It participates in a complex and dynamic RNA-RNA interaction network involving, among others, essential functional domains of the 3′ untranslated region and the internal ribosome entry site located at the 5′ terminus of the viral genome. A proper balance between all these contacts is critical for the control of viral replication and translation, and is likely dependent on host factors. Proteomic analyses identified a collection of proteins from a hepatoma cell line as CRE-interacting candidates. A large fraction of these were RNA-binding proteins sharing highly conserved RNA recognition motifs. The vast majority of these proteins were validated by bioinformatics tools that consider RNA-protein secondary structure. Further characterization of representative proteins indicated that hnRNPA1 and HMGB1 exerted negative effects on viral replication in a subgenomic HCV replication system. Furthermore DDX5 and PARP1 knockdown reduced the HCV IRES activity, suggesting an involvement of these proteins in HCV translation. The identification of all these host factors provides new clues regarding the function of the CRE during viral cycle progression.The  Hepatitis C virus (HCV) infection is controlled by a number of host and viral factors (RNA and proteins) that cooperate to initiate the different steps of the viral cycle. The use of secondary and tertiary, structurally conserved, functional genomic RNA elements capable of interacting with proteins and other RNA domains affords important advantages in terms of propagation efficiency. These genomic RNA elements direct and regulate translation, replication and encapsidation. The HCV genome is a single stranded, positive RNA molecule of ~9.6 kb that codes for a single open reading frame (ORF), flanked by untranslated regions (5′ and 3′UTR) of conserved sequence and structure1HCV protein synthesis initiation is guided by a subset of regulatory structural RNA elements that define an internal ribosome entry site (IRES)35661011121415cis signals that modulate essential steps during the viral cycle1718cis-acting replication element (CRE), located within the 3′ end of the ORF, consists of three stem-loops: 5BSL3.1, 5BSL3.2 and 5BSL3.3  and High Mobility Group Box 1 (HMGB1) exert biological effects on viral replication whereas DEAD box protein 5 (DDX5) and Poly [ADP ribose] polymerase 1 (PARP1) play a role in HCV translation. Given the involvement of these proteins in different pathways of cell metabolism, the present results support the idea that CRE plays a biological function in the viral cycle by interacting with the host factors here identified. This work provides the first evidence of association of a pool of host cell factors with HCV genome through CRE and the involvement of some of them in different stages of the HCV cycle.The present work provides evidence that the CRE associates with a collection of cellular proteins, most of them reported here for first time, which allows them to connect with the HCV genome. The identification of these proteins by liquid chromatography coupled to mass spectrometry (LC-MS/MS) showed that the CRE is pulled down with a pool of proteins with different biological functions, some of them previously identified as IRES- or 3′UTR-interacting proteins. Many of these proteins bear RNA-recognition motifs (RRMs) within their sequences. Additionally, To identify eukaryotic proteins able to interact with the HCV CRE, pull-down assays were performed involving CRE and Huh-7 cell lysates. To this end, a genomic HCV RNA fragment comprising the CRE plus the downstream hypervariable region (9181–9414) was transcribed . This RNTo identify the pool of proteins, pull-downs were subjected to trypsin digestion and LC-MS/MS. Biological replicates were compared two by two using PEAKS software, and the proteins enriched in at least three of the four replicates selected (see Methods). As shown in 1224The candidates that interact with CRE were classified using the Panther Classification System gene list analysis tool. An additional classification tool with a broader and both functional and structural focus was used. DAVID bioinformatics resources can extract biological features/meanings associated with large gene lists by simultaneously comparing terms from different annotation categorieshttp://rbpdb.ccbr.utoronto.ca/) to identify the type and number of RNA binding domains involved in the protein-CRE interaction. Interestingly, all showed the presence of RRMs in their sequence (All the proteins belonging to the RNA-binding cluster were checked against the RNA-Binding Protein (RBPDB) database  of the proteins identified in the pull-down experiments were predicted to bind as well to the CRE sequence  was transfected with siRNAs designed against the mRNA of these proteins. For this purpose those siRNA sequences that have already been proved to be effective were used2930To confirm the knockdown results we selected hnRNPA1 and HMGB1 and cloned their respective coding genes into the pcDNA3 eukaryotic expression vector. Huh-7 cells were transfected with pcDNA3 (as a control) or the corresponding hnRNPA1- or HMGB1-pcDNA3 constructs. Interestingly, 48 h after transfection a significant inhibition of the replicon levels was detected in both cases , reinforMoreover, direct interaction of hnRNPA1 with CRE was assayed through an RNA-immunoprecipitation protocol (RIP). Results shown in Firefly luciferase gene Renilla luciferase reporter (cap-RLuc) was used for normalization of translation data. Translation efficiency at 4 h post-transfection is shown in Since CRE was been shown to long-range interact with the IRES and inhibit its translation functionMany processes are run by viral genomes through the use of structurally conserved functional RNA domains which form a dynamic, complex network of direct RNA-RNA interactions that may be regulated or stabilized by the recruitment of host or viral factors. In the case of HCV, the CRE region plays an active role in the organization of an RNA-RNA contact network involved in the regulation of viral translation and replication20212232et al.To our knowledge, very few attempts to study CRE-protein association have been made. The present work identifies a pool of 55 host proteins that are pulled-down by an RNA transcript corresponding to the HCV CRE. The majority of these proteins are proposed as HCV-interacting proteins for the first time. Using MALDI-MS, Oakland As would be expected for an RNA-protein pull-down method, most of the proteins identified in this work were classified as nucleic acid binding proteins. This suggests direct association between them and the CRE. However, protein-mediated interactions cannot be ruled out since 6% of the factors were initially termed as protein-binding proteins. Using bioinformatics resources such as DAVID searches, which do not rely exclusively on GO terms, protein pool was clustered into eight different groups; that with highest score corresponded to RNA-binding proteins . InteresThe catRAPID algorithm, which studies RNA-protein interactions based on their sequences and secondary structure, strengthened the reliability of identification of the pulled-down proteins, even though some are termed DNA-binding proteins. HMGB1 provides an example of these proteins reported to bind with high affinity to branched RNA structures39Considering the involvement of CRE in HCV translation is interesting to remark that two proteins related to the regulation of translation were identified: RNA-helicase DDX5 and eukaryotic translation initiation factor I subunit I (eIF3I). In fact, other subunits such as eIF3E, eIF3F and eIF3H were also detected in one or two experimental replicates, and other proteins related to translation  were detected in previous pull-down experiments performed under less stringent conditions (data not shown). This is in good agreement with the HCV IRES translation regulation exerted by CRE41Flaviviridae21Finally, some proteins were selected to check whether they had any important involvement in HCV replication. In this work, replication experiments were performed on a hepatoma cell line containing a subgenomic HCV replicon defective for the viral protease (NS2/NS3). As an advantage, these assays reproduce quite accurately the HCV genome replication, being a good biological model. On the other hand one cannot exclude additional interactions of the assayed proteins with other regions of the HCV genome contained in the replicon, more than the CRE element. In fact some of the proteins tested for their implication in replication have been suggested to interact with other regions of the genome of the HCV  and the degree of protein and carbohydrate/phenolic contamination assessed from the A260/A280 and A260/A230 ratios respectively. The integrity of the RNA was confirmed by denaturing agarose-formaldehyde gel electrophoresis.DNA coding for the CRE transcript was obtained by PCR amplification, ensuring the presence of the precise 3′ end. Briefly, T7pC + HV was amplified from plasmid pU3′HCV9181 using the primers T7pHCV-9181in vitro transcription and purification as explained above. The generation of a capped RLuc RNA was generated as previously describedRNA construct (ICU) containing a luciferase reporter gene flanked by the HCV genomic ends was obtained by amplification of pGLICU plasmid with 5′pT7HCV and 3′HCV primers2 atmosphere. The knockdown assays were performed in the named cells and in a human hepatocarcinoma cell line harboring an HCV subgenomic replicon system (Huh-7 NS3-3′)53The cell lines used in this study were a gift from Dr. R. Aldabe . The human hepatoma Huh-7 cells were maintained in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum and 1 mM sodium pyruvate, at 37 °C in a 5% COg for 5 min. Cellular lysis was performed by adding 1.5 volumes of cold hypotonic buffer  and incubation at 4 °C for 20 min. Cell lysate was homogenized in a glass Dounce homogenizer (25 strokes) and equilibrated with 0.2 volumes of S10 buffer . Cellular debris was removed by centrifugation at 10,000 × g for 10 min. The resulting cleared lysates contained around 30 A280 units ml−1.S10 fractions of Huh-7 cell lysates for use in pull-down assays were essentially prepared as previously described® (Invitrogen) were washed and equilibrated following the manufacturer’s instructions. Ribonucleoprotein complexes were captured by incubation of the Dynabeads in the binding reaction in a rotation wheel for 30 min at room temperature. Unbound factors were removed after washing three times with buffer B (0.1 M NaCl and 0.1% Tween® 20) pre-warmed at 37 °C. CRE-protein complexes were eluted from the beads by heating for 2 min at 95 °C in the presence of 95% formamide and 10 mM EDTA. The recovered polypeptides were precipitated in methanol/chloroform. Some 2/3 of the sample volume was aliquoted for LC-MS/MS analysis and the rest used in SDS-PAGE. For the latter, samples were resuspended in Laemmli buffer 1X  and loaded onto a polyacrylamide gel . Proteins were stained by Sypro-Ruby (BioRad) and scanned in a Typhoon 9400 scanner . Both biotinylated and non-biotinylated reactions were conducted in duplicate .Transcript C + HV (500 pmol) was annealed to 1 nmol of the 5′ biotinylated oligonucleotide (b-asHCV-9414) (Eurofins MWG Operon) or to the respective unlabeled asHCV-9414 for control reactions (sequence detailed in DNA template and RNA synthesis). It was then incubated with Huh-7 S10 cell extracts (~10 mg of proteins) pretreated with 50 μg of tRNA and CL buffer . Reactions were performed at 25 °C for 20 min to allow for CRE-protein interactions to take place. For the affinity chromatography step, 5 mg of streptavidin-coupled DynabeadsThe rest of the samples were resuspended, digested and run in LC-MS/MS following a protocol similar to that used by others56Supplementary