyes from ANASPEC). For labeling the reaction mixture containing 300 µg primer (11 µl in H2O) and 220 µg one of diluted in DMSO fluorophores (14 µl) and 75 µl 0.1 M sodium tetraborate, pH 8.5 was incubated overnight in room temperature in shaker oscillating at low speed. Labelled primers were precipitated with ethanol and purified by electrophoresis on 12% denaturing PAGE.Primers for PCR, qRT-PCR and reverse transcription, DNA oligonucleotides for RNase H cleavage, ASOs Table –S3 were in vitro transcriptions of (+)RNA5 and (+)RNA5-ORF were obtained by PCR from vector pPol1 using primers For_c5, Rev_c5 and FM5, RM5, respectively (Supplementary Table In vitro transcription reaction was performed using an Ampliscribe T7 Flash kit (Epicenter) according to manufacture protocol. Product was purified using RNeasy MiniElute Cleanup Kit (Qiagen). Integrity and purity of samples were checked on agarose gel.DNA template for 2, 50 mM HEPES pH 7.0 or 300 mM KCl, 5 mM MgCl2, 50 mM HEPES pH 7.0) was heated in 65 °C for 5 min. and slowly cooled to room temperature. Folding was analyzed by native gel electrophoresis using 0.8% agarose gel running at 4 °C with low voltage. Under these conditions, one band was observed RNA5 and (+)RNA5-ORF) in folding buffer , 16.5 mM CMCT (1-cyclohexyl-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate), 6.7 mM kethoxal or 1 mM NMIA (N-methylisatoic anhydride) for 15, 20, 30 or 40 min, respectively, at selected temperature (23 °C or 37 °C). Control reaction were performed in the same conditions but without mapping reagents. Modified nucleotides were read out by primer extension using a stechiometry of 2 pmol primer/1.5 pmol RNA. Primer extension was performed at 55 °C with reverse transcriptase SuperScript III (Invitrogen) using manufacturer’s protocol. Primers labeled by 6-JOE were used for detection of modification by DMS, CMCT, NMIA or kethoxal. Primers labeled by 5-FAM were used for detection of reverse transcription products that serve as control of quality of RNA. For each primers labeled with 5-ROX and 6-TAMRA there were prepared ddNTP ladders: (most often ddGTP and ddATP). cDNA fragments were separated by capillary electrophoresis .44. Quantitative NMIA reactivities for individual datasets were normalized to a scale in which 0 indicates an unreactive site and the average intensity at highly reactive sites is set to 1.0. The normalization factor for each datasets was determined by first excluding the most reactive 2% of peak intensities and then calculating the average for the next 8% of peak intensities. All reactivities were then divided by this average. In this scale, reactivities ≥0.700 are considered as strong, 0.500–0.700 as medium and <0.500 as weak, but all calculated reactivities were used for prediction of secondary structure. Nucleotides with no data were indicated as −999. Normalized SHAPE reactivities from each primer extension reaction were processed independently. DMS, CMCT, kethoxal modifications analysis was equivalent to that described above, but only strong and medium modification were used in prediction. For all SHAPE and DMS, CMCT, kethoxal mapping analysis at least three datasets were obtained from each primer and the average of results was used in the RNAstructure 5.546 for prediction of secondary structure.Data were analyzed with the ShapeFinder program45 uses thermodynamic parameters47 and free energy minimization for prediction of base pairing48. SHAPE reactivities were converted to pseudo free-energy change terms to restrain predictions. For this purpose the text file with normalized SHAPE reactivity (as described above) was input to RNAstructure 5.5 using “Read SHAPE reactivity—pseudo free energy” mode with slope 1.6 and intercept −0.6 kcal/mol49. DMS, CMCT, and kethoxal mapping data were also introduced at the same time, using “chemical modification” mode to apply only strong DMS, CMCT and kethoxal modifications as constraints51.For determination of RNA secondary structure RNAstructure5.5 program was used with incorporated experimental data. RNAstructure29, only non-redundant, full-length sequences were taken. Genome alignment of influenza sequences was performed using MAFFT52 using the FFT-NS-1 fast alignment approach. Base pairing conservation was analyzed by counting model pair nucleotides in the alignment using a Perl script (Supplementary File). The base pair probabilities were calculated using the program RNAstructure45 and the mutual information (MI) score was calculated using a module within the alignment editor BioEdit53.All available influenza A strains were downloaded from the NCBI influenza virus resourceFor RNase H assays RNA was folded as described above. Then, 3 pmol of DNA oligonucleotide in buffer with DTT  and 5 units of RNase H were added to final volume 20 μl. A control reaction was prepared equivalently but without DNA oligomer. Reactions were incubated for 30 min at 37 °C and then RNase H was inactivated by incubation for 10 min at 65 °C. Samples were precipitated with ethanol and cleavage sites were identified with primer extension using stoichiometry 2 pmol primer/1.5 pmol RNA. Primers labeled by JOE were used for detection of RNA cleavage. Primers labeled by FAM were used for control reactions. For each primers labeled with ROX and TAMRA there were prepared ddNTP ladders: most often ddGTP and ddATP.54. Microarray probes were complementary to (+)RNA5 step-by-step giving that whole studied RNA was scanned , which should show no binding to RNA, were also printed on the microarray as internal negative controls. Silanized slides were coated with 2% agarose activated by NaIO4 and microarrays were printed in the European Center of Bioinformatics and Genomics in Poznan, Poland with NanoPrint microarray printer (Arrayit). Printed microarrays were incubated for 12 h at 37 °C in 50% humidity. The remaining aldehyde groups on microarrays were reduced with 35 mM NaBH4 solution in phosphate-buffered saline solution and ethanol (3:1 v/v). Then slides were next washed (3x) in water at room temperature, next in 1% SDS solution at 55 °C (1x), and finally in water at room temperature (3x) and dried at room temperature.Isoenergetic microarrays were prepared and microarrays mapping was performed similarly to published proceduresAbout 200,000 cpm of 5′ radioactive labeled RNA (ca. 10 nM) was folded as described above before hybridization to isoenergetic microarrays. Hybridizations were carried out in folding buffer for 18 h at 23 or 37 °C in 100% humidity. Microarrays were washed for 3 min at the same temperature and buffer composition as hybridization occurred and then dried by centrifugation. Hybridization was visualized by exposure to a phosphorimager screen and quantitative analysis was performed with ArrayGaugeV2.1. Binding was considered strong, medium and weak, when the integrated spot intensity was ≥1/3, ≥1/9, or ≥1/27 of the strongest integrated spot intensity, respectively. The average of at least three repeats is presented. Alternative binding sites were predicted using RNA/RNA thermodynamics.2. Influenza A/California/04/2009 (H1N1) titers were determined with standard plaque assays56. A/California/04/2009 (H1N1) virus was a gift from Prof. Luis Martinez-Sobrido, University of Rochester.Madin-Darby canine kidney (MDCK) cells (Sigma) were propagated in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% PSG  at 37 °C with 5% CO6 cells were seeded in a 100-mm dish. Before each transfection, Lipofectamine 2000 (Thermo Fisher Scientific) was diluted with the Opti-MEM I Reduced Serum Medium (Opti-MEM) and incubated 10 min at RT. Then 15 µl of this solution was mixed gently with 15 µl ASO in OptiMEM and incubated 30 min at room temperature. To each of prepared ASO solution, 300 µl of 7.5 × 104 MDCK cells in media was added. Next, the mixture was divided for 100 µl and added to three wells in a 96-well plate. Final concentration of ASO was 750 nM. Cells were incubated at 37 °C in air with 5% CO2 and after 12 h medium was changed for fresh one followed by incubation of another 6 h. Transfection efficiency was checked on flow cytometer and optimized using fluorescently labeled (TAMRA) oligonucleotide. Several concentrations were tested: 4 µM, 2 µM, 1 µM, 0.75 µM, 0.5 µM, 0.25 µM and 0.1 µM. The efficiency of 750 nM was the highest and similar