This increased sensitivity of peroxisome isolation as opposed to IP experiments was also striking when we analyzed the small RNA population bound by the Tomato bushy stunt virus-encoded P19, one of the best characterized viral suppressors of RNA silencing (VSR), artificially targeted to peroxisomes. These results support that peroxisomal targeting should be considered as a novel/alternative experimental approach to assess in vivo interactions that allows detection of labile binding events. The advantages and limitations of this approach are discussed.Peroxisomes are organelles that play key roles in eukaryotic metabolism. Their protein complement is entirely imported from the cytoplasm thanks to a unique pathway that is able to translocate folded proteins and protein complexes across the peroxisomal membrane. The import of molecules bound to a protein targeted to peroxisomes is an active process known as ‘piggybacking’ and we have recently shown that P15, a virus-encoded protein possessing a peroxisomal targeting sequence, is able to piggyback siRNAs into peroxisomes. Here, we extend this observation by analyzing the small RNA repertoire found in peroxisomes of P15-expressing plants. A direct comparison with the P15-associated small RNA retrieved during immunoprecipitation (IP) experiments, revealed that  Arabidopsis thaliana  proteins are responsible for small RNA biogenesis  precursors. They mediate a pan-eukaryotic regulation process known as RNA silencing, or RNA interference (RNAi), through their incorporation into Argonaute (AGO) proteins, where they act as sequence-specific guide to trigger either (i) cleavage or translational inhibition of the targeted RNA , or (ii)ogenesis . DCL1 progenesis . DCL3 maogenesis . DCL3 caogenesis . Finallyogenesis . Of notein vivo, and the nature of these small RNAs, was assessed by immunoprecipitation (IP) experiments.Given the crucial role of RNA silencing during antiviral defense, viruses have, in turn, evolved proteins that block or hinder this process. These proteins, collectively known as viral suppressors of RNA silencing (VSRs), employ a variety of strategies to inhibit antiviral RNAi . Among tPeanut clump virus (PCV)-encoded P15, a PTS1-containing VSR, was recently found to bind small RNAs of 21-22nt in length, with an apparent higher affinity for the latter size in vivo. Interestingly, as a result of P15 import into peroxisomes, P15-bound antiviral siRNAs are piggybacked into these organelles in order to efficiently neutralize their spread to naïve tissues and promote PCV systemic infection , it was not possible to discriminate whether this difference reflected a true advantage of the peroxisomal isolation approach, or resulted from an altered binding ability of the epitope-tagged P15 used for IP experiments. Therefore, to conclusively address this question we decided to compare the two approaches, using the same tagged version of P15, targeted or not to peroxisomes. These experiments confirmed our initial observations and supported that peroxisomal isolation is a sensitive technique to identify labile or weakly interacting complexes formed SKL construct was generated by amplifying P15FHA  or the SKL-encoding codons (P19HASKL), and SalI/PstI restriction sites were used. 35S:AGO2 and 35S:AGO2SKL were obtained by amplifying AGO2 (AT1G31280) genomic sequence from A. thaliana Col0, with primers containing attB sites for Gateway cloning (with SKL-encoding codons upstream of the stop in the case of AGO2SKL), inserted into pDONR221 through BP recombination, then into pH2GW7 binary vector through LR recombination. SUC-SUL (ago2-1 (SALK_003380) plants were transformed through floral dip as previously described (in vitro on MS medium containing hygromycin. The 35S:P15FHA/SUC:SUL line was previously described (35S:P15FHAg P15FHA  with a r SUC-SUL  and ago2escribed . Transfoescribed . All expg for 5 min at 4°C), an aliquot was set aside as input fraction, and 50 μl of anti-HA micro-beads  were added to the remaining lysate and incubated 30 min at 4°C on wheel. Next, the lysate containing the beads was deposited on a magnetic M column (Miltenyi Biotech) and allowed to flow through. An aliquot of the flow-through was kept for further analysis, and beads were washed by adding 2 × 500 μl of lysis buffer to the columns and allowed to pass through, then 100 μl of 20 mM Tris-HCl pH 7.5, after which excess liquid was removed. Beads were recovered in 1 ml of Tri-reagent (Sigma).HA-epitope IP was performed as previously described . 0.2 g oA. thaliana rosettes per sample were harvested and left on ice 2 h. Plant tissue was minced with a knife, then ground in 60 ml grinding buffer . The resulting pulp was filtered through Miracloth, the liquid divided into three tubes and centrifuged 1 min at 6700 g (Beckman rotor JA25.5). The clarified supernatant was pooled and deposited on 4 freshly prepared and chilled Percoll/sucrose gradients. These contain, from top to bottom, 3 ml of 15% Percoll , 9 ml of 38% Percoll , 2 ml of 2:1 mix 38% Percoll:36% sucrose, 2 ml of 1:2 mix 38% Percoll:36% sucrose, and 3 ml of 36% sucrose . Gradients were centrifuged 12 min at 13200 g, then without stop 20 min at 27000 g (Beckman rotor JA25.5), with medium brake. Top layers were discarded, while the bottom 2–3 ml were kept, pooled and diluted up to a volume of 60 ml in 36% sucrose solution, divided in 3 tubes and centrifuged 30 min at 38700 g (Beckman rotor JA25.5). Next, 1 ml of the organellar phase on the bottom of each tube was directly harvested with a cut-tip pipette, transferred to a potter and gently homogenized. Then, the samples were deposited on a sucrose 41.2 to 60% discontinuous gradient . Gradients were ultra-centrifuged 40 min at 110800 g (Beckman rotor SW41), with maximum acceleration and brake. 1.5 ml of visible white peroxisome fraction within the 50.5% sucrose phase were harvested and frozen at –80°C.Peroxisome isolations were performed as previously described , with miN-(3-Dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride powder  and 1% HCl 1 M. Detection of RNA species was achieved by hybridizing membranes in Sigma PerfectHybTM Plus buffer with PCR products labeled with α-32P-CTP through Klenow reaction  or with oligonucleotides labeled with γ32P-ATP through PNK reaction , miR159, miR160, miR169, miR173, miR403, miR408; Probing order is available upon request). Hybridization was carried out O/N at 42°C, followed by 3x10 min washes in 2X SSC, 2% SDS at 50°C. Radioactive signal was revealed with autoradiographic films (Fujifilm). Stripping of the membrane before reprobing is done by submerging the membrane in 500 ml of boiling stripping buffer (0.1% SDS) for 3 × 10 min. The efficiency of the stripping was assessed by checking the membrane with a Geiger counter radiation detector.RNA extraction was performed using Tri-reagent (Sigma), according to manufacturer’s instructions. In the case of immunoprecipitated and peroxisomal RNA, 1.5 μl glycogen was added during isopropanol precipitation, which was allowed O/N at 4°C. Small RNAs were resolved through PAGE  and blotted as previously described . RNA wasTotal protein was obtained from frozen tissues through phenol extraction followed by methanol/ammonium acetate precipitation, as previously described . PeroxisSKL and P15FHA, respectively). Addition of this artificial PTS1 should allow to target P15FHASKL to peroxisomes, as opposed to the P15FHA-expressing lines where the tag prevents the peroxisomal import of P15  construct, driven by the phloem-companion cell-specific AtSUC2 promoter, triggers RNAi of the endogenous SULHUR (SUL) mRNA and apparition of a chlorotic phenotype that expands 10-15 cells beyond the vasculature , and producing similar levels of SUL siRNAs than the one found in our SUC:SUL reference line .Previous experiments conducted with the VSR P15 have shown that although 21nt miRNAs could be detected in isolated peroxisomes, they were below detection level in northern analysis following IP experiments  and P15FHASKL peroxisomal fractions . Similarly, 22nt-long siRNAs deriving from the endogenous IR71 locus, and the 22nt-long miRNA miR173, were found associated to P15 using both approaches . However, the results were drastically different when we analyzed P15-binding to several 21nt-long miRNAs. Indeed, and in agreement with our previous report . In sharp contrast, all 21nt-long miRNAs were consistently found in three biological replicates of peroxisomal isolation performed on P15FHASKL-expressing plants . Given that these miRNAs were not detected in peroxisomal isolates of the P15FHA-expressing line, performed in parallel, this indicates that the miRNAs specifically detected in peroxisomes of plants expressing P15FHASKL result from P15-mediated piggybacking of these small RNAs. Moreover, detection in these peroxisomal isolates of the otherwise unstable miR160 passenger strand (miR160∗), strongly suggests that small RNAs are imported as duplexes by P15.Neither P15FHA- nor P15FHAs report , all 21nex vivo treatment performed during peroxisomal isolation, whether or not they remain bound to P15, thereby allowing their detection. By contrast, P15 interaction with 21nt miRNAs seems to be lost upon ex vivo conditions applied during IP experiments , the P15FHASKL protein was difficult to detect within peroxisomal isolates, using either antibodies raised against the HA tag or the P15 protein  prompted us to determine whether these observations were specific to P15, or if this trend could also be observed with another, unrelated, small RNA-binding VSR upon artificial targeting to peroxisomes. For this purpose, we decided to test the tombusviral P19. P19 has been extensively studied in the past and is potentially the best characterized VSR to date. The structure of P19 has been resolved and showed that a head-to-tail P19 homodimer binds preferentially 21nt-long small RNA duplexes with 2nt 3’ overhangs . Western blot analysis revealed that, despite lower accumulation of P19HASKL than P19HA in total fractions, only the former was detected in peroxisomal isolates , supporting functional targeting of this protein to peroxisomes. Importantly, both constructs efficiently impaired the appearance of the SUL-silencing phenotype , without affecting production of SUL siRNAs , indicating that addition of the SKL tripeptide did not hinder P19 ability to suppress siRNA-mediated silencing. Moreover, expression of P19HASKL triggered the appearance of similar developmental defects (mostly characterized by leaf serrations) as the one observed in P19HA-expressing lines . Given that this phenotype has been previously associated with a disruption of the miRNA pathway , and the documented size-specificity of P19 , indicating specific P19-mediated piggybacking of these small RNAs into peroxisomes. More intriguingly, DCL3-dependent 24nt long siRNAs deriving