NEDD4L and PBX3 were targeted by SOX5, while GNAQ, ANLN, and KIF11 were targeted by miR-429. The expression levels of these genes and miR-429 were confirmed by quantitative real-time polymerase chain reaction. Additionally, 109 DMRs were confirmed using additional public datasets. The regulatory pathways SOX5-NEDD4L/PBX3, miR429-GNAQ/ANLN—RHOA, and miR429-ANLN—KIF11 may participate in the progression of the androgen-independent phenotype in prostate cancer.This study aimed to investigate the mechanisms underlying the development of the androgen-independent phenotype in prostate cancer. Methylation patterns were detected in androgen-independent and androgen-dependent lymph node carcinoma of the prostate (LNCaP) prostate carcinoma cells based on methylated DNA immunoprecipitation-bisulfite sequencing data and differentially methylated regions (DMRs) were identified. Differentially expressed genes (DEGs) and micro RNAs (miRNAs) with DMRs (named MDEGs and MDEmiRNAs) were identified by combining transcriptome and methylation data, and transcription factor (TF)-DEGs with DMRs in promoter (PMDEGs) and MDEmiRNA-MDEGs networks were constructed. Furthermore, a time-course analysis of gene transcription during androgen deprivation was performed based on microarray data and DMRs, MDEGs, and DEmiRNAs were validated. In total, 18,447 DMRs, 3369 MDEGs, 850 PMDEGs, and 1 MDEmiRNA (miR-429) were identified. A TF-target network (94 PMDEGs and 5 TFs) and a miRNA–target network (172 MDEGs and miR-429) were constructed. Based on the time-course analysis of genes in the networks,  Prostate cancer (PC) is one of the leading causes of cancer death in males aged more than 60 years. There are estimated to have been 180,890 new cases of PC and 26,120 deaths caused by PC in the United States in 2016, accounting for 21% of total new cancer cases and 8% of total cancer deaths in males [lymph node carcinoma of the prostate (LNCaP)-AI-F [CASP8, CD14, and cyclin dependent kinase inhibitor 2A) were significantly different between androgen-sensitive/dependent (LNCaP) and androgen-independent (DU145 and PC3) cells [Previous studies have elucidated several molecular mechanisms underlying the development of the androgen-independent PC phenotype. Androgen receptor (AR) has been considered a driver of therapeutic resistance in PC via its aberrant activation ,4 or theaP)-AI-F . AdditioaP)-AI-F ,11,12,13aP)-AI-F . Based o3) cells . HoweverMethylated DNA immunoprecipitation sequencing (MeDIP-seq) is a quantitative high-throughput DNA sequencing approach to identify methylated sites with low DNA concentrations based on methyl-DNA immunoprecipitation with an antibody against 5-methylcytosine (5mC) . HoweverIn the present study, MB-seq was applied to investigate the global distribution of DNA methylation across the whole genomes of androgen-independent and androgen-dependent PC cells. Furthermore, RNA-sequencing, miRNA-sequencing, methylation-sequencing, and microarray data were comprehensively analyzed in combination with the MB-seq data. These results provide novel insights into the molecular mechanisms underlying the progression of the androgen-independent phenotype in PC cells.®, Life Technologies, Carlsbad, CA, USA) and 10% (v/v) fetal bovine serum (FBS) . LNCaP was chronically cultured in an androgen-deprived medium to establish an androgen-independent LNCaP line. First, LNCaP cells were cultured in an androgen-deprived medium composed of the phenol red-free dulbecco's modified eagle medium (DMEM)/F-12 medium (Gibco®) plus 10% (v/v) charcoal/dextran-treated FBS (Biowest) for 5 passages. As flutamide was able to competitively inhibit the binding of androgens to AR, LNCaP cells were then cultured in the above androgen-deprived medium plus 10−7 mol/L flutamide  [BCL-2 and BAX in a previous study [2.An androgen-dependent PC cell line, LNCaP, was purchased from the American Type Culture Collection . This was cultured in a routine medium and split once per week by trypsinization. The routine medium consisted of phenol red-positive F-12 medium   for 105 us study . Meanwhi® blood and tissue kit . After the verification of DNA integrity and DNA quantification, 1 μg DNA was sonicated into fragments of 100–500 base pairs (bp). Afterwards, the end-repair of DNA fragments, addition of adenine, and ligation to C-hydroxymethylated Illumina multiplexing adapters  [®-MeDIP-DNA mixture using phenol-chloroform and ethanol. Subsequently, MeDIP-DNA was bisulfite-treated twice using the EpiTect® bisulfite kit (Qiagen) and further amplified for 12 cycles with Illumina multiplexing polymerase chain reaction (PCR) primers 1.0 and 2.0  in an 8 × 25 µL reaction system. After purification of the PCR products and gel electrophoresis on a 2% (w/v) low melting agarose gel, libraries with insert sizes of 270–370 bp were selected and quantified using the Quant-iT™ PicoGreen® double stranded DNA (dsDNA) reagent and kits . Afterwards, the DNA was sequenced for 200 cycles on a next-generation sequencing instrument (Illumina Hiseq 2000 with a TruSeq SBS Kit v3-HS) [http://www.ncbi.nlm.nih.gov/sra) of the National Center for Biotechnology Information (NCBI) with accession number of SRP067471.Genomic DNA was extracted from LNCaP and LNCaP-AI-F cells using the DNeasyATCT-3′)  were perATCT-3′) ,21, and t v3-HS) . The rawhttp://hannonlab.cshl.edu/). Bases with a quality assessment score < 10 at the 5′-terminal or continuous “N” at the terminals and reads in which more than 20% of bases had a quality assessment score < 20 were removed. Only clean reads were subjected to the subsequent analyses. The raw data were preprocessed using GA pipeline software version 1.3 (Illumina) with the default settings. Sequencing reads were filtered using FASTX-Toolkit (http://genome.ucsc.edu). The methylation of C in CpG, CHG, and CHH sequences was detected using the Bismark software [The Bismark software , a flexisoftware . The soft-test was used to assess the significance of differential methylation, and the raw p-value was adjusted into a false discovery rate (FDR) using the Benjamini–Hochberg method [Based on the clean reads, differential methylation patterns were detected between LNCaP-AI-F and LNCaP samples within a window of 50 bp, and the adjacent windows were merged (length of merged window ≤ 2000 bp). A g method . Only thhttp://utrdb.ba.itb.cnr.it/), and information about enhancers was generated from the VISTA Enhancer Browser (http://enhancer.lbl.gov/). Based on the above information, transcription start sites, transcription termination sites, exons, introns, repeats, UTRs, and enhancers that overlapped with the DMRs were identified.The annotation information about the transcription start site, transcription termination site, exon, intron, and repeats were downloaded from the UCSC database. Information about untranslated regions (UTRs) was obtained from UTRdb (https://www.perl.org/), and motif enrichment analysis was performed using the online tool MEME Suite [http://jaspar.genereg.net) [Methylation within CpG islands can impede transcription factor (TF)-DNA binding, affecting the transcription of corresponding genes. The DMRs that overlapped with promoters were obtained using the Perl program (ME Suite . Afterwareg.net) .http://asia.ensembl.org/index.html), was input into Linux shell programming [The DEGs and differentially expressed miRNAs (DEmiRNAs) between LNCaP-AI-F and LNCaP cells were reported in our previous studies ,13. The gramming , and thehttp://targetmine.nibio.go.jp/) was used to perform pathway and functional enrichment analyses based on the KEGG [p-value was adjusted by the Holm–Bonferroni method, and the adjusted p-value < 0.05 was set as the cut-off criterion.To study the potential functions of MDEGs, the online tool Targetmine  [Promoters of PMDEGs were aligned with TF-binding motifs in the DMR-promoter-overlapping regions identified above using the MotifDb R package  (criteridb.org/)  were usedb.org/) .Based on the information of miRNA–target gene pairs in the online database miRDB , the tarhttp://www.ncbi.nlm.nih.gov/geo/). The raw data for this dataset were generated via the Affymetrix Human Genome U133 Plus 2.0 Array. GSE8702 contained six LNCaP samples cultured in normal medium (control group) and ten LNCaP samples cultured in androgen-deprived medium (deprivation group) for 3 weeks, 1 month, 5 months, 11 months, and 12 months. In our study, samples in the deprivation group were utilized to study the time-course changes in gene transcription during androgen deprivation. After data pre-processing using the Affy package [c-means clustering method [As the regulation of gene expression is a dynamic process, it is important to identify and characterize changes in gene expression over time. Arrays collected over a time course allow for the study of the dynamic behavior of gene expression and clustering analysis has been commonly applied to time-course microarray data . GSE8702 package , the fuzg method  in Mfuzzg method ,40 was ug method . To identify the genes that were consistently up-regulated or down-regulated during androgen deprivation, the genes in the above Mfuzz-clusters were compared with the DEGs identified in a previous study , PMDEGs The methylation profiling dataset GSE41701  depositeFurthermore, the non-coding RNA profiling GSE55829 was utilized to validate the expression of the above identified DEmiRNAs, which included four xenograft prostate cancer tissue samples at the androgen-dependent stage and four at the CRPC stage. DEmiRNAs between the androgen-dependent stage and the CRPC stage were identified using a previously reported method . Subsequ® reagent (Invitrogen) according to the manufacturer’s protocol. Afterwards, total RNA was reverse transcribed using Moloney murine leukemia virus reverse transcriptase . After complementary DNA (cDNA) synthesis, mRNA expression levels were measured using SYBR® Green quantitative polymerase chain reaction (qPCR) Supermix . Furthermore, total RNA for miRNA expression detection was isolated using the mirVana™ miRNA isolation kit  and purified by the miRNeasy® mini kit (Qiagen). miRNA expression levels were determined using the mirVana™ qPCR detection kit (Ambion). Primers were designed using Primer5 software . Each PCR was repeated at least three times.Total RNA for mRNA expression analysis was extracted and purified from LNCaP-AI-F and LNCaP cells using TRIzol−ΔΔCt method [Expression levels of both mRNA and miRNA were analyzed by a CFX96™ real-time detection system (Bio-Rad). Relative gene expression was calculated using the 2t method . The expt-tests and were considered significant if p < 0.05.The data were presented as the mean ± standard deviation. Statistical analysis was performed using SPSS 19.0 . Differences