RNA editing by C-to-U conversions is nearly omnipresent in land plant chloroplasts and mitochondria, where it mainly serves to reconstitute conserved codon identities in the organelle mRNAs. Reverse U-to-C RNA editing in contrast appears to be restricted to hornworts, some lycophytes, and ferns (monilophytes). A well-resolved monilophyte phylogeny has recently emerged and now allows to trace the side-by-side evolution of both types of pyrimidine exchange editing in the two endosymbiotic organelles.Our study of RNA editing in four selected mitochondrial genes show a wide spectrum of divergent RNA editing frequencies including a dominance of U-to-C over the canonical C-to-U editing in some taxa like the order Schizaeales. We find that silent RNA editing leaving encoded amino acids unchanged is highly biased with more than ten-fold amounts of silent C-to-U over U-to-C edits. In full contrast to flowering plants, RNA editing frequencies are low in early-branching monilophyte lineages but increase in later emerging clades. Moreover, while editing rates in the two organelles are usually correlated, we observe uncoupled evolution of editing frequencies in fern mitochondria and chloroplasts. Most mitochondrial RNA editing sites are shared between the recently emerging fern orders whereas chloroplast editing sites are mostly clade-specific. Finally, we observe that chloroplast RNA editing appears to be completely absent in horsetails , the sister clade of all other monilophytes.C-to-U and U-to-C RNA editing in fern chloroplasts and mitochondria follow disinct evolutionary pathways that are surprisingly different from what has previously been found in flowering plants. The results call for careful differentiation of the two types of RNA editing in the two endosymbiotic organelles in comparative evolutionary studies.The online version of this article (doi:10.1186/s12862-016-0707-z) contains supplementary material, which is available to authorized users. RNA editing that converts specific cytidines into uridines in chloroplast and mitochondrial transcripts is nearly omnipresent among land plants –4. As anWhereas the C-to-U type of RNA editing is nearly ubiquitous among land plants, the reverse process, U-to-C editing, appears to be more restricted in occurrence. U-to-C editing is unequivocally present in hornworts –19 and irbcL, atpB, rps4, and nuclear 18S rDNA demonstrated the monophyly of the monilophytes and placed them as the sister group to the spermatophytes (seed plants) [atpA, atpB, rbcL, rps4, and matK with the mitochondrial loci nad2, nad5, atp1, and rpl2 confidently placed the horsetails as sister group to all other monilophytes and Ophioglossales/Psilotales as the sister group to a joint clade of Marattiales and leptosporangiate ferns [The monilophytes are a morphologically heterogenous group that include the true eusporangiate ferns , such as Ophioglossales (moonworts) and Marattiales, the Equisetales (horsetails), the Psilotales (whisk ferns), and the species-rich group of leptosporangiate ferns . Early studies with  plants) , 26. Howte ferns .Adiantum capillus-veneris [Ophioglossum californicum [Psilotum nudum only 27 C-to-U and no U-to-C editing sites were detected [Previous studies on mitochondrial and chloroplast RNA editing in ferns already indicated highly differing frequencies of RNA editing in different fern taxa. In the chloroplasts of -veneris  and Ophifornicum  RNA editdetected . In the detected , 21, 30.Equisetum hyemale confirmed our assumptions of total absence of RNA editing.The now available backbone phylogeny of monilophytes allows for phylogenetic insights into the evolution of C-to-U and U-to-C RNA editing among ferns, thus complementing similar studies restricted to C-to-U editing in flowering plants. To this end, we analyzed RNA editing in a phylogenetically wide sampling of monilophytes for four selected mitochondrial loci and for all available chloroplast genomic data. In contrast to an overall loss of RNA editing in angiosperms, we observed (i) an increase of RNA editing in monilophytes after the diversification of later emerging lineages, (ii) a mostly uncoupled evolution of editing frequencies in chloroplasts and mitochondria, and (iii) a largely uncoupled evolution of C-to-U and U-to-C editing. In some taxa, such as the order Schizaeales, U-to-C editing even exceeds the canonical C-to-U editing. Additionally, a complete chloroplast transcriptome analysis of the horsetail atp1, nad5, rpl2, and rps1 [Our mitochondrial RNA editing analysis was based on four genes, which were previously included for phylogenetic studies in wide samplings of monilophyte taxa: and rps1 , 31, 32.Gleichenia and Anemia phyllitidis are exceptions with unusual high numbers of RNA editing. Gleichenia has higher editing than all other examined taxa in two loci, the 1104 bp amplicon of the nad5 gene (92 C-to-U and 9 U-to-C edits) and the 411 bp amplicon of the rps1 gene (12 C-to-U edits). A. phyllitidis shows highest editing numbers in the other two loci, the rpl2 gene (16 C-to-U and 46 U-to-C edits) and the atp1 gene (53 C-to-U and 70 U-to-C edits).Overall, the four different mitochondrial loci reflect comparable frequencies of RNA editing for each individual taxon Fig. . Howeveratp1 locus in A. phyllitidis has the most U-to-C editing and it was used for an exemplary outline of all 123 observed RNA editing events  remained unconfirmed in the Anemia or Gleichenia, we observed very low numbers of editing in the early-branching eusporangiate fern lineages and the horsetails . In contrast to Anemia and Gleichenia that each feature more than 200 editing sites in the four sampled mitochondrial loci, only 27 editing sites were found in the same four genes of Angiopteris evecta.Contrary to the high levels of RNA editing in G. dicarpa also shows widely divergent ratios of C-to-U vs. U-to-C editing ranging from 12:0 in rps1 to 19:21 in rpl2. The latter example is the earliest emerging case of U-to-C editing exceeding C-to-U editing in our phylogenetic sampling . While fewer U-to-C RNA editing sites were predicted (and confirmed) for the eusporangiate lineages Psilotales, Ophioglossales, and the Osmundales as the earliest-branching leptosporangiate lineage, we failed to find any evidence for U-to-C editing in Equisetales and Marattiales.Other than overall high RNA editing numbers, The ratio of C-to-U and U-to-C RNA editing becomes more balanced in the later emerging water ferns , tree ferns  and in the large order of Polypodiales, which contains most of the extant fern diversity . ResolviOur extended data set of 1794 mitochondrial editing sites identified by cDNA analysis  [accD gene is ACG, which may be subject to C-to-U editing. In order to investigate the issue, we performed an exhaustive cDNA analysis of the Equisetum hyemale chloroplast coding regions. All annotated introns are spliced out correctly and all negative controls did not show any traces of a potential contamination of genomic DNA. However, we did not find a single RNA editing site in any protein coding gene. Even the creation of the putative AUG start codon by C-to-U editing in accD could not verified. We therefore conclude that the Equisetum hyemale chloroplast is completely devoid of RNA editing.In the course of our studies we observed very low numbers of predicted chloroplast RNA editing in the C117177) . No in-fA. capillus-veneris [O. californicum and P. nudum [Alsophila spinulosa [Diplopterygium glaucum, Osmundastrum cinnamomeum [Angiopteris evecta [Marsilea crenata and Lygodium japonicum [For the comparison of mitochondrial and chloroplast RNA editing frequencies we investigated all available complete monilophyte chloroplast genomes. Extensive chloroplast editome analyses have previously been done for -veneris , O. caliP. nudum  and are pinulosa , Diploptnamomeum , Angiopts evecta , Marsileaponicum .All editing frequencies were calculated as numbers of editing sites per 1000 nucleotides of coding sequence Fig. . Like inndhF, chlB and chlL to test for the low numbers of predicted RNA editing in Angiopteris evecta for comparison to the low levels of mitochondrial editing we identified  are shared by all three species, indicating shared ancestry from a common ancestor of polypods, water ferns and tree ferns , are particularly interesting for comparative studies of organelle RNA editing evolution for two main reasons. First, the evolution of editing spans a much larger time frame since the monilophyte clade is about three times as old as the angiosperms. Second, the plant-typical C-to-U-type of RNA editing is accompanied by U-to-C editing in the reverse direction in monilophytes. Moreover, a well-supported backbone phylogeny of ferns has been obtained very recently , now offSeveral interesting observations emerge. Our analysis shows a high variance of RNA editing in monilophytes. There is a clear trend towards more editing in the more diversified and species-rich orders of the leptosporangiate ferns compared to the basal eusporangiate fern lineages. This is in full contrast to the large-scale evolution of RNA editing in angiosperms, where RNA editing is frequent in early lineages but significantly lower in later emerging lineages, both in mitochondria –46 and iGleichenia and U-to-C editing with more than 45 events per kb in Anemia.Despite extreme differences in morphology and development, the three extant lineages of crown leptosporangiates, the Polypodiales, the Cyatheales (tree ferns) and the Salviniales (water ferns) show comparable mitochondrial RNA editing frequencies of ca. 30 editing sites per kb both for C-to-U and U-to-C editing  in hornworts –19. HornOther than the highly variable numbers of C-to-U and U-to-C editing in the two endosymbiotic organelles across monilophyte diversity, the entirely different patterns of conservation of individual editing sites are striking. Only 3.5 % of chloroplast RNA editing sites are shared between the representatives of Salviniales, Cyatheales and Polypodiales Fig. . In contAzolla filiculoides is ongoing and the nuclear genome of Ceratopteris richardii is proposed as a possible project [Gains and losses of organelle RNA editing sites are expected to be accompanied by corresponding changes in the nuclear-encoded co-factors addressing individual RNA editing sites in chloroplast or mitochondria. Key editing factors are unique RNA-binding pentatricopeptide repeat (PPR) proteins of the so-called “PLS-type”, which are particularly abundant in the plant lineage –49. The  project .Other than their tremendous diversity of editing patterns and the simultaneous existence of reverse U-to-C editing, ferns offer yet another feature distinguishing them from flowering plants with regard to organelle RNA editing. RNA editing in angiosperms is affected by additional protein factors called multiple organelle RNA editing factors (MORFs) or RNA editing factor interacting proteins (RIPs) –61, whicIsoetes engelmannii [Most importantly, any forthcoming fern genome data will help to elucidate the yet enigmatic biochemical machinery of reverse U-to-C editing. The DYW domain with cytidine deaminase similarity present at the carboxyterminus of many PLS-type RNA editing factors is the bona fide candidate to perform the deamination of cytidine to uridine . No convelmannii . WhetherOphioglossum californicum and Psilotum nudum assumed that reverse editing is entirely lost in Psilotum and that it may also be lost in Equisetum, Angiopteris and Osmundastrum based on the absence of in-frame stop codons in chloroplastid genes [Psilotum nudum mitochondria , feature highly frequent and highly diverse RNA editing in both organelles, exclusively of the C-to-U type in Selaginellales [Isoetes and Selaginella are taxon-specific, notably also in comparison to the low-editing, early-branching lycophyte Phlegmariurus  and in Marattiales. Within the leptosporangiate ferns, however, RNA editing of both types is highly abundant with record amounts of reverse editing in Schizaeales. Mitochondrial RNA editing sites in the leptosporangiate ferns are strikingly conserved in contrast to RNA editing in the chloroplasts. Hence, C-to-U and U-to-C RNA editing is evolving independently in the two organelles and the results call for careful differentiation between the different types of RNA editing in mitochondria and chloroplasts. Its great variability in the fern organelles promises an interesting field of co-evolution of C-to-U and U-to-C editing sites and their hitherto unknown nuclear-encoded specificity factors. Importantly, the biochemical mechanisms of “reverse” U-to-C editing, evolutionarily much more restricted than the more widespread C-to-U editing both within and outside of the plant kingdom , are comEquisetum hyemale chloroplast primers binding in the UTRs of single or polycistronic genes were used. As a negative control samples without reverse transcriptase treatment were used to test for remnants of genomic DNA.Plant material was obtained from the Botanic Garden Bonn. Nucleic acids were isolated according to the CTAB method  followedEscherichia coli. Sequencing was done by Macrogen Europe . New cDNA sequences were submitted to GenBank . Gel-eluted products were either sequenced directly or after cloning into the pGEM-T Easy vector (Promega) and amplification in atp1, nad5, rpl2, rps1), cDNA sequences were determined for at least 10 fern taxa (Fig. For each of the four investigated mitochondrial loci (Adiantum capillus-veneris, Amborella trichopoda, Arabidopsis thaliana, Chara vulgaris, Equisetum hyemale, Marchantia polymorpha, Ophioglossum californicum, Pellia endiviifolia, Physcomitrella patens and Psilotum nudum. All editing sites were labeled according to a previously published nomenclature proposal [Chloroplast RNA editing sites were predicted with the BLASTX prediction tool of PREPACT. Again, a threshold for prediction of 80 % and ten selected reference sequences were used: proposal , 71 usin


The final paragraph in the Results section is duplicated. The first version of the paragraph is correct and is provided here.We then turned to the 23 female-specific sites. These sites were concentrated in two loci overlapping non-coding RNAs (X56 and X130), largely identical to sites previously identified as being involved in a repeat-specific X chromosome behaviour [38]. Although there are far fewer sites to analyse than the other classes, the female specific sites are all enriched for binding to YY1, which is known to tether XIST to the inactive X nucleation centre [39]. Horakova et al [38] explored the RNA expression of these ncRNAs in female cells; we performed fluorescence in situ hybridization (FISH) for RNA in both male and female cells. Consistent with the published results [38], we detected RNA from the active X at these loci in female cells . In male cells we also detected RNA expression , suggesting that these CTCF sites are likely to be involved in a female-specific inactivation process at these loci. Using the data from Kilpinen et al, we can show that these sites are active in female lymphoblastoid cell lines, but not male . It is notable how few of these sites there are on the X chromosome, compared to the far more numerous single-active and both-active categories.


We investigate the role of water molecules in 89 protein–RNA complexes taken from the Protein Data Bank. Those with tRNA and single-stranded RNA are less hydrated than with duplex or ribosomal proteins. Protein–RNA interfaces are hydrated less than protein–DNA interfaces, but more than protein–protein interfaces. Majority of the waters at protein–RNA interfaces makes multiple H-bonds; however, a fraction do not make any. Those making H-bonds have preferences for the polar groups of RNA than its partner protein. The spatial distribution of waters makes interfaces with ribosomal proteins and single-stranded RNA relatively ‘dry’ than interfaces with tRNA and duplex RNA. In contrast to protein–DNA interfaces, mainly due to the presence of the 2′OH, the ribose in protein–RNA interfaces is hydrated more than the phosphate or the bases. The minor groove in protein–RNA interfaces is hydrated more than the major groove, while in protein–DNA interfaces it is reverse. The strands make the highest number of water-mediated H-bonds per unit interface area followed by the helices and the non-regular structures. The preserved waters at protein–RNA interfaces make higher number of H-bonds than the other waters. Preserved waters contribute toward the affinity in protein–RNA recognition and should be carefully treated while engineering protein–RNA interfaces. When the protein components in two entries had more than 35% sequence identity, only the one with better resolution was kept. Additionally, we kept the complexes with homologous protein chains if they bind different RNA sequences, otherwise removing one of them would lose the result of diversity of interactions shown by them. We also performed the pairwise sequence alignment using structural superposition in PDBeFold (http://www.ebi.ac.uk/msd-srv/ssm/cgi-bin/ssmserver). The sequence identity values obtained using structural superposition are essentially similar to those obtained using BLAST. Finally, we retained 89 complexes reporting at least one interface water molecule (Supplementary Table S1). Water molecules within a distance of 4.5 Å from at least one of the interface atoms of both the interacting partners were identified at the interface. We have used shorter (or longer) cut-off distances (3.0–5.0 Å) for selecting the interface waters. This gave us somewhat less (or more) interface waters , but did not alter the general conclusions that we obtained using 4.5 Å. When the asymmetric unit contains multiple copies of the complex, only one copy was kept. In case of oligomeric structures, the data corresponding to the biologically significant oligomeric state were selected after verification from the literature and the PISA server  buried in contact. The web server PRince  was usedE-value <10−3. When multiple candidates for an unbound structure were found, entry with the highest sequence similarity and the maximum alignment length with the bound structure was kept. While selecting the unbound structures, we kept the same resolution limit as that of the bound structures. It was further confirmed that the unbound forms corresponding to the bound structures contained water molecules. This finally gave us 38 unbound proteins and seven unbound RNAs for the complexes listed in Supplementary Table S1. An interface water was considered preserved if it makes an H-bond with the same donor or acceptor atoms of protein or RNA chains in both the bound and unbound structures. The dataset of protein-DNA and protein-protein complexes are taken from Setny et al.  are at the interface (Table R = 0.49). Exceptionally, the interface between RNA-dependent RNA polymerase of bacteriophage phi6 and a 6 nt RNA (PDB id: 1UVI) contains only two waters. Here, the small RNA molecule interacts with the core region of the polymerase, which is almost dehydrated (2 of B) compared to interfaces with single-stranded RNA (11.0 per 1000 Å2 of B) or duplex RNA (12.4 per 1000 Å2 of B). In contrast, interfaces with ribosomal proteins are most hydrated (14.5 per 1000 Å2 of B). This lowest density of immobilized waters at the tRNA interfaces is justified by their relatively more hydrophobic nature compared to the other three classes  obtained is 0.0091, rejecting the null hypothesis .The dataset consists of 89 X-ray structures of protein–RNA complexes with resolution 2.6 Å or better (Supplementary Table S1), reporting a total of 19 327 crystallographic waters bound to them. We have identified 2440 waters  bound at the interface, contributing to the recognition process. Thus, an average protein–RNA complex contains about 217 bound waters, of which, 27  obtained from ANOVA test on mean number of water-mediated H-bonds is 0.0052, showing the significant statistical difference in mean values among the four classes.Polar interactions at the protein–RNA interface are quantified in terms of H-bonds. The dataset of 89 complexes contains 2440 interface waters, of which 442 ∼18%) do not make any H-bond with the polar groups of the binding partners. The remaining 1998 waters make 4625 H-bonds with a biased distribution: 58% with the RNA polar groups and 42% with the protein polar groups. This asymmetry is also observed in interface area, where the RNA side contributes more compared to the protein side . Of the  Å2 of B . Table 18% do not2 of B. The density of bridging waters varies in different classes; it is lower in interfaces involving tRNA and single-stranded RNA, and higher in interfaces involving ribosomal proteins and duplex RNA. The P-value (at α = 0.05 level of significance) for mean number of bridging waters among the four classes is 0.0088, confirming the significance of statistical differences.More than one-third (36%) of the interface waters make at least one H-bond with the polar groups on both sides of a protein–RNA interface. They are defined as bridging waters. Table et al. , whereas the phosphate and the bases contribute 30% (9 per interface) and 25% (7 per interface), respectively. This contribution differs in direct protein–RNA H-bonds, where the phosphate group contributes slightly more (36% or 7 H-bonds per interface) than the bases or the ribose (both contribute about 32% or 6 H-bonds per interface). Among the different bases, guanine and uracil are more frequently found in water-mediated H-bonds than adenine and cytosine. Similar trend is observed for different bases involved in direct H-bonds. Polar groups in all the four bases are frequently found as acceptors in water-mediated H-bonds rather than donors Figure . The relThe presence of the 2′OH in RNA enhances its ability to make polar