from the exogenous SUL-IR transgene, the endogenous IR71 locus or the p4-siRNA REP2 loci, were also specifically detected in these fractions, suggesting that this small RNA size class can also be imported into peroxisomes along with P19HASKL. Although surprising given that (i) in vitro binding assays showed that P19 has a 22-fold lower affinity for 24nt siRNA than for its 21nt counterpart , both IP and peroxisome isolations were performed in triplicate from P19HA- or P19HASKL-expressing plants, grown in parallel in the same conditions and harvested simultaneously. In agreement with previous reports, IP experiments showed that, out of the 21nt and 24nt SUL-siRNAs produced in these plants, P19HA only efficiently binds to the former species . In addition, analysis of the endogenous IR71-derived siRNAs, which are produced by DCL2, DCL3 and, to a lower extent, by DCL4, revealed that both 21nt and 22nt siRNAs can be immunoprecipitated by P19HA, although with an apparent bias toward the 21nt species . Importantly, neither the SUL-, IR71-, nor REP2-derived 24nt siRNAs were found associated to P19HA in these IP experiments.To confirm these observations, we decided to perform a side-by-side comparison of the P19-bound small RNAs retrieved using these two approaches. Similarly, to the experiments described above in the case of P15 , which most likely correspond to the recently described 3′-end trimming products of P19-bound miRNAs . Although the mechanism behind this specific destabilization remains unknown, detection with the miR173 probe of several shorter bands in both total and IP fractions suggests that this specific miRNA duplex may be particularly sensitive to the P19-induced 3′end trimming enzymatic activity.Similarly, to the 21nt siRNAs, 21nt miRNAs were also efficiently immunoprecipitated by P19HA, together with 1 or 2 nucleotide-shorter derivatives  yielded interesting differences compared to the IP experiments. First, in addition to the 21nt and 22nt siRNA species, both SUL- and IR71-derived 24nt siRNAs were readily detected in peroxisomes of P19HASKL-expressing plants, as were the REP2-derived 24nt siRNAs . Importantly, none of these siRNA species were detected in peroxisomal isolates of P19HA-expressing plants performed in parallel, supporting that their detection in peroxisomes of P19HASKL-expressing plants specifically results from their piggybacking into these organelles by the P19HASKL. In addition, western and mass spectrometry analysis of these peroxisomal extracts revealed that presence of these small RNA species in peroxisomes cannot be attributed to import of AGO proteins or other known silencing factors within these organelles upon P19HASKL expression , suggesting that peroxisomal import of P19-bound miRNAs protects them from the 3′-end trimming activity present in the cytoplasm. Consequently, miR173 accumulation, which was at or below detection level in P19HA-expressing plants, is restored to wild-type levels in the presence of P19HASKL. Therefore, both P15 and P19 experiments  support peroxisomal isolation as an alternative approach to obtain more information about VSR-associated small RNAs in planta than that provided by conventional IP experiments.In parallel, the results obtained from northern analysis of the small RNAs retrieved in our peroxisomal isolates , detection of the P19HASKL within peroxisomal isolates was also difficult to achieve using both anti-HA or anti-P19 antibodies, and required the loading of a higher amount of peroxisomal protein than the one required to detect HPR  seems to promote degradation of the tagged protein within peroxisomes, or their prompt export, by an unknown mechanism.Of note, and while not as drastically different as what we observed above in the case of P15FHAago2-1 mutant background, transgenic lines expressing AGO2, fused or not in C-terminal to the canonical PTS1 tripeptide SKL . Unfortunately, despite high levels of AGO2SKL accumulation in total fractions, we were unable to detect any import of this protein in peroxisomal fractions , suggesting that addition of a C-terminal PTS1 does not lead to peroxisomal import of AGO2. While the reason for this lack of AGO2 import into peroxisomes remains to be formally determined, we believe this is most likely caused by the inaccessibility of the PTS1 signal to PEX5, as crystal structures of human and yeast Argonaute proteins have shown that their C-terminal ends are not exposed on the surface, but rather buried within the RNA binding cleft , typically observed in plants deficient for TAS3 trans-acting (ta)-siRNA production . We hypothesized that this reduction could be a consequence of direct competition for miR390 loading between AGO7 and AGO2. This could result from the overexpression of AGO2, assuming that the AGO2/miR390 complex would be less efficient than the AGO7/miR390 complex to trigger TAS3 ta-siRNA production. Surprisingly, we found that miR390 steady-state level was also strongly reduced in our 35S:AGO2 transgenic lines, whereas the other miRNAs tested accumulated to wild-type levels . As mutants impaired in TAS3 ta-siRNA production, such as rdr6, are not affected in miR390 accumulation (∗) is rapidly degraded, we next hypothesized that AGO2 in our transgenic lines might be preferentially loaded with miR390∗, thereby triggering degradation of the miR390 guide strand. The corollary of this hypothesis is that the miR390∗ should be stabilized in AGO2 over-expressing compared to wild-type plants, as previously observed in the case of miR393∗ loading into AGO2 during Pseudomonas syringae infection  ta-siRNA accumulation was indeed strongly reduced in our 35S:mulation , this innfection . HoweverFigures 1, 3). Therefore, these results delineate the potential of peroxisomal targeting as a tool to probe more accurately the small RNA interactome of VSRs in vivo. We believe that the increased sensitivity of this approach, when compared to IP experiments, mainly results from three combined factors. Firstly, while during IP, interactions must withstand ex vivo conditions for a considerable amount of time (from sample grinding to washing of the IPed immune complex) before RNA extraction and analysis, peroxisomal targeting and isolation allows to detect an interaction the moment a VSR-bound small RNA enters the peroxisome in vivo. Indeed, these organelles will work as small containers that will preserve the small RNA enclosed within the peroxisomal membrane from the ex vivo conditions applied during the peroxisome isolation procedure, whether they remain bound to the VSR or not. Secondly, although the fate of small RNAs piggybacked into peroxisomes remains an open question, their easy detection supports that a considerable amount remains intact. Moreover, as evidenced by the protection of P19-bound small RNAs from the 3′-end trimming activity present in the cytoplasm , another advantage of this peroxisomal targeting strategy is to protect/shelter VSR interactors from in vivo factors that may affect their nature or stability. Thirdly, while the small RNA co-IPed with a VSR are strictly the ones associated at the time of tissue harvest, the small RNA present in peroxisomes are most likely the result of VSR-dependent piggybacking during a certain period of time, spanning the life (or the period of importomer function) of each peroxisome. Therefore, interactors that are below detection level after IP could, thanks to their accumulation within peroxisomes, be detected by peroxisomal piggybacking.The experiments described in this paper have shown that the addition of a PTS1 peptide to two distinct VSRs led to the piggybacking of their bound small RNAs into the peroxisomal matrix. More importantly, we found that these proteins were able to piggyback into peroxisomes small RNA species that were not detected following IP experiments performed in parallel . Indeed, structural data combined with in vitro binding assays clearly established that P19 dimers form a molecular caliper that specifically accommodate 21nt-long small RNA duplexes for which they have the strongest affinity  factors, involved in the biogenesis and action of p4-siRNAs such as REP2, have been described as being exclusively located in the nucleus , whereas pathway . In this pathway . TherefoFigure 4), is that the PTS1 tripeptide must be accessible for recognition by PEX5 and that, as opposed to tag fusion for IP experiments, this targeting signal must be on the C-terminal end of the studied protein. An alternative to this latter limitation could be the use of an N-terminal fusion sequence containing the PTS2 nonapeptide (Although promising in terms of accurate characterization, or even discovery, of potential interactors for a given protein, the peroxisomal targeting approach also entails several technical or biological limitations. From a practical point of view, peroxisome isolation is a longer and more technically challenging procedure than IP. It also requires an important amount of plant material and cannot be performed on frozen tissues. A second potential issue, as suggested by the apparent failure to import AGO2 in peroxisomes (apeptide  that canapeptide , but thia priori. Firstly, recognition and binding to PEX5 of the peroxisomal-targeted protein of interest must not disrupt its association with its interactors. Secondly, the protein of interest and its interactors must be successfully shuttled onto the docking complex and pass through the peroxisomal pore, which may not be possible for large protein complexes due to potential size/conformation restrictions. Finally, once delivered within peroxisomes, the interactors must remain stable and not be degraded by peroxisomal enzymes. Because of these actual or potential limitations, this experimental approach must therefore be tested empirically for each candidate protein, in a case-by-case manner. However, we believe that this conceptually new approach of using peroxisomes as storage units that rely on cell machinery-driven accumulation of interactors within closed membranes, holds interesting promise for the full characterization of any given protein, particularly for the identification of RNA or protein components that are only weakly or transiently associated to it. Combining this approach with a more sensitive detection procedure such as high-throughput sequencing technologies should really be considered in the future in order to obtain a genome-wide view and full-spectra of the RNAs potentially bound by the protein of interest.In addition to these points, there are other potential issues that cannot be predicted MI and PD designed and planned the experiments. MI performed the experiments. MI, CR, and PD analyzed the data. MI drafted the manuscript. CR and PD reviewed/revised the manuscript. All authors approved the final version to be published.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.