as for 1–4 µM concentration and therefore was chosen for further experiments. In each experiment, transfection was checked using labeled NEG5 (ROX) under fluorescence microscope.One day before transfection, 2.5 × 102 for 24 h. Next, cell culture supernatants (CCS) were collected. Viral titers were analyzed with the immunofocus assay and vRNA amount is measured using qRT-PCR.After 18 h posttransfection with ASO, 0.01 MOI (multiplicity of infection) of A/California/04/2009 (H1N1) was used to infect MDCK cells. Infection medium consist of PBS supplemented with 0.3% bovine albumin (BA) and 1% PS . After 1 h incubation at RT on a rocking platform, the infection medium was changed for the postinfection medium containing DMEM supplemented with 0.3% BA, 1% PSG, and 1 mg/ml tosyl–sulfonyl phenylalanyl chloromethyl ketone (TPCK)-treated trypsin (Sigma). Infected MDCK cells were next incubated at 33 °C in air with 5% CO2 for 8–10 h. Next, 4% paraformaldehyde and 0.5% Triton X-100 in PBS were used to fixed and then permeabilize cells. Cells were incubated with a blocking buffer containing 3% bovine serum albumin in PBS for 1 h. After blocking, the solution was replaced with mouse anti-influenza A monoclonal primary antibody targeting NP protein (MAB8257 Merck Merc Millipore) diluted in the blocking buffer (1 µg/ml) and incubated for 1 h at 37 °C. FITC-conjugated secondary rabbit anti-mouse IgG antibody (AP160F Merck Millipore) diluted in blocking buffer (1:150) was used to detect primary antibody by incubation 1 h at 37 °C. Fluorescent-forming units (FFU/ml) were counted under a fluorescent microscope. The mean titer from triplicates and the standard deviation were calculated with Microsoft Excel software.Serial dilution of cell culture supernatants were used to infect MDCK cells cultured on 96-well plates. Infection was conducted for 1 h at RT. After incubation, the infection medium was changed for the postinfection medium and infected cells were maintained at 33 °C in air with 5% CO57 with TRI Reagent (Sigma-Aldrich). RNA quality was checked on agarose gel.For real-time PCR cells were seeded on 24-well plate. Transfection and infection were conducted as describe above . Following the removal of supernatant, infected cells were washed with PBS and total RNA was extracted from cells using Chomczynski and Sacchi method58. To obtain vRNA7 standard first total RNA was extracted from infected MDCK cells. Reverse transcription was carried out using Superscript III reverse transcriptase (Invitrogen) and primer RT (Supplementary Table in vitro transcribed using the AmpliScribe T7-Flash Kit (Epicentre Biotechnologies), following manufacturer’s protocol. The RNA transcript was purified using the RNeasy MinElute Spin Columns (Qiagen). Quality of RNA sample was verified by electrophoresis on agarose gel and concentration was determined by spectrophotometry. 10-fold serial dilutions of RNA were prepared and reverse transcription with RT primer was carried out. cDNA was then amplified in qPCR reaction to obtain standard curve59.Viral RNA of segment 7 (vRNA7) was used as a reference standard for the quantitation procedureTM Thermal Cycler (BioRad) using software version v.3.1.1517.0823 in 96-well format. qPCR reaction components were set-up in triplicate according to the manufacturer’s instruction. 1ul cDNA from the RT step and 10 pmol of QF, QR and 10 pmol Taqman probe Q  according to manufacture protocol. 500 ng of total cellular RNA serve as temple for reaction and 2 uM of RT primer -2,5-diphenyltetrazolium bromide (tetrazolium). MDCK cells were incubated for 2 h with tetrazolium at 37 °C in air with 5% CO2 and after 2 h, solution was changed for DMSO (100 ul/well) and incubated for 15 min. in dark. The rate of formazan formation was determined by measuring the absorbance (570–650 nm) on xMarkTM Microplate Spectrophotometer (BioRad). Cells enzymes are capable of reducing the tetrazolium to its insoluble formazan. The 570–650 nm reading value is directly proportional to the number of living cells. Cytotoxicity assays were normalized to viability of cells treated only with Lipofectamine 2000. The mean and standard deviation were calculated with Microsoft Excel software from three independent experiments each containing three technical repeats.MDCK cells were transfected with ASOs in concentration from 1 uM to 4 uM and seeded on 96-well plate as for experiments of antiviral tests described above. After transfection cells were incubated at 37 °C in air with 5% COSupplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary File


SRP093707 and No. SRP093842 respectively.Current prostate cancer (PCa) diagnostic tests suffer from insufficient sensitivity and specificity. Novel biomarkers that can be detected by minimally invasive methods are of a particular value. Here we provide two datasets. The first one is on the whole transcriptome profiling by RNA-seq of urine and plasma obtained from patients with PCa and benign prostatic hyperplasia (BPH). The second one represents targeted sequencing of DNA from urine and plasma of patients with PCa and BPH. Both datasets are available at NCBI Sequence Read Archive under Accession No.  Specifications TableValue of the data•The data on protein coding and non-coding RNAs detectable in urine and plasma of patients with PCa is valuable for the screening of novel biomarkers for early diagnostics of PCa.•Targeted DNA sequencing data can be used to develop methods of revealing resistance to commonly used anti-androgen therapy at the early stages of PCa progression.•Both datasets include samples obtained from patients with BPH which can be used as a control group to ensure high specificity of found markers towards PCa  •Access to the raw sequencing data allows researchers to perform further bioinformatic analysis based on their own computational algorithms.11.1Urine and plasma samples were taken from 14 patients with PCa and 3 patients with BPH from Moscow City Clinical Hospital No. 50. Urine samples were obtained after prostate massage. Each urine and plasma sample was collected either before or after the operation  depending on patient׳s diagnosis). RNA and DNA extractions from urine were performed using RNA/DNA Purification Kit (Norgen Biotek). Isolation of these molecules from plasma was carried out by QIAamp Circulating Nucleic Acid Kit (Qiagen). Overall, 19 RNA (12 from urine and 7 from plasma) samples and 41 DNA (14 from urine and 27 from plasma) samples were obtained. 1.2DNA contaminations from extracted total RNA were removed with the use of DNAse I (Fermentas) treatment following the manufacturer׳s recommendations. RNA concentration was determined by fluorometer Qubit 2.0 using Qubit RNA HS Assay Kit (Thermo Fisher). Transcriptomic libraries were constructed using Ion Total RNA-Seq Kit v2 (Life Technologies) with the following modifications of the protocol. For RNA fragmentation 1 µl of 10x RNase III buffer (Life Technologies) was added to 9 µl of RNA solution and heated for 10 min at 95 °С followed by immediate snap-cooling on ice. After that 1 µl of 10 µM ATP and 1 µl of polynucleotide kinase (Fermentas) were added to the solution from the previous step and the whole mix was incubated at 37 °С for 30 min. Fragmented RNA was cleaned up using Micro Bio-Spin Chromatography Columns (Bio-Rad). Further steps of library preparation including adapter ligation, first-strand cDNA synthesis, and amplification were carried out in accordance with manufacturer׳s instructions. Fragments of cDNA corresponding to rRNA were depleted using duplex-specific nuclease (Evrogen) as described in 1.3GeneRead DNAseq Targeted Human Prostate Cancer Panel (Qiagen) was used for targeted enrichment of the extracted DNA. Considering the quality of circulating cell-free DNA the number of cycles in this amplification step was raised to 20 and 22 for DNA extracted from urine and plasma respectively. Subsequent library construction was performed using GeneRead Library Prep workflow (Qiagen) following the manufacturer׳s recommendations.1.4The sequencing of constructed libraries was performed on Ion Proton platform using ION PI HI-Q Sequencing 200 Kit and Ion PI Chip Kit v2 (Thermo Fisher Scientific) following the recommendations of the manufacturer. Base calling was performed by Torrent Suite 5.0, fastqCreator v3.4.56313.