interactions with the waters compared to DNA. In the present dataset, the 2′OH is involved in 31% of all water-mediated H-bonds Table . This isdr  to quantify the ‘dry’ and ‘wet’ protein–RNA interfaces. Figure dr of 0.86 and 0.98, respectively, whereas the interfaces with ribosomal proteins and single-stranded RNA are ‘dry’ with an average dr of 1.17 and 1.07, respectively , where the waters are distributed along the periphery of the interface, making it ‘dry’  where the waters are distributed throughout the interface, making it ‘wet’ . The distribution of the dr values for the 89 interfaces is shown in the Supplementary Figure S4. A two-tailed t-test (at α = 0.05 level of significance) shows that there is a significant difference  between the ‘dry’ and ‘wet’ interfaces separated by cut-off value 1.0.Depending on the spatial distribution of the interface waters, macromolecular interfaces can be distinguished into ‘wet’ and ‘dry’ categories . We usedly Table . The int2 of B. This density varies among the different classes: the lowest in interfaces with tRNA and the highest in interfaces with single-stranded RNA. Moreover, we found that the preserved waters make an average of 2.5 H-bonds compared to the other interface waters, which make only 1.9.Preservation of waters at the protein–RNA interfaces was identified by comparing the bound structures with their corresponding unbound structures of the protein or the RNA . When both protein and RNA are available in the unbound form, we considered both of them to assign preserved waters. The number of preserved waters varies from 0 to 17 in the present dataset with an average of six (∼21% of the interface waters) per interface. This low average definitely under-represents the preserved waters, since in most of the cases we found either protein or RNA in the unbound form. This is exemplified by two complexes , where the corresponding protein and the RNA are available in the unbound form. In both the cases, the number of preserved waters increases when we considered the bound waters in the complex along with the free form of its partners. Moreover, it should be noted that the poor assignment of the waters in the bound and the unbound crystal structures results in the lack of preserved waters. We did not find any preserved water in two interfaces with single-stranded RNA: the one with TRAP (PDB id: 1C9S), and the other with ERA (PDB id: 3IEV). Although the complex TRAP-RNA is determined with high resolution (1.9 Å), it reports 86 bound waters, of which only six are at the interface. The complex between interferon-induced protein with tetratricopeptide repeats 5 (IFIT5) and a single-stranded RNA (PDB id: 4HOR) contains the highest number . These c10-Helix and π-helix; the category ‘strands’ includes residues in isolated β-strand, in extended strands or in β-ladder; and the category ‘others’ includes non-regular secondary structures. We found, about 42% of B is contributed by the helices; accordingly they are involved in 43% of all water–protein H-bonds. In contrast, the strands account for only 17% of B, and they are involved in only 20% of all water–protein H-bonds. The others account for 41% of B, and they are involved in 37% of all water–protein H-bonds. This suggests that the helices occur most frequently at the interfaces; thereby they are involved in the highest number of water-mediated H-bonds. However, when we calculated the density of water-mediated H-bonds (number per unit B), we found that, in spite of the lowest contribution to B as well as to the number of water-mediated H-bonds, the strands has the highest density, the helices has the intermediate density, and the others has the lowest density  than the average size of a protein–DNA interface (3100 Å2), but larger than that of a protein–protein interface (1886 Å2). This is consistent with the data obtained in the previous studies  with a biased distribution; RNA makes more than its partner protein. This is translated into one water-mediated H-bond per 47 Å2 of B, which is much higher than the direct H-bond density (one per 125 Å2 of B). This suggests that the water-mediated polar interactions play a greater role in the stability of protein–RNA interfaces than the direct H-bonds. The water-mediated H-bond density of an average protein–RNA interface is similar to that of an average protein–DNA interface, but higher than that of an average protein–protein interface. About one-third of the interface waters bridge the protein–RNA interfaces by making H-bonds with the polar groups on both sides. This value is almost similar to the observation made by Treger and Westhof (Interface waters are involved in polar interactions through H-bonds. We observe that the frequency of waters involved in one or two H-bonds is higher than those involved in three or four H-bonds. Moreover, we find a number of interface waters do not make any H-bond. These non-H-bonded interface waters are most often trapped at the interface cavities , and are Westhof  in a smaOur study shows that the neutral polar side chains of amino acids contribute about one-third of all water–protein H-bonds in a protein–RNA interface. In comparison, the charged side chains contribute little less. This observation flipped in case of direct protein–RNA H-bonds, where the charged side chains, mainly from Arg and Lys, contribute more than the neutral polar side chains. This is in agreement with the findings made in the previous studies that the positively charged amino acid side chains play a greater role in RNA recognition ,34,36,49A systematic dehydration analysis of B-DNA showed that water molecules can be easily removed from the ribose and the bases compared to the phosphate ,55. ThisPolar interactions with the bases determine the sequence specificity ,56–58. HWe find the helices contribute highest to the protein–RNA interface area followed by the non-regular secondary structures and the strands. The highest frequency of occurrence of the helices in protein–RNA interfaces was also observed by Treger and Westhof . BecauseThe major groove of DNA is wide and easily accessible than its minor groove, so that the sequence-specific recognition of DNA helices is generally determined by the major groove interactions. This easy accessibility makes the major groove atoms participate in water-mediated interactions more frequently than the minor groove atoms ,26,29,60et al. (It has been well studied that the dehydration of the non-polar groups upon macromolecular binding can lead to an increased binding affinity owing to the favorable gain in entropy associated with the release of well-ordered water molecules into the bulk solvent . On the et al.  suggesteet al. ,67. ThisUnderstanding protein–RNA interactions at the structural level is immensely important to apprehend the cellular processes occurring in the aqueous environment, where the water molecules invariably play an important role. We have performed a comprehensive analysis to understand the role of water molecules in protein–RNA recognition, and identified interface waters that do not displace upon complexation. Our understanding will help in drug design targeting protein–RNA interfaces, by knowing which waters are important in mediating the interaction between a protein and a RNA, and which, instead, can be targeted for displacement. Our analysis will also help to improve accuracy of protein–RNA docking methods, as well as binding affinity predictions, which are becoming the active field of interest in understanding protein–RNA interaction and designing novel protein–RNA complexes.Supplementary Data are available at NAR Online.


P-test analyses indicate that the recognition between DNA/RNA hybrid and polymerase might follow an induced fit mechanism for DNA/RNA hybrid and conformation selection for polymerase. Furthermore, this method could be used to relative studies of specific recognition between nucleic acid and protein.RNA polymerase catalyzes transcription with a high fidelity. If DNA/RNA mismatch or DNA damage occurs downstream, a backtracked RNA polymerase can proofread this situation. However, the backtracked mechanism is still poorly understood. Here we have performed multiple explicit-solvent molecular dynamics (MD) simulations on bound and apo DNA/RNA hybrid to study backtracked recognition. MD simulations at room temperature suggest that specific electrostatic interactions play key roles in the backtracked recognition between the polymerase and DNA/RNA hybrid. Kinetics analysis at high temperature shows that bound and apo DNA/RNA hybrid unfold via a two-state process. Both kinetics and free energy landscape analyses indicate that bound DNA/RNA hybrid folds in the order of DNA/RNA contracting, the tertiary folding and polymerase binding. The predicted Φ-values suggest that C7, G9, dC12, dC15, and dT16 are key bases for the backtracked recognition of DNA/RNA hybrid. The average RMSD values between the bound structures and the corresponding apo ones and Kolmogorov-Smirnov (KS)  RNA polymerase efficiently catalyzes the template-directed transcription with a low error rate   Baker, . Fortuna−1.The atom coordinates of DNA/RNA-polymerase complex were obtained from the Protein Data Bank, with the accessory code 3GTQ  is sufficient to capture the average properties of the protein  were used to study the unfolding kinetics.Tertiary contact assignment was handled with in-house software  or Cα (protein) was calculated in both bound and apo structure, respectively. Then the landscape of base-to-base (or residue-to-residue) distances in apo-structure minus the corresponding distances in bound structure was plotted. This indicates a structural contraction when the color in red is positive and an extension when the color in blue is negative. In order to exclude the effect of thermal fluctuations, we averaged all the distances in the whole 10 ns' simulation instead of a certain time point.According to the definition of protein Φ-values which computed with a strategy similar to those used in other studies  test was used to evaluate the statistical significance of RMSDs' variations. The equation below was used to quantitatively describe relative differences between conformational selection and induced fit.There are two mainstream hypotheses to explain binding process: “Induced fit” model  or induced fit (IF), and NCS and NIF are the numbers of data points for the distribution of CS or IF, respectively.P) will be 50%. In order to determine TSE, we have scanned MD snapshots for TSE structures in all six unfolding trajectories for each of bound DNA/RNA and apo-DNA/RNA, respectively . TSE structures can either fold or unfold, and the transition probability  and 40 Å (the whole molecule) for DNA/RNA hybrid and polymerase, and shown in Figure Distance analyses could be used to further confirm the conformation adjustment in the binding process. C5′ and Cα fluctuations for bound and apo states