Table 1.The Panther gene list analysis tool was used to classify the pulled-down proteinshttp://david.abcc.ncifcrf.gov/home.jsp). Functional annotation clustering of the protein list was performed under the highest classification stringency. A total of 13 annotation categories were selected (by default) for the search. As indicated by Huang and co-workersThe same protein list was examined using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 bioinformatics resources . The CRE sequence was run against the Homo sapiens nucleotide-binding proteome using the catRAPID omics tool. A search was performed for RNA- or DNA-binding full-length proteins. Proteins were ordered based on their Rating Star Score, which considers the protein-RNA Interaction Propensity score (i.e. the interaction probability) and the presence of RNA/DNA binding domains and motifs. Additionally, nine different FASTA protein sequences, taken from the Uniprot database, were run (using the catRAPID graphic tool) against the complete CRE sequence, and nucleotide-aminoacid interaction heat-maps obtained.The catRAPID webserver was used for predicting RNA-protein interaction  in 6 well-plates to obtain a confluence of at least 80% after 24 h. Cells harbouring a subgenomic HCV replicon were transfected with small interference RNAs (siRNAs) designed against the mRNAs of the proteins of interest. For hnRNPA1, DDX5, G3BP1 and HMGB1, a pool of four targeting siRNAs for each protein was designed (Dharmacon). For PARP1 inhibition, a specific siRNA (Sigma) was used. As a negative control, a single non-targeting (NT) siRNA (Dharmacon) was selected. siRNAs were mixed with TransfectinRNA extraction was performed using Trizol reagentTM Western ECL Substrate Kit (BioRad).For protein extraction, cells were scraped after adding 500 μl of cold PBS, and centrifuged at 2500 rpm. The resulting pellet was resuspended in 200 μl of protein Lysis Buffer  for 30 min at 4 °C. Lysates were centrifuged at 12,500 rpm and the supernatant stored at −20 °C. The protein concentration was measured using BioRad Protein Assay reagent. Proteins were separated by SDS-PAGE and transferred to nitrocellulose membranes in a SemiDry Trans Blot apparatus (BioRad). Membranes were blocked in 5% dry milk in TBS-Tween and incubated with specific primary antibodies against hnRNPA1, DDX5, HMGB1  G3BP1 (Santa Cruz Technology) or PARP1  in 1% BSA-TBS/Tween solution at 4 °C overnight. Secondary HRP-linked antibodies were added for 1 h. Chemiluminescence was then detected using the ClarityEcoRI and XhoI for HMGB1 sequence and BamHI and XhoI for hnRNPA1 and inserted in pcDNA3 vector digested with the corresponding enzymes. Plasmid isolation was perfomed using Qiagen® Plasmid Maxi Kit (Qiagen). Huh-7 NS3-3′ cells were cultured in 6 well plates and allowed to grow up to 80% confluence, then cells were transfected with 2 μg/well of pcDNA3 (as a control) or pcDNA3-hnRNPA1 or pcDNA3-HMGB1, and total RNA was extracted 24 h or 48 h after transfection using Trizol reagent. Quantification of HCV RNA levels was performed by RT-qPCR as described above. Relative amounts of hnRNPA1 and HMGB1 mRNAs to GAPDH mRNA in the total RNA mix was analyzed by RT-PCR amplification with the following primers: 5′A1-2D (GCGTATGGATCCTCCAGCCAAAGAGGTCGAAGT) and asA1 for hnRNPA1; asHMGB1-A (GCGTATCTCGAGGAACTTCTTTTTTGTCTCCCCTT) and 5′HMGB1 for HMGB1. Amplification products were resolved in a 2% agarose gel.Total RNA from Huh-7 cells was extracted using Trizol reagent. Primer extension was performed as described above using instead an oligodT and annealing at 25 °C. cDNA was amplified by PCR using 5′HMGB1-EcoRI (GCGTATGAATTCATGGGCAAAGGAGATCCTAAGAA) and asHMGB1 (GCGTATCTCGAGTTCATCATCATCATCTTCTTCT) primers for HMGB1 and 5′A1 (GCGTATGGATCCATGTCTAAGTCAGAGTCTCCTAAA) and asA1 (GCGTATCTCGAGTTAAAATCTTCTGCCACTGCCATA) for hnRNPA1. PCR amplicons were digested with The RIP assay was carried out following the protocol described by Niranjanakumari and coworkersFirefly or Renilla luciferase reporter genes and translation efficiency measured after 4 h using Dual Luciferase Reported Assay System (Promega). Translation efficiency was determined by the ratio FLuc/RLuc .


DMPK gene 3’UTR. The nature, severity and age at onset of DM1 symptoms are very variable in patients. Different forms of the disease are described, among which the congenital form (CDM) is the most severe. Molecular mechanisms of DM1 are well characterized for the adult form and involve accumulation of mutant DMPK RNA forming foci in the nucleus. These RNA foci sequester proteins from the MBNL family and deregulate CELF proteins. These proteins are involved in many cellular mechanisms such as alternative splicing, transcriptional, translational and post-translational regulation miRNA regulation as well as mRNA polyadenylation and localization. All these mechanisms can be impaired in DM1 because of the deregulation of CELF and MBNL functions. The mechanisms involved in CDM are not clearly described. In order to get insight into the mechanisms underlying CDM, we investigated if expanded RNA nuclear foci, one of the molecular hallmarks of DM1, could be detected in human DM1 fetal tissues, as well as in embryonic and neonatal tissues from transgenic mice carrying the human DMPK gene with an expanded CTG repeat. We observed very abundant RNA foci formed by sense DMPK RNA and, to a lesser extent, antisense DMPK RNA foci. Sense DMPK RNA foci clearly co-localized with MBNL1 and MBNL2 proteins. In addition, we studied DMPK sense and antisense expression during development in the transgenic mice. We found that DMPK sense and antisense transcripts are expressed from embryonic and fetal stages in heart, muscle and brain and are regulated during development. These results suggest that mechanisms underlying DM1 and CDM involved common players including toxic expanded RNA forming numerous nuclear foci at early stages during development.Myotonic dystrophy type 1 (DM1) is caused by an unstable expanded CTG repeat located within the  Myotonic dystrophy type 1 (DM1) is a dominantly inherited neuromuscular disorder characterized by very variable symptoms. Several forms of DM1 can be differentiated: a late onset form with pre-senile cataracts and early frontal balding; an adult form which presents myotonia, insulin resistance, cardio-respiratory problems, muscle weakness, hypersomnia and cognitive impairments; and the congenital form (CDM), the most severe form. CDM is characterized by high perinatal mortality 30–40%), mainly due to respiratory distress, hypotonia, sucking and swallowing difficulties. Children who survive the neonatal period will later show delays in motor development and intellectual disability . Interes0–40%, maDMPK) ..DMPK) [DSIX5 regulatory region downstream of the CTG repeat has been described [DM1 locus, to form a chromatin insulator element. In CDM myoblasts, the expanded allele is associated with loss of CTCF binding and propagation of heterochromatin [DMPK late in embryogenesis. Expression of high levels of expanded CUG repeat-containing RNA at this stage might contribute to the earlier disease phenotype in CDM. More recently, it has been reported that RNA carrying CTG/CAG expansions can be translated by repeat associated non-ATG (RAN) translation in homopolymeric proteins in the absence of an ATG start codon. Homopolymeric proteins generated by antisense DMPK mutant RNA can form potentially toxic aggregates found in DM1 mouse models and human tissues [DMPK and the CTG repeat length is still unclear during development.An antisense transcript emanating from the adjacent escribed . This trhromatin , 23. It  tissues . The conDMPK 3’UTR from embryonic stages reproduced cardinal features of DM1 from 2 weeks of age, more severely than in adult mice [DMPK gene with normal (DM20) or expanded (DMSXL) CTG repeats under the control of its own human promoter [DMPK RNA are both expressed from early stages and are developmentally regulated.Until now, the mechanisms of DM1 were mainly studied in the context of the adult form and few models have recreated early features of DM1. Furthermore, the extent and distribution of RNA foci during development is unknown. An inducible mouse model overexpressing the ult mice . A zebrault mice . In thispromoter , 28. We DMPK allele with 20 CTG repeats. The animals were only used for tissues recovering after sacrifice. This project has been conducted with the authorization for animal experimentation n° 75 003 in the animal facility with the approval n°: B 91 228 107 both delivered by Prefecture de police and the French veterinary department.DMSXL and DM20 mice (>90% C57BL/6 background) carried 45 kb of human genomic DNA cloned from normal or mutated DM1 patient allele as previously described . TransgePost mortem tissues of DM1 infants and tissues of fetuses were sampled at autopsy at least 18 years ago and stored at-80°C. No law required approvals at the time of tissues collection. The diagnosed CTG repeats ranged between 1100 and 2500 CTG.DMPK gene  and in exons 4 and 5 of the mouse Dmpk gene . Additional controls were performed using the same primers but without reverse transcriptase.Tissues were homogenized in Trizol (Life Technologies) using a tissue lyser (Retsch MM400): twice for 2 min 30 sec at 23 pulses/sec with 2 stainless 5 mm steel beads (Qiagen). After chloroform extraction, the aqueous phase was mixed with 70% ethanol and transferred to a Spin Cartridge of the PureLink RNA Mini Kit (Ambion by Life Technologies). Total RNA was extracted according to the manufacturer's protocol. A PureLink DNase step was inserted in the protocol, after binding of the RNA to the column as recommended. The quantity of RNA was determined by absorbance at 260 nm with a Nanodrop spectrophotometer and its quality was verified on a 1% agarose gel. Each RNA was controlled for absence of DNA contamination by RT-PCR using for RT random primers and for PCR primers located in exons 3 and 4 of the human DMPK using the strategy described by Cho et al [For strand-specific RT-PCR, we attached a linker sequence, LK 5’-CGACTGGAG CACGAGGACACTGA-3’, to the 5’ end of a primer specific for the antisense or sense strands of ho et al . cDNA waFor each RT with different quantity of RNA input, we performed a quantitative RT-PCR (qRT-PCR). We analysed the results of qRT-PCR and compared with theoretical results obtained with cDNA from 0,125 μg, 0,25 μg, 0,5 μg, 1 μg and 2 μg of RNA. The RNA quantity was selected when the quantification of the studied gene was proportional to RNA quantity used for the RT. This step was made for each tissue and each gene studied. See 18S rRNA was used as internal control. mRNA levels were quantified relative to 18S transcripts and the ratios were normalized using the same standard sample. For experiments using mouse samples, RNAs from 4 to 6 sex- and age-matched animals were pooled. Oligonucleotide primer sequences are described in The transcripts were amplified in a 7300 Real Time PCR System (Applied Biosystems) using Power SybrGreen detection (Life Technologies). Annealing temperatures and sample dilutions were optimized for each amplicon. For qRT-PCR, we used standard curves established with 6 known dilutions of plasmid carrying the amplicon. Samples were quantified in triplicate and experiments were repeated at least twice. 