Caenorhabditis elegans data set. Our results have important implications for medical genomics, as STR allelic variation is associated with >40 diseases. STR nonallelic transcript variation can also contribute to disease phenotype. The MLE and empirical rates presented here can be used to evaluate the probability of disease-associated transcripts arising due to RDD.Transcript variation has important implications for organismal function in health and disease. Most transcriptome studies focus on assessing variation in gene expression levels and isoform representation. Variation at the level of transcript sequence is caused by RNA editing and transcription errors, and leads to nongenetically encoded transcript variants, or RNA–DNA differences (RDDs). Such variation has been understudied, in part because its detection is obscured by reverse transcription (RT) and sequencing errors. It has only been evaluated for intertranscript base substitution differences. Here, we investigated transcript sequence variation for short tandem repeats (STRs). We developed the first maximum-likelihood estimator (MLE) to infer RT error and RDD rates, taking next generation sequencing error rates into account. Using the MLE, we empirically evaluated RT error and RDD rates for STRs in a large-scale DNA and RNA replicated sequencing experiment conducted in a primate species. The RT error rates increased exponentially with STR length and were biased toward expansions. The RDD rates were approximately 1 order of magnitude lower than the RT error rates. The RT error rates estimated with the MLE from a primate data set were concordant with those estimated with an independent method, barcoded RNA sequencing, from a  Transcription transfers genetic information from DNA to RNA, and multiple types of transcripts  have critical functions in the cell. Therefore, the modifications or errors that occur in transcripts can lead to phenotypic variation among tissues and individuals. RNA–DNA differences (RDDs) are created by specific enzymatic machinery leading to RNA editing , or arisWith the availability of next-generation sequencing (NGS) data from whole genomes and transcriptomes of many different species, RDDs have been extensively studied, particularly with respect to base-substitution RNA editing , 2013. MRDDs in the form of insertions and deletions, particularly at short tandem repeats (STRs), have been less studied than base-substitution RDDs. Indeed, RNA editing that expands or contracts STRs is yet to be demonstrated. Transcription errors at STRs have been shown both in vitro and in vivo . Althougn repeat number change from n = 11 to n = 12 in the PABPN1 gene can cause Oculopharyngeal Muscular Dystrophy  that utilizes sequencing data from replicate cDNA libraries. Our method can be employed with conventional RNA sequencing procedures, and as such represents an attractive alternative to barcoded RNA sequencing. Using our method, we addressed three questions. First, what are the levels of RDDs and of RT errors at STRs, and do they exhibit contraction or expansion biases? To address this question, we generated DNA and RNA sequencing data from the same tissue of the same individual to eliminate the effects of somatic genetic variation, and simultaneously estimated RT error and RDD rates at STRs. Second, what are the precision and accuracy of our estimates? To assess these properties, we validated the estimated rates with a replicated trial and compared them with those obtained from the published barcoded RNA sequencing data . Finally6 to (A)7 due to RDD, then we should expect to observe (A)7 in both replicated cDNA libraries sequenced. However, if (A)7 was not sampled in one library, then we will observe (A)7 only in the other library, thereby misclassifying this situation as an RT error. Therefore, we developed a full likelihood method that permits sampling errors in the likelihood calculation to avoid error misclassifications.To study the RT error and RDD rates at STRs, we designed the following experiment . We isolD, and we observe variants with repeat lengths D − 1 at high frequency in one cDNA library and D + 1 at high frequency in another cDNA library. Such a scenario is likely to have occurred due to substantial RT errors at this locus, as a large portion of the distribution is different between the libraries. However, now suppose that we instead observe variants with repeat length D + 1 at high frequency in both cDNA libraries. Such a scenario could have occurred through either RDDs or RT errors, though this probability is lower for RT errors than for RDDs, with the uncertainty accounted for within our likelihood method. Finally, suppose that we instead observe variants with repeat length D + 2 at high frequency in both cDNA libraries. Such a scenario is likely to have occurred due to both substantial RDDs and RT errors, because at each step the stepwise mutation model only permits a change in the STR repeat length by one unit. By taking the likelihood across independent loci, we are accumulating evidence for the prevalence of each scenario, and are also directly accounting for the uncertainty by modeling the unobserved states .The rationale behind the method is in the correlation of variants observed between cDNA libraries. RDDs lead to correlated shifts in the distribution of variants between cDNA libraries, whereas RT errors lead to independent shifts between these distributions. For example, suppose at a locus the repeat number in the DNA is supplementary table S1, Supplementary Material online), a higher concordance than that achieved in previous studies  to reduce complexity of MLE estimation . They constitute 99.5% of our data. After additional filtering , we retained 5,582,009 mono-, 2,768,451 di-, 309,546 tri-, and 78,454 tetranucleotide STR-containing loci.Sequencing of the PCR-containing and PCR-free genomic DNA libraries resulted in the estimated genome-wide mean sequencing depth of 6.7× (267 million reads) and 1.8× (73 million reads), respectively. We employed our previously published software, STR-FM , to loca studies . After rFor the RNA-seq data, we generated a total of 56.6, 55.3, 39.7, and 38.9 million paired-end reads for library 1 batch A, library 2 batch A, library 1 batch B, and library 2 batch B, respectively. These sequencing depths are higher than those recommended by the best practice guidelines for gene expression studies of species with a reference genome . Though To profile STRs in the RNA-seq data, we followed the same procedure as that used for DNA data. Briefly, STR-containing RNA-seq reads were mapped to the reference genome, and reads with uniquely mapping flanking sequences (20 bp upstream and 20 bp downstream from an STR) were retained. This procedure resulted in length profiles (a collection of lengths from the reads mapping to this locus) for each STR locus. Each RNA-seq library and each sequencing batch was analyzed separately .n motif ((A/U)n for RNA). We call this motif (A)n for brevity. Most analyses were performed in the range of (A)5–(A)10 because of the high abundance of STRs with this repeat number  , which is expected based on the distribution of STRs in the genome (n-containing loci with low expression level (proxied by the number of RNA-seq reads per locus) were considerably more prevalent than the loci with high expression level . Thus, most (A)n-containing loci in our data set were short and had low expression levels.We focused our analysis on STRs with the (A/T) online) , and due online) . Other me genome . For eac7), our method calculates the likelihood of the data being generated from all possible combinations of RNA forms and all possible combinations of cDNA forms given the set of four parameters . By identifying the parameter set that results in the highest likelihood value, our model makes use of the replicated cDNA library structure tructure  to enhansupplementary figs. S3, S4, and S7–S10, Supplementary Material online), provided certain conditions are met. First, the chosen bin size M must be close to the number of reads per locus of the RNA-seq data, proxying gene expression level . Although the optimal combination between bin size and the number of RNA-seq reads per locus varies among parameter sets , the MLE performs reasonably well when the number of RNA-seq reads per locus is between M and 2M. For example, the estimated RT error and RDD rates for a bin size of 2 are the most accurate when the simulated data were generated using three molecules of RNA and three molecules of cDNA . Second, the number of loci must be at least the inverse of the error rates. The higher the number of loci, the more accurate the estimates. When both conditions are met, the true parameters are bound by 95% of the estimated parameters, and the median estimates deviate from the true parameters by less than 10% .To evaluate the ability of the MLE to infer the four parameters of interest, we conducted simulations using several sets of model parameters, numbers of loci, and bin sizes. The bin size is the number of sampled molecules at the RNA or cDNA stages, which determines the set of possible distinct STR length distributions for RNA and cDNA. This bin size affects the sampling process of each cDNA library from an RNA sample and each RNA sample from the DNA sample. Small bin sizes will yield a high sampling error, leading to distortions in the distribution of RNA or cDNA STR forms relative to the distribution expected under the stepwise mutation model. The results of the simulations indicate that the MLE can estimate all four parameters with a high level of precision and accuracy  as compared with that in our original, or “full, MLE” . We validated this method using loci