are illustrated in Figure To investigate the relative movement in the binding process, distances from last 4 base pairs of bound DNA/RNA hybrid to the mass center of the protein are analyzed and shown in Figure The landscape of distance difference for base pairs between apo and bound DNA/RNA is shown in Figure The conformation adjustment in the binding process could be attributed to the electrostatic, hydrophobic and hydrogen-binding interactions between DNA/RNA and polymerase. The electrostatic interactions in the simulation of six trajectories are shown in Figure Unfolding state along high temperature simulations was performed to detect the binding and folding details of DNA/RNA hybrid and the polymerase. The fraction of tertiary native contacts (Qf) and binding native contacts (Qb) is used to monitor unfolding and unbinding kinetics, respectively. The stable native contacts were calculated for the folded state, including inter-chain and intra-chain. Qf is defined as the fraction of intrachain native contacts which are still stable at unfolding state; Similarly, Qb is the fraction of interchain native contacts at 498 K. Time evolutions of Qb and Qf for bound DNA/RNA are shown in Figure To further understand the interdependence between the DNA/RNA folding and polymerase binding, the unfolding landscape of bound DNA/RNA was analyzed with the variables Qf and Qb, and shown in Figure Kinetics analysis shows that the unfolding of bound and apo-DNA/RNA obeys first order kinetics, which suggests that bound and apo-DNA/RNA unfold via a two state process. Therefore, there is a transition state ensemble (TSE) corresponding to the free energy maximum. TSE structures can either fold or unfold, and the transition probability (P) will be 50%. In order to determine TSE, we have scanned MD snapshots for TSE structures in all six unfolding trajectories for each of bound DNA/RNA and apo-DNA/RNA, respectively  with a population around 25%. For Thr831, there are two stable native contacts (Thr831/A10 and Thr831/G11) with the population about 55%. This is consistent with the observation of X-ray experiment.Backtracked state could also be caused by the nascent mismatched RNA. In order to compare DNA damage and DNA/RNA mismatch, six trajectories of 10.0 ns each were also simulated for the DNA/RNA mismatch. In the distance analyses Figure , the proInteractions between the protein and the mismatched DNA/RNA hybrid were also analyzed and shown in Figure The RMSD for A10, G12, and C13 of bound DNA/RNA mismatch was analyzed respectively, and shown in Figure In our simulation, the RMSF of bound polymerase is significant smaller than that of apo polymerase. This is also consistent with the previous work that RMSFs of free polymerase are ranging from 0.6 to 2.3 Å and bound polymerase from 0.5 to 1.3 Å  unfolded state, (2) tertiary contraction , (3) tertiary folding , (4) tertiary binding , and (5) folded state. Average structures along binding and folding pathway are shown in Figure P-test analyses indicate that the DNA/RNA hybrid might follow an induced fit mechanism and the polymerase with the conformational selection in their binding process. Furthermore, this method could be used to relative studies of specific recognition between nucleic acid and protein.Both 298 K and 498 K molecular dynamics simulations are performed for bound and apo DNA/RNA hybrid. At folded state, conformation of bound DNA/RNA hybrid shows significant adjustment and becomes more unstable upon the polymerase binding, especially the 3′-end of the RNA. Kinetics analysis of MD simulations at 498 K shows that bound and apo DNA/RNA hybrid unfold via a two-state process. Bound DNA/RNA hybrid folds in the order of DNA/RNA contracting, the tertiary folding, and the protein binding. C7, G9, dC12, dC15, and dT16 are key bases to bound DNA/RNA hybrid folding. Inter-chain interaction analyses and binding pathway construction show that the recognition is in the order of non-specific closing, specific binding and conformation adjustment. dC14, G9, A10, and G11 are key bases to the protein recognition. The average RMSD values between the bound structures and the most “like” apo-structure and Kolmogorov-Smirnov (KS) 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.


RNA-based approaches are among the most promising strategies aimed at developing safer and more effective therapeutics. RNA therapeutics include small non-coding miRNAs, small interfering RNA, RNA aptamers and more recently, small activating RNAs. However, major barriers exist to the use of RNAs as therapeutics such as resistance to nucleases present in biological fluids, poor chemical stability, need of specific cell targeted delivery and easy entry into the cell. Such issues have been addressed by several recent reports that show the possibility of introducing chemical modifications in small RNAs to stabilize the molecular conformation and increase by several fold their integrity, while still preserving the functional activity. Further, several aptamers have been developed as excellent candidates for the specific recognition of cell surface targets. In the last few years, by taking advantage of recent advances in the small RNA field, molecular bioconjugates have been designed that permit specific targeting and may act as cargoes for cell internalization of small RNAs acting on gene expression that will be discussed in this review. From this optic short non-coding RNAs were revealed to be attractive molecules. In the last decades significant advances have been attained in the knowledge of molecular mechanisms leading to selective inhibition of gene expression and protein function. However, in order to successfully translate RNA-based therapeutics to the clinic several challenges must be addressed, including appropriate stability in biological fluids, high efficiency and specificity of delivery, durable safety and target selectivity.Innovative targeted therapeutic strategies aim at developing new molecules with high target affinity and specificity with suitable pharmacokinetic properties for Several classes of molecules have been characterized with potential applications as RNA therapeutics in the treatment of human diseases. These include ribozymes, RNA decoys, aptamers, small interfering RNA (siRNA) and microRNA (miRNA) .guide strand while the other strand, named the passenger, is degraded. In the cytoplasm, the RNA-induced silencing complex (RISC) drives the guide strand of the dsRNA to hybridize with the target mRNA to prevent translation or induce degradation depending on the degree of complementarity .,72.71,72This means that aptamers function as specific recognition ligands to target cells, which is especially significant given the whole cell-SELEX strategy to target specifically cell surface epitopes . Once deThe cell-SELEX method allows for the generation of aptamers against cell surface targets by replicating the native conformation and glycosylation pattern of the extracellular regions of proteins. Recently, multiple groups have reported selections using living cells as the target to identify receptor-specific aptamers and those that bind to a specific cell type ,74. SomeIn the aptamer-based delivery approach, the last goal is to develop an aptamer to the extracellular portion of such a protein and then use the aptamer to deliver the secondary reagent to the targeted cell population via binding the targeted protein on the surface of the targeted cell type. Because this binding in some cases also results in the endocytosis of the aptamer/secondary reagent complex, this approach can be used to deliver reagents such as siRNAs that depend on delivery to intracellular compartments for their proper function .To date, the best-characterized aptamers for targeted delivery are the two 2′-F-Py-RNA aptamers (A9 and A10) that have been generated against the extracellular domain of the prostate-specific membrane antigen (PSMA) . These aA first study reports the non-covalent conjugation of siRNA with A9 aptamer via a streptavidin connector . The 27met al. [In the same year, McNamara et al.  describein vivo application [In addition, the PSMA-siRNA chimeric molecule has been further modified for lication . The aptet al. [To date several groups have adapted the covalent assembly approach to aptamer-mediated siRNA delivery . In theset al.  linked atat/rev siRNAs into HIV infected cells via binding to envelope expressed on the cell surface, resulting in internalization of the aptamer and delivery of a dicer substrate siRNA to RISC. In vivo delivery of the aptamer and aptamer-siRNA conjugates into a humanized mouse model for HIV infection suppressed HIV replication and completely protected T-cells from HIV mediated T-cell killing.Rossi and colleagues have extensively characterized the HIV glycoprotein gp120 as a target for aptamer-mediated siRNA delivery -84. In tWith the development of the conjugation strategies, the list of aptamers against surface epitopes that are being used as delivery agents is growing rapidly and now includes those against PTK7 ,86, nucl4.Even if only one nucleic acid aptamer has been approved and is on the market, aptamers hold an extraordinary potential in drug development and it is plausible that the global interest for their development will increase in the next few years. Accordingly, a new technical market research report, from BCC Research , estimatTo date, Archemix Corp. is a leading biopharmaceutical company in the development of aptamers as therapeutics. It is the owner of the aptamer technology patent and it collaborates with other pharmaceutical companies  to develop and commercialize a pipeline of partnered aptamers in the cardiovascular disease, hematology and oncology areas.Moreover, the development of aptamers as delivery agents for therapeutic RNAs can have a considerable impact on aptamer market in the near future. Indeed, intracellular delivery has been a key challenge for RNA modalities and the potential of bringing together the properties of aptamers and microRNA therapeutics will allow to overcome this limitation and open further potential for RNA-based therapeutics.Recently Archemix Corp. started a collaboration with miRagen Therapeutics Inc., a biopharmaceutical company focused on developing innovative microRNA-based therapeutics for cardiovascular and muscle disease, for the development of conjugated aptamer-microRNA molecules capable of intracellular delivery and subsequent microRNA targeting. Combining aptamers and microRNA therapeutics has the potential to solve the intracellular delivery challenge for certain RNA-based therapeutic approaches. In this perspective, even if aptamer-miRNA chimeras have not been already described in literature, it is plausible that the approaches discussed in this review for aptamer-siRNA conjugation could be as well adapted to generate aptamer-miRNA molecules of fundamental therapeutic value.