5 PNA probe as described [5 PNA probe using the same protocol. For simultaneous FISH detection of sense and antisense foci, we first performed hybridization using the 5’-Alexa 488-(CAG)5 PNA probe to detected sense ribonuclear inclusions. After 3 washes in 1X PBS for 2 min at room temperature, the slides were hybridized with 5’-Cy3-labeled (CTG)5 PNA probe. The specificity of the RNA foci was determined by treating some slices before FISH detection with either RNase A (100 μg/mL in 2× SSC) or DNase  at 37°C for 30 min as shown in Frozen sections of tissues (10 μm) were prepared: transversally for mouse embryo brains, and sagittally for muscle and heart studies. Sense ribonuclear inclusions were detected with a 5’-Cy3-labeled (CAG)escribed . For antAll statistical analyses were performed with the GraphPad Prism software (GraphPad Software Inc.) using two-tailed Student's t-test when distributions were normal with equal or unequal variance as appropriate and Mann-Whitney test (for non-Gaussian distributions). The significance level was set at 0.05 for all statistical analyses.5 fluorescent probe, we investigated if we could detect expanded DMPK sense RNA foci, one of the hallmarks of DM1, in human fetal tissues samples. Interestingly, we observed very abundant foci in heart, skeletal muscle and brain as early as 12 weeks of gestation. RNA foci were also detected in these 3 tissues at later stages, up to 33.5 weeks (DMPK sense RNA foci. We studied the distribution of foci per nucleus (Using (CAG).5 weeks , despite nucleus , and fou nucleus . HoweverDMPK sense RNA foci at early stages in the DMSXL mouse model. DMSXL are transgenic mice carrying the human DMPK gene with >1000 CTG that is expressed in various tissues under the control of its own human promoter and regulatory sequences [in situ hybridization, we observed a very high number of mutant RNA foci in heart, skeletal muscle and brain from DMSXL embryos at embryonic day 14.5 (E14.5) and DMSXL neonates on postnatal day 7 (P7) (We investigated the presence of expanded equences . Using iy 7 (P7) . At bothy 7 (P7) .DMPK sense RNA foci sequestered MBNL1 and MBNL2 in embryonic and fetal DM1 tissues. Using combined in situ hybridization and immunohistochemistry, we observed that the majority of RNA foci co-localized with MBNL1 and MBNL2 in heart, muscle and brain from human DM1 fetal samples and from DMSXL embryonic and postnatal samples  fetuses . HoweverDMPK RNA foci n probe combined with MBNL antibodies, resulting in the detection of very faint foci. Nevertheless, in those cells where we could observe antisense foci, they did not appear to co-localize with MBNL proteins (data not shown).In DMSXL embryos and neonates, we also detected mutant antisense RNA foci . In bothRNA foci . The antRNA foci , and appRNA foci  and 6. FDMPK transcripts in control and DM1 fetal heart and brain. Unfortunately, we did not have enough tissues to extract RNA from fetal muscle samples. DMPK transcripts are detected at higher levels in heart than in brain samples. No statistical significant difference was observed between control and DM1 samples of the same ages. Antisense DMPK transcripts are expressed at lower level when compared to sense transcripts both in heart and brain. Interestingly, levels of antisense transcripts appeared increased in DM1 heart and brain compared to control samples. The difference reached significance only in brain .Using specific qRT-PCR and standard curves established with a known number of plasmid molecules coding the amplicons, we studied the levels of sense and antisense DMPK sense and antisense RNA over a wider development period, we studied by qRT-PCR the levels of transcripts in transgenic mice from E14.5 up to postnatal age 29 days (P29)  . We compn muscle . In DMSXn muscle . FurtherDMPK RNA are the central player in DM pathogenesis and exhibit a deleterious gain-of-function. The molecular hallmark of DMPK RNA toxicity in patients is the formation of abnormal nuclear foci formed by aggregation of the expanded transcripts [DMPK transcripts have been well documented in the case of adult DM1, less is known about the formation of toxic RNA foci during development in DM1 and CDM fetal tissues.During the last decade, numerous studies have clearly demonstrated that expanded nscripts . These fnscripts . If the DMPK RNA foci, during development in DM1 fetal tissues and in DMSXL mice, in the major affected tissues: heart, skeletal muscle and brain. In all three tissues, we observed very abundant sense DMPK RNA nuclear foci as early as 12 weeks in DM1 fetuses and E14.5 in DMSXL mouse embryos. In mouse embryos and neonates, the highest intensity was observed in heart. The foci intensity observed are consistent with the profile of DMPK expression that is higher in heart and muscle at both stages  is integrated in different locations in DMSXL and DM20 mice and we cannot exclude a position effect on the regulation of DMPK. Furthermore, the developmental pattern of sense DMPK expression is different in both transgenic mice (DMSXL and DM20) compared to the endogenous Dmpk gene in muscle with a decrease of DMPK sense transcript in mice at P29. This could reflect also position effect or inadequate regulation of the human DMPK expression  by mouse transcription factors in muscle at later stage.In order to better characterize the expression of sense and antisense ansgenes , 32. Thunscripts , 40. In  of DMPK , 41. In DMPK transcripts are expressed from early stages during development suggesting that expanded RNA could trigger pathogenic events at early stages, contributing to developmental disease features, such as mental retardation and muscle immaturity described in CDM. Sense foci clearly sequester MBNL protein at early stages demonstrated that MBNL proteins as in adult DM1 are probably also a pathological intermediate in CDM. Antisense transcripts that appeared to be expressed in affected tissues and for which expression is higher at early stages could also participate to RNA toxicity but independently of MBNL proteins that do not appeared sequestered at high levels by the antisense foci. RAN translation is an appealing possible mechanism of toxicity and could have more impact at early stages when the antisense transcripts are expressed at higher levels.In conclusion, we showed that sense and antisense S1 FigThe number of foci per nucleus was determined using 3D images. Graphs represent the percentage of cells showing a given number of foci per nucleus. A: heart; B: skeletal muscle; C: brain, 12–13 wk: 12 to 13 week-old fetuses; 16–17 wk: 16 to 17 week-old fetuses; 33–33.5 wk: 33 to 33.5 week-old fetuses.(TIF)Click here for additional data file.S2 Fig5 PNA probe in heart samples from a 12 week-old DM fetus (DM-12wk), and in DMSXL P7 neonates heart samples (DMSXL-P7), with or without treatment with RNase (+ RNase) or DNase (+DNase).RNA foci containing (CAG)n expansions were labeled in red, using a 5’-Cy3-labeled (CTG)(TIF)Click here for additional data file.S3 FigDmpk transcripts were studied by qRT-PCR in heart, skeletal muscle and brain from DMSXL embryos and neonates at embryonic E14.5 to postnatal P29 stages. Levels of Dmpk transcripts were reported on graphs using 18S as internal control, in arbitrary units (a.u.) with standard deviation of the mean for repeated experiments.Mouse endogenous sense (TIF)Click here for additional data file.


DNA microarrays and RNA sequencing (RNA-seq) are major technologies for performing high-throughput analysis of transcript abundance. Recently, concerns have been raised regarding the concordance of data derived from the two techniques. Using cDNA libraries derived from normal human foreskin fibroblasts, we measured changes in transcript abundance as cells transitioned from proliferative growth to quiescence using both DNA microarrays and RNA-seq. The internal reproducibility of the RNA-seq data was greater than that of the microarray data. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarray values were moderate. The two technologies had good agreement when considering probes with the largest (both positive and negative) fold change (FC) values. An independent technique, quantitative reverse-transcription PCR (qRT-PCR), was used to measure the FC of 76 genes between proliferative and quiescent samples, and a higher correlation was observed between the qRT-PCR data and the RNA-seq data than between the qRT-PCR data and the microarray data. DespiRNA sequencing (RNA-seq) has recently become popular as an alternative method for measuring transcript abundance . Like thet al. [et al. [While several previous studies have compared RNA-seq data to DNA microarray data –17, to oet al. ) used qu [et al.  performe2.2.1In order to determine the concordance between transcript abundances as measured by RNA-seq and by DNA microarrays, two RNA-seq reactions and four two-channel DNA microarray assays were performed. We first determined the level of internal reproducibility of the microarray data. Labelled cDNA libraries prepared from paired proliferative and quiescent cells were hybridized to each of four microarrays , with biological replicates used for each microarray. The four microarrays were labelled QP1, QP2, QP3 and QP4. ‘Dye-swaps’ were performed for arrays QP2 and QP4 to ensure that there were no biases in the labelling protocol. Analysis of raw datasets was performed using the online microarray database software BioArray Software Environment (BASE) , with whEach microarray contained 35 355 probes, each approximately 70 bp in length. Correlations between probe intensity values  and fold change (FC) values (QUI/PRO) were determined for all a), while microarrays QP1 and QP4 exhibited a negative relationship (figure 1b). These negative correlations were unexpected; however, they might arise from the method used to label specific cDNA libraries. Each library was constructed to bind specific tags called dendrimers  following hybridization of the cDNA to the arrays. Given the complex nature of dendrimer binding, it is possible that a number of spots on the arrays are giving values consistently with one dye regardless of the dye swaps for the samples and therefore disrupting the array correlations. For the majority of probes, there appeared to be little relationship between the log-transformed FC values between QP1 and QP4 (the ‘cloud’ in the middle of figure 1b); however, probes with very high FC values in one microarray tended to have very low FC values in the other (figure 1b), resulting in a negative Pearson correlation. Scatterplots comparing FC values for the remaining pairs of microarrays are given .Relative to the correlations between intensity values, the Pearson correlations between FC values were generally lower, ranging from −0.01 to 0.71 . This wafigure 12.2nsembl [opHat 2 [eqMonk (http://www.bioinformatics.babraham.ac.uk/projects/seqmonk), 149 135 probes were generated based on Ensembl-annotated mRNA transcripts, and read counts were normalized using the widely used reads per kilobase of gene per million reads (RPKM) method [eqMonk for three genes. The two proliferative RNA-seq replicates were labelled PRO1 and PRO2, while the two quiescent replicates were labelled QUI1 and QUI2.We previously performed comparative transcriptome analysis of RNA-seq datasets to identify genes that had changed expression more than or equal to fivefold as fibroblasts were induced from proliferative growth to quiescence using serum starvation . Our anansembl  using To[opHat 2 . Using S) method –29. ElecThe concordance between the RNA-seq replicates was determined both in terms of normalized read counts  and in terms of FC values (i.e. QUI1/PRO1 versus QUI2/PRO2). A fixed value of 0.05 was added to the normalized read count for each probe to prevent division by zero when calculating FC values. While the Pearson correlations between read counts were fairly low (0.58 for PRO and 0.35 for QUI), the Pearson correlations between log-transformed read counts, as well as the Spearman correlations, were similar to or higher than those of the microarray data, ranging from 0.93 to 0.94 . These c2.3nsembl transcript IDs were used in order to generate a mapping of microarray probes to RNA-seq probes. Of the 35 355 microarray probes, 29 007 (82%) mapped to an RNA-seq