Saccharomyces cerevisiae shows that RNA polymerase II has unique activity profiles at both gene ends, which are highly dependent on positioned nucleosomes. This is the first demonstration of the in vivo influence of positioned nucleosomes on transcription elongation. The particular features at the 5′ end and around the polyadenylation site indicate that this polymerase undergoes extensive specific-activity regulation in the initial and final transcription elongation phases. The genes encoding for ribosomal proteins show distinctive features at both ends. BioGRO also provides the first nascentome analysis for RNA polymerase III, which indicates that transcription of tRNA genes is poorly regulated at the individual copy level. The present study provides a novel perspective of the transcription cycle that incorporates inactivation/reactivation as an important aspect of RNA polymerase dynamics.The particular behaviour of eukaryotic RNA polymerases along different gene regions and amongst distinct gene functional groups is not totally understood. To cast light onto the alternative active or backtracking states of RNA polymerase II, we have quantitatively mapped active RNA polymerases at a high resolution following a new biotin-based genomic run-on (BioGRO) technique. Compared with conventional profiling with chromatin immunoprecipitation, the analysis of the BioGRO profiles in  In vivTATA box ,6. This TATA box , but alsTATA box ). CurrenTATA box ,10, RNA TATA box ,12. In tTATA box . When RNTATA box . This paTATA box  or rescu(in vivo  and in vin vitro .in vitro (reviewed in (in vivo in specific genes (in vivo (Most studies into RNA pol elongation through nucleosomes have been conducted iewed in ) or in vic genes . In the ic genes ). In linic genes –20. Eachic genes ,21). Foric genes –20. Theyic genes ,23 detecic genes ,25. Theric genes ,26. RNA ic genes –29. RNA  and tiling microarrays, which enables the specific analysis of active RNA pol molecules for a large set of genes. This novel approach, which we called BioGRO, does not require sample amplification, so it strictly avoids any interference from contaminating mature RNA molecules that can affect the results of previous methods  and derived from the S288c background. BQS252: 600 = 0.55. Cells were collected by centrifugation and frozen in liquid nitrogen. Frozen pellets were transferred immediately to −20°C. After at least 3 h, cells were thawed on ice and permeabilized with 10 ml of 0.5% sarkosyl solution. Once permeabilized, cells were split into two aliquots: the first (control sample) was kept on ice, whilst the second (digested sample) was treated with RNase A (Roche). RNase trimming was achieved by incubating cells with 32 μl of RNase A (10 mg/ml) dissolved in 3.2 ml of 0.5% sarkosyl solution under agitation conditions for 10 min at 30°C. In order to remove RNase, cells were washed three times with 50 ml of 0.5% sarkosyl and were then transferred to an eppendorf tube. The cells from the control sample were resuspended in 120 μl of water, whilst the samples treated with RNase were resuspended in 115 μl of water plus 5 μl of RNase OUT to protect the integrity of nRNAs from any residual RNase that might be present. Then 120 μl of 2.5X transcription buffer , 6 μl 0.1 M DTT (dithiothreitol), 16 μl of NTPs (nucleotides triphosphates) ((adenosine triphosphate) ATP, (cytidine triphosphate) CTP and (guanosine triphosphate) GTP, 10 mM each) and 20.25 μl of 10-mM Biotin-11-UTP (Ambion) were added. The run-on reaction was performed by incubating the mixture at 30°C for 5 min. The reaction was stopped with 1 ml of cold water. Cells were kept on ice for 5 min, harvested by centrifugation and the supernatant (containing unincorporated nucleotides) was removed.Briefly, run-on reactions were performed as described elsewhere , but witRNA extraction was done using the ‘MasterPure Yeast RNA Purification Kit’ (Epicentre) following the manufacturer's instructions. Once extracted, genomic DNA was removed by digesting with 2 μl of RNase-free DNase I (Roche) for 30 min at 37°C. Purified RNA was resuspended in 32 μl of water and was spectrophotometrically quantified.In order to selectively inhibit the enzymatic activity of RNA pol II, the permeabilized cells were incubated 5 min before RNase A degradation with 50 μM of α-amanitin (Sigma). The inhibitor was added again (at the same concentration) as part of the run-on reaction mixture.RNase-degraded labelled nRNAs (<200 nt on average) were separated from the larger, unlabelled molecules using the miRNA NucleoSpin microRNAs isolation kit (Macherey-Nagel) following the instructions in Section Purification of siRNA from DICER and large dsRNA reactions: purification of siRNA from DICER and large dsRNA reactions. Small RNAs were eluted from the column in 30 μl of water and were spectrophotometrically quantified .et al. . An ECL chemiluminescent system  was used to develop the signal, which then was acquired with the ImageQuant LAS 4000 system .Once cross-linked, the biotinylated RNA-containing membranes were treated as follows: the membrane was blocked with blocking buffer  for 20 min at room temperature, incubated with Streptavidin-HRP  for 10 min at room temperature (RT), washed twice with blocking buffer and twice with wash buffer  screens and scanned with a Phosphorimager system (Fuji).For the size separation of biotinylated RNAs, 8% polyacrylamide gels (TBE-urea 7 M) were used. Separated RNAs were transferred to nylon membranes by the ‘Wet’ method with the MiniProtean Tetra System (BioRad) in 0.5X TBE for 90 min at 100 V in a cold room. The membrane was then dried between paper sheets, cross-linked with UV light and processed as described in the previous section.From each BioGRO sample, at least two biological replicates  were hybridized to Custom Tiling Array  . Then 5 For the Rpb3 ChIP-on-chip experiments, ChIP was performed as previously described . ImmunopcDNA hybridizations for ‘total RNA’ samples were taken from .et al., in preparation).Translational positioning of nucleosomes was mapped genome wide by digesting formaldehyde-fixed chromatin with micrococcal nuclease (MNase). Mononucleosomal DNA from each sample, used to create sequencing libraries, was subjected to 36-nucleotide single-read sequencing in a Solexa Genome Analyzer IIx. Nucleosome maps were generated with the DANPOS comprehensive bioinformatics pipeline . MNase-dS. cerevisiae reference genomic map, the Integrated Genome Browser (http://bioviz.org/igb.html) software was used. The ‘TilingArray’ Bioconductor (http://www.bioconductor.org/packages/2.11/bioc/html/tilingArray.html) and custom R scripting packages were used for the metagene analysis, scatterplot generation and k-means clustering. The ‘simple tiling array analysis of Affymetrix ChIP-chip data’  with the signal detection parameters set by default. To visually inspect the hybridization signals in relation to the annotations from the ’ STARR,  was alsoAll the BioGRO samples were normalized against genomic DNA (YJM789 strain) hybridized into the same custom Affymetrix arrays as in . The ArrThe total RNA hybridization used for plotting and comparing against the BioGRO, RNA pol II and nucleosome data was downloaded from . The Arr33P-UTP to label nRNA, which is hybridized to nylon macroarrays et al. (We modified the existing Genomic-run on (GRO) protocol to generate biotin-labelled nRNAs, which we then used to directly hybridize Affymetrix tiling arrays. We called it Biotin-GRO or BioGRO Figure . In ordeet al.  and founTo confirm that the observed fluorescence signal was due to nRNA, we compared the BioGRO results with those of conventional cDNA hybridization. Supplementary Figure S4 shows that the BioGRO signal was present at both introns and exons with a similar intensity, whereas the cDNA signal was absent in introns, as expected. BioGRO was also able to detect antisense (AS) transcription . It is iThe average BioGRO signal for 809 yeast ORF-containing genes, rRNA gene and all the RNA pol III genes in a wild-type strain growing in the log phase was at least >6 times above the background. For some analyses, we focused on this set of confident genes to improve the profiles quality. The BioGRO high-resolution data can also be used to quantify nTR as in conventional radioactive GRO experiments. We averaged signal intensity within the ORF to obtain a single nTR value. The plots of those nTR against the transcriptomics data from other sources  showed that our per gene signal best correlated with previous GRO results  whilst, To further prove that BioGRO measures nRNA, we treated cells with α-amanitin, a peptide which, at low concentrations, specifically inhibits RNA pol II . The celOne advantage of BioGRO over previously published methods for nRNA detection is that RNA pol activity itself is utilized to map its position. Since the use of tiling arrays allows BioGRO to determine the transcriptional activity of RNA pol II at a high resolution, we decided to focus on those regions where transcription regulation was expected, such as the transcription start site (TSS), the polyadenylation site (pA) and intron boundaries. In the 5′ region, the BioGRO average profile clearly differed from the mRNA profile Figure ). This iet al.  showed a relative accumulation of elongating RNA pol II in the 3′ end of the transcribed region. This result is compatible with the observation made by Carrillo-Oesterreich et al.  who, baset al. ,54,55.BioGRO profiles showed a regularly spaced peak-and-valley shape from the 5′ start towards the gene body Figure ,18, but In order to check if the BioGRO profile reflected interplay between RNA pol and nucleosomes, we decided to separate genes by their 5′-end nucleosomal profiles. We used the improved MNase genome mapping data of yeast nucleosomes  to dividisw2 mutant strain. Isw2 is a chromatin remodeler that spatially arranges nucleosomes at both ends of the transcribed region in a subset of yeast genes  sites Figure  was coheBioGRO also provided us with information about other nuclear RNA polymerases. Figure S. cerevisiae tRNA introns were 15% poorer in G+C content than exons. Thus it seems plausible that RNA pol III slows down in introns because the higher relative A+T content facilitates DNA unwinding and destabilizes the elongating bubble at the intron  and theS. cerevisiae following a new technique. This BioGRO method improves preceding techniques .We have quantitatively mapped active RNA polymerases at a high resolution in the yeast chniques ,19,22,32et al.  have been published for animal cells ,44 and fet al. . In thatet al. . Howeveret al. ,19 or Chet al. ; 71 techome see ,81)..et al.  and extends beyond the TSS and pA sites Figure . This reet al.  site, which can be released by nuclear exosome action .The existence of a run-on signal before the mapped TSS Figure  may indiet al.  publisheet al. ,59 and het al. , whereaset al. . This RNet al. . Thus thpability . The res  or changed position (Drosophila) in the absence of TFIIS, it has been suggested to be caused by enhanced backtracking. Our results also evidence an antinucleosomal profile for RNA pol II-specific activity  across the 5′ region showed the absolute maximum on nucleosome +1 Figure . This waant H2AZ . This nuant H2AZ . Our resant H2AZ . The difant H2AZ , a histoant H2AZ . Therefoenhancer .The comparison made between the high-resolution genome-wide profiles obtained after different RNA pol II mapping techniques offers a unique opportunity to discriminate between the transcription modes followed by different genes. For instance in Supplementary Figure S7, we show that the RP, TATA and TATA-like genes present different BioGRO and Rpb3-ChIP profile shapes. These differences not only imply that RP genes have a smaller proportion of active RNA pol II than the rest  but also confirm the diversity of different gene regulons in transcriptional elongation terms ,51.S. cerevisiae and accounts for ∼14% of total transcription . To datcription ; reviewecription ), there cription . Using tcription  of 3–6 mcription ). As expcription . Therefoin vivo, a strong dependency of RNA pol II elongation activity on nucleosome positioning. Such nucleosome dependence causes gene-specific profiles and reveals that RNA pol II-dependent genes differ not only at the transcription initiation level, as generally acknowledged (In summary, high-resolution run-on mapping reveals significant quantitative variations in RNA pol II and III activity along their genes in relation to initiation and termination phases, and reveals, for the first time owledged , but alsThe GEO accession number for new the BioGRO and RNA pol II ChIP data is GSE58859.Supplementary Data are available at NAR Online.


