The genomes of plus-strand RNA viruses contain many regulatory sequences and structures that direct different viral processes. The traditional view of these RNA elements are as local structures present in non-coding regions. However, this view is changing due to the discovery of regulatory elements in coding regions and functional long-range intra-genomic base pairing interactions. The ∼4.8 kb long RNA genome of the tombusvirus tomato bushy stunt virus (TBSV) contains these types of structural features, including six different functional long-distance interactions. We hypothesized that to achieve these multiple interactions this viral genome must utilize a large-scale organizational strategy and, accordingly, we sought to assess the global conformation of the entire TBSV genome. Atomic force micrographs of the genome indicated a mostly condensed structure composed of interconnected protrusions extending from a central hub. This configuration was consistent with the genomic secondary structure model generated using high-throughput selective 2′-hydroxyl acylation analysed by primer extension (i.e. SHAPE), which predicted different sized RNA domains originating from a central region. Known RNA elements were identified in both domain and inter-domain regions, and novel structural features were predicted and functionally confirmed. Interestingly, only two of the six long-range interactions known to form were present in the structural model. However, for those interactions that did not form, complementary partner sequences were positioned relatively close to each other in the structure, suggesting that the secondary structure level of viral genome structure could provide a basic scaffold for the formation of different long-range interactions. The higher-order structural model for the TBSV RNA genome provides a snapshot of the complex framework that allows multiple functional components to operate in concert within a confined context. The genomes of many important pathogenic viruses are made of RNA. These genomes encode viral proteins and contain regulatory sequences and structures. In some viruses, distant regions of the RNA genome can interact with each other via base pairing, which suggests that certain genomes may take on well-defined conformations. This concept was investigated using a tombusvirus RNA genome that contains several long-range RNA interactions. The results of microscopic and biochemical analyses indicated a compact genome conformation with structured regions radiating from a central core. The structural model was compatible with some, but not all, long-range interactions, suggesting that the genome is a dynamic molecule that assumes different conformations. The analysis also revealed new structural features of the genome, some of which were shown to be functionally relevant. This study advances our understanding of the role played by global structure in virus genome function and provides a model to further investigate its in role virus reproduction. We anticipate that organizational principles revealed by this investigation will be applicable to other viruses. Many viruses possess RNA genomes, and those with single-stranded plus-sense RNA genomes represent an important subgroup that includes many pathogenic plant, animal, and human viruses. These RNA genomes serve the traditional role as the blueprint for viral proteins; however, they also contain cis-acting RNA elements (RE) that direct different viral processes, such as protein translation, genome replication and transcription of subgenomic (sg) mRNAs i.e. phage Q-beta) i.e. tombusviruses) i.e. flaviviruses) One potential drawback of having an RE in a coding region is that it physically couples distinct activities to the same RNA sequence; therefore, some compromise in one or both functions may be required. Additionally, in some cases the relative location of REs within a genome may not be optimal for their activity, thus compensatory measures may be required. One strategy used by many RNA viruses to deal with suboptimally positioned REs is to reorganize their relative location within the genome via intramolecular long-range RNA-RNA interactions Tombusviruses, such as tomato bushy stunt virus (TBSV), are plus-strand RNA viruses that encode five proteins in a ∼4.8 kb long genome i.e. UL, AS2 and AS1), hinting at possible shared organizational strategies and/or functional or regulatory crosstalk . This type of analysis provides information on the overall topography of an RNA molecule adsorbed to a mica surface and has been used successfully to assess global conformations of different viral RNA genomes Due to conformational flexibility, variable surface adsorption, and limits of resolution, a single well-defined structure for all genome molecules was not anticipated. However, what was evident from the AFM analysis was the general conservation of a global organization consisting of irregular extensions emanating from a central core . Some vaWe next sought to determine the RNA secondary structure of the TBSV genome using high-throughput selective 2′-hydroxyl acylation analysed by primer extension (SHAPE) RNAstructure software using a pseudo free-energy change term RNAstructure-based analysis  predicted 60.4% of the genome to be base paired, while exclusion of the SHAPE data (i.e. SHAPE-minus: thermodynamics-only) yielded a structure with 65.5% base pairing. Accordingly, addition of the SHAPE data reduced overall predicted pairing by ∼5%. Approximately 70% of the base pairs in the SHAPE-plus structure were identical to those in the SHAPE-minus structure; therefore inclusion of the SHAPE data did result in altered pairing schemes and model refinement. Interestingly, this latter value is higher than the corresponding 47% determined during structural analysis of the HIV-1 genome Initially, we were interested in determining whether the SHAPE data contributed significantly to the refinement of the genomic structural model. The model that included the SHAPE data (i.e. closing stems) of several of these domains were closely associated, leading to their emergence from a common area and an overall floret-like organization . Accordingly, three small, three medium, and four large domains were defined , and are positioned more centrally in the genome; (ii) ∼66% of the genome is present in the four lDs, internal regions of these domains do not contain known functional RNA elements, and each domain corresponds to a different viral ORF; (iii) the sDs and mDs tend to be located closer to genomic termini; (iv) known functional RNA elements are located in both domain and inter-domain regions; (v) long-range RNA-RNA interactions are evident throughout the structure, but only a third of the known functional long-distance RNA-RNA interactions (i.e. AS2-RS2 and DE-CE) are present in the model.The linear 5′-to-3′ order of the ten defined domains is sD1-lD1-sD2-mD1-mD2-lD2-lD3-lD4-mD3-sD3, with inter-domain lengths ranging from as little as 1 nt  up to 91 nts (between sD2 and mD1). Analysis of the predicted structures of the optimal and 1000 suboptimal SHAPE-plus structures revealed that the assigned domains, with the exception of mD1, were well maintained in the population . Some ofi.e. ≥0.7) were abundant in both complementary sequences for the 5′UTR-3′CITE interaction, the UL sequence, and the AS2 element . Accordingly, the simultaneous presence of these two interactions may provide a glimpse at what a transcriptionally-active genome looks like. Although the AS2-RS2 interaction is not engaged in this model, the two sequences are juxtaposed by the DE-CE and AS1-RS1 interactions, which could facilitate transient AS2-RS2 pairing. Indeed, the DE-CE interaction is critical for AS2-RS2-dependent sg mRNAs transcription The two long-range interactions that were predicted to occur in the structural model correspond to AS1-RS1 and DE-CE  and 4. Ii.e. within its directly flanking sequence), it is predicted to be positioned in the loop of a local stem-loop structure . This finding suggests that, at these positions, maintaining this RNA structure was a higher priority than preserving the consensus amino acid identity in p92. To test the importance of this potential interaction, substitutions were introduced into the complementary sequences of S31 in the TBSV genome. Substitutions were made at degenerate codon positions so as not to change the corresponding amino acids in the p92 ORF. As no directly opposing degenerate codon positions were available, the introduction of silent compensatory mutations was not possible in the genomic context. Instead, two genomic mutants were made, TCL1 and TCL2, which were predicted to either disrupt or stabilize the interaction, respectively -SL and UL) suggested a possible role in mediating genome replication. To address this question in a replication context free from translational constraints, a non-coding defective interfering (DI) RNA replicon containing the interaction was used. The DI RNA, termed DICL, was able to replicate efficiently in co-inoculations with helper T100 (which provided p33 and p92 RNA replication proteins in trans) and, as this replicon was non-coding, it was possible to generate a full set of compensatory mutations within it to test the role of S31 , an antiviral host defence system that is based on the detection and destruction of viral RNA One of the driving forces in RNA virus evolution is recombination, in which discontinuous regions of a viral genome(s) are joined together For RNA virus characterization, a common strategy used to define the role of a viral protein is to mutate its corresponding ORF in the infectious clone. However, it is becoming increasingly evident that functional RNA elements also reside within coding regions of different RNA viruses e.g. different genera in the same family) display any likeness at the global genomic level and if unrelated viruses share similar organizational features that can help to define general rules of arrangement.With respect to the reliability of our SHAPE-guided structure, we feel that (i) the general agreement with the AFM structure, (ii) the presence of known local structures, (iii) the occurrence of some of the known long-range interactions, and (iv) identification of new structures that were functionally validated, all add confidence to the prediction. Nonetheless, the accuracy of many aspects of the structure still remains to be investigated. The current structural model provides a context to begin to understand the organization of local structures and long-range RNA-RNA interactions within the TBSV genome. At