Recent studies have revealed that large numbers of non-coding RNAs are transcribed in humans, but only a few of them have been identified with their functions. Identification of the interaction target RNAs of the non-coding RNAs is an important step in predicting their functions. The current experimental methods to identify RNA–RNA interactions, however, are not fast enough to apply to a whole human transcriptome. Therefore, computational predictions of RNA–RNA interactions are desirable, but this is a challenging task due to the huge computational costs involved.Here, we report comprehensive predictions of the interaction targets of lncRNAs in a whole human transcriptome for the first time. To achieve this, we developed an integrated pipeline for predicting RNA–RNA interactions on the K computer, which is one of the fastest super-computers in the world. Comparisons with experimentally-validated lncRNA–RNA interactions support the quality of the predictions. Additionally, we have developed a database that catalogs the predicted lncRNA–RNA interactions to provide fundamental information about the targets of lncRNAs.The online version of this article (doi:10.1186/s12864-015-2307-5) contains supplementary material, which is available to authorized users. Non-coding RNAs (ncRNAs), which are not translated into proteins but play essential roles in various biological processes, have been receiving increased attention , 2. AmonKnowing which RNAs and/or proteins are the interaction targets is essential for determining the functions of lncRNAs. The ncRNAs whose functional mechanisms have been identified are known to interact with other RNAs and/or proteins. Therefore, identifying the target RNAs or proteins with which an lncRNA interacts is the first step in characterizing the function of an lncRNA.′UTR of two mRNAs, leading to Staufen 1 (STAU1)-mediated messenger RNA decay (SMD); ) in-silico predictions of non-coding RNAs, a widely accepted software tool, RNAZ, has been applied to the human genome and had an impact on non-coding RNA research [in-silico predictions of RNA–RNA interactions are limited to small datasets, such as bacterial small RNAs (sRNAs) [For research . However (sRNAs) –17 becaujoint secondary structure, which involves both intra-molecular and inter-molecular base-pairs. Computational prediction of the joint secondary structures of two RNAs of length L has time complexity O(L4) to O(L6), depending on the complexity of the structures considered. It becomes infeasible to perform this calculation for all O(N2) pairs of RNAs when N (the number of RNA sequences) becomes large.For predicting interactions between two RNA molecules, an understanding of the base-pair interactions between the two RNAs is essential . RNA–RNAhttp://www.aics.riken.jp/en/k-computer/about/), which is one of the fastest super-computers in the world.In this study, we report comprehensive predictions of lncRNA–RNA interactions in the human transcriptome (including lncRNAs and mRNAs) for the first time. To achieve this, we have developed a fast pipeline for predicting RNA–RNA interactions for a large number of RNA sequences, and have implemented the pipeline on the K computer  to O(L6)), and for the number of times this computation must be performed (O(N2)). In order to reduce the computational cost for each pair, it is practical to separate the calculations into those for intra-molecular base-pairs and those for inter-molecular base-pairs. In the INTARNA [accessibility based on secondary structural energy of each RNA sequence and the hybridization energy between the two RNA sequences are evaluated as an approximation. Another screening that can reduce the total complexity of the computation is to find the mutually reverse complementary local pairs of subsequences from among all the pairs of RNA sequences, followed by computing RNA–RNA interactions between those local pairs. This rough screening on huge number of combinations can itself be computationally expensive, but recent progress in computational methods of sequence analysis enables us to avoid the O(N2) combinatorial explosion of the calculations. To ensure that the above screening works effectively, the pipeline first screens each RNA sequence by its accessibility because a subsequence that forms external base-pairs should not form internal base-pairs.For comprehensive prediction of RNA–RNA interactions, it is necessary to reduce the heavy computational costs, both for the computation for each pair of RNAs  and the other is called the target RNAs (denoted by T), our pipeline predicts RNA–RNA interactions between Q and T (the number of possible candidates of RNA–RNA interactions is |Q|×|T|). In Step 1, the accessible regions of each RNA sequence are extracted using the RACCESS program [TANTAN program [LAST [INTARNA [RACTIP [A summary of the pipeline is shown in Fig.  program , and tan program  in Step am [LAST , 22 in S[INTARNA , and can [RACTIP .Fig. 1SO(N) computation time, where N is the number of RNA sequences.First, each RNA sequence is screened to find its inaccessible regions and its tandem repeats, which allows us to extract candidate subsequences to form inter-molecule interactions. This screening requires an RACCESS program [accessible region ra is a region of length greater than s that satisfies ΔEacc(ra)<t, where s and t are parameters to be specified. Accessibility is defined by In Step 1, the accessible regions of each RNA sequences are extracted using the  program ; an acceR and T are the gas constant and the temperature, respectively, and where E denotes the free energy of secondary structure y of an RNA sequence x, x and Z(x) is the partition function.In Eq. Q and T), we compute the accessible energy Eacc(ra), where ra are the s consecutive nucleotides starting from the position, using the RACCESS program [RACCESS [s option). If Eacc(ra)<t holds, the positions are considered to be accessible. The parameter t in our pipeline is optimized as described in a later section.For each position in each RNA sequence . Specifically, a maximum non-gapped alignment is computed in which the score of G-C and A-U pairs (a.k.a. Watson-Crick base-pairs) is +1 and the score of the other pairs is −1. We consider a seed match whose integer score is more than LAST version 250 (http://last.cbrc.jp/) [s-m1000000 -ls, in which -s0 means LAST detects only matches with reverse complementary sequences, -j1 indicates gapless local alignment is conducted, and -es means that LAST reports those alignments whose score is greater than or equal to s. The option -m1000000 is the maximum multiplicity for initial seed match, for which we use a large value because we want to obtain as many seed matches as possible to maintain sensitivity. The option -ls indicates that the minimum length of initial matches is at least s. The threshold s is one of the parameters of the pipeline and was adjusted by using a bacterial dataset in our computational evaluations.In Step 3, the seed matches are detected using brc.jp/) , 22, wits are found. Note that LAST employs spaced seeds as the default; it was confirmed by a homology search that this realizes a sensitive seed match [Due to the scoring scheme described above, the seed matches whose lengths are more than ed match .Eint, is computed using the INTARNA [w base-pairs upstream and downstream of each seed match. The integer w is one of the parameters in our pipeline, and is optimized using bacterial sequences. Although calculating the optimal interaction energy between two RNA sequences entails heavy computational costs, INTARNA computes the minimum interaction energy between two local segments taking into account the approximated accessibility of the target sites [local RNA–RNA interactions. In Step 5, those interactions are ranked according to Eint, calculated in Step 4. We introduce two ranking methods for the predicted interacting pairs with respect to a query as follows. MinEnergy: The minimum interaction energy among the interactions contained in each pair of RNA sequences, computed by IntaRNA. This score is adequate for short RNA sequences or for cases in which the strongest local interaction is dominant.SumEnergy: The sum of the