Our method uses targeted RNase H-based digestion to remove contaminating poly(rA) carrier and ribosomal RNA. This depletion step improves both the quality of data and quantity of informative reads in unbiased total RNA sequencing libraries. We have also developed a hybrid-selection protocol to further enrich the viral content of sequencing libraries. These protocols have enabled rapid deep sequencing of both Lassa and Ebola virus and are broadly applicable to other viral genomics studies.We have developed a robust RNA sequencing method for generating complete The online version of this article (doi:10.1186/s13059-014-0519-7) contains supplementary material, which is available to authorized users. Lassa virus (LASV) and Ebola virus (EBOV) belong to a class of RNA viruses that cause hemorrhagic fevers with high case fatality rates, have limited or no treatment options, and have the potential for extensive transmission -6. The nArenaviridae family, is an ambisense RNA virus whose genome consists of an L and an S segment of 7.4 kb and 3.4 kb in length, respectively, encoding two proteins on each segment [Mastomys natalensis, its natural reservoir, which is asymptomatically infected with the virus [Filoviridae family of single-stranded negative-sense RNA viruses. Its genome is approximately 19 kb in length and it encodes seven proteins [LASV and EBOV are both single-stranded RNA viruses. LASV, a member of the  segment . LASV ishe virus -11. EBOVproteins ,13.LASV and EBOV genomics can inform surveillance, diagnostic, and therapeutic developments, yet few full length genomes have been published -16. The Massively parallel RNA sequencing (RNA-seq) based on randomly primed cDNA synthesis has the potential to transform LASV and EBOV genomics, providing a comprehensive, largely unbiased qualitative and quantitative view of all RNA in a sample -19. It tSequencing viral genomes directly from clinical and biological samples, however, holds special challenges. Samples may contain very little viral RNA and are heavily contaminated with human RNA; in some instances, the nucleic acid is severely degraded. While poor sample quality affects viral sequencing in general, it is exacerbated for EBOV and LASV. Here, sample quality is often compromised by cold chain gaps in remote rural areas in hot climates and by complications with handling, containment and biological inactivation at the highest biosafety level .The comprehensive and unbiased nature of total RNA-seq also presents a challenge in samples where non-viral RNA makes up the vast majority of material being sequenced. As with most RNA-seq approaches, unwanted RNA contaminants waste many sequencing reads and negatively impact sequencing performance. The largest single component of RNA in clinical samples is human RNA, particularly ribosomal RNA (rRNA). In addition, a prevalent artificial contaminant in RNA preparations is poly(rA) carrier RNA, present in commonly used commercial viral RNA extraction kits . Although non-nucleic-acid carriers such as linear polyacrylamide are suitable substitutes, many existing sample collections already contain poly(rA).et al., in preparation), including many from the 2014 EBOV outbreak, and can be broadly applied to a wide range of RNA viruses.Here we describe the development of efficient and cost-effective methods for sequencing of EBOV and LASV that are based on unbiased total RNA-seq. These techniques have already been used to rapidly generate large catalogs of LASV and EBOV genomes  RNA carrier resulted in high-molecular-weight byproducts .Second, after sequencing the libraries to >20 million Illumina reads per library, we found that only a small fraction <0.1%) aligned to the LASV-Josiah reference genome  in all b% alignedde novo assembly and for confident calling of intra-host variants. We therefore developed methods to: (1) deplete carrier poly(rA) before library construction; (2) deplete rRNA before library construction; and (3) to enrich LASV reads in libraries before Illumina sequencing. We then demonstrated the utility of these approaches to EBOV sequencing during the 2014 Ebola virus disease (EVD) outbreak.The median fraction of LASV reads in these test libraries was 0.0003%  RNA carrier on sequencing quality, we developed a targeted RNase-H-based depletion method  to removTo deplete host rRNA in human clinical samples, we pursued selective RNase H-based depletion using oligodeoxyribonucleotides tiled along human cytoplasmic and mitochondrial human rRNA sequences . We achiThe host rRNA depletion not only improved overall sequencing depth along the LASV genome Figure B but revAs most LASV isolates collected from human serum or plasma contain very little total RNA (sub-nanogram levels), we further developed a prescreening process to identify samples suitable for host depletion. We used a real-time qRT-PCR assay for 18S rRNA as a surrogate for quantification of total RNA. We then performed rRNA depletion on nine samples spanning a wide range (approximately 200-fold) of input RNA to determine the minimum amount of RNA required for efficient LASV enrichment. As shown in Figure Mastomys natalensis spleen sample . Using the same human rRNA probes, we depleted rRNA and enriched unique LASV reads approximately five-fold in a e Figure A. Most oe Figure B. Depletde novo assembly of LASV genome (>10×) and for detecting intra-host variants with minor allele frequencies as low as 5% (>100×) remains cost prohibitive.Despite efficient depletion of carrier RNA and host rRNA, in a number of cases the fraction of LASV sequencing reads stayed well below 1%. For these samples, sequencing to the depth required for et al., in preparation). We then synthesized the oligonucleotides on a microarray, PCR-amplified them as a pool, and prepared single-strand biotinylated RNA baits for hybrid capture [In order to capture LASV genomes in ultra-low coverage libraries, we used solution hybrid selection ,32 to fu capture .Mastomys) and geographical regions  that had been previously sequenced . This test set included libraries that contained high host content  or produced poor LASV genome coverage. We also included libraries with low duplication rates indicating under-sampling of LASV sequences. These libraries may potentially contain unique LASV sequences that were masked by host or other contaminating content in the library.We tested the LASV hybrid selection method on a set of 13 libraries from different sample sources  at high confidence . In both cases, hybrid selection increased the number of detectable iSNVs . Importantly, none of the 25 previously observed iSNVs dropped out during the selection process  and host rRNA depletion approach had worked well with a wide range of clinical LASV samples we examined its utility on the first cases from the outbreak in Sierra Leone . We sequUsing our approach, we were able to lower the rRNA contamination in all four samples from >80% to <0.5%  and rRNA contaminants can be efficiently removed by targeted RNase H-based digestion prior to library construction. Selective depletion is a cost-effective, high throughput alternative to size-selection for removing unwanted carrier RNA from viral samples. Since we are selectively depleting rRNA in our current protocol, there are no added steps when depleting carrier RNA. Further, depletion of poly(rA) prior to cDNA synthesis limits homopolymer A and T sequence in final libraries, resulting in cleaner preparations and ensuring higher quality sequencing runs.We have overcome key technical challenges in deep RNA sequencing and de novo genome assembly and detection of iSNVs in approximately two-thirds of our LASV samples. Moreover, the increased coverage permits deeper exploration of the genome: systematic unevenness along the genome, while it may in part be due to experimental biases, suggests biological features in genome organization such as stem-loop structures between genes and differences in segment copy numbers and expression levels during replication  library construction, we are able to decrease the overall process time three-fold. We were thus rapidly able to make our data available to the community, to enable timely insights for surveillance and control efforts and to inform diagnostic and therapeutic developments during the epidemic.Our approach, while designed for LASV, enables robust, universal, rapid sequencing and was readily transferrable to sequencing EBOV during the 2014 outbreak. We had initially developed and implemented our techniques to generate over 300 LASV genomes from Nigeria and Sierra Leone, and from humans and Hybrid selection in RNA-seq libraries can further enrich for virus in ultra-low input samples and can also serve as a cost-effective first-line sequencing method. As our data and previous exome studies indicate that single-base mismatches between target and bait sequences cause little allelic bias Figure , future de novo genome assembly and intra-host variant calls in diverse clinical and biological samples. Our RNase H-depletion-RNA-seq method may be more broadly applicable to sequence and assemble the genomes of many RNA viruses, known or unknown. We also developed a hybrid selection method to enrich viral content of libraries prior to sequencing, significantly lowering the cost of sequencing and rescuing RNA-seq libraries with very low coverage. While enrichment by hybrid selection requires prior sequence knowledge, hybrid selection with a complex multi-virus bait may prove to be a broadly applicable, viable and cost-effective approach to sequencing.Our newly developed viral sequencing protocol combines selective depletion of contaminating carrier RNA and host rRNA with unbiased total RNA-seq of randomly-primed cDNA. It thereby improves the quality of raw sequencing data and boosts the fraction of unique informative reads, producing sufficient LASV and EBOV reads for Lassa fever patients were recruited for this study using protocols approved by human subjects committees at Tulane University, Harvard University, Broad Institute, Irrua Specialist Teaching Hospital (ISTH), Kenema Government Hospital (KGH), Oyo State Ministry of Health, Ibadan, Nigeria, and Sierra Leone Ministry of Health. All patients were treated with a similar standard of care and were offered the drug Ribavirin, whether or not they decided to participate in the study. For Lassa fever (LF) patients, treatment with Ribavirin followed the currently recommended guidelines  and was Due to the severe outbreak for Ebola Virus Disease (EVD), patients could not be consented through our standard protocols. Instead use of clinical excess samples from EVD patients was evaluated and approved by Institutional Review Boards in Sierra Leone and at Harvard University. The Office of the Sierra Leone Ethics and Scientific Review Committee, the Sierra Leone Ministry of Health and Sanitation, and the Harvard Committee on the Use of Human Subjects have granted a waiver of consent to sequence and make publically available viral sequences obtained from patient and contact samples collected during the Ebola outbreak in Sierra Leone. These bodies also granted use of clinical and epidemiological data for de-identified samples collected from all suspected EVD patients receiving care during the outbreak response. The Sierra Leone Ministry of Health and Sanitation also approved shipments of non-infectious non-biological samples from Sierra Leone to the Broad Institute and Harvard University for genomic studies of outbreak samples.Human samples were obtained from patients with LF; all samples were acquired on the day of admission before any treatment regimens had been started. The time from onset of symptoms to admission at the hospital was similar between patients from Sierra Leone and Nigeria ; Nigeria = 9.7 days ). Human samples were obtained from patients suspected with EVD and stored in -20°C freezers; samples were collected using existing collection and processing protocols at Kenema Government Hospital (KGH), under the emergency response efforts established by KGH. For LF and EVD samples, 10 mL of whole blood was collected and plasma or serum was prepared by centrifugation at 2,500 rpm for 15 min. Diagnostic tests for the presence of LASV were performed on-site using PCR  and/or ERodents  were trapped in case-households, humanely sacrificed, and samples