RNA molecules play different roles in coding, decoding and gene expression regulation. Such roles are often associated to the RNA secondary or tertiary structures. The folding dynamics lead to multiple secondary structures of long RNA molecules, since an RNA molecule might fold into multiple distinct native states. Despite an ensemble of different structures, it has been theoretically proposed that the separation between the 5′ and 3′ ends of long single-stranded RNA molecules (ssRNA) remains constant, independent of their base content and length. Here, we present the first experimental measurements of the end-to-end separation in long ssRNA molecules. To determine this separation, we use single molecule Fluorescence Resonance Energy Transfer of fluorescently end-labeled ssRNA molecules ranging from 500 to 5500 nucleotides in length, obtained from two viruses and a fungus. We found that the end-to-end separation is indeed short, within 5–9 nm. It is remarkable that the separation of the ends of all RNA molecules studied remains small and similar, despite the origin, length and differences in their secondary structure. This implies that the ssRNA molecules are ‘effectively circularized’ something that might be a general feature of RNAs, and could result in fine-tuning for translation and gene expression regulation. RNAs play central roles in coding, decoding and gene expression regulation ,2. Moreoper se . Such roper se ,5. Most per se ,7. Calcuncreases –10. Thisequences  by appliequences  and the equences . Based o .The smFRET measurements use a modified Nikon E800 microscope. Briefly, a single-line 514-nm laser  was directed through a beam expander into the back of the microscope. A 100× Plan-Apochromat objective  focused the beam down to a tight focal spot within the sample chamber. Collected back fluorescence was separated from the excitation beam by a band-pass filter 67–118 Technspec  and focused into a 100 μm pinhole. The donor and acceptor fluorescence was separated by a dichroic mirror 540DRLP , filtered  and focused into single photon counting avalanche photodiodes . A SCB-68 card  stores the photon rates in 1 ms bins in a computer. Photon bursts were analyzed using a Matlab  algorithm. To identify single-molecule events, we consider only bins with total photon rates above a background threshold value. FRET efficiency values were computed using the usual equation for the energy transfer efficiency The smFRET system was calibrated by determining the transfer efficiencies of fluorescently end-labeled double-stranded DNA molecules of 10, 13, 16, 19, 20, 21, 22, 25, 28 and 45 bp in length taking into account that a DNA bp has a separation of 0.34 nm. UTP-Alexa Fluor-546 and CTP-Alexa Fluor-647 (Invitrogen) were used as FRET pairs, and they were attached to DNA fragments by Klenow-dependent filling of overhanging Adenines and Guanines on each end of the DNA molecules. Thereafter, DNA fragments were purified using Sephadex G-25 gravity flow columns . Calibration was performed with DNA fragments at a concentration of 90 pM in TE buffer .Trichoderma atroviride, IMI 206040 strain, was grown over a sterilized cellophane sheet on potato dextrose agar (Difco) plates and incubated at 26°C for 48 h in total darkness. The mycelium was collected from the surface of the cellophane with a scalpel and immediately frozen in liquid nitrogen to prevent RNA degradation. Total RNA was isolated using Trizol® Reagent  according to the manufacturer's protocol. Contaminating genomic DNA was removed by DNase treatment using the TURBO RNAse-free kit (Ambion). Complementary DNA (cDNA) synthesis was performed using SuperScript II Reverse Transcriptase (Invitrogen Life Technologies), following the manufacturer's recommendations. The cDNA was quantified with a Nanodrop spectrophometer  and used to obtain the fgen1 (triat1 (phr1 gene was amplified using cDNA obtained from T. atroviride (IMI 206040) mycelia exposed to a 5 min blue light pulse as described elsewhere (chi18-4 (T. atroviride strain P1 (ATCC 74058) setting in plate confrontation assays against the phytopathogenic fungus Rhizoctonia solani as described by (phr-1 and chi18-4 was performed using GoTaq DNA polymerase (Promega) with the following conditions: 5 min at 94°C, 35 cycles of 94°C (30 s), 55°C (30 s) and 72°C (30 s), and a final extension of 72°C for 10 min. The RT-PCR for fgen1 and triat1 was basically as described for phr-1 and chi18–4, only changing the temperature and annealing time, to 45°C for 35 s for fgen1, whereas the temperature for triat1 was 44°C for 38 s. The PCR products were visually inspected by loading 4 μl onto 1% agarose gel. Each PCR product was cloned into pBlueScript SK II (+) vector using KpnI (fgen1) or ApaI-BamHI . fgen1 was cloned also as antisense strand (fgen1-). Constructed plasmids were transformed into Escherichia coli strain TOP10F′ Cl2Ca competent cells. Plasmid DNA extraction from E. coli was performed by the alkaline lysis method and precipitated with ethanol. CCMV, RNA2 and RNA3 were already cloned into pMJ5 using StuI-XbaI (In vitro RNA transcription was performed using the T7 RiboMax kit as described by the manufacturer (Promega). DNA templates were removed by treatment with RNase-Free Dnase RQ1 (Promega). No 5′-cap nor 3′-Poly(A) tail were added to any of the mRNAs synthesized.To obtain the different fungal messenger RNAs (mRNAs), he fgen1  and tria  RNA molecules were dephosphorylated using alkaline phosphatase, calf Intestinal (NEB), (ii) a thiophosphate group was added into the dephosphorylated 5′-end using ATPγS and T4 polynucleotide kinase (NEB), (iii) the thiol-reactive C-2 maleimide-Alexa Fluor 647 was allowed to react with the thiophosphate for 30 min at 65°C. All labeled RNA molecules were phenol extracted and precipitated using absolute ethanol, and resuspended either in TE buffer (magnesium-free condition) or TM buffer .On all the RNA molecules, Alexa Fluor-546 on the 3′-end and Alexa Fluor-647 on 5′-end were used as FRET pairs. Custom made r-Adenosine-3′,5′-(bis)phosphate-8-[(6-Amino)hexyl]-amino-Alexa Fluor-546  was linked to the 3′-OH of the RNA molecule by the action of T4 RNA Ligase in 10 mM MgClR0 and γ that depends on the quantum yields and detection efficiencies for both donor and acceptor  and plotted as a function of the fluorophore separation. The DNA calibration curve for the smFRET signal is shown in Figure acceptor . The fit account .−4 nm/nt in TM buffer. Increasing the RNA length by a factor of 10, as in our experiments, changes the fluorophore separation by less than 50%. Despite the important role of Mg ions on RNA tertiary stability  tails. However, despite all those differences we find no significant difference in the end-to-end separation in all tested mRNAs. Although different biological roles of sense-antisense RNA have been observed and proposed . The error bars in Figure Out of the 11 RNA sequences studied, 8 RNA sequences were monocistronic, including 1 antisense complementary sequence, and 3 were dicistronic  is much2  with a p2  that resℒ) equivalent to 11–19 nt for range 1 and 9–16 nt for range 2. If instead we use a persistence length of 2.1 nm and the end-to-end distribution (The separation between fluorophores we found is in the range between 6.5 and 10.5 nm (range 1). To extract the separation between 5′-3′ ends (range 2) we need to subtract from range 1 the contribution coming from the fluorophore linker lengths Figure . If we cribution  we obtairibution –17. It iribution . Howeverribution , nor theThe length of the majority of conserved proteins found in eukaryotes and prokaryotes species are between 70 and 1500 aminoacids (aa) in length –40. TherSupplementary Data are available at NAR Online.