probe via at least one of the two methods. The remaining microarray probes, as well as the RNA-seq probes that did not have a matching microarray probe, were not used in comparing the microarray data to the RNA-seq data. Some RNA-seq probes were mapped to by more than one microarray probe; the total number of unique RNA-seq probes used was 22 041 . Note that because the RNA-seq probes overlap in the genome, it is not the case that 85.2% of the RNA-seq data are being thrown away in the comparison to the microarray data; see the electronic supplementary material, Discussion, for more details.In order to compare the RNA-seq data to the microarray data, the correspondence between the microarray probes and the RNA-seq probes had to be ascertained. Both a sequence-based method and a method based on ETo simplify the comparisons, the reads from the two PRO RNA-seq replicates were combined, as were the reads for the two QUI RNA-seq replicates. This was justified by the high reproducibility of the RNA-seq biological replicates . These cIn §2.1, we reported that the inter-array correlations between some pairs of microarrays were low. In response to this, we compared the microarray data to the RNA-seq data in two different ways: individually, as well as collectively. Comparing each microarray individually to the RNA-seq data has the advantage that the degree of concordance can be ascertained without inter-array variation being a contributing factor. By contrast, comparing the combined microarray data to the RNA-seq data has the advantage that it is more consistent with how experiments are typically done. More specifically, biological inferences are almost never drawn from the results of just a single microarray; it would be far more common to perform several replicates and then combine their results . In this study, the correspondence of the FC values between RNA-seq and the individual microarrays was very low, ranging from 0.02 to 0.07 for the Pearson correlation, 0.17 to 0.23 for the Pearson correlation between log-transformed values, and 0.16 to 0.21 for the Spearman correlation . Specifip-value was determined which indicates the likelihood that the level of overlap between a given pair of lists was greater than would be expected at random. For lists of size 10, there was no overlap between the RNA-seq data and microarrays QP1, QP3 and QP4, while there was one probe in common between the RNA-seq data and microarray QP2 , as well as for the combined RNA-seq replicates. For each list size, the RNA-seq list was compared separately to the list for each microarray. Using the empirical statistical test described in Material and methods, a rray QP2 . Two proFinally, of the 29 007 instances in which there was a corresponding probe between the RNA-seq data and the microarray data, the number of microarray probes that had an FC value more than 5 was 225, 785, 256, 975 and 50 for QP1, QP2, QP3, QP4 and the geometric mean of the four arrays, respectively. Of these, the numbers of corresponding RNA-seq probes that also had an FC value more than 5 were 31 (13.8%), 30 (3.8%), 26 (10.2%), 27 (2.8%) and 27 (54.0%), respectively. Thus, consistent with the results given above, the geometric mean of the four microarrays was far more concordant with the RNA-seq data than the individual microarrays.The reason for the modest agreement between the RNA-seq data and the microarray data is unclear. Although all samples used in this study were biological replicates, the high internal reproducibility of both the microarray data and the RNA-seq data indicates that biological variability is an unlikely explanation. It is possible that the inability of DNA microarrays to distinguish between splice variants may play a role, as could the difficulty exhibited by DNA microarrays in detecting transcripts with low abundances. It is also possible that there are unforeseen biases that impact the results from RNA-seq.et al. [et al. [liver/kidney) from the RNA-seq data and the FC values from the microarray data. The correlations from both methods were similar, ranging from 0.67 to 0.75. In a review article, Wang et al. [et al. [Drosophila pseudoobscura were characterized to identify genes whose transcription is sex biased [The degree of concordance between DNA microarray data and RNA-seq data has been examined in several previous studies. For instance, Sultan et al.  examined [et al.  comparedg et al.  comparedg et al.  and a stg et al. . The aut [et al.  used thrx biased . High cox biased  and in ax biased . A summa2.4n=35). In particular, there were 23 genes for which the log-transformed RNA-seq FC value was closer to the qRT-PCR FC value than was the mean of the log-transformed microarray FC values, versus 12 for the reverse .In order to provide measurements via an independent technique, qRT-PCR was used to measure transcript abundance for 76 genes. Each of these genes had a corresponding probe in both the microarray data and the RNA-seq data. Several genes were selected because they had FC values more than 5 in either the RNA-seq data only, the microarray data only, or both. Other genes were selected arbitrarily. Over all 76 genes, the RNA-seq data were more consistent with the qRT-PCR data than were the microarray data  ,32 were r 7 days ,32.4.2More than 10 million cells for both proliferating and quiescent samples were harvested using Trypl Express (Invitrogen). Cells were pelleted by centrifugation and RNA extracted using either Trizol (Invitrogen) or FastRNA Pro Green Kit with FastPrep-24 instrument  according to the manufacturer's instructions. Polyadenylated mRNAs were isolated from the RNA pools using the Oligotex mRNA isolation kit (Qiagen). Five micrograms of RNA from both proliferating and quiescent cells were used in cDNA synthesis reactions in combination with Superscript III (Invitrogen) as per manufacturer's instructions with random hexamers (50 ng per reaction). The final reaction was diluted to a final volume of 200 μl.4.3Microarray analysis was carried out using Op Human ReadyArray HS1200 (Microarrays Inc.) slides, with the 3DNA Array 900 labelling kit (Genisphere) as previously described . Four mi4.3.12O. One microlitre of RT primer was added to the RNA, with the correct dendrimer target sequence for labelling of the samples on the microarray. The mixture was heated to 80°C for 5 min to denature RNA secondary structures and placed on ice for 2 min. The following reagents were added to each reaction: 2 μl of first-strand buffer, 1 μl of 0.1 M DTT, 0.5 μl of SUPERase-In (provided with the 3DNA 900 kit), 0.5 μl of dNTP mix (provided with the 3DNA 900 kit) and 0.5 μl of SuperScript III (Invitrogen). The reaction was incubated for 2 h at 42°C. The reaction was then stopped by adding 1 μl of 1 M NaOH/100 mM EDTA solution and incubating at 65°C for 10 min to denature the cDNA/RNA hybrids and degrade the template RNA. The reverse transcription reaction was then neutralized by adding 1.2 μl of 2 M Tris-HCl (pH 7.5). One microlitre of H2O was then added to each cDNA sample and the samples mixed. One microlitre of sample was assessed using the Qubit® single-stranded DNA assay on a Qubit 1.0 Fluorometer to verify that a sufficient quantity of cDNA was present.One microgram of RNA was taken in a volume of 5 μl H2O, to a final volume of 80 μl. The mixture was heated to 80°C for 10 min in order to denature secondary structures, and then cooled to 60°C to prepare it for addition to the slide.The appropriate samples were then mixed to form the hybridization mix for the microarray slides. The total amounts (12.7 μl) of each cDNA were mixed with 40 μl of 2× sodium dodecyl sulfate (SDS)-based hybridization buffer and 14.6 μl of H4.3.2−1 bovine serum albumin solution in a volume of 50 ml. The slides were then washed in MilliQ water for 1 min, in isopropanol for 1 min and dried using a Microarray high-speed centrifuge (Arrayit Corporation). The slide was then pre-scanned again with the GenPix 5.1 scanner to verify that it was clean and undamaged.The microarray slides were pre-scanned with the GenPix 5.1 scanner to check for any manufacturing faults. The slides were then pre-hybridized at 65°C for 20 min in a Coplin jar containing 3.5× saline sodium citrate (SSC), 0.1% SDS and 10 mg mlThe microarray slide was then placed into a clean SlideBooster  on a layer of 45 μl AS100 AdvaSon coupling solution (Beckman Coulter), with 60 μl more in the thumb hole at the base of the slide. The wells of the slide booster were each filled with 500 μl of AdvaHum AM102 humidifying solution (Beckman Coulter), and a 24×60 mm LifterSlip was placed on top of the microarray slide. The assembly was pre-warmed to 55°C, and when it reached temperature, the hybridization solution was pipetted underneath the LifterSlip. The microarrays were then hybridized for 16 h.The slides were then washed in 2× SSC, 0.2% SDS at 55°C for 10 min with orbital rotation of 150 r.p.m., followed by a wash in 2× SSC at room temperature with orbital rotation of 150 r.p.m. for 10 min, followed by a wash in 0.2× SSC at room temperature with orbital rotation of 150 r.p.m. for 10 min. The slides were dried using a Microarray high-speed centrifuge (Arrayit Corporation).2O to a final volume of 80 μl to make the second hybridization mix. This was heated at 80°C for 10 min, and then cooled to 55°C to prepare it for addition to the slide. The SlideBooster was assembled as before, and pre-warmed to 50°C. When it was warm, the second hybridization mix was added, and the microarrays were incubated for 4 h. The washing and drying steps described above were then repeated.For each slide, 2.5 μl of the Cy3 dendrimer capture reagent was mixed with 2.5 μl of the Cy5 dendrimer capture reagent, with 40 μl of 2× SDS-based hybridization buffer and 35 μl of H4.4ioArray Software Environment (BASE) tool [Microarray images were imported into BlueFuse v. 3.2 and spots with insufficient signal above background removed from the datasets either manually or by automatic exclusion. Output from BlueFuse was modified into a fused file format and imported into the BSE) tool . Using BSE) tool , and theSE) tool  was used4.5http://genome.ucsc.edu/ENCODE/protocols/dataStandards/ENCODE.RNAseq_Standards.V1.0.pdf). RNA was isolated using the FastPrep-24 instrument  according to the manufacturer's instructions. The integrity of the RNA was determined using the Bioanalyzer (Agilent Technologies) with RNA having an RNA integrity number above 9.0 used for further analysis. For sequencing library synthesis, polyadenylated RNAs were purified using oligo dT-beads (Invitrogen) with random hexamers, then used as primers for the cDNA library construction prior to paired-end sequencing. One PRO sample and one QUI sample were sequenced using the Illumina GxII platform at the Centre for the Analysis of Genome Evolution and Function . These datasets were denoted PRO1 and QUI1, respectively. Another PRO sample and QUI sample  were sequenced using the same platform at the Babraham Institute (UK), and the corresponding datasets denoted PRO2 and QUI2, respectively. All sequencing reactions resulted in the generation of 50 bp paired-end reads.Two RNA-seq replicates were used, as recommended by the ENCODE Consortium's Standards, Guidelines and Best Practices for RNA-Seq . The feature probe generator function in SeqMonk was used to generate probes based on mRNA annotations from Ensembl. The number of reads that mapped to each probe was then quantitated, and normalized using the widely used RPKM method [RNA-seq reads were subjected to quality control using the standard Illumina pipeline. Raw sequence reads were mapped against the reference genome (the GRCh37 assembly from Ensembl ) using t[opHat 2 : --bowtiM method –29. A co4.72O and 2.5 μL 3 μM forward and reverse primers. All reactions for each gene were run in