a global level, the genome assumes a floret-like structure that includes multiple long-range RNA-RNA interactions. The simultaneous presence of the AS1-RS1 and DE-CE interactions in our model suggests that transcriptional-related interactions are structurally compatible and points to functional coupling of this process. Conversely, the absence of other known long-distance interactions in the structure implies the requirement for dynamic structural transitions, some of which may be mutually exclusive. However, the basic framework of secondary structure observed may mediate the formation of alternate structures without the need for large-scale rearrangements. Importantly, our structure provides a foundation to further test and refine the model and to identify alternative global structures that allow other long-range interactions to occur. Such dynamic transitions may occur readily in a cellular environment that includes both viral and host proteins that could mediate structural rearrangements. A possible candidate for this role is the abundant viral protein p33, which has been shown to possess RNA chaperone activity The infectious clone of the wild-type TBSV genome, T100 2). A 20 µl volume of the diluted RNA sample was deposited on freshly cleaved mica and incubated for 1 min at room temperature. Following the incubation period, the sample was rinsed with double distilled water, dried under a stream of nitrogen gas, and imaged using AFM tapping mode in air. Tapping mode AFM was performed with a Dimension 3100 microscope and Nanoscope IIIa controller . Tapping mode AFM images in air were obtained using a silicon cantilever-tip assembly with a resonance frequency of 200–300 kHz. The images were captured at scan rate of 1–1.5 Hz and 512×512 pixels resolution. Image height scale was typically of the order of ca. 3 nm; exact scales are provided for each image. Images were viewed and analyzed using V614r1 Nanoscope program . Images were subjected to third order flattening to remove image bowing artifacts.For AFM imaging, TBSV RNA genome derived from in vitro transcription of clone T100 was diluted to 1.5 ng/µl in deposition buffer  was added, followed by incubation at 37°C for 30 min.High-throughput SHAPE analysis was carried out using the methodology described by Low and Weeks Three hundred and sixty µL of the folded RNA was treated with 40 µL of 50 mM 1-methyl-7-nitroisatoic anhydride (1M7) in DMSO and another 360 µL aliquot of the folded RNA was treated with 40 µL neat DMSO. Both were subsequently incubated at 37°C for 4 min. 1M7-modified RNA (+), and DMSO control RNA (−) were recovered by ethanol precipitation  and resuspended in 400 µL of 0.5× TE.Mfold RNA folding software TBSV genomic RNA secondary structure was predicted by 2 16.7 mM DTT, 1.67 mM dATP, 1.67 mM dTTP, 1.67 mM dCTP, 1.67 mM dITP) was added to each tube and incubated at 52°C for 2 min, to which 1.5 µL of SuperScript III (Invitrogen) was added and incubated at 52°C for 30 min. The extension reaction was stopped by incubating on ice for 2 min and then adding 4 µL of 3 M NaOAc (pH 5.2). For sequencing reactions, 7.2 pmol RNA in 40 µL of 0.5× TE was incubated at 95°C for 4 min and transferred to ice for 2 min to denature the RNA. Six µL primer  was added to 20 µL denatured RNA (Seq-G) and 6 µL primer  was added to another aliquot of 20 µL denatured RNA (Seq-T). After primer and RNA annealing at 65°C for 5 min and then 37°C for 5 min, 2 µL of ddGTP (0.5 mM) and 2 µL ddTTP (10 mM) were added to Seq-G and Seq-T, respectively, and the rest of the procedure was as described above for the 1M7-treated and untreated samples. The four reactions were combined and precipitated with ethanol and 2 µL of glycogen (20 mg/ml). The cDNA pellet was washed twice with 70% ethanol, dried, and resuspended in 40 µL of deionized formamide.Fluorescently-labeled primer (6 µL at 4 µM) was added to 20 µL RNA (+) (4 µM WellRED D4) and 20 µL RNA (−) (4 µM WellRED D3) reactions and incubated at 65°C for 5 min and 37°C for 5 min. Twelve µL SHAPE enzyme mix  capillary using a Beckman CEQ800 DNA sequencer. Samples were denatured at 90°C for 120 seconds, injected at 2.0 kV for 6 to 15 sec, and separated at 4 kV for 80 min.SHAPEfinderi.e. structural prediction in these regions was based only on thermodynamics.) For the rest of the genome, the raw SHAPE reactivities were normalized using the box-plot normalization method i.e. the normalized reactivity was set to  = 1 if t was equal to the average of the top 10% non-outlier data. These preprocessing steps were carried out in MATLAB.SHAPE electropherogram intensities were quantified using RNAstructure software RNAstructure. No limit was imposed on the maximal base pairing range (which is the default in RNAstructure). Genome RNA structures were generated using RnaVis2 software The average reactivity at each position from two separate SHAPE analyses of the TBSV genomic RNA were incorporated into the RNAstructure, and by using the constraints at the 5′ and 3′ ends. The base-pairing probability pfs file, which is a prerequisite in RNAstructure for drawing a stochastic sample from the ensemble, could not be successfully generated in the software by simultaneously placing restraints (i.e. SHAPE data) and hard constraints . To circumvent this limitation we followed a modified procedure in simulating a sample of plus-SHAPE suboptimal structures: For the SHAPE-constrained structures the pfs file was generated over the range of nts 167–4696 , and the 1000 sampled structure were concatenated with the separate foldings of 5′ and 3′ ends , for which latter the constraints, but no SHAPE reactivity data restraints, were applied. Suboptimal structures were processed and interrogated for alternative or additional interactions not seen in the SHAPE-plus mfe structure, using MATLAB scripts.Suboptimal structures: 1000 structures each were stochastically sampled from the ensemble by using i) the SHAPE-constrained .pfs file and ii) the thermodynamic pfs file generated in Mountain plots and maximum ladder distance calculations: Mountain plots of the SHAPE-plus structure, suboptimal structures, and structures of 8 incremental variations in pseudo-free energy parameters  = +/− were generated with MATLAB scripts.The maximum ladder distance (MLD) for the SHAPE-plus and SHAPE-minus structures and their mean values <MLD> over the ensembles values for i) 1000 SHAPE-plus sampled structures and ii) 1000 SHAPE-minus sampled structures were calculated together with their maximum and minimum values over the ensembles.RNAstructure software Computational analyses were carried out via Perl/Python and MATLAB scripts interfacing with the Figure S1Accumulation of TBSV genome in transfected plant protoplasts. Cucumber protoplasts were transfected with 1 or 3 ug of viral RNA transcripts that either were or were not subjected to the RNA folding protocol as describe In the (TIF)Click here for additional data file.Figure S2Mountain plots for SHAPE-plus TBSV genome optimal and suboptimal structures. The red graph line indicates the mountain plot for the optimal structure. The graph lines in black above and below the red graph line show, respectively, the maximum and minimum values for the number of enclosed basepairs along the sequence in the sampled 1000 suboptimal structures. Below, the corresponding assigned domains are indicated. The maximum and minimum enclosure values within the suboptimal population mirror that for the optimal structure, indicating that global structure is largely maintained and the domain boundaries are well preserved within the suboptimal population.(TIF)Click here for additional data file.Figure S3Dot plots for SHAPE-plus TBSV genome optimal and suboptimal structures. Dot plot showing interactions for the optimal (red dots) and 1000 sampled suboptimal structures (black dots), with annotation of the general areas in which the known 6 long-range interactions in the TBSV genome would reside (blue boxes). The four interactions absent in the optimal structure  are also absent in all suboptimal structures.(TIF)Click here for additional data file.Figure S4RNAstructure-predicted secondary structure for the TBSV genome. No SHAPE data were used in this prediction. Formation of the confirmed local RNA structures in the 5′UTR (nts 1–166) and 3′UTR (nts 4697–4778) were added as constraints in the input file.(TIF)Click here for additional data file.Figure S5Mountain plots of SHAPE-plus structures generated with varying pseudo-free energy parameters. The pair-wise values used in variants were: m = 2.5, 2.6, 2.7; b = −0.7, −0.8, −0.9. The structure for the default parameter  is shown in red, the maximum and minimum values for number of enclosed basepairs along the sequence in the sampled parameter variants are shown in blue. Good robustness of overall domain structure with respect to incremental changes in  was seen in the simulations. In each pairwise comparison, more than 90% of the basepairs occurring in the structure with the default parameters were identical to the basepairs in the structures with parameter variants. We were not able to compute stable structures for larger parameter variations, due to software instability for large RNAs with enforced constraints. Below the graph the corresponding assigned domains are indicated.(TIF)Click here for additional data file.Figure S6Dot plots for SHAPE-plus TBSV genome structures. Dot plot showing interactions for the SHAPE-plus optimal structure generated using the default values  =  (red dots) and those in 8 SHAPE-plus optimal structures using parameter variations  = +/− (black dots). The general areas in which the known 6 long-range interactions in the TBSV genome would reside are denoted by blue boxes. The four interactions absent in the optimal structure  are also absent in all 8 variant structures.(TIF)Click here for additional data file.Table S1Shape reactivities for the TBSV genome.(XLSX)Click here for additional data file.Table S2Shape reactivities for complementary partner RNA elements that did not form cognate pairs in the SHAPE-plus structure.(DOC)Click here for additional data file.Table S3List of primers used for SHAPE analysis along with their coordinates relative to the TBSV genome sequence.(DOC)Click here for additional data file.Table S4Primer coverage and overlap in primer extension analysis. For each primer, the nucleotides for which reactivity data was generated is shown under “Covered nt”, while the number of nucleotides of overlap with that from the next primer is shown under “# of overlap”.(DOC)Click here for additional data file.