Hantavirus; family Bunyaviridae) is an emerging pathogen associated with cases of acute kidney injury in several countries across the globe. We report here the whole-genome sequence of the Tchoupitoulas strain of Seoul virus isolated in New Orleans, LA.Seoul virus (genus  Bunyaviridae family; as such, they are enveloped negative-sense single-stranded RNA viruses with a trisegmented genome. The large (L) segment encodes a viral RNA polymerase, the medium (M) segment encodes a glycoprotein precursor, and the small (S) segment encodes the nucleocapsid protein (Rattus norvegicus (brown rat) (Hantaviruses are members of the  protein . Seoul vown rat) . SEOV prown rat) . Six casown rat) . Within own rat) –7. Desown rat) . SEOV stown rat) . Currentown rat) . Here, wThe virus was isolated in 1984, propagated in Vero E6 cells by the staff of the Rare and Imported Pathogens Laboratory in 1992, and deposited into the National Collection of Pathogenic Viruses in 2001, under catalog no. 0107221v. Viral RNA was extracted using the QIAamp viral RNA minikit (Qiagen), following the addition of 10 ng/µl GenElute-linear polyacrylamide (Sigma-Aldrich). The sample was digested with Turbo DNase I (Thermo Fisher Scientific) and purified using the RNA Clean & Concentrator kit (Zymo). Single-primer isothermal linear amplification (SPIA) cDNA was prepared from total RNA using the Ovation RNA-seq version 2 system (NuGen). One and a half nanograms of SPIA cDNA was prepared for sequencing, according to the Illumina Nextera XT protocol, and the 2 × 150-bp paired-end library was run on a MiSeq (Illumina).Reads were quality trimmed to a minimum score of Q30 across the read. BWA (version 0.7.5)  was usedKU204958 to KU204960. This paper describes the second version of the sequences.The whole-genome sequences have been deposited in GenBank under the accession numbers


Intriguingly, yeast Rat1/Rai1 does not terminate Escherichia coli RNAP, implying that a specific interaction between Rat1/Rai1 and RNAPII may be required for termination. Furthermore, the efficiency of termination increases as the RNA transcript undergoing degradation by Rat1 gets longer, which suggests that Rat1 may generate a driving force for dissociating RNAPII from the template while degrading the nascent transcripts to catch up to the polymerase. These results indicate that multiple mechanistic features contribute to Rat1-mediated termination of RNAPII.Within a complex with Rai1, the 5′-3′ exoribonuclease Rat1 promotes termination of RNA polymerase II (RNAPII) on protein-coding genes, but its underlying molecular mechanism is still poorly understood. Using  Three classes of RNA polymerases (RNAPs) exist in eukaryotes: RNAPI transcribes rRNAs, RNAPII transcribes mRNAs and a majority of non-coding RNAs , and RNAPIII transcribes 5S rRNA and tRNAs. Accurate transcription termination is important because early or late termination may disrupt normal gene regulation and produce abnormal RNAs, which may be harmful to cellular fitness.Saccharomyces cerevisiae, there are at least two distinct pathways for RNAPII termination  that might facilitate the access of Rat1 to RNAPII by interacting with the connective tissue disease (CTD), which is phosphorylated at serine 2  –7. Rat1 serine 2 ,9. A stuin vitro . Howevern + 1 template DNA base (n + 1 NTP) can be incorporated through template misalignment, leading to a temporary movement of a DNA base to an extrahelical position (−5) (RPB9 or TFIIS (DST1) gene significantly decreases the transcriptional fidelity of yeast RNAPII -driven power strokes –13, freqposition ,15. If nposition ,15. The ion (−5)  because ion (−5) –20. Suppt RNAPII . NTP mist RNAPII ,23. In aive site ,25. Seveive site ,27, but in vitro assays, we show that when an NTP misincorporates, RNAPII becomes catalytically disrupted and is more efficiently terminated by Rat1/Rai1. These results suggest that the pausing of RNAPII, which often occurs in vivo near the termination site, might generate a similar configuration that facilitates termination by Rat1/Rai1. Although NTP misincorporation also catalytically disrupts Escherichia coli RNAP, Rat1/Rai1 does not terminate it, indicating that a specific interaction between Rat1/Rai1 and RNAPII may also be important to induce termination. Additionally, we found that RNAPII termination efficiency depends on the length of RNA transcript undergoing degradation by Rat1, which suggests that Rat1 may generate a driving force to dislodge RNAPII while degrading the nascent transcripts to catch up to the polymerase.In this study using S. cerevisiae Rat1, Rai1 and Rtt103 were cloned into pET21b  tagged with hexahistidine (6xHis) . Human Xrn2 (hXrn2) was cloned from cDNA (Harvard DNA Resources) into pET21b tagged with 6xHis. Catalytic mutants of rat1  and rai1  were prepared by site-directed mutagenesis. Xrn1 was purchased from NEB (cat# M0338S).The genes encoding for The rat1 E203A/D233A/D235A (rat1EDD) mutant was generated by polymerase chain reaction mutagenesis and cloned into either pET21b or pRS41H. Rtt103 was cloned into pRS415 or pRS423 and transformed into a yeast strain (rat1Δ::KanMX/pAJ202-Rat1/pRS41H-rat1EDD) to test whether extra copies of the Rtt103 gene could rescue the rat1EDD lethal phenotype when wild-type Rat1 was shuffled out by 5-Fluoroorotic Acid (5-FOA) selection.The 6xHis-tagged recombinant proteins of Rat1, Rai1, Rtt103 and TFIIS were over-expressed in a BL21 CodonPlus (DE3) RIL (Stratagene) strain via IPTG induction (0.25 mM IPTG) at 25°C for 6 h.2, 1 mM Dithiothreitol (DTT), 10% (v/v) glycerol), quick frozen in liquid nitrogen and stored at −80°C. The Rat1 variants were purified by the same procedure. For Rtt103, cells were lysed by sonication in freezing buffer B  and the cell lysate was sequentially applied to Ni-NTA, MonoQ and Superose 6 columns. The purified protein was eluted in final buffer B  glycerol). For TFIIS, purification procedure was performed as described previously ). The lysates were cleared by centrifugation and sequentially applied to an Ni-NTA agarose column (Qiagen), a HiTrap Heparin HP affinity column , and a MonoQ 10/100 GL anion exchange column  and underwent Superose6 10/300 GL size-exclusion chromatography. The purified proteins were eluted in final buffer  glycerol).The Rat1/Rai1/Rtt103 and rat1EDD/Rai1/Rtt103 complexes were purified using the same procedures but with slightly different buffer conditions. In the affinity and ion-exchange chromatography steps, Tris–HCl, pH 7.6 at 24°C was used. In the Superpose 6 size-exclusion chromatography step, protein complexes were eluted with low-salt storage buffer  was fermented and purified as described previously (For eviously . The Rpbeviously .32P] uridine 5′-triphosphate (UTP) by RNAPII.Transcription-competent elongation complexes were assembled as previously described . The tem2, 1 mM DTT) and the EC bound beads were resuspended in reaction buffer . A mixture of four NTPs was added to EC and the samples were incubated for 30 min at 28°C to allow RNAPII elongation. The reactions were stopped and RNA samples were analyzed by 7 M Urea-polyacrylamide gel electrophoresis. The radioactively labeled RNA was detected by phosphorimaging .For RNA digestion, 6 pmol of Rat1/Rai1 was added to the ECs and the samples were incubated for 1 h at 30°C in the presence or absence of each NTP. After the reaction was complete, the nuclease and NTP were removed with washing buffer  plate (Merck) and developed in 0.6 M KH2PO4 (pH 3.4). The extent of ATP hydrolysis was detected using phosphorimaging.An adenosine triphosphatase (ATPase) activity assay was performed as previously described . Each 20in vitro transcription termination assay that was previously developed by Cramer etal. . Once it had interacted with Rat1/Rai1, if RNAPII was terminated, the RNA would no longer be protected by the polymerase and would be degraded by Rat1. In contrast, if RNAPII was not terminated, the template-bound polymerase would continue to elongate in the presence of NTPs, generating an ∼54/55 nt run-off transcript . Th. Thin vit Figure . The RNAy Figure .in vitro in the presence of ATP. Notably, the addition of ATP with RNase If did not lead to a similar decrease in the amount of RNA, indicating that this ATP-dependent effect seems to be unique to Rat1/Rai1 in triggering RNAPII termination  readily hydrolyzed ATP, Rat1/Rai1 did not show ATPase activity  that remained were higher than those observed in the ATP-treated controls Figure , indicatn + 1 and n + 2 positions of the template DNA strand . Supporting this hypothesis, Xrn1 processively degrades RNA substrates containing stem-loop structures  Figure . We primTo determine which protein induces the RNA length effect observed, the same experiment was performed without Rai1. Similarly to Rat1/Rai1, Rat1 terminated RNAPII more effectively as it degraded longer RNA Figure , implyinE. coli cells. Because rat1 EDD does not have 5′-3′ exoribonuclease activity, the elongation efficiency  rather than the remaining RNA level was measured to determine the extent of RNAPII termination. rat1EDD/Rai1 did not degrade RNAs or decrease RNAPII elongation  form of Rat1 by mutagenizing three conserved acidic residues in the active site . This rat1 mutant (referred to as rat1EDD) was co-expressed and purified as a complex with Rai1 in n Figure . Howevern Figure . Furthern Figure . This fiin vitro system  are relatively far from the p(A) sites, degradation of the downstream RNA transcript and subsequent specific RNAPII interaction by Rat1 would be sufficient to trigger termination. However, if the p(A) sites and TTSs are in close proximity, pausing of RNAPII by either NTP misincorporation or pausing specific sequences might help Rat1 to displace the polymerase. However, all these features would be important factors affecting Rat1 termination of transcription at the 3′-end of genes.Supplementary Data are available at NAR Online.


Globally, farmed seaweed production is expanding rapidly in shallow marine habitats. While seaweed farming provides vital income to millions of artisanal farmers, it can negatively impact shallow coral reef and seagrass habitats. However, seaweed farming may also potentially provide food subsidies for herbivorous reef fish such as the Siganidae, a valuable target family, resulting in increased catch. Comparisons of reef fish landings across the central Philippines revealed that the catch of siganids was positively correlated to farmed seaweed production whilst negatively correlated to total reef fish catch over the same period of time. We tested the generality of this pattern by analysing seaweed production, siganid catch, and reef fish catch for six major seaweed-producing countries in the tropics. We hypothesized that increased seaweed production would correspond with increased catch of siganids but not other reef fish species. Analysis of the global data showed a positive correlation between farmed seaweeds and siganids in Southeast Asia  but not Africa (Tanzania and Zanzibar), or the Western Pacific (Fiji). In Southeast Asia, siganid catch increased disproportionately faster with seaweed production than did reef fish catch. Low continuity, sporadic production and smaller volumes of seaweed farming may explain the differences. The commercial cultivation of seaweeds occurs in approximately 35 countries around the world and provides a variety of products that, in 2011, produced 21 million tonnes with a total annual value of US$7.35 billion )Regional data on seaweed production and catches of herbivorous siganids and common reef fish species (in tonnes per year) have been compiled by the FAO since 1950 in varying levels of detail. We extracted these data for six countries in three regions: Southeast Asia ; Africa ; and Western Pacific (Fiji) . A rangehttp://www.fao.org/fishery/statistics/software/fishstatj/en). These statistics mainly represent commercial operations as artisanal, subsistence, and recreational fisheries are not typically reported . Inby nation, and regby nation. In Afriation[-2 , with faation[-2 . HoweverS. canaliculatus is the major siganid targeted by fisheries. It tends to settle directly on algal beds rather than coral reefs and consumes macroalgae [Siganus sutor, common in the Indian Ocean Region and east Africa, while known to settle in algal beds like S. canaliculatus, exhibits a dietary preference for turf algae [S. sutor as they would for S. canaliculatus. In the absence of taxonomic resolution within the FAO global database, regional studies will help elucidate the relative importance of seaweed farming in terms of the provision of shelter vs. the provision of additional food sources.Differences in the relationships between siganids, reef fish, and seaweed production in Southeast Asia and Africa may also reflect differences in the ecology and feeding strategies of the siganids found in these regions ,35,66–69croalgae , and therf algae –73, suggS. sutor in the Indian Ocean appear to be enhanced by the presence of algal dominated degraded reefs [S. sutor’s dietary preference for turf algae within these systems. As seaweed farms result in the clearing of turf algae, seaweed farming may present a dietary penalty for S. sutor rather than a benefit as it does for S. canaliculatus. Such a scenario would imply that the effects of seaweed farming may vary depending on location and species, and highlights the need for further investigation into both the ecological and dietary strategies of affected fish assemblages.The differences in feeding ecology of the main targeted species between the two regions may also underpin the lack of relationship between siganid catches and seaweed production in the Eastern Indian Ocean. Siganid fisheries for ed reefs –73, reflAnother possibility is that siganid catch may not be a direct result of seaweed farming itself, but an effect of algal domination as a result of coral reef degradation present where seaweed farming tends to occur, but initiated before commercial seaweed farming. Coral reefs in Southeast Asia show significantly higher levels of human impact from a variety of activities than either Fiji or Africa , and degIn the face of declining returns from fisheries depleted by over-extraction, destructive fishing practices, and habitat degradation over an extended period of time , the potS1 Table(DOCX)Click here for additional data file.