expressed at low levels and compared its results with those obtained using the full MLE. The parameter estimates and their corresponding 95% confidence intervals of the same data sets are strikingly similar between the full and lumping MLE methods . For example, at a bin size of 5, both the full MLE and the lumping MLE can estimate RDD rates for the data with ten RNA-seq reads per locus with less than 5% error. Because of a similar performance but applicability to a larger range of expression levels than for the full MLE, we will use the lumping MLE to estimate RT error and RDD rates for STR loci with six or more RNA-seq reads per locus.Because the optimal bin size for MLE increases with the number of RNA-seq reads per locus (expression level), it is computationally challenging to estimate RT error and RDD rates from loci expressed at high levels. Therefore, we developed an approximation to the MLE, which we call the n-containing loci expressed at low levels . The exceptionally low RT error rate for repeat (A)5 was most likely due to our detection threshold for mononucleotide STRs—we only collected such STRs starting from five repeats and thus could not observe RT errors (and RDDs) that changed (A)5 to (A)4. As the repeat number increased from 6 to 9 bp, the RT error rates increased exponentially from 2.1 × 10−4 to 7.7 × 10−2, . Our estimates of RT error rates had narrow 95% confidence intervals and were highly similar between the two sequencing batches (blue and red lines in 6 to (A)9 exhibited an expansion bias ; the 95% confidence intervals also widened because the number of loci evaluated decreased . Similar to the pattern observed for RT errors, RDD rates increased with STR length , and the 95% confidence intervals for those that we could estimate were wide . The same is true for our estimates of RDD expansion probability .Using the full MLE and the bin size of 2, we first estimated the RT error and RDD rates, as well as RT error and RDD expansion probabilities, at (A)ion bias D. The exR length . Howeversupplementary table S5, Supplementary Material online) using the bin size of 5 . For example, we simultaneously analyzed both cDNA libraries 1 and 2 for both batches A and B (four sequencing data sets) with lumping MLE with bin size of 5. The estimated RT error and RDD rates  are similar to those obtained after analyzing batches A and B separately . For example, the next most abundant group of STRs in our data after the (A)n-containing STRs were (AC)n- and (AG)n-containing STRs . Among them, we identified only 5,142 loci with three to five RNA-seq reads per locus in batch A . For such loci (combined for these two motifs), we only detected one deviant STR form at the consensus repeat number of 4 (one locus contained two reads of (AG)3), and inferred RDD rate of 0 and RT error rate of 5.84 × 10−4  . The RT error expansion probability was inferred to be 0, indicating a contraction bias; however, the 95% confidence interval was wide . We conclude that we presently lack a sufficient amount of data to accurately evaluate the RT error and RDD rates at STRs others than (A)n.We also attempted to estimate RT error and RDD rates for other STR motifs. However, the numbers of loci were insufficient to estimate these rates accurately . In the barcoded RNA data, using the modified STR-FM . Because most of the cDNA families were present in just one of the three RNA-seq libraries . No errors were detected among the reads for each RNA molecule allowing us to estimate only maximal RDD rate as one divided by the number of loci for a specific motif 5; supplementary table S9, Supplementary Material online).To validate the MLE, we analyzed publicly available RNA data  and evald STR-FM , we deteibraries , we couln-containing motif and that the RT error rates increased with increasing repeat number . The 12 erroneous reads stemmed from RNA families with only two reads, and so we could not immediately determine whether these were expansion or contraction errors. However, based on the consensus repeat numbers of all reads mapped to these loci, we concluded that the RT errors had a preference toward expansions .For this data set, the barcoded RNA was also reverse transcribed (and sequenced) independently three times, allowing one to infer RT error rates. We found that all RT errors occurred at the (A)C. elegans data set (where they were inferred with the RNA barcoded approach) and orangutan data set  appears to be higher than but overall is comparable to the RDD estimates for orangutan . Because we can only estimate the maximal RDD rate from the C. elegans data (as one over the number of studied loci), we are not in a position to compare it with the RDD rate obtained from the orangutan data rigorously.Notably, the point estimates of RT error rates were remarkably concordant between the ratories . Indeed,erlapped . The maxC. elegans data . The resulting RT error rate estimates for the C. elegans data set were strikingly similar between the lumping MLE and barcoded RNA methods. This validates the use of the MLE method for reliable RT error rate estimation.To additionally test the performance of our MLE, we applied the lumping MLE to the ans data  after reKnowing RT error rates can be instrumental in functional genomics analysis where RNA-sequencing data are one of the most important sources of information. RNA-sequencing data have been used to study differences in gene expression among tissues , betweenApplications of RT error rates are not limited to functional genomics. STRs have been widely used as markers in population genomics due to their high polymorphism level . Accordin-containing STRs, we found that the RDD and RT error rates increase exponentially with repeat number—a pattern similar to that previously identified for germ-line mutations utations  and sequutations . This siutations  . The MMLV-RT enzyme has an in vitro error rate of 1/29,000 nucleotides synthesized using an RNA template, and 1/37,000 nucleotides using a DNA template, as determined using a genetic reporter assay . Althougreported . Many prreported . These rreported , 1991. TC. elegans sample—produced in two separate laboratories. This concordance suggests that our estimates are reliable.The RT error rates we obtained were congruent between two independent methods—MLE and barcoded RNA sequencing—and between two independent data sets—the one obtained from an orangutan sample and the one obtained from the We followed several procedures to control for technical errors that could distort RT error and RDD rates. We used the same tissue sample for both DNA and RNA sequencing to prevent somatic variation among different tissues. We genotyped the sample with two separate library preparation techniques, and tested for genotype concordance to ensure the correct genotype. We utilized a flank-based mapping approach in the read-mapping process to avoid bias in the STR-length profiling . This flSeveral factors may still affect our RT error and RDD rate estimates. First, the RNA expression levels at the same locus vary even among cells from the same tissue. Such variation is stochastic , and ourSecond, the MLE possibly reported a suboptimal solution if local maxima exist. However, we do not believe our estimates were distorted by this potential limitation because 1) we considered five sets of initial parameters for each bootstrap procedure of the loci and the initial parameters for the RDD and RT error rates were randomized on a log scale to accommodate error rates that have a large search space; 2) in most cases, all five of the initial parameters converged to the same solution, which suggests that the landscape of our maximum-likelihood surface may not contain many local maxima; and 3) despite the independent analysis of error rates by STR length, the estimated error rates increased exponentially with the STR length, as expected based on the known STR sequencing error and mutation patterns . Also, tIn this study, we provide the first model-based method to estimate the rates of RT errors and RDDs at STRs from RNA sequencing of replicated cDNA libraries. This method can be applied to existing RNA data sets with replicated cDNA libraries and a known genotype (or existing DNA-sequencing data). The merit of our approach is in that it does not require significant changes to be made to the established, general RNA-sequencing procedures. Also, our approach allows one to utilize a large number of STR loci throughout the genome, thus reducing the contextual bias due to sequencing composition around STRs. Therefore, the MLE provides a suitable alternative for estimating batched RT errors to the barcoded RNA sequencing approach . The curn from a larger data set. The MLE method we developed can be used for this purpose.Future studies should evaluate RDD error rates at STRs with more precision. Unlike RT error rates, which depend on an enzyme used for RT during library preparation, RDD rates might differ among species as they depend on species-specific biology. Moreover, both RT error and RDD rates should be evaluated for STRs others than (A)supplementary table S1, Supplementary Material online), therefore including them would not have substantially changed our estimates, while analyzing them is computationally challenging for our model. The increased genetic polymorphism of STRs at heterozygous loci has been controversial both in terms of observations and in terms of mechanistic explanations . Polymerase chain reaction (PCR)-containing and PCR-free libraries with insert size of 250–280 bp were constructed with the TruSeq DNA LT Sample Preparation Kit (Illumina) and the TruSeq DNA PCR-Free LT Sample Preparation Kit (Illumina), respectively, following the manufacturer’s