Control of nuclear RNA stability is essential for proper gene expression, but the mechanisms governing RNA degradation in mammalian nuclei are poorly defined. In this study, we uncover a mammalian RNA decay pathway that depends on the nuclear poly(A)-binding protein (PABPN1), the poly(A) polymerases (PAPs), PAPα and PAPγ, and the exosome subunits RRP6 and DIS3. Using a targeted knockdown approach and nuclear RNA reporters, we show that PABPN1 and PAPα, redundantly with PAPγ, generate hyperadenylated decay substrates that are recognized by the exosome and degraded. Poly(A) tail extension appears to be necessary for decay, as cordycepin treatment or point mutations in the PAP-stimulating domain of PABPN1 leads to the accumulation of stable transcripts with shorter poly(A) tails than controls. Mechanistically, these data suggest that PABPN1-dependent promotion of PAP activity can stimulate nuclear RNA decay. Importantly, efficiently exported RNAs are unaffected by this decay pathway, supporting an mRNA quality control function for this pathway. Finally, analyses of both bulk poly(A) tails and specific endogenous transcripts reveals that a subset of nuclear RNAs are hyperadenylated in a PABPN1-dependent fashion, and this hyperadenylation can be either uncoupled or coupled with decay. Our results highlight a complex relationship between PABPN1, PAPα/γ, and nuclear RNA decay, and we suggest that these activities may play broader roles in the regulation of human gene expression. In eukaryotes, mRNAs include a stretch of adenosine nucleotides at their 3′ end termed the poly(A) tail. In the cytoplasm, the poly(A) tail stimulates translation of the mRNA into protein, and protects the transcript from degradation. Evidence suggests that poly(A) tails may play distinct roles in RNA metabolism in the nucleus, but little is known about these functions and mechanisms. We show here that poly(A) tails can stimulate transcript decay in the nucleus, a function mediated by the ubiquitous nuclear poly(A) binding protein PABPN1. We find that PABPN1 is required for the degradation of a viral nuclear noncoding RNA as well as an inefficiently exported human mRNA. Importantly, the targeting of RNAs to this decay pathway requires the PABPN1 and poly(A) polymerase-dependent extension of the poly(A) tail. Nuclear transcripts with longer poly(A) tails are then selectively degraded by components of the nuclear exosome. These studies elucidate mechanisms that mammalian cells use to ensure proper mRNA “quality control” and may be important to regulate the expression of nuclear noncoding RNAs. Furthermore, our results suggest that the poly(A) tail has diverse and context-specific roles in gene expression. Mistakes in any of these processes lead to aberrant mRNAs that may code for proteins with deleterious effects. As a result, cells have developed RNA surveillance or S. cerevisiae. The yeast Trf4-Air2-Mtr4 polyadenylation (TRAMP) complex is a co-factor for the degradation of aberrant rRNA, tRNA, snoRNAs, snRNAs, long noncoding RNAs (lncRNAs), and mRNAs poly(A) polymerase (PAP) that polyadenylates mRNA 3′-ends poly(A)-binding protein (PABP), Nab2, recruits the exosome to polyadenylated RNAs, suggesting that yeast PABPs couple hyperadenylation with decay The addition of a poly(A) tail is essential for normal mRNA biogenesis, but polyadenylation can stimulate RNA QC pathways in S. cerevisiae appear to be conserved in mammals. For example, mammalian TRAMP homologs promote polyadenylation and decay of aberrant rRNA and unstable promoter-associated transcripts Few studies on mammalian nuclear RNA QC systems have been published, but some roles of the poly(A) tail in polyadenylated nuclear PAN RNA further support the idea that the poly(A) tail plays an important role in mammalian nuclear RNA decay. PAN RNA is a polyadenylated, capped, RNA polymerase II (pol II) transcript that accumulates to high levels in the nucleus, thereby making it a useful model to study nuclear RNA decay pathways. The high nuclear levels of PAN RNA depend on the presence of a 79-nt stability element near its 3′ end termed the ENE Our previous studies using the noncoding KSHV S. pombe nuclear PABP, Pab2, is homologous to PABPN1, but is not related to S. cerevisiae Nab2. Pab2 promotes the exosome-mediated nuclear RNA decay of specific pre-snoRNAs, pre-mRNAs, and meiotic mRNAs S. pombe Pab2 does not stimulate PAP activity Drosophila decrease steady-state poly(A) tail lengths Comparison of nuclear PABP functions in mRNA 3′-end formation and nuclear RNA decay suggest important mechanistic distinctions among fission yeast, budding yeast, and mammals. The human nuclear PABP, PABPN1, stimulates PAP processivity and controls poly(A) length in 3′ processing reactions in vitro Here we define a pathway that promotes the degradation of polyadenylated nuclear RNAs in mammalian cells. This pathway depends on PABPN1, the canonical PAPs, PAPα and PAPγ, and the nuclear exosome components RRP6 and DIS3. The pathway targets ENE-lacking PAN RNA and intronless β-globin mRNA reporters, but does not degrade a spliced β-globin mRNA. Interestingly, PANΔENE RNA and intronless β-globin RNAs are polyadenylated upon PABPN1 knockdown or PAPα and PAPγ co-depletion, but the poly(A) tails are substantially shorter. In contrast, co-depletion of RRP6 and DIS3 stabilizes hyperadenylated forms of both intronless β-globin mRNA and PANΔENE RNA reporters and this hyperadenylation depends on PABPN1 and canonical PAP activity. Bulk RNA analysis reveals that a significant fraction of newly synthesized cellular polyadenylated RNAs is subject to PABPN1-dependent hyperadenylation. Efficiently spliced endogenous mRNAs appear to be unaffected by this decay pathway, but an endogenous polyadenylated nuclear noncoding RNA is, consistent with the interpretation that this pathway targets non-exported RNAs. Together, our results suggest that PABPN1 and the PAPs, PAPα and PAPγ, promote the exosome-mediated degradation of nuclear polyadenylated transcripts in human cells.1/2≤15 min), while the remaining RNA pool is degraded more slowly. Regression analysis allows us to estimate the percentage of transcripts in each pathway and their corresponding half-lives. The presence of the ENE protects polyadenylated RNAs from the rapid decay pathway, but the cellular factors driving rapid decay have not been previously elucidated.To measure PANΔENE RNA stability, we used a well-characterized transcription pulse-chase assay S. pombe PABPN1 homolog promotes RNA decay, we tested the effects of PABPN1 depletion on rapid PANΔENE decay. We used siRNAs to efficiently (∼95%) knockdown PABPN1 in 293A-TOA cells 1/2∼7 min, S1), but predicted that only ∼6.6% of the transcripts had t1/2≤15 min in PABPN1-depleted cells. Thus, we conclude that PABPN1 is necessary for the rapid decay of PANΔENE RNA in vivo.Interactions between the ENE and the poly(A) tail stabilize PAN RNA, suggesting that a poly(A) tail may promote rapid RNA decay. Because PABPN1 binds to poly(A) tails in the nucleus and the 40 to the RNase H reaction  tail lengths of PANΔENE RNA in the presence or absence of PABPN1. RNA samples taken immediately after the 2-hr transcription pulse (T = 0) were cleaved with RNase H targeted by DNA oligonucleotide complementary to a region near the PANΔENE RNA 3′ end NC30, , and thereaction , lane 3 To determine whether human mRNAs are subject to PABPN1-dependent decay, we initially examined the effects of PABPN1 knockdown on two TetRP-driven β-globin reporter constructs. One contains no introns  while the other retains the second intron of β-globin (βΔ1). BecauseWe further examined the effects of PABPN1 knockdown on the poly(A) tail lengths of the intronless and spliced β-globin RNAs after a two-hour transcription pulse . The intPABPN1 stimulates processive polyadenylation by PAPα  We next examined the effects of PAPα and PAPγ depletion on PANΔENE poly(A) tail length . ConsistThe results above show that depletion of either PABPN1 or the canonical PAPs stabilizes transcripts that would otherwise be rapidly degraded. Because PABPN1 directly promotes PAP activity Several pieces of data suggest that poly(A) tail extension precedes the rapid nuclear RNA decay observed here. First, the polyadenylation factors PABPN1 and the canonical PAPs are required for rapid decay. Second, the PAP-stimulating activity of PABPN1 is necessary for decay. Third, stabilization of PANΔENE or intronless β-globin RNA correlates with shorter poly(A) tails. Fourth, transient increases in PANΔENE length are sometimes observed at the earliest time points after transcription inhibition e.g. . CompariWe next tested the effects of adding cordycepin simultaneously with transcription shutoff T = 0; . Under tGiven its roles in RNA QC pathways, we next tested whether the nuclear exosome is required for PABPN1-mediated decay. We used siRNAs to knockdown both catalytic cofactors of the nuclear exosome, RRP6 and DIS3 . Using oIf PABPN1 stimulates polyadenylation by PAPα or PAPγ as a prerequisite to exosome-mediated decay, then knockdown of RRP6 and DIS3 should lead to the accumulation of hyperadenylated (∼150 to ∼400 nt) RNAs. Indeed, upon exosome depletion, the poly(A) tails were equivalent in length to those from siControl treated cells , rather To examine whether the canonical PAPs