RNA editing is a mutational mechanism that specifically alters the nucleotide content in transcribed RNA. However, editing rates vary widely, and could result from equivalent editing amongst individual cells, or represent an average of variable editing within a population. Here we present a hierarchical Bayesian model that quantifies the variance of editing rates at specific sites using RNA-seq data from both single cells, and a cognate bulk sample to distinguish between these two possibilities. The model predicts high variance for specific edited sites in murine macrophages and dendritic cells, findings that we validated experimentally by using targeted amplification of specific editable transcripts from single cells. The model also predicts changes in variance in editing rates for specific sites in dendritic cells during the course of LPS stimulation. Our data demonstrate substantial variance in editing signatures amongst single cells, supporting the notion that RNA editing generates diversity within cellular populations. RNA editing rate detected from bulk RNA-seq data can vary widely. Here, by constructing a hierarchical Bayesian model, the authors report substantial variance in editing signatures detected by RNA-seq data from both single cells and a cognate bulk sample. The central dogma of biology assumes faithful transmission of information from DNA to RNA to protein. However, changes in DNA methylation or the chromatin state strongly affect not only the flow of information but also its heritability. In addition to these epigenetic alterations, there has been growing interest in investigating the epitranscriptome, or modifications that occur at the RNA level, which can affect both the regulation of gene expression, and what is actually being expressed by directly altering the decoding of proteins.One type of modification of interest is RNA editing, which involves the dynamic alteration of specific nucleotides in transcribed RNA. The advent of RNA-seq technology has facilitated the identification of RNA editing events in the transcriptome, and numerous studies cataloguing such events in diverse systems have been published125789Specific RNA editing events found from RNA-seq are typically presented in the literature with their detected editing rates, that is, the number of edited reads divided by the total number of reads mapped to a specific site. RNA editing rates vary widely, from <1 to >90% per transcript per site; in our own analyses using stringent filtering, putative C-to-U sites are edited at an average of ∼15−20% . To dateTo test these hypotheses, we sought to compare editing frequencies derived from population-based RNA-seq data with RNA-seq data from single cells. There are a number of factors that affect our ability to detect editing, including site mappability, editing frequency and coverage. RNA editing detection, especially of sites that are not highly edited, is complicated by variations in capture efficiency. This is not a concern in conventional bulk RNA-seq, which is performed using a large amount of cells or tissue, since the loss of even a large portion of the starting material may be tolerated if the remaining fraction can still provide a representative sample of the population's gene expression profile. But these sampling issues substantially impact the ability to detect editing when libraries are made from single cells. As noted by ref. Here we have compared APOBEC1-mediated C-to-U RNA-editing rates derived from single cells with ‘bulk' editing rates recovered from populations of cells. To account for the stochasticity inherent in single-cell RNA-seq, we have taken a statistical approach: we have used a hierarchical Bayesian method to model the levels of variability of rates per site across single cells and in bulk samples, and to quantify the posterior variance of editing rates in single cells. Our approach reveals that while some transcripts are edited with low variance across cells (as predicted by Gommans and Maas), others exhibit high editing rate variance across cells (as hypothesized by Pullirsch and Jantsch). The existence of the latter set of transcripts supports the hypothesis that ‘bulk' RNA editing represents a population average of cells that show a wide spectrum of editing rates. Our work suggests that the sequence diversity contributed by RNA editing might provide subsets of cells with distinct informational content. Further, our work implies that RNA editing might underlie or anticipate the functional heterogeneity apparent in populations of cells of monocytic lineage , the mechanism for which is currently not understood.APOBEC1−/− bulk RNA-seq data sets, using an in-house pipeline , and there appears to be a wide range of editing rates for some specific sites . For insj as consisting of a stack of coins (with each coin corresponding to the same site on a different copy of the transcript), each having the same probability of falling on ‘Heads', denoted by pj. Finally, we can think of the population of cells as a bag full of stacks of coins, where the probability of ‘Heads' for each stack is drawn from an unknown distribution on . The variance of this distribution quantifies the diversity among editing rates of different cells (or stacks of coins). The single-cell experiments correspond to randomly picking J stacks from the bag and tossing some of their coins, whereas the bulk experiment corresponds to randomly picking a large number of individual coins from the bag, after emptying all the stacks in and mixing them together.The biological problem to be modelled can be described in the following statistical context . Similarly, we denote by x, n and p the number of edited reads, total number of reads and editing rate, respectively, for the bulk RNA-seq experiment. v will be the variance of editing rate among cells.More formally, for a fixed genomic coordinate and given single cell The first component of our hierarchical model is a binomial distribution:xj ‘Heads' when tossing nj coins, where pj is the probability of getting ‘Heads.' In this case, the binomial distribution is a natural choice. The second component of the hierarchy is used to couple together the different editing rates {pj}Jj=1, throughUsing our analogy, this models the probability of observing Here Beta represents the beta distribution in the non-standard mean/variance parametrization, as detailed in the J single cells are randomly sampled from the bulk RNA-seq experiment and that the number of cells n comprising the population of a ‘bulk' RNA-seq data set is large (at least 105 cells), the mean editing rate is well approximated by its standard mean estimatorAssuming that the n that comprise the population of a ‘bulk' RNA-seq data set is typically large, the mean editing rate is accurately approximated by its standard mean estimator.That is, since the number of cells v∈ is highly interpretable, with v=0 corresponding to identical editing rates across cells (no variability) and p(1−p) corresponding to the largest possible editing rate variability allowed by the model.Note that pj}Jj=1 and their variance v. Because we assume that the edited sites can be treated independently, we can restrict attention to just a single genomic location, for which we can estimate the posterior distribution by applying Bayes' rule and the appropriate Markov chain Monte Carlo algorithms , we take a Bayesian approach to construct ‘penalized complexity' (PC) priors, as proposed recently inv). PC priors are therefore well suited to investigate the scientific null hypothesis (v=0).An important part in building a successful Bayesian model is the choice of the prior. Formally, the prior distribution encodes our P(d)=λeλd− on the distance v)||(0)) is the Kullback–Leibler divergence between the distributions(v) and(0). This explicitly penalizes model complexity, quantified in terms of distance to the base model. Since the Kullback–Leibler distance is parametrization invariant, it follows that so is the PC prior P(v). The prior specification is completed by assuming that the probability of the variance being greater than some value vL is smaller than a chosen threshold—in this case, 0.01. In mathematical notation, P[v>vL]<0.01 (vL=0.142).More specifically, the PC prior is defined by placing an exponential distribution vL]<0.01 . Resultsv provides a quantification of RNA editing variance. Statistically, this can be formalized through the calculation of highest posterior density (HPD) credible intervals , where a 95% HPD interval C for v means that the probability of v falling in C given the observed data D is at least 95%. Thus, HPD intervals enable direct probability statements about the chance of v falling in C.The posterior density of J=20 cells exhibiting high levels of editing variance for a theoretical site, generated by randomly sampling the number of edited reads to attain effective editing rates only in the region of 0−5% and 95−100%.To test the model, we performed simulations using artificial data sets comprising of To assess the effect of the number of reads mapping to an edited site on inference, simulations were run for varying levels of coverage while fixing all other parameters . The firJ=5 cells appearing to be sufficient to detect high editing rate variance.v (hereafter referred to as simply the posterior variance) is shifted away from zero, as expected for high variance in editing rates, whereas in the latter scenario, it shifts in opposite direction accumulating towards zero. When the variance is high, the posterior editing rates per cell are dispersed. In contrast, they concentrate around the bulk mean of 0.5 when the variance is low. The marginal editing rate histogram exhibits fat tails when the sampled editing rates come from extreme quintiles, while it is peaked around the bulk mean of 0.5 when there is nearly no editing rate variance.To further validate our model, we ran simulations on two distinct data sets: one with very high variance , samplinJ=20 cells and coverage at 20 reads per site. Minimal variance (mass still centred at 0) is observed when sampling single-cell editing rates over the range of 0–20% . We screened the pooled RNA-seq data for genomic common, coding biallelic variants in dbSNP 138, and found that the mouse sequenced was potentially heterozygous at 47 sites . However, coverage across the 24 cells for these 47 sites was poor and the cells exhibited expression from only one of the two alleles for many of the sites, consistent with previous reports19The use of artificial data allowed us to derive certain constraints: minimally, the model requires some level of local read coverage  and a minimum number of cells queried (≥5 cells with a minimum of 20 reads per cell). However, by these standards, most editable sites, identified from bulk RNA-seq data, are insufficiently covered within single-cell data sets to be tested with the model. For example, whereas 275 genes are identified as edited within the bulk macrophage data sets, only 29 of them are consistently detected as expressed in scRNA-seq data , and only 18 sites in 11 genes show sufficient coverage over the editable site by our more stringent guidelines of having at least 20 reads covering a site in at least 20 cells .v=0 is not contained in the 95% HPD interval of the posterior distribution of v for any of the three tested sites. This can be seen as analogous to rejecting the hypothesis ‘there is no variability' with P value that is ≤0.05, and is exactly determined by the area under each curve . The non-zero variance indicates that there is heterogeneity in editing rates amongst the 24 cells profiled ; Anxa5 ; and Cybb . For these