were collected from spleens.Previously collected cynomolgous macaques tissue samples were used  from macAll viral samples were inactivated in AVL buffer (Qiagen) or TRIzol (Life Technologies) following standard operating procedures. Samples were stored in liquid nitrogen or at -20°C. In some cases, RNA was isolated at the clinical site using the QIAamp Viral RNA Minikit (Qiagen), lyophilized using RNAstable (Biomatrica)  and stored at room temperature in desiccator cabinets. Inactivated samples were shipped on dry ice to Tulane or Harvard University and stored at -80°C  or room temperature (Biometrica) until further processing.RNA (from AVL) was isolated using the QIAamp Viral RNA Minikit (Qiagen) according to the manufacturer’s protocol, except that 0.1 M final concentration of β-mercaptoethanol was added to each sample. RNA (from Trizol) was isolated according to the manufacturer’s protocol with slight modifications. Briefly, 200 μL 1-bromo-2 chloropropane (BCP) was added for every 1 mL TRIzol used. After phase separation, 20 μg of linear acrylamide was added to the aqueous phase. All extracted RNA was resuspended in water and treated with Turbo DNase (Ambion) to digest contaminating DNA.Host RNA (18S rRNA) were quantified using the Power SYBR Green RNA-to-Ct 1-Step qRT-PCR assay (Life Technologies) and human 18S rRNA primers (5′-CCTGAGAAACGGCTACCACATC-3′ (forward), 5′-AGAGTCCTGTATTGTTATTTTTCGTCACT-3′ (reverse)). Human genomic DNA (Promega) was used as a standard control. All reactions were performed on the ABI 7900HT (Applied Biosystems).Poly(rA) and host rRNA was depleted using RNase H selective depletion . BrieflyFor the experiments in this study, selectively-depleted EBOV and LASV RNA were fragmented for 4 minutes at 85° C using NEBNext Fragmentation buffer (New England Biolabs). After fragmentation, samples were purified using 2.2x volume AMPure RNA clean beads (Beckman Coulter Genomics). In the production protocol implemented after this study we removed the fragmentation step . Random-in vitro transcription . RNA baits for each clade were prepared separately and mixed at the equal RNA concentration prior to hybridization. LASV libraries were added to the baits and hybridized over a 72 h. After capture and washing, libraries were amplified by PCR using the Illumina adapter sequences. Libraries were then pooled and sequenced on the MiSeq platform.Bait design and hybrid selection was done similarly to a previously published method . BrieflyRaw sequencing reads were demultiplexed using the Picard v1.4 pipeline  and savede novo assembly. This was done using the program Lastal r247 [de novo assembled using Trinity r2011-11-26 with a minimum contig size of 300 [BAM files were converted to Fastq format and then all viral reads were extracted prior to tal r247  with a ce of 300 . Contigse of 300 . If multE.coli full genome (gi|48994873) or DNA polymerase I .To determine the composition of each library, reads were aligned to viral and host references as previously described . The refRarefaction analysis was performed by down sampling the reads at 200 intervals using using custom scripts ,48. For Reads were realigned to a consensus sequence and variants were called using mpileup: samtools mpileup -Q 0 -B -q 1 -d 10000 and VarScan v2.3  with theNext-generation viral RNA-seq data can be found in the NCBI database  under BiAdditional file 1:Supplementary material including: Figures S1 to S3, Tables S1 to S3.Additional file 2:Probe design for Lassa virus hybrid selection.Additional file 3:Accession numbers for data submission.


Tris(2-aminobenzimidazole) conjugates with antisense oligonucleotides are effective site-specific RNA cleavers. Their mechanism of action is independent of metal ions. Here we investigate conjugates with peptide nucleic acids (PNA). RNA degradation occurs with similar rates and substrate specificities as in experiments with DNA conjugates we performed earlier. Although aggregation phenomena are observed in some cases, proper substrate recognition is not compromised. While our previous synthesis of 2-aminobenzimidazoles required an HgO induced cyclization step, a mercury free variant is described herein. O-methyloligoribonucleotide complementary to the targeted RNA [Sequence specific artificial ribonucleases are an attractive research target for several reasons. On the one hand, they can improve our mechanistic understanding of natural ribonucleases and of phosphodiester reactivity in general. On the other hand, a wide range of practical applications is conceivable for such RNA cleavers ranging from tools in molecular biology to chemotherapeutics targeting mRNAs or miRNAs. Although most site-selective artificial ribonucleases consist of a catalytic unit  attached to a DNA oligonucleotide or 2’-eted RNA –3, possieted RNA . The beneted RNA  as well eted RNA . Furthereted RNA .7 can be obtained in a six-step reaction sequence starting from tris(2-aminoethyl)amine (1) [PNA conjugates of both metal-containing and metal-free RNA cleavers have been effectively used as artificial nucleases towards different substrates –11. Mostmine (1) . However2 or HgCl2 and carbodiimides such as 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDCI) or N,N’-diisopropylcarbodiimide (DIC) [N-methylpyridine-2(1H)-thione resulting from Mukaiyama's reagent is soluble and can be easily removed by chromatography. As we were searching for a heavy metal-free approach towards tris(2-aminobenzimidazole), we adopted Lipton's method to the synthesis of aminobenzimidazoles.Alternative reagents to convert thioureas into guanidines include metal salts like CuClde (DIC) . Anotherde (DIC) . The rea3 was prepared as described before [1) followed by the stepwise reaction of the free NH2 group of product 2 with thiocarbonyldiimidazole and methyl 3,4-diaminobenzoate (3 with Mukaiyama’s reagent in the presence of NEt3 at rt yielding 88% of 4. Although the yield is slightly lower compared to the cyclisation with HgO (97% yield), the mild and metal-free reaction conditions are a big advantage of this new method. For the subsequent two conversions, again the known procedures were used. Thiourea 6 was prepared by deprotection of 4 with SOCl2 in MeOH and addition of 2-nitrophenylisothiocyanate followed by reduction of the nitro groups with H2. In the last step of the synthesis, again Mukaiyama’s reagent was used for the formation of the two benzimidazole moieties. Stirring at rt for 22 h in the presence of NEt3 yielded 56% of tris(2-aminobenzimidazole) 7 after HPLC purification. Ester hydrolysis with aq HCl led to the free carboxylic acid 8, which after removal of the solvent was used for conjugation to PNA without further purification  was attached. In both steps, DIC and HOBt were used as reagents. Coupling of the cleaver proceeded in high yields with no unconjugated PNA detectable in the MALDI mass spectra of the crude products. All conjugates were purified by RP-HPLC and isolated as trifluoroacetate salts. Starting from 20–30 mg of Rink amide MBHA resin (0.66 mmol/g) even manual synthesis routinely yielded milligram amounts of the purified final product. This compares favorably to the yield of DNA conjugates we have studied earlier [Conjugation of tris(2-aminobenzimidazole)  earlier .15 and 16 have been used for comparison with our previous studies [17 has the sequence of miRNA 20a, a member of the oncogenic miRNA 17–92 cluster [15–17 are embedded into a stretch of nonreacting deoxythymidine residues to improve the separation and resolution of substrate and fragment peaks in the sequencer.Cleavage experiments were run with Cy5-labeled RNA, the fluorescent dye permitting the detection and quantification of fragments by gel electrophoresis in a DNA sequencer . Substra cluster  that is 15 with conjugate 12, which has the same 15mer sequence as a previously tested DNA conjugate [15 by the corresponding DNA conjugate. Similar results were obtained by reaction of substrate 17 with 15mer-conjugate 14 (lane e). Here, A(18) and G(19) are the main cleaving sites. Unexpectedly, no distinct cleavage pattern could be observed with the 15mer-conjugate 13 and its cognate RNA 16. Instead, the electropherogram showed a shift of several signals including the substrate peak , making it difficult to assign the signals to individual cleavage fragments. However, the position of the signals relative to the substrate peak suggests that cleavage occurred only in the non-hybridized part of the RNA. We explain this effect by incomplete PNA–RNA strand separation during electrophoresis due to stronger hybridization resulting from the G/C-rich sequence of the conjugate . The fact that incubation of RNA 15 with PNA 12 also leads to a slight, but much weaker shift of signals (lane b), supports this assumption. The first four nucleotides in the single stranded part of RNA 16 where cleavage takes place have only pyrimidine bases, while RNA 15 and 17 have mainly purine bases in the corresponding regions. If the tris(2-aminobenzimidazole) cleaver interacts with the purine bases  it is not impossible that this could also be related to the apparent lower rate and lower selectivity in cleavage of RNA 16 with both 11 and 13. Consistent with that idea, we previously had observed similar effects when RNAs 15 and 16 were cleaved by DNA-catalyst conjugates analogous to 12 and 13 [Incubation of RNA onjugate , led to 10 and 11 complementary to substrates 15 and 16 were also tested in cleavage experiments. Compared to the 15mers, the first 5 monomers were omitted in order to keep the cleavage site unchanged. Results for the incubation of 15 with 10 were as expected (lane a). In contrast to incubation of substrate 16 with 15mer-conjugate 13, no signal shift is seen with the 10mer-conjugate 11. However, the cleavage occurs in a nonspecific way in nearly all positions not protected by hybridization with PNA (lane c). Though site specificity is low in this case, conjugate 13 is nevertheless substrate specific and does not degrade RNAs 15 or 17 under identical conditions  in absence and presence of different PNA conjugates (750 nM) was studied by fluorescence correlation spectroscopy (FCS) [10 to noncognate DNAs 18/19, indicating that no aggregates were formed. In contrast, addition of 10mer conjugate 11 or of 15mer conjugates 12, 13, and 14 to DNAs 18/19 led to considerable changes in diffusion times, which was most evident for PNAs 11 and 13. This clearly shows that molecular aggregates of oligonucleotides and PNA conjugates are formed in all of these cases [12 and 14. Conjugates 11 and 13 still caused an effect but less pronounced than in the absence of DMSO, indicating that DMSO prevents aggregation partially. We also investigated RNA 16 by FCS . Upon addition of 10mer PNA 11, a large increase of diffusion times was observed that can only be explained by aggregation.To examine whether PNA conjugates py (FCS) ,20. No sse cases . Althougse cases , this re10–14 in all possible combinations with substrates 15–17. Exemplarily, 12 and 14 with RNAs 15 and 17. Consistent data was obtained for all other possible combinations of conjugates and substrates  [For the reaction of substrate n before , a small 12.4 h) .We have shown that PNA conjugates of tris(2-aminobenzimidazole) are potent hydrolytic cleavers of RNA, exhibiting similar efficiency and substrate specificity as the corresponding DNA conjugates tested previously. Our optimized synthesis of tris(2-aminobenzimidazole) provides a new way to obtain the catalyst avoiding mercury salts with all their disadvantages . Conjugation to PNA oligomers can be readily achieved in high yields, providing an easy way to synthesize sequence specific metal-free artificial nucleases for a wide range of RNA substrates. Due to the tendency of PNA conjugates to form aggregates, a phenomenon not seen with their DNA analogs, optimization of the PNA oligomer length is necessary, especially for strands rich in G/C base pairs. Reduced activity of long PNA conjugates caused by aggregation can be a minor disadvantage in comparison to DNA conjugates. However, the higher affinity of PNA towards RNA allows using shorter PNA oligomers. In addition, the stability of PNA against biodegradation is another advantage especially with respect to possible in cell applications.File 1Experimental procedures and characterization data.