local interaction energies that are lower than x kcal/mol. This score is adequate for long RNA sequences such as lncRNAs and mRNAs and for cases in which several strong interactions exist.In Step 4, the interaction energy, denoted by  INTARNA  program,et sites . For eacjoint secondary structure of the strongest interaction site in each pair of RNA sequences is calculated using RACTIP [RACTIP estimates precise internal and external base-pairs by sophisticated integer programming using the principle of maximum expected accuracy [In Step 6, additionally, the g RACTIP . RACTIP accuracy . This stK computer (http://www.aics.riken.jp/en/k-computer/about/), including 88,128 SPARC64 VIIIfx CPUs . The other steps were calculated using the Chimera cluster system at AIST, including 176 Intel Xeon E5550 CPUs . In our experiments, a part of those cores is utllized . The analyses of Human transcriptome were performed on two computational environments as follows. Steps 3–6 in our pipeline, which require much more computational time than the other steps, were implemented on the http://www.ncbi.nlm.nih.gov/nuccore/NC_000913).As the known interactions for training the parameters of the pipeline, 44 interacting ncRNA–mRNA pairs were taken from . These phttp://www.gencodegenes.org/releases/19.html). The average and maximum lengths of the lncRNAs were 955 and 91,677, respectively. While most human protein-coding genes have alternative transcripts, the longest mRNA transcripts were selected for each gene in our analysis. As a result, the number of mRNA sequences was reduced to 20,185, and the average and maximum lengths were 3382 and 109,224, respectively.We used 23,898 long ncRNA (lncRNA) and 81,814 mRNA sequences obtained by the Gencode project  (http://MINENERGY or SUMENERGY), and the number of true positive predictions (denoted by “nTPs” in the following) are counted for all query RNAs with a given rank.In this study, we use the same evaluation method as . Specifit; (ii) the threshold of the score of the seeds, denoted by s; and (iii) the length w of the flanking sequences around the seed region. Flanking sequences of length w upstream and downstream around a seed are used for computing the  interaction energy. Those three parameters affect both the prediction accuracy and the computation time. For example, smaller values of t and larger values of s reduce the computational time because the number of candidates decreases. For optimization of those parameters, the known ncRNA-mRNA interactions in E. coli (see Section “MINENERGY method was used as a ranking method for the procedures described in this section because the query RNA sequences in this dataset were short (the average length is 117.2).Our pipeline includes three adjustable parameters cf. Fig. : (i) theWe compared the running time and accuracy of our pipeline with those of a method proposed by , which ww and s . The computation time decreased as s became larger and as w became smaller, as expected. For most of the combinations of s and w, our pipeline was faster than Richter’s pipeline, whose running time for the same dataset was 9.27 h.Additional file s=8 and w=20 achieved the best performance on accuracy, which was almost the same as the performance of Richter’s pipeline.Additional file t, the parameter for the threshold of accessibility, while the other parameters were held fixed at s=8 and w=20. Figure t. When t=4.3 kcal/mol was used, our pipeline (1.00 h) was 9.27 times faster than Richter’s pipeline (9.27 h) while the prediction performances were similar . For steps 3 to 5, 40,000 cores on the K computer (http://www.aics.riken.jp/en/k-computer/about/), one of the fastest computers in the world, were used for the calculation, which took 45 h. For a machine with a single core, however, these processes are estimated to take 1.8×106 h (205 years). This indicates that it is presently infeasible to make comprehensive predictions about RNA–RNA interactions in the human transcriptome without using a supercomputer. . Finally, in step 6, the joint secondary structure of the strongest interaction site in each pair of RNA sequences is predicted. For step 6, 16,000 cores on the K computer were used for the calculation, which took 7.3 h.The step 1 (accessibility filtering) was performed on the Chimera cluster system using 128 cores, which took 1.16 h. After Step 1, 80.7 % and 79.2 % of the total lengths of lncRNA and mRNA sequences, respectively, were maintained for the next step. The step 2 (tandem repeat masking) was performed on the Chimera cluster system using a single core, which took only about one minute. After Step 2, 78.4 % and 76.7 %, respectively, were maintained. In Step 3, about 4.7×10We validated our predicted interactions for the human transcriptome by comparing our predictions with three reliable studies of RNA–RNA interactions –10, all et al. [enriched segment by RIA-Seq  was included in our human transcriptome dataset. Figure ter Fig. . These r′UTR of the mRNA.Finally, we analyzed the pattern of local interactions between TINCR and mRNAs in our predictions. Additional file ′UTRs of two mRNAs (SERPINE1 and ANKRD57). These interactions lead to Staufen 1 (STAU1)-mediated messenger RNA decay (SMD) [Recently, Gong and Maquat  reporteday (SMD) .MINENERGY ranking, the ranks of SERPINE1 (ENST00000223095) and ANKRD57 (ENST00000356454) are 28 (within the top 0.06 %) and 1827 (within the top 4.14 %), respectively, among the predicted interactions with 1/2-sbsRNA (ENST00000548810) as the query. Interestingly, for this lncRNA, ranking with MINENERGY achieved better performance than ranking with SUMENERGY , while the SUMENERGY ranking achieved better performance on TINCR–mRNA interactions. This indicates that ranking by MINENERGY ranking is more appropriate than by SUMENERGY for such interactions. Figure ′UTR in mRNA. Additionally, the joint secondary structure of the two subsequences (processed by our pipeline) of the two 1/2-sbsRNA–mRNA interactions .The biological functions of NEAT1 have been well-studied from molecular biology viewpoints , with rMINENERGY . In contrast, in the list sorted by SUMENERGY  and users can check the annotation of the target/query RNA sequences in the genomic context. The database is available at http://rtools.cbrc.jp/cgi-bin/RNARNA/index.pl.To make our comprehensive predictions of lncRNA–RNA interactions in the human transcriptome publicly available, we have developed a database that contains all the predicted RNA–RNA interactions along with the following information: the rankings by Our pipeline achieved a performance for bacterial sRNA–mRNA interactions that is comparable to the performance of existing methods  but with significantly faster calculation. Several promising results were found in the predicted interactions in the human transcriptome in spite of the fact that our pipeline utilizes only sequence information of RNAs. There is room, however, to improve the accuracy of the pipeline and minimize false positive predictions. In the future, we will work to combine our method with additional information, such as expressions (RNA-seq) and localizations. Extensive proteome data for humans  and infoIn our pipeline, we used the parameters trained using sRNA-mRNAs in E.coli, which might not be optimal for predicting lncRNA-mRNA interactions in human transcriptome. It is, however, interesting that our pipeline achieved modest performance for predicting lncRNA-mRNA interactions with the parameters. This would imply a similarity between the mechanisms of mRNA recognition by sRNA and lncRNA. When the instances of lncRNA-mRNA interactions are accumulated, the similarity and difference between sRNA-mRNA and lncRNA-mRNA interaction will be elucidated. Moreover, it would be interesting to investigate the relation of two ranking methods (MINENERGY and SUMENERGY) with functions of interactions, because using SUMENERGY sometime provided better prediction results than using MINENERGY (TINCR and 7SL lncRNA), whereas the three parameters were optimized for MINENERGY.Recently, Engreitz et al. have pointed out the importance of interactions between RNA and nascent pre-mRNAs . AlthougE. coli RNA–RNA interaction dataset and was applied to the human transcriptome using the K computer, one of the fastest computers in the world. We compared our predicted RNA–RNA interactions in the human transcriptome with predictions in three existing studies of RNA–RNA interactions. Moreover, we have developed a database to compile predicted interactions, which will be useful for biologists who are interested in specific lncRNAs. To the best of our knowledge, this is the first study to comprehensively predict RNA–RNA interactions in the human transcriptome.In this study, we have developed a novel pipeline for predicting RNA–RNA interactions. Our pipeline was tuned and validated using an