triplicate and were conducted using the RotorGene qPCR machine (Qiagen).Templates from cDNA synthesis reactions were diluted 1 : 100 in nuclease-free water. Ten microlitre reactions were set up using 5 μl IQ 2X Master Mix (BioRad), 1 μl template, 1.5 μL H4.8z-transformation to the correlation coefficients. Log-transformation of variables was done after other transformations .Correlations between variables were determined in three different ways: Pearson correlation between untransformed values, Pearson correlation between log-transformed values (i.e. the base-2 logarithm was taken of each variable before determining the Pearson correlation), and the Spearman rank correlation. The statistical significance of differences between correlations was calculated by applying Fisher's 4.9The consistency among the four microarrays was evaluated by comparing both normalized intensity values and FC values among pairs of microarrays. Specifically, for each of the 4.10The reproducibility of the RNA-seq data was evaluated by comparing both normalized read counts and FC values. In the first method, the read counts from PRO1 were compared to those from PRO2, and read counts from QUI1 were compared to those from QUI2. The second method involved finding the correlation between QUI1/PRO1 and QUI2/PRO2. All RNA-seq probes were used, not just those for which a corresponding microarray probe was identified.4.11eqMonk, two different methods were used: a method based on probe sequences and a method based on Ensembl transcript IDs.In order to determine the correspondence between the  probes on the DNA microarrays and the  probes generated by SMi was the sequence MSi of that probe, as well as the chromosome MCi on which that sequence is found. The record associated with each probe Rj generated by SeqMonk contained the chromosome RCj on which that probe is found, as well as the start and end position of the probe on that chromosome. Let RSj denote the sequence bounded by those chromosome locations. For each microarray probe Mi, it was determined whether there was a SeqMonk probe Rj on chromosome MCi for which MSi was a subsequence of RSj. If there did exist such a probe, then Mi was considered to correspond to Rj.The sequence-based mapping method was performed as follows. Associated with each microarray probe Rj for which MSi was a subsequence of RSj, then a second mapping method was attempted based on Ensembl transcript IDs. Each SeqMonk probe had exactly one Ensembl ID REj associated with it. Some microarray probes had exactly one associated Ensembl ID; others had more than one or none at all. Let MEi denote the (possibly empty) set of Ensembl IDs associated with microarray probe Mi. For each e∈MEi, it was determined whether there was a SeqMonk probe Rj such that e=REj. If there was, then Mi was considered to correspond to Rj (and no further elements of MEi were examined). For microarray probes having MEi=∅, this mapping method could not be performed.If there did not exist a probe nsembl IDs, then that RNA-seq probe was selected. If not, then one of the identified RNA-seq probes was arbitrarily selected to correspond to that microarray probe.In many cases, a given microarray probe mapped to more than one RNA-seq probe  based on sequence. In such cases, if one of the RNA-seq probes was also a match based on Ensembl IDs, then it was not included in the comparison between the microarray data and the RNA-seq data.If no mapping could be found for a given microarray probe via either sequence or E4.12Prior to comparing the RNA-seq data and the microarray data, the reads from the two RNA-seq replicates for each cell state were combined, giving a single read count for PRO and a single read count for QUI for each probe. These combined data were then compared to each individual DNA microarray. All comparisons were performed using only microarray probes for which a corresponding RNA-seq probe was found (and vice versa).S represent the set of all probes for which a correspondence was found between the microarray data and the RNA-seq data. The k=10 probes in S with the highest FC values according to the RNA-seq data were identified, as were the k=10 probes from S having the highest FC values in a given DNA microarray. The number of probes n that were in both lists was then ascertained. To determine whether n was greater than would be expected by chance, an empirical statistical distribution was calculated by performing 10 000 random trials. In each trial, k probes were randomly selected without replacement from S. The selected probes were then placed back into S, and k additional probes were selected without replacement. The number of probes p found in both lists was recorded. The associated p-value was equal to the number of random trials for which p≥n. The above procedure was repeated for k=50, 100, 500 and 1000.The RNA-seq data were compared to the microarray data in three ways. First, the RNA-seq read counts for PRO were compared to the PRO intensity values from each microarray, and similarly for QUI. Second, the FC values (QUI/PRO) from the RNA-seq data were compared to the FC values from each microarray . Third, the degree of overlap between the two techniques was determined in terms of the probes having the greatest FC values (QUI/PRO). Let 4.13CT was calculated for each gene, as well as for four normalizing genes . The FC in transcript abundance was calculated relative to each normalizing gene using the ΔΔCT calculation [qRT-PCR reactions were conducted using both PRO and QUI samples for 76 genes. Each of these genes had a corresponding probe in both the RNA-seq data and the microarray data. Some genes were selected arbitrarily, while others were selected because they were upregulated in either the RNA-seq data, the microarray data or both. For both PRO and QUI, the cycle threshold culation . The ave


The debate on whether artificial reefs produce new fish or simply attract existing fish biomass continues due to the difficulty in distinguishing these processes, and there remains considerable doubt as to whether artificial reefs are a harmful form of habitat modification. The harm typically associated with attraction is that fish will be easier to harvest due to the existing biomass aggregating at a newly deployed reef. This outcome of fish attraction has not progressed past an anecdotal form, however, and is always perceived as a harmful process. We present a numerical model that simulates the effect that a redistributed fish biomass, due to an artificial reef, has on fishing catch per unit effort (CPUE). This model can be used to identify the scenarios  that pose the most risk of exploitation due to fish attraction. The properties of this model were compared to the long‐standing predictions by Bohnsack (1989) on the factors that increase the risk or the harm of attraction. Simulations revealed that attraction is not always harmful because it does not always increase maximum fish density. Rather, attraction sometimes disperses existing fish biomass making them harder to catch. Some attraction can be ideal, with CPUE lowest when attraction leads to an equal distribution of biomass between natural and artificial reefs. Simulations also showed that the outcomes from attraction depend on the characteristics of the target fish species, such that transient or pelagic species are often at more risk of harmful attraction than resident species. Our findings generally agree with Bohnsack's predictions, although we recommend distinguishing “mobility” and “fidelity” when identifying species most at risk from attraction, as these traits had great influence on patterns of harvest of attracted fish biomass. Artificial reefs can produce new fish biomass and attract existing fish biomass Bohnsack , but it For reefs open to fishing, it has been largely assumed that attraction of fish to an artificial reef is a harmful process, and species‐specific variation is usually ignored. Although numerous studies have attempted to quantitate or infer production . So, for a given level of effort, the catch before reef deployment was compared to the catch after. If CPUE increased after reef deployment, we considered this potentially harmful and evidence toward an artificial reef increasing the risk of exploitation of fish biomass. By varying the multiple factors that can influence the magnitude of fish attraction, it was possible to identify scenarios in which the risk of exploitation is greatest.Atypichthys strigatus  at which biomass density halves and is given as follows:The first step in the model was to distribute fish biomass around a natural reef. The decline of biomass with increasing distance from a reef was defined by an exponential function, based on research that shows this can approximate the change in abundance with distance  and of relative reef quality . A logistic function  attraction occurs, and D50 is the distance at which half the fish in a cell will be attracted a reef. D99 was fixed at twice the value of D50 to ensure a symmetric curve. The logistic was chosen given its flexibility of shape and the logic of the D50 parameter for decision‐based processes, but other functions could be used. Reef quality was simply used in this model to define a reef's attractiveness which, ecologically, means appealing to the “behavioral preferences” of a species  that defines the relative biomass between artificial and natural reefs as Di approaches zero:The second step was to add an artificial reef and calculate the attracted fish biomass. The reef was deployed at a variable distance Bohnsack  without Q = 2 (Q0 = 2/3), an artificial reef will have twice the biomass of an immediately adjacent natural reef after attraction occurs.For example, when n cells in the modeled system, the biomass that was attracted to the artificial reef and redistributing it based on cell i's relative biomass density , the biomass Bai was estimated as the maximum biomass for that cell . Figure The final step was to redistribute, around the artificial reef, the biomass that was attracted to the artificial reef. This was performed by summing, for all p) with the largest biomass. Thus, fishing effort is related to the size of p, as this represents how many cells are simultaneously being fished. Exploitation risk can be evaluated by comparing the CPUE before (CPUEb) and after (CPUEa) artificial reef deployment:CPUE was the metric used to examine the risk of exploitation of the fish biomass due to attraction to an artificial reef. CPUE was measured in this model as the sum of the fish biomass in a given number of cells .This formulation assumes that CPUE is linearly related to fish biomass density, that fishing effort per unit area is constant and effort is varied by changing the amount of area  fished, and that fishers will always fish the cell(s) with the largest fish biomass. These assumptions were made to generalize the “worst case scenario.” However, CPUE is not always linearly related to biomass density . This was then followed by a second round of attraction due to the addition of the artificial reef.It is important to allow for species‐specific responses in attraction to an artificial reef, and the parameters Dh, Q, D50, Dr, and p (Table Sr) was varied at three levels  to account for the increased fish biomass available for attraction on larger reefs, and to account for the increased number of reef cells that can be simultaneously fished on larger reefs. The artificial reef was kept constant at 1 × 1 cell. These six factors were systematically varied to illustrate numerous possible scenarios and fish responses; for example, an artificial reef placed near to a large natural reef of much poorer quality attracts more fish than an artificial reef placed far from a smaller natural reef of equal quality. Simulations were performed