The microbial community in anaerobic digestion is mainly monitored by means of DNA-based methods. This may lead to incorrect interpretation of the community parameters, because microbial abundance does not necessarily reflect activity. In this research, the difference between microbial community response on DNA  and RNA (active community) based on the 16S rRNA (gene) with respect to salt concentration and response time was evaluated.Methanosaeta was identified as the most abundant methanogen on DNA level, but its relative abundance decreased on RNA level, related to salt perturbation.The application of higher NaCl concentrations resulted in a decrease in methane production. A stronger and faster response to salt concentration was observed on RNA level. This was reflected in terms of microbial community composition and organization, as richness, evenness, and overall diversity were differentially impacted. A higher divergence of community structure was observed on RNA level as well, indicating that total community composition depends on deterministic processes, while the active community is determined by stochastic processes. This research demonstrated the need for RNA-based community screening to obtain reliable information on actual community parameters and to identify key species that determine process stability.The online version of this article (doi:10.1186/s13068-016-0652-5) contains supplementary material, which is available to authorized users. Anaerobic digestion (AD) contains a complex microbial community that produces biogas from organic waste streams. The four different steps in the AD process are carried out by distinct groups of micro-organisms, each requiring specific conditions to ensure optimal performance . OperatiMonitoring of the microbial community could lead to a better stability prediction and even prevent operational failure , 9. The The majority of studies performing amplicon sequencing focused on the 16S rRNA gene, which resulted in relative abundance profiles of the various microbial groups potentially important for AD process performance , 11. Thiproxy of the active microbial community, while the DNA level represents the total microbial community. The potentially strong difference in 16S rRNA gene copy number between micro-organisms has to be considered [In this research, the effect of an increased salinity on total and active microbial community composition was evaluated. The actual effects of salt concentration in function of time were assessed on the level of the 16S rRNA gene (DNA) and the 16S rRNA itself, to validate their difference in response. The RNA level is considered a nsidered , which insidered , 33. Hennsidered , 35, wouA mixture of primary sludge and waste activated sludge in a 70:30 weight ratio was used as substrate to feed the reactors  to avoid overloading. The inoculum was diluted with tap water to a volatile solids concentration of 15 g L−1. Feeding of the reactors was performed three times a week, and fresh feed was prepared for every feeding. Biogas production and composition were determined three times a week, and reported at standard temperature and pressure  conditions. The pH was also measured three times a week before feeding, while alkalinity, Ripley index, and VFA concentration were determined on weekly basis.Three identical lab-scale continuous stirred tank reactors with a working volume of 2 L were operated for a period of 42 days at mesophilic temperature (37 °C). Stirring took place on a shaker at 120 rpm. Each reactor was connected to a Ritter milligas counter  to monitor biogas production. A sludge retention time of 15 days was applied during the first 7 days of the experiment after which it was increased to 30 days for the entire remaining period. The organic loading rate was fixed at 1 g COD L−1. Finally, a single pulse of NaCl was added, resulting in a surplus concentration of 0 (control treatment), 5, 10, and 20 g Na+ L−1, respectively, and the reactors were sealed to maintain anaerobic conditions. The twelve reactors were operated at mesophilic conditions (37 °C) on a shaker (120 rpm) for a period of 14 days. Biogas production and composition were measured on a daily basis by means of pressure build-up, using an in-house constructed pressure transducer, and reported at STP conditions. Samples for pH, VFA, and cation analysis were taken at the end of the test. Samples for DNA and RNA analysis were taken on day 0, 1, 2, 4, 7, and 14 (T0–T5), and stored at −80 °C prior to analysis.The short-term perturbation test consisted of four different treatments, each carried out in triplicate in identical lab-scale continuous stirred tank reactors with a total volume of 500 mL and a working volume of 400 mL. First, each reactor was inoculated with 400 mL of anaerobic sludge from the mixture of the three mother reactors , respectively, according to manufacturer’s instructions after which the Illustra Ready-To-Go RT-PCR Beads  were used for conversion of the RNA to cDNA. The quantity and quality of the resulting DNA and cDNA were determined with a Nanodrop 2000c . Microbial community analysis was carried out by means of high-throughput amplicon sequencing using 250 bp paired-end sequencing chemistry (MiSeq Illumina), both on the DNA and cDNA samples. The general primers 807F and 1050R, targeting the V5–V6 region of the 16S rRNA gene of both bacteria and archaea [unique.seqs was used to extract the unique reads. The paired-end reads were merged to fully cover the V5–V6 region, and no mismatch was allowed in the overlapping of the forward and reverse reads  [phyloseq [vegan packages [vegan package was also used to determine the diversity indices, and to calculate Bray–Curtis dissimilarity matrices (vegdist function). A table containing all samples with the abundance of different phylotypes and their taxonomic assignments was created . Permutational multivariate analysis of variance (PERMANOVA) of the Bray–Curtis dissimilarity matrices was carried out with the vegan package (adonis functions). For multivariate abundance analysis, all analyses were conducted with the mvabund package and seed 777 [P-values, calculated after 5000 resampling runs, which accounted for inter-variable correlations.The resulting data set containing the relative abundance of each phylotype in all samples was analysed using the R software, version 3.2.3. (ect.org) . Rarefacect.org) , 44, usiphyloseq  and vegapackages . The vegseed 777 . Samples2 Suppressor.The pH was measured with a Crison 506 standard pH meter , equipped with an Ingold U 455 electrode . Total solids (TS), volatile solids (VS), total alkalinity, partial alkalinity, and total ammonia nitrogen (TAN) were determined according to the standard methods . The fre3PO4 (2%), a flame ionization detector and an automatic injector . The column, injector, and detector temperatures were set at 105, 260, and 280 °C, respectively. The carrier gas was nitrogen gas, saturated with formic acid, at a flow rate of 24 mL min−1. Dry air and hydrogen gas were used as auxiliary gases, with flow rates of 400 and 30 mL min−1, respectively. Pivalic acid was used as internal standard. Calibration of the gas chromatograph was carried out using the Volatile Free Acid Mix , and values were corrected for the partition coefficients of the different VFA in diethyl ether. The lower detection limit was between 10 and 20 mg L−1.The volatile fatty acids (VFA) were extracted from 2 mL samples using diethyl ether (2 mL), and analysed by means of a gas chromatograph  with a glass column (3 m × 2 mm), filled with Chromosor WAW (mesh 100/120), and impregnated with Neopentylglycoladipate (25%) and H−1.Biogas composition was determined by gas chromatography , equipped with a thermal conductivity detector. The stainless steel column has a total length of 2 m and an internal diameter of 3.175 mm, and was filled with Poropack Q (mesh 80/100). The temperatures of the injector, column and detector were set at 110, 35, and 110 °C, respectively. Helium was used as carrier gas at a flow of 15 mL min4 L−1 d−1 on day 42  and treatment with 5 g Na+ L−1, respectively , and, as a consequence, reads of replicates were added for the generation of heat maps. Microbial community composition was determined on phylum, class, order, family, and phylotype level  was evaluated by means of ANOVA. No significant differences were observed among replicates P < 0.000, and, asvel Fig. a. The PrMethanosaeta (Phy1), Rikenella (Phy2), and two unclassified Bacteroidetes phylotypes (Phy 3 and 4) were each higher on DNA level compared with RNA, and also showed a high level of variation with respect to salt concentration and time. Methanosaeta (Phy1) was by far the most abundant methanogen, both on DNA and RNA levels, with the highest relative abundance at low salt concentrations on DNA level, but this was not confirmed on RNA level. The relative abundance of both Phy2 and Phy4 increased on RNA level between day 2 and 7), compared with day 1 and day 14, indicating a higher activity during this period. The relative abundance of Phy15  showed an increased abundance on RNA level only in the control treatments, indicating its preference for a low salt concentration and/or a competitive advantage over other micro-organisms at these conditions [While the microbial community profile appeared to be rather uniform at phylum level, this was not the case for the most abundant phylotypes Fig. b. Methannditions .P = 0.001) and time (P = 0.001), both on the total (DNA) and active (RNA) microbial community  between the total and active community. This was confirmed by a significantly different (P < 0.0001) overall degree of variation between the DNA