Non-coding RNAs up to 1,000 nucleotides in length are widespread in eukaryotesand fulfil various regulatory functions, in particular during chromatinremodeling and cell proliferation. These RNAs are not translated into proteins:thus, they are non-coding RNAs (ncRNAs). The present review describes theeukaryotic ncRNAs involved in transcription regulation, first and foremost,targeting RNA polymerase II (RNAP II) and/or its major proteinaceoustranscription factors. The current state of knowledge concerning the regulatoryfunctions of SRA and TAR RNA, 7SK and U1 snRNA, GAS5 and DHFR RNA is summarizedherein. Special attention is given to murine B1 and B2 RNAs and human Alu RNA,due to their ability to bind the active site of RNAP II. Discovery of bacterialanalogs of the eukaryotic small ncRNAs involved in transcription regulation,such as 6S RNAs, suggests that they possess a common evolutionary origin. According to transcriptome analysis, only 1.5% of the total amount of RNAs ineukaryotic cells encodes proteins, while other transcripts are non-coding(ncRNAs). Apparently, the ”repertoire” of genes that encodeproteins has remained relatively static in the course of evolution, and thenumber of ncRNA genes has increased when proceeding to more complex organisms.Ribosomal, transfer, small nuclear, and small nucleolar ncRNAs, which areconstantly expressed in the cells, are conventionally classified ashousekeeping ncRNAs, by analogy with the name of the most important cellulargenes -4.The lUsually ncRNAs are classified into short (~20–30 nt), which includemicroRNAs (miRs), small interfering (siRNAs), and PIWI-interacting RNAs ; small nFig. 1).These ncRNAs are involved in complex multistep regulatory mechanisms and usuallyinteract with cascades of proteins, indirectly affecting a transcriptionprocess. On the contrary, the murine B1 and B2 RNAs and human Alu RNA encodedby mobile genetic elements (SINE) are able to directly bind RNAP II[Considering the diversity of the classes and functions of ncRNAs, it is nosurprise that many of them are involved in the regulation of transcription ineukaryotes. This occurs primarily through various epigenetic mechanisms; inparticular, chromatin remodeling (this area of ncRNA functioning has beenstudied much better than others) ,13. The RNAP II. To date RNAP II. Since t RNAP II.According to the conventional criteria, TAR RNA, B1 RNA, B2 RNA, and U1 snRNAshould be classified as small ncRNAs, while Alu RNA, 7SK snRNA, DHFR RNA, SRARNA, and GAS5 RNA are lncRNAs. The specific properties and functions of each ofthese ncRNAs are discussed in detail in this review. The structural features ofthe interaction between FC RNA and RNAP II are comprehensively described in.SINE (short interspersed elements) are retrotransposons 80 to 500 bp in lengthlocated randomly in the genome of higher eukaryotes. The nucleotide sequencesof SINE characterized by 65–90% similarity are clustered into families,and the number of homologous SINE can vary from 103 to 106 copies per cell. HistoriHuman Alu RNA and murine B1 RNAArthrobacter luteus (R.AluI).The human genome contains more than 1 million copies of Alu encoding Alu RNAs,which accounts for about 10.6% of nuclear DNA. B1 RNA-encoding SINE are morerare in the murine genome, less than 550,000 per cell. Both of these RNAsbelong to the family of retroelements of small cytoplasmic 7SL RNA[Fig. 2).The full-length Alu RNA, sized ~280 nt in length, isa tandem repeat of two B1-like elements connected by a 20-nt A-rich linker. AluRNA processing produces scAlu RNA of 118 nt in length, which is localized inthe cytoplasm and is a complete analog of murine B1 RNA[Fig. 2A),although chimeric RNA consisting of B1 RNA and Alu-RA has all the properties of the full-size Alu RNA[SINE element Alu was named due to the presence of recognition sites ofrestriction endonuclease from  7SL RNA and poss. Alu RNA Alu RNA, 25.Fig. 2B).Correspondingly, B1 RNA and scAlu RNA possess only a RNAP binding domain. According tocryoelectron microscopy, both Alu and B1 RNA interact with the “clamp” domainof RNAP II near the active site of the enzyme [Fig. 2).At the same time, no direct contact between TFIIF and B1 or scAlu RNA was detected[in vivo TFIIF is usually associated with RNAP II priorto PIC assembly on the promoter, “useless” binding of non-regulatoryncRNAs  probably does not occur.Besides the two RNAP binding domains located in the “left arm” and“right arm,” Alu RNA has two domains disposed in the central region of the “rightarm” and in the A-rich linker which are responsible for transcription inhibition, whose number is estimated to be about 105 copies per cell[in vitro [in vivo,while, the mechanism of action of this ncRNA has been studied in detail.B2 RNA is transcribed by RNAP III in the presence of TFIIIB and TFIIIC factorsfrom the respective B2 SINE  due to polyadenylation, whereas the degradationtime of a 180 nt-long transcript is just 3–4 minutes. The shortest 150-ntvariant of B2 RNA is more stable and characterized by a τ1/2value of 20 min [Fig 3A):(1) a long double-stranded region (1–72 nt)with unwound moiety in the center; (2) a poorly structured region (73–153nt) containing three small hairpins, and (3) a short 3'-terminal unstructuredAU-rich region (154–178 nt) which is conserved in all SINE.Murine cells contain at least four variants of B2 transcripts of differentlengths: ~150, ~180, ~240 and ~500 nt. The two longest variants are very stable, and a 5'-terminal hairpin is requiredneither for B2 RNA binding to RNAP II nor for transcription repression.Analysis of various deletion mutants of B2 RNA has determined the 51 nt-longregion 81–131 nt) which directly interacts with RNAP II and inhibitstranscription  nt whichhB2 RNA . Therefoinvitro with B2 RNA and the promoter simultaneously[The similarity of the structural organization of B2 RNA and Alu RNA indicatesthat, besides the active site , RNAP II contains an additional docking site highly specific toncRNAs. As in the case of Alu RNA, RNAP II forms a ternary complex aneously. Therefoaneously.in vitro that B2 RNA can inhibit transcription even in thecase when PIC had been previously bound to B1 RNA[in vivo have not been established. Since humanAlu RNA also has a non-functional analogue, it can be assumed that these inactivencRNAs, B1 and scAlu RNA, in certain circumstances may replace B2 and Alu RNA,respectively, and re-stimulate transcription.Let us recall that murine cells also express B1 RNA binding to RNAP II, but areincapable of inhibiting transcription. B1 RNA possesses affinity to polymerasecomparable to that of B2 RNA and can displace B2 RNA from PIC. Therefore, B1RNA must prevent the functioning of B2 RNA. However, it has been showno B1 RNA. The mecFig. 