C9orf72 gene is the most common genetic cause of amyotrophic lateral sclerosis and frontotemporal dementia. Neurodegeneration may occur via transcription of the repeats into inherently toxic repetitive sense and antisense RNA species, or via repeat-associated non-ATG initiated translation (RANT) of sense and antisense RNA into toxic dipeptide repeat proteins. We have previously demonstrated that regular interspersion of repeat RNA with stop codons prevents RANT (RNA-only models), allowing us to study the role of repeat RNA in isolation. Here we have created novel RNA-only Drosophila models, including the first models of antisense repeat toxicity, and flies expressing extremely large repeats, within the range observed in patients. We generated flies expressing ~ 100 repeat sense or antisense RNA either as part of a processed polyadenylated transcript or intronic sequence. We additionally created Drosophila expressing > 1000 RNA-only repeats in the sense direction. When expressed in adult Drosophila neurons polyadenylated repeat RNA is largely cytoplasmic in localisation, whilst intronic repeat RNA forms intranuclear RNA foci, as does > 1000 repeat RNA, thus allowing us to investigate both nuclear and cytoplasmic RNA toxicity. We confirmed that these RNA foci are capable of sequestering endogenous Drosophila RNA-binding proteins, and that the production of dipeptide proteins  is suppressed in our models. We find that neither cytoplasmic nor nuclear sense or antisense RNA are toxic when expressed in adult Drosophila neurons, suggesting they have a limited role in disease pathogenesis.A GGGGCC hexanucleotide repeat expansion in the The online version of this article (10.1007/s00401-017-1798-3) contains supplementary material, which is available to authorized users. Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are two adult onset, neurodegenerative diseases, leading to loss of the upper and lower motor neurons, or frontal and temporal lobe cortical neurons, respectively. In recent years, it has become apparent that these diseases represent extremes of a clinical and pathological spectrum of disorders, since genetic evidence shows that either or both of these clinical manifestations can be caused by mutations in the same set of genes .n hexanucleotide expansion within the first intron of the C9orf72 gene is the most common genetic cause of both ALS and FTD, causing up to 40% of familial ALS and 25% of familial FTD , before being incubated overnight at 4°C with anti-Glorund primary antibody  at a concentration of 1/100 in blocking solution. The following day the tissue was washed three times in PBST before being incubated in Alexa-488-conjugated goat anti-mouse secondary antibody  at 1/1000 in block at room temperature for 1 h. Following three further washes, the brains were mounted as described above. Images were taken using a Zeiss LSM 700 confocal microscope, using the same settings. Foci overlap with Glorund puncta was quantified automatically using Volocity analysis software (Perkin Elmer).7, or (GR)7 followed by affinity purification and a proportion biotinylated (Eurogentec) [g for 20 min at 4 °C. Supernatant was collected in fresh tubes. The concentration of protein was determined using the DC protein assay (BioRad). Concentration of lysates were made equivalent by the addition of further RIPA buffer with protease inhibitors. The total amount of protein loaded per well in each assay was 24 μg for poly-GR and 14 μg for poly-GP.Antibodies were produced by immunising rabbits with either (GP)ogentec) . Adult f7, (GR)7, (GA)7, (PR)7, and (AP)7 synthetic peptides (Biogenes) at a concentration of 100 ng/mL. For Drosophila assays lysates from wild-type flies (w1118) were run on each plate, and the resultant values subtracted from all samples to correct for background. A four-parameter logistic regression curve was fit to the values obtained from a standard curve of peptide calibrators using graph pad prism, and concentrations were interpolated.Immunoassays were performed using 96-well SECTOR plates  as described previously , 50. DetLifespan data were compared using log rank test. For negative geotaxis analysis, counts of flies at each height were compared using ordinal logistic regression. For percentage foci positive nuclei data, a linear model with genotype as the explanatory variable was fitted. Comparisons were made using a priori orthogonal contrasts.Drosophila capable of inducible expression of 108 sense RO repeats as part of a capped and polyadenylated (polyA) transcript [C9orf72 gene or within the promoter, depending on the splice variant [Drosophila model capable of expressing 106 RO hexanucleotide repeats from within a strongly, constitutively, spliced artificial intron introduced into an eGFP coding sequence  driver, with expression induced after eclosion. In both types of fly, foci were observed in adult neurons  in adult ons Fig. b and notons Fig. b, 2b. HoC9orf72 sense RNA can lead to neurodegenerative phenotypes, we assessed the effect of adult pan-neuronal expression of sense polyA and sense intronic RO repeats on the survival of Drosophila. We observed that expression of sense polyA or sense intronic repeats did not reduce adult survival compared to controls where no expression was induced; in fact, a significant extension of lifespan was observed . No RO lines, polyA or intronic, showed an eclosion defect. A slight rough eye phenotype was observed in female  flies expressing polyA repeats, potentially due to the production of small amount of DPRs in these flies . In addition, we found lines carrying expansions larger than expected, consistent with de novo expansion events. We, therefore, selected one of these lines Sense-> 1000 PolyA (approximately 2000–5000 repeats) for further characterisation . repeats , 5. We, When we expressed these constructs in adult neurons, we observed abundant nuclear and cytoplasmic RNA foci in up to 47% of cells Fig. a, b. UneDespite the presence of abundant RNA foci, Sense-800 PolyA, Sense-1000 PolyA and Sense > 1000 PolyA flies did not show a reduction in lifespan in females Fig. c. We addTo ensure that the lack of typical neurodegenerative phenotypes was not caused by repeat length instability, we performed Southern blotting on the flies used in the lifespan and negative geotaxis assays, and observed conservation of repeat length .C9orf72 repeat RNA in neurodegeneration has thus far not been investigated in Drosophila. Because the stop codons present in the RO constructs are also able to block translation in the antisense direction, we created flies capable of expressing ~ 100 antisense RO repeats by reversing the orientation of the repeat construct. We reasoned that intronic localisation would enhance nuclear antisense RNA foci formation as found for sense repeats; therefore, we created two sets of constructs, polyadenylated and intronic. We derived multiple independent transgenic lines and screened for repeat expansions of the correct size using Southern blotting , deriving two lines capable of expressing ~ 100 RO sense repeats either as part of a processed polyadenylated RNA transcript (AS-PolyA-1 and AS-PolyA-2) or from an intron (AS-Intronic-1 and AS-Intronic-2). eGFP expression was confirmed in intronic flies demonstrating correct splicing . Due to repeat instability, we were unable to create DNA constructs of > 108 antisense RO repeats.The potential role of antisense Drosophila neurons using FISH. As in the sense flies, in antisense polyA expressing flies, we observed frequent cytoplasmic RNA signals in adult neurons, with occasional intranuclear puncta , we failed to observe either a rough eye phenotype, or eclosion defect compared to controls . This suggests that even if the RNA foci were sequestering large amounts of Glo, this would be unlikely to translate into a toxic phenotype.To quantitatively assess whether production of DPRs is prevented in our models, we developed in-house Meso Scale Discovery (MSD) immunoassays, using antibodies specific for poly-GP or poly-GR   using western blotting , and, asC9orf72 mutation-associated repeat RNA does not produce typical neurodegeneration-associated phenotypes in Drosophila, despite the formation of sense and antisense cytoplasmic RNA, or nuclear RNA foci.Here, we have demonstrated that expression in adult neurons of both sense and antisense C9orf72 repeat RNA is extremely toxic to Drosophila neurons, resulting in a dramatically reduced lifespan [We, and others, have previously demonstrated that overexpression of lifespan , 37, 56.C9orf72 models [Drosophila ortholog of XPO1 (emb) significantly rescues toxicity in repeat-expressing flies, but enhances toxicity in recodonised DPR-expressing flies implicating XPO1 in the nuclear export of repeat GGGGCC RNA, and suggesting that preventing nuclear export of repeat RNA may be of therapeutic value [We have here demonstrated that the genomic context of repeat RNA (intronic vs. polyA) affects intranuclear RNA foci formation, presumably because splicing of the intronic repeats prevents the nuclear export of repeat RNA. This is in line with previous studies that have confirmed that intronic localisation of repeat RNA favours its nuclear retention and nuclear RNA foci formation , 54. It 2 models , 27, 58.C9orf72 mutation-associated ALS/FTD is currently contentious. C9orf72 repeat RNA forms secondary structures such as hairpins and G-quadruplexes, leading to the formation of intranuclear RNA foci, which are generally assumed to become toxic by the sequestration of RNA-binding proteins [The role of repeat RNA in the pathogenesis of proteins . One stuproteins ; howeverproteins , 38. Addproteins , 42, 44.proteins .Drosophila neither cytoplasmic nor nuclear RNA are sufficient to induce a strong pathological phenotype. This is consistent with the results of a previous study examining intronic sense foci in Drosophila, which also did not observe a neurodegenerative phenotype [It is possible that hexanucleotide RNA is toxic through mechanisms independent of intranuclear RNA foci formation. Recently, it was demonstrated that toxicity correlates with the presence of neuritic RNA foci in sense RNA expressing primary neurons . Our curhenotype .Drosophila expressing (GGGGCC)15–CTCGAG–(GGGGCC)15 repeats [48-58 repeats [Drosophila models, without having to employ the strong overexpression paradigm necessary to detect DPRs by immunoblot [Drosophila. Notably, the only consistent toxic phenotype we observed in any genotype was a modest reduction in climbing ability in ~ 100 repeat Sense-PolyA flies as well as a slight rough-eye phenotype when these flies were crossed to GMR-Gal4. These flies showed the largest production of poly-GP protein, suggesting that DPRs present at low levels, or below the level of detection may still be capable of inducing mildly toxic phenotypes.Previous studies have observed toxicity in  repeats , 58 or  targeting the sense RNA stand [Drosophila, suggesting that therapeutic interventions that act to reduce dipeptide repeat protein production will be of importance as a therapeutic endpoint.A full understanding of the mechanisms by which the NA stand , 31, 47,NA stand , 57, 58.Supplementary material 1 (PDF 13299 kb)Below is the link to the electronic supplementary material.