protocol. The libraries were sequenced with the 150 bp × 150 bp paired-end reads on HiSeq2500 (Illumina).Using the DNeasy Blood and Tissue Kit (Qiagen), we extracted genomic DNA from testis of a Bornean orangutan . We limited the subsequent analysis to homozygous loci because 1) they represent the majority of our data  and 2) heterozygous loci can display biased expression between the two alleles  were removed. Conversion between hg19 and GRCh38 was performed with the lift-over tool in Galaxy . The extracted RNA was divided into two aliquots that were utilized to generate two separate sequencing libraries (libraries 1 and 2) using the TruSeq RNA sample preparation kit (Illumina) with the stranded protocol . Each ofsupplementary fig. S1, Supplementary Material online). Each RNA-seq library and each sequencing batch was analyzed separately  was also employed for mapping the RNA-seq reads in our analysis to 1) minimize differences between the current procedure and the procedure used to estimate RNA sequencing errors in previous studies that also used BWA e.g., , 2) guarparately . As a repRDD, and the contraction probability can be computed as 1 – pRDD. The same is true for an expansion probability for RT errors, pRT. The model used in the MLE is based on the following key assumptions:All loci are independent.The error rates and the expansion probabilities for both RT errors and RDDs are identical for all DNA loci with the same STR motif and repeat number.6), there are three possible STR forms for RNA 5, (AG)6, and (AG)7), and five possible STR forms for cDNA 4, (AG)5, (AG)6, (AG)7, and (AG)8). We denote the number of possible STR forms at a given stage (RNA or cDNA) as K.Both RT errors and RDDs follow the stepwise mutation model  that onlM), which represents the number of sampled RNA or cDNA molecules after transcription or RT, respectively. This finite bin size M permits alterations in the expected distribution of STR forms in a given stage (RNA or cDNA) by conditioning on the number of STRs of a given form passed on from the previous stage. For example, suppose that at the RNA stage the relative proportions of STR forms for four, five, and six repeats are 0.1, 0.5, and 0.4, respectively. Based on the previous point, five possible cDNA forms are expected—those with three, four, five, six, and seven repeats. If we sample only a small number M of STRs to be passed from the RNA to the cDNA stage, then it is likely that the STR form with three repeats will not be represented in the cDNA stage. However, if M is sufficiently large, then the probability of observing all possible forms at the cDNA stage is high. This sampling permits different cDNA libraries for the same RNA sample to be correlated, as the STR forms observed in these libraries are conditional on the RNA STR forms that they share, and allows us to take advantage of the structure of our experimental design 5(AG)5, (AG)5(AG)6, (AG)5(AG)7, (AG)6(AG)6, (AG)6(AG)7, and (AG)7(AG)7) and 15 possible compositions of cDNA forms 4(AG)4, (AG)4(AG)5, … , and (AG)8(AG)8). Considering all possible compositions allows us to calculate the probability of changes from DNA to RNA, and from RNA to cDNA, which permits the derivation of the distribution of STR forms at the RNA and the cDNA stages.With this formulation, the likelihood function at locus j can be represented asThe model uses a fixed bin size  in STRs obtained from RNA-seq. The model includes an expansion probability for RDD, M under the stepwise mutation model (assumptions 3 and 4), to ensure that the estimation is not distorted by an incorrect inference of RNA and cDNA STR profiles. In the transition from cDNA to sequencing reads, we incorporated the sequencing error rates estimated by We calculated the probability that the observed data are generated from all possible distributions of RNA and cDNA STR lengths for a given number of sampled molecules −9 to 0.5 for the RT error and RDD rates, and from 0 to 1 for the expansion probabilities. We used the lower bound of 10−9 as it is several orders of magnitude lower than known STR germ-line mutation rates lump and lump forms D + 1 and D + 2 into a single form (D + 1)lump. This formulation reduces the complexity of the calculation as we now have K = 3 forms instead of K = 5 forms at the cDNA stage, and this substantially reduces the number of compositions needed to be evaluated from M + 5 − 1 choose 5 − 1 to M + 3 − 1 choose 3 − 1, thereby permitting consideration of larger bin sizes for a fixed amount of computing time. The probability of state (D − 1)lump is the sum of the probabilities of states D − 2 and D − 1, and the probability of state (D + 1)lump is the sum of the probabilities of states D + 1 and D + 2. As an example, if the genotype is (A)8, then based on the stepwise mutation model there are three possible forms of RNA ((A)7, (A)8, (A)9) and five possible forms of cDNA ((A)6, (A)7, (A)8, (A)9, (A)10). We lump the probabilities of (A)6 and (A)7 and those of (A)9 with (A)10 to reduce the complexity of the calculation. We will refer to this algorithm as “lumping MLE.” Its full description can be found in supplementary text S2, Supplementary Material online. The full and lumping MLE were implemented in R and the resulting software, STR-RNA-MLE, can be downloaded from https://github.com/Arkarachai/str-rna-mle.Due to the computationally intensive nature of the algorithm when bin size To test the ability of our method to estimate its four parameters, we performed simulations to generate random STR length profiles based on fixed RT error and RDD rates (0.01 or 0.05) and expansion probabilities , the number of studied loci , two replicated cDNA libraries, and the number of RNA and cDNA molecules (ranging from 2 to 17 molecules). We then employed our MLE to infer the parameters for each simulation set using bin sizes of 2, 3, and 5. We generated 100 replicate data sets for a given parameter set, and estimated the parameters for each replicate. The 95% confidence interval for each estimated parameter was calculated from the average of the second and third lowest inferred values to obtain the lower bound, and the average of the second and third highest inferred values to obtain the upper bound, from a set of 100 replicates. We also tested the lumping MLE by comparing the estimated parameters from the data with the number of RNA and cDNA molecules set at 6 and 10, RT error and RDD rates set at 0.1, expansion probabilities of RT errors and RDDs of 0.8, 1,000 loci, and two cDNA libraries using both our standard MLE model and lumping MLE model at a bin size equal to 5.supplementary fig. S2, Supplementary Material online) and 2) their estimated rates of RT errors and RDDs are less than 2-fold different from the expected values based on our simulations . To ensure that our estimated rates are valid for the loci with higher expression levels, we used the lumping MLE model to analyze 1) a subset of data with 6–16 RNA-seq reads per locus, using a bin size of 5, and 2) a subset of data with 49–102 RNA-seq reads per locus, using a bin size of 40. For each sequencing batch, each STR length, and each binning of the data, we generated 100 bootstrap replicates in which the loci had the same DNA length and STR length profiles as in our RNA sequencing data from the two replicated cDNA libraries. We analyzed each bootstrap replicate with the MLE starting with five random initial sets of the four parameters of interest, and chose the parameter estimates with the highest likelihood. We started with five initial parameter sets to avoid hitting local maxima. We then calculated the 95% confidence interval for each of the four model parameters by taking the average of the second and third lowest inferred values as the lowered bound, and the average of the second and third highest values as the upper bound, with parameters estimated at each of 100 bootstrap replicates.For each batch of RNA sequencing, the RNA profiling data of replicated libraries were paired with the DNA genotypes at the same loci. Each batch of replicated sequencing data was analyzed separately. We selected subsets of data with an appropriate number of RNA-seq reads to analyze specific bin sizes. Initially, we chose a bin size of 2 and analyzed a subset of data that had a mean number of RNA-seq reads of three to five reads per locus, requiring a minimum of two reads. The loci within this range of RNA-seq reads were chosen because 1) 65% of the STR-containing loci in our data set have low expression level  based on the shared barcode and shared starting genomic mapping coordinate . We mapped flanking regions of the STR-containing sequencing reads to the C. elegans genome assembly version Ce10 using BWA (Li and Durbin 2009), and applied a modified STR-FM pipeline , to polarize the direction of an error. For example, if two cDNA reads in a certain family had an STR with lengths of 10 and 11 bp at the same locus, and the most common cDNA STR length for all the families at this STR locus was 10 bp, then we inferred that the STR of 11 bp was erroneous. Our error estimation did not include any cDNA families or RNA molecules for which we could infer only one cDNA molecule, or one RNA molecule, at a certain locus, as errors could not be inferred in such cases.C. elegans  replicated cDNA libraries with the highest sequencing depths were chosen, and the data were processed exactly as for the orangutan data above (with M = 2). To infer cDNA molecules, we employed the sequencing error rates from As an alternative method of comparison, the RNA sequencing STR length profile of  elegans  was alsoSupplementary texts S1–S3, figures S1–S12, and tables S1–S10 are available at Molecular Biology and Evolution online .