3B).TFIIH contains cyclin-dependent kinase 7 (CDK7), which, under normal conditions,phosphorylates serine residues within the heptapeptide repeats YSPTSPS (mainlySer5) at the C-terminal domain (CTD) of the large subunit (Rpb1) of RNAP II.Modification of Ser2 and Ser5 at the CTD of Rpb1 is extremely important fortranscription. It occurs at various stages of transcription: the domain is notphosphorylated in the initiation complex and, on the contrary,hyperphosphorylated during transcription elongation[Interestingly, in addition to direct “physical” RNAP II active siteblocking, B2 RNA specifically inhibits the kinase activity of the TFIIH transcription factor.Elongation of the B2 RNA leads to the dissociation of the molecule from PIC and,apparently, enables reversibility of inhibition. The released extended B2 RNAundergoes degradation. An analysis of computer modeling data suggests that theelongation of the B2 RNA strand (or any other RNA located in the active site ofthe polymerase) should lead to the partial opening of the “clamp”domain of RNAP and, as a consequence, the weakening of ligand binding to theenzyme. In fact, a newly formed structural element of the elongated B2 RNA“extrudes” the molecule from PIC. Notably, B2 RNA elongation wasobserved in vitro only after treatment of the B2 RNA-RNAP IIcomplex with the cell extract [The more surprising fact is that B2 RNA can promote self-elongation in acomplex with RNAP II . The enz extract . Itis bSince most RNA polymerases are DNA-dependent ,elongation of B2 RNA is a kind of exception to the rule, since the enzymemodifies its substrate specificity. To date, there are only a few suchexamples, which are also related to the functioning of ncRNAs. For instance,the same mechanism is used by the hepatitis delta virus and plant viroids toreplicate their own genome. These pathogenic circular ncRNAs lack inherent RNApolymerases and use host cell RNAPs, reprogramming them for RNA synthesis onRNA templates . This phU1 snRNAFig. 4A).Studies of in vitro transcription showed that the presence of U1 snRNA in thereaction mixture increases the rate of formation of the first phosphodiesterbond and that the efficiency of transcription initiation increases more thantenfold. Furthermore, U1 snRNA stimulates abortive initiation and re-initiationof transcription from the promoter preceding the 5'-terminal splicing site[U1 snRNA is one of the five major snRNAs forming the core of the spliceosome.Human U1 snRNA, 164 nt in length, is associated with U1-A, U1-C, and U1-70kproteins, as well as with eight Sm proteins, together forming the U1 snRNPcomplex (~245 kDa). The main function of U1 snRNP is to recognize pre-mRNA atthe first  stage of spliceosome assembly, which occurs due tocomplementary interactions between the 5'- end region of U1 snRNP and theintron splice site . Howevering site. Besidesing site. For allDHFR ncRNADHFR gene encodes dihydrofolate reductase, one of the keyenzymes of folate metabolism. About 99% of DHFR mRNAs are transcribed from themajor promoter and contain six exons. During serum starvation and cell growthretardation, an alternate promoter located ~450 nt upstream from the maintranscription initiation site is activated. Early transcription termination atthe second intron results in the formation of a short product from the minorpromoter, DHFR ncRNA, whose length varies from 800 nt to 2–3 thousand nt[The usand nt. The funusand nt. Thus, Dusand nt.Since T7SK and TAR RNAHuman 7SK snRNA and TAR RNA of HIV are probably the most well-known eukaryoticncRNAs involved in the regulation of transcription elongation. Both of thesencRNAs act as platforms for the assembly of protein associates, modulating theactivity of the RNAP II elongation complex, and they interact with the factor P-TEFb-45..P-TEFb is attracted to the polymerase by various DNA-binding proteins, first ofall Brd4, but also by some general transcriptional factors, such as NF- κB,HSF, p53, c-Myc, etc. After overcoming the pause stage, P-TEFb binds severalother proteins, forming a super elongation complex (SEC) of RNAP II[P-TEFb is a key transcription factor that stimulates the proceeding of RNAP IIarrested at the promoter  to the activation of elongation. P-TEFbconsists of cyclin-dependent kinase 9 (CDK9) and cyclin T1 or its analogs,CycT1b and CycT2b (hereinafter CycT). Its main function includesphosphorylation of Ser2 in the CTD of Rpb1 RNAP II, as well as thetranscription repressors NELF and DSIF is transcribed by RNAP III from a singlegenuine gene located on the sixth chromosome. The nucleotide sequence of thisgene is highly conserved in vertebrates[7SK snRNA is 332 nt in length and consists of four main long hairpin structuresconnected by disordered areas and additional small hairpins. Although the humangenome contains hundreds of 7SK snRNA pseudogenes, this RNA  of HEXIM binds the 5'-terminal hairpin of 7SK snRNA,resulting in conformational change in the protein, so that it can interact withCycT of P-TEFb. Additionally, the C-terminal domain of LARP7 binds to CDK9,providing a stable structure of the whole complex. Apparently, 7SK RNA is alsoinvolved in the formation of contacts with P-TEFb. As a result, the factorloses its kinase activity, which prevents it from promoting transcription elongation[Approximately 90% of 7SK snRNA in the cell is bound to MePCE and, together withthe LARP7 protein, forms a so-called core of the ribonucleoprotein complex, 7SKsnRNP ongation, 51, 52..Another mechanism concerns temporary inactivation of P-TEFb, since only the activatedform of the protein  can interact with 7SK snRNP. Serine-threonine phosphatasesare responsible for this process, including PPM1G, attracted by the NF-κB.Some proteins may also acetylate CycT, phosphorylate HEXIM, demethylate 7SKsnRNA at the 5'-end, or carry out MePCE proteolysis, which leads to adestabilization of the complex and dissociation of P-TEFb. After the releasefrom 7SK snRNP, the factor is again modified[However, not all P-TEFb molecules are bound to 7SK snRNP. A wealth ofexperimental data shows that there is continuous equilibrium between the freeand bound forms of P-TEFb in the cell nucleus, which is controlled throughvarious signaling pathways. For example, a number of heterogeneous nuclearribonucleoproteins (hnRNPs) and RNA helicase A (RHA) block access to P-TEFbthrough binding to the 7SK snRNPcore modified. Let us modified..TAR RNA is a 5'-terminal structural element (hairpin) of the nascent strand ofviral RNA synthesized from 5'-LTR. In the absence of additional activation,RNAP II is incapable of synthesizing transcripts longer than 60-80 nt from5'-LTR, and TAR RNA consisting of 59 nt is the smallest fragment, followed by atranscription pause. In order to stimulate elongation, TAR RNA binds to a viralprotein, Tat, which attracts various transcription factors to 5'-LTR, includingP-TEFb [.This region of the molecule mimics the5'-terminal hairpin of the 7SK snRNA, which enables TAR RNA binding to the ARMHEXIM, thus preventing the activation of P-TEFb in the absence of Tat.Moreover, Tat directly interacts with CycT and CDK9, forming