in the mRNA expression levels of genes and miRNA between LNCaP-AI-F and LNCaP cells were analyzed by two-tailed Student’s The MB-seq analysis of the LNCaP-AI-F and LNCaP cell lines generated approximately 48.0 and 50.6 million reads, respectively. After data filtering, approximately 80% clean reads were uniquely mapped to the human genome. The ratio of total methylated cytosine in LNCaP-AI-F cells was 8.45%, which was a little higher than that in LNCaP cells (8.20%). In addition, the methylation levels in CpG were much higher than those in CHG and CHH in both LNCaP-AI-F and LNCaP cells.A total of 18,447 DMRs were identified between LNCaP-AI-F and LNCaP cells, including 10,135 hypermethylated and 8312 hypomethylated DMRs in LNCaP-AI-F cells. DMRs were present in 55.6% of genes and 24.54% of intergenic regions A. AdditiA total of 2816 DMRs were identified to overlap with the promoters of 2587 genes, and these genes were mainly associated with the functions of cellular processes (GO:0009987) and binding (GO:0005488) C. AdditiBased on the obtained DMRs in LNCaP-AI-F cells, and DEGs (4653 up-regulated DEGs and 4074 down-regulated) identified in our previous study , a totalMeanwhile, based on the 86 up-regulated DEmiRNAs and 91 down-regulated DEmiRNAs found in our previous study , only onThe up-regulated MDEGs were significantly enriched in eight pathways and 119 GO terms. The pathways and the top five terms were mainly related to cell cycle, mitotic, nuclear part, and nucleic acid binding . MeanwhiSOX5 targeted 26 up-regulated PMDEGs and 36 down-regulated PMDEGs.To uncover the potential regulatory relationships among the identified PMDEGs, TFs were predicted from the PMDEGs. The promoters of the 850 PMDEGs were aligned with the ten TF-binding motifs that were identified above . Five TFRHOA interacted with 15 MDEGs. ZEB1, shown in Furthermore, 768 potential target genes of hsa-miR-429 were predicted and 172 of them were MDEGs, including 98 up-regulated ones and 74 down-regulated ones. In the miRNA–target network of 172 MDEGs and hsa-miR-429 B, RHOA iBased on the mRNA levels at five time-points, a total of six gene-clusters involving 1367 genes were identified due to their differential expression patterns A. A totaGenes in clusters 1, 2, and 6 were first compared with the 4653 up-regulated DEGs found in the previous study , and resDDX41 was found between cluster 1 and up-regulated PMDEGs  and ANLN also interacted with KIF11 .Genes in clusters 1, 2, and 6 were compared with the up-regulated MDEGs in the miRNA–target network B. PKIA i network E. Genes  network B, and REse MDEGs F. AdditiFBXO44, VPS72, and RIMS1) are also shown in A total of 109 DMRs identified in this study overlapped with those in the dataset GSE41701. The TF targets containing these confirmed DMRs miRNAs found in the previous study  overlappNEDD4L, PBX3, and GNAQ, as well as hsa-miR-429 were significantly lower in LNCaP-AI-F cells than in LNCaP cells (p < 0.05). Furthermore, the mRNA expression levels of ANLN, RHOA, and KIF11 were significantly higher in LNCaP-AI-F cells than in LNCaP cells  and their targets need to be confirmed by experiments. Furthermore, the driving effect of DNA methylation on gene expression and the associations between DNA methylation and the development of androgen-independent PC need to be further experimentally investigated. These questions will be addressed in our future studies.Despite the aforementioned results, there are several limitations to this study. The regulatory relationships of regulators (e.g., miR-429 and SOX5-NEDD4L/PBX3, miR429-GNAQ/ANLN—RHOA, and miR429-ANLN—KIF11 may participate in the transition from androgen-dependent LNCaP cells to androgen-independent LNCaP-AI-F cells. Our results may provide novel information for the study of the molecular mechanisms underlying the progression of the androgen-independent phenotype. Those genes and miR-429 may serve as therapeutic targets for delaying the development of androgen-independent PC.In conclusion, the regulatory pathways,


RNA is known to play diverse roles in gene regulation. The clues for this regulatory function of RNA are embedded in its ability to fold into intricate secondary and tertiary structure.We report the transcriptome-wide RNA secondary structure in zebrafish at single nucleotide resolution using Parallel Analysis of RNA Structure (PARS). This study provides the secondary structure map of zebrafish coding and non-coding RNAs. The single nucleotide pairing probabilities of 54,083 distinct transcripts in the zebrafish genome were documented. We identified RNA secondary structural features embedded in functional units of zebrafish mRNAs. Translation start and stop sites were demarcated by weak structural signals. The coding regions were characterized by the three-nucleotide periodicity of secondary structure and display a codon base specific structural constrain. The splice sites of transcripts were also delineated by distinct signature signals. Relatively higher structural signals were observed at 3’ Untranslated Regions (UTRs) compared to Coding DNA Sequence (CDS) and 5’ UTRs. The 3′ ends of transcripts were also marked by unique structure signals. Secondary structural signals in long non-coding RNAs were also explored to better understand their molecular function.Our study presents the first PARS-enabled transcriptome-wide secondary structure map of zebrafish, which documents pairing probability of RNA at single nucleotide precision. Our findings open avenues for exploring structural features in zebrafish RNAs and their influence on gene expression.The online version of this article (10.1186/s12864-018-4497-0) contains supplementary material, which is available to authorized users. RNA is a multitasking biomolecule, which not only acts as a messenger molecule to transfer genetic information from DNA to proteins, but also plays a vital role in regulation and catalysis of major biological reactions like transcription , post-trIn addition to mRNAs, non-coding RNAs also display secondary structural features. The secondary structural elements in non-coding RNAs have been shown to regulate gene expression –12 and oZebrafish has been extensively used to study spatiotemporal expression profiles of genes including protein-coding genes , 29 and In this study, we probe the zebrafish transcriptome using Parallel Analysis of RNA Structure (PARS) , 37 to rAbout, 400 million reads were generated in total, with approximately 200 million reads in RNase V1 and S1 Nuclease cleaved samples respectively (Table n = 169 million) were aligned to 54,083 transcripts. Load score for all the transcripts were evaluated to check their abundance in the data. All the transcripts  displayed a load ≥ 1  and S1 nuclease  cleaved samples  cleaved by RNase V1 and S1 Nuclease were determined as per the formula described in v = 1.17 And Ks = 0.88 were used to normalise the read counts for every position.PARS scores for all the respective positions  were aligned to 54,083 transcripts in zebrafish genome assembly (v79/Zv9). Amongst these transcripts, a total of 25,158 transcripts had positions represented by overlapping peaks in both the datasets with ratio score greater than one and 11,450 transcripts were termed as multi-conformation transcripts as they constituted at least five positions with overlapping peaks  was estimated. Out of 54,083 transcripts, 544 transcripts had more than 85% position represented by enzyme cleavage across the length of the transcript. While 7366 transcripts showed coverage from 40 to 85%, but a majority of transcripts 46,173 had coverage less than 40% Fig. . The biorpl35 in zebrafish  and human  have 81% sequence homology in the CDS region encompassing 372 nucleotides  positions are structurally conserved.The homologs of des Fig. . The invted Fig. . Out of ubiquitin c , a candidate protein-coding gene. Ubiquitin c was chosen as it had 97% nucleotide positions represented by enzyme cleavage across the length of the transcript. Since UTRs are known to play important role in gene regulation owing to the secondary structural features  ATP  for overnight at 37 °C [Gel purified RNA was radiolabelled using the Kinase max kit  as per the protocol provided by the manufacturer. Briefly, the RNA was dephosphorylated using Calf Intestinal phosphatase (CIP) and purified using Phosphatase Removal Reagent (PRR). The transcript was further incubated with T4 polynucleotide kinase and [γ-at 37 °C . The lab2 for overnight to facilitate structure formation. Folded RNA was subjected to digestion with RNase V1 (1:1600 U for 45 s) and S1 Nuclease (1:100 U for 1 min) at 28 °C respectively. RNA ladder for G residues was obtained by digesting the RNA with 1 U of RNase T1 (Fermentas) in the presence of 1 M LiCl and 100 mM MgCl2 for 2.5 min at 37 °C. Alkaline hydrolysis of RNA was performed at 90 °C in 0.5 M sodium bicarbonate buffer for 8 min. All reactions were stopped using equal volumes of gel loading buffer II  containing 95% formamide and 18 mM EDTA and snap-chilled on ice. Equal counts of digested products were separated on a 12% denaturing gel in 0.5× Tris-borate EDTA buffer and exposed to a phosphorimager screen. The gel images were scanned on a Typhoon scanner . Cleavage profiles were visualised using ImageQuant 5.2 software .5′-end-radiolabelled RNA  was added to 1 μg of unlabelled zebrafish RNA and was subjected to heating at 90 °C for 5 min and then allowed to cool to 28 °C in RNA structure buffer (Life Technologies) and 5 mM MgClrpl35 , a candidate gene encoding the protein component of 60S ribosome subunit was compared with its human homolog.Several studies have highlighted that protein-coding genes are well conserved across species based on nucleotide sequence and function. The RNA secondary structures of such conserved genes are also known to be preserved. We tested the validity of PARS based pairing probability in zebrafish using a well-conserved protein-coding gene across human and zebrafish. Pairing probability of p-values were plotted as a heatmap for further inference.Gene ontology enrichment analysis was performed for the transcripts showing position coverage of least 85% of the transcript length (209 genes) using WebGEStalt . The enrPeriodicity across CDS regions was determined using Discrete Fourier Transform analysis . PARS scAverage PARS scores of first 100 CDS positions were separated into 33 codons. Anova Test  was used to affirm that the PARS score for every position in a codon differs from the rest of the two positions. If significant, pairwise comparisons of PARS scores using t-test with pooled Standard Deviation (SD) was performed.HOTAIR [HOTAIR was folded and probed at 37 °C with 10 μl of (1:1600 U) of RNase V1 for 45 s and 10 μl of  S1 Nuclease for 5 min. Similarly, RNA structures of two zebrafish candidate non-coding RNAs viz. y-rna and tie-1as were elucidated using PARS. Approximately, one picomole of the above mentioned in-vitro synthesised transcripts were pooled with zebrafish poly-A RNA to constitute 2 μg of the starting material. They were enzymatically probed by both RNase V1 and S1 nuclease as mentioned above and RNA libraries were prepared for sequencing. The data analysis was performed for the candidate ncRNAs using the pipeline described previously. The PARS scores were computed for each ncRNA using the method described in the above section. The oligo sequences used in the study are provided in Additional file Human HOTAIR  transcriHOTAIR  was emplAdditional file 1:Supplementary Tables and Figures. (PDF 454 kb)Additional file 2:Load score and percentage coverage of 54,083 transcripts. (TXT 3635 kb)Additional file 3:Multi-conformation position counts in transcripts with overlapping peaks. (TXT 712 kb)Additional file 4:Number of read starts for every position covered in RNase V1 sample. (TXT 32051 kb)Additional file 5:Number of read starts for every position covered in S1 nuclease sample. (TXT 45980 kb)Additional file 6:Total number of read starts for every position in both RNase V1 and S1 nuclease sample. (TXT 64960 kb)Additional file 7:Positions with ratio score more than one in V1 dataset. (TXT 15410 kb)Additional file 8:Positions with ratio score more than one in S1 dataset. (TXT 15865 kb)Additional file 9:PARS scores of 54,083 transcripts and non-coding RNAs. (TXT 35418 kb)