and RNA levels, as observed by the relative distance from the centroid, based on the Bray–Curtis dissimilarity index , with a significant increase in distance on day 7 (P = 0.009) and 14 (P = 0.004) of the short-term batch test, while salt concentration did not significantly contribute to this relative distance (P = 0.87).Beta diversity analysis, including both DNA and RNA samples, revealed a significant correlation with salt concentration P = 0.00 and timeity Fig. . PERMANOP < 0.0001), Fisher’s alpha diversity (P < 0.0001), and Pielou’s evenness (P = 0.026) between the DNA and RNA profiles of the different samples  between DNA and RNA relative abundance , Methanospirillum  and Methanobacterium  showed a lower relative abundance on RNA level, which was also the case for Rikenella  and other phylotypes belonging to the Bacteroidetes phylum. In contrast, two unclassified Actinomycetales , two unclassified Armatimonadetes , and an unclassified Synergistaceae  showed a significant higher relative abundance on RNA level.After validation of normality of residuals and homogeneity of variance in the residuals , and also an unclassified Methanomicrobiales phylotype  was negatively affected by salt, while an unclassified Archaeon  was positively affected by salt. Time had a significant positive effect on the unclassified Methanomicrobiales phylotype .Both salt concentration and time also affected specific phylotypes, with 32 and 20 phylotypes significantly influenced by salt concentration and time, respectively  clearly showed a different response on RNA and DNA levels and with respect to salt concentration and time , with the strongest decrease at high salt concentrations. The unclassified Methanomicrobiales did not show a change in RNA/DNA ratio over time or related to salt, or difference in relative abundance on RNA and DNA levels, but, nonetheless, a significant negative (P = 0.031) effect was observed in relation to salt, while there was a significant positive effect of time (P = 0.008). Overall, the methanogenic phylotypes showed the strongest differential response between DNA and RNA, both to salt concentration and time.The effect strength of time was additionally dependent on the RNA/DNA ratio for 21 phylotypes, while this could only be observed for 2 phylotypes for the salt effect  and active microbial community (RNA), related to both salt concentration and time. The multivariate regression approach allowed to determine not only the phylotypes that experienced a significant effect of salt concentration and time, but also the size of the effect. This was reflected in the differential response of specific phylotypes, amongst which the dominant archaeon The comparison of the microbial community via DNA- and RNA-based measurements revealed an overall higher diversity on DNA level, and a higher degree of variation on RNA level. A high species richness and diversity can relate with an increased resilience, as a larger pool of available micro-organisms enhances the potential to maintain functionality, and respond to disturbances , 16, 51.Salt perturbation often promotes a change in microbial community composition and/or organization , 56, whiThe higher degree of overall variation on RNA level could not be attributed to the salt concentration. This unexplainable variation on RNA level indicates that the selection of active species depends both on deterministic and stochastic processes. Deterministic factors, such as pH and temperature, determine which species can be present (DNA) in the anaerobic digester , 58, whiThe active microbial community in AD seems to aim for a high degree of evenness, regardless the lower level of evenness in the total community, which undoubtedly relates with the requisite for a well-balanced succession of the different phases in the AD process. This high degree of evenness on RNA level is, however, insufficient to assure stable process performance in AD, because methane production declined nonetheless at high salt concentrations. This implies the importance of specific active (groups of) micro-organisms to ensure process stability.Methanosaeta (Phy1), as this phylotype covered on average 80% of archaeal reads, both on DNA and RNA levels. Next to Methanosaeta, several bacterial dominant phyla, such as Rikenella (Phy2), also showed a clear decrease on RNA compared with DNA level, which also explains the higher evenness on RNA level.Microbial community analysis on different phylogenetic levels revealed an overall uniform pattern on the higher phylogenetic levels in relation to salt concentration and time, while a much stronger degree of variation could be observed between different phylotypes. The overall dominance of the Bacteroidetes, Proteobacteria, and Firmicutes bacterial phyla is to be expected, given the versatile consortia of bacteria in these phyla involved in different phases of the AD process , 21, 60.Methanosaeta for methane production in the AD process has been confirmed by numerous studies, as well as its susceptibility to changes in environmental conditions outside the optimal range for methane production [Methanosaeta in AD is compared between the DNA and RNA levels. The RNA profile of Methanosaeta (Phy1) suggests a lower activity than estimated by its DNA profile, but this is similar for several other methanogenic phylotypes. This is in contrast with other studies in which a higher  activity in relation to the DNA level was observed [Methanosaeta on RNA level related to the increase in salt concentration, which was not the case for the other methanogens, with the exception of an unclassified Methanomicrobiales phylotype (Phy54), indicating that salt perturbation strongly affects Methanosaeta in AD. The persistence of a high abundance of Methanosaeta at suboptimal conditions [Methanosaeta is often replaced by Methanosarcina as primordial acetoclastic methanogen [Methanosarcina could be detected. This may explain the inhibition of methane production at 10 and 20 g Na+ L−1, as the hydrogenotrophic methanogens were unable to take over the methane production process. None of the observed known syntrophic acetate oxidizing bacteria, which are necessary to redirect acetate conversion from acetoclastic to hydrogenotrophic methanogenesis, showed an increase in activity or abundance in relation to salt perturbation [The overall importance of oduction , 61, 62.observed –26. In tnditions  does notthanogen , but in urbation , 66.Methanosaeta in AD is apparent, which is amplified by the fact that in the absence of Methanosarcina and/or a sufficient response of potential syntrophic acetate oxidizing bacteria, the system collapses if Methanosaeta is not able to maintain its activity. Hence, despite its low relative abundance on RNA level in this study, Methanosaeta can be considered the most important methanogen in AD to obtain stable methane production.The crucial role of Methanosaeta, showed a lower relative abundance on RNA level compared with DNA level, caused by salt perturbation. Although microbial community activity analysis based on the rRNA profile should be interpreted with care, given its limitations [This research demonstrates a different response between the total (DNA) and active (RNA) microbial community in AD with respect to salt perturbation. Salt concentration and time more strongly affected the microbial community, and especially the archaeal phylotypes, on RNA level both in terms of overall diversity and specific phylotypes. The most abundant archaeal phylotype, itations , a clear


Post-transcriptional regulation of gene expression is an important process that is mediated by interactions between mRNAs and RNA binding proteins (RBP), non-coding RNAs (ncRNA) or ribonucleoproteins (RNP). Key to the study of post-transcriptional regulation of mRNAs and the function of ncRNAs such as long non-coding RNAs (lncRNAs) is an understanding of what factors are interacting with these transcripts. While several techniques exist for the enrichment of a transcript whether it is an mRNA or an ncRNA, many of these techniques are cumbersome or limited in their application. Here we present a novel method for the immunoprecipitation of mRNAs and ncRNAs, Urb—RNA immunoprecipitation (Urb-RIP). This method employs the RRM1 domain of the “resurrected” snRNA-binding protein Urb to enrich messages containing a stem-loop tag. Unlike techniques which employ the MS2 protein, which require large repeats of the MS2 binding element, Urb-RIP requires only one stem-loop. This method routinely provides over ~100-fold enrichment of tagged messages. Using this technique we have shown enrichment of tagged mRNAs and lncRNAs as well as miRNAs and RNA-binding proteins bound to those messages. We have confirmed, using Urb-RIP, interaction between RNA PolIII transcribed lncRNA BC200 and polyA binding protein. While there exist many molecular techniques for purification of RNAs of interest [in silico tools for identification of RNA binding protein (RBP) or ncRNA binding sites on your RNA of interest [Regulation of gene expression at the post-transcriptional level is a complex process involving many s (RNPs) , 2. In ointerest –18 and iinterest –24, manyinterest –10, aptainterest –18, or dinterest , 26. WhiPulldown of RNA of interest using aptamers or oligonucleotides relies on base pairing of an biotinylated-oligonucleotide to an RNA of interest or binding of a structure inserted into an RNA of interest to compound, usually a metabolite. While both techniques have been used successfully they both suffer from the same difficulty, RNA structure. Folding of the RNA can disrupt the formation of the aptamer or occlude the binding site of an oligonucleotide. For