RNA splicing is a critical step in the human immunodeficiency virus type 1 (HIV-1) replication cycle because it controls the expression of the complex viral proteome. The major 5′ splice site (5′ss) that is positioned in the untranslated leader of the HIV-1 RNA transcript is of particular interest because it is used for the production of the more than 40 differentially spliced subgenomic mRNAs. HIV-1 splicing needs to be balanced tightly to ensure the proper levels of all viral proteins, including the Gag-Pol proteins that are translated from the unspliced RNA. We previously presented evidence that the major 5′ss is regulated by a repressive local RNA structure, the splice donor (SD) hairpin, that masks the 11 nucleotides (nts) of the 5′ss signal for recognition by U1 small nuclear RNA (snRNA) of the spliceosome machinery. A strikingly different multiple-hairpin RNA conformation was recently proposed for this part of the HIV-1 leader RNA. We therefore inspected the sequence of natural HIV-1 isolates in search for support, in the form of base pair (bp) co-variations, for the different RNA conformations. The human immunodeficiency virus type 1 (HIV-1) untranslated leader RNA region can fold several stem-loop structures that have important regulatory functions, including the transacting responsive element (TAR), polyadenylation signal (polyA), dimerization initiation signal (DIS), splice donor (SD) and psi hairpins A 1,2,3],32,3]. W282–U300 instead of G282–C300) of diverse HIV-1 isolates, thus providing some initial phylogenetic support for the proposed base pairing scheme [286–U295 was suggested to form a novel base-triple platform with the bulged nt A296 [The presence of the SD hairpin was first suggested in 1993 based on the unusual migration of a 44 nucleotide (nt)-leader RNA molecule encoding the SD and psi sequences during native gel electrophoresis . The SD  nt A296 . SHAPE  of the NL4-3 strain by the Michael Summers team suggested a rather different folding of the SD RNA region 5 of the . The 3WJThe new NMR study proposes that the 3WJ structure is intrinsically involved in the regulation of different leader RNA processes, such as mRNA translation and packaging of the dimeric RNA genome in virions, but only the latter was tested experimentally. Analysis of NC protein binding and RNA packaging for wt and mutant RNAs suggested the importance of unpaired and weakly-paired “junction” guanosines that are exposed by the 3WJ scaffold  but results in the formation of two alternative bps (A287–U295 and C288–G294). Some virus isolates hold multiple nt differences in this leader RNA domain, but as previously shown [Comparison of the sequence of 1128 HIV-1 group M isolates with the prototype subtype B LAI strain resulted in the identification of 459 variations in the SD region , 270 nt variations were observed in 1135 isolates that affect one of the bps in the stem  and the extended U5-AUG interaction in the 3WJ structure. Analysis of the SD-3WJ nts yielded 642 variations in 708 isolates that affected 10 of the 12 putative bps in this region  and polyA . We cannot formally disproof the SDa and 3WJ structures and it remains of interest to test their candidate functions, possibly as part of a riboswitch, as multiple structures may co-exist or distinct structures may exist under different conditions. Such a riboswitch has been proposed for other leader RNA domains [Some of the confusion about the folding of this leader RNA domain may come from the fact that all of the proposed foldings are semi-stable. For example, MFold RNA structure analysis predicts a thermodynamic stability of −7.1 and −3.6 kcal/mol for the SD and SD domains ,45. Dist domains .Although the SD sequence analysis was informative, we previously obtained much stronger phylogenetic support for other HIV-1 RNA structures. For instance, we documented multiple co-variations and even clustered co-variations in a single duplex for other leader domains like the TAR and polyA hairpins, thus providing very strong phylogenetic support for the existence of these structures ,50,51,52In summary, the SD hairpin structure is not only supported by the results of different experimental analyses, including virus mutant/revertant analysis, RNA structure probing, gel electrophoresis and NMR analysis ,9,10,11,


We quickly form first impressions about newly encountered people guiding our subsequent behaviour . Such instant judgments might be innate and automatic, being performed unconsciously and independently to other cognitive processes. Lying detection might be subject to such a modular process. Unfortunately, numerous studies highlighted problems with lying detection paradigms such as high error rates and learning effects. Additionally, humans should be motivated doing both detecting others’ lies and disguising own lies. Disguising own lies might even be more challenging than detecting other people’s lies. Thus, when trying to disguise cheating behaviour, liars might display a mixture of disguising (fake) trust cues and uncontrolled lying cues making the interpretation of the expression difficult (perceivers are guessing). In two consecutive online studies, we tested whether seeing an increasing amount (range 0–4) of lying cues (LC) and non-lying cues (NLC) on a standard face results in enhanced guessing behaviour (studies 1 and 2) and that enhanced guessing is accompanied by slower responding (study 2). Results showed that pronounced guessing and slowest responding occurred for faces with an intermediate number and not with the highest number of LC and NLC. In particular, LC were more important than NLC to uncertain lying decisions. Thus, only a few LC may interfere with automatic processing of lying detection (irrespective of NLC), probably because too little lying cue information is yet available. Beyond such superficial impression formations, we even judge upon the character of a new person such as her/his leadership abilities or trustworthiness [Daily, we meet known and unknown people in various situations. If unknown, we very quickly establish a first impression, rthiness ,2. Most rthiness ,4,5. Givrthiness . This larthiness ,8,9.e.g. Is the possible mating partner healthy?) and social investment . Most relevant to the present study is the social investment argument. Evolution might have equipped us with a processing system that quickly signals us whether co-species are likely to be trustworthy or cheaters, the latter better being avoided [i.e. unconscious, domain specific, fast, automatic, and encapsulated mechanisms [A common assumption to such theories in evolutionary psychology is that we have developed survival-enhancing strategies that should benefit mating decisions  [Before rejecting the idea of modular processing systems for survival-relevant lying cues, we note various reasons that might explain why lying detection has been disappointing in some of these previous studies. For instance, the experimental material was uncontrolled concerning the role of various possible lying cues ( speech) . Others  speech) ,4,18 or  speech) . Most im speech) ,23. Foll speech) ,15,16 by speech) , that mi speech) ,26.We here investigated the proposition that at least two processes are at stake when people make lying judgments and that these processes result from concurrent conflicting information (lying and non-lying cues). In two independent subsequent online studies, we investigated whether faces, to which we had added an increasing number of lying cues (LC) and non-lying cues (NLC), would be increasingly hard to judge as representing a lying or non-lying face. To give a concrete example, we expected that participants would find it harder to judge whether a face is lying or not when a presented face consists of 4 LC and 4 NLC as compared to a face consisting of 2 LC and 2 NLC. Moreover, we expected that the former decision would take longer than the latter. Ideally, as done in the studies reviewed above, we would present real faces to which a lying history is attached or not. Yet, we do not see the possibility to create real lying and non-lying faces to which we could add a controlled number of LC and NLC. The downside to our approach is that we might measure a propensity to lying judgments and not to actual lying detection . The features that resulted in the most pronounced lying and non-lying judgments were selected as LC and NLC. For study 1, we added an increasing amount of these LC and NLC to the same face . Again, participants judged whether a face was lying or not. In the subsequent study 2, we repeated the procedure of study 1, but enabled the assessment of reaction times when participants decided whether a face is lying or not. Thus, we could test whether individuals were most unsure whether a person is lying when an increasing number of conflicting information (i.e. LC AND NLC) were added to the same face, and whether this uncertainty would be reflected by enhanced reaction times.In a stimuli selection part, we initially tested lying judgements for previously described LC and NLC . To do se.g. family, friends acquaintances) asking them to participate themselves, but also to distribute the online link to further potential participants. This invitation was sent in a bulk email. The addresses were blind-copied. We also posted the online link on our Facebook pages. Thus, participation was entirely anonymous. We were unable to trace back who had actually taken part. The online link remained active for about 7 days. The original sample consisted of 68 participants .Participants were recruited through personal contact by distributing the online link of the study to personally known people (http://en.softonic.com/), we added individual facial features of different intensities  to a standard neutral face. Based on the review by DePaulo et al. [i.e., some ranging in intensity from zero to 10 and others from zero to 100 . In some cases (e.g. look downwards to the right), however, two attributes had to be added (looking down and looking to the right). Sometimes, we were limited by “shape” properties, i.e. when we changed one attribute we automatically changed another. Thus, when adding facial cues that were dependent on shape properties, we had to use what the program forced us to do in order to preserve the natural look of the generated face. In practical terms, when we changed the intensity of one facial cue, FaceGen Modeller adjusted another accordingly. All in all, we had 39 pictures including one neutral picture with the remaining 38 pictures consisting of 19 pictures that were presented in two intensities . We also informed participants that their continued study participation would be taken as written informed consent including their willingness that we can use their anonymous data for scientific purpose. When participants continued, they received instructions on the actual experiment. For each sequentially presented face, they were informed that they should respond to the question: “is this person lying or not”. If they considered the person to be lying, they were asked to press the F