Organellar RNA editing involves the modification of nucleotide sequences to maintain conserved protein functions, mainly by reverting non-neutral codon mutations. The loss of plastid editing events, resulting from mutations in RNA editing factors or through stress interference, leads to developmental, physiological and photosynthetic alterations. Recently, next generation sequencing technology has generated the massive discovery of sRNA sequences and expanded the number of sRNA data. Here, we present a method to screen chloroplast RNA editing using public sRNA libraries from Arabidopsis, soybean and rice. We mapped the sRNAs against the nuclear, mitochondrial and plastid genomes to confirm predicted cytosine to uracil (C-to-U) editing events and identify new editing sites in plastids. Among the predicted editing sites, 40.57, 34.78, and 25.31% were confirmed using sRNAs from Arabidopsis, soybean and rice, respectively. SNP analysis revealed 58.2, 43.9, and 37.5% new C-to-U changes in the respective species and identified known and new putative adenosine to inosine (A-to-I) RNA editing in tRNAs. The present method and data reveal the potential of sRNA as a reliable source to identify new and confirm known editing sites. Arg (ACG). This type of editing enables hydrogen bond formation with more than one base in the corresponding codon position  produces the wobble nucleotide described as essential for efficient chloroplast translation  performs this deamination  and less frequently from uracil to cytosine (U-to-C), in some sites of coding sequences  are a highly diverse protein family. In the plant evolutionary landscape of PPR proteins, 109 genomes/proteomes were analyzed, resulting in a total of 49,204 PPR genes and 616,206 motifs  of plastid messenger RNAs in which several chloroplast cDNA fragments are cloned into vectors and further sequenced  have considerably increased, particularly associated with the deep sequencing of microRNAs (miRNAs) and other small non-coding RNAs (ncRNAs) from nuclear origin, producing a large amount of new sequence data. These studies have focused on the roles of sRNAs in genome maintenance, development and plant responses to environmental stresses  with accession numbers GSE85070 , GSE69571  and GSE77046  were used as input data to evaluate the proposed method. These libraries were produced from samples with no qualitative influence on RNA editing and did not use any method to enrich the isolation of plastid RNAs. The Arabidopsis mutant data present in the libraries were not used. For sRNA analyses, only reads with 18–24 nucleotides were selected from the libraries. Complete chloroplast genome, coding sequences and tRNAs from Arabidopsis (NC_000932), soybean (NC_007942), and rice (NC_001320) were obtained separately at the Index of Genomes from The CpBase: Chloroplast Genome Database (http://chloroplast.ocean.washington.edu/).Public RNA libraries deposited in NCBI GEO (http://prep.unl.edu/)   Mower,  was usedThe sRNA/mRNA libraries were primarily mapped using Bowtie  Merrill], we collected the roots, leaves and petals from the soybean cultivar Conquista. These tissues were collected as biological triplicates. All samples were immediately frozen in liquid nitrogen, and total RNA was extracted using Trizol . The RNA quality was evaluated through electrophoresis on a 1% agarose gel, and the RNA amount was verified using a Qubit fluorometer and Quant-iT RNA assay kit according to the manufacturer's instructions .To validate predicted and new C-to-U RNA editing sites from the sRNA data in soybean chloroplast transcripts [Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed to validate the C-to-U RNA editing rates for some predicted editing sites in soybean chloroplast genes across three different tissues . To validate and quantify new RNA editing sites, only leaf samples were used. The cDNA synthesis was performed with approximately 1 μg of total RNA. Each reaction was primed with 1 μM dT25V oligonucleotide . Prior to transcription, RNA and the oligo(dT)25V primer oligo were mixed with RNase-free water to a total volume of 10 μL and incubated at 70°C for 5 min, followed by cooling on ice. The reactions were reverse transcribed with 1X M-MLV RT buffer, 0.5 mM dNTPs  and 200 U of M-MLV RT Enzyme  in a final volume of 30 μL. The synthesis was performed at 40°C for 60 min. All cDNA samples were diluted 100-fold with RNase-free water and subsequently used as templates in RT-qPCR analysis. The subsequent PCR amplification was performed using a set of primers designed according to Chen et al. , with mo2, 0.25 U Platinum Taq DNA Polymerase  and 200 nM of each forward and reverse primer. The samples were analyzed as biological triplicates and technical quadruplicates in a 384-well plate. A non-template control was also included. The PCR reactions were run under the following conditions: an initial polymerase hot start at 94°C for 5 min, followed by 40 cycles at 94°C for 15 s, 60°C for 15 s and 72°C for 10 s. A melting curve analysis was programmed at the end of the PCR run over the range of 65 to 99°C, and the temperature increased stepwise by 0.5°C. The threshold and baseline were manually determined using Bio-Rad CFX manager software.All RT-qPCR reactions were performed on a Bio-Rad CFX384 real-time PCR detection system  using SYBR Green I  to detect double-stranded cDNA synthesis. The reactions were conducted in a 10 μL volume containing 5 μL of diluted cDNA (1:100), 0.2X SYBR Green I, 0.1 mM dNTP, 1X PCR buffer, 3 mM MgClTo calculate the RNA editing rates, we used the threshold cycle (Ct) generated during the qPCR amplifications. To calculate the percentage of editing, an equation that considered the difference between the Ct-values of each editing variant was used:The sRNA libraries sequenced without plastid RNA isolation were mapped to Arabidopsis, soybean and rice chloroplast genomes using an in-house pipeline Figure . Approxihttp://prep.unl.edu/)   Mower, . The PRE) Mower, . Even wi) Mower, . Four edy Figure . The comTo provide information concerning sRNA data reliability, the C-to-U RNA editing profiles were compared to the PREP predicted editing sites between the sRNA and mRNA (messenger RNA) libraries in Arabidopsis, soybean and rice. The mRNA-Seq data confirmed 27 predicted editing sites in Arabidopsis, 37 sites in soybean and 20 sites in rice, corresponding to 39.13, 40.21, and 25.31% of the predicted sites, respectively Table . One preP-value of ≤ 10−10 to identify sites with C-to-T changes. This parameter enabled the identification of 59 potential editing sites in Arabidopsis, 43 sites in soybean, and 19 sites in rice. Among these editing sites, 58, 37, and 15 sites encode amino acid changes in Arabidopsis, soybean and rice, respectively  and confirmed with at least one method used for all species evaluated. Considering exclusive edited genes, Arabidopsis showed 14 editing sites distributed among nine genes identified using SNP analysis. The editing in the rice atpA gene, detected through SNP analysis, was predicted by PREP. Soybean presented four exclusive editing sites confirmed by sRNA reads and predicted by PREP. They sites were distributed among the petB, rps2, and rps14 genes. C-to-U changes promote a serine to leucine amino acid change in petB and rps14 and a histidine to tyrosine amino acid change in rps2. Arabidopsis, soybean and rice SNP analysis revealed 19, 15, and 7 C-to-T changes distributed among 11, 10 and five exclusive genes, respectively. All genes and their respective editing sites are listed in Table When the edited transcript distribution was evaluated in all species Figure , a highendhB gene was the most frequently edited gene detected through PREP prediction in all plants. The number of editing sites predicted by PREP in this gene varied between species: 9 sites in Arabidopsis, 13 in soybean and 10 in rice. The number of editing sites confirmed by sRNA alignment was 7 sites in Arabidopsis, 9 sites in soybean and 7 sites in rice, representing 77.7, 69.23, and 70% of the predicted editing sites, respectively. Other editing sites could not be confirmed, reflecting insufficient read coverage  RNA editing screening. tRNA genes were used to evaluate editing events, by searching for a guanosine (G) SNP in sRNA mapping since inosine is read as G by cellular machineries Kim, .tRNA genes showed at least one position with an A-to-G change in at least two species Table , totalinndhA (position 1073), ndhB (position 149), rps14 (position 80), and rps16 (position 212) editing sites were comparatively quantified in different soybean tissues , ndhE (position 233), petB (position 611), rps2 (position 248), and rps3 (position 383)  than in petals (20.11%) or roots (30.23%) , ndhE  petB (54.3%), rps2 (71.52%), and rps3 (20.02%)  Figure . RT-qPCR) Figure . The sam% Figure , while vy Figure . The rpss Figure . With re) Figure .In the present study, we propose an additional resource and new method to identify conserved and new RNA editing sites in plastid RNA sequences. Currently, an increasing number of high-throughput sequencing data have become available. Among these datasets, there are substantial