and exosome were required for mRNA QC, we co-depleted PAPα and PAPγ or RRP6 and DIS3 and monitored intronless β-globin RNA stability. Consistent with the PANΔENE results, intronless β-globin was stabilized upon depletion of these factors . In addiWe next evaluated the impact of PABPN1 on bulk cellular polyadenylated RNAs. To ensure that we monitored RNAs synthesized after functional knockdown of each factor and thereby diminish ambiguities that arise from long-lived transcripts generated prior to knockdown, we developed an in vivo RNA labeling technique to monitor newly made poly(A) tails We also tested other factors involved in PANΔENE and intronless β-globin rapid decay in this assay. Similar loss of long poly(A) tails was observed when PAPα and PAPγ were co-depleted . ImportaTo investigate the role of PABPN1 on specific endogenous RNAs, we first examined the effects of PABPN1 depletion on the levels of several mRNAs and nuclear lncRNAs. Upon depletion of PABPN1, the abundance of GAPDH, β-actin, or ARGLU1 mRNAs was unaffected . In addiNext, we examined the steady-state levels of three abundant and well-studied nuclear lncRNAs, MALAT1, NEAT1, and XIST. Each of these RNAs undergoes a slightly different maturation process. The MALAT1 RNA has no introns, and its 3′ end is produced by RNase P-mediated endonucleolytic cleavage and stabilized by formation of a triple helix structure We also examined the relative poly(A) tail lengths of several transcripts in the presence or absence of PABPN1. Knockdown of PABPN1 had no effects on the mobility of GAPDH mRNA, β-actin mRNA, or the MALAT1 lncRNA . HoweverDespite significant progress in uncovering the mechanisms of nuclear RNA decay in yeast, these processes remain relatively unexplored in higher eukaryotes. In this study, we used a viral nuclear RNA and intronless β-globin reporters to identify components of a rapid human nuclear RNA decay pathway. Through a targeted knockdown approach, we identified PABPN1, the canonical PAPs, PAPα and PAPγ, and the nuclear exosome components RRP6 and DIS3 as central players in this rapid decay pathway. We propose that this pathway promotes the decay of nuclear mRNAs undergoing RNA QC and polyadenylated nuclear noncoding RNAs.The relationship between RNA polyadenylation, hyperadenylation and RNA decay is complex. Hyperadenylated RNAs are often observed when RNA decay factors or other RNA processing factors are compromised, but it is difficult to empirically determine whether the hyperadenylation promotes RNA decay or if hyperadenylated transcripts appear as a consequence of manipulating nuclear RNA metabolism. Several of our observations suggest that poly(A) tail extension is mechanistically linked to decay. Knockdown of either PABPN1 or co-depletion of the PAPs leads to more stable RNAs with shorter poly(A) tails. Importantly, the tails observed upon PABPN1 knockdown are not completely lost, but are still ∼50–150 nt for PANΔENE RNA  and ∼100How might PABPN1 and PABPN1-dependent hyperadenylation be linked to RNA decay? PABPN1 and PAPs may provide the exosome a suitable binding site by stimulating the polyadenylation of a “naked” poly(A) tail . Recent S. pombe Pab2 appears to function in this fashion (see below). Notably, PABPN1 has been reported to physically interact with RRP6 and RRP40 PABPN1 may further contribute to decay by direct recruitment of the exosome. The exosome has relatively weak affinity for poly(A) RNA In an alternative model, PABPN1 and PAP activities do not directly stimulate decay but are essential to “license” the RNA for degradation. In this case, PABPN1 and PAP-mediated polyadenylation is a necessary upstream step in RNA biogenesis. Prior to the completion of this step, RNAs are resistant to decay by the exosome, perhaps due to subcellular localization or RNP structure. When PAPs or PABPN1 are knocked down, the RNA does not progress from this state and therefore the transcript has a longer half-life. Several aspects of this model are similar to yeast systems in which accumulation of the RNAs at the site of transcription is coupled with RNA QC NAB2 mRNA by promoting Rrp6-mediated decay S. cerevisiae PABPN1 homolog has been described The present work complements recent studies in both fission and budding yeast pointing to novel roles for nuclear PABPs in RNA decay. In budding yeast, the nuclear poly(A)-binding protein, Nab2, binds to hyperadenylated tails generated by TRAMP and recruits Rrp6 to degrade the transcript Drosophila PABPN1 homolog mimics the human PABPN1 in that it stimulates its cognate PAP and its depletion leads to poly(A) tail shortening S. pombe suggests the primary function of Pab2 in decay is to recruit the exosome to polyadenylated transcripts. Upon Pab2 deletion, the exosome is less efficiently recruited, resulting in increased Pla1 activity and hyperadenylation. Thus, while nuclear poly(A)-binding proteins from fission yeast, budding yeast, and humans all promote nuclear RNA decay, the mechanistic details appear to differ considerably.Perhaps a better model for human PABPN1 is the fission yeast Pab2 protein, which shares 47% identity and 66% similarity with PABPN1 A recent RNA-seq study demonstrated that PABPN1 depletion had little effect on the levels of most transcripts, but a subset of nuclear lncRNAs were stabilized PABPN1 and PAPα are generally thought to be required for the initial polyadenylation of nascent transcripts. However, under conditions in which we observed effects on hyperadenylation and RNA stability of our reporters and the endogenous NEAT1, we see little or no effect of PABPN1 knockdown on steady-state mRNA levels, mRNA nucleocytoplasmic distribution or in the accumulation of IFN-stimulated mRNAs. Moreover, changes in poly(A) tail lengths of spliced β-globin reporters or bulk newly-made cytoplasmic poly(A) tails were not as large as those observed for nuclear RNAs. There are several possible interpretations of our seemingly contradictory data. First, PABPN1 and PAP are required for both initial polyadenylation and subsequent hyperadenylation, but our knockdown was sufficient only to affect hyperadenylation. Because the PAPs are catalytic factors, low amounts may be sufficient for activity. Additionally, low levels of PABPN1 can stimulate PAPs in vitro The TetRP-driven PANΔENE and β-globin reporter constructs were described previously (TRP-PANΔ79) Cells were transfected with 10 nM siRNA , and plus or minus 0.5 µM dT40. We found that the addition of poly(A) was necessary to counter a trace deadenylation activity present in the reaction. In 5′ CAATGTTCTTACACGACTTTGAAACTTCTGACAAATGCC 3′). Following digestion, 180 µL of G50 buffer  was added to stop the reaction. After PCA extraction, the RNA was precipitated in 70% ethanol and run on a 1.6% agarose gel alongside a 0.1 to 2 kb RNA ladder (Invitrogen). Blots were probed with an end-labeled oligo probe specific to the 3′ end of PANΔENE RNA. Poly(A) tail lengths were estimated by comparison with the size marker. For analysis of endogenous transcripts 90 µg total RNA was treated with RNase H and DNA oligos complementary to the 3′ ends of MALAT1 (5′ AAGCACCGCTTGAGATTTGGG 3′), NEAT1 (5′ TTCCAAACTGATTTTAGGTGA 3′), and XIST (5′ TAGACAAACCTTGTAAATGC 3′). The final concentration of each DNA oligo was 0.5 µM in a 60 µL reaction. Following RNase H treatment, RNA was poly(A) selected according to standard protocols and half of each sample was treated with RNase H and oligo(dT) and the 3′ cleaved fragments were detected by northern blot.Total RNA (1–3 µg) was incubated at 37°C for 1 hour in a 20 µL mixture containing 0.3 U RNase H (Promega), 20 U RNasin (Promega), 300 ng poly(A) RNA (Sigma), 10 mM dithiothreitol, 100 mM KCl, 10 mM MgClTranscription pulse-chase assays were performed as previously described fast) pathway was set to ≥0.046 corresponding to a t1/2≤15 2 values  using default parameters with two additional constraints. First, the decay constant for the rapid decay , anti-PAPα , anti-PAPγ , anti-RRP6 (EXOSC10) , anti-DIS3 , anti-β-actin . Quantitative westerns were performed using infrared detection with an Odyssey Fc and quantification was performed using ImageStudio software (LI-COR Biosciences).5′ GATATTGAATCGAAAATCATATCTTTGATAATAGACTACTCAAGACTTTGTCCCGATTCTCCTTTAAACTTGAAG-Btn 3′) was added to the reaction in order to control for differences in recovery. The loading control was detected with a complementary DNA oligo (5′ GTCTTGAGTAGTCTATTATCAAAGATATGATTTTCGATTC 3′).Click-iT Nascent RNA Capture Kit (Invitrogen) was used with modifications described in 40 probe end-labeled with T4 polynucleotide kinase.Total or SA-captured biotinylated RNA was incubated at 37°C for 15 minutes in a 30 µL reaction containing 20 mM TrisHCl (pH 6.8), 8 U RNasin and 150 U RNase T1 (Ambion). The reaction was stopped by adding 170 µL G50 buffer containing 0.1 mg/mL Proteinase K (Fisher) and incubated at 37°C for 30 minutes. After PCA extraction and ethanol precipitation, products were separated on a 1.8% agarose-formaldehyde gel, and analyzed by northern blot using a dT2, 4 mM spermidine, 10 mM DTT, 200 ng T7-driven template, 0.5 mM ATP, CTP, and GTP, 2 U/µL RNasin, 50 µM UTTPαS (TriLink Biotechnologies), 25 µCi of α-32P-UTP (800 Ci/mmol), and T7 RNA polymerase. After visualizing the target RNA, the membrane was incubated at 65°C for 30 