pulldown with oligonucleotides this can be abrogated by tiling across the entire RNA with multiple oligonucleotides; however this increases the chance of pairing with other RNAs beside the RNA of interest.in vitro synthesis of an RNA of interest and tagging with biotin, this is usually done with a biotinylated cap or 5’ nucleotide. The biotinylated RNA is then incubated with a cell lysate and subsequently precipitated with a streptavidin matrix. The biggest drawback of this technique is that the RNA is introduced to a lysate as opposed to being transcribed within the cell as normal. It is well appreciated that numerous proteins bind to RNAs concurrent with transcription or splicing. These interactions may not occur when an in vitro transcribed RNA is incubated with a cell lysate, potentially leading to false negative results.Direct purification of a biotinylated RNA involves Likely the most common technique for affinity based RNA-purification is RNA-immunoprecipitation (RIP) using the bacteriophage MS2-coat protein. This approach uses an epitope tagged MS2-protein to enrich an RNA of interest containing the MS2 hairpin. While this technique has been widely and successfully used –8 it is Here we report a new method for targeted RNA pull-down that is both efficient and highly adaptable. Our approach, which we have named Urb—RNA immunoprecipitation (Urb-RIP), utilizes the RNA recognition motif 1 (RRM1) domain of the “resurrected” snRNA-binding protein Urb to enrich transcripts containing a stem-loop tag. The RRM1 domain of Urb binds stem-loop II (SLII) of the U1-snRNA and SLIV of the U2-snRNA with high affinity . Urb-RIPNarI restriction site in between the coding region for the TEV protease and N-terminus of URB-RRM1. The pENTR-2HA-TEV-URB-RRM1 plasmid containing the inserted NarI site was then digested with NarI and ligation was performed to insert a single FLAG tag using the FLAG oligonucleotides 1 and 2. LR-Clonase II (Invitrogen) was used to transfer the pENTR-2HA-TEV-FLAG-URB-RRM1 insert to the destination plasmid pT-RexDEST31 (Invitrogen), making pT-REx-2HA-TEV-FLAG-URB-RRM1 (referred to above in the text as pT-REx-2HA-URB). The pENTR-2HA-TEV-FLAG-URB-RRM1 plasmid was also recombined with pCDNA5-Frt-TO (Invitrogen) to make a pCDNA5-2HA-URB.All primers used for cloning can be found in SacII and ligation was performed to insert the SLII-tag using the SLII Tag SacII oligonucleotides 1 and 2To facilitate SLII-tagging of mRNAs a destination vector was constructed that would place a SLII-tag in the 3’UTR. The SLII-tag was inserted into the 3’UTR of pcDNA-DEST40 to make pcDNA-DEST40-SLII. pcDNA-DEST40 was digested with LR-Clonase II (Invitrogen) was used to transfer the pENTR-mCherry insert to the destination plasmid pcDNA-DEST40 or pcDNA-DEST40-SLII, making pcDNA-mCh and pcDNA-mCh-SLII.SpeI and XbaI and ligated into pSM2 vector (Addgene) digested with the same restriction enzymes. The SLII-tag with XbaI/SpeI overhangs, SLII Tag SpeI oligonucleotides 1 and 2, was ligated into the pSM2-BC200 plasmid digested with SpeI. The inserts from pSM2-BC200 and pSM2-SLII-BC200 were recombined into pcDNA-DEST40ΔCMV using LR-Clonase II (Invitrogen) to make pcDNAΔ-BC200 and pcDNAΔ-SLII-BC200. pcDNA-DEST40ΔCMV was constructed by digest to remove the CMV promoter with SpeI (NEB) and SacI (NEB) followed by ligation with the CMV deletion oligonucleotides 1 and 2.The transcript sequence for the lncRNA-BC200 was amplified from HEK 293 genomic DNA purified using DNeasy Blood & Tissue Kit (Qiagen) by the BC200 forward and BC200 reverse (with PolII terminator) primers. The PCR product was digested with let-7-A114-N40-HhR [let-7 binding sites was amplified using the pAWH let-7 sites forward and reverse primers. This PCR product was gel purified and phosphorylated using T4-PNK (NEB). This product was then ligated into the pcDNA-mCh and pcDNA-mCh-SLII plasmids that had been digested with PmeI and dephosphorylated with Antarctic phosphatase (NEB).A region of pAWH-Rluc--N40-HhR  containiTo make EGFP-FLAG-AGO2, EGFP was PCR amplified using the EGFP forward and reverse-overlap primers and Ago2 was amplified using the Ago2 forward-overlap and reverse primers. The PCR products were stitched together using overlap PCR and cloned into pENTR-D-TOPO. LR-Clonase II (Invitrogen) was then used to transfer the EGFP-FLAG-Ago2 cassette into pcDNA-DEST40 making pcDNA-EGFP-FLAG-Ago2.™-293 cells and Flp-In T-REx™-293 (Invitrogen) were grown in DMEM (Gibco) supplemented with 10% heat-inactivated FBS (Gibco), 1x Penicilin streptomycin and glutamine (Gibco) and 1x MEM Non-Essential Amino Acids (Gibco). T-REx™-293 were kept under selection with 5 μg/mL blasticidin. Transfection was performed using Xtreme Gene 9 (Roche) per manufacturer’s recommendations. The plasmid was transfected at a ratio of 1 μg per 2 μL of transfection reagent. For a 10 cm dish 8 μg of plasmid was used. Two stable cell lines were made for expression of 2HA-Urb. The pT-REx™-2HA-Urb plasmid was transfected into T-REx™-293 cells and the cells were selected with 0.5 mg/mL Geneticin (Invitrogen) to produce a stable cell line. This cell line (T-REx™-293 -2HA-Urb) was maintained in 5 μg/mL blasticidin and 0.5 mg/mL geneticin. The pCDNA5-2HA-Urb plasmid was co-transfected with the Flippase expressing plasmid pOG-44 (Invitrogen) into Flp-In T-REx™-293 and the cells were selected with 0.1 mg/mL hygromycin to produce a stable cell line. This cell line (Flp-In T-REx™-293-2HA-Urb) was maintained in 5 μg/mL blasticidin and 0.1 mg/mL hyrgomycin. These cell lines were used interchangeably with minimal differences in Urb-RIP efficiency (data not shown).T-REx2 using a Stratalinker 1800. The cells were suspended in cold PBS by pipetting and transferred to a conical tube. The cells were pelleted, resuspended in PBS and transferred to a 1.7 mL microfuge tube before pelleting a second time. The pelleted cells were lysed in 1% NP-40 Lysis Buffer containing 1x Complete Protease Inhibitor (Roche) and 0.5 units/μL RNase inhibitor . Lysis occurred over 20 minutes on ice and was followed by centrifugation at 15,000 g for 20 minutes at 4°C to clear the insoluble fraction. The protein concentration of the lysate was quantified by DC Protein Assay (Bio-Rad). At least 1 mg of total protein was loaded onto anti-HA magnetic beads (Pierce) that had been previously blocked for 1 hr in 4% BSA with 0.5 μg/μL yeast tRNA. An aliquot of the lysate (5% of the amount used for IP) was kept for western analysis and RNA isolation. The lysate was incubated on the anti-HA beads for 1 hr with rotation at 4°C. Following binding the beads were washed twice with Low Salt Wash Buffer and twice more with High Salt Wash Buffer. The beads were resuspended in water and transferred to two fresh microfuge tubes for elution. Elution of protein was carried out by suspending the beads in reducing sample buffer  and heating at 95°C for 7 minutes. Elution of RNA was performed by resuspending th beads in 200 μL of Proteinase K Buffer containing 32 units of proteinase K (NEB) and incubation for 20 minutes at 37°C. After 20 minutes an equal volume of Proteinase K Urea Buffer was added and the samples were incubated another 20 minutes at 37°C. Following incubation the RNA was extracted using low pH phenol:chloroform and precipitated by ethanol precipitation with glycogen added as a carrier. The precipitated RNA was washed with 70% ethanol, dried and resuspended in water. The RNA was DNase treated  Isolation of RNA from the input sample was performed in the same manner as elution of the beads.A detailed protocol for the Urb-RIP method and the recipes for all buffers listed below can be found in the Supplemental Methods , this prlet-7 reverse transcription primer [t) values were normalized to GAPDH, except where indicated. All qPCR and reverse transcription primers can be found in Total cDNA synthesis was performed using iScrpt Supermix per manufacturer protocol (Bio-Rad). For miRNA reverse transcription 6 pmol of the n primer  was addeProtein samples were resolved on a 4–12% Bis-Tris precast gel (Bio-Rad). The resolved proteins were transferred using Trans-Blot SD (Bio-Rad) onto Immuno-Blot PVDF (Bio-Rad). The membrane was blocked in 5% milk in 1x PBS with 1% Tween (PBST) for a minimum of 1 hour. The following primary antibodies were used in western analysis at the given dilution: HA-HRP, 1:2000 ; PABP, 1:1000 ; FLAG-HRP, 1:5000 ; Beta-Actin-HRP, 1:2000 ; GAPDH-HRP, 1:2000 ; Nop56, 1:2000, ; Anti-mouse IgG HRP, 1:10,000 ; Anti-Rabbit IgG HRP . All antibodies were diluted in 5% milk in PBST and incubated with the membrane for 2 hours at room temperature or overnight at 4°C. The membrane washed with PBST prior to incubation with secondary antibody against mouse or rabbit coupled to horse-radish peroxidase (HRP) . The secondary was diluted 1:10,000 in 5% milk in PBST and allowed to incubate for 1 hour at room temperature. The membrane was washed with PBST and HRP activity was detected using SuperSignal West Pico or Dura (Thermo Scientific). The membrane was imaged by Bio-Rad Molecular Imager ChemiDoc XRS System with Image Lab software (Bio-Rad).