Methods: Forty-four aptamers were tested for their ability to interfere with viral RNA synthesis in a subgenomic replicon system. Some of the most efficient inhibitors were further evaluated for their potential to affect the recruitment of the HCV RNA-dependent RNA polymerase (NS5B) and the viral translation in cell culture. Results: Four aptamers emerged as potent inhibitors of HCV replication by direct interaction with functional RNA domains of the CRE, yielding a decrease in the HCV RNA levels higher than 90%. Concomitantly, one of them also induced a significant increase in viral translation (>50%). The three remaining aptamers efficiently competed with the binding of the NS5B protein to the CRE. Conclusions: Present findings confirm the potential of the CRE as an anti-HCV target and support the use of aptamers as molecular tools for investigating the functionality of RNA domains in viral genomes.Background: Hepatitis C virus (HCV) contains a (+) ssRNA genome with highly conserved structural, functional RNA domains, many of them with unknown roles for the consecution of the viral cycle. Such genomic domains are candidate therapeutic targets. This study reports the functional characterization of a set of aptamers targeting the  Systematic Evolution of Ligands by Exponential enrichment) process, which consists on iterative sets of synthesis, binding, positive selection, and amplification steps over a randomized oligonucleotide pool. The resulting population is enriched in those molecules able to bind to the desired target molecule. The highly dynamic folding of nucleic acids is the key to understand the specific and efficient interaction of aptamers to their cognate target, thus demonstrating the versatility and flexibility of nucleic acids.The hypothesis of an all-RNA based world inspired the recreation of the natural molecular selection and evolution processes in a test tube, with the aim of isolating nucleic acids with diverse activities. These efforts yielded the identification of the so-called aptamers, oligonucleotides able to recruit a wide variety of ligands ,2. AptamPotential application of aptamers is an expanding field . Since Flaviviridae family and is responsible for a worldwide health threat, infecting more than 180 million people (WHO data). Current treatments based on the combination of α-interferon with direct-acting antivirals (DAAs) targeting the viral protease NS3 are partially effective due to the appearance of resistant viral variants . ,9. 8,9].g region  . T. Tcis-acg region A. (i) ThWe had previously reported the isolation of a collection of RNA aptamers targeting the CRE region of the HCV genome . Some ofIt had been previously observed that a small set of aptamers targeting the HCV CRE region interfered with viral replication in cell culture ,34, poinThe results showed that around 35% of the tested aptamers promoted a significant reduction (>50%) in the relative HCV RNA levels . Four ouInterestingly, though most of the aptamers showed more than one theoretical interacting site within the CRE region , around In summary, from this functional screening, four aptamers—P6-89, P6-96, P6-103, and P7-49—emerged as potential tools for HCV detection and inhibition.Aptamers-mediated inhibition is directly related to their biochemical features, such as their three-dimensional folding and binding affinity to their target. Therefore, further efforts were focused on analyzing those properties.In silico structural analysis of the RNA molecules P6-89, P6-96, P6-103, and P7-49 was performed with the aim of identifying common structural motifs that could define a functional domain within the inhibitor RNA. The TurboFold tool was employed for that goal [hat goal . TurboFohat goal . This hy32P-internally labeled aptamer (~2 nM) with increasing amounts of the unlabeled construct CU [d values and complex formation yields above 50% , appeared as the less effective binder, with calculated Kd values in the range of low micromolar  in the IRES activity (p < 0.05) were detected for the cap-dependent translation, pointing to a specific effect on HCV protein synthesis.No effect was observed on protein synthesis for the inhibitors P6-96 and P6-103, suggesting that their potent inhibitory activity is mainly restricted to the replication step B. A signactivity B, suggesTaken together, these data suggest that the tested inhibitors P6-96 and P6-103 specifically interfere with the HCV cycle mainly by blocking the replication step, while the molecules P6-89 and P7-49 exert a double role as HCV replication interfering agents and translational enhancers.Advances in clinical approaches for HCV detection and therapeutics are limited by the emergence of viral mutants. This phenomenon promotes the lack of effective antiviral sustained responses and false-negative tests. Therefore, it is desirable to search for novel targets that allow the development of efficient drugs and robust molecular diagnostic methodologies. Among the potential novel targets, the genomic RNA domains that play important roles in the consecution of essential viral processes are excellent candidates, which are worthy to be tested. In the context of both basic and applied research, the use of aptamers is recognized as a promising tool . The preWe had previously reported the isolation of a collection of RNA aptamers targeting the highly conserved CRE region of the HCV genome . InteresThe effectiveness of the CRE domain as a targeting region was evaluated. From the forty four molecules tested, 15 showed inhibition of HCV replication at rates higher than 50%. With the exception of variant P6-89, which targets the bulge of the 5BSL3.2, all of these efficient aptamers harbor sequence motifs complementary to the apical loop of the 5BSL3.2 domain. These results confirm the potential of the 5BSL3.2 as an anti-HCV target.Being the 5BSL3.2 a critical regulatory partner in the HCV IRES-dependent translation, some of the most efficient inhibitors of the viral replication were tested for their ability to affect the viral translation. Interestingly, two of the selected molecules, P6-89 and P7-49, promoted a significant increase in the IRES activity B. In theFinally, neither P6-96 nor P6-103 promoted any variation in the IRES activity B, which In summary, this study provides evidences for the development of molecular tools based on aptamers with the aim of elucidating multiple aspects of the viral biology. From a clinical point of view, the design of alternative therapeutic strategies combining several antiviral molecules with different activities and specificities is also a feasible option that is gaining increasing support in recent years. Therefore, aptamers can be considered as multivalent partners in the Virology lab.2 atmosphere.The human hepatoma cell line derivative Huh-7.5 are highly permissive for the initiation of HCV replication since they bear mutational inactivation of the retinoic acid-inducible gene I (RIG-I) . Cell moneo), the EMCV IRES, the coding sequence for non-structural HCV proteins (NS3-NS5) and the HCV 3′UTR [Culture of the human hepatoma cell line Huh-7 NS3-3′ET supporting a subgenomic HCV replicon was carried out with DMEM-high glucose supplemented with 20% heat-inactivated fetal bovine serum, 1 mM sodium piruvate and 0.5 mg/mL of G-418 (Sigma-Aldrich Chemie) . The subCV 3′UTR .194 DNA was obtained by SalI digestion of the plasmid construct pUC18-T7HCV9181-9371, as reported [389FLucNS3-3′ET, the plasmid pFK-I389FLucNS3-3′ET containing two cell-culture adaptive mutations in NS3 and one in NS5A, was restricted with SpeI. The RNA CU, bearing the CRE plus the whole HCV 3′UTR, was obtained as reported [DNA templates encoding the aptamer sequences were obtained by amplification as previously described . The HCVreported . DNA temreported . For thereported  for the in vitro RNA synthesis with the TranscriptAid T7 high yield transcription kit (Thermo Fisher Scientific), following the manufacturer instructions. Transcripts were purified as previously described [The purified DNA molecules were used for escribed . RNA quaInhibitory activity of the RNA aptamers on HCV replication was assayed in the cell line Huh-7 NS3-3′ET as reported before ,38. Brie389FLucNS3-3′ET, and the corresponding RNA aptamers or the RNA80. In addition, the reporter cap-RLuc transcript was included as internal transfection control. Cell were essentially electroporated as described [7 cells/mL in Cytomix [® 630, Holliston, MA, USA). Cells were seeded onto 6-well plates with 4 mL of DMEM and 1.25% DMSO and harvested at 4 and 20 h post-transfection for Firefly and Renilla luciferase measurements.The effect of the aptamers on HCV translation was evaluated in Huh-7.5 cells. This cell line was transiently co-transfected with the reporter dicistronic subgenomic HCV replicon transcript, Iescribed , with mi Cytomix  suppleme® supermix (Bio-Rad) and amplified over 40 cycles with specific oligonucleotides targeting the IRES region (C-149 and C-342) [Intracellular relative HCV RNA amount was quantified by qRT-PCR as previously reported . Brieflyd C-342) . qRT-PCRd C-342) .32P aptamer with increasing amounts (0–1 pmol) of the unlabeled target RNA CU. RNA molecules were independently denatured by incubation at 95 °C during 2 min and then chilled on ice for 15 min. Reactions were initiated by mixing both RNA molecules in the presence of binding buffer . Complex formation proceeded during 15 min at 37 °C and the resulting products were loaded in 8% native polyacrylamide gels supplemented with MgCl2 2 mM, in TBM buffer . Electrophoresis proceeded at 12 mA and 4 °C during 3 h. Gels were then dried and further scanned using a Storm 820 PhosphorImager . Analysis was accomplished with the ImageQuant v5.2 software . Dissociation constants Kd were calculated with the Sigma Plot v8.0 software  and fitted to the equation y = (Bmax × x)/(Kd + x), where y is the percentage of bound aptamer, Bmax is the amplitude of the reaction, and x is the concentration of the substrate RNA CU [RNA aptamer:CRE binding efficiency was monitored by incubation of 20 fmol of the internally labeled e RNA CU .32P-internally-labelled HCV-CRE194 transcript were mixed with a 10-fold excess of the NS5BΔ21 in competition buffer . This interaction was competed with increasing amounts of the selected aptamers or a non-related molecule, the glycogen (Ambion by Life Technologies). Binding reactions were performed at 37 °C during 30 min. They were then diluted in the competition buffer and applied to 0.45 µm nitrocellulose membranes . Filters were previously presoaked in the competition buffer and assembled in a dot blot apparatus . Samples were filtered under vacuum. Membranes were dried, scanned, and analyzed as indicated above. The EC50 values were calculated from the equation y = ymax/(1 + 10(LogEC50−x)), where y is the complex ratio, ymax is the maximum binding of the HCV-CRE194 RNA to the NS5BΔ21 protein, x is the aptamer concentration and EC50 the aptamer concentration that produces 50% of the maximum observed effect.Recombinant viral RNA polymerase NS5BΔ21 was obtained as reported  and usedFirefly and Renilla luciferase activities were determined with the Dual-Luciferase reporter assay system . All experiments were performed in triplicate and data are shown as normalized mean values ± standard deviation.