minutes in a solution of 200 mM sodium phosphate buffer (pH = 7.2), 50% formamide, 7% SDS, and 6.25 mM I2. Immediately following the cleavage reaction, the probe was stripped by washing the membrane twice in boiling 0.5% SDS.Standard northern blotting techniques AGCCTCAAGATCATCAGCAATG, GAPDH 3′ ATGGACTGTGGTCATGAGTCCTT. Viperin (QT01005256) and OAS2 (QT00005271) primers were obtained from Qiagen.Two days following siRNA transfection, cells were treated for five hours with 100 U/mL human interferon-α A (PBL Interferon Source). RNA was harvested, and cDNA was made with random hexamers as primers using standard molecular biology techniques. Quantitative real-time PCR was performed using iTaq Universal SYBR Green Supermix (BIO-RAD). Primers: GAPDH 5′ 2, 2 mM MgCl2, 0.1 mM EDTA, 10 mM Tris (pH = 8.0), 1 mM DTT, 0.4 U/µL RNasin, and 0.5% Igepal) and incubated for 5 minutes on ice. The cell lysates were spun at 500 g for 5 minutes at 4°C. The supernatant (“cytoplasmic” fraction) was added to 5 mL TRI Reagent and the pellet (“nuclear” fraction) was resuspended in 600 µL Buffer I before addition of 5 mL TRI Reagent. In order to ensure consistent loading between samples, we poly(A) selected 40 µg of RNA from the cytoplasmic fraction and 10 µg of RNA from the nuclear fraction. To control for the quality of the fractionation, an aliquot of non-poly(A) selected RNA was probed for pre-ribosomal RNA using a DNA oligo probe (5′ AACGATCAGAGTAGTGGTATTTCACC 3′).Two days following knockdown, cells from 10 cm plates were trypsinized and resuspended in ice-cold media. Cells were pelleted with gentle centrifugation at 500 g for 3 minutes at 4°C, and then washed with ice-cold PBS. Following the wash step, cells were resuspended in 600 µL of ice cold Buffer I  Quantitative western blot analysis of total lysate from cells transfected with either a control siRNA or an siRNA pool against PABPN1. The blot was probed with antibodies against PABPN1 and β-actin (loading control) and the signal was quantified relative to the β-actin loading control. (C) Western blot analysis of protein from the time zero samples in Related to (TIF)Click here for additional data file.Figure S2Related to (TIF)Click here for additional data file.Figure S3A) Decay curves of PANΔENE from cells transfected with the indicated siRNAs (n = 3). (B) Northern blot analysis of PANΔENE RNA from cells transfected with independent pools of siRNAs targeting PAPα and PAPγ (C) Quantitative western blot analysis of total lysate from cells transfected with either a vector control, or LALA PABPN1. The blot was probed with antibodies against either PABPN1 (green) or β-actin (red) as a loading control. The relative abundance of PABPN1 normalized to β-actin is indicated below each lane. Importantly, because these values include protein from untransfected cells, they are likely an underestimate of the degree of PABPN1 overexpression. (D) Relative poly(A) tail lengths of PANΔENE RNA following LALA overexpression. RNA from cells treated with siPABPN1 was included for comparison. (E) Relative poly(A) tail lengths of PANΔENE RNA following cordycepin treatment. PANΔENE RNA was cleaved with RNase H targeted by NC30 and oligo(dT) as indicated. The cleaved RNA was analyzed by northern blot using a 3′ end-specific probe. We estimated that the poly(A) tails in the presence of cordycepin were between ∼20–50 nt. (F) Representative transcription pulse assay of βΔ1,2 from cells induced in the presence or absence of cordycepin. (G) Linear interpolation of the results from (C)(n = 3). (H and I) Same as in (F) and (G) except spliced β-globin (βΔ1) was assayed. The same blot and exposure are shown for both panels in (H).Related to (TIF)Click here for additional data file.Figure S4A) Decay curves of PANΔENE from cells transfected with the indicated siRNAs (n = 3). (B) A comparison of the relative lengths of PANΔENE from cells transfected with the indicated siRNAs. RNA is from the time zero samples (lanes 2 and 10 in C) Results from a transcript pulse assay of PANΔENE from cells transfected with two independent pools of siRNAs targeting RRP6 and DIS3.Related to (TIF)Click here for additional data file.Figure S5A) Quantitation of the percent of intronless β-globin which was “extremely hyperadenylated” in each lane of B) Results from a transcription pulse assay of PANΔENE from cells transfected with the indicated siRNAs. Blot was overexposed to reveal the extremely hyperadenylated RNAs (double asterisks).Related to (TIF)Click here for additional data file.Figure S6A) Bulk poly(A) tail analysis from cells transfected with either siControl or siPABPN1. Prior to harvesting the RNA, cells were incubated with EU for the indicated pulse times. To compensate for the shorter pulse times, increasing amounts of total RNA was used for the click reaction: 0.5 µg for the 120′ pulse, 1 µg for the 60′ pulse, and 2 µg for the 30′ pulse and the no EU sample. An exogenously added biotinylated DNA oligo (“Loading control”) was used to control for recovery. Note that this DNA only controls for RNA recovery and loading during the experimental procedure and does not necessarily reflect the total amounts of input RNA. (B) Nuclear/cytoplasmic distribution of poly(A) tails made in the presence or absence of PABPN1. Cells were separated into nuclear and cytoplasmic fractions following a two-hour EU pulse.Related to (TIF)Click here for additional data file.Figure S7A) Northern blot analysis of nuclear and cytoplasmic fractions following PABPN1 depletion. The blot was probed for β-actin and GAPDH, as well as pre-ribosomal RNA in order to control for the quality of the fractionation. The amount of RNA loaded was kept constant at a 4∶1 cytoplasmic∶nuclear ratio. (B) Quantification of the results in panel (A) (n = 3).Related to (TIF)Click here for additional data file.Table S1Kinetic parameters estimated from regression analysis of decay data.(DOCX)Click here for additional data file.Table S2siRNAs used in this study.(DOCX)Click here for additional data file.


Sobemovirus genome consists of polycistronic single-stranded positive-sense RNA. The first ORF encodes P1, a suppressor of RNA silencing required for virus movement. The coat protein (CP) is expressed from the 3′ proximal ORF3 via subgenomic RNA. In addition to its structural role, the CP of some sobemoviruses has been reported to be required for systemic movement and to interact with P1. The aim of this study was to analyse the role of Cocksfoot mottle virus (CfMV) CP in the suppression of RNA silencing and virus movement.The Agrobacterium-mediated transient expression method was used for testing CfMV CP capacity to suppress RNA silencing. CP substitution and deletion mutants were generated to examine the role of this protein in CfMV infection, using three host plants . The viral movement was characterised with CfMV expressing EGFP fused to the C-terminus of CP.In the current study we show that CfMV CP is an additional RNA silencing suppressor. Interestingly, we observed that all CP mutant viruses were able to infect the three tested host plants systemically, although usually with reduced accumulation. CfMV expressing EGFP was detected in epidermal and mesophyll cells of inoculated leaves. Although EGFP fluorescence was not detected in upper leaves, some plants displayed CfMV symptoms. Analysis of the upper leaves revealed that the viruses had lost the EGFP sequence and sometimes also most of the CP gene.The present study demonstrates that CfMV CP suppresses RNA silencing but, surprisingly, is dispensable for systemic movement. Thus, CfMV does not move as virion in the tested host plants. The composition of the movement RNP complex remains to be elucidated. Sobemovirus is a small genus of plant viruses with single-stranded positive-sense RNA genome (for review -end labelled DNA oligo. As loading control we detected U6 snRNA in the same membrane with a [32P]-end labelled oligo according to Akbergenov et al. [32P]-labelled in vitro transcript corresponding to the anti-sense strand of GFP. The membrane was washed with greater stringency. Radioactive signals were detected using Personal Molecular Imager FX (BioRad) after 20 h (for siRNA detection) or 1 h exposure (for mRNA detection).Total RNA was extracted from the infiltrated patches 7 dpi using TRIzol reagent (Invitrogen) according to the manufacturer’s instructions. For the analysis of GFP siRNA 20 μg of total RNA were denatured and loaded on 15% polyacrylamide gel . The gel was run at 400 V for 3.5 h and then cut in the middle. The lower half of the gel was transferred to Hybond N + -membrane by electroblotting in 0.5 X TBE buffer at 10 V overnight. ULTRAhyb-Oligo buffer (Ambion) was used for overnight hybridisation at 42°C. As a radioactive probe DNA oligo containing a sequence complementary to GFP (5′-CTCTTGAAGAAGTCGTGCCGCTTCATATGA-3′) was end-labeled with v et al. . The NorThe authors declare that they have no competing interests.AO conceived the study, constructed CP mutants and carried out all experiments involving these mutants. KK performed the experiments involving EGFP expressing CfMV viruses. CS and JG carried out the RNA silencing suppression experiments. AO, KK and CS drafted the manuscript. ET conceived the study and helped to draft the manuscript. All authors read and approved the final manuscript.