Whole blood expression profiling is frequently performed using PAXgene (Qiagen) or Tempus (Life Technologies) tubes. Here, we compare 6 novel generation RNA isolation protocols with respect to RNA quantity, quality and recovery of mRNA and miRNA.matrix metalloproteinase 9, arginase 1) and miRNAs , shown to be altered by AMI, were analyzed.3 PAXgene and 3 Tempus Tubes were collected from participants of the LIFE study with (n = 12) and without (n = 35) acute myocardial infarction (AMI). RNA was extracted with 4 manual protocols from Qiagen (PAXgene Blood miRNA Kit), Life Technologies (MagMAX for Stabilized Blood Tubes RNA Isolation Kit), and Norgen Biotek (Norgen Preserved Blood RNA Purification Kit I and Kit II), and 2 (semi-)automated protocols on the QIAsymphony (Qiagen) and MagMAX Express-96 Magnetic Particle Processor (Life Technologies). RNA quantity and quality was determined. For biological validation, RNA from 12 representative probands, extracted with all 6 kits (n = 72), was reverse transcribed and mRNAs  followed by the choice of blood collection tube. (Semi-)automation reduced overall RNA extraction time but did not generally reduce hands-on-time. RNA yields and quality were comparable between manual and automated extraction protocols. mRNA expression was not affected by collection tubes and RNA extraction kits but by RT/qPCR reagents with exception of the Norgen Kit II, which led to mRNA depletion. For miRNAs, expression differences related to collection tubes (miR30b), RNA isolation (Norgen Kit II), and RT/qRT reagents (miR133a) were observed.We demonstrate that novel generation RNA isolation kits significantly differed with respect to RNA recovery and affected miRNA but not mRNA expression profiles. Gene expression profiling in peripheral blood is frequently performed to identify susceptibility genes or biomarkers for human traits or diseases. Upon collection, RNA profiles change within minutes In the meantime, additional suppliers have launched extraction kits for PAXgene and Tempus Blood RNA Tubes, which are also optimized for parallel mRNA and small RNA extraction (Norgen). Extraction methods vary depending on the manufacturer and are either based on silica-membranes  or magnetic beads (e.g. Life Technologies) Besides miRNA/mRNA quality and quantity, the duration of extraction is critical and automated sample processing is warranted especially in larger studies. Qiagen and Life Technologies provide companion instruments to facilitate RNA extraction, allowing parallel processing of up to 96 samples. To date, however, a comparison of the duration of sample processing as well as required hands-on times using different manual and (semi-)automated extraction methods has not been performed.After extraction, RNA expression may be analyzed by genome-wide approaches, either using array matrix metalloproteinase 9 (MMP9) and arginase 1 (ARG1) Thus, the aim of the current study was (1) to compare different extraction systems  for PAXgene and Tempus Blood RNA Tubes with regard to the quantity and quality of RNA, (2) to test different extraction protocols for the duration of extraction, hands-on time and processing time per sample, and (3) to evaluate mRNA and miRNA expression depending on the collection tube, RNA extraction method, RT and qRT-PCR reagents that were used. Studies were performed in probands of the LIFE study, which is an cohort study of the population of Leipzig, and in the LIFE Heart substudy, which recruits patients with myocardial infarction (AMI) Studies were approved by the local ethics committee of the University of Leipzig . Research participants gave informed written consent. Whole blood samples were collected from 35 probands of the LIFE study and 12 probands with acute myocardial infarction (AMI) from the LIFE Heart study 6 RNA extraction protocols using 4 different RNA isolation kits were compared . Four ki72 RNA samples, which were isolated with 6 RNA extraction protocols from 12 representative probands (6 LIFE and 6 LIFE-AMI probands), were selected for reverse transcription (RT) with Qiagen and Life Technologies reagents, respectively . RT of mbeta actin , matrix metalloproteinase 9  and arginase 1 . miRNA quantification was performed with miScript Primer Assays for miR1 (MS00008358), miR16 (MS00031493), miR30b (MS00003276) and miR133a (MS00031423). Assays were run with the miScript SYBR Green PCR Kit for quantitative PCR (for further information see ACTB (HS 99999903) MMP9 (HS 00234579) and ARG1 (HS 00968979) and TaqMan MicroRNA Assays for miR1 (ID 002222), miR16 (ID 000291), miR30b (ID 000602) and miR133a (ID 002246) were used in combination with the TaqMan Fast Advanced Mastermix. Reaction setups and manufacturers’ instructions are summarized in For mRNA analyses, samples were diluted with RNase free water to a final amount of 10 ng template cDNA/sample in a volume of 2.5 µl. For miRNA quantification, 1 ng cDNA/sample/2.5 µl input were used . qRT-PCRStatistical analysis was done using SAS , GraphPad PRISM , and Excel . Normality of distribution was tested using the Kolmogorov-Smirnov test. For the calculation of RNA extraction efficiency, outliers deviating more than 2xSD from arithmetic mean were excluded from analysis. Comparison of two groups was done using Wilcoxon-Mann-Whitney test for non-normally distributed data, Welch’s t-test or Student’s t-test for normally distributed data with unequal or equal variances, respectively. Bonferroni correction for multiple testing was applied. The ΔCt and ΔΔCt method was calculated according to Two whole blood stabilizing systems, PAXgene blood RNA system (Qiagen) and Tempus RNA system (Life Technologies), were evaluated for total RNA yield, duration of RNA preparation and mRNA and miRNA preservation. To this end, three PAXgene and three Tempus Blood RNA Tubes were collected from each proband of the LIFE study with (n = 12) and without AMI (n = 35) A major difference between both types of stabilization tubes was the time recommended for thawing. According to the manufacturer’s instructions, PAXgene Blood RNA Tubes should be thawed for 2 h at room temperature whereas Tempus Blood RNA Tubes may be processed after 30 min thawing on ice. Upon thawing, 24 or 16 samples for PAXgene or Tempus Blood RNA Tubes, respectively, were processed in batches. The differences in numbers were due to the capacity of the available centrifuge (Heraeus Multifuge 3SR), which was limited to 16 conical tubes with a volume of 50 ml required for RNA isolation from Tempus Blood RNA Tubes with all investigated kits .Manual sample preparation of 24 PAXgene Blood RNA Tubes was faster using the Norgen Preserved Blood RNA Purification Kit II . Manual sample preparation for 16 Tempus Blood RNA Tubes was faster with the Norgen Preserved Blood RNA Purification Kit I , compared to the MagMAX Stabilized Blood Tubes RNA Isolation Kit .Using the semi-automated MagMAX Express-96 Magnetic Particle Processor, RNA from 16 Tempus Blood RNA Tubes was isolated within 139 min, requiring 69 min of hands-on time. Using the fully-automated QIAsymphony, RNA from 24 PAXgene Blood RNA Tubes was isolated in 145 min requiring 20 min of hands-on time. Differences in hands-on times may largely be explained by the fact that the QIAsymphony is a fully automated instrument, in which thawed PAXgene Blood RNA Tubes can directly be loaded, whereas the MagMAX Express-96 Magnetic Particle Processor mainly supports washing and elution steps. To achieve the most economic use of reagents, it is recommended to work within batches of 24 