Qualitative psychological principles are commonly utilized to influence the choices that people make. Can this goal be achieved more efficiently by using quantitative models of choice? Here, we launch an academic competition to compare the effectiveness of these two approaches. Choice architecture utilizes qualitative psychological principles to shape behavior. Can this goal be more effectively achieved using quantitative models? In the natural sciences, quantitative models underlay the development of engineering. Therefore, we ask whether quantitative models can revolutionize the field of choice architecture into choice engineering, defined as the use of quantitative models to shape choice behavior.Influencing human choices has been a principal objective of parents and educators, as well as of salesmen and politicians for millennia. In economics, psychology and neuroscience, there is considerable interest in the principles underlying decision-making and in the ways in which they can be used to bias human choice. The 2017 Nobel prize in economics, was awarded to Richard Thaler for his contributions to the development of behavioral economics and its applications to policy-making. Thaler coined the term choice architecture to describe how insights from behavioral economics can be used to nudge choices without changing their objective valuesBoth qualitative principles and quantitative models identify factors that affect behavior. The difference between them is that the latter, but not the former, quantitatively describe the magnitudes of the effects. Operant learning is a process, in which the strength or likelihood of a behavior is modified by rewards and punishments. We demonstrate the difference between choice architecture and choice engineering in the framework of an operant learning task.2. In line with this law of behavior, a choice architect (and common-sense intuition) would recommend allocating all available rewards to the desired alternative 1  in a repeated, two-alternative forced-choice task with binary rewards Fig. . How shoe 1 Fig. . Subtler3 the architect may recommend to place all rewards that are associated with alternative 1 at the beginning of the sequence and those of alternative 2 at its end  model6 provides a quantitative description of learning in the repeated, two-alternative forced-choice task. According to this model, humans compute the expected reward associated with each of the alternatives based on their past experience, and choose more often the alternative associated with the higher estimated expected reward. The QL model provides a quantitative prediction of future choices—the probability that the participant would choose each of the alternatives in each trial, based on her specific history of actions and their outcomes (see Supplementary Methods). A choice engineer that is equipped with such an accurate quantitative description of the learning process can design a more sophisticated reward schedule for this task  of each alternative , which is far less restrictive then explained variance in a particular experimental setting. It can be used to compare models of arbitrary complexity—the models are simply compared by their effectiveness in shaping behavior. Moreover, and perhaps more importantly, the competition can compare the potency of quantitative models of choice (choice engineering) to that of qualitative principles (choice architecture) in choice design.Traditional methods for model evaluation are associated with an estimate of the model’s explained variance. While this approach has proven useful in many studies, it suffers from several shortcomings. First, it is not clear how the complexity of a model—a necessary ingredient in model comparison—should be quantified and utilizedThe principles underlying operant learning are formally studied in different disciplines, including neuroscience, psychology, economics and computer science. However, one does not need to be formally trained in any of these fields in order to be a good choice designer and participate in the competition. We are all social creatures and have good intuitions as to how to incentivize other humans to make specific choices.We offer two participation tracks. In the first static track, choice designers are invited to propose static reward schedules in the form of a sequence of rewards  will be tested in a single session. To efficiently identify the best schedules, we will use the method of successive rejects13 (see Supplementary Methods) that allocates more sessions (more samples) to the so-far better performing schedules. Each participant in the competition is allowed to submit a single static and a single dynamic schedule. Each schedule must be accompanied by a short explanation of the principles or models which guided the design of the submitted application. Should the number of submissions be too large, these explanations will be used for an initial screening of those submitted applications that will enter the competition. The winners of the static and the dynamic schedules competitions will be invited to present their schedules as a talk in a workshop summarizing the competition. If appropriate, they will also be invited to coauthor the paper summarizing the results of the competition. In addition, all the datasets collected during the competition will become publicly available after the competition is concluded.The full details of the competition appear in the competition website The deadline for submissions is set to four months from the published date of this commentary.In conclusion, we believe that the Choice Engineering Competition is a first step in the field of Choice Engineering, providing both a novel way of comparing the potency of different quantitative models to qualitative principles (and to common-sense intuition) and a way of using these quantitative models to shape behavior. In this competition we focus on choices in a specific domain of operant learning—repeated, two-alternative choices with partial feedback. However, this framework can be readily expanded to other operant tasks, e.g., tasks involving multiple-choices, different constraints on the reward schedule, full-feedback, etc. Finally, it can be further generalized to help search for effective learning strategies, from low-level perceptual learning to higher-order skill acquisition.Supplementary Information


To evaluate skin tissue gene expression patterns correctly, extracting sufficient quantities of good quality RNA is essential. However, RNA extraction from skin tissue is challenging, as the hyaluronic acid-collagen matrix is extremely difficult to homogenize. Although there are multiple ways to extract RNA from skin, there are no comparative studies that identify the most critical steps, e.g. sample collection, storage and homogenization. We analysed the various steps involved in RNA extraction . We hypothesised that domestic pig skin is applicable as a model for human skin studies. Altogether twenty different workflows were tested on pig skin and the four most promising workflows were tested on human skin samples. The optimal strategy for extracting human skin RNA was to collect, store and homogenize the sample in RLT lysis buffer from the RNeasy Fibrous Tissue Kit combined with beta-mercaptoethanol. Both stator-rotor and bead motion-based homogenizing were found to result in high quality and quantity of extracted RNA. Our results confirmed that domestic pig skin can be successfully used as a model for human skin RNA studies. The present study aimed to find an optimal solution for overcoming the difficulties in extracting RNA from human skin tissue by testing the relative benefits and disadvantages of several different but widely used methods.Skin is a challenging tissue for gene expression analysis. Firstly, because it is exposed to the external environment it is contaminated with various biomolecules including nucleases. Furthermore, due to high stability of hyaluronic acid-collagen matrix, skin is one of the most difficult tissues to homogenize5. Homogenization of skin tissue is usually achieved via mincing with razor blades, using mechanical homogenizers or using a mortar and pestle in combination with liquid nitrogen, which is an efficient, but time-consuming process10. Additionally, enzymatic digestion of hyaluronic acid-collagen matrix with collagenase and hyaluronidase prior to mechanical homogenization might increase the efficiency of homogenization ), however, there is no clear evidence about its efficiency. Two commonly used solutions for homogenization contain phenol and guanidine thiocyamte , , which commercial lysis buffers often combine with beta-mercaptoethanol (BME) , and which have both lysis and RNA inhibiting capacities.There are several methods available to extract RNA from skin. However, there have been no comparative and systematic investigations aimed at evaluating sample collection, storage and homogenizing steps. These are the most critical steps when working with skin tissue. Currently, two widely used strategies to block the activity of RNases are either immediate freezing after sample collection or applying stabilizing reagents at room temperature and/or lower temperatures15. Hence, domestic pig has successfully been used in studies regarding percutaneous absorption11, different skin injuries such as bite marks16, burns and autografting17, diseases such as melanoma18, and in toxicology studies19.A critical limitation for accurate and meaningful comparisons of different RNA extraction methods for human tissue is the scarcity and difficulty of obtaining sufficient tissue itself. Thus, in the current investigation we initially used domestic pig skin tissue for comparing different techniques. Pig skin is widely used in various fields of dermatological research, as it is easily accessible and has similar skin structure to humans . These characteristics outweigh the dissimilarities, such as higher vascularisation in human and higher fat content in pig skinThe present study aimed to find an optimal solution for overcoming the difficulties in extracting RNA from human skin tissue by testing the relative benefits and disadvantages of different but widely used methods. We tested 1) four different sample collection/storing strategies , (2) the presence or absence of enzymatic treatment prior homogenizing, (3) two different homogenizing solutions (phenol-based and BME-based), and (4) two different homogenizing instruments  motion-based homogenizing). The final