Arabidopsis thaliana) was performed.RNA editing is a transcript-based layer of gene regulation. To date, no systemic study on RNA editing of plant nuclear genes has been reported. Here, a transcriptome-wide search for editing sites in nuclear transcripts of Arabidopsis  maturation.MPSS  and PARE  data retrieved from public databases were utilized, focusing on one-base-conversion editing. Besides cytidine (C)-to-uridine (U) editing in mitochondrial transcripts, many nuclear transcripts were found to be diversely edited. Interestingly, a sizable portion of these nuclear genes are involved in chloroplast- or mitochondrion-related functions, and many editing events are tissue-specific. Some editing sites, such as adenosine (A)-to-U editing loci, were found to be surrounded by peculiar elements. The editing events of some nuclear transcripts are highly enriched surrounding the borders between coding sequences (CDSs) and 3′ untranslated regions (UTRs), suggesting site-specific editing. Furthermore, RNA editing is potentially implicated in new start or stop codon generation, and may affect alternative splicing of certain protein-coding transcripts. RNA editing in the precursor microRNAs (pre-miRNAs) of To our knowledge, the results provide the first global view of RNA editing in plant nuclear transcripts. RNA editing, defined as any site-specific alteration in RNA sequences including insertion or deletion of nucleotides and base conversion, is an effective way of post-transcriptional gene regulation, and has been widely investigated in animals and plants -3. DiffeHere, we carried out an extensive search for potential editing sites in nuclear transcripts utilizing mRNA MPSS and PARE data. The results indicate that RNA editing is an essential RNA-based regulatory layer not only for mitochondrial and chloroplast genes but also for nuclear genes. The data presented could serve as a repository for further analyses, and it will lead to a shift of RNA editing research from well-characterized mitochondrial and plastid transcripts to nuclear transcripts in plants.ftp://ftp.arabidopsis.org/home/tair/) [http://www.mirbase.org/cgi-bin/mirna_summary.pl?org=ath) [http://mpss.udel.edu/at/) and the PARE database (http://mpss.udel.edu/at_pare/), respectively [The Arabidopsis genome information and the GO annotations were retrieved from TAIR  . The miRorg=ath) . The MPSectively ,14.in planta, and the possibility that the editing site may be a feint one generated by sequencing errors can be greatly reduced. The clustering results were visualized by using Treeview [We retrieved MPSS sequences from 17 different libraries with normalized expression data . The editing ratio for each editing site was defined as the expression value of all edited reads divided by that of the total reads surrounding the editing site. The single-base sequencing error rate of MPSS was estimated to be ~5.00% ,16. ThusTreeview .http://weblogo.berkeley.edu/logo.cgi) was used for sequence conservation analysis. GO::TermFinder [http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi) was used for pre-miRNA secondary structure prediction. miRU [http://bioinfo3.noble.org/miRNA/miRU.htm) was used for miRNA target prediction.WebLogo   signatures) or ~4.25% 17-nt ones) for MPSS sequencing platform ,16, and 7-nt onesThe result indicates that all 12 RNA editing patterns may exist in the nuclear transcripts, although the number of editing sites in a specific pattern varies widely Fig. . Previoucis-element surrounding the editing site was required for the recognition by PPR-associated editing enzyme in plants [cis-elements surrounding the editing sites may exist in nuclear transcripts. To better understand the mechanisms implicated in various RNA editing processes, delicate experiments are needed for cis-element identification, editing enzyme isolation, and editing site validation.The 100-nt sequences  cleavage, has disappeared after editing , is subjected to leaf-specific editing : polyadenylation; nt: nucleotide; DCL1: Dicer-like 1.The authors declare that they have no competing interests.YJM collected high-throughput sequencing data, designed major parts of the experiment, and wrote the paper. DJC performed the analytical research. YFJ and CZM gave many useful suggestions for our analyses, and helped to improve the manuscript. PW and MC conceived of the study, and participated in its design and coordination, and helped to draft the manuscript. All the authors have read and approved the final manuscript.Prediction criteria: more than two candidate editing sites should be present in one transcript and each editing site must be supported by more than five distinct short-read sequences.Click here for filePrediction criteria: each editing site in one pre-miRNA should be supported by more than two distinct short-read sequences.Click here for fileData S7 Detailed information of the MPSS and PARE data utilized in this study.Click here for fileData S3 100-nt sequences surrounding the editing sites in protein-coding genes, which were utilized for conserved element detection. The random sequences for control analysis are included.Click here for fileData S4 Results of searching for conserved elements surrounding the editing sites in protein-coding genes. The results of three replicates of control analysis are included.Click here for fileData S5 Result of GO term enrichment analysis of edited protein-coding genes in Arabidopsis.Click here for fileFigure S1 Clustering analysis of RNA editing sites.Click here for fileData S6 Codon variation by RNA editing in nuclear protein-coding genes (only the edited codons resided in CDSs were considered).Click here for fileath-MIR854a, ath-MIR854b, and ath-MIR854d.Figure S2 Secondary structure transformation of edited Click here for file


Carcharhinus amblyrhynchos), whitetip reef sharks (Triaenodon obesus), and the group “all reef sharks” increased substantially as human population decreased and as primary productivity and minimum sea surface temperature  increased. Simulated baseline densities of reef sharks under the absence of humans were 1.1–2.4/ha for the main Hawaiian Islands, 1.2–2.4/ha for inhabited islands of American Samoa, and 0.9–2.1/ha for inhabited islands in the Mariana Archipelago, which suggests that density of reef sharks has declined to 3–10% of baseline levels in these areas.Sharks and other large predators are scarce on most coral reefs, but studies of their historical ecology provide qualitative evidence that predators were once numerous in these ecosystems. Quantifying density of sharks in the absence of humans (baseline) is, however, hindered by a paucity of pertinent time-series data. Recently researchers have used underwater visual surveys, primarily of limited spatial extent or nonstandard design, to infer negative associations between reef shark abundance and human populations. We analyzed data from 1607 towed-diver surveys (>1 ha transects surveyed by observers towed behind a boat) conducted at 46 reefs in the central-western Pacific Ocean, reefs that included some of the world's most pristine coral reefs. Estimates of shark density from towed-diver surveys were substantially lower (<10%) than published estimates from surveys along small transects (<0.02 ha), which is not consistent with inverted biomass pyramids (predator biomass greater than prey biomass) reported by other researchers for pristine reefs. We examined the relation between the density of reef sharks observed in towed-diver surveys and human population in models that accounted for the influence of oceanic primary productivity, sea surface temperature, reef area, and reef physical complexity. We used these models to estimate the density of sharks in the absence of humans. Densities of gray reef sharks  es obstaculizada por la falta de datos de series de tiempo pertinentes. Recientemente, los investigadores han utilizado muestreos visuales submarinos, de extensión espacial limitada o de diseño no estándar, para inferir asociaciones negativas entre la abundancia de tiburones de arrecife y las poblaciones humanas. Analizamos datos de 1607 muestreos por remolque de buzos (transectos >1ha muestreados por observadores remolcados por una lancha) realizados en 46 arrecifes en el Océano Pacífico centro-occidental, arrecifes que incluyeron algunos de los más prístinos del mundo. Las estimaciones de densidad de tiburones fue sustancialmente menor (<10%) que estimaciones publicadas a partir de muestreos a lo largo de transectos pequeños (<0.02 ha), lo cual no es consistente con las pirámides de biomasa invertidas (la biomasa de depredadores es mayor que la biomasa de presas) reportadas para arrecifes prístinos por otros autores. Examinamos la relación entre la densidad de tiburones de arrecife observados en los muestreos por remolque de buzos y la población humana en modelos y consideramos la influencia de la productividad oceánica primaria, la temperatura de la superficie del mar, la superficie del arrecife y su complejidad física. Utilizamos estos modelos para estimar la densidad de tiburones en ausencia de humanos. Las densidades de  Sharks are high-level predators whose importance in oceanic ecosystems is increasingly recognized . Over thGinglymostoma cirratum), reef sharks are largely absent on Caribbean reefs and occur primarily where human population densities are low. 2 in Sandin et al.  of iterations). We present model results for only the variable humans within 200 km because it performed best statistically and we believe it is conceptually better for this region than log of humans per kilometer of reef, which does not account for the effect of humans around unpopulated islands near large population centers.The WinBUGS model diagnostics showed clear model convergence (Supporting Information). Of the 3 variables pertaining to human effects, the log of humans per square kilometer of reef and the log of humans within 200 km were selected in almost every MCMC iteration (average w < 0.25) in any of our models. Goodness of fit  for the best models of all 3 model sets were reasonably close to 0.5 (0.59 for model including temperature and 0.4 for model including reef area).Variable selection in both model sets (A with minimum temperature and B with reef area) followed a similar pattern for all reef sharks and gray reef sharks: models including humans within 200 km, primary productivity, and either minimum temperature (model set A) or reef area (model set B) had the highest posterior weight (>0.94) . For whi−2·day−1 increase in primary productivity .In all cases, shark densities increased as oceanic primary productivity and minimum SST increased, but decreased as humans within 200 km and reef area increased . Densitiuctivity , respectThe human effect on shark density had a similarly declining power function for all 3 groups of sharks; the strongest effect was on gray reef sharks , regardless of oceanic conditions , whereasPossible explanations for the reduced abundance of reef sharks near human populations include fishing and a reduction of the sharks’ prey base. Although there are currently no active commercial reef shark fisheries around any U.S. Pacific island, reef sharks may be fished recreationally, taken incidentally, killed because they are perceived as a nuisance, and possibly taken illegally for their fins . AssociaAs expected, our models indicated reef shark abundance is also substantially influenced by oceanic primary productivity. Isolated reefs located in warm waters with the highest productivity had the highest shark