−9 M and 1.5 x 10−8 M respectively [We turned to the recently “resurrected” urbilatarian homologue of the SNF/U1A/U2B family of proteins, Urb, to create Urb-RIP as a tool for pull-down of RNA of interest, ectively . This hiectively . It is eectively . In factectively . Hence, ectively . Protein™-293 cell line for the inducible expression of 2HA-Urb, we will refer to this cell line as 293-2HA-Urb. As a control, a parallel transfection was performed with a mCherry reporter lacking the SLII-tag. These reporters were used to optimize the pull-down conditions, as well as the amount of UV-irradiation and the procedure for blocking anti-HA-beads, 2 of UV prior to lysis provided enrichment over non-irradiated samples (approximately 2 fold) for the SLII-tagged mCherry RNA, We first sought to validate Urb-RIP using a mCherry reporter containing a single SLII-tag, To validate Urb-RIPs ability to identify RBP bound to RNA of interest we assessed enrichment of PABP bound to a tagged mRNA. We used the mCherry reporters described above, in silico tools to identify potential binding sites for a miRNA, it is often the case that their predictions produce false positives [let-7 reporter construct which contains multiple binding sites for let-7 in the 3’UTR of the reporter gene [let-7 sites has been shown previously to reduce expression of such reporters [let-7 reporter by reduction in mCherry fluorescence (data not shown). We inserted the SLII-tag between the let-7 sites and the polyadenylation signal, let-7 bound to the mCherry-let-7 reporter, let-7 reporter along with GFP-FLAG-Ago2 and performed Urb-RIP as before. In parallel we assayed the GFP-FLAG-Ago2 binding to the control reporter lacking the SLII-tag. Analysis of the Urb-RIP product by western blot revealed modest enrichment of GFP-FLAG-Ago2 in the sample containing the SLII-tagged mCherry-let-7 reporter, A common desire in the field of post-transcriptional regulation of gene expression is the ability to identify miRNAs and miRNA-induced silencing complexes (miRISC) that target an RNA of interest. While there exists several ositives , 37. An ositives , 38. In ter gene . The inseporters  and we hin vitro to interact with PABP [In order to show the adaptability of Urb-RIP for other RNA transcripts we used our method to identify factors bound to the lncRNA BC200. BC200 is a well described lncRNA transcribed by RNA PolIII –43. The ith PABP –43. We tWe have presented Urb-RIP, an adaptable and efficient approach to affinity purify specific RNAs and to identify interacting RNAs, RBPs or RNPs. Our method uses a novel affinity tag for RNA affinity purification. We utilize the RRM domain of a recently “resurrected” snRNA-binding protein, Urb . Urb-RIPThis method provides many advantages over the commonly used MS2 system for RNA purification. Urb-RIP requires only one SLII-tag there by limiting the mass added to the RNA of interest. The single tag also makes cloning much easier as the tag can be synthesized using a single DNA oligonucleotide and its complement and simply ligated into a plasmid of interest or added to the template sequence of an RNA of interest through PCR. We have not observed any aggregation of yellow fluorescent protein tagged 2HA-Urb or negative effects on cellular homeostasis upon continuous expression of 2HA-Urb in stable cell lines (data not shown). This gives Urb-RIP an advantage over other RNA pull down methods. Aggregation of the MS2 protein is a common problem and requires tight control of expression in order to be mitigated , 45. Whitrans regulators: ncRNAs, RBPs and RNPs, Figs Urb-RIP proved capable of enrichment of tagged mRNAs and lncRNAs from cell lysates as well as for their in vivo transcribed human lncRNA BC200. We showed that human BC200 lncRNA can be efficiently immunoprecipitated using our Urb-RIP method, in vitro by either electrophoretic mobility shift assay (EMSA) or by immunoprecipitation of PABP bound RNAs from cells transfected with in vitro transcribed BC200 [Using our method we were able to confirm binding of PABP to the PolIII ed BC200 –43. By uIn addition, our Urb-RIP method has been recently coupled with mass-spectrometry to identify RBPs bound to the H/ACA snoRNA ACA11. These analyses confirmed previous results  and reveAn important consideration when tagging an RNA with the SLII-tag is the location the tag is to be inserted. Here we have inserted the tag into the 3’UTR of mRNAs and the 5’ end of the lncRNA BC200. For lncRNAs we suspect the tag could be placed at either end of transcript. It would likely be best to avoid the middle as to not perturb the structure of the RNA. For mRNAs the tag should be placed in the 3’UTR. Inserting the tag into the 5’UTR or coding sequence will likely lead to displacement of 2HA-Urb from the message by the translation machinery, or stalling of the ribosome or pre-initiation complex. In all cases the ideal location of the tag may need to be empirically determined.One of the pitfalls of the Urb-RIP approach is the ability of the Urb-RRM1 domain to interact with endogenous U1-snRNA. However, this binding has not prevented us from identifying RBPs and RNAs bound to Urb-RIP purified mRNAs and lncRNAs. It may be possible to mitigate this issue by pre-clearing the lysate with an antibody against a U1-snRNP factor. Additionally, mutants of Urb RRM1 domain, which show similar or higher affinity to SLII hairpin , can be Taken together our results show that Urb-RIP provides an adaptable and efficient approach for pull down of RNA of interest and their interacting proteins and ncRNAs. We predict Urb-RIP will work efficiently in most cell lines and can be coupled with many techniques for the analysis of interacting proteins and ncRNAs. Urb-RIP has the potential to become a useful tool in the study of post-transcriptional regulation of mRNA and the function of lncRNAs.S1 FigA stable HEK-293 cell line for the inducible expression of 2HA-Urb was transfected with a plasmid expressing mCherry-mRNA untagged or tagged with SLII. Two days after transfection the cells were UV-irradiated at doses shown and subsequently lysed. Immunoprecipitation was performed using the Urb-RIP protocol and RNA was eluted with proteinase K treatment. qRT-PCR was performed using mChery and GAPDH primers. Pulldown efficiency was quantified by qRT-PCR analysis of enrichment of mCherry+SLII relative to mCherry, the abundance of both messages was normalized to GAPDH.(TIF)Click here for additional data file.S2 FigRNA was isolated from control or UV irradiated (400 mJ/cm2) 293-2HA-Urb cells using the Qiagen RNeasy Kit per manufacturer’s protocol. Two micrograms of RNA was mixed with 3 μL of 10x MOPS buffer, 6 μL of formaldehyde and formamide to 30 μL prior to denaturation at 80°C for 15 minutes. The RNA was cooled on ice and 2x RNA Loading Dye was added  along with ethidium bromide. The samples were loaded on a 1.2% denaturing agarose gel, resolved and the gel was imaged.(TIF)Click here for additional data file.S3 Fig2 and subsequently lysed. The lysate was loaded onto untreated beads or beads blocked with 300 μL of 4% BSA, 0.5 μg/mL yeast tRNA in TBST. For one sample the lysate was cleared by incubation with Protein A/G beads for 1 hour prior to loading on the blocked beads. Following binding the beads were processed following the Urb-RIP protocol and RNA was eluted with proteinase K treatment. qRT-PCR was performed using mChery and GAPDH primers. Pulldown efficiency was quantified by qRT-PCR analysis of enrichment of mCherry+SLII relative to mCherry, the abundance of both messages was normalized to GAPDH.A stable HEK-293 cell line for the inducible expression of 2HA-Urb was transfected with a plasmid expressing mCherry-mRNA untagged or tagged with SLII. Two days after transfection the cells were UV-irradiated at 400 mJ/cm(TIF)Click here for additional data file.S4 Fig2 and subsequently lysed. The lysate was loaded onto untreated beads or beads blocked with 300 μL of 4% BSA, 0.5 μg/mL yeast tRNA in TBST. For one sample the lysate was cleared by incubation with Protein A/G beads for 1 hour prior to loading on the blocked beads. Following binding the beads were processed following the Urb-RIP protocol and RNA was eluted with proteinase K treatment. qRT-PCR was performed using mChery, U1-snRNA and GAPDH primers. a Enrichment of U1-snRNA relative to the input abundance was determined by qRT-PCR, normalized to GAPDH. b Abundance of U1-snRNA in the immunoprecipitate relative to mCherry was determined by qRT-PCR, normalized to GAPDH.A stable HEK-293 cell line for the inducible expression of 2HA-Urb was transfected with a plasmid expressing mCherry-mRNA untagged or tagged with SLII. Two days after transfection the cells were UV-irradiated at 400 mJ/cm(TIF)Click here for additional data file.S5 Fig2 and subsequently lysed. Immunoprecipitation was performed using the Urb-RIP protocol and RNA was eluted with proteinase K treatment. qRT-PCR was performed using BC200 and GAPDH primers. a and b Enrichment of BC200 by qRT-PCR, normalized to GAPDH, from two independent experiments. c and d Western blot analysis of PABP and 2HA-Urb abundance in the Urb-RIP product and input. Samples labeled input represent 5% of the total sample used for Urb-RIP.Enrichment of BC200+SLII by Urb-RIP as determined by qPCR. A stable HEK-293 cell line for the inducible expression of 2HA-Urb was transfected with plasmids expressing the constructs described in (TIF)Click here for additional data file.S6 Fig2 and subsequently lysed. Immunoprecipitation was performed using the Urb-RIP protocol and RNA was eluted with proteinase K treatment. The eluted RNA as well as RNA from the isolated from the Urb-RIP input was analyzed by Agilent 2100 Bioanalyzer. The analysis for the input samples a and b are shown as well as the IP eluate c and d. The analysis of the IP eluate shows a strong peak for BC200 in the pull-down of BC200+SLII, d, this peak was absent in the control pulldown, c. There is a peak for the U1 and U2-snRNA in both IP eluates.A stable HEK-293 cell line for the inducible expression of 2HA-Urb was transfected with plasmids expressing the constructs described in (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S1 Text(DOC)Click here for additional data file.