RNA purification step combined with DNase treatment was conducted on silica-membrane columns. Altogether twenty different workflows (WFs) were analysed  and QIAzol Lysis Reagent ) with each other. Additionally, we did not test an RNA extraction protocol with phenol-based solution alone, but only as combined with silica-membrane columns. The latter was due to our previous experience that without additional on-columns purification, the RNA purity values tend to be poor (unpublished observations by Ene Reimann).tissueweight < 0.013 and PRNAoutput < 0.0001). We found a significant association between the tissue input and RNA output: 27% of the variability in RNA output was explained by input tissue amount . We found that only samples from one pig showed a significant correlation between tissue input and RNA output .For domestic pig, the weight range of collected biopsies prior to RNA extraction was 14 to 76 mg (average 40.1 mg ± SD 17.56)  and according to the Spearman’s correlation analysis there was no correlation between the weight of skin biopsy and extracted total RNA amount . After RNA extraction, the total RNA amount was in a range of 2.3 to 13.3 (μg) (average 5.8 μg with ±SD 3.0). If applying the pooled values from human skin biopsies, the weight range passed the normality test, but the total RNA amount did not , collection into QIAzol Lysis reagent (QIAzol) or collection into RLT lysis buffer containing additional beta-mercaptoethanol (BME + LB) with delayed freezing. Samples collected as dry biopsy or into BME + LB resulted in higher total RNA amounts and quality values  rates, RNA Integrity Number (RIN)) . Fifty percent of enzymatically digested tissues (WF9 and WF12) and 33.3% of non-digested biopsies (WF10 and WF13) homogenized with GentleMACS Dissociator with M tubes were fully homogenized, however, the difference was not statistically significant. In case of the Fastprep-24 instrument with lysing matrix D or S tubes, all enzymatically treated skin samples remained visually intact. Furthermore, after incubation for 2 hours at 37 °C, the RNA quality decreased rapidly (P < 0.0001); the average RINs with and without enzymatic digestion were 2.4 (±SD 1.6) and 8.8 (±SD 0.6), respectively.The next step in the RNA extraction workflows after collection/storage and/or enzymatic digestion was the mechanical homogenization, for which QIAzol and BME + LB were tested as homogenizing buffers. Although we found the homogenizing efficiency to be similar between these two solutions, skin samples homogenized in BME + LB resulted in higher total RNA amounts, RNA purity values  and RIN numbers compared to samples homogenized in QIAzol  and tissue fast spinning in tubes with stator and rotor (GentleMACS Dissociator). The Fastprep Instrument with D tubes (ceramic beads) or S tubes  was not able to fully homogenize the skin samples – after five cycles, the homogenizing solution was cloudy, but the tissue piece remained intact. Conversely, the GentleMACS Dissociator with M tubes was able to fully homogenize 8 samples out of 18 (44.4%) after two cycles; the rest of the samples had similar results to the Fastprep Instrument. Additionally, comparing these two instruments and three tubes, the achieved RNA quantity and quality values were similar , and workflows which comprised sample collection into APTR and latter homogenizing in QIAzol or collecting and homogenizing in BME + LB applying GentleMACS M tubes (WF10 and WF14)  were similar to each other. Furthermore, the ΔCq values were lower for samples with higher RIN .The expression patterns of four reference genes  there was no correlation between the input tissue weight and RNA yield , and environmental factors (such as water consumption prior to sample collection)25. In the present study, pigs were all immature females from the same age group and grew up together in same environmental conditions. However, in the case of human samples, the patients’ age, sex and body areas were distinct. Additionally, pathological skin conditions such as irritated and inflammatory skin26 tend to be associated with higher RNA expression levels. This is also true in the case of lesional psoriasis skin that yields a higher quantity of RNA compared to unlesional skin obtained from the same patients (unpublished observations by Ene Reimann).Between the three domestic pigs (front limb), only in one case was there a significant correlation between the skin biopsy weight and the RNA yield. There was no correlation between the skin biopsy weight and the RNA yield in the case of human samples. Thus, we cannot expect higher RNA yield from larger skin biopsies. In previous studies, the correlation between tissue weight and RNA yield has been controversial. In the case of human (upper thigh or arm) and mouse (back) skin tissues, a linear relationship has been demonstratedAdditionally, the Qiagen RNeasy Fibrous Tissue Mini kit handbook states that incomplete lysis of the starting material will decrease the RNA yield from the sample . In our experiment, we had incomplete tissue homogenization in most cases and thus probably also insufficient lysis of the samples. However, in the case of six human samples fully homogenized by the GentleMACS Dissociator, there was still no correlation between tissue input and RNA yield (data not shown). Furthermore, it is often complicated to measure the weight of biopsies, and thus the amount of the sample applied to the spin column may overload it, which is another cause for decreased RNA yield and also quality . If we analysed only the samples which had weight a quarter lower than the columns’ capacity, there was no correlation between the sample input and RNA yield or quality (OD rates and RIN) values (data not shown). These data support the suggestions from previous paragraph that the RNA output is not only affected by tissue input and homogenizing efficiency, but also by other factors which may have been specific to the sample.Compared to collecting skin samples into APTR or QIAzol with delayed freezing, sample collection as dry biopsies with immediate freezing or into BME + LB with delayed freezing resulted in RNA with higher quality and quantity values. QIAzol is known to contain phenol and guanidine thiocyanate , and thus there may have been some contamination even after column purification. However, in case of APTR, precise information about the ingredients is not available, and the cause of lower purity values cannot be suggested. We found no previous literature to compare with our findings. Thus, according to our results, skin samples collected as dry biopsy or into BME + LB should be preferred to sample collection into APTR or QIAzol.27 or tumour lymphocytes28 from human skin biopsies. Furthermore, it has been suggested that combining collagenase and hyaluronidase digestion prior to human skin homogenization would be advantageous ), however, there is no information regarding the effect on homogenizing efficiency. In the case of porcine tissue, overnight incubation was conducted to induce dendritic cell migration29 from skin biopsy. However, at the time of this study, no literature was available regarding treating pig skin enzymatically prior to homogenization.Our attempt to make the domestic pig skin tissue more fragile and susceptible to mechanical homogenization by applying combined hyaluronidase-collagenase digestion was not successful, as there was no significant difference in homogenizing efficiency between digested and not digested samples. Previously, collagenase treatment has been successfully applied to extract fibroblasts31.Additionally, the purpose of the enzymatic digestion was to evaluate whether it improves the homogenizing efficiency, and thus we did not add RNase inhibitors to our samples. Thus, obtaining highly degraded RNA from enzymatically treated samples was an expected result. However, if, for some reason, enzymatic digestion at 37 °C is needed, RNase inhibitors are strongly recommended7. However, the stator and rotor based GentleMACS Dissociator was able to fully homogenize almost half of the pig skin biopsies and all the human skin biopsies. The RNA quality and quantity were nearly identical between these two homogenizing strategies. From previous literature, there are data available regarding comparison between different types of homogenizers, including these two, but not specifically on skin33. Thus, when relying on the information received from the present study, both the Fastprep-24 Instrument and the GentleMACS Dissociator might be considered suitable for homogenizing skin tissue.The bead motion-based Fastprep-24 Instrument was incapable of fully homogenizing skin tissues, although the manufacturer’s suggested homogenizing program was applied19. The results presented here showed that the domestic pig samples were indeed applicable as a model tissue for finding potentially good workflows for human skin experiments. Furthermore, applying pig skin tissue helped us to achieve the results with less human tissue.Analysis of human skin experimental data confirmed the findings previously obtained from the domestic pig skin experiments. Applying WF7_D or WF14_M, it was possible to gain RNA with better quality and quantity, but with higher DNA contamination. Furthermore, for skin samples, the on-column DNase digestion seems to be insufficient for full digestion of the genomic DNA, and thus other strategies such as post-RNA-extraction DNase treatment should be considered. Although in the literature, there were no studies confirming our findings, we expected to obtain these results, as human and pig skin have similar characteristics in various aspects, but most importantly in morphologyIn conclusion, we demonstrated that domestic pig can be used as a model for testing the recovery of RNA from human skin. Utilizing pig skin tissue helped to exclude less efficient strategies prior to the testing of the particular methods on human skin biopsies. We were able to find the optimal workflow for extracting RNA from human skin biopsies. Furthermore, skin tissue collection directly into lysis buffer proved to be an efficient strategy and that could be used instead of collecting samples as dry biopsies. The enzymatic digestion did not increase the homogenization efficiency and, in fact, degraded the RNA. The rotor and stator-based homogenizer was more efficient than the bead motion-based homogenizer for skin tissue homogenization, however, the RNA output after purification was rather similar. Nevertheless, thirteen