a stable complex:that crystal structure was resolved in 2010[.Formation of a Tat-TAR-P-TEFb ternary complex is regulated by a number of enzymesthat perform the acetylation, phosphorylation, methylation, and ubiquitination of Tat[The best known mechanism of P-TEFb dissociation from its complex with 7SK snRNPis represented by TAR RNA in HIV-infectedcells gP-TEFb . This in in 2010.When bi in 2010. This aln of Tat.Obviously, there should be competition between the Tat-TAR RNA and 7SK snRNPcomplexes for binding to P-TEFb. As it turns out, Tat can displace theelongation factor from its complex with 7SK snRNP due to a direct interactionbetween Tat and CycT and conformational changes in P-TEFb, 58.A sSRA RNAHuman SRA RNA (steroid receptor activator) is a long ncRNA involved in theactivation of estrogen (ER), progesterone (PR), glucocorticoid (GR), and othernuclear receptors. Similarly to 7SK snRNA, SRA RNA serves as a platform for thebinding (including competitive) of various transcription factors. CTCF, SLIRP,and SHARP, as well as the RNA helicases p68 and p72, are the most important among them, 61.Bessra1 gene encoding SRA RNA is highly conserved in thegenome of mice, rats, and humans. It is about 6,500 bp in length and consistsof five exons. At least 20 different isoforms of SRA RNA, from 700 to 1,500 nt,have been detected in human cells. Most transcripts contain a core element of687 nt, which corresponds to exons 2–5, and differ in their 5'- and3'-end regions [[The  regions . In 2012egions [.Analysiegions [.Nevertheless, the D3 domain 494–699 nt) is the most important one forthe interaction with nuclear receptors. Its constituent element H15–H18(505–575 nt) is highly conserved in vertebrates. Interestingly,individual expression of this element leads, on the contrary, to the inhibitionof the transcription of ERα-dependent genes. Switchi9 nt is tFig. 6B).STR7 of SRA RNA interacts with the RNA-recognition motifs (RRM) of the factorsSHARP and SLIRP and, thereby, initiates both activation and inhibition of thetranscription of various genes. Another SRA RNA partner, the receptorPPARγ, regulates the expression of the genes involved in the control ofadipogenesis and insulin sensitivity .Clearlysra1 gene transcript. At the moment, it remainsunclear whether SRAP is capable of direct binding to SRA RNA and inhibition ofits interaction with other proteins, or whether this process is carried outthrough the transcription factors or nuclear receptors associated with this RNA[sra1 genetranscription is one of the key factors of transcriptionalregulation in the cell. For example, in the case of myocyte differentiation,equilibrium is strongly shifted towards the non-coding SRA RNA and it cansmoothly interact with transcriptional activators, attracting them to theMyoD-dependent promoter and activating the transcription of the respectivegenes [Similarly to other ncRNAs, some SRA RNA suppression mechanisms exist that inparticular involve the SRAP protein. The latter is encoded in 39% of SRA RNAtranscripts and represents an example of specific self-regulation. In fact, SRARNA is a coding RNA, although the key function is fulfilled by a non-codingvariant of the this RNA. Neverthvegenes .GAS5 RNA.GAS5 RNA can interact not only withGR, but also with other nuclear receptors binding GRE, in particular androgen,progesterone receptors, etc. [in vitro and in vivo studies clearlyindicate the important role of GAS5 RNA in the initiation of apoptosis invarious types of tumor cells and inhibition of proliferation and metastasis, aswell as in immune response regulation in various inflammatory, bacterial, and viral diseases[GAS5 RNA (growth arrest-specific 5) is another long ncRNA that controlstranscription through regulation of nuclear receptors. Under normal conditions,GAS5 RNA is rapidly degraded. However, in case of serum starvation in cells,arrested at a certain stage of growth, or after treatment with translationinhibitors, expression of GAS5 RNA is induced and its stability enhances, thatgave the name for this ncRNA . Themairs, etc. .Recent diseases, 75.gas5 gene,which comprises 12 exons interspersed with 10 introns encoding smallnucleolar RNAs. After transcription performed by RNAP II, pre-mRNA undergoespolyadenylation and alternative splicing, which results in different isoformsof GAS5 RNA. The most important of them are GAS5a (612 nt) and GAS5b (651 nt)RNAs, containing 7a or 7b exons, respectively. Longer variants of GAS5 RNA(~1200–1800 nt) are less common and contain one or more sequencesencoding small nucleolar RNAs [Human GAS5 RNA is encoded by the lar RNAs ,76. UndFig. 7A)and is found in all GAS5 RNA isoforms. The main contacts are formed between theGR and the GRE-1/ GRE-2 hairpin stem (539–544 and 553–559 nt),which mimic the conformation of the palindromic GRE-sequence of DNA:d(5'-AGAACANNNTGTTCT-3'/ 3'-TCTTGTNNNACAAGA-5'), where N = A, T, C, G). TheG540 and C554 residues in GAS5 RNA are conserved among the human consensusGRE-sequences and interact with K442 and R447 in the DNA-binding domain of theGR protein, respectively. C554U substitution in GAS5 RNA, maintaining thestability of the double helix, results in a lost of the ability of this RNA toinhibit GR-dependent transcription from the MMTV (mouse mammary tumor virus)promoter in vivo [The GAS5 RNA secondary structure is represented by several hairpins. Thefunctional region  is located at the3'-terminal region of the molecule  system implementing the degradation of “nonsense” mRNAsequences and the mTOR  kinase-dependentsignaling pathway. It is assumed that during active cell growth, mTOR-dependenttranslation of the short open reading frame (ORF) located in GAS5 RNA can occur. However, the large number of stop codons in the ORF and the shortlength of the potentially synthesized peptide lead to the activation of NMD anddegradation of GAS5 RNA. In the case of cellular arrest and low level of themTOR complex, GAS5 RNA is not translated and its concentration increases..In addition to its primary function, GAS5 RNA binds oncogenic miR-21, miR-222,and miR-103, thereby serving as a siRNA-sponge and preventing them from impacting gene expression, 78.MorDozens of ncRNAs modulating the activity of transcription factors are currentlyknown. Most of them “work” only under certain (stress) conditionsand are often tissue-specific , 17. ForSpecial attention should be paid to circular RNAs (circRNAs or ciRNAs),products of alternative splicing, which results in closure of the 5'- and3'-ends of the molecule. According to recent data, more than 100 circRNAs areassociated with RNAP II and at least some of them activate transcription oftheir own genes . It is aIn recent years, increasingly detailed information has appeared regarding thevarious ncRNAs involved in transcription regulation in eukaryotic cells both atthe level of specific genes and on a more global scale. Most often, they arelong ncRNAs that regulate transcription during chromatin remodeling. In thisreview, we described those ncRNAs whose action mechanisms are closely relatedto the supervision of the functioning of the RNAP II transcription complex. Theaforementioned ncRNAs have a number of other features which are equallyimportant. For example, Alu RNA binds SRP9/14 proteins and, as part of this RNP, inhibits translation initiation. Incontrast, free Alu RNA is able to activate this process. U1 snRN