CCL3, CCL3L1, JUN, IL8, and IL1B were identified in inflammation mediated by chemokine and cytokine signaling pathway (P00031) in the hepatic carcinoma samples with metastasis, and subsequently confirmed by quantitative real-time polymerase chain reaction. In conclusions, CCL3, CCL3L1, JUN, IL8, and IL1B have the potential to be considered as candidates for future molecular diagnosis of the hepatic carcinoma with metastasis. This work may provide us with new visions into the metastasis process and potential efficient clinical diagnosis in the future.Tumor metastasis is a multistep process involving a number of genetic alterations so that the genetic diagnosis is got increasingly attentions today. The aim of this study was to use RNA-seq to screen the effective differential expression genes in the peripheral blood mononuclear cells for the hepatic carcinoma with metastasis. The results showed that hepatic carcinoma samples gathered according to different metastasis.  Hepatic carcinoma is the fifth most common cancer and the third leading cause of cancer-related death worldwide . AlthougMetastasis is a complex and multistep process and consists of several stages such as disruption of intercellular adhesion and dispersal of single cells from solid tumor, invasion of blood and lymphatic vessels, immunologic escape in circulation, attachment to endothelial cells, extravasation from blood and lymph vessels, and proliferation and induction of angiogenesis –6. ThoseRecently, many related studies have been reported. Zhang et al.  identifiTo shed light on these inconclusive problems, instead of the tumor tissue, the peripheral blood mononuclear cells (PBMCs), an easily accessible and minimally invasive sample, were used to investigate the gene expression profile among patients with hepatic carcinoma in this study. With simple components, PBMCs would be beneficial for increasing the reliability of the results through decreasing the intra-tumor heterogeneity. Initial screening of dys-regulated mRNAs was conducted using RNA-seq. Then, the correlation analysis (CA) and principal component analysis (PCA) were performed to group the samples according to their similarity on the gene expression, which could decrease the inter-tumor heterogeneity through reducing the dimension of effectors. Finally, the dys-regulated pathways or biological processes were selected and novel potential mRNAs were confirmed by SYBR Green quantitative real-time polymerase chain reaction (qRT-PCR). This work may help to understand the progression of tumor metastasis and provide us with new visions into the metastasis process and potential efficient clinical diagnosis in the future.We used paired-end RNA-Seq to present the gene expression profiles of 23 PBMC samples of patients with hepatic carcinoma and 23 PBMC samples of age-matched healthy people (as the control). RNA-Seq generated from 4,513,370 to 69,018,006 raw reads that were aligned to the human reference hg19, representing 1,117,549 to 21,463,998 mapped reads . The basTo gain insights into the characteristics of gene expression profiles of the hepatic carcinoma, PCA and CA were jointly carried out to generate an evaluation of the similarities or dissimilarities of the RNA-seq outputs.PCA is a linear projection method that allows visualization of high-dimensional data in a lower dimensional space. The results of it showed that the first principal component PC1) accounted for 86.09% of the overall variance of the data, and the second principal component (PC2) accounted for 12.66%. As shown in Figure  accounteCA is a multivariate statistical technique used to group elements (or variables) and try to achieve maximum homogeneity in each group as well as the biggest difference between them. It was performed using an agglomerative hierarchical clustering algorithm in the present study, and the results were shown in Figure Pearson correlation coefficient of each group was 0.9839 ± 0.0063 (HCDM), 0.9821 ± 0.0129 (HCIM), and 0.9753 ± 0.0194 (HC))  , the group of hepatic carcinoma with intrahepatic metastases (HCIM), and the group of hepatic carcinoma without metastases (HC). We computed the whole transcriptome correlations for each group. As anticipated, the gene expression profiles of these three groups were very homogeneous (the average ) Figure , 5, 6, a) Figure , reflectSpearman correlation coefficient: S = 0.928, P value < 0.001)  to recognize the functions of the DEGs, and the statistical overrepresentation test was performed to distinguish the significant biological processes and pathways which were involved in the hepatic carcinoma with metastasis. As shown in Tables Analysis of DEGs was performed to identify the differential expression genes (DEGs) for HCDM, HCIM, and HC. The results showed that when compared with the healthy samples, 3 DEGs were identified for HC, 24 DEGs were identified for HCIM, and 84 DEGs were identified for HCDM  Figure . SubsequCCL3) was common to HC, HCIM, and HCDM, which was down-regulated in these three groups, and 10 DEGs were common to HCIM and HCDM, including 6 down-DEGs  and 4 up-DEG . GO analysis was carried out to recognize their functions. Surprisingly, we found that 5 of them, including CCL3, CCL3L1, JUN, IL8, IL1B, were involved in the inflammation mediated by chemokine and cytokine signaling pathway (P00031), which was shared by HCIM and HCDM, indicating that these 5 DEGs had potential to distinguish the hepatic carcinoma with metastasis from the healthy samples, and among them, CCL3L1, JUN, IL1B, and IL8, which were not involved in HC, could be used to distinguish the hepatic carcinoma with metastasis from those without metastasis.Moreover, we draw a Venn-Diagram to identify the common DGEs. As Figure CCL3 6.6246 ± 1.51270 vs. 14.0031 ± 1.95683 P < 0.001, CCL3L1 5.4790 ± 0.59239 vs. 11.6479 ± 2.33065 P = 0.003, JUN 14.5420 ± 2.12853 vs. 8.6600 ± 1.94387 P = 0.002, IL8 10.7340 ± 1.43404 vs. 7.7540 ± 2.03102 P = 0.028, IL1B 15.5340 ± 1.87811 vs. 12.4520 ± 1.85442 P = 0.031; healthy samples vs. HCDM: CCL3 6.6246 ± 1.51270 vs. 13.5701 ± 0.75597 P < 0.001, CCL3L1 5.4790 ± 0.59239 vs. 11.6377 ± 2.45480 P = 0.001, JUN 14.5420 ± 2.12853 vs. 8.3920 ± 1.54747 P = 0.001, IL8 10.7340 ± 1.43404 vs. 7.5860 ± 1.08408 P = 0.004, IL1B 15.5340 ± 1.87811 vs. 8.8560 ± 2.05059 P = 0.001) , and showed a positive correlation to the progression of hepatic carcinoma . No significant difference of other 4 mRNAs was found (P > 0.05)  to classify the samples. The results showed that CCL3, CCL3L1, and JUN could identify the patients with metastasis from the healthy people, while IL1B could optimally classify the disease progression status  Figure . Further) Figure . Finallyn status .P = 0.531). It was different from the previous study which presented a well-recognized phenotypic heterogeneity of advanced liver cancer (r = 0.78) [Tumor metastasis is a multistep process involving a number of genetic alterations, so that the genetic diagnosis is got increasingly attentions today. However, the existed evidences were still inconclusive due to the tumor heterogeneity. In this study, the correlation of the gene expression profiles for HCDM, HCIM, and HC were very homogeneous Figure  and no s = 0.78) . The incSpearman correlation coefficient: S = 0.928, P value < 0.001)  Figure . These iCCL3, CCL3L1, JUN, IL8, and IL1B.Then, GO analysis was performed to identify the functions of the selected DEGs. Three processes were found to be shared by both HCIM and HCDM. They were immune system process GO:0002376), response to stimulus process (GO:0050896) and inflammation mediated by chemokine and cytokine signaling pathway (P00031)  and human CC ligand 3-like protein 1 (CCL3L1), respectively, which belong to the family of chemotactic cytokines known as chemokines. MIP-1α is produced by macrophages and has the ability to induce the migration of monocytes, which then differentiate into dendritic cells (DCs) to recognize an antigen and activate tumor-specific T-cell responses via their potent antigen-presenting capacity to efficiently recognize and kill stem-like cancer cells [IL8 was found to be down-regulated in the samples of hepatic carcinoma with metastases in the present study, which was consistent with previous researches. IL8 is located on human chromosome 4 and encodes interleukin-8 (IL-8), which is produced by macrophages, endothelial cells, monocytes, neutrophils and fibroblasts. It was reported to be correlated with tumor size and tumor stage of hepatocellular carcinoma [JUN and IL1B in the samples of hepatic carcinoma with metastases. JUN encodes JUN protein, which is the AP-1 transcription factor subunit and involved in the JNK pathway. IL1B encodes interleukin-1β protein (IL-1β). It indicated that IL-8 might be mainly induced by IL-1β through the JNK pathway to participate in the process of hepatic carcinoma cells metastasis [CCL3, CCL3L1, JUN, IL8, and IL1B were illuminated in Figure er cells –26. Whener cells –28. Howearcinoma –32. Thustastasis . The posCCL3, CCL3L1, JUN, IL8, and IL1B were reliable and available to be used as the candidates for hepatic carcinoma with metastasis  and provided written informed consents. Ethics approval was obtained from the Ethics Committee of Zhongda Hospital Affiliated Southeast University. All experiments were performed in accordance with relevant guidelines and regulations set out by the ethical committee.The PBMCs were isolated from 2 ml fresh EDTA-blood of each human subject by Ficoll-Paque™ PREMIUM according to its commercial protocols. And then the PBMC samples were used for RNA extraction using TRIZOL  following standard procedures as previously described . RNA qua46 RNA samples were selected for sequencing. Among them, 10 were extracted from the hepatic carcinoma patients with solitary tumor, 6 were extracted from the hepatic carcinoma patients with intrahepatic metastases, 5 were extracted from the hepatic carcinoma patients with distant metastases, 2 were extracted from the hepatic carcinoma patients with direct invasion of adjacent organs, and 23 were extracted from the age-matched healthy people. The poly-(A) enriched RNA sequencing libraries were prepared according to a previously published protocol , using 0cufflinks.cbcb.umd.edu/igenomes.html) with Tophat2 (version 2.0.7) calling Bowtie2 (version 2.1.0) using the default settings. The alignment and differentially expression genes analysis were performed with Cufflinks (version 2.0.2) [q-value -adjusted p-value [2 fold change (FC) < 2 were used as the thresholds to judge the significance of differences in gene expression.Raw reads from the image data output from the sequencing machine were generated by Base Calling and saved in FASTQ format. Clean reads were generated by removing reads with adaptors, reads where the number of unknown bases was more than 10%, and low-quality reads  using SOAPnuke (version 1.0.1) and then were mapped to the human (hg19) genomes provided by Illumina iGenomes (downloaded from n 2.0.2) . The q-v p-value –38) ≤ 0.http://www.pantherdb.org/) [For functional analyses, GO analysis was carried out with the PANTHER (protein annotation through evolutionary relationship) classification system (db.org/) . The sta® Premix Ex Taq™ II  on an Applied Biosystems 7500 real-time PCR machine (Life Technologies) by using 2 μl of the cDNA obtained in the RT reaction. The primers were synthesized by Takara Bio, Inc. and shown in Table 15 RNA samples were selected for qRT-PCR. Among them, 5 were extracted from the hepatic carcinoma patients with intrahepatic metastases, 5 were extracted from the hepatic carcinoma patients with distant metastases, and 5 were extracted from the age-matched healthy people. For the RT reactions, 1 ul total RNA was used with a PrimeScript™ RT Master Mix   at 37°C for 15 min and 85 °C for 5 second with a final volume of 10 μl. The following qPCR was performed using SYBR® (version 2010b). PCA, CA, Pearson correlation analysis was performed to analyze the similarity of the samples, and Spearman correlation analysis was performed to analyze the correlations. Venn-Diagrams were generated using the VENNY software (bioinfogp.cnb.csic.es/tools/venny/index.html). Statistical differences were examined by a t-test or a one-way ANOVA. K-means clustering algorithm was performed to classify the samples according to the selected DEGs. All statistical tests were performed as two-sided tests. And P values < 0.05 were considered statistically significant.All of data were performed with MATLABCCL3, CCL3L1, JUN, IL8, and IL1B, were identified by RNA-seq. They have the potential to be considered as candidates for future molecular diagnosis of the hepatic carcinoma with metastasis. And the number of identified DEGs and the level of IL1B presented the potential to be used to supervise the progression of hepatic carcinoma. This work may help to understand the progression of tumor metastasis and provide us with new visions into the metastasis process and potential efficient clinical diagnosis in the future.To our knowledge, it is first to use the easily accessible and minimally invasive PBMC samples to explore the gene expression profiles of hepatic carcinoma with metastasis. 5 DEGs, including