Results from surface plasmon resonance (SPR) verified the direct binding of AZA-9 to HuR. NMR methods mapped the RNA-binding interface of HuR and identified the involvement of critical RNA-binding residues in binding of AZA-9. Computational docking was then used to propose a likely binding site for AZA-9 in the RNA-binding cleft of HuR. Our results show that AZA-9 blocks key RNA-binding residues of HuR and disrupts HuR-RNA interactions in vitro. This knowledge is needed in developing more potent AZA-9 derivatives that could lead to new cancer therapy.The RNA-binding protein Hu antigen R (HuR) binds to AU-rich elements (ARE) in the 3’-untranslated region (UTR) of target mRNAs. The HuR-ARE interactions stabilize many oncogenic mRNAs that play important roles in tumorigenesis. Thus, small molecules that interfere with the HuR-ARE interaction could potentially inhibit cancer cell growth and progression. Using a fluorescence polarization (FP) competition assay, we identified the compound azaphilone-9 (AZA-9) derived from the fungal natural product asperbenzaldehyde, binds to HuR and inhibits HuR-ARE interaction (IC AU-rich elements (ARE) in 3’-untranslated region (UTR) of mRNAs are common cis elements that promote rapid degradation of mRNAs [The limited lifetime and subsequent decay of messenger RNA (mRNA) is an important mechanism for posttranscriptional regulation of gene expression. In mammalian cells, mRNA decay is dependent on both of mRNAs , 2. Specof mRNAs –4.The RNA-binding protein Hu antigen R (HuR), a ubiquitous member of the ELAV/Hu protein family, binds and stabilizes ARE-containing mRNAs that encode oncoproteins, cytokines, growth factors and transcription factors –7. HuR iHuR is overexpressed in a wide variety of cancers, including colon, ovarian, brain, breast, cervical, and pancreas , 12–14. We have previously reported screening of ~6000 small molecule compounds for HuR inhibitors using a fluorescence polarization assay . To expa15N-labeling, we also used ILV-labeling, where the Isoleucine Cδ1 and the geminal Leucine Cδ and Valine Cγ methyl groups are 13C-labeled by growing E. coli in M9 minimal media supplied with 13C alpha keto acids. His6-tagged HuR RRM1/2 simultaneously labeled with 15N and ILV was prepared by expression in E. coli BL21 (DE3) grown in 1 liter of M9 minimal media supplemented with 1 g of 15N-ammonium chloride and 3 g of glucose at 37°C. At OD600 ~0.4, the growth medium was supplied with 60 mg of 2-ketobutyric acid-4-13C (Sigma #571342) to label the 13Cδ1 methyl group of isoleucine and 100 mg of 2-keto-3-(methyl-13C)-butyric acid-4-13C (Sigma #571334) to label the two leucine 13Cδ and the two valine 13Cγ methyl groups [600 of ~0.8), the culture was induced with 0.7 mM isopropyl-β-D-thiogalactopyranoside (IPTG), and cell growth was continued overnight in a 15°C shaker incubator to a final OD600 ~2.5. Cells were harvested by centrifugation , resuspended in binding buffer , and lysed by sonication in the presence of 0.1 mM phenylmethanesulfonylfluoride (PMSF). Cellular debris was removed by centrifugation , and 600 μL of 5% (v/v) polyethyleneimine was added to the supernatant to precipitate the nucleic acids. Following centrifugation , the supernatant was loaded into a 5 mL Ni2+ column, washed with binding buffer and the His6-tagged HuR RRM1/2 was eluted with elution buffer . The HuR RRM1/2 construct used herein retained an N-terminal His6-tag. Purified protein was dialyzed in buffer  and concentrated using Amicon Ultra 3K centrifugal filter (Millipore). Protein concentration was determined by A280.The protein expression and purification of full length HuR (326 residues) and HuR RRM1/2 (residues 18–186) have been described . For NMRl groups . Approxi13C-alpha keto acid was enough to obtain 2D 1H,13C HSQC to assign the ILV peaks of the 12 isoleucine and L39, L61, V66, and L138 residues of HuR RRM1/2.Mutagenesis was used to assign the ILV resonances by introducing conservative mutations  in HuR RRM1/2. The QuikChange kit (Stratagene) was used to introduce site-directed mutants and mutations were verified by DNA sequencing. Proteins obtained from cell growth in 200 mL of M9 minimal media supplied with the appropriate Msi1 oligo derived from the 3’-UTR of Musashi RNA-binding protein 1 (Msi1) with the sequence 5’-GCUUUUAUUUAUUUUG-3’ and 11-mer AREc-fos RNA oligo (5’-AUUUUUAUUUU-3’) identical to the c-fos RNA sequence used in the crystal structure of HuR-RNA complex [c-fos RNA oligo [Aspergillus nidulans that was engineered to overproduce this compound as described elsewhere [6-DMSO) was used.Synthetic RNA oligos were from Dharmacon. For FP assay, fluorescein-tagged ARE complex  were useNA oligo  was usedlsewhere , 27. ComMsi1 or protein-AREc-fos complex. To form HuR-ARE complex, 10 nM full length HuR and 2 nM Msi1 oligo or c-fos oligo were used. To form HuR RRM1/2- AREc-fos complex, 50 nM HuR RRM1/2 and 2 nM c-fos oligo were used. Measurements were taken using a BioTek Synergy H4 hybrid plate reader  after incubating for 2 hours at room temperature. IC50, the drug concentration causing 50% inhibition, was calculated by sigmoid fitting of the dose response curve using GraphPad Prism 5.0. Percent inhibition was calculated by comparing to the DMSO (0% inhibition) and labeled free RNA only (100% inhibition) controls.FP competition assay for screening and hit validation were carried out as reported previously . BrieflySurface plasmon resonance (SPR) datasets were acquired using a BIACORE 3000  at 20°C as described . Briefly4 pH 6.8, 10% D2O). 15N NMR data were acquired using Bruker Avance 800 MHz spectrometer with a TCI cryoprobe. ILV 13C NMR data were acquired using on a Bruker Avance III 600 MHz spectrometer. NMR data were acquired at 25°C, processed using NMRPipe [15N and ILV chemical shift mapping, 80 μM 15N/ILV-labeled HuR RRM1/2 was titrated with unlabeled AREc-fos RNA at increasing molar ratios of 1:0, 1:0.5, 1:1, and 1:1.7. AZA-9 was titrated at 1:1 and 1:2 molar ratios into 50 μM 15N/ ILV-labeled HuR RRM1/2 in NMR buffer with 10% d6-DMSO. Higher protein concentrations or higher molar ratios of AZA-9 resulted in sample precipitation. The 15N titrations were monitored by acquiring 2D 1H-15N TROSY spectra, and the ILV titrations were monitored by acquiring 2D 1H-13C HSQC spectra. The published backbone amide assignments of HuR RRM1/2 (BMRB entry 26628) [Proteins for NMR were dissolved in NMR buffer   and the A docked model of how AZA-9 may bind to HuR RRM1/2 was built starting from the crystal structure of HuR RRM1/2 in complex with RNA (PDB 4ED5) . FRED (vE. coli BL21 (DE3) and purified under native conditions by Ni2+-affinity chromatography , beyond which, it precipitated at higher concentrations.Full length HuR (326 residues) and a shorter HuR construct consisting only of the two tandem RRM1 and RRM2  were overexpressed in tography . TypicalMsi1 RNA oligomer from the 3’-UTR of Musashi1 (Msi1) mRNA previously identified novel small molecule inhibitors of HuR-ARE interaction [Msi1 interaction  was the most potent compound among the nine azaphilone derivatives and thus was chosen for further characterization. To confirm that the inhibition of HuR-ARE interaction is not mRNA specific and to further characterize the interaction between AZA-9 and HuR by NMR methods, we used another HuR target ARE, AREc-fos, which is identical to the c-fos RNA sequence used in the crystal structure of HuR-RNA complex [Msi1, AREc-fos shows tight binding to full length HuR and a smaller fragment, HuR RRM1/2, with a Kd of 3.3 nM and 22.2 nM, respectively  used in the co-crystallization of HuR RRM1/2 [15N-labeled HuR RRM1/2 was titrated with increasing concentrations of unlabeled AREc-fos at 1:0.5, 1:1, and 1:1.7 molar ratios, and the titration was monitored by acquiring 2D 1H-15N TROSY spectra. The stepwise addition of AREc-fos induced mainly peak broadening of specific HuR RRM1/2 resonances  for each non-overlapped peak at an HuR RRM1/2:RNA