sites, we computed the prior and posterior distribution of the variance –PCR amplification protocol, to amplify regions of specific transcripts (containing editable sites) using barcoded RT primers from single cells  co-exist in the population with cells that are hardly edited . Amplicons for Cybb from additional single cells are shown in et al. showed that while these dendritic cells are transcriptionally and genomically homogeneous while at rest, they quickly separate into two groups in response to lipopolysaccharide (LPS) stimulation: a group of ‘early responders' and a group that eventually catches up later in the response. To investigate if RNA editing may be anticipating the emergent diversity of this population, we applied our model to 20 single-cell RNA-seq data sets and the bulk RNA-seq data set for each of the following conditions: 0 (or unstimulated); 1; 2; 4; and 6 h after LPS exposureTo investigate if the model could detect the biological regulation of editing rates, we applied our model to a single-cell RNA-seq data set profiling bone marrow-derived dendritic cells under various stimulation conditionsThe idea of variability in RNA editing rates as a mechanism for increasing functional heterogeneity across cell populations is an intriguing new possibility that has thus far been largely overlooked. Here, using APOBEC1-mediated RNA editing as a model, we demonstrate that there is a significant range in editing across different single cells, supporting the hypothesis presented in ref. To arrive at this conclusion, we used RNA-seq to quantify editing in single cells. The normalization of single-cell RNA-seq libraries is the biggest challenge to accurate quantification, given the high variability in capture efficiency from cell to cellBecause of the shortfalls of these experimental approaches, we have used statistical modelling to deconvolute technical and biological effects in assessing the variability of RNA editing rates across single cells. An assumption of the modelling is that RNA-seq samples from the transcriptome in a completely unbiased manner, when in actuality, RNA-seq libraries exhibit a 3′-transcript and GC-content biasThe model is also robust to errors due to RT–PCR, or sequencing. We note that reverse transcriptase has characteristic features  should improve our ability to detect editing along specific, contiguous transcripts. Future work will be geared towards modelling editing profiles for all editable transcripts per cell, allowing for a comparison of individual cells by editing signatures, in analogy to population classification based on expression profiles.Overall, the model presented here can only be used to build reasonable confidence intervals for sites that are highly covered in single-cell RNA-seq data, which limits the number of sites we can evaluate. Poorly covered transcripts are truly representative of few mRNA molecules, exacerbating issues of sampling, especially in the context of editing . Thus, oet al., submitted), anticipates the phenotypic heterogeneity observed on stimulation.Despite these caveats, we were able to validate experimentally using targeted single-cell RT–PCR the statistical model's prediction of substantial sequence heterogeneity from cell to cell for specific edited sites in macrophages and dendritic cells. This result is especially notable because data from either cell type suggest that there are no significant gene expression differences between individual cells while at rest. Furthermore, we find few if any gene expression differences that are dependent on editing at the population level. However, macrophages and dendritic cells are known to acquire substantially different functional attributes within minutes of stimulationThe statistical framework introduced here has numerous potential applications. The model can be used to determine if the editing profiles of specific sites are regulated (as demonstrated in this study for LPS stimulation), for instance, under different environmental conditions, or between developmental or cell cycle states. This model can also be easily adapted to examine other RNA modifications at single-nucleotide resolution, including ADAR-mediated RNA editing, pseudouridylation and m6A methylation. It may be especially useful in the study of single cells taken from tumours (for example ref. 89GSE48968—see We prepared RNA-seq libraries from bone marrow-derived macrophages (both bulk and single cell). Single-cell and bulk libraries prepared from dendritic cells that were unstimulated. Dendritic cells exposed to LPS for 0, 1, 2, 4 or 6 h from ref. APOBEC1−/−  C57BL/6J mice. To obtain bone marrow-derived macrophages, cells were cultured in DMEM, supplemented with FBS, non-essential amino acids, beta-mercaptoethanol and 20 ng ml−1 macrophage colony-stimulating factor (M-CSF)  on bacterial plates. One day after initial isolation, the cells were replated at 2 million cells per 10 ml media (per 100-mm dish). Half of the media was replaced every 3–4 days. Seven to nine days after initial isolation, the cells were checked for maturation by FACS  and collected.Bone marrow was flushed from femurs of 6- to 8-week-old wild-type  or APOBEC1−/−). A unit of 1 μg of the total RNA collected per condition was then treated with DNase, and then processed using the NEBNext Ultra Directional RNA Library Prep Kit for Illumina .To generate single-cell libraries, wild-type bone marrow-derived macrophages were flowed into a C1 IFC for mRNA seq (10–17 μm) chip using the Fluigidm system. Lysis, RT and PCR were performed using the SMARTer Kit designed for the C1 . The efficiency of chip loading (capture) was confirmed by microscopy and any of the wells that contained either none or more than one cell were noted and discarded from further library preparation and analysis. In all, 24 single cDNA libraries were selected for library preparation on the basis of concentration and size range, as determined via Agilent Bioanalyzer. Sequencing libraries were made from the cDNA using the Nextera XT DNA Sample Preparation Kit (Illumina). To generate conventional bulk RNA-seq libraries, total RNA was extracted using Trizol  from 500,000 to 1,000,000 macrophages of each genotype (wild type and http://broadinstitute.github.io/picard).Libraries were sequenced on the Illumina HiSeq 2000, generating 100-nucleotide, single-end reads. Reads were trimmed for quality and adapters using Trim Galore!, and then aligned to mm10 (using the reference sequences and annotations provided by iGenomes (Illumina)) using Tophat2 ref. , allowinAPOBEC1−/− bulk RNA-seq alignments that were deduplicated using Picard (http://broadinstitute.github.io/picard). For each coordinate that exhibited a C-to-T change only in the wild-type sample  and met a number of stringent quality control thresholds , the angle between the corresponding vectors for the wild-type and knockout were compared. Putative hits were retained if the magnitude of the wild-type vector was at least 15 and the angle between the wild-type and knockout vectors was at least 0.11 radians . Potential sites were also filtered against genomic DNA-derived SNPs in dbSNP138, and removed if they occurred within four base pairs of a splice junction  pileup engine. RNA editing events were then validated by designing primers proximal to the sites of interest, amplifying those regions from cDNA and genomic DNA from both wild-type and APOBEC1−/− cells, and performing colony sequencing.APOBEC1-dependent C-to-U RNA editing was detected from the bulk RNA-seq data sets using a modified version of our previously published bioinformatics pipeline−1 RNAse Inhibitor and 0.18 U μl−1 Superase-In (Ambion). RT–PCR amplification was done with gene specific primers and the OneStep RT–PCR kit (Qiagen), using a modified protocol. Single-transcript molecules were tagged with barcoded gene-specific primers that have an additional universal sequence, used in RT. These primers were then digested with 1 U of Exonuclease I To analyse editing of specific sites in single cells, single wild-type macrophages were sorted into 96-well PCR plates with 5-μl of lysis buffer, containing 0.45% NP-40, 0.36 U μlTranscript specific primer sequences were as follows: B2m primers: RT: 5′-GGCCAGTGAATTGTAATACGACTCACTATAGGNNNNNNAAAGCAGAAGTAGCCACAGGGTTG-3′. PCR-reverse: 5′-TTAAGCATGCCAGTATGGCCGA-3′; Anxa5 primers: RT: 5′-GGCCAGTGAATTGTAATACGACTCACTATAGGNNNNNNGTCGCCAATGTTTTGGAT ACTACCATC-3′. PCR-reverse: 5′-GCGACACATCTGGAGACTATAA GAAGGC-3′; Cybb primers: RT: 5′-GGCCAGTGAATTGTAATACGACTCACTATAGGNNN NNNGAGGGTTTGTGCCTAGTCTTATTGCA-3′. PCR-reverse: 5′-GCATGCGCTCATCTTGTTTTGACTTC-3′. Universal PCR-forward: 5′-GGCCAGTGAATTGTAATACGACTCACTATAGG-3′.j, 20 of 20 reads may be randomly selected as edited, for 100% editing within that cell). An implementation of our model is publicly available as a package written using the Julia programming language: https://github.com/scidom/CellwiseEditingDifferentiation.jlFor full methods, please see GSE74720. The experimental data used to generate the graphs presented in the paper are provided in Bulk and single-cell macrophage RNA-sequencing data are publicly available in the NCBI GEO repository under the accession number Accession codes: Bulk and single-cell macrophage RNA sequencing data are publicly available in the NCBI GEO repository under the accession number GSE74720.How to cite this article: Harjanto, D. et al. RNA editing generates cellular subsets with diverse sequence within populations. Nat. Commun. 7:12145 doi: 10.1038/ncomms12145 (2016).Supplementary Figures 1-6, Supplementary Tables 1-2, Supplementary Methods and Supplementary ReferencesC-to-U edit lists as identified from bone marrow-derived macrophages. Modeled sites are bolded, and editing rates are provided for bulk and single cell data. Editing rates and coverage for both single cells and bulk for the simulated sites are also provided on a separate sheet.APOBEC1 expression levels in TPM from bulk and single cell RNA-seq dataEditing rates and coverage for simulated sites for single cells and bulk ("tube control") samples from LPS-stimulated dendritic cellsValues for 95% HPD intervals on posterior distribution of variances for presented simulationsSRA coordinates for dendritic cells' RNA-seq datasets used from GSE48968