BRCA1 and BRCA2. The Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium completed a multicentre investigation to evaluate differences in assay design and the integrity of published data, raising a number of methodological questions associated with cell culture conditions and PCR-based protocols. We utilized targeted RNA-seq to re-assess BRCA1 and BRCA2 mRNA isoform expression patterns in lymphoblastoid cell lines (LCLs) previously used in the multicentre ENIGMA study. Capture of the targeted cDNA sequences was carried out using 34 BRCA1 and 28 BRCA2 oligonucleotides from the Illumina Truseq Targeted RNA Expression platform. Our results show that targeted RNA-seq analysis of LCLs overcomes many of the methodology limitations associated with PCR-based assays leading us to make the following observations and recommendations: (1) technical replicates (n > 2) of variant carriers to capture methodology induced variability associated with RNA-seq assays, (2) LCLs can undergo multiple freeze/thaw cycles and can be cultured up to 2 weeks without noticeably influencing isoform expression levels, (3) nonsense-mediated decay inhibitors are essential prior to splicing assays for comprehensive mRNA isoform detection, (4) quantitative assessment of exon:exon junction levels across BRCA1 and BRCA2 can help distinguish between normal and aberrant isoform expression patterns. Experimentally derived recommendations from this study will facilitate the application of targeted RNA-seq platforms for the quantitation of BRCA1 and BRCA2 mRNA aberrations associated with sequence variants of uncertain clinical significance.PCR-based RNA splicing assays are commonly used in diagnostic and research settings to assess the potential effects of variants of uncertain clinical significance in  BRCA1 (MIM #113705) or BRCA2 (MIM #600185)  approach, with only very recent work expanding into a quantitative analysis. To assess key elements for splicing assay design and the integrity of published splicing data, a multicentre quality control investigation was conducted by the ENIGMA Splicing Working Group  non-standardized use of NMD inhibitors, (3) isoforms infrequently confirmed by sequencing, and (4) a qualitative or semi-quantitative approach to assess mRNA expression patterns, as opposed to quantitative assessment  previously utilized by the ENIGMA-led multicentre study .BRCA1 or BRCA2 rare variant carriers, and 10 healthy controls  were obtained from Kathleen Cuningham Consortium for Research into Familial Breast Cancer. Eighteen of the cell lines (sample IDs LCL1–8 and LCL18–27) were previously used in a multi-center methods splicing analysis study coordinated through the ENIGMA consortium (27 LCLs derived from 17 nsortium .BRCA1 (GenBank accession—NM_007294.3) and BRCA2 (GenBank accession—NM_000059.3) (Variants included in this study are referred to by the recommended Human Genome Variation Society00059.3) .2 atmosphere. RNA was isolated from cycloheximide treated (4 h 100 µg/mL) and untreated cells using the Qiagen RNeasy Mini Kit. RNA was reverse transcribed into cDNA using Superscript III First Strand Synthesis System (Invitrogen), according to the manufacturer’s instructions.Cell lines were cultured in RPMI 1640 media, with fetal calf serum (10%) and penicillin–streptomycin (1%), while incubated at 37°C in a 5% COBRCA1 and 28 BRCA2 oligonucleotides chosen from a database of validated predesigned probes . Capture of the targeted cDNA sequences was performed according to the manufacturer’s specifications . Sequence analysis was carried out using the Illumina MiSeq platform.Targeted RNA-seq was undertaken on all cycloheximide treated and untreated LCLs using the Illumina Truseq Targeted RNA Expression kit. The targeted sequencing assay was custom designed in Illumina’s design studio using 34 BRCA1 and BRCA2 mRNA isoform splicing expression patterns were also obtained. RNA from LCL24 was sequenced with and without cycloheximide treatment on a HiSeq2000 using the Truseq® Stranded mRNA kit (Illumina).Whole transcriptome sequencing analysis was also carried out for one control (LCL24). Using these data, Targeted RNA-seq and whole transcriptome read mapping was undertaken as previously described . BrieflyBRCA1 and BRCA2, respectively) was used as the reference junction (RJ) (hereon referred to as “reference junction”) to calculate the relative expression of the full-length and alternative mRNA transcripts for both BRCA1 and BRCA2, respectively. To achieve this, the sum of all non-overlapping exon 2–3 alternative splicing events was deducted from the RJ for each gene independently. This leaves a proportion of reads that solely represent the full-length transcript  or were common NAG events . All qPCR assays were carried out in triplicate.The expression levels observed for the BRCA1 and 17 BRCA2 alternate isoforms in LCLs from non-variant carrier controls cultured with or without a NMD inhibitor . These include 25/63 of the BRCA1 isoforms identified by Colombo et al.  were due to target probe placement (restricted to the options predesigned by Illumina), while the remainder (11/32 BRCA1 and 4/17 BRCA2) were presumably not expressed at levels that were detectable in our cell lines . From our observations, and those reported in previous publications  were not found to be involved in an exon skipping event in this study or by Fackenthal et al. (BRCA1 and eight BRCA2) were detected solely in the samples treated with a NMD inhibitor . Of the 19, 14 are out of frame.Of the previously reported naturally occurring isoforms not detected in  RNA-seq , 13, theications , all BRCBRCA1 and 11 BRCA2 alternative splicing events were identified by targeted RNA-seq in addition to those detected by reverse transcriptase-PCR (RT-PCR) from the multicentre study . We found that 33/37 BRCA1 and all 11 BRCA2 of these events fell outside the region targeted by the RT-PCR assays. By comparison, 26 BRCA1 and 12 BRCA2 splicing events were exclusively detected in these samples in the multicentre study. However, 21 of these events were not detected by the Truseq Targeted RNA Expression platform due to the absence of probes targeting those regions, while 11 were due to the events involving multiple separate regions, which are not detectable together using this platform. Of the remaining six events not detected by Targeted RNA-seq , three  were respectively identified by three laboratories, which always included the two laboratories utilizing capillary electrophoresis for detection. Further to this, BRCA1 Δ5–6 was identified solely by laboratories utilizing capillary electrophoresis, which was the most sensitive detection method used in the multi-center study , we compared our data with those previously reported from the PCR-based ENIGMA multicentre study  . In contrast to the multicentre study, targeted RNA-seq was unable to detect the Δ5 event associated with the pathogenic variant BRCA1 c.135−1G>T, likely as a result of a low read count . These data further highlight the complexity associated with detection and interpretation of BRCA1 and BRCA2 splicing patterns when assays are designed across the whole transcript.Targeted RNA-seq identified another five splicing events described by Whiley et al.  that werBRCA1 (n = 25) and BRCA2 (n = 14) transcripts from the study LCLs that did not contain known splice disrupting variants in the respective gene assayed. The number of alternative splicing events detectable across multiple LCLs was double in cycloheximide treated cell lines compared to that found in non-treated cells  and BRCA2 (R2 = 0.69) . The questionable probe efficiency observed for alternative event BRCA1 Δ9–10 using targeted RNA-seq was confirmed with qPCR to be overinflated  and so was excluded from the normalization calculations.To derive quantitative information from the targeted RNA-seq data, we separately calculated the relative expression range for RCA1 n = 5 and BRCBRCA1 and BRCA2 when comparing the relative levels of all mRNA isoforms expressed for each gene, while they also had the greatest expression variability , which expressed an 8.8-fold increase compared to controls. The isoforms Δ15 and ▾21 were also upregulated in this sample . Expression differences were also found for ▾25 (4.5-fold increase) for LCL8 (BRCA2 c.8632+1G>A); ▾20 (2.3-fold increase) and ▾25 (2.4-fold increase) for LCL1 (BRCA2 c.426-12_426-_8delGTTTT); and ▾21 and Δ11 (1.3-fold increase) for LCL4 (BRCA1 c.671−2A>G)  in LCL5 (BRCA1 c.594−2A>C) and compared to controls. Consistent with recent findings (RNA-seq – 16.262-foldqPCR), similar levels of Δ9–10 (0.869-foldRNA-seq – 1.150-foldqPCR), and reduced 10–11 junction levels (0.718-foldRNA-seq – 0.427-foldqPCR) in LCL5 compared to the controls .To compare targeted RNA-seq and qPCR-derived expression levels, the relative expression of BRCA1 and BRCA2 isoform detection between laboratories using different cell processing and assay protocols  was more consistent across time points and between technical replicates than it was for the minor events . Together these results suggest that expression variability does exist at the intra-laboratory level and this variability is greatest for mRNA isoforms detected at low levels, such as the samples not treated with NMD inhibitors  and Δ11 (BRCA1 c.671−2A>G)  than expected in controls, which have not previously been reported, likely because these small differences are not easily observable with semi-quantitative technologies. However, these changes are not associated with pathogenicity in BRCA1 c. carriers  long read sequencer and obtained whole transcript information for in BRCA1 . FurtherBRCA1 and BRCA2. While qualitative analysis alone has been assumed to be sufficient for identifying aberrant events, the more quantitative RNA-seq offers improvements to the accuracy and capabilities of PCR-based assays, overcoming many of the detection limitations presented by the earlier technologies. These results lead us to make the following recommendations: (1) technical replicates (n > 2) of the variant carrier are necessary to capture methodology induced variability associated with RNA-seq assays, (2) LCLs can undergo multiple freeze/thaw cycles and can be cultured up to 2 weeks without noticeably influencing isoform expression levels, (3) NMD inhibitors are essential prior to splicing assays for comprehensive mRNA isoform detection, (4) quantitative assessment of exon:exon junction levels across BRCA1 and BRCA2 can help distinguish between normal and aberrant isoform expression patterns. While advances in probe design, and possibly the uptake of long read sequencers, are essential to allow detection of all expressed mRNA isoforms using this platform, the decreasing costs of RNA-seq technology, alongside an increasing understanding of bioinformatics capabilities, will likely increase the progression away from PCR-based assessment of gene expression, as evidenced by recent work by Davy et al. (BRCA1 and BRCA2, but further exploration is required to determine whether these variants are influencing expression. Quantitative assessment of BRCA1/2 isoforms in a greater number of control samples using other RNA-seq platforms will further improve our understanding of “normal” expression and provide an invaluable reference for establishing the occurrence of aberrant splicing when assessing genetic variants. Furthermore, careful assay design will be crucial for obtaining data across the gene, thus enabling an interpretation of splicing changes for the entire transcript as opposed to selected regions.Here, we utilize targeted RNA-seq technology to provide a comprehensive review and quantitative assessment of naturally occurring mRNA isoforms in y et al. . In addiConception or design of the work—VL, LW, JP, BR, MC, and AS. Resources—kI and LW. Data collection and drafting the article—VL and LW. Data analysis and interpretation—VL, JP, and LW. Critical revision of the article and final approval of the version to be published—all authors.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor declared past co-authorship with the authors AS and LW.