An unprecedented Ebola virus (EBOV) epidemic occurred in 2013–2016 in West Africa. Over this time the epidemic exponentially grew and moved to Europe and North America, with several imported cases and many Health Care Workers (HCW) infected. Better understanding of EBOV infection patterns in different body compartments is mandatory to develop new countermeasures, as well as to fully comprehend the pathways of human-to-human transmission. We have longitudinally explored the persistence of EBOV-specific negative sense genomic RNA (neg-RNA) and the presence of positive sense RNA (pos-RNA), including both replication intermediate (antigenomic-RNA) and messenger RNA (mRNA) molecules, in the upper and lower respiratory tract, as compared to plasma, in a HCW infected with EBOV in Sierra Leone, who was hospitalized in the high isolation facility of the National Institute for Infectious Diseases “Lazzaro Spallanzani” (INMI), Rome, Italy. We observed persistence of pos-RNA and neg-RNAs in longitudinally collected specimens of the lower respiratory tract, even after viral clearance from plasma, suggesting possible local replication. The purpose of the present study is to enhance the knowledge on the biological features of EBOV that can contribute to the human-to-human transmissibility and to develop effective intervention strategies. However, further investigation is needed in order to better understand the clinical meaning of viral replication and shedding in the respiratory tract. An unprecedented Ebola outbreak occurred in 2013–2016 in West Africa. In order to better understand EBOV infection patterns in different body compartments, we have longitudinally explored the presence of already assessed markers of ongoing EBOV replication (negative sense genomic RNA and positive sense RNA) in the upper and lower respiratory tract, as compared to plasma and other body compartments, in a Health Care Worker infected with EBOV in Sierra Leone, who was hospitalized in the high isolation facility of the National Institute for Infectious Diseases “Lazzaro Spallanzani” (INMI), Rome, Italy. The presence of total EBOV RNA and replication markers was observed in specimens of the lower respiratory tract, even after viral clearance from plasma, suggesting possible local replication. Our results contribute to the knowledge on the biological features of EBOV and shed light on the potential role of respiratory compartment in human-to-human transmissibility. The Ebola virus (EBOV) Makona variant is a negative-sense RNA virus and a member of the Filoviridae. From December 2013 to January 2016, this EBOV Makona caused the largest viral hemorrhagic fever epidemic ever reported in West Africa. . Like ot8 copies/mL at day 1) with a marked decrease at day 3, also overlapping with the administration of Mil77. Declining EBOV viremia continued after further administration of MIL77 on day 6, and reached undetectable levels on day 9. In contrast, total EBOV RNA in sputum peaked at day 6 with above 107 copies/mL and remained high at around 106 copies/mL up to day 9 before becoming undetectable at day 11. 6 copies/mL up to day 11, and pos-RNA was present at a concentration of 104 copies/mL up to day 10 (Panel D). The levels of neg-RNA and pos-mRNA in sputum samples were 2 or 3-Log higher than neg-RNA and pos-RNA values in plasma, respectively.6 copies/mL) until day 5, and detected at low levels (103 copies/mL) until day 12 before becoming undetectable at day 13 (Panel E). The prolonged detection of total RNA was paralleled by that of neg-RNA, which was detectable (102−103 copies/mL) until day 10 (Panel B) while pos-RNA became undetectable on day 6 (Panel F). In ocular swabs and urine samples, total RNA remained detectable up to day 15 (103 copies/mL) and day 4 (104 copies/mL), respectively. Interestingly, pos-RNA was always undetectable in all specimens analyzed from these compartments, whereas neg-RNA was detectable around 102 copies/mL both in urine and ocular swab.The presence of total, neg- and pos-RNA in nasopharyngeal swab is shown in in vitro experiments [5) did not influence tests results as compared to tissue culture medium. Further, no appreciable background noise signal was observed in unspiked plasma and sputum matrices (Ct > 45). Hence, we longitudinally explored the persistence of viral replication markers in clinical samples from the upper and lower respiratory tract of the second EVD patient in Italy, and made comparison with plasma samples. The results indicate the decay of EBOV total RNA, neg-RNA and pos-RNA overtime, which was significantly different in plasma when compared to respiratory samples. In particular, we observed a persistence of total viral RNA and replication markers in sputum, until the second week of hospital stay (up to day 10), when viremia was already undetectable. From these data the possibility that the prolonged detection of EBOV RNA in sputum merely reflects enhanced stability of EBOV RNA in this compartment cannot be ruled out [This study presents novel insights in the potential role of the respiratory compartment in EBOV replication in a human case of EVD. Several clinical findings suggest lung involvement in different cases of EVD who received care in Europe  and the eriments . We confuled out ; neverthuled out . Moreoveuled out . In pigsuled out . Guinea uled out  while rhuled out  with celuled out . The hypuled out . This anuled out . Unfortuuled out –20. To tuled out . The premucus and phlegm coughed-up from the lower airways) were repeatedly collected (starting from day 4) throughout hospitalization. We made all efforts to uniform the time (morning) and size (4 mL) of sample collection, in order to minimize this as possible source of difference. Inactivation of samples was performed in the BSL4 facility and RNA extraction was carried out in the BSL2 laboratory. RNA extraction was performed with QIAamp Viral RNA Mini Kit (Qiagen) following manufacturer’s instructions. Identical volumes (140 μl) of each sample have been used for RNA extraction. The quality of all the samples was checked using a house-keeping gene (RNaseP) in qRT-PCR according to [9 copies/mL down to 102 copies/mL.Patient’s clinical specimens were longitudinally collected for diagnostic purposes. Plasma samples, nasopharyngeal swabs, ocular swabs and urine samples were collected daily  while sputum samples  of the National Institute for Infectious Diseases “All participants to the study are adults.