samples on the QIAsymphony allowing parallel processing of up to 96 samples. In contrast, any number of samples (1 −96) can be processed with the MagMAX Express-96 Magnetic Particle Processor without loss of reagents.P<10–9) and LIFE-AMI probands (P<10–5), respectively  and 6 LIFE-AMI probands (mean RNA yield 10.4 µg) from all 6 extraction methods were used for reverse transcription and subsequent qRT-PCR (n = 72). All reactions were carried out with reagents from Qiagen or Life Technologies, respectively, and the same amount of RNA was used in all experiments. Schematics about all specifications and working steps are given in MMP9 and ARG1 expression, since both genes have been shown to be induced in AMI Beta actin (ACTB) was selected as house-keeping gene. For miRNAs miR133a and miR1, an increased expression has been shown in AMI For biological validation, we quantified In general, RNA extraction from PAXgene Blood RNA Tubes using the Norgen II Kit resulted in higher Ct-values for all quantified mRNA transcripts  but loweFor mRNA, we observed no major effects using different collection tubes or RNA isolation kits, whereas the use of RT and qRT-PCR reagents had a significant effect on mRNA expression profiles: Life Technologies reagents resulted in higher Ct-values, an effect more pronounced in higher expressed mRNAs , which mThe miRNA expression data were less homogeneous. miRNA quantification was in part affected by the choice of collection tubes  of the Ct-values . For allectively . For miRectively .MMP9 and ARG1 revealed a trend towards increased expression in LIFE-AMI compared to LIFE probands irrespective of the collection tube, RT or qRT-PCR reagents. Expression of house-keeping gene ACTB was not significantly altered between the two groups  and Tempus Blood RNA Tubes (Life Technologies) and RNA extraction with manual and (semi-)automated novel generation RNA isolation kits. Furthermore, we show that mRNA expression profiling was unaffected whereas miRNA expression levels were altered by the choice of collection tube, RNA extraction kits and RT/qRT-PCR reagents.Previous studies investigated mRNA expression in PAXgene and Tempus Tubes using older generation RNA extraction kits With respect to RNA yields, the Norgen Kit I in combination with Tempus Tubes revealed highest RNA yields, whereas the Norgen Kit II with PAXgene Tubes revealed the lowest RNA yields (60 −70% less), compared to all other isolation kits . The disFor biological validation, we used samples from apparently healthy individuals from a population-based cohort (LIFE) and a cohort of patients with acute myocardial infarction (LIFE-AMI) With respect to RNA quality, we found overall average RNA integrity numbers (RIN) of 5.9–8.6, average OD ratios 260/280>2.0, and 260/230>1.0 , which wWe also evaluated the duration of sample preparation and compared manual and (semi-) automated extraction protocols. In general, automated extraction protocols were slightly faster than manual protocols. The fastest manual RNA extractions were those from Norgen Biotek for both, PAXgene and Tempus Tubes , which also required least hands-on-time compared to manual kits from Qiagen and Life Technologies . The fasMMP9 and ARG1 mRNA expression was investigated because these genes were shown to be induced in patients with AMI We further evaluated potential impacts of the novel generation RNA isolation kits on mRNA and miRNA profiles. With exception of the Norgen Preserved Blood RNA Purification Kit II, we demonstrate that mRNA expression profiles of selected candidate genes were not affected by the type of collection tube and by different RNA isolation kits . MMP9 anIn contrast, miRNA expression profiles were affected by the type of collection tubes (e.g. miR30b), RNA extraction kit , and RT/qRT-PCR reagents (e.g. miR133a) . miR1 anTaken together, our study shows significant differences in RNA recovery and quality using different RNA collection tubes in combination with manual and (semi-)automated novel generation RNA extraction kits. It demonstrates that mRNA expression was rather stable but miRNA levels were affected by the collection tubes and processing steps. In case of multi-center studies or replication studies, where samples might have been collected and processed according to different protocols, special attention should be paid to adjust for these potentially confounding factors.Figure S1Extended information on reverse transcription (RT) and qRT-PCR study design. RNA samples from 12 probands  were selected for downstream application using reagents from Qiagen and Life Technologies. Analysis of mRNA and miRNA was carried out using the ViiA 7 (Life Technologies). The detailed experimental setup for RT and qRT-PCR is summarized in (TIF)Click here for additional data file.Figure S2RT and qRT-PCR results of selected mRNAs and miRNAs in dependency of RNA extraction kits. Expression of (A) matrix metalloproteinase 9 (MMP9) and arginase 1 (ARG1) and (B) miR133a and miR1 in LIFE-AMI relative to LIFE probands. mRNA and miRNA was normalized to beta-actin (ACTB) or either to miR16 or miR30b, respectively. Fold changes and SEM were calculated according to (TIF)Click here for additional data file.Table S1Characteristics of the extracted RNA. Total amount, concentration and OD values of RNA extracted from PAXgene Tubes (A) and Tempus Tubes (B) and blood weight for LIFE probands (LIFE1– LIFE35) and LIFE-AMI probands (LIFE-AMI1– LIFE-AMI12) were measured. Before starting the RNA extraction procedure, the RNA Blood tubes were weighted. Empty PAXgene Tubes and Tempus Tubes were used as tare weight. After each RNA extraction (using the six methods described in the manuscript), RNA yield was measured using Nanodrop. Samples were excluded (–) where total RNA amount was too low likely due to inaccurate handling. Furthermore, cells in green indicate RNA yield out of the 95% distribution. These values were omitted for the analyses in (XLSX)Click here for additional data file.Table S2Specifications of reverse transcription (RT) and qRT-PCR using reagents from Qiagen and Life Technologies. Detailed information on the used volumes and dilutions is given in (XLSX)Click here for additional data file.Table S3Setup for RT and Real-time PCR. Overview including all reaction setup for the processing of Reverse Transcription (RT) and quantitative real-time PCR using kits from Qiagen and Life Technologies. rct = reaction.(XLSX)Click here for additional data file.


RNA editing results in a post-transcriptional nucleotide change in the RNA sequence that creates an alternative nucleotide not present in the DNA sequence. This leads to a diversification of transcription products with potential functional consequences. Two nucleotide substitutions are mainly described in animals, from adenosine to inosine (A-to-I) and from cytidine to uridine (C-to-U). This phenomenon is described in more details in mammals, notably since the availability of next generation sequencing technologies allowing whole genome screening of RNA-DNA differences. The number of studies recording RNA editing in other vertebrates like chicken is still limited. We chose to use high throughput sequencing technologies to search for RNA editing in chicken, and to extend the knowledge of its conservation among vertebrates. We performed sequencing of RNA and DNA from 8 embryos. Being aware of common pitfalls inherent to sequence analyses that lead to false positive discovery, we stringently filtered our datasets and found fewer than 40 reliable candidates. Conservation of particular sites of RNA editing was attested by the presence of 3 edited sites previously detected in mammals. We then characterized editing levels for selected candidates in several tissues and at different time points, from 4.5 days of embryonic development to adults, and observed a clear tissue-specificity and a gradual increase of editing level with time. By characterizing the RNA editing landscape in chicken, our results highlight the extent of evolutionary conservation of this phenomenon within vertebrates, attest to its tissue and stage specificity and provide support of the absence of non A-to-I events from the chicken transcriptome.