data corresponding to sRNA sequencing libraries. After analyzing some of these libraries, we observed that even without previous isolation of chloroplasts for further RNA extraction and sequencing, millions of chloroplast-derived sRNA reads could be recovered, reflecting mapping against the chloroplast genome. An important constraint of the presented method refers to the library quality and the read coverage of reference genomes.In the present study, Arabidopsis libraries had the highest mean coverage using sRNA reads, which likely facilitated the recovery of the largest number of confirmed editing sites. The coverage percentage across genomes was different between species, with lower values detected in Arabidopsis. This result demonstrated that the use of sRNA libraries for mapping editing events is not directly related to a significant coverage across the entire plastid genome. Although this method has the capacity to confirm and discover editing sites in chloroplasts, a smaller number of mitochondrial reads would likely affect RNA editing analysis in this organelle. In the present study, the approach for the identification of editing sites was compared to the PREP and SNP strategies. The editing sites and percentage editing may vary between species because some species may already possess a thymine in the genome. In these cases, C-to-U editing will not occur. The same situation can occur with some A-to-I editing sites, which could affect the general percentage of editing among species. The use of a different PREP score, resulting in distinct cut-off values, may also affect these percentages. In addition, editing factors and their editing sites may evolve differently among species.The elementary step employed in the pipeline used in the present study was the initial sRNA library mapping against the chloroplast genome, considering 0 mismatches. Plastid DNA insertions in nuclear genomes have been demonstrated for partial, intact or even truncated coding sequences in several species  core protein, showed a putative new editing event in rice at positions 1006 and 1007. However, reflecting low coverage, these new editing sites still require further experimental confirmation. Maintenance of the D2 protein structure is important not only for proton transport  gene was previously evaluated in the non-photosynthetic tissues of Arabidopsis. An RNA editing pattern previously demonstrated in Arabidopsis H dehydrogenase complex  and plastocyanin  A-to-I editing event was also observed in all three species in the present study. This change corresponds to an inosine in the wobble position, which encodes three arginine codons CGU, CGC, and CGA that play a critical role in plastid protein synthesis (Rogalski et al., In addition to the high amount of data currently available in public databases that can readily be assessed, there are some plastid sRNAs biological features that can reveal important mechanisms of RNA editing. The precise plastid sRNA biogenesis remains unknown because there is no evidence of any RNAi machinery in organelles that could originate small RNAs thus far. Notably, there is evidence of a relaxed plastid genome transcription mechanism, resulting in full plastid genome transcription (Hotto et al., The identification of editing sites and measurement of editing levels have demonstrated differences among tissues (Tseng et al., Analysis of sRNA libraries can be used to identify and quantify RNA editing events. Using this source of sequence data and pipeline of analyses, we obtained, for the first time, a consistent set of non-conserved and new editing sites in soybean. We propose the use of plastid sRNA libraries as a novel source and approach to study RNA editing events. Until recently, no other studies have taken advantage of such data to screen for RNA editing sites. Thus, the results from the present study should encourage researchers to use small RNA libraries to compare RNA editing in different plants under different conditions to improve knowledge on the editing role of plastid RNA in plant biology.in silico analysis. NR and FK conducted the RT-qPCR experiments. NR and GdF analyzed the data. NR and AC drafted the manuscript. All authors have read and approved the manuscript.RM, NR, and AC conceived and designed the study. NR conducted 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.


Nucleic acid material of adequate quality is crucial for successful high-throughput sequencing (HTS) analysis. DNA and RNA isolated from archival FFPE material are frequently degraded and not readily amplifiable due to chemical damage introduced during fixation. To identify optimal nucleic acid extraction kits, DNA and RNA quantity, quality and performance in HTS applications were evaluated. DNA and RNA were isolated from five sarcoma archival FFPE blocks, using eight extraction protocols from seven kits from three different commercial vendors. For DNA extraction, the truXTRAC FFPE DNA kit from Covaris gave higher yields and better amplifiable DNA, but all protocols gave comparable HTS library yields using Agilent SureSelect XT and performed well in downstream variant calling. For RNA extraction, all protocols gave comparable yields and amplifiable RNA. However, for fusion gene detection using the Archer FusionPlex Sarcoma Assay, the truXTRAC FFPE RNA kit from Covaris and Agencourt FormaPure kit from Beckman Coulter showed the highest percentage of unique read-pairs, providing higher complexity of HTS data and more frequent detection of recurrent fusion genes. truXTRAC simultaneous DNA and RNA extraction gave similar outputs as individual protocols. These findings show that although successful HTS libraries could be generated in most cases, the different protocols gave variable quantity and quality for FFPE nucleic acid extraction. Selecting the optimal procedure is highly valuable and may generate results in borderline quality specimens. Large archives of formalin-fixed paraffin-embedded (FFPE) tissues are held in clinical laboratories, representing an invaluable source of material for biomedical research. FFPE blocks enable prolonged storage of clinical samples such as tumour biopsies and operation material, preserving tissue morphology for pathological examination and nucleic acids for molecular analysis. However, as we enter the era of molecular pathology and personalized medicine, the molecular preservation and quality of proteins and nucleic acids become critical. The ability to reliably investigate nucleic acids from FFPE material is thus important, and should also be taken into account in tissue fixation procedures.In general, nucleic acid extraction from FFPE samples yields inferior quality material compared to extraction from fresh frozen-tissue, and detecting low frequency genetic variants from FFPE samples poses significant challenges. However, high concordance has been reported between paired fresh frozen and FFPE samples in molecular analyses . The nucin situ hybridization (ISH), Sanger sequencing and PCR experiments. In addition, variant detection based on DNA or RNA sequencing is becoming increasingly important for diagnostics and therapeutic decision making, and high-throughput sequencing (HTS) is now being implemented into routine molecular pathology.Robust DNA and RNA extraction protocols are required for accurate identification and quantification of somatic variants in FFPE tissue specimens, and this will improve both research and routine clinical management of cancer. Multiple analyses are routinely performed on the same FFPE sample, such as immunohistochemistry (IHC), Considerable efforts have been undertaken to optimize methods for extraction of high-quality DNA from FFPE samples, and several commercial extraction protocols have been developed for this specific purpose . In the Various studies have addressed the crucial choice of the DNA and RNA extraction method, its impact on nucleic acid integrity and downstream performance. These cover both manual and automatic extraction systems for DNA or RNA, evaluating amplifiability using PCR or HTS , 8–12. LIn this study, we have performed an extensive comparison of different nucleic acid extraction protocols for FFPE material, using eight different extraction protocols from seven kits from three different commercial vendors. This included two protocols where both DNA and RNA were obtained simultaneously that have not previously been compared in other studies. In addition to quantity and quality of the nucleic acids extracted, performance on HTS applications using Agilent SureSelect XT  for variant detection in DNA and Archer FusionPlex Sarcoma Assay  for fusion detection in RNA were evaluated.Archival FFPE blocks from five sarcoma samples were selected from the biobank at the Department of Pathology, Oslo University Hospital. Preparation of the FFPE blocks was performed following standard procedures at the Department of Pathology. Varying levels of cellularity, necrosis, tumour content and tissue size were present among the five sarcoma samples analysed. All tumours were diagnosed according to the World Health Organization (WHO) classification , and cliDNA and RNA were extracted from FFPE sections using eight different protocols from seven kits from three different commercial vendors . For DNADNA and RNA extractions were performed three times per kit, except for the AllPrep DNA/RNA FFPE, FormaPure and truXTRAC FFPE DNA and RNA kits, where extraction was performed twice due to limited material. DNA and RNA were extracted from a single 10 μm FFPE section from the five sarcoma samples, using a total of 95 sections. Equal amount of starting material was used for all methods of extraction, and consecutive FFPE sections for each block were randomized before extraction. DNA and RNA were extracted as soon as possible after sectioning in order to prevent oxidation of the nucleic acids.The DNA extractions were done following the manufacturers’ instructions. All samples were treated with RNAse and the DNA was eluted in a volume of 100 μl. The RNA extractions were done following the manufacturers’ instructions, with some exceptions for the AllPrep and FormaPure kits, where specific recommendations from ArcherDX were followed since the Archer FusionPlex Sarcoma Assay from ArcherDX was used for HTS analysis of the RNA samples. For AllPrep, the protocol for extraction of total RNA that does not include small RNAs was followed, deparaffinization was performed using Deparaffinization Solution (QIAGEN), and the RNA was eluted in 30 μl water. For FormaPure, the samples were digested with proteinase K overnight instead of 60 minutes, and the RNA was eluted in 40 μl water. For the other methods, the RNA was eluted in a volume of 30 μl. For all RNA protocols, no DNAse treatment was performed.DNA and RNA yield were measured using the Qubit dsDNA BR Assay Kit and RNA BR/HS Assay Kits, respectively, and the Qubit 3.0 Fluorometer from Life Technologies. DNA integrity (DIN) values were determined using the 2200 TapeStation system with the D1000 ScreenTape. RNA integrity (RIN) values were determined using the 2100 Bioanalyzer system with the RNA 6000 Nano Kit .DNA amplifiability was measured using the FFPE QC kit from Illumina  according to the manufacturer’s instructions using 4 ng of FFPE DNA input. RNA amplifiability was measured using the PreSeq QC assay from ArcherDX according to the manufacturer’s instructions. 