In summary, these data elucidate how RNA transcripts cooperate with RAD52 to coordinate homology-directed DNA recombination and repair in the absence of a DNA donor, and demonstrate a direct role for transcription in RNA−DNA repair.Genetic studies in yeast indicate that RNA transcripts facilitate homology-directed DNA repair in a manner that is dependent on RAD52. The molecular basis for so-called RNA−DNA repair, however, remains unknown. Using reconstitution assays, we demonstrate that RAD52 directly cooperates with RNA as a sequence-directed ribonucleoprotein complex to promote two related modes of RNA−DNA repair. In a RNA-bridging mechanism, RAD52 assembles recombinant RNA−DNA hybrids that coordinate synapsis and ligation of homologous DNA breaks. In an RNA-templated mechanism, RAD52-mediated RNA−DNA hybrids enable reverse transcription-dependent RNA-to-DNA sequence transfer at DNA breaks that licenses subsequent DNA recombination. Notably, we show that both mechanisms of RNA−DNA repair are promoted by transcription of a homologous DNA template in  Homologous recombination (HR) typically uses DNA as a donor template to accurately repair DNA breaks. Here, the authors elucidate two mechanisms by which RAD52 uses RNA as a template for HR: one involving RNA-mediated synapsis of a homologous DNA break, and the other involving reverse transcriptase dependent RNA-to-DNA sequence transfer at DNA breaks. Whether non-canonical forms of recombinational repair exist and contribute to genome integrity, in particular within expressed genes, is an important area of investigation. For example, recent genetic studies in yeast demonstrate that DSBs can be repaired by RAD52 in a manner that depends on the use of a homologous RNA transcript as a template3. Specifically, spliced RNA transcripts were implicated in serving as templates for DNA repair synthesis at homologous DSBs either in cis or in trans of the transcription site3. This form of RNA transcript-dependent DNA recombinational repair (RNA−DNA repair) was shown to be promoted by RAD52 and putative reverse transcriptase (RT) activity, while being suppressed by RNase H3. RAD52 was also shown to promote RNA−DNA annealing in vitro3. Hence, these studies support RAD52-mediated RNA−DNA hybrid formation in transcript-dependent RNA−DNA repair. Additional studies suggest a conserved role for RAD52 in transcript-dependent DNA repair. For instance, mammalian RAD52 was shown to be preferentially recruited to DSBs in a transcription-dependent manner during G0 and G1 cell-cycle stages4, and multiple reports demonstrate that RAD52 associates with RNA polymerase II and RNA transcripts in mammalian cells6.Homologous recombination (HR) utilizes a DNA donor as a template for DNA repair synthesis and is therefore the most accurate form of double-strand break (DSB) repair that is essential for suppressing genome instability and tumorigenesis3. In the first, RAD52 is predicted to promote RNA-bridging of a homologous DSB, thus facilitating sequence-directed DNA synapsis and ligation  and efficiently promotes RNA−DNA annealing in a similar manner to DNA−DNA annealing, as shown previously  Fig. . Short sion Fig. . RNA-briion Fig. . We noteion Fig. . RPA DNAion Fig. 16,17. trans was used to generate homologous RNA transcripts during the RNA−DNA repair reaction  was shown to more efficiently promote strand exchange between RNA and pssDNA compared to reactions performed with RAD52 pre-bound to RNA12. We assessed whether pre-incubation of RAD52 with duplex DNA vs. RNA affected the efficiency of our RNA−DNA repair reactions using the model DSB substrate in Fig. Recent studies suggest RAD52 reverse strand exchange activity may play a role in RNA−DNA repairWe next examined whether RAD52-dependent RNA-bridging of a DSB can serve as a recombination intermediate for ligation. Here, we repeated the conditions used in Fig. 20. However, the RTs involved in this form of repair remain to be elucidated. First, it was tested whether RAD52 promotes the formation of a recombinant RNA−DNA half-bridge on a RPA-coated model DSB end harboring a 3′ overhang (left flank), and whether the overhang can then be extended by RT in the presence of deoxy-ribonucleoside triphosphates (dNTPs) and template RNA  of the transcription bubble to the DNA break may provide a mechanistic advantage for RNA−DNA repair. For example, transcription complex unwinding of DNA near the break could conceivably facilitate RNA−DNA annealing by RAD52. In contrast, transcription in trans of the DNA break likely has no direct mechanistic link to the repair process aside from generating the necessary homologous transcript.We note that particular reactions involving transcription in23. Although this type of repair process may contribute to genome integrity in transcribed regions, it may alternatively facilitate genomic plasticity by enabling promiscuous RNA-to-DNA sequence transfer events at DSB junctions harboring little or no microhomologous sequence to the RNA. Considering that our data demonstrate that RAD52 requires relatively long tracts of homology to assemble recombinant RNA−DNA hybrids that direct accurate DNA recombination and repair, they suggest RAD52-mediated RNA−DNA repair might contribute to genome integrity such as in transcribed regions. Consistent with this idea, mammalian RAD52 is recruited to DSBs in a transcription-dependent manner, and associates with RNA polymerase II and RNA transcripts5. Future studies are needed to determine whether the transcription-associated functions of RAD52 are conserved and involved in genome maintenance specifically within transcribed regions.An important consideration is whether RNA-mediated DNA repair is widespread and important for particular aspects of genome maintenance or plasticity. Multiple lines of research in yeast and mammalian cells support the use of RNA in DNA repair, suggesting an evolutionary advantage for this non-canonical form of genome repairm Tris-HCl pH 7.5, 1 mm DTT, 10 mm NaCl, 0.01% NP-40, 0.1 mg/ml BSA, 10% glycerol) at 37 °C. Various concentrations of MgCl2 were used as follows: Fig. m MgCl2; Fig. m MgCl2. Twenty units RNase inhibitor (Ambion) was added to reactions in Fig. m) was pre-incubated with 4 nm RNA (SM44R) for 5 min. Next, equimolar concentrations of indicated 5′-32P-radiolabeled ssDNA or pssDNA (4 nm) were added. Reactions were terminated