Bunyaviruses have become a major threat to both humans and livestock in Europe and the Americas. The nucleocapsid (N) protein of these viruses is key to the replication cycle and knowledge of the N oligomerisation state is central to understanding the viral lifecycle and for development of therapeutic strategies.E. coli as hexahistidine-SUMO-tagged fusions, and the tag removed subsequently. Noncovalent nano-electrospray ionisation mass spectrometry was conducted in the presence and absence of short RNA oligonucleotides. Instrumental conditions were optimised for the transmission of intact protein complexes into the gas phase. The resulting protein-protein and protein-RNA complexes were identified and their stoichiometries verified by their mass. Collision-induced dissociation tandem mass spectrometry was used in cases of ambiguity.Bunyamwera virus and Schmallenberg virus N proteins (BUNV-N and SBV-N) were expressed recombinantly in Both BUNV-N and SBV-N proteins reassembled into N-RNA complexes in the presence of RNA; however, SBV-N formed a wider range of complexes with varying oligomeric states. The N:RNA oligomers observed were consistent with a model of assembly via stepwise addition of N proteins. Furthermore, upon mixing the two proteins in the presence of RNA no heteromeric complexes were observed, thus revealing insights into the specificity of oligomerisation.Noncovalent mass spectrometry has provided the first detailed analysis of the co-populated oligomeric species formed by these important viral proteins and revealed insights into their assembly pathways. Using this technique has also enabled comparisons to be made between the two N proteins. Bunyaviridae family of segmented, negative sense, single-stranded RNA viruses comprises five genera: Hantavirus, Nairovirus, Phlebovirus, Tospovirus and Orthobunyavirus. Amongst the 330-plus viruses in the Bunyaviridae family are several human pathogens, including Rift Valley fever virus , Crimean-Congo haemorrhagic fever virus  and Sin Nombre virus . Studies of these viruses are particularly timely, given the migration of many arthropod vectors into northern Europe as a result of changing climate.1The Orthobunyavirus, the largest of the family. This genus also contains Bunyamwera virus (BUNV), the prototypic member of the family.In 2011, a novel bunyavirus was isolated in Europe and found to cause severe birth defects and abortion in livestock such as sheep, cattle, and goats.The orthobunyavirus genome consists of three separate strands of negative-sense single-stranded RNA. These are termed the large (L), medium (M), and small (S) segments and are packaged into viral ribonucleoprotein complexes (RNPs) incorporating the nucleocapsid protein (N). The N protein protects the viral genome and is essential for the viral replication cycle; the viral RNAs are only able to participate in transcription and replication when complexed with N as RNPs, and only intact RNPs are packaged into virions.6Recently, there have been several advances in the field of orthobunyavirus structural biology, with the crystal structures of the N proteins from four different viruses, namely SBV,Previously, we have conducted structural studies on both SBV-N and BUNV-N.et al. show agreement between the width of the orthobunyavirus N tetramer and the width of intact RNPs isolated from infectious virions,et al.et al.With the establishment of the crystal structures of these orthobunyavirus N proteins came a significant leap forward in the knowledge of the architecture of these viruses; however, many of the details of the RNP superstructure and RNA-binding properties remained elusive. To date, orthobunyavirus RNPs have been shown by electron microscopy (EM) as circular, loose, string-like particles; however, the results of observations of the width of the 'strings' have varied. Our previous EM data and those of Reguera Electrospray ionisation mass spectrometry (ESI-MS) has been employed in a wide range of studies investigating the structure and function of protein assemblies and has been found to be particularly powerful in the study of virus systems. This technique has been used to detect and interrogate whole virus capsids,In this communication, we demonstrate the use of ESI-MS to investigate the RNA-binding and oligomerisation properties of the N proteins of both the prototype BUNV and the recently emerging SBV. We rationalise the observed species and propose a simple model of assembly. For example, if a higher-order RNA-N complex is composed of stacked tetrameric rings of N proteins, we may expect to see addition of 'blocks' of tetramers of N proteins during oligomerisation. However, here we observed a step-wise addition of monomers and/or dimers of N. Also of note is the lack of a heteromeric N-N interaction between BUNV-N and SBV-N, revealed by tandem mass spectrometry (MS/MS) and collision-induced dissociation (CID), which is somewhat unexpected considering the similarity in the folds of the two proteins. We attribute these observations to differences in the sequences of the unstructured N-terminal arm, a region revealed by crystallographySBV-N was produced as described previously.–1 in water). Data were analysed using the MassLynx 4.1 software package . Samples of each N protein were analysed at a target protein concentration of 2.5 μM tetramer in ammonium acetate buffer (pH 6.8). Spectra were acquired using a capillary voltage of 1.2–1.4 kV, a cone voltage of 60 V, a trap collision voltage of 6 V, a transfer collision voltage of 14 V, a trap DC bias of 20 V, a trap flow rate of 3 mL min–1, and a source pressure of 4 mbar. MS/MS was performed by precursor mass-selection in the quadrupole analyser followed by CID in the argon-filled trap ion guide of the Synapt HDMS mass spectrometer. Trap collision voltages in the range 40–90 V were used to dissociate the protein complexes in order to verify their identities. The product ions generated were m/z analysed using the time-of-flight analyser.All mass spectra were acquired using a Synapt HDMS orthogonal acceleration quadrupole-travelling wave time-of-flight mass spectrometer . Samples were analysed using nano-ESI from platinum/gold-plated borosilicate capillaries fabricated in-house using a P-97 micropipette puller  and a sputter coater . All spectra were calibrated using cluster ions generated from a separate introduction of caesium iodide solution .For ion mobility experiments, the following parameters were set on the Synapt HDMS mass spectrometer: a wave height of 11 V and a wave velocity of 300 m s2, the experimental collision cross-sectional area measured for SBV N. The centre-to-centre distance of protein spheres in the tetramer model was optimised so that its theoretical collision cross-section was in agreement with the experimental value of tetrameric SBV N. This facilitated the building of a range of oligomer models as it gave an estimate of the buried protein surface upon N oligomerisation. Theoretical single-ring (trimers-hexamers) and double-ring  structures were built using the experimentally optimised protein radius and centre-to-centre distance for comparison with experimental data. All theoretical collision cross-sections were calculated using the Leeds' Method projection approximation algorithm.20Experimentally optimised coarse-grained structural models were built approximating each N monomer as a sphere, for comparison with the experimentally calculated collision cross-sectional areas. The radius of the spheres was set at 2.5 nm, the radius required to give a collision cross-section of 21.6 nmThe oligoribonucleotides (RNAs) 12-mer , 24-mer , 48-mer , and 60-mer  were purchased from Integrated DNA Technologies . It was not feasible to produce longer RNAs by chemical synthesis at the purity required for these experiments. The masses of the RNAs were confirmed by negative ion ESI-MS in solutions of 50:49:1 acetonitrile/50 mM ammonium acetate/triethylamine (v/v/v).RNAs were added to samples of either BUNV-N or SBV-N in various molar ratios as stated in the Results and Discussion section using a protein concentration that would lead to a final concentration of 10 μM, based on the monomer. MS and MS/MS were used to confirm the oligomeric states that were formed in the presence of the RNAs.The N proteins were mixed in a 1:1 molar ratio in 50 mM ammonium acetate (pH 6.8). The 24-mer RNA was added in a 2:1 RNA/total protein ratio. Mass spectra were acquired under the same conditions as above.E. coli expression host.m/z (m/z 5000–7000) were observed which are consistent with tetrameric and pentameric N bound to RNA, the latter of which had survived the RNA-removal step. However, there was no evidence for protein-only oligomers. Together