50 ng of RNA was used as input whenever possible, but for some samples all RNA amount available (< 50 ng) was used. Real-time qPCR was performed using the Applied Biosystems 7900HT Fast Real-Time PCR system (Thermo Fisher Scientific). The QC Template Reagent and the Universal RNA from Ambion (Thermo Fisher Scientific) were run in parallel to serve as a control sample for DNA and RNA, respectively. Undetected samples were censored to Ct 45. For each sample, a delta Ct value was computed by subtracting the average Ct value for the QC Template from the average Ct value for each sample.For DNA HTS library preparations, DNA was selected from a parallel of extraction giving average yields, for all the different methods. For the truXTRAC extraction method, DNA simultaneously extracted with RNA was used. Libraries for DNA HTS were prepared from 1 μg DNA following the SureSelect XT protocol (Agilent Technologies) with a custom SureSelect in-solution 900 cancer genes capture panel developed by the Norwegian Cancer Genomics Consortium (cancergenomics.no) . The libraries were sequenced paired-end (2 x 151 bp) on a HiSeq4000 instrument  using Sequencing by Synthesis chemistry.BCL files generated by the Illumina sequencing system were processed using the Illumina Bcl2fastq (v. 2.20) software to generate and demultiplex fastq files. The mapping, alignment, quality assessment and somatic variant calling were performed as previously described . A Randomized downsize of the data based on coverage per sample was done to compare variant calling for all DNA extraction methods. Variant calling was done with MuTect  and StreFor RNA HTS library preparations, RNA was selected from a parallel of extraction giving average yields, for all the different methods. For the truXTRAC extraction method, RNA simultaneously extracted with DNA was used. Libraries for RNA HTS were made using the Archer FusionPlex Sarcoma Assay and the Archer Universal RNA Reagent Kit v2 for Illumina (ArcherDX), following the manufacturer’s instructions. For samples SARC3 and SARC4, 200 ng RNA was used as input, and all available amount of RNA (20–160 ng) was used for SARC5. In brief, cDNA was first synthesized from the RNA using random priming, then end repair, dA-tailing and adapter ligation were performed, using the molecular barcode (MBC) adapters A13-24 for Illumina (ArcherDx). Then two rounds of PCR with gene-specific primers from the Archer FusionPlex Sarcoma Assay were performed. The final libraries were quantified using the KAPA Biosystems qPCR Kit for Illumina , assuming a 250 bp fragment length. Barcoded libraries were pooled at equimolar concentrations and sequenced using the MiSeq Sequencing System from Illumina with the MiSeq v2 300 cycle reagent kit (Illumina).Fastq files from MiSeq were analysed using the Archer Analysis 5 bioinformatics platform from ArcherDX using default settings for the FusionPlex Sarcoma Assay. The fusion detection algorithm relies on the specificity of the gene specific primers used in the amplification steps in the anchored multiplex PCR (AMP) process. The software requires a single read spanning two separate genes to be considered a fusion candidate. Archer Analysis performs all secondary analysis, and many tertiary analysis steps with annotations from the Archer Quiver Fusion Database, as well as sources such as Ensembl VEP, Clinvar and COSMIC. In addition, a randomized downsize of the total number of reads for each sarcoma sample across extraction methods was done to be able to compare all the methods under similar number of reads and to assess the impact of read depth on fusion detection. The down-sampling was done according to the sample with lowest total number of reads (> 1.5 million reads). Access to the HTS data is provided upon request.Statistical analysis was performed to determine if there was a significant difference in DNA and RNA yield, DIN and RIN values, DNA and RNA amplifiability and yield of DNA HTS libraries and QC metrics for RNA HTS libraries, obtained using the different extraction methods. A Wilcoxon Signed Ranks test was performed taking the median value of each sample when two or three replicates were available, and testing the methods against one and each other.The project (S-06133) was approved by the Regional Ethical Committee for Southern Norway, and informed, written consent was obtained from the patients.DNA and RNA were extracted from the same archival FFPE blocks from five sarcoma samples using different extraction kits from commercial vendors in order to determine the best extraction method for downstream HTS applications. The QIAamp DNA FFPE Tissue Kit, miRNeasy FFPE Kit and AllPrep DNA/RNA FFPE Kit from QIAGEN have been shown to perform well in comparison with other DNA and RNA protocols in previous studies , 12, 14.Equal amount of starting material and a randomized order of the consecutive FFPE block sections were used for all methods of extraction. The extracted DNA and RNA were quantified using fluorescent dye-based quantification, and the average total amount for the different samples and extraction methods are shown in For RNA, the yields from the different extraction methods were more similar, although the FormaPure kit gave slightly lower RNA yields than the other extraction methods. Both protocols using the truXTRAC kit gave significantly higher RNA yields compared to the FormaPure kit (p = 0.043). As also seen for the DNA amounts, sample SARC5 gave by far a lower RNA yield than the other samples.Nucleic acid integrity, including both fragment length and absence of chemical modifications, is important for successful amplification. The fragment length distributions for the extracted DNA and RNA obtained using the different extraction protocols are shown in In order to determine the amplifiability of the extracted DNA and RNA obtained using the different extraction protocols, qPCR was performed using the FFPE QC kit and PreSeq QC assay, respectively. The average delta Ct values for the different samples and extraction methods are shown in For RNA, the PreSeq QC quality score takes both concentration, length and crosslinking into consideration. For sample SARC5, the extractions used for sequencing library preparation were not included in the PreSeq QC quantification due to limited amounts of RNA. No significant differences in delta Ct values, reflecting the amplifiability, were observed between the methods. In contrast to DNA, high yields of extracted RNA did not necessarily correspond to low delta Ct values. For sample SARC1, RNA extracted using the FormaPure kit was the only RNA to obtain a delta Ct value although the yield obtained was by far lower than with the other extraction methods.Libraries for DNA HTS were made for four of the samples, SARC1-4, using Agilent SureSelect XT with a custom SureSelect in-solution 900 cancer gene panel. The quality and yield of SARC5 DNA were considered too low for library preparation. Two of the samples, SARC1 extracted using the GeneRead kit and SARC2 extracted using the AllPrep kit, did not have enough library yield to proceed to capturing. Thus, for two of the samples, DNA HTS was performed on libraries representing only three DNA extraction methods, while all four methods were included for the remaining two samples. No significant differences were seen between the methods for DNA library yield, neither for the libraries before capturing nor the final libraries, although the QIAGEN methods showed a trend towards more yield Tables  and S1.To better compare the suitability of the various sequenced libraries for somatic variant calling, a randomized downsize of the data was done to be able to compare all the methods under similar coverage per sample, giving a mean coverage of 245-320x . The somIn order to compare the complexity of the different libraries, we downsized the number of reads and calculated the duplication rates. No significant differences were seen between the extraction methods, although the libraries generated with AllPrep in general showed a lower duplication rate, indicating a more diverse library .For the three samples with known fusion genes, SARC3-5, libraries for RNA HTS were made using the Archer FusionPlex Sarcoma Assay. The library yields are given in Tables Fusion detection was performed using the Archer Analysis 5 bioinformatics platform using default settings for the FusionPlex Sarcoma Assay Tables  and S1. To better compare the fusion detection capability of the different extraction methods, the number of reads per sample was normalized by down-sampling according to the sample with the lowest number of reads (> 1.5 million reads) for each sarcoma sample separately. The fusion gene analysis was then re-run, giving the same results for fusion detection as when all reads were included Tables . Hence, The different DNA and RNA extraction methods examined in this study gave variable quantity, quality and performance of the isolated nucleic acids. For DNA extraction, the truXTRAC FFPE DNA kit from Covaris gave in general higher yield and more intact DNA fragments, however, no significant differences were observed in the quantity of libraries, DNA sequencing or variant calling among the different extraction methods tested. For detection of RNA transcripts from fusion genes, the truXTRAC FFPE RNA kit from Covaris and FormaPure kit from Beckman Coulter performed better identifying a fusion that was not detected with the kits from QIAGEN. The FormaPure and truXTRAC kits also had the highest percentage of unique read-pairs identified, providing a higher complexity of the HTS libraries and data. For both DNA and RNA, the truXTRAC simultaneous DNA and RNA extraction protocol performed just as well or even better than the separate truXTRAC protocols. In general, the choice of extraction method seems to be more crucial for RNA than for DNA. However, the findings show that selecting the optimal procedure for nucleic acid extractions is highly valuable, and may be essential for successful HTS analysis.S1 Fig(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.