molar ratio of 1:1 . The 2D 1H-13C methyl HSQC spectrum of HuR RRM1/2  within 19–28 13C ppm range. To assign the 16 ILV residues used herein, fifteen point mutations were introduced in HuR RRM1/2 , and the resultant protein expressed and purified under native conditions. Comparable to the wild type construct, the mutant proteins were soluble at ~6 mg/mL in 100 mM NaCl, 10 mM sodium phosphate pH 6.8 buffer. To illustrate the assignment method to assign I23 for example, comparison of the 2D 1H-13C HSQC spectrum of the I23L point mutant that was selectively 13C-labeled at the isoleucine δ1 methyl with the spectrum of the wild type protein  and a valine residue, V66  in the bound and free form at 1:1 molar ratio ,,Fig 2), 15N TROSY spectra of the free and RNA-bound HuR RRM1/2  that are involved in the phosphorylation and ubiquitinylation of HuR, and these posttranslational modifications affect the RNA-binding, nucleo-cytoplasmic shuttling, and protein stability of HuR  and the RRM1/2 fragment (24 kDa). Lanes were loaded with low and high amounts of purified proteins.Coomassie stained SDS-PAGE of purified recombinant full length HuR ((TIF)Click here for additional data file.S2 Fig(A) Structures of azaphilone compounds tested in the inhibition assay. (B) Dose-response curves of azaphilones disrupting HuR-AREMsi1 binding in FP assay using 10 nM HuR and 2 nM fluorescein-labeled AREMsi1 RNA. Data are representative of three independent experiments. AZA-15 does not inhibit HuR- AREMsi1 interaction and serves as negative control; all other azaphilone derivatives show dose-dependent inhibition of HuR-AREMsi1 interaction.(TIF)Click here for additional data file.S3 Fig1H-13C HSQC spectra for HuR RRM1/2 mutants (red) overlaid with the wild type spectra (black). (A-K) A single missing 13Cδ1 methyl peak for Ile; and (L) two 13Cδ1 and 13Cδ2 methyl peaks for Leu corresponding to the mutated residue are indicated.2D (TIF)Click here for additional data file.S4 Fig(A) RNA-bound (PDB ID: 4ED5) and (B) RNA-free HuR RRM1/2 (PDB ID: 4EGL). (C) Superposition of the (A) RNA-bound and the (B) RNA-free forms of HuR RRM1/2. The structures are superimposed on the RRM1 domain. The structures are colored as follows: RRM1 (green), the inter-domain linker region (yellow), RRM2 (orange), and RNA (blue).Our NMR data does not detect this large interdomain motion of RRM1 and RRM2 upon RNA-binding. Crystal structures of (TIF)Click here for additional data file.


Most DNA viruses express small regulatory RNAs, which interfere with viral or cellular gene expression. For adeno-associated virus (AAV), a small ssDNA virus with a complex biphasic life cycle miRNAs or other small regulatory RNAs have not yet been described. This is the first comprehensive Illumina-based RNA-Seq analysis of small RNAs expressed by AAV alone or upon co-infection with helper adenovirus or HSV. Several hotspots of AAV-specific small RNAs were detected mostly close to or within the AAV-ITR and apparently transcribed from the newly identified anti-p5 promoter. An additional small RNA hotspot was located downstream of the p40 promoter, from where transcription of non-coding RNAs associated with the inhibition of adenovirus replication were recently described. Parallel detection of known Ad and HSV miRNAs indirectly validated the newly identified small AAV RNA species. The predominant small RNAs were analyzed on Northern blots and by human argonaute protein-mediated co-immunoprecipitation. None of the small AAV RNAs showed characteristics of bona fide miRNAs, but characteristics of alternative RNA processing indicative of differentially regulated AAV promoter-associated small RNAs. Furthermore, the AAV-induced regulation of cellular miRNA levels was analyzed at different time points post infection. In contrast to other virus groups AAV infection had virtually no effect on the expression of cellular miRNA, which underscores the long-established concept that wild-type AAV infection is apathogenic. Membranes were hybridized for 16 hours at 37°C or at 55–65°C with the labeled DNA- or DNA/LNA-probes, respectively. Subsequently, membranes were washed three times for 10 min with low stringency buffer (2x SSC / 0.1% SDS), followed by two washing steps at higher stringency (1x SSC / 0.1% SDS) for 5 min at 37°C or at 55–65°C for membranes hybridized with DNA- or DNA/LNA-probes, respectively. Membranes were exposed to X-ray films between intensifying screens at -80°C overnight or longer.For electrophoresis 15% polyacrylamide gels containing 8 M urea and 0.5x MOPS running buffer  were used. Prior to loading of the samples the gel was pre-run at 100 V for 30 min. RNA samples were treated using the FDF-PAGE method as described . In shorescribed . The memescribed  with seq2, 0.5% Nonidet P40, 5 mM dithiothreitol (DTT), protease inhibitor  and 40 U/ml RNase inhibitor  for 30 min at 4°C on an overhead tumbler. Cell debris was removed by centrifugation at 20,000 g for 30 min at 4°C. For co-immunoprecipitation, 100 μl Protein G-Agarose beads (Roche) per preparation were washed three times in ice-cold nuclease-free PBS and subsequently resuspended in 1 ml nuclease-free PBS supplemented with protease inhibitor cocktail. 15 μg of anti-pan Ago mAb clone 2A8 (Merck Millipore) or anti-Rep mAb 76–3 (Progen) were added to the pre-washed beads, respectively, and incubated for 6 hours at 4°C under constant rotation. Antibody-coupled beads were blocked at 4°C over night in blocking solution consisting of nuclease-free PBS containing 1.2 mg/ml BSA, 0.6 mg/ml yeast tRNA (Roche) and protease inhibitor cocktail. The beads were washed three times in ice-cold blocking solution and once with cell lysis buffer. For immunoprecipitation, the cell lysates were incubated with the prepared antibody-coupled beads under constant rotation at 4°C over night. Following, the beads were washed twice with cell lysis buffer supplemented with BSA and tRNA, three times with high salt washing buffer (25 mM Tris-HCl (pH 8.0), 900 mM NaCl, 2 mM MgCl2, 1% Nonidet P40, 5 mM DTT, protease inhibitor cocktail, 40 U/ml RNase inhibitor, BSA and tRNA) and twice with low salt washing buffer (25 mM Tris-HCl (pH 8.0), 150 mM NaCl, 2 mM MgCl2, 0.05% Nonidet P40, 5 mM DTT, protease inhibitor cocktail, 40 U/ml RNase inhibitor, BSA and tRNA). Co-immunoprecipitated RNAs were isolated from the beads using the TRIzol Reagent (Ambion) followed by RNA precipitation and purification.Co-immunoprecipitation was carried out as described . In brie1 μg of each of the eight different total RNA samples  were subjected to small RNA library generation using the TruSeq Small RNA Sample Prep Kit according to the manufacturer’s protocol (Illumina). Briefly, after ligation of the RNA 3’- and the RNA 5’ adapter, total RNA samples were reverse transcribed to cDNA libraries and subsequently PCR-amplified. The PCR products were run on 6% PAGE gels and bands of adapter-ligated DNA libraries between 145 and 160 basepairs (bp) were excised from the gel and purified. These bands correspond to RNAs of a length of approximately 20 to 35 nucleotides. Subsequently, the libraries were validated by bioanalyzer, pooled and sequenced on a HiSeq 2000 platform (multiplexed 1x51+7).http://www.mirbase.org) using Bowtie [The sequencing reads were demultiplexed and the sequencing adapters were removed. Reads shorter than 16 bp were discarded; the remaining reads were mapped without mismatch to the human genome (hg19), transcriptome (generated from RefSeq annotations) and pre-miRNA sequences . For secondary structure prediction of possible precursor RNAs the Vienna RNAfold WebServer was used (http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi). To search for putative target sites of described human miRNAs the miRNA target prediction tool “Target Scan” was applied (http://www.targetscan.org).Known human, HSV1 and Ad microRNAs were obtained from miRBase, the database of microRNAs  higher than 8.90. Using the Illumina TruSeq