out of twenty workflows in the case of domestic pig skin resulted in average RIN > 7, and thus could be considered as potential strategies for further RNA expression analysis.To analyse how different sample collection/storage, enzymatic treatment and homogenization methods work together, several commonly used strategies were analysed Table . Below, Collecting domestic pig skin applying method described below does not require an ethical approval. In case of human subjects, the Ethical Review Committee on Human Research of the University of Tartu approved the study protocols and informed consent forms. All participants signed written informed consent.Domestic pig  skin from the front limbs of three female piglets was collected immediately after slaughter. The pig samples were collected free of charge in Tartu County (Estonia) slaughterhouse owned by OÜ Rotaks-R. The skin was cut so that it contained the layers of epidermis, dermis and minimum amount of subcutis. Four different sample collection/storage strategies were tested – samples were (1) collected as dry biopsies and immediately frozen on dry ice; (2) collected into Allprotect Tissue Reagent , kept overnight at +4 °C and then transferred to −80 °C; (3) collected into QIAzol Lysis reagent (QIAzol) or (4) into RTL lysis buffer from RNeasy Fibrous Tissue Mini kit, containing beta-mercaptoethanol (BME + LB)  and frozen at −80 °C 2–3 hours after collection.All six human subjects in the study were Caucasians living in Estonia and were recruited from among the patients at the dermatologic outpatient clinic. The skin tissues collected for the study were derived from the edges of the unpigmented skin areas from a birthmark removal surgery (two pieces per patient). Among six patients there were two men and four women, the average age was 43 years (±SD 13) and the body areas were abdomen, thigh, chest, arm, and side of a trunk. For human, only two sample collection and storage strategies were used – samples were collected into QIAzol or BME + LB, frozen at −20 °C ~1 hour after collection and transferred to −80 °C within a week after collection (best possible conditions available in clinic).All domestic pig and human samples were stored at −80 °C for two months before RNA extraction. All the skin pieces were weighed prior homogenizing and for the following procedures full biopsies were used. Each workflow had three biological replicates.The enzymatic digestion was tested only on domestic pig samples. For that, the whole biopsies were treated at 37 °C for 2 hours with combined collagenase and hyaluronidase solution . After that, samples were centrifuged, the supernatant was removed, and samples were transferred into homogenizing tubes containing the homogenizing buffer (QIAzol or BME + LB) and homogenized immediately.Two homogenizing solutions (QIAzol or BME + LB), two instruments with three different types of disposable tubes (bead motion-based Fastprep-24 instrument with lysing matrix D (ceramic beads) or S tubes   and stator and rotor-based GentleMACS Dissociator with M tubes  were tested for homogenizing domestic pig samples. In case of human samples, two homogenizing solutions (QIAzol or BME + LB) and Fastprep-24 instrument with lysing matrix D tubes or GentleMACS Dissociator with M tubes was applied. The homogenizing efficiency was evaluated visually and termed as “fully homogenized” if no intact skin piece was seen. After homogenizing, RNA purification was carried out immediately according to RNA purification protocol.For samples homogenized in QIAzol, chloroform was added, and tubes were shaken vigorously and centrifuged, separating the preparation into three phases. The upper aqueous phase, containing the RNA, was transferred into a new tube and ethanol was added. Then the column purification combined with DNase treatment was conducted according to manufacturer’s protocol, applying RNeasy Fibrous Tissue Mini kit with RNase-Free DNase Set .For samples homogenized in BME + LB with additional nuclease free water, a standard protocol of RNeasy Fibrous Tissue Mini kit with RNase-Free DNase Set was applied, however, the proteinase K and absolute ethanol amount was adjusted to the BME + LB volume applied for homogenizing.For measuring the quantity of extracted RNA and evaluating DNA contamination the Qubit 2.0. fluorometer with Qubit RNA HS Assay Kit or DNA HS Assay kit  was applied, respectively. This method is able to clearly distinguish between DNA and RNA molecules and thus provides reliable information about RNA concentration and possible DNA contamination , . For evaluating RNA integrity the Agilent 2100 Bioanalyzer and RNA 6000 Nano kit  was applied. Based on the entire electrophoretic trace of RNA sample, the RNA Integrity Number (RIN) is calculated by 2100 Expert Software and given as a numerical value ,  RIN = 7 is often taken as an RNA sample quality threshold for the suitability for expression analysis (Ion T RNA-Seq Kit v2 - User Guide. MAN0010654 (2017). A NanoDrop spectrophotometer  was used to evaluate the purity of RNA samples. According to the Nanodrop manual the A260/280 ratio of ~2.0 and A260/230 ratio of 1.8–2.2 is generally accepted as “pure”; a low A260/280 ratio indicates the presence of protein, phenol or other contaminants that absorb strongly at or near 260 nm; similarly, a low A260/230 ratio indicates the presence of residual phenol, guanidine, magnetic beads, carbohydrates or proteins .36. Although, it is possible to screen the whole transcriptome with arrays or sequencing platforms39, these approaches are still rather expensive. With qPCR it is possible to analyse both miRNA and mRNA expression, however, as we applied the protocols for which only RNA molecules longer than 200 bp are purified , we analysed only mRNA expression with qPCR. In RNA samples with poor quality, the higher grade of molecules has degraded, which stands out in the results as higher Cq values, compared to samples with high quality. This effect can be seen due to the lower amount of cDNA molecules synthesized, as the process starts from the polyA tail, which is often damaged in degraded RNA molecules .To additionally confirm the RNA quality at RNA expression level, we applied the frequently used qPCR methodHPRT1, OSBP, PGK1, SDHA) previously used in different skin studies43 were applied to confirm the RNA quality derived from human samples – RNA samples with higher RIN values should have lower ΔCq values. In the case of all four genes, we have previously validated the even expression level between psoriasis patients lesional and unlesional skin and control skin applying RNA-seq and/or qPCR (data not published). We analysed both high-quality (RIN > 7) and lower-quality RNA samples (RIN < 7) to confirm that the expression pattern of selected genes is affected by the general RNA integrity values.Four reference genes  for cDNA synthesis, according to the manufacturer’s protocol. cDNA was used as a template for qPCR using the Quantstudio 12k Flex Real-Time PCR system platform . The qPCR conditions for all four reference genes  were the same. The qPCR was conducted in four replicates and with reaction volume 10 ul on 384 plate formats using EvaGreen qPCR Supermix , final primer concentration 400 nM and cDNA input 2.5 ng (10 × dilution) per reaction. The qPCR program was as follows: hold stage −95 °C, 15 min; 40 cycles of PCR stage −95 °C, 20 sec; 60 °C, 20 sec; 72 °C, 20 sec, melt curve stage 95 °C, 20 sec; 60 °C, 20 sec; 95 °C, 20 sec. The pool of all twelve human RNA samples in equal amounts (ng) (CS_Hs) was used as a calibration sample on each PCR plate.The qPCR experiments were conducted according to the MIQE guidelinessample − CqCs_Hs. For correlation and comparative analysis, ΔCq was used.Cq values were taken as average from the four (or less) technical replicates. The formulas for ΔCq calculation was as follows: ΔCq = CqFor data analysis and generation of graphs and figures, GraphPad Prism 6 , Microsoft Excel and PowerPoint  were applied. For evaluating the efficiency of the different strategies or whole workflows, all the quality and quantity values  were considered; however, the high total RNA amount and RIN values were considered critical for higher efficiency. In Figs. Supplementary Materials


Optimal localization of ribosomes requires the proper identification of the ribosome P-site in each ribosome protected fragment, a crucial step to determine the trinucleotide periodicity of translating ribosomes, and draw correct conclusions concerning where ribosomes are located. To determine the P-site within ribosome footprints at nucleotide resolution, the precise estimation of its offset with respect to the protected fragment is necessary. Here we present riboWaltz, an R package for calculation of optimal P-site offsets, diagnostic analysis and visual inspection of ribosome profiling data. Compared to existing tools, riboWaltz shows improved accuracies for P-site estimation and neat ribosome positioning in multiple case studies. riboWaltz was implemented in R and is available as an R package at  PLOS Computational Biology Software paper.This is a Ribosome profiling (RiboSeq) is an experimental technique used to investigate translation at single nucleotide resolution and genome-wide scale ,2, throuFig 1A).Much of this information relies on the ability to determine the exact localization of the P-site, i.e. the site holding the t-RNA associated to the growing polypeptide chain during translation, within ribosome protected fragments . This pAccurate determination of the PO is a crucial step to verify the trinucleotide periodicity of ribosomes along coding regions ,17, deriFig 1A) [Typically, the PO is defined as a constant number of nucleotides from either the 3' or 5' end of reads, independently from their length  . This apFig 1A) , non-traFig 1A) , nucleasFig 1A)  and sequFig 1A) . This prFig 1A) ,29. As sFig 1A) ,31 and cFig 1A) . RecentlFig 1A)  and RiboFig 1A) . Both toHere, we describe the development of riboWaltz, an R package aimed at computing the PO for all reads from single or multiple RiboSeq samples. Taking advantage of a two-step algorithm, where offset information is passed through populations of reads with different length to maximize the offset coherence, riboWaltz computes with extraordinary precision the PO and shows higher accuracy and specificity of P-site positions than the other methods. riboWaltz provides the user with a variety of graphical representations, laying the foundations for