The modification plays significant roles in biological processes by regulating RNA metabolism in both eukaryotes and prokaryotes. It may also, however, cause cancers and other major diseases. Given an uncharacterized RNA sequence that contains many C residues, can we identify which one of them can be of m5C modification, and which one cannot? It is no doubt a crucial problem, particularly with the explosive growth of RNA sequences in the postgenomic age. Unfortunately, so far no user-friendly web-server whatsoever has been developed to address such a problem. To meet the increasingly high demand from most experimental scientists working in the area of drug development, we have developed a new predictor called iRNAm5C-PseDNC by incorporating ten types of physical-chemical properties into pseudo dinucleotide composition via the auto/cross-covariance approach. Rigorous jackknife tests show that its anticipated accuracy is quite high. For most experimental scientists’ convenience, a user-friendly web-server for the predictor has been provided at http://www.jci-bioinfo.cn/iRNAm5C-PseDNC along with a step-by-step user guide, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the approach presented here can also be used to deal with many other problems in genome analysis.Occurring at cytosine (C) of RNA, 5-methylcytosine (m So faetic one  formed band rRNA .5C modification site in RNA has various biological functions, including the one that can regulate RNA metabolism in both eukaryotes and prokaryotes [Actually, the mkaryotes . Furtherkaryotes . It is akaryotes .5C sites in RNA , it 5C sites via the powerful PseKNC approach [Very recently, in a pioneering study, Feng et al.  proposedapproach –11. But A predictor called “iRNAm5C-PseDNC” has been established. The success rates achieved by it on the benchmark dataset constructed based on experimental servations  arewhere the definitions for the metrics Sn, Sp, Acc, and MCC are given in Eq.5C sites in RNA sequences, it is not possible to demonstrate its power by comparing with its counterparts for exactly the same purpose. Nevertheless, we can indirectly show its power via a cohort of the anticipated success rates . To http://www.jci-bioinfo.cn/iRNAm5C-PseDNC. Furthermore, to maximize users’ convenience, a step-to-step guide of how to use it is given in As shown in a series of recent publications ,As practiced in a series of recent studies , 46–53 i5C modificationsite sample can be generally expressed byTo make the description simpler and cleaner, the Chou's sequential scheme, which had been used by many previous investigators for analyzing the enzyme specificity , signal where the center symbol where In literature the benchmark dataset usually consists of a training dataset and a testing dataset: the former is for training a model, while the latter for testing it. But as elucidated in a comprehensive review , there i5C samples, where the positive and negative subsets, y see Eq.; while ∪t theory .(1) The genomic sequences downloaded from RMBase [(2) As done in [3) If the upstream or downstream in a RNA sequence was less than ξ or greater than L is the length of the RNA sequence concerned, the lacking code was filled with the same code of its nearest neighbor. (4) The RNA segment samples thus obtained were put into the positive subset 5) To reduce redundancy and bias, none of included RNA segments had pairwise sequence identity with any other in a same subset. By strictly following the above procedures, we obtained an array of benchmark datasets with different The benchmark dataset used in this study was derived from RMBase , which im RMBase  are in t done in , by slid RMBase [ As done But it was observed via preliminary tests that when http://www.jci-bioinfo.cn/iRNAm5C/Supp-S1.pdf. It contains 1,900 RNA samples, of which 475 belong to the positive subset The benchmark dataset An RNA samples in the aforementioned benchmark dataset can be generally expressed aswhere Based on the sequential model of Eq.k-tuple nucleotide (or k-mers) composition; i.e.,One of the well-known vector models for DNA/RNA sequences is the i-th k-mer, and the symbol T is the transpose operator.where When where When where k value, causing the so-called “high-dimension disaster” [As we can see from above, the vector's dimension will rapidly increase with the isaster”  or overfisaster” , and makk-mers approach is useful only when the value of k is very small. In other words, it can only be used to incorporate the local or short-range or local sequence-order information, but certainly not the long-range or global sequence-order information. To approximately cover the long-range sequence-order effects, one popular and well-known method is to use the pseudo components that were originally introduced in dealing with protein/peptide sequences [Therefore, the equences –73 and requences , 74–80.According to the concept of pseudo components, the RNA sequence can be generally formulated by , 54where the subscript In this study, we used the approach called “physical-chemical property matrix combined with auto/cross-covariance” proposed by Liu et al.  to definwhere Being a powerful algorithm, the random forest (RF) has been increasingly used to analyze various different problems in computational biology .The final predictor obtained via the aforementioned procedures is called as iRNAm5C-PseDNC, where “i” stands for “identify”, and “RNAm5C” for “RNA 5-methylcytosine modification sites”, and “PseDNC” for “pseudo dinucleotide composition”.One of the important procedures  in develThe following metrics are generally used to measure the prediction quality from four different angles: (1) Acc for measuring the overall accuracy of a predictor, (2) MCC for its stability, (3) Sn for its sensitivity, and (4) Sp for its specificity . Unfortu5C sites investigated, while 5C sites incorrectly predicted to be of false m5C site; 5C sites investigated, while 5C sites incorrectly predicted to be of true m5C site.where 5C sites are incorrectly predicted to be of false m5C site, we have the sensitivity 5C sites are incorrectly predicted to be of false m5C site, we have the sensitivity 5C sites are incorrectly predicted to be of m5C site, we have the specificity 5C sites are incorrectly predicted to be of true m5C sites, we have the specificity 5C sites in the positive dataset and none of the false m5C sites in the negative dataset are incorrectly predicted, we have the overall accuracy 5C sites in the positive dataset and all the false m5C sites in the negative dataset are incorrectly predicted, we have the overall accuracy According to Eq.13, it is crystal clear to see the following. When Note that, however, the set of equations defined in Eq.Now let us discuss what kind of test method should be used to score the four metrics in Eq.Accordingly, here we also used the jackknife test to check the quality of iRNAm5C-PseDNC predictor. During the jackknifing process, both the training dataset and testing dataset are actually open, and each sample will be in turn moved between the two. The jackknife test can exclude the “memory” effect. Also, the arbitrariness problem  rooted i


Many critical biological processes are strongly related to protein-RNA interactions. Revealing the protein structure motifs for RNA-binding will provide valuable information for deciphering protein-RNA recognition mechanisms and benefit complementary structural design in bioengineering. RNA-binding events often take place at pockets on protein surfaces. The structural classification of local binding pockets determines the major patterns of RNA recognition.In this work, we provide a novel framework for systematically identifying the structure motifs of protein-RNA binding sites in the form of pockets on regional protein surfaces via a structure alignment-based method. We first construct a similarity network of RNA-binding pockets based on a non-sequential-order structure alignment method for local structure alignment. By using network community decomposition, the RNA-binding pockets on protein surfaces are clustered into groups with structural similarity. With a multiple structure alignment strategy, the consensus RNA-binding pockets in each group are identified. The crucial recognition patterns, as well as the protein-RNA binding motifs, are then identified and analyzed.Large-scale RNA-binding pockets on protein surfaces are grouped by measuring their structural similarities. This similarity network-based framework provides a convenient method for modeling the structural relationships of functional pockets. The local structural patterns identified serve as structure motifs for the recognition with RNA on protein surfaces.The online version of this article (doi:10.1186/s12859-016-1410-1) contains supplementary material, which is available to authorized users. Protein-RNA recognitions play many fundamental and vital roles in crucial molecular interactions involved in biochemical reactions and signaling transduction , 2. ReveProtein surfaces are among the major locations where RNA-binding events take place . PocketsIn this work, we conduct a large-scale analysis of the RNA-binding pockets on protein surfaces to identify the structural motifs and patterns of protein-RNA recognition. We first identify the RNA-binding residues in 3D protein structures and extract the surface pockets involved in the binding events from a compiled non-redundant protein-RNA complex dataset. Then, the local structure similarities among these RNA-binding pockets and the global structure similarities among their containing or parent proteins are measured via structure alignment methods. Using a similarity-network-based framework, the RNA-binding pockets and proteins are clustered into certain groups. The correspondences of RNA-binding pockets, domains and proteins are then investigated. The patterns of binding structure motifs and their functional implications in protein-RNA recognition are revealed accordingly.http://doc.aporc.org/wiki/PRNAclass.We download the RNA-binding protein complexes from the PDB . There aThe residues that exhibit binding between protein amino acids and RNA nucleotides are defined by Entangle . SeveralFigure To detect the structural similarities among RNA-binding pockets, we improve our non-sequential-order structure alignment algorithm SAMO to compare the local structures in an all-against-all manner.SAMO formulates the structure alignment problem as a multiple-objective mixed integer programming problem. The first objective function is to minimize the total square distances of the aligned residues, and the second maximizes the total number of aligned residues. The optimization problem is solved by an iterative algorithm. The details of the SAMO algorithm for protein structure alignment can be found