small RNA sample preparation kit, libraries of RNAs with sizes between 20 to 35 nucleotides were generated. The integrity of the libraries was re-checked immediately prior to sequencing on the Illumina HiSeq 2000 platform.For each data set between 18.0 and 24.9 million RNA reads were obtained by Illumina small RNA-Seq analysis . The majThe population of AAV2-specific small RNA reads for each data set was mapped to the wild-type AAV2 genome. For a better overview RNA reads were initially counted in closed intervals of 10 nucleotides. In the presence of Ad2, AAV2 infected cells showed a high number of small RNAs located within the ITRs . Since sL (TRL) (S-HS / TRS-HS). This hotspot has also been reported in another study during early lytic HSV1 infection just recently [Comparative analysis of Ad2 and HSV1 as helper viruses for productive AAV2 replication allowed the validation of small RNA-Seq data by analysis of known Ad or HSV encoded miRNAs, respectively. Indeed, the majority of Ad2-specific small RNA reads (>95%) could be assigned to the adenoviral mivaRNAs in the absence, as well as in the presence of AAV2 . These sL (TRL) . Furtherrecently .For more precise mapping of the small AAV2-specific RNAs the regions with high read counts were enlarged to single nucleotide level in the data set of AAV2/Ad2 co-infected cells . The 5’ To confirm the presence of AAV2-specific small RNAs by an alternative method, total RNA was harvested from AAV2 infected and AAV2/Ad2 co-infected HeLa cells and respective controls. RNA samples treated with RNase-free DNase were analyzed on Northern blots with specific probes directed against the most prominent AAV2-specific small RNAs. As internal controls, the human miRNA hsa-let-7a-1 and the splicosomal U6-snRNA were detected in all RNA extracts  showing To determine whether sR-108, sR-271 and sR-1862 represent functional miRNAs, their loading into the RNA-induced silencing complex (RISC) was studied. Ago2 is a component of RISC, which binds the mature miRNA and thus enables mRNA target recognition. We performed co-immunoprecipitations (Co-IP) of human Ago2 complexes with total RNAs from infected cells, using an antibody that detects the human argonaute proteins 1 to 4. Subsequently, the RNAs isolated from the precipitates were analyzed on Northern blots using probes directed against the AAV2-specific small RNAs sR-108, sR-271 or sR-1862, respectively. Probes directed against the human miRNA hsa-let-7a and the cellular U6-snRNA, a component of the splicosome, were used as controls. The human miRNA hsa-let-7a-1 could be detected in all total RNA and Co-IP RNA extracts, with the exception of co-immunoprecipitated RNAs in lane 7 for which an unrelated control antibody was used . In the To evaluate the relevance of the newly identified small RNAs the genomes of AAV serotypes 1 to 7 were aligned and compared to the AAV2 sequences of sR-108, sR-271 and sR-1862 . The smaTo assess whether AAV2 infection has an effect on the cellular miRNA expression profile, the read counts of 979 cellular miRNAs were determined, that are listed in the miRNA database (miRBase) . Their eIn this report we present the first comprehensive analysis of AAV-encoded small RNAs from infected cells using Illumina small RNA-Seq. Whereas the cellular miRNA profile was largely unchanged by AAV infection, previously unrecognized small AAV RNAs were detected. Their abundance, AAV serotype conservation and predominant localization in close proximity to the established AAV promoters, p40 and p5, calls for roles as transcriptional regulators during latency and active replication.Based on their location we postulate that sR-108 and sR-271 are generated from the recently identified anti-p5 promoter, whereas sR-1862 is expressed from the p40 promoter. We recently detected the novel anti-p5 promoter on the AAV (-) strand . The pretrs) during Rep-dependent, strand-specific nicking of the AAV genome required to resolve the ssDNA genomes during AAV DNA replication [trs RNA-bound Rep, would result in the here-described small RNAs starting at nucleotide position 1 or 125, respectively (trs adjacent to the Rep binding element (RBE) [In order to initiate transcription, second-strand synthesis of the AAV ssDNA genome is required . Therebylication , 55. Reclication . Nickingnt (RBE) , Rep coucis-regulatory element for the inhibition of recombinant adenovirus replication [Transcription of the small RNA sR-1862, located a few nucleotides downstream of the p40 TSS (nt 1853) could also lead to a secondary structure . Recentllication . Here weInterestingly, in all stem loop models the small RNAs, sR-108, sR-271 and sR-1862, span sequences, which cover both sides of the stem loop including the terminal loop . It is aTypically, virus encoded small non-coding RNAs are expressed either to support virus propagation by generating beneficial conditions or to evade the cellular immune response . The majtrs, the described mutations might have inadvertently abolished the generation of the newly detected small RNAs.The functional role of the small RNAs located within the ITRs are difficult to analyze by genetic means, due to an overlap with elements required for DNA replication and/or virus packaging , 61. A sIn a recent study we have shown that short transcripts initiated at the anti-p5 promoter are generated . Due to In addition, a heterogeneous group of small AAV p40 promoter-associated transcripts has been described . A mechain vivo. The discovery of the small AAV RNA species some derived from transcripts from within the AAV-ITRs and associated with the major AAV promoters adds a new piece of information to the unsolved puzzle of the AAV switch from latency to productive replication. In the absence of a suitable animal model to study the AAV biology in vivo, systems biology approaches to study viral interaction with the host cell transcriptome offer detailed insight onto potentially pathogenic host cell effects. Surprisingly and in contrast to most other virus infections studied, AAV infection neither induces own miRNAs nor regulates host cell miRNA levels to any significant extend. These findings are in line with earlier microarray-based cellular mRNA analysis, where AAV infection showed virtually no regulation [in vivo will be required to unravel the role of the newly identified, obviously regulatory small AAV RNAs for the AAV life cycle.The complex regulation of the AAV life cycle has not yet been fully unraveled, particularly not gulation . TogetheS1 FigThe small AAV2-specific RNAs sR-108, sR-271 and sR-1862 were aligned to the corresponding sites of AAV serotypes 1 to 7. Gray areas represent conserved nucleotide regions; white areas indicate aberrations to the AAV2 small RNAs in the given AAV serotype.(TIF)Click here for additional data file.S2 Fig(A) The expression levels of 979 described cellular miRNAs were analyzed by small RNA-Seq analysis in AAV2/Ad2 co-infected versus uninfected HeLa cells at 27 hpi. Dots near the red line represent equally expressed miRNAs in both data sets; the solid gray lines mark 2-fold and the dashed gray lines 5-fold up- or down-regulation of miRNAs. The most highly up- or down-regulated miRNAs were highlighted and specified. Cellular miRNAs below a level of 100 read counts, within the dashed square, were considered as not representative. (B-D) Expression level of 979 described cellular miRNAs in (B) AAV2/HSV1 co-infected versus uninfected HeLa cells 8 hpi; (C) Ad2 infected versus uninfected HeLa cells 27 hpi; (D) HSV1 infected versus uninfected HeLa cells 8 hpi, displayed as in (A).(TIF)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableListed are the top hundred AAV2-specific small RNA candidates detected by small RNA-Seq analysis in the presence of the helper viruses Ad2 and HSV1, respectively. Given are genomic location, read counts, median read length and sequence of the respective small AAV RNAs.(XLS)Click here for additional data file.