Furthermore, Phen-DC3, a specific G-quadruplex binder, is able to inhibit HCV viral replication in cells in conditions where no cytotoxicity was measured. Considering that this domain of the negative RNA strand is well conserved among HCV genotypes, G4 ligands could be of interest for new antiviral therapies.DNA and RNA guanine-rich oligonucleotides can form non-canonical structures called G-quadruplexes or “G4” that are based on the stacking of G-quartets. The role of DNA and RNA G4 is documented in eukaryotic cells and in pathogens such as viruses. Yet, G4 have been identified only in a few RNA viruses, including the Flaviviridae family. In this study, we analysed the last 157 nucleotides at the 3′end of the HCV (−) strand. This sequence is known to be the minimal sequence required for an efficient RNA replication. Using bioinformatics and biophysics, we identified a highly conserved G4-prone sequence located in the stem-loop IIy’ of the negative strand. We also showed that the formation of this G-quadruplex inhibits the  These structures are also present in pathogens and can control their virulence3. In addition to bacteria and protozoa, several DNA viruses are concerned (for a review3). The Epstein–Barr virus encodes a genome maintenance protein  that binds G-rich sequences at the viral replication origin, recruits the replication complex and is involved in metaphase chromosome attachment. This interaction ensures the genome maintenance throughout mitosis4. The human papillomaviruses (HPV) genome also contains G-quadruplexes that can carry out regulatory functions5. In SV40, the last regulatory region contains six GC boxes (GGGCGG) in close proximity involved in a quadruplex structure that constitutes a binding motif for SP16. The early transcription of SV40 is also controlled by G4 structures. The HSV-1 genome displays multiple clusters of repeated sequences forming very stable G-quadruplexes that are involved in the viral DNA replication7. Beside these DNA viruses, there are very few descriptions of G-quadruplexes in RNA viruses. HIV-1 is the first one in which RNA G4 structures have been shown to be essential in the replication cycle. They are needed for genome dimerization8 and for capsid attachment9. A conserved G-rich region from HIV-1 promoter was shown to form G4 structures10 and being involved in the regulation of viral replication11. Very recently, G-rich sequences were identified and analyzed in the genome of Ebola virus and Marburg virus13. A recent study has also revealed the first evidence of G-rich sequences to be found in the negative-sense RNA of Zaire ebolavirus L gene and likely involved in the L gene expression14. Finally, several potential G4 forming sequences were identified in Zika genome, belonging to the Flaviviridae family15. Their role in the viral replication, if any, still has to be determined.DNA and RNA guanine-rich regions can fold into non-canonical secondary structures called G-quadruplexes or “G4”. These structures result from the planar association of four guanines connected through hydrogen bonding. These G-quartets stack on top of each other and are stabilized by monovalent cations such as sodium or potassium. Since the 1960s, when the structural basis of the G-tetrads was elucidated, numerous works have described their implication in biological regulatory mechanisms. Control of telomeric functions, DNA replication, gene expression, genome stability, translation regulation or mRNA processing can involve DNA or RNA G-quadruplexes17. These interactions are essential for viral replication20. The HCV genome encodes a unique polyprotein, which is cleaved into three structural proteins , and seven non-structural proteins . The latter is an RNA-dependent RNA polymerase (RdRp) that, associated to cellular and viral non-structural proteins in a replication complex (RC), ensures the RNA synthesis. Plus-strand genomic RNA ((+)RNA) is copied into a negative strand ((−)RNA) that is used as template for the synthesis of new (+)RNAs. The RC binds to the 3′-end RNA of each strand to ensure the synthesis of a complete genome. As a result, the 3′-end of the (−)RNA is used by the RC to initiate the RNA synthesis of the (+)strand. However, the intimate mechanism involved in the initiation of the RNA synthesis is not known. The secondary structure of the 3′end of the (−)RNA was established from RNA footprinting experiments strand constitute the minimal sequence required for efficient RNA replication27.Hepatitis C virus (HCV), the causative agent of hepatitis C, a liver disease, also belongs to the Flaviviridae family. Its plus-strand RNA genome of 9600 nt is flanked by 5′ and 3′ untranslated regions (UTRs) that fold in several stem-loop structures engaged in short or long-range RNA-RNA tertiary interactionsnts Fig. 21,22. So28. In this work, we identified another G-rich region in the stem-loop IIy’ of the 3′-end of the negative strand of HCV. Using bioinformatics, biophysics, biochemical and cellular experiments, we show that this G-rich sequence is able to fold into a G4. In vitro RNA synthesis by the RdRp is affected by the formation of this quadruplex. Furthermore, we show that Phen-DC3, a known G-quadruplex ligand, inhibits HCV viral replication in cells. As this region of the (−)strand RNA of HCV is highly conserved, targeting the G4 structure by ligands might be a new therapeutic pathway.Recently a G-rich region located in the core coding sequence on the genomic (+)strand RNA of HCV was shown to adopt a G4 structureOligonucleotides were purchased from Eurogentec  with RP cartridge gold purification. Oligonucleotide strand concentrations were determined by measuring absorbance at 260 nm  using the extinction coefficients provided by the manufacturer.2 atmosphere. The fully replicative whole HCV genome  was obtained by gene synthesis  and cloned into pUC19 (pUC/LMTV). The gene encoding Gaussia luciferase (GLuc) ending by 60 nt that coded for the FMDV 2 A protein, was obtained by gene synthesis and was further introduced between the p7 and NS2 genes in position 2779 (pUC/LMTV-GLuc) as described in28.Huh7 cells were grown in Dulbecco’s modified Eagle medium supplemented with 10% fetal calf serum and gentamicin (50 µg/mL) at 37 °C in a 5% CO29. TDS were calculated by subtracting the UV spectrum at 5 °C from the one at 95 °C30.A SAFAS UVmc2 double-beam spectrophotometer  equipped with a 10-cells holder regulated by a Peltier controller was used to perform the UV melting experiments. Oligonucleotide strand concentrations ranged between 3.3 and 7.2 µM. The melting curves at 295 nm were recorded both ways between 95 °C and 5 °C with a temperature gradient of 0.2 °C/minCD spectra were measured at 20 °C between 220 and 335 nm (0.5 nm data pitch) in 1 cm path-length quartz cells using a Jasco J-815 instrument. Each spectrum was the average of four scans recorded at 50 nm/min (2 nm bandwidth and 1 s data integration time). The oligonucleotide concentration was 2.5 µM. CD melting curves were obtained by measuring the CD signals from low temperature to high temperature with 1 °C increments and a temperature gradient of 0.3 °C/min. The ellipticity was then plotted at 263 nm against temperature.1H 1D NMR experiments were acquired using a pulse sequence with Spin-Echo Water Suppression. The oligonucleotides were dissolved in a 20 mM potassium phosphate buffer, pH 7 at 20 °C, and 70 mM KCl at a concentration of 0.15 mM.NMR experiments were performed on a 700 MHz Bruker spectrometer equipped with a TXI probe. 1/2) were determined from FRET melting experiments carried out in 96-well plates on a Stratagene Mx3005P real-time PCR equipment, as previously described30. The excitation and detection wavelengths were set to 492 and 516 nm, respectively. After an initial stabilisation at 25 °C for 5 min, the temperature was increased by a 1 °C step every minute until 95 °C. When the RNA sequence is in the G-quadruplex folded state, FRET occurs between the two fluorophores while denaturation of the G-quadruplex structure upon heating abolishes the FRET process. The fluorescently labelled HCV110-131 sequence (f-HCV110-131-t 5′-FAM-CGGGAGGGGGGGUCCUGGAGGC-Tamra-3′) was dissolved to a 0.1 mM stock concentration and stored at −20 °C. The stock concentrations were determined from the absorbance at 260 nm measured on a SAFAS UVmc2 double-beam spectrophotometer  . The melting experiments were performed at a final 0.2 μM strand concentration of labeled oligonucleotide with 0.4 μM of ligand Phen-DC3. The experiments were carried out in a 10 mM lithium cacodylate buffer (pH 7.2 at 20 °C) containing either 10 mM KCl or 90 mM LiCl.The ligand-induced thermal stabilisations (∆Tin vitro transcription to produce the HCV (−)RNA 3′-end (157nt)22. PCR was performed with the GoTaq® Flexi DNA Polymerase (Promega) with primers designed to introduce a T7 RNA polymerase promoter in the correct orientation. The primers sequences are Stop_157: GCCAGCCCCCTGATGGGGGCGACAC, T7_157_WT: TAATACGACTCACTATAGGTTCCGCAGACCACTATGGC or T7_157_MutG4: TAATACGACTCACTATAGGTTCCGCAGAGGACTATGATTTTTTTGGGAAAAAAAATCCTCCAGGCTGCACG. Control RNA 5BSL3.2 (555 nt) was produced similarly using pGEM-T/5UTR-H2AE-5BSL-3UTR vector31 and specific primer (Stop_555: ACTTGATCTGCAGAGAGGCCAG and T7_555: TAATACGACTCACTATAGGGCATGGACGACTGTACAAGTAATC). The template for producing the LMTV genomic RNA was obtained by cleavage of the XbaI site of pUC/LMTV-Gluc. The RNA was then obtained from 1 µg of DNA by in vitro transcription using the MEGAscript kit (Ambion). DNA templates were digested with DNase I for 15 min then precipitated after acidic phenol/chloroform extraction. HCV107-151 negative strand including the sequence corresponding to the inverse complementary of the T7 promoter was obtained from Eurofins Genomics SAS (GCAGCCTCCAGGACCCCCCCTCCCGGGTGTGCCATAGTGGTCTGCCCTATAG TGACTCGTATTA). One µg of HCV107-151 and 4 µg of portable promotor pT7 (CCTATAGTGACTCGTATTA) were incubated with 2 µl of the transcription buffer MEGAscript kit (Ambion) for 1 min at 95 °C and decrease to 30 °C for 30 min before adding the other components of the transcription kit and transcribed for 2 hours. DNA templates were digested with DNase I for 15 min then precipitated after acidic phenol/chloroform extraction. The purity and integrity of the RNAs were determined using capillary electrophoresis on a Bioanalyzer 2100 (Agilent).The pGEM9Zf(–) containing the 341 nucleotides of the 5′UTR of HCV (H77 strain) was used for 2, 40 mM NaCl or KCl, 13.2 U RNasin (Promega), 0.5 mM each of the 3 NTP , 500 nM G4-ligand when indicated, 10 pmoles of RNA template, 8 pmoles of purified NS5B (purified as in22) and 2 µCi [3H]UTP (40 Ci/mmol). The reaction mixture was incubated for 10 min at 30 °C before addition of the purified NS5B for 2 h at 30 °C. The reaction was stopped by the addition of 10% trichloracetic acid (TCA) onto 20 µl of the mixture. The radioactivity incorporated into neo-synthesized RNA was then determined by scintillation counting and normalized according to the number of UTP contained in each template.The assay was performed in a final volume of 25 µL containing 20 mM Tris-HCl buffer (pH 7.5), 1 mM DTT, 5 mM MgClHuh7 cells (25000 per well) were seeded in a 96-wells plate 24 hours before infection. Serial dilutions of G4 ligand or DMSO were added on the cells. Infection was then performed using supernatant containing LMTV particles. After 48 hours of culture, the GLuc activity from 4 µl of viral supernatant diluted in 1 µl of lysis buffer  was measured using 30 µL of BioLux GLuc Substrate (BioLabs) and the VariosKan apparatus (ThermoScientific). Cytotoxicity analysis was performed in similar conditions without virus and measured with the CellTiter 96® AQueous One Solution Cell Proliferation Assay System (Promega).32. The mean of the scored nucleic acid sequence was computed for a sliding window arbitrary set at 25 nt. Only scores above 1.0 were taken into consideration.The G4Hunter algorithm was used to predict putative G4 structures in the genomewww.hcv.lanl.gov). A G4-prone fragment of the FASTA alignment was detected. This G4 sequence is located in the stem-loop IIy’ of the 3′-end of the (−)RNA RNA from the “HCV genome database”  melting assay32 with a 5′-FAM/3′-TAMRA labelled oligonucleotide . In this test, a T1/2 of 70 °C was determined for the labelled sequence in the absence of ligand . The assay was first performed in the