button, otherwise to press the “J” button. After two training trials, participants saw the next slide on which they were reminded that timing would matter, i.e. to respond as spontaneously and accurately, to leave the left and right index fingers on the “F” and “J” button respectively, and to expect the task to last about 5 minutes. We also displayed a green progress bar on top of the screen. Overall, we presented each face twice with face sequence being randomized between participants. Each face was presented in the centre of the computer screen (face pictures being 624x519 pixels in size) until participants responded. After a short inter-stimulus interval of 300ms, the next picture was presented. For each participant, we recorded lying and non-lying decisions.Swiss Law does not require a specific ethic confirmation for these types of study. Yet, studies are required to follow the guidelines of the Helsinki Declaration . When pai.e. unfinished data sets, and participants who might have responded randomly (50% yes responses or 100% responses for the same response key). We accounted for the consistency with which individuals responded for the same pictures (each picture was presented twice). We summed how often individuals responded in the same way for both pictures and divided it by the number of times individuals gave different responses to the picture pairs. Overall, we discarded data from individuals who yielded a consistency value <60% or >95% ensuring that participants responded above chance level without using the same button across all pictures (100% consistency). Because 100% consistency is indeed possible, we verified that to-be-excluded participants had a response bias. This procedure resulted in the omission of 4 participants who yielded a very low (3 women) consistency and 5 participants (4 women) who yielded a very high consistency. Four of these latter participants finished less than 50% of the trials (n = 6). Excluding these 11 participants left us with 57 participants for the final analysis.We screened for participants with potential careless performance, For our stimuli selection, we needed distinct LC and NLC. We calculated the proportion with which the 57 participants judged a face to be lying. We aimed to select facial cues that resulted in the most pronounced lying or non-lying judgments. In addition, we had to determine facial cues that could be added individually and in combination to the same face.i.e. individuals were not sure whether the facial cue is representing a LC. We did not consider these facial cues further. In addition, percent lying judgments were below 50% for many facial cues (these faces were not considered to be lying). We inferred that very low percentages indicate that the person is perceived of not lying. Finally, some facial cues could not be combined. For instance, it would be impossible to add the facial cue Smile Open and the facial cue Smile Closed to the same face. Based on these constraints, we selected as LC Look Down Left , Look Down Left , Lip Thin , Eyes Blink . As NLC we selected Smile Open , Mouth Retracted , Fear , Brows Up/Down   judged the 39 faces to be lying is provided in f scale) . Note thi.e. they showed a low proportion of lying judgments, or seemed to guess . For the actual studies, we selected facial cues that were either strong or very weak lying indicators and could be added individually and in combination to the same face.We here determined facial cues resulting in the highest proportion of lying and non-lying judgments. Based on a previous review on LC  and whati.e. whether participants as a group would become increasingly unsure as to whether a person is lying or not when exposed to an increasing amount of conflicting information (LC and NLC combined). We added either only facial cues from one category (non-conflicting faces: only LC or only NLC) or facial cues from both categories (conflicting faces). A priori, we assumed that the faces with the highest amount of conflicting information would result in the highest level of uncertainty as to whether the person is lying, i.e. a proportion of lying judgments around 50%.These facial cues were used to create faces to which an increasing number of LC and NLC were added. These faces are key to test our main study question, We recruited a new sample of 57 participants . The strategy was the same as in the stimuli selection part.Face stimuli: Having determined the four LC and the four NLC, we again used the FaceGen Modeller software to generate facial stimuli composed of different combinations of the eight facial cues. For each facial category , we could add 0, 1, 2, 3, or 4 facial cues resulting in 5 x 5 = 25 basic face combination possibilities see . For insWe used the same data cleaning procedure as in the stimuli selection part. This procedure resulted in the omission of 6 out of 57 participants among them five participants (two women) who yielded a very low consistency in performance and one woman who yielded a very high consistency in performance.i.e. those of very low, low, high, very high conflict. We defined conflict as the value resulting when multiplying the number of LC and the number of NLC  by the number of non-lying answers (NLA). If the number of LA was smaller than the number of NLA, we divided the number of NLA by the number of LA. Thus, values ranged from 0 to 1 with higher values indicating more pronounced random answering. These values were used for statistical analysis; we performed a one-factor ANOVA with conflict group as within-subject factor on the randomness values. Post-hoc comparisons were performed using paired t-tests. To account for multiple comparisons, we applied a more conservative significance level to our data; we tested at an alpha level of 1% . We applF = 15.101, p<.001, PES =. 23. Post-hoc pairwise comparisons showed that 4 out of the 6 comparisons were significant  apart from the differences between the low conflict group and the high conflict group (p =. 35) and the very low conflict group and the very high conflict group (p = 0.012)  as within-subject measure showed a significant main effect, = 0.012) . Thus, t= 0.012) .i.e. as a group, people responded rather consistently across these faces. The remaining results were inconsistent with our prediction, though. Participants were not most strongly guessing about the lying intention in a depicted face when seeing faces of the very high conflict group. We predicted the highest probability of random responding for the very high conflict group, because both modular and non-modular systems might be equally activated  apart from the differences between faces with 0 LC and 4 LC (p = 0.01), between faces with 1 LC and 2 LC (p =. 17) and between faces with 3 LC and 4 LC (p =. 71) (PES =. 03 (To further this observation in p =. 71) . The anaES =. 03 .i.e. LC seemed more influential to lying decisions than NLC, because faces with 1 LC as well as 2 LC resulted in the most enhanced random responding, irrespective of how many NLC had been added to these faces. In line with our initial proposition, it may be evolutionarily most advantageous to develop strategies that prevent negative outcomes (being cheated at) as compared to attending to potential positive outcomes [i.e. a lying detection system that rests on our proposed two distinct systems . Probably, some non-modular cognitive evaluation might lead to random lying and non-lying decisions across faces and by inference across people.Results from study 1 indicate that increasing conflict (LC and NLC added to a standard face) does not enhance the subjective uncertainty whether a person may be lying or not. An intermediate number of LC resulted in the highest amount of random answering (adding 1 or 2 LC). Moreover, the influence of LC and NLC on lying judgements is not symmetric; outcomes . The conIn study 2, we tested whether the randomness results of study 1 can be replicated and complemented by reaction times for lying judgements. As already pointed out in the introduction, non-modular processing as compared to modular processing should go along with slower responses . Thus, wWe recruited a new sample of 68 participants with an age range of 20–44 years . We used the same recruitment strategies used in study 1.We used the same faces as the ones used in Study 1 . We usedThree participants were excluded because response consistency was > 95% . These exclusions left us with a sample of 65 participants .n = (T—μ) / σ, where σ is the standard deviation of time, μ is the mean value of response time, and T is the absolute response time. Accordingly, larger values represent slower responding. These normalized response times were averaged across faces of the same conflict group. To check for uncommon interruptions in task performance, we individually inspected performance to find particularly long reaction times that could indicate that the participant was interrupted or stopped performing the task for unknown reasons.For statistical analysis, we performed the same analysis on randomness scores as performed in study 1. For reaction times analysis, we first accounted for within and between subject variations in response times. We normalized reaction times for each participant according to the following formula, RTThe two scores  were subjected to separate one-factor ANOVAs with conflict groups as repeated measure. Post-hoc comparisons were performed using paired sample t-tests. In line with the previous study, we again tested at a significance level of 1%. Effect sizes are provided by reporting partial eta-square (PES).F = 32.731, p<.001, PES =. 34  = 9.06, p <. 001, PES =. 12  LC and NLC. Results showed that random decisions were most pronounced for faces to which an intermediate amount of LC had been added. Interestingly, the NLC seemed to contribute little to this effect. In study 2, we replicated this effect. We additionally showed that reaction times were slowest for these intermediate faces. The current results show that our expected behavior  was not observed for the most conflicting faces, but for faces to which an intermediate amount of LC had been added.i.e. mainly 1 LC, but also 2 LC) had been added. When we want to consider the first point, we also need to consider the second point. NLC seem to play a minor if not negligible role when making lying decisions on faces. Results showed that random decisions for faces with 1 LC  were not further modulated by the presence of 1, 2, 3 or even 4 NLC (study 1). It seems as if the information of the NLC was already filtered out or not attended to. Thus, the increasing amount of LC was not competing with the information conveyed by the NLC. Our very high conflict faces were therefore not ambiguous.To discuss the implications of our results we have to account for two major observations. Firstly, random decision taking co-occurring with increased reaction times was not observed for faces of very high conflict. Instead, and secondly, this decision taking occurred for faces to which an intermediate number of LC . Using ce , potentce . One woce (see, ,39,40.