Deletions that compromised the ability of B2 RNA to function as a transcriptional repressor were also tested for their ability to bind directly to Pol II, which enabled us to distinguish regions uniquely important for repression from those important for binding. We found that transcriptional repression requires a pattern of RNA structural motifs consisting of an extended single-stranded region bordered by two stem‑loops. Hence, there is modularity in the function of the stem-loops in B2 RNA—when one stem‑loop is deleted, another can take its place to enable transcriptional repression.B2 RNA is a mouse non-coding RNA that binds directly to RNA polymerase II (Pol II) and represses transcription by disrupting critical interactions between the polymerase and promoter DNA. How the structural regions within B2 RNA work together to mediate transcriptional repression is not well understood. To address this question, we systematically deleted structural regions from B2 RNA and determined the effects on transcriptional repression using a highly purified Pol II Non-coding RNAs (ncRNAs) are now understood to be active participants in regulating the process of transcription ,2. One sBiochemical studies have provided insight into the mechanism by which B2 RNA functions as a transcriptional repressor. B2 RNA binds directly to core Pol II with high affinity and kinetic stability . Mass spWe previously determined a model for the secondary structure of B2 RNA using RNase protection assays and in-line probing, which identified six distinct structural regions  to the repression of Pol II transcription. To do so, we took a two-pronged approach. First, we internally deleted from full length B2 RNA the 3 regions we previously found to be important in the context of a minimal functional piece of B2 RNA. Second, we serially deleted each of the distinct structural regions from the 5’ and 3’ ends of B2 RNA and evaluated the effect on transcriptional repression in vitro. The transcription assays contained highly purified human Pol II, the general transcription factors TBP, TFIIB, and TFIIF, and a strong core promoter contained on a negatively supercoiled plasmid. The promoter was upstream of a G-less cassette that allowed an RNA product of defined length (390 nt) to be transcribed in the presence of ATP, CTP, and UTP. This product was resolved on a gel and quantified.We previously found that structural regions 2, 3, and 4 were each important for transcriptional repression and/or Pol II binding when perturbed in the context of the minimal, functional B2 RNA construct (B2 RNA(81–130) . We firs32P‑labeled full length B2 RNA and B2 RNAΔ(99–113) were incubated with Pol II, and the mixtures were run on native gels to resolve bound RNA from unbound RNA. These electrophoretic mobility shift assays were quantitated to determine the amount of RNA bound to Pol II, and the data were normalized to binding by full-length B2 RNA, which was set to a value of 1.0. Representative raw binding data for the constructs tested in Prior to concluding that the 99-113 region of B2 RNA is important in mediating transcriptional repression, we asked whether the impaired ability of B2 RNAΔ(99–113) to repress transcription was due to impaired ability to bind to Pol II. The other B2 RNA deletions, which maintained full ability to repress transcription were not tested in binding assays , since all previous experiments that we have done with ncRNA repressors have conclusively shown that full repression requires high affinity binding to Pol II ,9,11. ToThe observations in As a strategy to test the model proposed above we systematically truncated B2 RNA by serially removing structural regions from the 5’ end or the 3’ end, and then determined the effect on transcriptional repression. A schematic of the deletion mutants that removed structural regions from the 5’ end is shown at the top of We continued deletions from the 5’ end to determine if other regions were important for transcriptional repression, or Pol II binding. The two remaining B2 RNA constructs that serially deleted regions 3 then 4, B2 RNA (B2 RNAΔ(1–115) and B2 RNAΔ(1–130)), were each significantly impaired in their ability to repress transcription when compared to full-length B2 RNA, as expected. To specifically ask if regions 3 and 4 contribute to repression, we compared each deletion to its next longest neighbor in the deletion series. Only the difference in repression between B2 RNAΔ(1–130) and B2 RNAΔ(1–115), its next longest neighbor, was significant (indicated by two asterisks), suggesting region 4 was important for repression. However, as shown in the plot on the right side of We performed an analogous set of experiments, deleting structural regions from the 3’ end of B2 RNA  was removed, generating B2 RNAΔ(99–178). B2 RNAΔ(99–178) showed a slight but statistically significant reduction in Pol II binding; however this reduction was not enough to explain the entire effect of the deletion on transcriptional repression. This is consistent with the earlier observations that single-stranded region 3 is important for transcriptional repression in the context of both full length B2 RNA B and B2  versus the potency of transcriptional repression. As shown by the dashed line in R2 = 0.18). We further tested this by separately considering the RNAs that potently repressed transcription (plotted as circles) and the RNAs that were impaired for transcriptional repression (plotted as triangles). In both cases there was no correlation between the level of transcriptional repression and RNA size, as shown by the two solid lines and corresponding R2 values.In analyzing the B2 RNA deletions for transcriptional repression, we noticed a trend that the least potent transcriptional repressor RNAs were in general shorter RNAs. Hence, we questioned whether the repressive ability of an RNA would simply correlate with its size. To evaluate this for full-length B2 RNA and each of the 13 deletion RNAs shown in i.e., regions 2 and 4) did not affect transcriptional repression, because deleting these stem-loops in the context of a minimal functional piece of B2 RNA had strong effects ). After 20 min of transcription the reactions were stopped and subjected to denaturing PAGE as previously described [Recombinant human TBP, TFIIB, TFIIF, and native human Pol II were prepared as previously described ,16. Negaescribed . Every et tests. The data for each deletion construct were compared to the data for full-length B2 RNA; in separate t tests the data for each deletion in the 5’ series and the 3’ series were also compared to the data for its nearest larger neighbor. All two-tailed p values < 0.05 were considered to be statistically significant, as indicated by asterisks in the figures.The gels were visualized using phosphorimagery and the bands were quantified using ImageJ software. The fold-repression in reactions containing a B2 RNA (full-length or deletion) was calculated by comparison to transcript production in reactions without B2 RNA. The average fold-repression by B2 RNA across all experiments was 7.8. For purposes of plotting, the fold-repression for each sample was divided by −7.8, which set the relative repression by full length B2 RNA to −1.0 and normalized the repression data for the deletions accordingly. The one-sided Dixon's test for single outliers was used to find low and high outliers, which were omitted . For statistical comparisons between samples, the fold-repression prior to normalization was used in unpaired 32P-labeled B2 RNA or B2 RNA deletion was incubated with 2 nM Pol II for 10 min at 30 °C in 20 μL Buffer A plus 25 μg/mL BSA. 50 ng of poly-dGdC was added and the incubation was continued for 5 min at 30 °C. Complexes were subjected to electrophoresis through 4% native polyacrylamide (37.5:1 acrylamide:bis ratio) gels containing 0.5X TBE, 4% glycerol, and 5 mM magnesium acetate. The gels were visualized using phosphorimagery and the bands quantified using ImageJ software [32P-labeling of the RNAs. Within each experiment, the data were then normalized to the amount of bound full-length B2 RNA, setting this value to 1.0 and obtaining fractional binding for each of the B2 RNA deletions. The composite data for full-length B2 RNA from all experiments is shown in each figure. For statistical comparisons between samples, the fractional binding was used in unpaired t tests. The data for each deletion construct were compared to the data for full-length B2 RNA; in separate t tests the data for each deletion in the 5’ series or the 3’ series were also compared to the data for its nearest larger neighbor. All two-tailed p values < 0.05 were considered to be statistically significant, as indicated by asterisks in the figures.5 nM software . The amo


Malignant intraventricular meningiomas are quite rare and may spread along the craniospinal axis or extraneurally. However, simultaneous cerebrospinal dissemination and distal extraneural metastasis has seldom been reported.A 51-year-old woman presented with recurrent anaplastic meningioma in the trigone of right lateral ventricle over a 1.5-year period. Suggested radiotherapy was refused after each operation. The patient showed a local relapse and dissemination around the previous tumoral cavity and along the spinal canal during the last recurrence. Left pulmonary metastasis was also found. She died despite multiple lesion resections.Malignant intraventricular meningiomas are an uncommon subset of intracranial meningiomas, and have a great potential for intraneural and extraneural metastasis. Systemic investigation for metastasis is required after surgery, especially for those without adjuvant therapies. Meningioma is the second most common intracranial tumor in adults, and usually occurs on the surface of brain with extracranial metastasis reported occasionally. IntraveA 51-year-old woman presented with persistent headache in the right parietooccipital region and blurring of vision. Computed tomography (CT) of the head revealed a well-defined, irregular lobulated lesion in the trigone of the right ventricle. The lesion was hyperdense with intratumoral necrosis in its center and slight peritumoral edema Figure A. InfiltIntraventricular meningiomas are rare tumors, accounting for less than 3% of all intracranial meningiomas. They originate from the choroid plexus stroma and the tela choroidea absent of dural attachment. The vast majority of IVMs are benign and MIVMs are extremely rare; to the best of our knowledge, only 10 cases have been described so far. NeverthThe clinical data of the 10 MIVMs with CSF dissemination or extracranial metastasis and our case are described in detail (Table et al. first depicted a malignant IVM with distal metastasis to the liver[Although extracranial metastasis is far more common than CSF dissemination for meningiomas of other locations, whether they are benign or malignant, this is not true for MIVMs. A singlhe liver.Compared with previous patients, there were two characteristics in the present case that may elucidate its aggressiveness. First, no auxiliary radiotherapy or chemotherapy was applied after operations. Theoretically, free viable cells in the bloodstream or subarachnoid space could reproduce easily to form new metastasis without further radiochemotherapy. Secondly, at the time of second recurrence, the tumor was observed to infiltrate the ipsilateral transverse sinus both on preoperative MRI and during the operation. It is postulated that blood-borne passage of tumor cells through dural sinuses is the most likely mechanism for distal metastasis.Many risk factors were suggested to be responsible for the systemic spread, but no definite criteria could be determined, to identify the subgroup of aggressive tumors that will recur or metastasize. Therefore, for MIVM, owing to its susceptibility to spreading via the CSF and the possibility of extraneural metastasis, both neuraxis investigation and assessment of other systemic organs, particularly the lung and liver, should be carefully performed to detect and treat any metastasis as early as possible when tumor relapses.Malignant IVMs are extremely rare and may metastasize intracranially, extraneurally alone or simultaneously. Systemic imaging for early metastasis detection must be performed when local recurrence occurs.Written informed consent was obtained from the patient’s father for the publication of this report and any accompanying images. A copy of the written consent is available for review by the editor-in-chief of this journal.CSF: cerebrospinal fluid; CT: computed tomography; H & E: hematoxylin and eosin; IVM: intraventricular meningioma; MIVM: malignant intraventricular meningioma; MRI: magnetic resonance imaging.The authors declare that they have no competing interests.T C-Y carried out the acquisition of the patient’s clinical data, and drafted the manuscript. W J-J and L H carried out the collection of pertinent literature. Y C carried out the guidance and revision of the manuscript. All authors read and approved the final manuscript.