for both reef‐resident species (which have a biomass = 0 in the absence of a reef) and reef‐associated pelagic species (which have a biomass > 0 in the absence of a reef). Figure Simulations using the above model were performed to identify the scenarios (in terms of reef and fish characteristics) for which the risk of exploitation of fish biomass was highest. The parameters varied in this simulation were as follows:  p Table . In addiD50 which had 5 levels between 100 and 300 m). This was iterated 2000 times, and a linear model fitted to the resulting dataset , some attraction (Dr = 250 m), and little attraction (Dr = 510 m).The results of simulations are best presented graphically due to some clear nonlinearities. Given that each simulated scenario is a function of six variables, multiple sets of 2‐dimensional plots are required to communicate results. A sensitivity analysis was also performed as an alternative illustration of parameter importance. The levels of each parameter were varied randomly in a Monte Carlo simulation, and the CPUE ratio stored at each iteration  increases for reef residents  does become the most influential at intermediate distances, due to its importance for influencing partial attraction (and hence inversely the CPUE ratio) at these distances. For reef‐associated pelagics , fidelity (Dh), and mobility (D50) are the most influential parameters on the CPUE ratio, but fidelity becomes more influential as distance increases, because attraction from the NR declines and fidelity (inversely) drives the CPUE ratio by influencing how easily fish are located. Generally, it is the properties of the species that are generally most important (mobility and fidelity) for driving change in CPUE, followed by the attractiveness of the AR. Surprisingly, neither fishing intensity (p) nor natural reef size (Sr) was as influential over the spatial and parameter scales tested here.The sensitivity analysis showed that all parameters become less influential on the CPUE ratio as distance from NR . This means that a threshold or asymptotic relationship between CPUE and fish density reduces the effect of both partial attraction and concentration, by maintaining a more constant catch rate across a broader range of scenarios.To test the general effect of a threshold CPUE , rather than a linear relationship between CPUE and fish density, the model was run with maximum CPUE occurring at 70% of the maximum fish density observed before the artificial reef was deployed. Doing so increased the area in which maximum CPUE occurred, which meant that the CPUE ratio in many scenarios was unchanged (=1) or approached this value , especially for low fishing effort . Although reef residents and reef‐associated pelagics showed different patterns of attraction, they shared this pattern of maximum risk. The greatest dispersion of fish biomass, and thus the largest reduction  in CPUE, occurred when there was partial attraction, and this was true for both functional groups. Our results show that attraction is a complex process that is not always harmful, that the role of attraction in influencing the success of artificial reefs is a function of reef, species, and harvest characteristics, and that the fidelity and mobility of the attracted fish species may be the key drivers of the outcomes of harvesting attracted fish biomass.Evaluation of our attraction model reveals some important generalities. In terms of exploitation of fish biomass, the typically maligned process of attraction is not always bad; in fact, attraction can sometimes disperse existing fish biomass instead of concentrating it, thus making it harder to exploit. There remain numerous scenarios under which attraction does create opportunities to exploit fish biomass, and, in general, maximum risk occurs given high‐quality artificial reefs very close to existing reefs, for species with low Dh) because they are easier to locate. Reef‐associated pelagics are attracted under all scenarios, and their CPUE increases in almost all scenarios. Their often patchy abundance may reduce this risk in the long term, however, and only a part of their biomass is associated with a reef at any one time (as opposed to reef residents). Most interestingly, the least risk for either functional fish group occurred when there was partial attraction. The ideal level of partial attraction is when the dispersion of fish biomass is most likely (as opposed to the concentration of fish biomass) and generally coincided with an equivalence of fish biomass on the artificial and natural reefs. The distance between artificial reefs and existing reefs is one factor over which fisheries managers have some control, and selecting the distance that ideal level of partial attraction will reduce the risk of attraction in an artificial reef deployment. If the system is too data poor to estimate the distance at which this level of attraction occurs, then a greatly isolated artificial reef may be the best choice as this reduces attraction for obligate reef residents (Carr and Hixon Table D50) would also increase risk. Our findings show, however, that a tighter association with a reef  puts you at greater risk of exploitation, which may be at odds with Bohnsack's prediction. And finally, Bohnsack predicted that species loosely associated with reefs are at more risk from attraction than reef obligates. Our findings generally agree, such that reef‐associated pelagics are attracted in most scenarios, even when no natural reefs are nearby. But again it is important to distinguish between the roles of mobility (D50) and fidelity (Dh), and we found that reef residents are probably easier to exploit given that all their biomass is easily located (not just their attracted biomass). Our recommendation is to improve the scenarios given by Bohnsack by specifying the mobility and habitat traits of species in terms of a willingness to move to a new reef  and the scale of the association with a reef , as these have clear, numeric identities, and are generally the most influential parameters across a range of scenarios.Factors that might increase fish attraction (or the harm from fish attraction) were proposed early on in artificial reef research by Bohnsack , and we This study identified scenarios that have the highest risk of harmful attraction. However, some of these scenarios may also be those that would maximize production . This means that one might inadvertently reduce the potential for fish production by deliberately reducing the risk of possible attraction. Unfortunately, this will remain the case until the processes that drive fish production are better identified and modeled. It is also the case that not in some management scenarios fish attraction may be not considered harmful, such as artificial reefs deployed in protected areas  were kept small for model tractability and probably underestimate the ability of many species to move to new habitat, although there is some evidence that reefs <600 m to 1 km apart show reduced connectivity , attracted biomass may be overestimated. However, we believe that even without this density‐dependent attraction, our patterns of risk in terms of distance and fishing effort are robust.It is assumed in our model that fishing effort is equivalent before and after reef deployment. If fishing effort increases due to a reef's deployment, and some have argued that artificial reefs may increase fishing effort on hard substrates in general (Grossman et al. There is unlikely to ever be a one‐size‐fits‐all model of the value of artificial reefs to fish production, given the possible variation in reef design, location, target fish species, and site‐specific food and larval supply. However, we believe that attraction is not as harmful a process as is generally reported, because it does not always increase maximum fish density. This lessens the burden to create models of fish production before a reef can be responsibly deployed. What is more, if bag limits as a management strategy is used and enforced, and fishers are generally reaching their bag limits, then attraction that does increase maximum fish density may simply result in reduced trip times and increased angler satisfaction. Plus, if an increase in fisheries harvest in a system is sustainable, an increase in fish density due to attraction may not be harmful at all. It may be that the most effective method to reduce exploitation is to encourage sustainable harvest through monitoring and management action (Powers et al. None declared.Figure S1 Evaluation of the impact of attraction for reef fish, with maximum CPUE occuring at 70% of the maximum fish density before artficial reef deployment.Figure S2 Evaluation of the impact of attraction for reef‐associated pelagic fish, with maximum CPUE occuring at 70% of the maximum fish density before artficial reef deployment.Click here for additional data file.


After more than two decades of research, the efforts to translate the concept of RNA based vaccination have reached a critical mass. Several preclinical and clinical projects located in the academic or industrial setting are underway and the coming years will allow us to get broad insight into clinical feasibility, safety, and first efficacy data. It can be anticipated that some RNA based vaccines will be approved within the near future. in vitro transcribed RNA is now viewed as an attractive approach for vaccination therapies, with several features contributing to its favorable characteristics. RNA allows expression of molecularly well-defined proteins and its half-life can be steered through modifications in the RNA backbone. Moreover, unlike DNA, RNA does not need to enter the nucleus during transfection and there is no risk of integration into the genome, assuring safety through transient activity. Rapid design and synthesis in response to demand, accompanied by inexpensive pharmaceutical production, are additional features facilitating its clinical translation.The use of ex vivo transfection of mRNA into autologous dendritic cells (DCs) which was initially described by Boczkowski et al. [The seminal work of Wolff et al. which showed that RNA injected directly into skeletal muscle can lead to protein expression opened the era of RNA based therapeutics . This obi et al. . Along wi et al. , severali et al. . In a dii et al. –10. Persi et al. , 12 are i et al. –15. in vitro analytics such as cytotoxicity  or effects of RNA on transcriptome of DCs . Finally, E. Hattinger et al. will also demonstrate, with a different disease focus, the efficacy of prophylactic RNA vaccination against allergy.In this special issue, a number of papers will illustrate and summarize the advances in this emerging field. M. A. McNamara et al. will provide a comprehensive review on RNA based vaccines in cancer immunotherapy, which is further detailed for the use of mutanome engineered RNA by M. Vormehr et al. These will be complemented by a review from K. K. L. Phua describing targeted delivery systems for RNA based nanoparticle tumor vaccines. Other contributions will describe RNA based methods forIn conclusion, this special issue covers many aspects of RNA based vaccines. As RNA based vaccination is not the only application of the RNA technology , we hope to have sparked the readers interest in RNA based therapies in general.Sebastian KreiterSebastian KreiterMustafa DikenMustafa DikenSteve PascoloSteve PascoloSmita K. NairSmita K. NairKris M. ThielemansKris M. ThielemansAndrew GeallAndrew Geall