in .Q-score as follows:R0 is a normalizing factor (set as 3.0), and N1 and N2 are the sequence lengths of the two pockets.For pocket similarity in the work, the structure alignment determined the similarity metrics of RMSD (root mean square deviation) between two RNA-binding pockets and the number of aligned residues,. Unlike , in thisP-value instead of the RMSD of an alignment in SAMO [P-value is computed based on the Q-score of a particular alignment and the total distribution of Q-scores in the population of pocket pairs. In detail, we fit the distribution of all the Q-scores by an extreme value distribution (EVD) and estimate the parameters of the distribution function as,, and. Then, the P-value for an alignment with Q-score is given byWhen applied to small local structures such as pockets, the structure alignment method tends to produce many false positives. To evaluate the statistical significance of the similarity in a population of alignments between any pair of local structures, we consider the similarity  in SAMO –42. Spec in SAMO . These p in SAMO . Then, tP-value is set to 0.05.In this work, the significance threshold of the structure similarity We embed the overall similarities among pockets into a pocket similarity network built by linking an edge between any two pockets with significant structure similarity. The RNA-binding pockets are then clustered into groups based on the topological structure of the constructed similarity network. By employing a fast community detection algorithm , we decoxikc denotes whether residue k in pocket i is matched to residue c of the consensus structure. M and N refer to the number of pockets in a group and the residue length of the pocket, respectively. The objective function is to maximize the number of aligned residue pairs consistent with the consensus structure. The constraints guarantee one-to-one residue matching between any pocket and the consensus structure. The optimization problem is solved using a greedy strategy. We first identify a seed of three pockets in a group with maximal consistent aligned residue pairs. Then, the other pockets are greedily added to the seed to maximize the objective function by solving a maximum weight matching problem [Underlying each pocket group, the pairwise alignments of any two pockets might be inconsistent with each other. To obtain a consistent multiple structure alignment for all pockets in a group, we develop a novel multiple structure alignment algorithm based on the pairwise alignments of SAMO. Supposing that residue of pocket is aligned with the residue of pocket in the output of SAMO, we denote ; otherwise. The aim of multiple pocket alignment is to derive a consensus structure from these pairwise alignments. The problem can be formulated as an optimization problem as follows: problem . The seehttp://www.cytoscape.org), and the structures are depicted using Pymol (http://www.pymol.org).To detect the upper-level structure characteristics of RBPs that contain these grouped pockets, we coordinate and map the RNA-binding pockets with their parent RBPs. The sequence domains and global structures where the pockets are located are identified accordingly. A “bottom-up” hierarchical model of RNA-binding structures, from pockets and domains to global tertiary structures, is built up. Joint analyses of their structural complementarity in protein-RNA recognition are then performed. Here, the networks are illustrated using Cytoscape  is the representative pocket of this group. The middle number refers to the pocket ID in the protein chain. Several pockets in the RBP, e.g., 1P6V:A, are classified into the same group, e.g., 1P6V:38:A, 1P6V:43:A, 1P6V:49:A and 1P6V:55:A, while some pockets in the same protein are classified into other groups, e.g., 1P6V:33:A in Group 2. This finding indicates that the pockets in the same protein are not always classified into the same group.The sequence patterns of RNA-binding pocket groups are investigated by concatenating the component amino acids for each pocket, as illustrated in Additional file For each group, we extract a multiple structure alignment, and the sequences of some representative pockets are exhibited in Additional file Note that these proteins are selected without sequence similarity, and the pocket similarities are measured by the non-sequential-order structure alignment algorithm. There is also no requirement of global structure similarity. Although there is no significant similarity among the protein sequences, global structures, and even the concatenated pocket sequences, the local structure similarity among pockets determines the functionality of RNA-binding. There is no significant structure similarity across the six pocket groups. Different groups contain their own spatial patterns. The pocket structures, such as the representative structures shown in Fig. The low sequence similarity underlying each group also indicates few enriched residues playing crucial roles in RNA-binding. Instead of sequence equivalence, a few residues adopt a local spatial shape that interacts with RNA on the protein surfaces. Several specific amino acid residues play essential roles in one pocket, and several pockets cooperate with each other in the binding. For example, the RBP 1GAX:A contains four RNA-binding pockets, of which 1GAX:115:A, 1GAX:118:A and 1GAX:168:A are clustered in Group 1 and 1GAX:236:A in Group 2. The pocket 1GAX:168:A is the hub pocket shown as the representative 3D structure in Fig. In the former sections, we constructed the pocket similarity network by comparing the local RNA-binding cavities in an all-against-all manner. Using network-based clustering, we identified six major groups of RNA-binding pockets, which are local spatial patterns and structural templates involved in the recognition of RNA. Obviously, these local structure motifs occur in their parent domains and proteins. From a hierarchical perspective, from the pocket and domain to the global structure, it is valuable to reveal the high-level upstream similarity among these parent RNA-binding domains and global structures.Checking the sequence components of these RBPs, we find that most of the RNA-binding pockets are located in certain RNA-binding domains. Most of the RBPs 102/158) contain at least one annotated RNA-binding domain, some of which are shown in Additional file 02/158 coAlthough the RBPs lack significant sequence similarity with each other (less than 25%), some of them contain the same RNA-binding domains. For instance, both proteins 1YTU:A and 3 F73:A contain the ‘Piwi domain’. The same domain underlying two proteins determines their structure similarity. As shown in Fig. The pockets are local regions and particular subparts of the RNA-binding domains. Based on the similarity network framework, we identified the major groups of RNA-binding local structures, which illustrates that the RNA-binding domains fold into certain local 3D structure patterns, and the resulting pockets formed on protein surfaces facilitate the local sites and environments for binding to RNA partners. We should analyze the RNA-binding local structures on protein surfaces to decipher their functional importance and complexity, as well as to identify specific RNA-binding structure motifs .P-value < 0.05), two nodes are linked by an edge. In this way, we implement a rapid community structure detection method to classify the RBPs into 12 groups. Figure Note that sequence alignment cannot reveal homologous relationships among these RNA-binding proteins. The classification of global protein structures provides a higher-level overview of the relationships among proteins in the recognition of RNA. In a network-based framework similar to pocket group identification, we build a protein similarity network for describing the global structure similarities among these RBPs. That is, one node represents one protein, and when the global structure similarity between two proteins exceeds a given threshold of significance   moleculaWe classified the RNA-binding pockets by a similarity-network-based framework via all-against-all structure comparisons. The correspondence between different levels of RNA-binding components such as sequences, structures, domains, pockets, and residues has been analyzed accordingly. For a detailed illustration of the identified RNA-binding structure motifs, Fig. In Fig. Notably, the RNA-binding pockets in these proteins are significantly similar, shown as a green clique in the center of Fig. Figure In this paper, we conducted a systematic analysis of RNA-binding pockets on protein surfaces to reveal the binding structure motifs in protein-RNA recognition. After extracting the large-scale RNA-binding pockets on protein surfaces, we improved our non-sequential-order structure alignment algorithm, SAMO, to better measure local similarities and proposed a similarity-network-based framework to cluster the pockets into similar groups. Then, in these groups, we developed a multiple structure alignment strategy to identify their consensus alignment and coordinate the correspondences between the RNA-binding pockets and their parent proteins. The multi-level analyses demonstrate the RNA-binding structure neighbors in different spaces of protein components, i.e., global structure, domain and pocket. Using the similarity-network-based strategy, we revealed the major groups of RNA-binding structure motifs in the form of pockets and their patterns of RNA-binding.Specifically, we identified the protein-RNA binding pockets in non-redundant RBPs. By implementing a similarity-network-based clustering method, we identified the major classes of RNA-binding pockets and the corresponding classes of the parent sequence domains and global structures. The similarities in these RNA-binding domains and global proteins are not inherently consistent with the similarities among RNA-binding pockets. Very different proteins and domains might contain similar RNA-binding pockets. These findings provide direct evidence for the importance of binding pockets on protein surfaces in the protein-RNA recognition. The results highlight that the RNA-binding pockets are the functional units providing structural specificity for recognizing RNAs. The classified binding pockets and their structure patterns are potentially valuable in RNA-interaction-related protein design and engineering. As increasing numbers of structures of protein-RNA complexes become available, the set of identified pocket groups will be expanded, and some novel patterns will be revealed using the classification strategy. The proposed framework of identifying RNA-binding structure motifs can be flexibly generalized and extended to study other local structures, such as ligand-binding residues and protein-interacting hotspots.