Moreover, PFOR is yet to identify a new viroid capable of initiating independent infection. Here we report the development of PFOR2 that adopted parallel programming in the C++ language and was 3 to 8 times faster than PFOR. A new computational program was further developed and incorporated into PFOR2 to allow the identification of circular RNAs by deep sequencing of long RNAs instead of small RNAs. PFOR2 analysis of the small RNA libraries from grapevine and apple plants led to the discovery of Grapevine latent viroid (GLVd) and Apple hammerhead viroid-like RNA (AHVd-like RNA), respectively. GLVd was proposed as a new species in the genus Apscaviroid, because it contained the typical structural elements found in this group of viroids and initiated independent infection in grapevine seedlings. AHVd-like RNA encoded a biologically active hammerhead ribozyme in both polarities, and was not specifically associated with any of the viruses found in apple plants. We propose that these computational algorithms have the potential to discover novel circular RNAs in plants, invertebrates and vertebrates regardless of whether they replicate and/or induce the in vivo accumulation of small RNAs.Replicating circular RNAs are independent plant pathogens known as viroids, or act to modulate the pathogenesis of plant and animal viruses as their satellite RNAs. The rate of discovery of these subviral pathogens was low over the past 40 years because the classical approaches are technical demanding and time-consuming. We previously described an approach for homology-independent discovery of replicating circular RNAs by analysing the total small RNA populations from samples of diseased tissues with a computational program known as progressive filtering of overlapping small RNAs (PFOR). However, PFOR written in PERL language is extremely slow and is unable to discover those subviral pathogens that do not trigger  in vivo accumulation of extensively overlapping sets of small RNAs to target viroids. Here we report the development of PFOR2 that adopted parallel programming in the C++ language and was significantly faster than PFOR. We also describe a simple computational program that after incorporation into PFOR2 is capable of identifying viroids from deep sequencing of long RNAs instead of small RNAs. Moreover, we report the identification of Grapevine latent viroid (GLVd) and Apple hammerhead viroid-like RNA by the computational approach. Since our new algorithms do not depend on the analysis of viroid-derived small RNAs produced in vivo, it is possible to discover viroids in a wide range of host species including plants, invertebrates and vertebrates.Viroids are a unique class of subviral pathogens found in plants, and they are difficult to identify since they are free circular non-coding RNAs and often replicate to low levels in host cells. We previously described the computational algorithm PFOR that discovers viroids by analyzing total small RNAs of the infected plants obtained by next-generation sequencing platforms. However, the algorithm written in PERL language is very slow, and viroid identification depends on the  Viroids and a group of satellite RNAs (satRNAs) have single-stranded circular RNA genomes that range in size from 220 to 457 nucleotides (nt) Viroids infect many crops and cause severe symptoms in susceptible hosts that result in economically important diseases Two major limitations of the first version of PFOR restrict its application in the discovery of circular RNAs. First, the iterative filtering of small RNAs that are not derived from a replicating circular RNA is a slow process and takes up more than 90% of the PFOR running time. Because PFOR was written in the explanatory PERL language, analyzing complex small RNA libraries using PFOR may take hours or days. Second, circular RNAs are not identifiable by PFOR if they neither replicate nor trigger Dicer-dependent siRNA production in a eukaryotic cell.In this study, a considerably improved version of PFOR was developed by adopting parallel programming in the C++ language. The use of the new version of PFOR, designated PFOR2, led to the discovery of a new viroid from grapevine and a viroid-like RNA from apple tree. Moreover, a new program was developed and incorporated into PFOR2 for the discovery of distinct classes of circular RNAs, including those that neither replicate nor induce in vivo accumulation of Dicer-dependent siRNAs. We propose that the application of PFOR2 would speed up the discovery of novel circular RNAs and expand the list of known host species that can be independently infected by viroids.The computational algorithm of PFOR has been developed to identify replicating circular RNAs including viroids by deep sequencing of small RNAs Hop stunt viroid (HpSVd), Grapevine yellow speckle viroid (GYSVd) and Grapevine hammerhead viroid-like RNA (GHVd RNA) were each identified, and their full-length genomic RNA sequences were obtained by both PFOR and PFOR2 from the grapevine tree sRNA library, which contains 4,701,135 reads of 18–28 nt in length (GEO accession no. GSM458928). However, PFOR2 required only 67 seconds and was 3.3 times faster than PFOR. The second sRNA library was from a peach tree infected with Peach latent mosaic viroid (PLMVd) and contained 7,862,905 reads of 18–28 nt in length (GEO accession no. GSM465746). Both PFOR and PFOR2 were again able to identify PLMVd and to recover the complete sequence. Instead of 2 hours by PFOR using a default k-mer of 17, PFOR2 required only 22 minutes. These results demonstrated that PFOR2 was indeed faster than PFOR in viroid discovery.To test the performance of PFOR2, two publicly available small RNA libraries, in which both known viroids and viroid candidates have been verified by RT-PCR and Northern-blot hybridization, were used to compare the running time between PFOR and PFOR2. Apple scar skin viroid (ASSVd) (accession no. KC110858) isolated previously from apple in China and was hence considered to be a new isolate of ASSVd. The second RNA species was 434 nt in length and showed no sequence similarity to any of the known entries in GenBank. Interestingly, the second predicted circular RNA also contained the conserved sequences found in hammerhead ribozymes as shown previously for GHVd RNA PFOR2 was next applied to determine whether an apple plant with typical symptoms of apple scar skin disease contained new circular RNAs. The sRNA library was constructed from this apple plant, and 15,321,500 clean reads of 18–30 nt in length with a predominant size of 21 nt were obtained after deep sequencing . Two putTo verify the predicted sequence and the circular nature of AHVd-like RNA, total RNAs from the diseased apple were isolated for divergent RT-PCR analysis. According to the sequence of AHVd-like RNA assembled by PFOR2, two pairs of adjacent primers with opposing polarities  in vivo.AHVd-like RNA did not contain the characteristic central conserved region (CCR) found in the viroid family Both strands of AHVd-like RNA could form natural hammerhead structures  containiChicory yellow mottle virus (CYMV) and Tobacco ringspot virus (TRSV) The hammerhead structures of AHVd-like RNA were carefully compared with those of other known viroids and circular satRNAs (namely viroid-like RNAs), revealing some common salient features . i) In min vitro from linearized plasmids and were found to be self-cleaved during transcription and after purification when incubated under standard self-cleavage conditions in a protein-free buffer , Apple stem grooving virus (ASGV), and Apple stem pitting virus (ASPV). The presence of these three plant viruses was further confirmed by RT-PCR , which have been previously detected by RT-PCR and Northern-blot hybridization in this old grapevine tree Citrus viroid VI (CVd-VI) (accession no. AB019508). Because CVd-VI had not been previously isolated from grapevine and the sequence similarity between CVd-VI and the identified RNA molecule was below the viroid species demarcation criteria of 90% sequence similarities The obtained sRNA library contained 14,033,487 clean reads of 17–30 nt in length, with 21 nt as the most dominant size class . PFOR2 aTo confirm the viroid nature of GLVd, we first determined whether GLVd existed in a circular form in vivo. Two sets of adjacent primers of opposite polarity  Apscaviroid. The terminal conserved region (TCR) of GLVd was also similar to that found in apscaviroids  formed by the upper CCR strand and the flanking inverted repeat unpaired . Howeverunpaired . The nucunpaired . DetectiTo further verify the viroid nature of GLVd, dimeric head-to-tail transcripts of GLVd were transcribed in vitro from the constructed cDNA clones of GLVd. Virus-free grapevine seedlings (cv ‘Beta’) grown in early spring were mechanically inoculated with the GLVd transcripts by slashing the stems with razor blades. Uninoculated healthy seedlings from the same batch were kept as controls. Because GLVd was undetectable by Northern-blot hybridization 3 and 6 months post inoculation, we re-inoculated the seedlings with a higher dose of GLVd transcripts and detected weak hybridization signals from 4 of the 18 inoculated grapevine plants 3 months after the secondary inoculation. To facilitate GLVd detection in the young tissues, the apical shoots of the inoculated grapevine plants were removed, and the leaves from the young lateral branches that emerged 6 months after the secondary inoculation (or 12 months after the first inoculation) were collected for RNA extraction. We found that GLVd infection became readily detectable in 6 of the 18 inoculated grapevine plants using either Northern-blot hybridization or RT-PCR. The progeny sequence was determined via DNA sequencing of the cloned RT-PCR products and was found to be the same as the inoculated GLVd transcripts. Therefore, GLVd autonomously replicated in its natural host grapevine, fulfilling the most critical criterion to be considered as a viroid.Apscaviroid. The phylogenetic tree constructed using viroids of genus Colviroid as the out-group revealed two subgroups of apscaviroids (Diospyros virginiana L.) Apscaviroid. Interestingly, careful inspection of the apscaviroid alignments identified two types of repeat sequences between GLVd and PVd-2 , as part of PFOR2 to discover biologically active circular RNAs via the deep sequencing of long RNAs instead of small RNAs. The program cut the sequenced long RNAs into 21-nt virtual sRNAs of 20-nt overlap with their 5′ and 3′ neighboring sRNAs before PFOR2 analysis to retain only 21-nt virtual ISRs for the final assembly of circular RNAs  approaches can readily identify viral and subviral pathogens in samples of plant and animal diseased tissues that are related in nucleotide sequence or encoded protein sequence to a known pathogen. The development of PFOR for viroid discovery thus represents a conceptual advance because, unlike NGS and several available classical approaches, PFOR does not depend on sequence homology with a known viroid. The major improvements described in this study overcame the limitations of the published version of PFOR that restrict its potentially widespread applications in pathogen discovery. PFOR2 was 3.3, 5.4, and 7.1 times faster than PFOR in the analysis of the three small RNA datasets from grapevine, peach, and apple, respectively. The enhanced speed is likely to be critical for viroid discovery when targeting hosts with large genome sizes and/or abundant small RNA populations. For example, our analysis of a small RNA library from Areca catechu with 46,637,488 reads took 8 hours and 40 minutes by PFOR2 instead of 110 hours by PFOR (unpublished data). The efficacy of PFOR2 was verified with the discovery of GLVd as a novel viroid that initiates independent infection in its natural