densities . In contrast, isolated reefs in warm but relatively unproductive waters  had fewer reef sharks (around 1.7 sharks/ha). The positive influence of primary productivity on marine animal production has been established theoretically  and empir=–0.66), however, complicates interpretation of their effect on shark densities because it is impossible to separate their effects . Contrary to expectations, shark densities were higher at smaller reefs. Such patterns may arise for several reasons. First, reef area was positively correlated with human-effect variables (r∼ 0.22) because larger islands tended to be the most heavily populated. Second, small, isolated islands are often the only available shallow habitat in large ocean expanses and may thus attract transient reef sharks. Movement patterns of reef sharks are poorly understood, but gray reef sharks, for example, are capable of traversing large (>120 km) stretches of open water   and times (daylight hours), not of the entire shark community. Peak densities of reef sharks may occur at night  or at deumbeus])  not encoOne important difference between our results and those of previous studies in the region is the use of data from towed-diver surveys , which gAlthough biased estimates of shark density may be of little consequence when they are used only to compare relative differences in shark populations , such biases are important when shark abundance (or biomass) estimates are compared with other components of the reef fish community. The very high shark densities reported in Our simulation of baseline shark densities, combined with other recent studies , supportThe absence of sharks and other large predators on coral reefs influenced by humans may affect these ecosystems via trophic cascades , prey be


Hepatitis delta virus (HDV) is a distant relative of plant viroids in the animal world. Similar to plant viroids, HDV replicates its circular RNA genome using a double rolling-circle mechanism. Nevertheless, the production of hepatitis delta antigen (HDAg), which is indispensible for HDV replication, is a unique feature distinct from plant viroids, which do not encode any protein. Here the HDV RNA replication cycle is reviewed, with emphasis on the function of HDAg in modulating RNA replication and the nature of the enzyme involved. BeingUnlike other RNA satellite viruses which rely on the RNA-dependent RNA polymerase (RdRp) provided by the coexisting helper virus for genome replication, the dependence of HDV on HBV is limited to the supply of HBsAg for the production of HDV viral particle. Similar to plant viroids which do not encode RdRp, HDV undergoes robust RNA replication autonomously once inside the cells. Thus, it is certain that HDV and plant viroids have to replicate their RNA genome using a cellular enzyme(s). Unlike plant viroids which do not encode any protein, HDV encodes a protein, hepatitis delta antigen (HDAg), which is intimately involved in its RNA replication. In addition, HDV RNA not only has to replicate itself but also needs to transcribe a subgenomic mRNA species coding for HDAg. The transcription of the HDAg-encoding mRNA has all of the hallmarks of the cellular mRNA transcription except for the nature of the template (DNA versus RNA). Therefore, distinct from plant viroids, HDV represents a hybrid of the conventional DNA-dependent transcription and the unique RNA-dependent RNA synthesis in the absence of an RdRP. To coordinate with this sophisticated and unique RNA amplification process in mammalian cells, HDAg plays important regulatory roles which will be reviewed herein. Additionally, the nature of the enzyme involved in HDV RNA replication will also be addressed.2.The HDV virion is a spherical particle of about 36-nm in diameter, which contains an envelope with HBsAg and a nucleocapsid containing an RNA genome in complex with HDAg ,8. Genom3.l-HDAg; 27 kDa). The amino acid sequences of these two isoforms are identical except that l-HDAg contains an additional 19 amino acids at the very end of its C-terminus, which results from a specific RNA editing event at the termination codon during the late stage of viral replication [C-terminal extension in the l-HDAg, these two HDAg isoforms share several functional domains, including RNA-binding motifs, a nuclear localization signal (NLS), a coiled-coil domain, a helix-loop-helix motif, and a C-terminal stretch of proline- and glycine-rich sequence  and the large isoform of 214 amino acid residues in length , which is absolutely required for HDV replication . This sutrp-coding sequence, thus extending the reading frame for an additional 19 amino acids to encode l-HDAg. At the late stage of HDV replication, interaction of l-HDAg with HBsAg, which is provided by its helper virus HBV, leads to the production of virus particles [During HDV RNA replication, an RNA editing event occurs on the antigenomic strand ,35 by a articles ,4.5.in trans [in vitro transcription of partial HDV RNA sequences have been achieved in the absence of S-HDAg. Nevertheless, the length of the transcripts obtained in such in vitro reactions was usually very short [Unlike plant viroids which do not have coding capacity, approximately 3/4 of the length of the HDV RNA rod-like structure encompasses the HDAg-coding region . Due to in trans –38. Thusry short –41 and try short . Thus, S6.S-HDAg has been known to transport HDV RNA to its site of replication. A nuclear import assay demonstrated that HDAg mediates nuclear import of HDV RNA, and that both the NLS and RNA-binding motif of S-HDAg are required for the RNA-transporting activity . Additioin vitro transcription system, S-HDAg was shown to be able to promote RNA elongation by displacing NELF-A [in vitro [Given that S-HDAg possesses no RdRp activity and that HDV undergoes robust RNA replication autonomously once inside the cells, it is certain that the replication of HDV RNA, similar to that of plant viroids, is mediated by a cellular polymerase(s). Several lines of evidence suggest that cellular DNA-dependent RNA polymerase II (Pol II) is involved in HDV RNA replication (see below). Other than serving as a transporter of HDV RNA, S-HDAg is also likely a component of the RNA synthetic machinery. S-HDAg has many features reminiscent of transcription regulators. It is a nuclear protein which contains coiled-coil and helix-loop-helix domains. Similar to many DNA-dependent transcription factors and core histones, which are subject to posttranslational protein modifications, S-HDAg can also be phosphorylated, acetylated and methylated, and these modifications play regulatory roles in HDV RNA replication –22. Besig NELF-A . Furtherg NELF-A . Althougin vitro ,44,45. Ain vitro . Collectin vitro studies showed that the N-terminal domain of HDAg can modulate the self-cleavage activities of HDV RNA fragments and facilitate a trans-acting hammerhead ribozyme to find its target in RNAs of various sequences and lengths [HDAg may also play a role in posttranscriptional RNA processing. As mentioned above, HDV RNA replication is thought to be carried out through a double-rolling-circle mechanism, in which the autocatalytic self-cleavage and the subsequent ligation process are involved. In an in vivo study, HDAg was shown to be able to enhance the self-cleavage activity of HDV RNA and the subsequent circularization of HDV RNA . Moreove lengths –50. Basel-HDAg is accumulated through time. l-HDAg contains all the functional domains of S-HDAg, with additional 19 amino acids at the C-terminus. However, the biological effects of these two isoforms are much different. In addition to playing a prerequisite role in virion packaging [l-HDAg has been shown to potentially inhibit HDV RNA replication, thus playing a modulating role in viral RNA replication [l-HDAg to regulate HDV RNA synthesis in the natural HDV life cycle is still in debate. Although the excessive RNA editing was shown to result in the inhibition of RNA replication of some HDV genotypes [l-HDAg per se or the editing-induced hypermutations of the HDV genome [l-HDAg did not affect the steady-state level of HDV RNA late in the viral replication cycle [l-HDAg does or does not inhibit HDV RNA replication, its role in HDV RNA replication is of considerable interest, as l-HDAg is usually co-localized with S-HDAg [During HDV replication, the amount of ackaging ,18, l-HDlication ,52. Howeenotypes ,53, it iV genome . Furtheron cycle . Regardlh S-HDAg  and has h S-HDAg , which ih S-HDAg .7.in vitro approaches, provided further evidence on the involvement of this polymerase in HDV RNA replication [Cellular Pol II is responsible for DNA-dependent RNA synthesis during gene transcription. Nevertheless, there is evidence that Pol II also possesses RdRP activity ,60. Pol lication ,62–64. Rlication ,66. Furtlication . In thatlication ,41, it il-HDAg when the latter is expressed at the beginning of the replication cycle, whereas the antigenomic RNA synthesis is not inhibited [HDV RNA replication includes the transcription of HDAg-encoding mRNA (from the genomic template), the synthesis of antigenomic RNA , and the synthesis of genomic RNA (from the antigenomic template). Indeed, it has been demonstrated that HDV mRNA transcription and genomic RNA synthesis are both inhibited by α-amanitin at 1 to 5 μg/mL, consistent with α-amanitin sensitivity of Pol II ,63,68. Snhibited . FurtherThe finding that the synthesis of the antigenomic RNA is insensitive to high concentration of α-amanitin ,63,68 su8.In recent years, the prevalence of HDV infection is decreasing partly ascribed to HBV vaccination. Nevertheless, HDV remains an important model system for the understanding of RNA biology. HDV RNA genome replication involves its only protein HDAg and several steps of RNA amplification and processing. There are still many unanswered questions concerning these events; chief among them is the nature of the RNA replication enzymes involved in HDV life cycle. Pol II has long been suspected, but Pol I has now been implicated in the antigenomic RNA synthesis. Furthermore, it has been recently shown that not only Pol II and Pol I but also Pol III directly bind to HDV RNA genome ,72, suggWhat is the detailed mechanism of individual posttranslational modification of S-HDAg involved in modulating Pol II-mediated HDV antigenomic RNA synthesis and HDAg-encoding mRNA transcription? For this aspect, antibodies to posttranslationally modified S-HDAg may be powerful tools to dissect roles of the modified S-HDAg.What is the crosstalk between different posttranslationally modified S-HDAg? Whether S-HDAg can be posttranslationally modified  sequentially and/or synergistically for its function in redirecting the Pol-II transcription complex is worthy of further exploration.The fact that unmodified S-HDAg can support HDV antigenomic RNA synthesis implies that the crosstalk between S-HDAg and Pol I transcription complex (or other novel polymerases) is simpler as compared to the crosstalk between modified-S-HDAg and Pol II transcription complex. Hence, an in-vitro reconstitution assay of Pol I-mediated HDV antigenomic RNA synthesis might be feasible.A unique feature of HDV RNA replication is its strict requirement for HDAg. There are a number of important issues in this regard requiring further investigation:HDV RNA replication represents a new facet of cellular transcription machinery. The answers to how the cellular enzymes are redirected and regulated for HDV RNA synthesis will reveal new insights into the molecular biology of cells and viruses.