further accurate RiboSeq analyses and better interpretation of positional information.Fig 1B). Optionally, a third file containing transcript sequence information in FASTA format can be provided as input to perform P-site specific codon sequence analysis. The user is also free to specify a genome build and the corresponding BSGenome object in R will be used for sequence retrieval .riboWaltz is an R package that requires two mandatory input data files: 1) alignment files, in BAM format or as GAlignments objects in R, ideally from transcriptome alignments of RiboSeq reads, and; 2) transcript annotation files, in GTF/GFF3 format or provided as TxDb objects in R. Alternatively, annotation can also be provided as a tab separated text file containing minimal transcript annotation: the length of the transcripts and of their annotated coding sequences and UTRs , as proposed by . It is kFig 1C). For each bin, temporary 5’ and 3’ POs (tPOL) are defined as the distances between the first nucleotide of the TIS and the nucleotide corresponding to the global maximum found in the profiles of the 5’ and the 3’ end at the left and at the right of the start codon, respectively . Therefore, considering the occupancy profile as a function f of the nucleotide position x with respect to the TIS, the temporary 5’ and 3’ POs for each length bin are such that:riboWaltz specifically infers the PO in two-steps. First, riboWaltz groups the reads mapping on the TIS according to their length. Each group of reads with a specific length (L) corresponds to a bin. To avoid biases in PO calculation, reads whose extremities are too close to the start codon (9 nucleotides by default) are discarded from the computation of the PO. This parameter, called “flanking length” (FL), can be set by the user. Next, for each length bin, riboWaltz generates the occupancy profiles of read extremities, i.e. the number of 5’ and 3’ read ends in the region around the start codon  mapping on the TIS the temporary POs corresponding to its length is assigned, obtaining two sets of read-specific tPOs:S12 Text), this approach can produce high variability in tPOL values of reads differing in only one nucleotide in length, especially across length bins with low number of reads.Despite good estimation of P-site positions, artifacts may arise from either the small number of reads with a specific length or the presence of reads from ribosomes nearby the TIS, but not translating the first codon. In other words, the offset estimated independently from the global maximum of each read length is not necessarily always the best choice. In fact, while the most abundant population of reads are less subjected to the above mentioned biases and show consistent tPOs  associated to the predominant bins as a reference value for correcting the temporary POs of smaller bins. Briefly, the correction step defines for each length bin a new PO based on the local maximum, whose distance from the TIS is the closest to the oPO. The complete procedure is illustrated below.Mode(5′tPOR) and Mode(3′tPOR)) as the one with the highest frequency.The optimal PO at either 5’ or 3’ extremities  are chosen as reference points to adjust the other tPOs. The optimal PO is selected between the two modes of read specific tPO sets  satisfies the following condition:The correction step is specific for each bin length and works as follows: if the offset associated to a bin is equal to the optimal PO, no changes are made. Otherwise, i) the local maxima of the occupancy profiles are extracted; ii) the distances between the first nucleotide of the TIS and each local maxima is computed; iii) the corrected PO is defined as the distance in point ii) that is closest to the optimal PO. Summarizing, given the set of local maxima positions (LMP) of the occupancy profile for the optimal extremity, the corrected PO for reads of length L . The first is a list of sample-specific data frames containing for each read i) the position of the P-site (identified by the first nucleotide of the codon) with respect to the beginning of the transcript; ii) the distance between the P-site and both the start and the stop codon of the coding sequence; iii) the region of the transcript  where the P-site is located and iv) the sequence of the triplet covered by the P-site, if a sequence file is provided as input. The second data structure is a data frame with the percentage of reads aligning across the start codon (if any) and along the whole transcriptome, stratified by sample and read length. Moreover, this file includes the P-site offsets from both the 5’ and 3’ extremities before and after the optimization . The third data structure is a data frame containing, for each transcript, the number of estimated in-frame P-sites on the CDS. This data frame can be used to estimate transcript-specific translation levels and to perform differential analysis comparing multiple samples in different conditions.riboWaltz returns three data structures that can be used for multiple downstream analysis workflows .To illustrate the functionalities of riboWaltz, we analyzed seven ribosome profiling datasets in yeast, mouse and human samples : this is a useful preliminary inspection tool to understand the contribution of each bin to the final P-site determination, and eventually decide to remove certain bin from further analyses. Second, the percentage of P-sites located in the 5’ UTR, CDS and 3’ UTR regions of mRNAs compared to a uniform distribution weighted on region lengths, which simulates random P-site positioning along mRNAs . This analysis is a good way to verify the expected enrichment of ribosome signal in the CDS. Third, to understand to which extent the obtained P-sites result in codon periodicity in the CDS, riboWaltz produces for every read group a plot with the percentage of P-sites in the three possible translation reading frames  for 5’ UTR, CDS and 3’ UTR . Fourth, riboWaltz returns for every read length the meta-gene read density heatmap for both the 5’ and 3’ extremities of the reads . This plot provides an overview of the occupancy profiles used for P-site determination and allows the visual inspection of PO values reliability. Fifth, to understand what codons display higher or lower ribosome density, riboWaltz provides the user with the analysis of the empirical codon usage, i.e. the frequency of in-frame P-sites along the coding sequence codon by codon, normalized for the frequency of each codon in the sequences . Indeed, the comparison of these values in different biological conditions can be of great help to unravel possible defects in ribosome elongation at specific codons or aa-tRNAs use. Finally, single transcripts profiles and meta-gene profiles based on P-site position can be generated ( see  for examples) with multiple options: i) combining multiple replicates applying convenient scale factors provided by the user, ii) considering each replicate separately, or iii) selecting a subsets of reads with defined length.riboWaltz integrates several graphical functions that provide multiple types of output results. First, the distribution of the length of the reads , mouevisiae, ; whole bevisiae, ; MCF-7, evisiae, ) and Plaevisiae, ). Both PFig 3A and  for individual examples, Fig 4A and Table 3 for a resume. The median values are: riboWaltz: 57.07; RiboProfiling: 51.45; Plastid: 39.04).To evaluate the three methods, we considered two performance scores. First, we estimated the percentage of P-sites with correct frame within the CDS region (Periodicity score). The higher this measure, the better the performance. For RiboWaltz and RiboProfiling, this measure was comparable in almost all datasets, while Plastid performed worse , with almost no signal along the UTRs, neither in the proximity of the start nor of the stop codons. By contrast, both Plastid and RiboProfiling generated a shift toward the 5’ UTR in the beginning of the periodic region . The presence of periodic peaks in the 5’UTR is undoubtedly a source of biological inaccuracy, conflicting with basic concepts in translation. In fact, outside the coding sequence, ribosomes are generally in non-translating mode. Translation can indeed occur outside the CDS, with upstream ORFs being the most documented examples. Nonetheless, occasional translation outside the CDS is unlikely to affect the codon periodicity in 5’ UTR regions, especially when metagene plots are anchored on the annotated AUG start codons. The presence of prominent codon periodicity in the 5’UTR in this latter case most likely results from a technical mistake, such as the inaccurate computation of the P-site offset. To quantify this effect, we determined a “TIS accuracy score”, comparing the amount of periodic signal in a local window before and after the translation initiation site. Considering the occupancy profile as a function f of the nucleotide position x with respect to the TIS, the TIS accuracy score is defined as follows:Next, we took into consideration the meta-profiles. In all datasets riboWaltz displayed a neat periodicity uniquely in the CDS .In the ideal scenario, this score should be equal to 1, meaning that the periodicity can be detected only within the CDS region. Lower scores are associated with a progressive increase of periodicity in the 5’UTR, indicative of ribosome mislocalization. Importantly, riboWaltz shows significantly higher TIS accuracy scores with respect to both RiboProfiling and Plastid . The results show correlation values ranging from 0.075 to 0.999. This analysis is a descriptive evaluation of the difference between riboWaltz and the other tools in computing the codon usage, depending on the different approach used for the P-site determination.The correct localization of ribosomes is a crucial step for obtaining estimations of the codon usage and for any downstream analyses. Empirical codon usage determination is a popular analysis for ribosome profiling data, and it is equally important for the biological interpretation of results and for the development of reliable mathematical models of translation –22,38–40In summary we show that the choice of the strategy for P-site positioning has a strong impact on downstream analyses and that riboWaltz is a more reliable tool for the identification of P-site offsets and the positional analysis of ribosome profiling data.riboWaltz identifies with high precision the position of ribosome P-sites from ribosome profiling data. By improving on other currently-available approaches, riboWaltz can assist