host. Moreover, the development of SLS-PFOR2 eliminates the requirement for the in vivo production and accumulation of Dicer-dependent siRNAs to target the circular RNAs to be identified. As a result, small RNA sequencing becomes unnecessary, and RNA-seq libraries depleted of either ribosomal RNAs or linear RNAs can be analyzed by SLS-PFOR2 for the discovery of both replicating and non-replicating circular RNAs in diverse organisms. In principle, SLS-PFOR2 can identify novel viroid circular RNAs in host species that replicate but do not trigger the biogenesis of viroid-specific siRNAs. Therefore, SLS-PFOR2 has the potential to expand the list of both viroids and host species that support viroid infection.PFOR and FOR2 separate small RNAs in the pool into TSR and ISR groups based on the presence of the minimal overlapping k-mer among reads and remove all TSRs progressively. As a result, the overlapping sets of small RNAs retained after the filtering process might be different when different k-mers are used, leading to the variations in the sequences assembled by PFOR and PFOR2 that may not reflect the natural heterogeneity of viroids. The successful detection of each viroid by PFOR and PFOR2 is dependent on whether the circular RNA has been completely covered by a set of overlapping small RNAs with the minimal length defined by k-mers at both ends after removing all TSRs. Because each ISR is allowed to be used only once during the assembling step, only one viroid would be revealed when two or more viroids share small RNAs with lengths defined by k-mers or longer. For example, although ASSVd was revealed by PFOR2 analysis of the apple library using k-mers in the size range of 18 to 20 nt, AHVd-like RNA was identified using k-mers of 18 or 19, but not of 20. In the grapevine sample, HpSVd was identified by PFOR2 using k-mers of 17 to 21, whereas GYSVd-1 and GYSVd-2 were each revealed with a specific k-mer most likely because the two viroids are 80% identical in sequence and share small RNAs. Therefore, it is necessary to analyze each small RNA library using PFOR2 with k-mers from 17 to 21 and to verify the assembled sequences of viroid candidates by RT-PCR and cDNA sequencing.Citrus medica L.) with mild petiole necrosis and leaf bending The evolutionary origin of viroids remains unknown Apscaviroid. Although we found that GLVd was able to independently infect grapevine seedlings (cv ‘Beta’), the accumulation of GLVd was low, and no obvious symptoms were observed in infected grapevine plants. Although future studies on biological properties of GLVd may further differentiate this viroid from those previously reported, the conserved structural elements, the low sequence identity (maximum of 79% with CVd-IV) with other members in the genus Apscaviroid, and the natural host of GLVd, strongly support the possibility of annotating it as a new species in the genus Apscaviroid.Our conclusion that GLVd is a novel viroid is supported by the molecular and biological evidence presented here including its circularity, typical structural elements of viroids, and self-replication in grapevine seedlings. The phylogenetic analysis indicates that GLVd is a member of the genus Avsunviroidae viroids It is currently unclear whether AHVd-like RNA is a viroid or a satellite RNA, in contrast to GLVd. AHVd-like RNA shared no homology with the apple genome and was not amplified by PCR without a RT step, indicating that AHVd-like RNA was exogenous. Given that AHVd-like RNA encoded self-cleavage activities and was not specifically associated with any of the viruses identified in apple trees, we propose that AHVd-like RNA is a viroid. However, we were unable to demonstrate independent infectivity in apple seedlings for either the in vitro transcripts from dimeric AHVd-like RNA cDNA clones or AHVd-like RNA purified from apple tissues. In this regard, AHVd-like RNA may be related to ASSARNA-2, a circular RNA that was previously isolated from diseased apple plants in Japan and China, known to migrate more slowly than the 330-nt ASSVd RNA and unable to establish independent infection in apple seedlings Malus pumila Mill. cv. Fuji) plant, the fruits of which showed typical symptoms of apple scar skin viroid disease, in Shandong province China, in July 2012. The grapevine (Vitis vinifera L.) leaf samples used for determination of GLVd were from Tulufan in Xinjiang China. This grapevine plant (cv. Thompson seedless) for sample collections is more than 100 years old. Young leaves of both apple and grapevine were immediately put into RNAlater stabilization solution  after collection and sent to a laboratory for deep sequencing analysis. Moreover, approximately 10 g of apple and grapevine leaves were packaged with ice, kept fresh at low temperature, and sent to a laboratory for RNA analysis using PAGE and northern-blot hybridization.For the initial identification of viroids and viroid-like RNAs from apple, leaves were collected from an apple  following the manufacturer's instructions. The integrity of the resulting RNA preparations was evaluated before preparing cDNA libraries using an Agilent Technologies 2100 bioanalyzer. For RNA analysis by PAGE and northern-blot hybridization, nucleic acid preparations were obtained with buffer-saturated phenol followed by ethanol precipitation, as reported previously To prepare templates of RT-PCR amplification performed for cloning full-length sequence of viroid-like apple RNA, the obtained crude extracts were run on a non-denaturing 5% polyacrylamide gel stained with ethidium bromide, and the region of the gel delimited by the 250-bp and 400-bp DNA markers was excised and eluted as described previously In the experiment involving the RNA-seq of the potato samples, the extracted total RNAs by TRIzol reagent were purified by depleting ribosomal RNAs and non-circular RNAs. RNA species smaller than 200 nt, such as 5S ribosomal RNA, were first removed using the RNeasy Mini Kit , and 28S and 18S ribosomal RNAs were depleted by hybridization with specific probes following the instructions for the RiboMinus Plant Kit . To enrich circular RNAs, the total RNAs from the same sample were digested with RNase R  at 37°C for 90 min to remove non-circular RNAs.The RNA extracts were separated using two-dimensional PAGE (2D-PAGE) under non-denaturing and denaturing conditions and stained with ethidium bromide, as previously described The small RNA libraries were constructed using Illumina's small RNA sample preparation Kit  following Illumina's method. The protocols for sRNA purification, adaptor ligation, RT-PCR amplification, library purification and high-throughput DNA sequencing on an Illumina HiSeq-2000 have been reported previously The RNA-seq libraries of potato samples were constructed with a modified Not Not So Random (NNSR) sequencing method PFOR in the PERL language was converted to PFOR2 initially by using the C++ language. OpenMP is an Application Programming Interface (API) that supports multi-platform shared memory multiprocessing programming Splitting Longer read into Shorter fragments) program was developed to cut longer reads into virtual sRNAs. The final number of generated virtual sRNAs was dependent on two metrics: sRNA size and overlap size between neighboring sRNAs (step size). Typically, a longer read was cut into contiguous 21-nt sRNAs covering the whole read, in which each sRNA overlapped 20 nt with its 5′ and 3′ neighboring sRNAs (step size  =  1).The SLS  to generate full-length sequences of viroids and viroid-like RNA. The products of RT-PCR amplification were ligated with additional adenine (A) at the end using Taq DNA polymerase  and cloned into pGEM-T vectors (Promega) with protruding 3′-terminal thymine (T). The recombinant plasmids were amplified by transforming DH5α Escherichia coli cells, and positive clones were randomly selected for sequencing.The primers of HpSVd, GYSVd-1, GYSVd-2 and AGVd were described previously Nco I or Sal I to generate linear plasmids. RNA transcripts in both orientations were synthesized by T7 and SP6 RNA polymerase as described previously in-vitro transcription were purified by RNeasy Mini Kit . The purified transcripts were incubated at 37°C for 1 h and then separated by 5% denaturing PAGE containing 8 M urea and visualized by ethidium bromide staining. Full-length of AHVd transcripts and the longer fragments resulting from their in vitro self-cleavage were excised from the gels and eluted, separately. The ribozyme activities of the purified transcripts were assessed according to previously described methods The sequenced recombinant plasmids containing full-length of AHVd-like cDNA amplified with primers of AHVd-88F and AHVd-87R were digested with Head-to-tail dimmers of the entire sequence of GLVd and AHVd-like RNA were prepared by ligation of unit-length inserts and cloning into pGEM-T vectors (Promega), as described previously The secondary structures with minimum free energy for GLVd and AHVd-like RNA were predicted by the circular version of the MFold program S1 FigureSize distribution of sRNA library from the diseased apple plant. The total reads and the non-redundant reads are summarized separately. For total reads, reads of 21 and 22 nt are most in the library; however, among the unique reads, reads of 24 nt are predominant.(TIF)Click here for additional data file.S2 FigureMultiple alignments of 14 variants of AHVd-like RNA. The full length sequences of AHVd-like RNA were amplified by RT-PCR with the primers AHVd-13F and AHVd-12R. The sequences forming the hammerhead structures are delimited by flags and the predicted self-cleavage sites are marked by arrowheads. Solid and open symbols refer to plus and minus polarities, respectively.(TIF)Click here for additional data file.S3 FigureConfirmation of the predicted self-cleavage sites of AHVd-like RNA by 5' RACE-PCR and sequencing. The 3′F+ and 3′F- fragments of AHVd-like RNA were eluted and purified from 5% denaturing polyacrylamide gel shown in (TIF)Click here for additional data file.S4 FigureRT-PCR detections of ACLSV, ASPV, and ASGV. Lane 1, the negative control; lane 2, the diseased apple plant for deep sequencing; lane 3, the positive control. The primers used in RT-PCR reactions are shown in (TIF)Click here for additional data file.S5 FigureSize distribution of the sRNA library from the old grapevine sample.(TIF)Click here for additional data file.S6 FigureHPI structures of some apscaviroids.(TIF)Click here for additional data file.S7 FigureEnrichment of circular RNAs from total RNA and the flow chart of the NNSR method. (A) Depletion of 28S and 18S rRNAs by hybridization using specific probes. Equal amounts of total RNA before and after rRNA hybridization were visualized by ethidium bromide staining. (B) Digestion of linear RNAs by RNase R. Equal amounts of total RNA before and after RNase R digestion were checked on agarose gels. (C) Flow chart of the NNSR method. The RNA samples after RNase R digestion or rRNA depletion were used for first-strand synthesis primed by an adaptor A-tagged random hexamer primer (shown in blue). After RNase H treatment, the second-strand cDNA was synthesized using an adaptor B-tagged random hexamer primer (shown in yellow). Then, the library was amplified with adaptor A and adaptor B, the approximate size of amplicons were selected, and the amplicons were purified for sequencing.(TIF)Click here for additional data file.S8 FigureProposed kissing loops in AHVd-like RNA. The predicted interactions between loops are indicated with red lines.(TIF)Click here for additional data file.S1 TableSequences of the primers used in this study.(XLS)Click here for additional data file.S2 TableSurvey of the occurrence of ACLSV, ASGV, ASPV, and AHVd-like RNA in the field by RT-PCR.(DOC)Click here for additional data file.