these data suggest that, under these experimental conditions, BUNV-N is unable to form stable higher oligomers. This suggests that the RNA may be required to provide thermodynamic stability to the complex, whilst promoting an oligomerisation-competent conformation of N.Recombinant BUNV-N has been shown previously to be purified predominantly as a tetramer bound to endogenous RNA co-purified from the For comparison with BUNV-N, ESI-MS was conducted on high salt-treated SBV-N  2], [(BUNVN)3 + (12-mer)3], and [(BUNVN)4 + (12-mer)4], the latter two being the major species, with no evidence for the monomeric protein . The RNA sequences were taken from the 5′ terminus of the BUNV antigenome S segment, in line with the RNA used in our previous fluorescence polarisation assays.3 + (24-mer)1], [(BUNVN)4 + (24-mer)2] (the major species), [(BUNVN)5 + (24-mer)2], and [(BUNVN)6 + (24-mer)3] were observed in the mass spectrum , signals corresponding to the complexes [(BUNVN)5 + (48-mer)1], as the dominant species 1] was observed  with BUNV-N gave rise to the pentamer, [(BUNVN)4 + (12-mer)4] (major species observed), [(SBVN)8 + (12-mer)8], [(SBVN)10 + (12-mer)10], and [(SBVN)12 + (12-mer)12] were observed, corresponding to tetramers, octamers, decamers and dodecamers of SBV-N.Similar experiments were performed with SBV-N for comparison Fig.  (again u4 + (24-mer)2] and [(SBVN)5 + (24-mer)2], respectively 1], [(SBVN)4 + (48-mer)1] (major species observed), [(SBVN)4 + (48-mer)2], and [(SBVN)5 + (48-mer)2] 1] , [(SBVN)5 + (60-mer)1] , [(SBVN)6 + (60-mer)1], and [(SBVN)8 + (60-mer)1], respectively 1] complex, some of the SBV-N monomers cannot be in contact with RNA, which is consistent with SBV-N being able to oligomerise without RNA The preferred oligomerisation state of the SBV-N protein in all experiments was the tetramer, in complex with varying molecules of RNA, in contrast to BUNV-N. Furthermore, the SBV-N protein was detected in a wider range of complexes than the BUNV-N protein, although some of these may have arisen from the presence of the protein-only complexes observed in the absence of RNA 2] and [(BUNVN)3 + (24-mer)1], respectively, are observed. In these trimeric complexes there are 33 RNA base binding sites but only 24 are filled, leaving 9 unoccupied. It follows that not all available sites have to be filled in order for oligomerisation to occur, suggesting that there is a minimal region on the protein sensitive to RNA binding that induces oligomerisation. The observation of the complexes [(BUNVN)5 + (24-mer)2], [(BUNVN)6 + (24-mer)3], [(BUNVN)5 + (48-mer)1], and [(BUNVN)6 + (60-mer)1] in the spectra is also consistent with the accepted protein-RNA stoichiometry of 11 nt per monomer, but again there must be some empty sites. The observation of such a wide range of stoichiometries in these data raises the question as to the structure of these higher oligomers of N when in complex with RNA. The tetramers are likely to be planar ring structures, as supported by X-ray crystallography and EM.In addition to illustrating the diversity in the range of low oligomeric states formed by BUNV-N and SBV-N, these mass spectrometric data also provide insights into the nature of RNA binding. From the X-ray crystal structures of orthobunyavirus N,3 + -mer2] anWith tetramers being the dominant oligomeric state observed for SBV-N and one of the most highly populated complexes observed for BUNV-N, one might have expected to observe dimers, especially since in the presence of the 24-mer RNA, there are approximately the correct number of bases to fill all sites  on the protein. However, there was no trace of dimeric complexes for SBV-N or BUNV-N, in the presence or absence of RNA. Seemingly in conflict with this observation, the higher oligomers of the N proteins varied by two subunits (no odd-numbered oligomers were detected above the size of pentamers), implying that the dimer may indeed be an important structural building block. Together, these observations suggest that the dimeric form of N is transient and consumed on a timescale much faster than can be resolved by MS.4 + (24-mer)2] and [(SBVN)4 + (24-mer)2], indicating that no mixed complexes had been formed, and also the preferred tetrameric complexes with stoichiometries of 12 nucleotides per protein molecule were observed for both BUNV-N and SBV-N, whether analysed alone or within a mixture 2] complex yielded signals corresponding to SBV-N monomers and the charge-reduced 'stripped' complex [(SBVN)3 + (24-mer)2], as expected 2] complexes yielded signals corresponding to BUNV-N monomers and the 'stripped' complex [(BUNVN)3 + (24-mer)2]  is not the sole building block of the orthobunyavirus RNP, consistent with EM images of the native RNPs and a crystal form showing a helical geometry.The oligomerisation process is highly cooperative between protein and RNA and is not dominated by the electrostatic interactions between N proteins and the phosphate backbone, but likely involves protein condensation onto the RNA followed by the conformational promotion of oligomer growth. As not all nucleotide-binding sites on N proteins need to be occupied in order to promote this oligomerisation, a minimal RNA-sensitive binding region is likely. This leads to a model reminiscent of the assembly of tobacco mosaic virus via formation of a split ring (or lockwasher) of protein subunits in order to generate a nucleus for addition of further subunits to form a helical RNP complex.


Formalin-fixed paraffin-embedded (FFPE) tissue samples are routinely archived in the course of patient care and can be linked to clinical outcomes with long-term follow-up. However, FFPE tissues have degraded RNA which poses challenges for analyzing gene expression. Next-generation sequencing (NGS) is rapidly becoming accepted as an effective tool for measuring gene expressions for research and clinical use. However, the feasibility of NGS has not been firmly established when using FFPE tissue.We optimized strategies for whole transcriptome sequencing (RNA-seq) using FFPE tissue. Ribosomal RNA (rRNA) was successfully depleted by competitive hybridization using the Ribo-zero™ Kit (Epicentre Biotechnologies), and rRNA sequence content was less than one percent for each library. Gene expression measured by FFPE RNA-seq was compared to two different standards: RNA-seq from fresh frozen (FF) tissue and quantitative PCR (qPCR). Both FF and FFPE tumors were sequenced on an Illumina Genome Analyzer IIX with an average of 10 million reads. The distribution of FPKMs (fragments per kilobase of exon per million fragments mapped) and number of detected genes were similar between FFPE and FF. RNA-seq expressions from FF and FFPE samples from the same renal cell carcinoma (RCC) correlated highly (r = 0.919 for tumor 1 and r = 0.954 for tumor 2). On hierarchical cluster analysis, samples clustered by patient identity rather than method of preservation. TaqMan qPCR of 424 RCC-related genes correlated highly with FFPE RNA-seq expressions . Expression fold changes were considered, to assess biologic relevance of gene expressions. Expression fold changes between FFPE tumors (tumor 1/tumor 2) correlated well when comparing qPCR and RNA-seq (r = 0.890). Expression fold changes between tumors from different risk groups  also correlated well when comparing RNA-seq from FF and FFPE tumors (r = 0.887).FFPE RNA-seq provides reliable genes expression data, comparable to that obtained from fresh frozen tissue. It represents a useful tool for discovery and validation of biomarkers.The online version of this article (doi:10.1186/1471-2164-15-1087) contains supplementary material, which is available to authorized users. RNA expression profiling may lead to the discovery of molecular markers for disease diagnosis, assessing prognosis, and targeting with drugs. Quantitative (qPCR) has been the “gold standard” for measuring gene expressions, 2 due tThe optimal tissue for RNA-seq is fresh frozen (FF) tissue with high quality RNA. Unfortunately, frozen tumors are not widely available because they are costly to collect and maintain. However, formalin-fixed paraffin-embedded (FFPE) tissue samples are routinely archived in the course of patient care and can often be linked to clinical outcomes with long-term follow-up. Unfortunately, FFPE tissues yield relatively low quantities of degraded RNA. A small number of studies have reported using FFPE tissue for whole transcriptome mRNA expression profiling–14. HereIn this study, transcriptome-wide RNA-seq was successfully performed on matching FFPE and FF clear cell renal cell carcinomas (ccRCCs). The expression profiles generated from FFPE and FF tumors correlated well. The tumor RNA was also assessed by