with the detailed interrogation of ribosome profiling data, providing precise information that may lay the groundwork for further positional analyses and new biological discoveries.riboWaltz is written in the R programming language, and is compatible with Linux, Mac, or Windows PCs. riboWaltz depends on multiple R packages such as GenomicFeatures for handling GTF/GFF3 files, Biostrings, BSgenome and GenomicAlignments for dealing with sequence data and ggplot2 for data visualization. Furthermore, to easily handle datasets with several millions of reads preserving a high efficiency in terms of RAM usage and running-time, riboWaltz employs an enhanced version of data frames provided by the data.table package. Installation instructions for the dependencies are provided in the manual.https://github.com/LabTranslationalArchitectomics/riboWaltz. The package includes the R implementation of riboWaltz, data used in this article, extensive documentation and a stable release.riboWaltz is an Open-Source software package that can be extended in future releases to include other analysis methods as they are developed. Source code for riboWaltz is distributed under the MIT license and is available at the following GitHub repository: S1 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR from ribosome profiling in Hek-293 . The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained by using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz. (C) Comparison between the codon usage index based on in-frame P-sites from riboWaltz and RiboProfiling (left panel) and between the codon usage index based on in-frame P-sites from riboWaltz and Plastid (right panel). The length of the reads ranges from 25 up to 34 nucleotides (see (ides see  with the(TIF)Click here for additional data file.S2 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR from ribosome profiling in MCF-7 (GSE111866). The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained by using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz. (C) Comparison between the codon usage index based on in-frame P-sites from riboWaltz and RiboProfiling (left panel) and between the codon usage index based on in-frame P-sites from riboWaltz and Plastid (right panel). The length of the reads ranges from 20 to 45 nucleotides (see (ides see  with the(TIF)Click here for additional data file.S3 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR from ribosome profiling in mouse after immunoprecipitation of ribosomes using the ribosomal protein RPL10 as tag . The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained by using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz. (C) Comparison between the codon usage index based on in-frame P-sites from riboWaltz and RiboProfiling (left panel) and between the codon usage index based on in-frame P-sites from riboWaltz and Plastid (right panel). The length of the reads ranges from 19 up to 50 nucleotides (see (ides see  with the(TIF)Click here for additional data file.S4 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR from ribosome profiling in mouse after immunoprecipitation of ribosomes using the ribosomal protein RPL22 as tag . The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained by using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz. (C) Comparison between the codon usage index based on in-frame P-sites from riboWaltz and RiboProfiling (left panel) and between the codon usage index based on in-frame P-sites from riboWaltz and Plastid (right panel). The length of the reads ranges from 19 up to 50 nucleotides (see (ides see  with the(TIF)Click here for additional data file.S5 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR from ribosome profiling in yeast . The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained by using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz. (C) Comparison between the codon usage index based on in-frame P-sites from riboWaltz and RiboProfiling (left panel) and between the codon usage index based on in-frame P-sites from riboWaltz and Plastid (right panel). The length of the reads ranges from 20 to 46 nucleotides Click here for additional data file.S6 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR from ribosome profiling in yeast . The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained by using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz. (C) Comparison between the codon usage index based on in-frame P-sites from riboWaltz and RiboProfiling (left panel) and between the codon usage index based on in-frame P-sites from riboWaltz and Plastid (right panel). The length of the reads ranges from 21 to 40 nucleotides (see (ides see  with the(TIF)Click here for additional data file.S7 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR. The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz.((TIF)Click here for additional data file.S8 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR. The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz.((TIF)Click here for additional data file.S9 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR. The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz.((TIF)Click here for additional data file.S10 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR. The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz.((TIF)Click here for additional data file.S11 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR. The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz.((TIF)Click here for additional data file.S12 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR. The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz.((TIF)Click here for additional data file.S13 FigA) Percentage of P-sites in the three frames along the 5’ UTR, CDS and 3’ UTR. The statistical significances from two-tailed Wilcoxon–Mann–Whitney test comparing RiboProfiling and Plastid with respect to riboWaltz are reported . (B) Meta-profiles showing the periodicity of ribosomes along the transcripts at the genome-wide scale. The three metaprofiles are based on the P-site identification obtained using riboWaltz, RiboProfiling and Plastid. The shaded areas to the left of the start codon highlight the shift of the periodicity toward the 5’ UTR that is absent in the case of data analysed using riboWaltz.((TIF)Click here for additional data file.S1 TextThe PO computed from both read extremities are reported. The optimal PO used in the correction step of riboWaltz corresponds to 12 nucleotides from the 5’ end.(DOCX)Click here for additional data file.S2 TextThe PO computed from both read extremities are reported. The optimal PO used in the correction step of riboWaltz corresponds to 11 nucleotides from the 5’ end.(DOCX)Click here for additional data file.S3 TextThe PO computed from both read extremities are reported. The optimal PO used in the correction step of riboWaltz corresponds to 11 nucleotides from the 5’ end.(DOCX)Click here for additional data file.S4 TextThe PO computed from both read extremities are reported. The optimal PO used in the correction step of riboWaltz corresponds to 11 nucleotides from the 5’ end.(DOCX)Click here for additional data file.S5 TextThe PO computed from both read extremities are reported. The optimal PO used in the correction step of riboWaltz corresponds to 15 nucleotides from the 3’ end.(DOCX)Click here for additional data file.S6 TextThe PO computed from both read extremities are reported. The optimal PO used in the correction step of riboWaltz corresponds to 13 nucleotides from the 5’ end.(DOCX)Click here for additional data file.S7 TextThe PO computed from both read extremities are reported. The optimal PO used in the correction step corresponds to 12 nucleotides from the 5’ end.(DOCX)Click here for additional data file.S8 TextThe PO computed from both read extremities are reported. The optimal PO used in the correction step corresponds to 11 nucleotides from the 5’ end.(DOCX)Click here for additional data file.S9 TextThe PO computed from both read extremities are reported. The optimal PO used in the correction step corresponds to 11 nucleotides from the 5’ end.(DOCX)Click here for additional data file.S10 TextThe PO computed from both read extremities are reported. The optimal PO used in the correction step corresponds to 11 nucleotides from the 5’ end.(DOCX)Click here for additional data file.S11 TextThe optimal PO used in the correction step corresponds to 15 nucleotides from the 3’ end.(DOCX)Click here for additional data file.S12 TextThe PO computed from both read extremities are reported. The optimal PO used in the correction step corresponds to 13 nucleotides from the 5’ end.(DOCX)Click here for additional data file.S13 Text(DOCX)Click here for additional data file.


By comparing the transcriptome of these cell lines, we identified more than 150 polymerase II transcripts that are extensively edited, and we attributed most editing events to ADAR1p150. Editing is focused on inverted transposable elements, located mainly within introns and untranslated regions, and predicted to form duplex RNA structures. Editing of these elements occurs also in primary human samples, and there is evidence for cross-species evolutionary conservation of editing patterns in primates and, to a lesser extent, in rodents. Whereas ADAR1p150 rarely edits tightly encapsidated standard measles virus (MeV) genomes, it efficiently edits genomes with inverted repeats accidentally generated by a mutant MeV. We also show that immune activation occurs in fully ADAR1-deficient (ADAR1KO) cells, restricting virus growth, and that complementation of these cells with ADAR1p150 rescues virus growth and suppresses innate immunity activation. Finally, by knocking out either protein kinase R (PKR) or mitochondrial antiviral signaling protein (MAVS)—another protein controlling the response to duplex RNA—in ADAR1KO cells, we show that PKR activation elicits a stronger antiviral response. Thus, ADAR1 prevents innate immunity activation by cellular transcripts that include extensive duplex RNA structures. The trade-off is that viruses take advantage of ADAR1 to elude innate immunity control.The interferon (IFN)-mediated innate immune response is the first line of defense against viruses. However, an IFN-stimulated gene, the adenosine deaminase acting on RNA 1 (ADAR1), favors the replication of several viruses. ADAR1 binds double-stranded RNA and converts adenosine to inosine by deamination. This form of editing makes duplex RNA unstable, thereby preventing IFN