Overall binding constants were estimated to be K(SFN–DNA)=3.01 (± 0.035)×104 M-1 and K(SFN–RNA)= 6.63 (±0.042)×103 M-1. At high SFN concentration (SFN/RNA = 1/1), DNA conformation changed from B to A occurred, while RNA remained in A-family structure.Sulforaphane (SFN) is an isothiocyanate found in cruciferous vegetables with anti-inflammatory, anti-oxidant and anti-cancer activities. However, the antioxidant and anticancer mechanism of sulforaphane is not well understood. In the present research, we reported binding modes, binding constants and stability of SFN–DNA and -RNA complexes by Fourier transform infrared (FTIR) and UV–Visible spectroscopic methods. Spectroscopic evidence showed DNA intercalation with some degree of groove binding. SFN binds minor and major grooves of DNA and backbone phosphate (PO Sulforaphane (1-isothiocyanato-4-(methyl-sulfinyl)) butane, a molecule within the isothiocyanate group of organosulfur compounds ; is the Even though much is reported about antitumor activities of sulforaphane, there has been no report on the molecular aspects of sulforaphane interaction with DNA and RNA in aqueous solution. RNA, a versatile molecule playing essential roles in many biological processes is an attractive target for potential therapeutics. Recent progress in antiviral research has been mainly based on targeting RNA molecules for therapeutic intervention. Development of molecules capable of controlling RNA activity is now a focus of medicinal and chemico-biological research –24. Unde258/A280 ratio was 1.80 for DNA and A260/A280 ratio 2.10 for RNA, showing that DNA and RNA samples were sufficiently free from protein  were obtained using a sharp DNA and RNA band at 968 cm-1 as internal reference  + [SL], we getThe binding constants of the drug-DNA or drug-RNA complexes were calculated as reported . It is at/(1 + K11[L]). Eq  PDB ID: 1BNA-DNA and PDB ID: 2R22 and PDB ID:3CZW [http://autodock.scripps.edu). The sulforaphane structure was extracted from Pubchem  file. In order to use the structures for docking, the universal force field (for drugs) [The crystal structures of two DNA–drug and RNA-drug complexes were selected from Protein Data Bank   and Mercr drugs)  were shor drugs) . In AutovdW, ΔG hbond, ΔG elec, ΔG tor, and ΔG sol are free energy coefficients of van der Waals, hydrogen bond, electrostatic interactions, torsional term, and desolvation energy of oligonucleotide–drug complex, respectively. rij, Aij, Bij, Cij, and Dij represent the interatomic distance, the depths of energy well, and the equilibrium separations between the two atoms, respectively. The first three terms are in vacuo force field energies for intermolecular interactions. The fourth term accounts for the internal steric energy of the drug molecule. The energies used and reported by AutoDock should be distinguished: there are docked energies, which include the intermolecular and intramolecular interaction energies, and are used during dockings, and predicted free energies, which include the intermolecular energy and the torsional free energy, and are only reported at the end of a docking [binding and the binding free energy change of binding, ΔGbinding, using the following equation:-1 mol-1, and T is the absolute temperature, assumed to be room temperature, 298.15 K.In Eq , ΔG vdW, docking , 37. We http://mgltools.scripps.edu) and UCSF Chimera 1.6.1([http://www.cgl.ucsf.edu/chimera).In order to analyze and display docking results, we used AutoDock Tools 1.5.4 (ADT)  and 1088 cm-1 (PO2 symmetric stretch) [Evidence of sulforaphane–DNA complexation comes from the infrared spectroscopic results shown in Figs stretch) –42 were -1 (r = 1/5), thymine band at 1665 to 1661 cm-1 , adenine band at 1610 to 1614 cm-1 (r = 1/5)  . Major s-1, however at high concentrations (r = 1/5), it shifted to 1230 cm-1 which can be related to phosphate interaction with SFN at higher concentrations  and symmetric (vs) vibrations were altered upon phosphate interaction [vs PO2 (1088 cm-1) and vas PO2 (1226 cm-1) were changed, with the ratio vs/vas going from 2.16 (free DNA) to 1.95 (SFN–DNA complexes) in various molar ratios of sulforaphane–DNA.At low SFN concentrations r = 1/40), no major intensity changes was observed for the phosphate band at 1226 cmtrations . In addi, no majo-1 (2 group.In the difference spectra of sulforaphane-DNA (r = 1/40), the positive features at 1706, 1645, and 1218 cmThe UV results indicated intensity increase in the DNA band at 259 nm upon SFN interaction . Similar2 group  were observed for the bases and phosphate bands at low concentration (r = 1/80). However at r = 1/40, increase in the intensity of guanine, uracil and to a lesser extent adenine bands can be related to sulforaphane interaction with these bases. At r = 1/20, reduction in intensity of the G, U and A bands arise from RNA stabilization upon SFN interaction . No majo-1, the uracil band at 1650 shifted to 1656 cm-1, the adenine band at 1610 shifted to 1605 cm-1. The major spectral changes of RNA bands upon sulforaphane interaction can be related to major interaction of SFN with RNA.The intensity changes were accompanied by major shifting of the bases bands especially at higher SFN concentrations (r = 1/5); the guanine band at 1697 shifted to 1706 cm2 asymmetric stretching, no major shifting was observed at low SFN concentration; 1241 to 1239 cm-1 (r = 1/40), however, a drastic shifting was observed at higher concentration; 1241 to 1228 cm-1 (r = 1/1) (For the backbone POr = 1/1) . Some inr = 1/1) .-1 upon sulforaphane interaction with RNA is indicative of minor participation of cytosine in SFN complexation , the positive features at 1768, 1662, and 1106 cmAdditional evidence regarding sulforaphane–RNA interaction comes from the UV results. An increase in the intensity of RNA band at 258 nm can be related to the interaction of sulforaphane with RNA . SimilarOn the other hand, the UV band at 245 nm characteristics of sulforaphane absorption showed major red shift and appeared at 258 nm upon RNA interaction. The observed spectral changes can be indicative of structural alterations of the SFN upon its complexation with RNA .-1 (PO2 stretch), 1714 cm-1 (mainly guanine), and 836 cm-1 (phosphodiester mode)  . In a B 35 cm- 1 –48. In a216 cm-1 , 48. In ion Figs  and 2A.-1 (phosphodiester) is indicative of RNA remaining in A-conformation upon sulforaphane complexation , 1240–1247 (phosphate), 861–867 (ribosephosphate), and 815–809 cmion Figs  and 2B. 0) versus 1/(sulforaphane concentration) is linear, and the binding constant (K) can be estimated from the ratio of the intercept to the slope  = 3.01 (± 0.035) ×104 M-1 and K(SFN–RNA) = 6.63 (±0.042) ×103 M-1. The affinity of sulforaphane–DNA and—RNA is in the order of SFN–DNA>SFN–RNA. Sulforaphane binding to DNA is stronger, since DNA is double helix, but RNA is not. These binding constants are consistent with FTIR results, which we concluded a stronger interaction of sulforaphane with DNA than that of RNA complexes.The sulforaphane–DNA and—RNA binding constants were determined as described in the experimental section (UV–visible spectroscopy). The UV absorption spectra of sulforaphane–DNA and—RNA complexes are shown in he slope , where AOur results from FTIR and UV-Visible spectroscopy are accompanied by docking experiments. In order to determine the preferred binding sites on DNA and RNA, the sulforaphane were docked to DNA and RNA. The dockings results are shown in Figs For docking, PDB 3CZW was chosen as single stranded RNA candidate and PDB 2R22 as double stranded RNA candidate. PDB 3CZW and PDB 2R22 include all kinds of bases . PDB 1BNA-DNA with minor and major grooves including all kinds of bases  was chosen as candidate for double stranded DNA.The models show that SFN is surrounded by C13.B, G14.B, C15.B, G16.B, C9.A, G10.A, C11.A, G12.A  and phosOur study provided important quantitative data on the binding affinity of sulforaphane to DNA and RNA. Direct binding experiments and DNA denaturation assays need to be done to determine the effect of low and high concentrations of SFN on polynucleotides structure.via both intercalation and groove binding with the order of stability SFN-DNA>SFN-RNA. At high SFN concentrations, DNA conformational changed from B to A occurred, while RNA remained in A-family structure.We also showed distinct differences in sulforaphane binding to these biopolymers. Interaction of sulforaphane with DNA and RNA can be used to gain insight into the mechanism of action of sulforaphane in cancer therapy. Based on our spectroscopic results and docking studies, the following points are important; sulforaphane binds DNA and RNA


Piwi-interacting RNAs (piRNAs) are a class of small RNAs; distinct types of piRNAs are expressed in the mammalian testis at different stages of development. The function of piRNAs expressed in the adult testis is not well established. We conducted a detailed characterization of piRNAs aligning at or near the 3’ UTRs of protein-coding genes in a deep dataset of small RNAs from adult mouse testis.We identified 2710 piRNA clusters associated with 3’ UTRs, including 1600 that overlapped genes not previously associated with piRNAs. 35 % of the clusters extend beyond the annotated transcript; we find that these clusters correspond to, and are likely derived from, novel polyadenylated mRNA isoforms that contain previously unannotated extended 3’UTRs. Extended 3’ UTRs, and small RNAs derived from them, are also present in somatic tissues; a subset of these somatic 3’UTR small RNA clusters are absent in mice lacking MIWI2, indicating a role for MIWI2 in the metabolism of somatic small RNAs.The finding that piRNAs are processed from extended 3’ UTRs suggests a role for piRNAs in the remodeling of 3’ UTRs. The presence of both clusters and extended 3’UTRs in somatic cells, with evidence for involvement of MIWI2, indicates that this pathway is more broadly distributed than currently appreciated.The online version of this article (doi:10.1186/s12864-015-1662-6) contains supplementary material, which is available to authorized users. Among the numerous types of small RNAs that have been investigated in recent years, piRNAs have been of interest primarily because they seem to be essential for maintenance of germ cells. They range in size from 24–32 nt, complex with members of the PIWI clade of Argonaute proteins, and are enriched for a 5’ uridine –6. piRNATransposon-derived piRNAs are produced via a ping-pong amplification loop in which primary piRNAs antisense to transposon mRNA sequences direct the cleavage of the transposon mRNA, leading to the production of secondary sense piRNAs which then direct the cleavage of the complementary sequence from the primary piRNA transcript. This results in sense/antisense piRNAs characterized by a 10-bp 5’ overlap, and secondary piRNAs with an A at position ten .Less is known about the functions of pachytene piRNAs. They are derived from single-stranded precursors, are not enriched for transposable element sequences, and do not show evidence of ping-pong amplification, suggesting that they are not involved in transposon silencing , 14. TheWe sequenced small RNAs from the adult mouse testis to a depth of approximately 139 million reads. Adapter stripping, removal of known ncRNAs  and collapsing of identical reads left approximately 13 million distinct small RNA sequences  contain antisense reads with the 10-bp overlap typical of ping-pong amplification  used for the analysis. However by using an RNA-Seq dataset from adult mouse testis  we were Pdpr) by Northern blotting of testis RNA . Finally, in many cases the xUTR was present but no small RNAs were detected (585 (62 %) liver and 573 (61 %) spleen, bottom right quadrants in Fig. Since we find evidence of xUTRs in somatic tissues, we asked if small RNAs aligning to xUTRs are present in somatic tissues. To address this question we sequenced small RNAs from mouse liver and spleen. After processing the sequence data and removing sequences matching known noncoding RNAs, we were left with approximately 328,000 and 410,000 unique small RNA sequences in