qPCR using the OpenArray® NT Cycler system, which we’ve previously validated for use with FFPE tumors. RNA quaTaqMan qPCR is an established assay for determining expression levels using either FF or FFPE tumors. The OpenArray platform uses microfluidics to load nanoliter scale TaqMan qPCR chambers. We recently validated the OpenArray platform for use with FFPE tumors. We condTotal RNA was used to prepare libraries for RNA-seq. Ribosomal RNA (rRNA) constitutes more than 80% of the total tumor RNA. Therefore, to avoid wasting sequencing reactions generating repetitive reads, rRNA was removed by competitive hybridization using the Ribo-Zero™ kit. Total RNA was assessed by Agilent Bioanalyzer before and after Ribo-Zero treatment to show that large peaks corresponding to 18S and 28S rRNA disappeared  provided 4 renal tumors from 2 matching pairs of FFPE and FF ccRCCs. The study was approved by Cedars-Sinai Medical Center’s Institutional Review Board (IRB). For prospectively collected tumor tissue, patients gave written informed consent for data and tissue collection. For FFPE tissue, which already existed in our biobank, IRB provided an exemption for obtaining informed consent. The clinical information associated with each tumor is listed in Additional fileOur method for RNA extraction has previously been described, 16, 23.Ribosomal RNA was depleted from 4 micrograms of total RNA using the Ribo-Zero™ Magnetic Kit  following the manufacturer’s procedure. Sequencing libraries for whole transcriptome analysis were prepared using ScriptSeq™ v2 RNA-Seq Library Preparation Kit  following the manufacturer’s procedure. After 3′-terminal tagging, the di-tagged cDNA was purified using MinElute PCR Purification Kit . Four 6-base index sequences were used to prepare bar-coded libraries for sample multiplexing (ScriptSeq™ Index PCR Primers (Set 1); Epicentre Biotechnologies, Madison, WI). PCR was carried out through 13 cycles to generate the second strand of cDNA, incorporate barcodes, and amplify libraries. The amplified libraries were size selected by a solid phase reversible immobilization, paramagnetic bead-based process . The beads: PCR product ratio was 1.8:1.Libraries were quantified by Qubit® dsDNA HS Assay . 15ng libraries were visualized on a 2% E-Gel . The four RNA-seq Libraries were then multiplexed in a single lane on a paired end flow cell; clonal amplification was performed using an Illumina cBot . Sequencing-by-Synthesis chemistry was performed on an Illumina Genome Analyzer IIX using 35 bp single end sequencing chemistry.http://ccb.jhu.edu/software/tophat/index.shtml) software package. Transcripts were assembled using Cufflink2 (http://cufflinks.cbcb.umd.edu/). The number of sequenced reads that align to a gene of interest was conventionally called a tag count. To make tag count comparable among samples, we transformed the tag count into FPKM/RPKM (Reads per kilobase per million reads mapped) for normalization, which is widely used in RNA-seq analysis. FPKM value was shown in Additional fileBCL files were converted to FASTQ files using BCLconverter. The FASTQ files were aligned against human genome reference version 19 (or hg19), using TopHat2 (http://bedtools.readthedocs.org) was used to overlap TopHat2 mappings with mapping quality of at least 20 and defined exons and introns with a minimum overlap fraction of .8. To reconcile differing exon and intron annotations for a single gene due to alternative splicing, a read was defined as intronic if it was (1) within an intron and (2) overlapped with no exons; reads overlapping any exon were counted as exonic. The bedtools utility was used to determine concordance between the strands of reads mapping to exons and the strands of the exons. To determine the presence of rRNA in the samples, reads were mapped to rRNA human sequences  using the BWA utility (http://bio-bwa.sourceforge.net). Reads which mapped to an rRNA sequence with a mapping quality of at least 20 were considered to be rRNA derived.To get RNA-seq quality metrics, bedtools utility (Reverse transcription (RT) was performed using the High Capacity cDNA Reverse Transcription Kit  following the manufacturer’s procedure. Each RT reaction contained 150ng of total RNA, 1 μl of 10× RT buffer, 0.5 μl of 25× dNTP mixture, 1 μl of 10× random reverse primers, 1 μl of 10× gene-specific reverse primers (1μM) and 0.5 μl of MultiScribe RT (50 U/μl). The 10 μl reactions were incubated in a Life Technologies Thermocycler for 10 min at 25°C, 2 hours at 37°C and then held at 4°C. The same primers were used for the pre-amplification and the TaqMan qPCR. The 3’end primers used in the PCR were used for the RT.Preamplification was performed using the TaqMan® PreAmp Master Mix Kit . Each reaction included 2.5 μl of 2× TaqMan® PreAmp Master Mix, 1.25 μl of 0.09× pooled TaqMan assays (TaqMan primers and probe) and 1.25 μl of cDNA. The reactions were incubated in a Life Technologies Thermocycler for 10 min at 95°C following by 13 cycles of 95°C for 15 seconds and 60°C for 4 min, and then held at 4°C. 0.09× pooled Taqman assays were prepared by combining equal volumes of each 20× Taqman assay . Each cDNA was preamplified on two sets of 218 pooled assays.The two sets of OpenArray plates were custom made. The preamplified products were diluted 1:10; 10 μl of diluted cDNA was mixed with 10 μl of TaqMan Real-time PCR OpenArray Master Mix . The pre-amplified cDNA samples were dispensed using the Accufill System onto the corresponding OpenArray plate containing 218 gene assays. Twelve cDNA samples were tested simultaneously per plate, with 36 samples per qPCR run on the Openarray® NT Cycler system. Post-acquisition data processing generated fluorescence amplification for each assay, from which cycle threshold (CT) were computed. CT value was shown in Additional filehttps://tcga-data.nci.nih.gov/tcga/). Our tumors were clinically localized tumors that posed high risk for metastasis. To compare these tumors to low risk-RCC using expression fold change, all pT1N0M0, grade 1 or 2 ccRCCs were selected from TCGA (n = 53).Clinical information and Level 3 RNA-seq FPKM data were retrieved from the TCGA data portal . Complete-linkage Hierarchical clustering was performed using Gene Cluster 3.0 software. The similarity (distance) between gene expression data was defined using Pearson correlation (uncentered). The result was viewed using Java TreeView software.Fold changes in gene expression were calculated: for qPCR results, logThe TCGA sequence data and clinical data discussed in this publication are available from the TCGA Data Portal.The supplemental data was available from LabArchive with DOI of 10.6070/H4Q23X6N DOI: 10.6070/H4Q23X6N#doi.Deep sequencing files have been deposited into Sequence Read Archive under accession number SRP050335.Additional file 1: Figure S1: RNA integrity test before and after Ribo-zero treatment. 200ng of RNA was measured with the Eukaryote Total RNA Nano Assay of the Agilent 2100 Bioanalyzer. FF, fresh frozen; FFPE: formalin-fixed paraffin-embedded; Before, before ribo-zero treatment; After, after ribo-zero treatment. (TIFF 1 MB)th and 75th percentile. TCGA, The Cancer Genome Atlas RCC; CS FF, Cedars Sinai fresh frozen RCC; CS FFPE, Cedars Sinai formalin-fixed paraffin-embedded RCC. (TIFF 544 KB)Additional file 2: Figure S2: Box Graph for RNA-seq. Distribution of FPKMs determined from RNA-seq. Horizontal line inside the box is the median expression. The box contains expressions between the 25Supplemental Tables.(XLSX 973 KB)Additional file 3:Additional file 4: Figure S3: Distribution of detected genes for RNA-seq. Transcript counts are plotted against Log2 (FPKM) for genes determined from FF and FFPE RNA-seq. FF, fresh frozen; FFPE, fresh-frozen paraffin-embedded; FPKM, fragments per kilobase of exon per million fragments mapped. (TIFF 578 KB)Additional file 5: Figure S4: Rank correlation of RNA-seq and qPCR for matched FF and FFPE tumors. A) Tumor 1, FFPE B) Tumor 1, FF C) Tumor 2, FFPE D) Tumor 2, FF. FF, fresh frozen; FFPE, fresh-frozen paraffin-embedded; FPKM, fragments per kilobase of exon per million fragments mapped. (TIFF 1 MB)


A 52-year old man, chronic smoker for 20 years, presented at our department with skin nodule, firm, painful and ulcerated, localized in the trunk, rapidly grown for period of three months. Histological and immunohistochimical examination confirmed the lesion to be metastasis of lung adenocarcinoma. The patient underwent a CT scan of the abdomen and thorax, which revealed primytumor of the left lower lobe T4N2M+ with infiltration of the deep fascial and muscular planes by theneoplasm associated with hepatic metastasis and vertebral metastasis T9-T11. The patient died after 1 month.