presence of 40 mM NaCl and corresponded to 100% activity. Figure Because this RNA G4 is located at the 3′-end of the (−)strand, which is the initiation site for the (+)strand replication by the RdRp, we investigated the putative role of this structure on RNA synthesis by HCV polymerase To confirm the presence of a G-quadruplex at the 3′-end of the (−)RNA of HCV genome, the effect of Phen-DC3 on the RNA synthesis was analysed. Phen-DC3 was mixed with the RNA template before adding HCV RdRp, then the level of RNA synthesis was measured Fig. . Phen-DCTo demonstrate these results, mutations were introduced in the G-rich sequence of the template to abolish G4 formation. RNA synthesis on the resulting MutG4 template was decreased in comparison to the WT sequence Fig. . HoweverAltogether, these results show that the RNA synthesis of the WT template was reduced in the presence of potassium or with Phen-DC3, which both stabilize the RNA G4 structure. Thus, in the context of the 157 last nucleotides of the HCV (−)strand, the G-rich sequence can be structured in a G-quadruplex and its presence affects RNA synthesis.31. As showed in Fig. Because Phen-DC3 can bind the 3′-end of the intermediate strand of RNA replication, we wondered if this ligand could also inhibit the viral life cycle. To address this question, Huh7 cells were infected with the WT virus (LMTV) in the presence of increasing concentrations of Phen-DC3. The virus used in the infection assay express the Gaussia luciferase protein in frame with viral polyprotein such that the level of luciferase was directly related to viral replicationThe G4Hunter algorithm identified the G-rich sequence (nt 110–131) located in the IIy’ domain of the HCV 3′ (−)strand as a putative G4 forming sequence. Biophysical experiments confirmed that this RNA sequence could fold into a stable G4 structure. Mutations of this sequence were introduced within the last 157 nt to prevent G4 formation. Biochemical analysis confirmed these results by showing the effect of potassium and of the G4 ligand on the WT template but not the mutated sequence. We thus conclude that the G-rich sequence contained in the 157 last nucleotides of the HCV negative strand can form a G4 structure.When we analysed the polymerase activity, the stabilisation of the structure by Phen-DC3 led to a decrease in the RNA synthesis. Yet the inhibition is observed on the template corresponding to the end of the HCV negative strand and not when using a synthetic template (polyrA) or a HCV RNA template without the G-rich sequence. This establishes that the polymerase enzymatic activity of the protein NS5B itself is not affected by the G4 ligand. The introduction of mutations in the G-rich sequence strongly affected the sensitivity to Phen-DC3 confirming the link between the inhibition and the formation of the G4 structure.As mentioned in the Results section, the activity of the RdRp was reduced both when measured on (i) the MutG4 template where the G-rich sequence is mutated or (ii) on the WT template when the G4 is stabilized by the ligand or potassium. These surprising similar effects consisting in a decrease in the RNA synthesis raise the question of the requirement of an efficient dynamic between alternative structures for an optimal RNA synthesis Fig. .Figure 5et al. and Smith et al.21, showed a stem-loop in the IIy’ domain. The possibility of a G-quadruplex formation was not mentioned. In the structure proposed by Smith, G114 and G115 (in the middle of the IIy’ domain) were sensitive to RNAse T1 while the opposite nucleotides in the stem U142 and C143 were sensitive to RNase A. This suggests that these nucleotides may not be always engaged in Watson-Crick interactions. In addition, in the Schuster’s experiments, nucleotides G121,122,123 were slightly reactive to Pb2+ indicating that this region, at the bottom of the IIy’ domain, could be engaged in an alternative structure such as a G-quadruplex. These two models are compatible with our findings and suggest that the IIy’ domain in the (−)RNA strand of HCV could fold dynamically into mutually exclusive conformations, one being double-stranded as indicated by Smith and Schuster, the other organized into a G4 (Fig. The structure published previously by Schuster  G4 Fig. , while sSupplementary Information


The sequence of nucleotides in an RNA determines the possible base pairs for an RNA fold and thus also determines the overall shape and function of an RNA. The Swellix program presented here combines a helix abstraction with a combinatorial approach to the RNA folding problem in order to compute all possible non-pseudoknotted RNA structures for RNA sequences. The Swellix program builds on the Crumple program and can include experimental constraints on global RNA structures such as the minimum number and lengths of helices from crystallography, cryoelectron microscopy, or in vivo crosslinking and chemical probing methods.The conceptual advance in Swellix is to count helices and generate all possible combinations of helices rather than counting and combining base pairs. Swellix bundles similar helices and includes improvements in memory use and efficient parallelization. Biological applications of Swellix are demonstrated by computing the reduction in conformational space and entropy due to naturally modified nucleotides in tRNA sequences and by motif searches in Human Endogenous Retroviral (HERV) RNA sequences. The Swellix motif search reveals occurrences of protein and drug binding motifs in the HERV RNA ensemble that do not occur in minimum free energy or centroid predicted structures.Swellix presents significant improvements over Crumple in terms of efficiency and memory use. The efficient parallelization of Swellix enables the computation of sequences as long as 418 nucleotides with sufficient experimental constraints. Thus, Swellix provides a practical alternative to free energy minimization tools when multiple structures, kinetically determined structures, or complex RNA-RNA and RNA-protein interactions are present in an RNA folding problem.The online version of this article (10.1186/s12859-017-1910-7) contains supplementary material, which is available to authorized users. Approximately 80% of the human genome is transcribed into an RNA sequence, although only 2% of the genome codes for proteins . This di3), where N is the number of nucleotides. The number of possible structures is approximately 1.8N [The RNA folding problem is defined by base pairing rules for Watson-Crick and GU pairs. An RNA secondary structure consists of a set of base pairs, noncanonical pairs, and non-paired nucleotides Fig. . A minimely 1.8N , 10. Theely 1.8N , 12 and ely 1.8N . The theely 1.8N , 12, 14.ely 1.8N , computiely 1.8N , and comely 1.8N  providesely 1.8N .Fig. 1RNThe first approach to complete enumeration of RNA structures after the discovery of tRNA crystal structures took more than 3 days and led to the general misconception that it is not possible or practical to completely enumerate all the possible RNA secondary structures . UtiliziThe main conceptual advance in Swellix is to combine all possible helices rather than combine all possible base pairs in the generation of RNA structures. This helix abstraction is further developed by bundling together similar helices that exist in the same region of RNA sequence. The use of abstract representations for RNA helices has previously been applied to free energy minimization approaches , but hasConstructing the Pair TableConstructing the Component ListConstructing the Interval Look-up TableConstructing the Bundle ListIteratively Recursive Combinatorics (Make Jump Tree)Swellix builds on the Crumple software , 25, 26.Parts one through four serve solely to speed up the recursive step. These are tables constructed to reduce the number of operations needed when checking if a valid helix exists within an interval. The bundling step serves to group similar structures together and reduce the size of the linked list that is sent to the recursive step. The iteratively recursive combinatorics step in Swellix is based on the algorithm in Crumple and has improvements in memory use and parallelization.2), where n is the RNA sequence length.The input is an RNA sequence, or a string of A, C, G, and U that represent the 4 nucleotides in RNA. All pair-wise matching results of the entire RNA string are tabulated on a look-up table to speed up the extremely repetitive comparison procedure. The pairing rules are based on Watson-Crick (A-U and C-G) and wobble (G-U) base-pairing criteria. The data structure is a 2D integer array, where only the upper triangle region is used. The pseudo code is provided in supporting materials with time complexity O(n2) process to identify the identical helices inside slightly different intervals I’s, in this phase, Swellix explores all qualified helices that will possibly be used in the entire runtime and documents them in an array of linked lists. Each array index i represents the nucleotide at the ith position of the input RNA sequence. Each node in the linked list extending from array cell i represents one component  that begins at the ith nucleotide. In other words, the 5′-end outermost nucleotide of a helix is labeled the “type” of that component. Continuing with that terminology, the ith array cell represents the component type i, and all nodes of the linked list from the ith array cell share the same type (starting nucleotide).This phase features one of the key distinctions from the Crumple algorithm. Instead of iterating through the O(n2) for n > > L, where L is the prescribed minimum helix length based on the experimental constraints. There are two points to making the component list. First, k_1 and k_2 indices, iterating through the entire RNA sequence, mark the boundary of a candidate helix. i, j are the helix pair indices making the k_3 stepwise matching checks starting from k_1 and k_2, respectively. Second, at the conditional statement ‘if can be paired’, the pair table is called. The runtime saving for each if statement implementation is small, but the collective benefit is substantial.The pseudo code is shown in the supporting materials, with the time complexity of O(Ln2), where m is the number of components in the component list. The runtime saving of each empty-cell skip is tiny, but the collective benefit is substantial.In the array of the component list, not all array cells (component types) have corresponding components, ie., some cells are empty. For some long RNA sequences with many experimental constraints, most of the array cells will be empty. To facilitate the Make Jump Tree process, it is worthwhile to tabulate the bounds for each interval so that the scanning process inside each interval may skip the empty array cells. The pseudo code is listed in supplemental material with the time complexity of O))).,. ((.((….))))., and. ((..