after 1 min or indicated times by the addition 0.4 μm cold ssDNA, then treated with 2 μl of stop buffer  for 15 min. Nucleic acids were resolved in 15% non-denaturing polyacrylamide gels and visualized by autoradiography. Reactions containing RAD51 included 1 mm ATP and 4 mm CaCl2  was pre-incubated with 4 nm RNA for 5 min, then was combined with RPA-coated ssDNA/pssDNA substrates. Bridging was carried out for 30 min, then terminated by the addition of 0.4 μm cold ssDNA, and then treated with 2 μl of stop buffer  for 15 min. Nucleic acids were resolved in 15% non-denaturing polyacrylamide gels and visualized by autoradiography. The time course in Fig. m RAD52. ssDNA substrates: 38 bp homology: SM99, SM100, SM44R; 32 bp homology: SM97, SM98, SM44R; 26 bp homology: SM101, SM102, SM44R. pssDNA substrates: SM100/SM16, SM99/SM14.Reactions were performed in 20 μl volume of buffer A  at 37 °C. Various concentrations of MgCl2 were used as follows: Fig. m MgCl2, Fig. m MgCl2. All dsDNA bridging reactions contained 20 units RNase inhibitor (Ambion) except for Fig. m) was pre-incubated with left and right flanking dsDNA (40 nm each) for 15 min and was used in the reaction at final concentrations of 20 and 4 nm, respectively. RAD52 (75 nm) was pre-incubated with 4 nm RNA for 5 min, then was combined with RPA-dsDNA pre-incubation reactions, and bridging was carried out for 30 min. Reactions were terminated by the addition of 0.4 μm cold ssDNA, then treated with 2 μl of stop buffer  for 15 min. dsDNA substrates: SM142/SM142C, SM143/SM143C, SM44R.Reactions were performed in 20 μl volume of buffer A (25 mm bacteriophage T4 DNA ligase (New England Biolabs) with 0.5 mm MgCl2 . Reactions were resolved in 12% denaturing (urea) polyacrylamide gels and visualized by autoradiography. In the case of RNA-templated ligation of RPA-coated substrates . Reactions were resolved in 12% denaturing (urea) polyacrylamide gels and visualized by autoradiography.RAD52-dependent RNA bridging reactions were performed in 20 μl of buffer A as described above Fig. , followeCl2 Fig.  for 2 h tes Fig. , reactiotes Fig.  and contion Fig. , RAD52-dion Fig. , containm Tris-HCl pH 7.5, 1 mm DTT, 10 mm NaCl, 0.01% NP-40, 6 mm MgCl2, 0.1 mg/ml BSA, 10% glycerol) with RNase inhibitor (Ambion) at 37 °C. Reactions contained 448 μm GTP, and 150 μm ATP, CTP, UTP. Transcription was initiated on 2 nm of a dsDNA template (SM85/SM85C) containing a T7 RNAP promoter by the addition of 10 nm T7 RNAP for 10 min at 37 °C. Next, 200 nm RAD52 was added for 5 min, followed by the addition of RPA-coated 5′-32P labeled 4 nm ssDNA template (SM100/SM99P). After 10 min, reactions were supplemented with 0.4 μm cold left flanking ssDNA. Ligation was then performed at 25 °C for 2 h in the presence of 0.846 μm T4 DNA ligase and an additional 1 mm of ATP. Reactions were then terminated by the addition of stop buffer , and resolved in 15% denaturing (urea) polyacrylamide gels, and visualized by autoradiography.Fig. m MgCl2, 10 μm dNTPs and 20 units RNase inhibitor (Ambion) at 37 °C. RAD52 (100 nm) was pre-incubated with RNA (SM44R) for 5 min. RPA (20  nm) pre-bound to 4 nm of left flanking pssDNA (SM100/SM16) was added and RNA−DNA recombination (RNA-bridging) was performed for 30 min. Next, ten units of AMV-RT (New England Biolabs) was added for 1 h and MgCl2 was supplemented to 4 mm  was added and annealing was performed for 30 min . Transcription was initiated using 4 nm of a dsDNA template (SM85/SM85C) containing a T7 RNAP promoter by the addition of 10 nm T7 RNAP at 37 °C. After 2 min, 200 nm RAD52 was added for 5 min, followed by the addition of 8 nm RPA-coated left flanking pssDNA template (SM100/SM16). Annealing between transcript RNA and pssDNA was carried out for 30 min. Next, ten units of AMV-RT (New England Biolabs) was added for 1 h to allow for reverse transcription to occur. Finally, 6 nm of 5′-32P-labeled right flanking pssDNA (SM83/SM84) was added and annealing was performed for 30 min. In Fig. m Tris-HCl pH 7.5, 0.125% SDS, 8.3 mm EDTA) for 15 min at 37 °C. Nucleic acids were resolved in 15% non-denaturing polyacrylamide gels and visualized by autoradiography. In the case of Fig. m EDTA, then resolved in 15% denaturing (urea) polyacrylamide gels, and visualized by autoradiography.Reactions were performed in 20 μl of buffer A containing 2 m mm Fig. . Next, 6min Fig. . In the min Fig. , reactio32P labeled with T4 polynucleotide kinase (New England Biolabs) and [γ-32P]ATP (PerkinElmer).DNA and RNA was 5′-RNA−DNA recombination (RNA-bridging) templates: SM83/SM84 Fig. , SM99/SMTranscription template: SM85/SM85C Figs. , 5c.RNA-DNA recombinational repair templates: SM100 + SM99 Fig. , SM142/SDNA and RNA oligonucleotides (Integrated DNA Technologies) are as follows (5′−3′). SM14, GGATCTCGACGCTCTCCCT; SM16, TTGTACACGGCCGCATAAT; SM44R, GCUCUGAUGCCGCAUAGUUAAGCCAGCCCCGACACCCG;SM44C, CGGGTGTCGGGGCTGGCTTAACTATGCGGCATCAGAGC; SM46R, GAAGCAUUUAUCAGGGUUAUUGUCUCAUGAGCGGAUACAUAUUUGAAU; SM51R, /56-FAM (fluorescein)/UUUUUUUUUUUUUUUUUUUUUUUUUUUUU; SM83, GCAAGCTTATGCACGGGGCTCTGATGCCGCATAGTT; SM84, /5Phos/CGTGCATAAGCTTGC; SM85, ATCGATATTAATACGACTCACTATAGGGCTCTGATGCCGCATAGTTAAGCCAGCCCCGACACCCG; SM85C, CGGGTGTCGGGGCTGGCTTAACTATGCGGCATCAGAGCCCTATAGTGAGTCGTATTAATATCGAT; SM97, GCGGCATCAGAGCAGGGAGAGCGTCGAGATCC; SM98, ATTATGCGGCCGTGTACAACGGGTGTCGGGGC; SM99, /Phos/AACTATGCGGCATCAGAGCAGGGAGAGCGTCGAGATCC; SM100, ATTATGCGGCCGTGTACAACGGGTGTCGGGGCTGGCTT; SM101, /Phos/TATGCGGCATCAGAGCAGGGAGAGCGTCGAGATCC; SM102, ATTATGCGGCCGTGTACAACGGGTGTCGGGGCTGG; SM104P, /5Phos/AGACAATAACCCTGATAAATGCTTCAATTCATGTCCAGCCCAAACTCTG; SM105, AACCAATGGACCATTTAACCAATATTCAAATATGTATCCGCTCATG; SM129, GCAAGCTTATGCAGACGGCTCTGATGCCGCATAGTT; SM130, /Phos/CGTCTGCATAAGCTTGC; SM142, AAGCCAGCCCCGAC; SM142C, GTCGGGGCTGGCTT; SM132, AAATAAACATAAAGTAAGTAAGTATAAGGATAATACACAATAAGTAAATGAATAGACATAGAAAATAAAGTAAATTATATAAA; SM133, TTTATATAATTTACTTTATTTTCTATGTCTATTCATTTACTTATTGTGTATTATCCTTATACTTACTTACTTTATGTTTATTT; SM143, CTGCTCTGATGCCGCATAGTT; SM143C, /5Phos/AACTATGCGGCATCAGAGCAG27. T4 DNA ligase was purchased from New England Biolabs.RAD52, RPA, T7 RNAP, and Lig3 were purified using previously optimized methodsAll uncropped gels from the main text are available as Supplementary Fig. The data that support the findings of this study are available from the corresponding author upon request.Supplementary InformationPeer Review File