induction. To better understand how ADAR1 works at the cellular level, we generated cell lines that express exclusively either the IFN-inducible, cytoplasmic isoform ADAR1 The innate immune response is a double-edged sword. It must protect the host from pathogens while avoiding accidental recognition of “self” molecular patterns, which can lead to autoimmune reactions. Double-stranded RNA is among the most potent inducers of cellular stress and interferon responses. We characterize here a mechanism that prevents autoimmune activation and show that an RNA virus, measles virus, can exploit it to elude innate immune responses. This mechanism relies on the enzyme adenosine deaminase acting on RNA 1 (ADAR1), which converts adenosine residues within duplex RNA structures to inosine. We identify duplex RNA structures in the 3′ untranslated regions of over 150 cellular transcripts and show that they are heavily edited in ADAR1-expressing cells. We detect the same type of editing in duplex RNA–forming defective genomes accidentally generated by measles virus. Loss of RNA editing causes strong innate immune responses and is detrimental to viral replication. Thus, by keeping the amount of duplex RNA in cells below an immune activation threshold, ADAR1 prevents autoimmunity while also favoring pathogens. ADAR genes, but only ADAR1 and ADAR2 proteins edit RNA in vitro ), and an amino-terminal Z-DNA binding domain (Zβ). ADAR1p150 includes an additional amino-terminal Z-DNA binding domain (Zα) ) )  and dysc [AGS6]) . AltogetIndeed, ADAR1 can be proviral: it favors the replication of positive-strand RNA viruses such as yellow fever virus, Venezuelan equine encephalitis virus, and Chikungunya virus  and of nKO) ) . Thus, pKO and ADAR1KO cells compared to parental HeLa or p150wtLV cells  resulted in stronger PKR activation in p150LV cells . Since Ptivation . Thus, AKO, and ADAR1KO cells. To identify potential editing sites, we adopted the Genome-independent Identification of RNA Editing by Mutual Information (GIREMI) method  compared to HeLa cells , whereas the ratios of C>U and G>A transitions and of all transversions were unchanged  method  33]. As. AsKO, anchanged . SymmetrKO cells . This isKO cells . This raSince other studies indicate that ADAR1 editing preferentially occurs in Alu elements ,34,35, wp150 isoform (p150KO), remaining editing was more prevalent in introns  transcript, which had the highest A>G transition differential. Whereas GIREMI detected 58 editing sites in a 6-kb region, the coverage plot was more sensitive, detecting 209 U>C transitions with >10% conversion rate  ). The discontinuous gradient was prepared as follows (from bottom to top): 1 ml of 40% (w/v) CsCl; 2.5 ml of 30% (w/v) CsCl; 1.5 ml of 25% (w/v) CsCl; 1 ml of 5% (w/v) sucrose. Ultracentrifugation was carried out in a SW41 rotor in a LE-80 ultracentrifuge  at 36,000 rpm and 4 °C for 22 h. RNPs banded about 2 cm above the bottom of the tube and were harvested in approximately 1 ml volume by needle aspiration using a 16-gauge needle and syringe. RNPs were diluted in 8 ml LEH buffer , layered over 2 ml of 15% (w/v) sucrose in LEH buffer in SW41 centrifuge tubes and centrifuged a second time at 36,000 rpm and 4 °C for 6 h. Afterwards, the supernatant was discarded, and RNP pellets were carefully resuspended in 1 ml LEH buffer supplemented with 1% (w/v) SDS. Total RNA was extracted from this solution using Trizol LS  and precipitated with isopropanol according to the manufacturer’s instructions. Precipitated RNA was resuspended in 25 μl nuclease-free H2O and stored at −80 °C.HeLa cells were seeded in 150-mm dishes at 5 × 106 TCID50 was separated on 1% (w/v) denaturing agarose gels supplemented with 2% (v/v) formaldehyde, and northern blot analysis using the DIG-system  was performed as described previously ). Count tables of regions of interest were generated using IGVTools and analyzed using Microsoft Excel 2010.RNAseq data sets were retrieved from the GEO database using Fastq-dump of the SRA Toolkit . Human donor RNAseq data were from study GSE57353   was ≥1.75.Single nucleotide variant (SNV) calling was performed using the SAMtools (v. 1.3) and BCFtools (v. 1.3) . The proe[Ts]) for A>G, C>U, G>A and U>C transitions (Ts) were calculated using function . Negative editing scores occur if more transversions than transitions are reported and indicate either sequencing artifacts or single nucleotide polymorphisms at the analyzed position.Read base count tables of regions of interest were generated from aligned BAM files using IGVTools (igvtools count--bases–w 1) and imported to Microsoft Excel 2010. Editing scores (function :e(Ts)=nSignificance of the ADAR1-specific transitions was tested against transversions at the same nucleotide position using Pearson chi-squared test with one degree of freedom χ2= distributed, the sum of L:KO cells. A difference between HeLa and ADAR1KO cells in the interval enrichment by the RNA-editing Poisson-Model scores can be estimated by the Sdiff statistics:Sdiff = 5.72 with P = 5.3E-09.A difference between HeLa and ADAR1http://rna.tbi.univie.ac.at//cgi-bin/RNAWebSuite/RNAfold.cgi) , or treated with IFN A/D . Heatmap is ordered for highest expression in UI HeLa cells and shows FPKM values from RNAseq analysis. (B) Normalized expression levels of transcripts relative to GAPDH levels. Shown are median levels in the four conditions described in (A). (C) Comparison of ranks of the 16 ADAR1-edited genes identified in this study as well as by Chung and colleagues  or genomic  orientation or NT 15,641–15,890 . Arrowhead indicates band of full-length genomes/antigenomes ; * indicates band of L mRNA (approximately 6.8 kb); # indicates band of N mRNA (approximately 1.8 kb); & indicates band of N-P bicistronic mRNA (approximately 3.4 kb). Bands in CKO(GFP) lanes below 1.8 kb correspond to DI RNA genomes. DI, defective interfering; NT, nucleotide.RNA extracted from viral stocks Click here for additional data file.S10 FigKO, and ADAR1KO cells. Left panel: methylene blue staining shows presence of viral genomes and absence of ribosomal RNAs. Center panel: northern blot with single-strand RNA probe recognizing the 5′ end of the MeV (–)-strand genome (L-trailer) confirms presence of full-length genomes. Right panel: northern blot with probe against 18S rRNA shows presence of small amounts of degraded rRNA. (B) RNAseq analysis of RNP preparations. Reads were mapped against MeV-vac2(GFP) (green), human genome 38 , and rRNA (red). Bars show percentage of reads mapping to either reference. Underlying values can be found in KO(GFP) genomes (right panels) amplified on standard HeLa (top diagrams), p150KO (middle diagrams), or ADAR1KO cells (bottom diagrams). Total coverage of reads with >95% of nucleotides having a quality score of ≥30 is shown in gray on a logarithmic scale. Coverage plots for reads filtered for >5 , >8 , or >11 mutations  are shown on a linear scale. ADAR1, adenosine deaminase acting on RNA 1; ADAR1KO, fully ADAR1-deficient; MeV, measles virus; p150KO, selectively ADAR1p150-deficient; RNAseq, RNA sequencing; RNP, ribonucleocapsid.(A) Analysis of viral RNPs purified from infections of HeLa, p150(TIF)Click here for additional data file.S11 FigKO(GFP) bases with editing scores ≥0.05. Underlying values can be found in KO(GFP) genomes amplified in standard HeLa cells. n indicates the number of NT positions available for the calculation. Underlying values can be found in (A) Western blot analysis of ADAR1 expression in Vero cells untreated or treated with 1,000 U/ml IFN A/D for 24 h. (B) Proportion of MeV-vac2(GFP) and MeV-C(TIF)Click here for additional data file.S12 FigKO cells with MeV-vac2(GFP) or MeV-CKO(GFP) at an MOI of 0.1, 32 h post infection. Images show GFP fluorescence  and phase contrast. Scale bar equals 100 μm. ADAR1, adenosine deaminase acting on RNA 1; ADAR1KO, fully ADAR1-deficient; GFP, green fluorescent protein; MOI, multiplicity of infection; UI, uninfected.Infection of Vero and Vero-ADAR1(TIF)Click here for additional data file.S13 FigMAVS gene, encoding two isoforms, FL-MAVS and ΔMAVS lacking the CARD domain. gRNAs  target exon 3, which is only present in the transcript of FL-MAVS. gRNA sequences are underlined in the nucleotide sequence below (PAM in bold letters). Corresponding amino acid sequence of MAVS is indicated on the bottom. Residues comprising the CARD domain are boxed in red. (B) Schematic representation of the human EIF2AK2 gene encoding PKR. gRNAs  target exons 3 and 5, respectively. gRNA sequences are underlined in the nucleotide sequence below (PAM in bold letters). Corresponding amino acid sequence of PKR is shown on the bottom. Residues within RBM I and RBM II are boxed in blue or green, respectively. (C) Western blot analysis of cells transfected with 2.5 μg/ml poly(I:C) for 6 h (+), or untreated cells (−). (D) Quantification of pPKR(T446) signals (top diagram) and pIRF3(S386) signals (bottom diagram) of (C). Values are average ± standard deviation of two independent experiments. Underlying values can be found in KO, fully ADAR1-deficient; CARD, caspase activation and recruitment domain; FL-MAVS, full-length MAVS; gRNA, guide RNA; MAVS, mitochondrial antiviral signaling protein; PAM, protospacer adjacent motif; pIRF3, phospho-interferon regulatory transcription factor 3; PKR, protein kinase R; pPKR, phospho-PKR; RBM, RNA-binding motif.(A) Schematic representation of the human (TIF)Click here for additional data file.S14 FigKO(GFP) (E-H) at an MOI of 3. Images were taken 24 h post infection. Scale bar equals 100 μm. (A and E) HeLa cells; (B and F) ADAR1KO cells; (C and G) 5 independent clones of ADAR1KO-MAVSKO cells; (D and H) 5 independent clones of ADAR1KO-PKRKO cells. ADAR1KO, fully ADAR1-deficient; GFP, green fluorescent protein; MAVS, mitochondrial antiviral signaling protein; MeV, measles virus; MOI, multiplicity of infection; PKR, protein kinase R.GFP fluorescence (green) and corresponding phase contrast images of cells infected with MeV-vac2(GFP) (A-D) or MeV-C(TIF)Click here for additional data file.S1 TableThe table includes the top 156 genes specifically edited by ADAR1 in HeLa cells as identified by the number of A>G editing sites detected by GIREMI. ADAR1, adenosine deaminase acting on RNA 1; GIREMI, Genome-independent Identification of RNA Editing by Mutual Information.(XLSX)Click here for additional data file.S1 DataEach tab contains data values used to generate indicated figure panels.(XLSX)Click here for additional data file.