((…))))., then only one helix would be selected as the representative helix for the bundle, in this case. ((((……))))., the helix with the most stacked pairs. The Make Jump Tree phase is the most computation-intensive part of the entire program, which inherits the core spirit of the Crumple algorithm. It demands time for recursion implementations and disk space to store the solution structures. Thus, any reduction in the input for the Make Jump Tree phase will reduce computational time.The bundling step bypasses all recursion branches which are guaranteed to resemble the representative structure and therefore saves significant time and space. For two helices to be regarded as similar, we proposed this criterion: the distance from 5′ end to 3′ end must not exceed the length of 4 L + 2 hp – 1, where L is the minimum helix length and hp. is the minimum hairpin loop length, both of which are determined by experimental considerations. There are two reasons for defining similar helices in this manner:two adjacent helices “similar” with one helix inside the same interval, which is apparently incorrect. The 4 L +2 hp. −1 limit does not allow formation of a multibranch loop in the interval.a) From a computational perspective, 4 L + 2 hp. is the minimum length to fit in two adjacent helices. Performing similar helices reduction for 4 L + 2 hp. size or larger allows the possibility of treating b) The wider the span of the interval, the less similar the largest and smallest helices within the inside the same interval will be. Therefore, there must be a fine line somewhere as the cutoff.For a given interval of size 4 L + 2 hp. – 1 or less, all helices which fit in that sequence space are called similar and are grouped in the same bundle. All helices of the same bundle can be replaced by a representative helix. This representative is selected from the bundle, based on first the helix span and secondly on the degree of saturation, ie the representative helix has the most possible base pairs.2) where m is the number of components.Through this reduction, the total number of solution structures will be greatly reduced and therefore more manageable and analyzable. In addition, a reference list will be provided to contain all possible alternative helices of the same bundle. Note that it is possible for a small size helix to belong to more than one bundle, since bundles can overlap each other, and therefore it is possible that the overlap length is large enough to fit in a small helix. The bundle list implementation is optional. Its pseudo code is included in supporting materials and shows a time complexity of O. If there is no interval on queue and if it is at the root of recursion (recursion level 0), the entire RNA sequence length is regarded as the interval. For a given interval, components from the component list will be checked one at a time to fit into the interval. If it fits, this component will be inserted into the current structure either as a new helix or to replace a certain old helix. Two new intervals will also be made inside and behind the new helix. The bound of the new inside interval is the inner edges of the new helix. The boundary of the new behind interval is the 3′ end outer edge of the new helix and the 3′ end inner edge of the helix containing the new helix. Then the make_recursion function call is made, and another recursion round begins.The theoretical time complexity is O(nm) for the worst case where n is the length of the sequence and m is the number of components or bundles. In practice, most of the RNA sequences are rather well-randomized, and therefore the average time complexity is close to O(asqrt(m)), where 1 < a < 3, depending on the experimental constraints and m is the number of components.The supporting functions that ensure completeness and no duplication of structures add complexity to the code. The descriptions of Restore_Interval and Duplication Prevention, which are part of the Make Jump Tree step, are described next.For most of the time, whenever an interval is examined, it should be discarded to prevent duplication. However, in some cases, the behind intervals should be conditionally kept after examination and reused later. Fig. The duplication prevention step includes the 2 L – 1 & qLr rules, where L is the minimum length of helix, q is the quotient, and r is the remainder. The first rule states that for a given minimum length of helix, in the case where the minimum helix size and larger are generated, the upper limit of expansion is 2 L-1 in order to prevent duplication because any component of the length 2 L can be replaced by two helices of length L. The second rule is the variant of the general linear equation a = q*d + r, where a, q, d and r are all natural numbers; a is any random number; d is divider; q is quotient; and r is remainder. The qLr rule states that for a helix of any length, a, there must be a way to build it with the helix length of L, L + 1, L + 2... 2 L-2, 2 L-1 (note the 2 L-1 rule limit) without duplication or deletion of structures in the output. So the expression is a = q * L + k, or more precisely, a = (q – 1) * L + (L + k), where k is 0, 1, 2, …, L-1 (note k is remainder).Note that in Swellix, helix length constraints like a minimum helix length of 2, which effectively eliminates any single, isolated, unstacked pairs, will produce a smaller output set than an algorithm such as Crumple. For Swellix, these length constraints are enforced in the first step when it is analyzing the sequence for possible components, ie individual helices. With Crumple, however, these length constraints will not be enforced until a full possible structure has been assembled with base pairs. At this point, the program looks for violations. For example, if a minimum helix length of 2 is applied, the output of Crumple and Swellix may differ as shown in Fig. n-nucleotide sequence, processing n-many subsequences before beginning the standard recursion. This negative performance impact is magnified by factors such as sequence length and runtime options like minimum helix length constraints. Bundling was identified as a parallelization candidate due to the data-independence of those n-many subsequences to be processed. Bundling and the recursive jump tree are similar in that they both have high potential for parallelization. They are unique in the type of parallelization necessary for efficient scaling.Bundling and the recursive “jump tree” algorithm do not scale well with input size. The bundling process has the effect of, given an n nucleotides in a sequence, there will be n units of work in the bundling step. Each unit of work consists of running a slightly modified version of Crumple on some substring of the RNA input sequence. The results from these computations are consolidated and used to improve the speed of Swellix’s recursion. For each of these substrings from the input sequence, only the data in one substring is required for its respective computation. Since each “unit” of work is completely separate from the others, we chose to parallelize the bundling stage by dividing the “units” equally among our pool of MPI (Message Passing Interface) processing elements. The end result is akin to an OpenMP parallel for loop: each MPI core uses its own rank, the world size, and the length of the RNA sequence to calculate at what index to start, and how many iterations of the loop to process.The nature of the bundling algorithm can be abstracted as such: for For Make Jump Tree, parallelization is more complex. The same strategy used for bundling could be used here, but the recursion is much more imbalanced than bundling. In an effort to achieve the most balanced compute load as possible, we chose to implement a dynamic load balancing algorithm for the recursive parallelization. This parallelization relies on each node having a copy of the component list. Then each node creates an array of pointers to each component for rapid access instead of iterating through a linked list.n (n = # components) in which it probes the MPI network for work requests from idle nodes. When it detects such a request, the master sends its current loop index to the node, indicating which index in the component array the worker node should use for a new recursion. Immediately after the master sends the nth index, it sends another message to all nodes in the MPI pool indicating that Phase 1 is complete. During phase 1, the worker nodes begin their procedure by sending a work request message to the master node. Then, they pause execution to wait for some response. If they receive a response with an array index, then the worker accesses the component array at that index and begins a recursion using the same function call that serial Swellix uses normally. Once a recursion is complete, the worker again requests work and waits for a response. Upon receipt of the phase 1 completion message, a worker node simply continues to the data consolidation stage where it will send the information it generated back to the master node for display.Phase 1 of the parallelization strategy can be described as a coarse master/worker paradigm. The master node begins a loop from 0 to During phase 2, a version of Dijkstra’s token rings that were applied to parallelization of the Crumple algorithm were used again , 27, 28.Early- to mid-stage development was done on University of Oklahoma resources including both the Boomer and Schooner Linux cluster supercomputers. For the Boomer computations, the compute nodes used contained the following hardware: 2 Intel Xeon E5–2650 “Sandy Bridge” 8-core 2.0 GHz processors with 32 GB of RAM. The compute nodes that we used on Schooner have the following specifications: 2 Intel Xeon E5–2650v3 “Haswell” 10-core 2.3 GHz processors with 32 GB of RAM.Late-stage development took place almost exclusively on the Blue Waters supercomputer through the Blue Waters Project and Shodor Education Foundation’s Blue Waters Student Internship Program. The typical compute node used on Blue Waters was the XE6 node: 2 AMD 6276 Interlagos 16-core (“integer” core) 2.45 GHz processors with 64 GB of RAM.Swellix enables longer RNA sequences to be computed and analyzed. Swellix with a minimum helix length of one is nearly identical to Crumple. The performance analysis in Fig. The abstract helix representation in bundling further improves the ability of Swellix to compute longer sequences in a reasonable time. In order to test whether bundling of similar helices was the prime contributing factor to Swellix’s performance compared to Crumple, sequences of increasing lengths were used: 14, 28, 42, 50, 71, 74, and finally 76 nucleotides. The first three sequences are artificial sequences used for general testing and development. The “14-mer” is 5’GCUCUAAAAGAGAG3’ and was designed to produce a set of structures that contain an example of each possible kind of soft constraint for DMS-modified adenines. The 28- and 42-mer are 2 and 3 concatamers of the 14-mer sequence. The longest sequence is a tRNA sequence, yeast phenylalanine tRNA . As shown in Table As a computational exercise several tRNA, pRNA, and group I intron sequences were folded on the Blue Waters computer using the parallelized bundling option. The minimum helix length was varied from 2 to 10. Table One of the roles of modified nucleotides in tRNA is to reduce conformational space and prevent misfolding . SwellixTriticum aestivum, is −73.2 J/molK. Thus, our computations confirm and quantify one role for naturally modified nucleotides in reducing conformational space for RNA folding.Fig. The above example illustrates how chemical modifications in natural RNA limit folding space. One further application of the Swellix program in the field of synthetic biology would be to estimate the optimal location in a sequence to restrict folding with a nucleotide unable to pair. When designing RNA sequences, incorporation of chemically modified nucleotides are one method to direct folding and restrict folding space in particular ways or regions of the sequence. Thus Swellix may also facilitate rational design of sequences with engineered folds.The shortest Human Endogenous Retroviral RNA (HERV) sequence is 141 nucleotides (Genbank # AY944072.1). Folding this HERV RNA using the parallelized Swellix on the Blue Waters computer generated 643 components and 12,518,055,094 bundled structures in an average time of 23,951 s. Swellix does not save all the generated structures, but a motif search can be completed for each structure before it is discarded. A search for known RNA motifs –37 from Swellix effectively combines helix abstraction with a combinatorial approach to RNA structure determination in order to efficiently compute all possible non-pseudoknotted structures for an RNA sequence. Swellix can analyze an RNA up to 418 nucleotides on the Blue Waters Supercomputer, and thus demonstrates that computer time and nodes are the only limits to a combinatorial approach to the RNA folding problem. The current capabilities of modern supercomputers and efficient tools such as Swellix make combinatorially complete searches of RNA conformational space a realistic option and dispel the myth of impossible RNA computations. Swellix is capable of incorporating unique constraints, such as the minimum number and length of helices, from crystallography or cryoelectron microscopy experiments. The possible biological applications for Swellix are demonstrated by computing the entropic benefits of reducing conformational space with modified nucleotides in tRNA folding and motif abundance in HERV RNA folding. Swellix reveals protein and drug binding motifs that occur in the entire ensemble but do not occur in the predicted minimum free energy or centroid structures. Thus, Swellix provides an alternative approach to RNA structure analysis when the assumptions of free energy minimization do not apply or when multiple conformations are present.