liver and spleen, respectively  mRNA in liver and spleen , a pathway that stimulates the proliferation of stem cells and progenitor cells [Although our dataset overlapped with 3’ UTR piRNA clusters identified by reanalysis of previously published piRNA datasets , 17, dueor cells .The evidence we have found is consistent with the interpretation that piRNAs and smaller somatic RNAs are derived from extended 3’UTRs, implying that their production may be connected in some way to regulation of 3’UTR length. The 3’ UTR is critical for regulating mRNA stability, localization, and translation (reviewed in ). Part oMiwi2 (Piwil4) gene. The small RNAs aligning to extended 3’UTRs in somatic tissues differ from canonical piRNAs: they are shorter, lack the predominant 5’ uracil, and much less abundant than their testis counterparts. We assessed the role of MIWI2 in these clusters because we and others have found piRNAs in somatic tissues and cancer cells [Miwi2 mRNA in somatic tissues  cells [Miwi2−/− mice, but any consequences of this absence must be subtle: we find no apparent change in expression of the genes associated with these UTRs in Miwi2−/− mice, and no other effects have been reported. MIWI2 has been shown to associate with piRNAs in the prenatal testis, and to mediate transcriptional silencing and methylation of retrotransposons at this stage of development [Perhaps the most intriguing finding of this study is that small RNAs aligning to extended 3’UTRs are also found in somatic tissues, and that a subset of these somatic 3’UTR clusters is absent in mice lacking a functional er cells , 27, andues Fig. . No phen−/− mice , 27, 28,elopment . If it ielopment –12.Using a very deep dataset of piRNAs from mature mouse testis, we show that clusters of piRNAs mark extended 3’UTRs that have not been previously annotated. These clusters are present on many mRNAs expressed in the testis, but also in somatic cells. A subset of somatic 3’UTR-derived small RNA clusters are dependent on MIWI2. MIWI2 is a Piwi protein that has been shown to associate with piRNAs in the prenatal testis, and to mediate transcriptional silencing and methylation of retrotransposons at this stage of development. Its potential involvement in the generation or maintenance of small RNAs derived from 3’UTRs in somatic cells would imply some very different function. These findings extend the biological scope of MIWI2 function, and also indicate the existence of a class of extended 3’UTRs that are processed into small RNAs.For testis piRNAs, 10 μg of total RNA from adult mouse testes was electrophoresed on an 18 % denaturing polyacrylamide gel with NEB miRNA markers, the gel stained with SYBR Gold, and the readily visible band migrating at 29 nt (range ~27-31 nt) excised. RNA was eluted from the gel fragment and purified by standard methods. Illumina libraries were constructed from RNA specimens using the Illumina Small RNA Library kit, following the manufacturer’s protocol. Libraries were sequenced on an Illumina GAII apparatus to collect reads of 36 nucleotides. Raw data was processed with the Illumina pipeline v1.3.2. Data are deposited into the GEO database with accession number GSE26251.Miwi2−/− mice [For deep sequencing of somatic small RNAs, liver and spleen were harvested from WT and −/− mice  and flasAdapter sequences were removed from the 3’ ends of raw reads; up to 25 % mismatch was allowed between the adaptor and read sequences. Exact duplicate sequences were collapsed (only a single representative read was retained). Known contaminants and small RNAs were removed by alignment with Bowtie , allowinPublished small RNA sequences were downloaded from the NCBI Gene Expression Omnibus (GEO) , includiCleaned RNAs were aligned to the NCBI37/mm9 assembly of the mouse genome using Bowtie  allowingIn the testis dataset, we used the feature density estimator F-Seq  to identTo generate other piRNA clusters, we started with the F-Seq defined regions above. We then expanded these regions by 500 bp if there was 1 piRNA in a 500 bp window. As intergenic piRNA clusters are known to span regions with repetitive elements, which would not contain our uniquely aligned reads, we then merged the clusters with the annotated RepeatMasker track elements within 50 bp. We then expanded the clusters by 500 bp windows if they contained 1 piRNA and checked the clusters that had expanded by more than 20 % of their length. We separated clusters if two clusters with reads aligning to different strands had merged. We then shrunk the cluster boundaries to the locations of aligned piRNAs. Clusters were annotated based on the presence of 50 % or more of their contained sequences within a given annotation track . Clusters with fewer than 50 % of sequences aligning to a genic track were classified as intergenic.In the liver and spleen small RNA datasets, we identified 3’ UTR clusters by first identifying genes where the density of sense small RNAs in the 3’ UTR was at least two fold that of the density in the coding region. We then selected 3’ UTRs with at least 3 small RNAs and a density of over 7.5 reads per kilobase 3’ UTR per million reads aligned. We set the cluster boundaries as the region of the largest 3’ UTR isoform and extended the 3’ boundary if there was at least 1 sense small RNA per 2 kb.www.ingenuity.com). IPA core analysis was conducted using the default parameters. The Functional Analysis identified the biological functions that were most significant to the combined data set. A right-tailed Fisher’s exact test was used to calculate a p-value determining the probability that each biological function assigned to that data set is due to chance alone.Genes with 3’ UTR piRNA clusters identified in our testis dataset, Robine et al. , and GanWhen analyzing the spleen and liver datasets, enrichment of specific gene ontology terms for genes with clusters of small RNAs overlapping their 3’ UTRs was determined using the Functional Annotation tool of the DAVID Bioinformatics Resources , 49.To analyze antisense reads in 3’ UTR piRNA clusters, we first aligned the small RNA sequences to the genome allowing up to two mismatches and up to 25 alignments. Sites of 10-bp overlap were defined as regions within a 3’ UTR cluster where the first 10 bases of sequences aligned to the plus and minus strands overlapped each other. To determine the most likely genomic source of sequences aligning antisense within the 3’ UTR clusters, these sequences were extracted and re-aligned to the genome under strict conditions . These positions were then annotated based on their presence in an original 3’ UTR cluster, an intergenic piRNA cluster (see above), a different piRNA cluster, or any other location.3’ UTR clusters were selected that extended beyond the annotated 3’ UTR by more than 30 % of the length of the cluster. Clusters that completely overlapped an annotated gene, in the sense or antisense direction, were removed from this analysis. For the expression analysis, only the portion of the cluster that did not overlap the annotated 3’ UTR was considered.9. We then used BEDTools [To calculate the expression of the unannotated portion of the extended 3’ UTR cluster precursors, we used mouse testis RNA-Seq data  downloadBEDTools  to generExpression of the RefSeq mRNA for genes with extended 3’ UTR clusters was calculated as for the annotated portion of the clusters. To calculate expression of the unannotated portion of extended 3’ UTR piRNA precursors in somatic tissues, we first downloaded RNA-Seq data for spleen, lung, liver, kidney, heart, cortex, cerebellum and bone marrow from the ENCODE project  using a Tissue Tearor homogenizer (Biospec Products) and total RNA was purified according to the manufacturer’s instructions. mRNA was purified from total RNA using a polyA Spin mRNA Isolation Kit (New England Biolabs) according to manufacturer’s instructions. Northern blotting used the Ambion NorthernMax kit (Invitrogen) following the manufacturer’s instructions. 1 % agarose gels were run with 1 ug mRNA per well and transferred to Ambion BrightStar-Plus membrane (Invitrogen) using downward capillary transfer. RNA was then cross-linked to the membrane using a UV Stratalinker 1800 (Stratagene) and stored at −20 °C. Cross-linked membranes were hybridized to radiolabeled single-stranded DNA probe overnight at 42 °C, washed, and exposed to a phosphorscreen for one week. Phosphorscreens were scanned using a Storm 840 phosphorimager (Molecular Dynamics), and images were analyzed using ImageQuant 5.0.Pdpr, using primers NblotEx-F and NblotEx-R for the exon-specific probe and NblotUTR-F and NblotUTR-R for the extended 3’ UTR-specific probe  according to the manufacturer’s instructions. Liver DNA was then used as a template for PCR amplification of the probe sequences for Primer extension was then used to make radiolabeled single-stranded DNA probes. Radiolabeled probes were purified using SigmaSpin™ Sequencing Reaction Clean-up, Post-reaction Clean-up Columns (Sigma-Aldrich) according to the manufacturer’s instructions.Total RNA was purified as in the Northern blot analysis, and contaminating genomic DNA was removed with the RNase-Free DNase Set (Qiagen) according to the manufacturer’s instructions. RNA was then cleaned with the RNeasy Mini Kit (Qiagen) RNA cleanup protocol. cDNA was synthesized using the ProtoScript™ M-MuLV Taq RT-PCR Kit (New England BioLabs) according to the manufacturer’s instructions and used as template in the RT-PCR reactions. See Additional file Since our somatic small RNA datasets were so much smaller than the testis dataset, we used a different approach for identifying 3’ UTRs associated with small RNAs. In the liver and spleen small RNA datasets, we identified 3’ UTR clusters by first identifying RefSeq genes where the density of sense small RNAs in the 3’ UTR was at least two fold that of the density in the coding region. We then selected 3’ UTRs with at least 3 small RNAs and a density of over 7.5 reads per kilobase 3’ UTR per million reads aligned. We set the cluster boundaries as the region of the largest 3’ UTR isoform and extended the 3’ boundary if there was at least 1 sense small RNA per 2 kb.Miwi2−/− mice as above. If a cluster region had small RNAs in both the WT and Miwi2−/− datasets, it was determined to be MIWI2 independent. Clusters without any small RNAs in the Miwi2−/− dataset, and in the bottom quartile of density in the WT dataset, were filtered because they were too small to determine MIWI2 dependence. The remaining clusters, in the top quartiles of small RNA expression in the WT dataset and lacking small RNA alignments from the Miwi2−/− dataset, were called as MIWI2 dependent clusters.To determine which somatic 3’ UTR smRNA clusters were MIWI2 dependent, we first calculated the density of small RNAs in each 3’ UTR cluster region using datasets from WT and For quantitative RT-PCR of somatic expression of Miwi2 and of mRNA derived from genes with somatic 3’ UTR small RNA clusters, cDNA was purified as for “RT-PCR of extended 3’ UTR clusters” above. PCR reactions were carried out using FastStart Universal SYBR Green Master (Roche) on an ABI 7900 Real-Time PCR machine according to the manufacturer instructions. All reactions were run in duplicate (quadruplicate for Miwi2 expression), and the mean CT was used for downstream analysis. Expression values were calculated using a standard ladder of pooled cDNA, and normalized for GAPDH expression in that cDNA prep. Values shown are the average and standard deviation obtained from two different mice.http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26251) and GSE47093 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE47093).The data sets supporting the results of this article are available in the NCBI GEO repository, with accession numbers GSE26251 (