Cells regulate gene expression at multiple levels leading to a balance between robustness and complexity within their proteome. One core molecular step contributing to this important balance during metazoan gene expression is RNA editing, such as the co-transcriptional recoding of RNA transcripts catalyzed by the adenosine deaminse acting on RNA (ADAR) family of enzymes. Understanding of the adenosine-to-inosine RNA editing process has been broadened considerably by the next generation sequencing (NGS) technology, which allows for in-depth demarcation of an RNA editome at nucleotide resolution. However, critical issues remain unresolved with regard to how RNA editing cooperates with other transcript-associated events to underpin regulated gene expression. Here we review the growing body of evidence, provided by recent NGS-based studies, that links RNA editing to other mechanisms of post-transcriptional RNA processing and gene expression regulation including alternative splicing, transcript stability and localization, and the biogenesis and function of microRNAs (miRNAs). We also discuss the possibility that systematic integration of NGS data may be employed to establish the rules of an “RNA editing code”, which may give us new insights into the functional consequences of RNA editing. The transcriptome is the complete set of RNA molecules in a cell, expression of which is finely and dynamically regulated to meet the cellular needs associated with a particular developmental or physiological state. The field of transcriptomics entails investigating three main aspects of gene expression at the RNA level: the RNA species , the RNA transcript structure , and RNA expression levels (quantitative changes within a specific context). While extensive efforts have been devoted to characterizing the transcriptional and post-transcriptional mechanisms that demarcate the transcriptome, RNA editing remains one of the less well explored steps in terms of its regulation and functional consequences.Alu elements and in the brain transcriptome [RNA editing events, the majority of which are of the adenosine-to-inosine (A-to-I) type base changes, represent a core co-transcriptional process by which transcripts are covalently modified in a manner that results in an RNA sequence different from that encoded by the genomic DNA -3. Such criptome ,4,5, RNAcriptome .adenosine deaminse acting on RNA (ADAR) family proteins that mediates deamination of adenosine to inosine in structured or double-stranded RNAs [ADAR1 gene or altered A-to-I editing has been implicated in the regional onset of several human pathophysiological conditions, such as tumor malignancy [The enzymatic process of A-to-I RNA editing is catalyzed primarily by the ded RNAs . This meded RNAs . Conversded RNAs , the secded RNAs -11. For lignancy ,13, the lignancy , sporadilignancy , and neulignancy , reinforGlobal and unequivocal identification of RNA editing targets represents a critical first step towards further understanding this vital co-transcriptional modification. In principle, editing sites could be inferred on the basis of sequence differences between RNA (or cDNA) and the genome DNA from which it is expressed. To this end, early bioinformatic approaches applied to publically archived sequence data provided glimpses of the RNA editome, revealing the distribution and structural characteristics of RNA editing events ,17-19. TTo account for the likelihood of errors, subsequent analyses have been designed to incorporate quality control filters in their respective bioinformatics pipelines, as well as a larger extent of transcriptome sampling via sequencing depth. Consequently, recent studies reporting the use of target-specific RNA-Seq ,25, a coGria2 (Glutamate receptor B) R/G editing site, which is one base upstream of a 5’-splice site, an A-to-I nucleotide change was demonstrated to influence the splicing rate [cis-regulatory sequence that gives rise to alternatively spliced transcripts of the NARF[PTPN6[A-to-I editing of pre-mRNA by ADAR proteins in a few cases has previously been linked to alternative expression of transcript isoforms. In a negative feedback auto-regulatory loop, ADAR2 may edit its own pre-mRNA to create an alternative isoform that encodes an otherwise dysfunctional short protein product . In the ing rate . The occ the NARF or PTPN6ARF[PTPN6 genes, rARF[PTPN6.trans-acting factors implicated in splicing.Reinforcing such a link between the two RNA processing events, a more systematic approach utilizing high-throughput RNA-seq and exon-specific microarray analyses further revealed at the global scale that ADAR1 targets alternative splicing . It was nrb-dependent mechanism [RNA editing was previously hypothesized to mark aberrant and/or structured transcripts for retention in the nuclear compartment via a p54echanism -40. In techanism , showingechanism . FindingAnother means by which ADARs and/or RNA editing can determine transcript fate is at the level of stability. The aforementioned NGS-based studies all pointed to a significant enrichment of editing events in the 3’ UTR of target transcripts, a strong indication that editing is a functional determinant of transcript stability. Combining editome sequencing from ADAR1 knockdown cells and protein immunoprecipitation, Wang et al. strengthened this hypothesis by identifying an enrichment of HuR-binding sequences, which are implicated in transcript turnover and processing, around editing sites and on ADAR-bound transcripts, respectively . They fuIn addition to messenger RNAs, editing is also known to take place on microRNAs (miRNAs), which are short RNAs of around 22 nucleotides that regulate gene expression post-transcriptionally. Owing to the double-stranded structure, the primary transcripts of miRNAs (pri-miRNAs) are therefore potential substrates for A-to-I RNA editing . Early sin cis the binding of miRNA to targeted transcripts. In this capacity, transcriptome sequencing studies revealed an overlap between RNA editing sites and certain miRNA target sequences in this non-coding region [ARHGAP26[Reciprocally, given its prevalence in 3’ UTR, RNA editing is likely to affect g region ,42. More[ARHGAP26. Both hiGLI1), a transcriptional effector of the Hedgehog (HH) signaling [Despite the seemingly extensive crosstalk between RNA editing and gene expression, transcriptional regulation remains one aspect that is the least explored to which ADAR1 may make a contribution. Intriguingly, in addition to the structural motifs for double-stranded RNA binding, ADAR1 also possess a Z-DNA-binding domain ,54, implignaling . The conignaling . Howeverignaling  and globignaling  methods.In summary, great progress has been made with regard to identifying RNA editing sites in the non-coding components of the transcriptome. We now know that crosstalk between A-to-I editing and miRNA is more extensive than previously thought. With the growing body of sequencing data, one of the outstanding issues to address is the biological and regulatory significance of these sequence-altering events, particularly in the miRNA gene silencing process that stringently relies on sequence complementarity. Predicting the functional outcome of RNA editing remains a challenge, and may require thorough bioinformatics algorithms that cross-compare both sequence and quantitative information on all constituents of the transcriptome derived from the same samples. This type of integrative investigation is expected to bring to light new layers of target selection by miRNAs, a better understanding of miRNA maturation and stability, and insights into the regulation of the post-transcriptional gene silencing mechanisms.per se on gene regulation cannot be deciphered solely based on the gene knockdown or knockout manipulation, due to the notion that ADARs may have editing-independent functions [Similar analyses and studies can also be extended to deciphering the impact of RNA editing on post-transcriptional attributes of the transcripts, such as alternative splicing and transcript stability. Despite our knowledge of the prevalent distribution of RNA editing in non-coding regions of genes, evidence is still lacking as to how these binary nucleotide changes in combination could contribute as regulatory switches for gene and isoform expression. Also, as translation is the functional readout of gene expression, it is presently unclear whether RNA editing in the non-coding regulatory regions could influence the rate and/or output of protein translation. To address these issues, high-throughput sequencing studies that are designed to monitor ribosome occupancy on protein-coding transcripts  may reprunctions ; rather,unctions ,62, may Furthermore, as sequencing data acquired from large-scale studies are providing a more comprehensive view of the cellular transcriptomes, we are now poised to explore the uncharted territory of RNA editomes – including repetitive elements of transposons and retrotransposons as well as other small RNAs, such as endogenous small RNA (esiRNA). Finally, given that RNA editing is highly abundant in primates ,63, the The authors declare no financial or non-financial competing interests.HL, C-PM, Y-TC, SCS, K-PC, and BC-MT drafted the manuscript. BC-MT participated in the coordination and helped to finalize the manuscript. All authors have read and approved the final manuscript.