Next-generation sequencing plays a central role in the characterization and quantification of transcriptomes. Although numerous metrics are purported to quantify the quality of RNA, there have been no large-scale empirical evaluations of the major determinants of sequencing success. We used a combination of existing and newly developed methods to isolate total RNA from 1115 samples from 695 plant species in 324 families, which represents >900 million years of phylogenetic diversity from green algae through flowering plants, including many plants of economic importance. We then sequenced 629 of these samples on Illumina GAIIx and HiSeq platforms and performed a large comparative analysis to identify predictors of RNA quality and the diversity of putative genes (scaffolds) expressed within samples. Tissue types  varied in RNA quality, sequencing depth and the number of scaffolds. Tissue age also influenced RNA quality but not the number of scaffolds ≥1000 bp. Overall, 36% of the variation in the number of scaffolds was explained by metrics of RNA integrity (RIN score), RNA purity (OD 260/230), sequencing platform (GAIIx vs HiSeq) and the amount of total RNA used for sequencing. However, our results show that the most commonly used measures of RNA quality  are weak predictors of the number of scaffolds because Illumina sequencing is robust to variation in RNA quality. These results provide novel insight into the methods that are most important in isolating high quality RNA for sequencing and assembling plant transcriptomes. The methods and recommendations provided here could increase the efficiency and decrease the cost of RNA sequencing for individual labs and genome centers. The first challenge is to isolate RNA of sufficient quality  and yield. RNases are particularly problematic in this regard because they rapidly degrade RNA and are widespread in nature and laboratories www.onekp.com). To achieve 1KP’s goals we have overcome biological, logistical and technological impediments that had hitherto not been attempted on such a scale.1KP is seeking to address these problems by sequencing transcriptomes from more than 1000 plant species. The project involves an unprecedented interdisciplinary collaboration of over one hundred researchers from around the world. The overall goal of the project is to create genomic resources for one thousand plant species sampled from across the plant kingdom, including plant species of importance in medicine, agriculture, forestry and conservation. The vast majority of these species are non-model organisms with little to no existing molecular tools. Thus, these data will constitute a cornucopia of genetic information to be used for the biotechnological engineering of crops, development of biofuels, genetic and biochemical characterization of medicinally important plants, and a comprehensive phylogenetic understanding of how plant life has evolved during the past 1 billion years  leaf (489 samples), ii) flower (4), iii) fruit (10), iv) buds (leaf or flower) (15), v) shoot/stem (7), vi) below-ground , vii) mixed tissues (two or more of tissues i–vi) (276) and viii) algal cells (274). Care was taken to properly differentiate and categorize tissue types, but some samples inevitably had overlapping cell types with other tissue types and we therefore view differences among tissues as conservative patterns. For a subset of 71 species, we also tested the effects of tissue age on RNA quality and sequencing success by comparing “young” freshly expanding leaves and “mature” fully expanded but non-senescing leaves collected from tissue that was pooled from at least two healthy plants grown together in the greenhouse. For these samples we used approximately 0.1–0.5 g of tissue from young leaves and roughly 2× as much (up to 1 g) for old leaves; more tissue was required from mature leaves to achieve equivalent concentrations as young leaves (see Results). The complete data set, including the list of all samples, tissues and data, is provided in No single protocol was optimal for isolating total RNA from all tissues and species. We therefore used 18 distinct protocols to isolate total RNA from samples, where the method used for specific species and tissues was shaped by the experience of individual researchers. Protocols involved commercially available kits , non-commercial RNA extraction lab protocols  and hybrid methods that combined components of both commercial kits and lab protocols. The detailed protocols used for all isolations are available online in For all isolations, tissues were collected and flash frozen in liquid nitrogen as soon after collection as possible. In most cases, flash freezing was performed immediately, although in some field collections there was a short delay before freezing, which might account for cases of severe degradation. Slightly less than half of the samples (493) were sent as frozen tissue on dry ice to BGI-Shenzhen (hereafter ‘BGI’), China, for RNA isolation. The remaining samples were isolated by individual labs and sent to BGI as either frozen RNA extracts on dry ice or as dehydrated total RNAs shipped using Genvault’s GenTegra RNA kit . A comparison of a subset of our samples (N = 168) allowed us to compare effects of shipping liquid RNA versus Genvault dehydrated RNA (see Results).In answering our first research question relating to the success rates of Qiagen’s RNeasy Plant Minikit and alternative protocols, we deemed samples acceptable for sequencing if they met the following criteria: total RNA concentration ≥150 ng/µl  or TE (pH 8.0) is expected to exhibit a ratio of 2.0–2.1; deviations below 2 indicate acidic pH or contamination by proteins, phenol and other impurities . We also measured OD 260 nm/230 nm from 406 samples, where pure RNA gives a ratio close to 2. Deviations of OD 260/230 below 2 indicate contamination by polysaccharides, phenol, TRIzol or low pH, which absorb light around 230 nm To ensure consistency in measurement of RNA quality, all measurements were performed at BGI. This was especially important because measures of RNA quality outside BGI often deviated substantially from measurements taken at BGI on the same samples, suggesting that methodological differences and degradation during shipping can be important sources of variation in measures of RNA quality. RNA purity was determined by assaying 1 µl of total RNA extract on a NanoDrop 1000 or NanoDrop 8000 spectrophotometer . We measured the optical density (OD) ratio between 260 nm and 280 nm from 526 samples, where pure RNA eluted in HThe concentration, integrity and yield of total RNA were determined by assaying 1 µl of diluted total RNA using Agilent’s 2100 Bioanalyzer with the Plant RNA Nano or Pico chip assay in accordance with the manufacturer’s instructions . The Bioanalyzer uses electrophoretic technology on a chip to separate RNA fragments by size, which are read by laser induced fluorescence and translated into gel-like bands and peaks (electropherograms) showing the distribution and relative amounts of RNA of different sizes . ConcentOur study sought to isolate and sequence mRNA from all cells using Illumina’s GAIIx and HiSeq sequencing platforms. Our standard procedure was to isolate polyA RNA from 20 µg of total RNA treated by DNaseI  using Dynabeads mRNA purification kit . We used up to 50 µg when the use of a lower mass  was insufficient for successful library construction, which we assessed by running final PCR products on an agarose gel; the library construction was considered to have failed when there was no visible band. We used less than 20 µg of total RNA when isolation of an important sample yielded low RNA mass but library construction was successful. Purified polyA RNA was fragmented in a fragmentation buffer  at 70°C for 90 seconds to 200–300 nt fragment sizes. The first cDNA strand was synthesized with random hexamer primers using the SuperScript II reverse transcription kit . The second-strand synthesis was performed by incubation with RNase H  and DNA polymerase . Short double-stranded cDNA fragments were purified using one of two methods. Our standard procedure was to use the QIAquick PCR purification kit , whereas for samples with low RNA mass we used Agencourt AMPure beads . Both methods were followed by end-repair with Klenow polymerase, T4 DNA polymerase and T4 polynucleotide kinase . A single 3′ adenosine (A base) was added to the double-stranded cDNA using Klenow (3′ to 5′ exo-)  and dATP . The Illumina PE Adapter oligo mix was ligated onto the A base on repaired double-stranded cDNA ends and DNA fragments of a selected size were gel-purified to make sure the insert size was 200 bp (±10% deviation). Thereafter, libraries were amplified by 15 cycles of PCR with Phusion DNA polymerase  and “indexed” paired-end PCR primers; the prepared libraries were 322 bp long. The amplified libraries were denatured with sodium hydroxide and diluted to 2.5 pM in hybridization buffer for loading onto a lane of an Illumina GAIIx or HiSeq flowcell. Read length on the GAIIx platform was typically adjusted to 73–75 bp (mean = 74 bp), but four samples were read at 100 bp. Read length on the HiSeq platform was predominately 90 bp with a small number of sequences with 84–87 bp. All samples were sequenced as paired-end reads, and up to eleven samples were multiplexed into a single lane of the Illumina flow cell.Prior to assembly of sequences into scaffolds we filtered sequence reads based on four criteria. First, all samples were indexed with unique 6–7 bp sequences and we retained reads with 0 or 1 bp sequence mismatch (>99.5% of reads). Second, we checked for the adapter sequences in the paired sequence reads and rejected pairs with more than 15 bp of continuous adapter sequence, allowing a maximum of three mismatches; this resulted in rejection of ca. 3% of reads. Third, we removed all sequence read pairs where either read had >5% uncalled bases (N’s) (<0.5% of reads). Similarly, we removed sequence read pairs when either read had more than 20 bp with Phred-type Q-score<10 (3.5% of reads). Together these four criteria ensured that contaminant sequences were eliminated from our data and only the highest quality reads were used in assemblies.We extracted several response variables for analyses from the sequencing results to depict sequencing depth. Specifically, we used total number of bases sequenced, which is the number of bases that exceeded the Phred quality 20 threshold  and the total number of sequence reads.http://soap.genomics.org.cn/SOAPdenovo-Trans.html) that is faster than Trinity and recovers more full-length transcripts from the same data set, based on a comparison to the annotated genomes for rice and mouse (unpublished results). We find that the number of large (≥1000 bp) scaffolds from our original assemblies and the newer assemblies are highly correlated , indicating that the original assemblies capture a large amount of the variation explained by higher yielding newer assemblies. We therefore decided to base our analyses on the older assemblies, since they more honestly reflect the basis on which project decisions were made and SOAPdenovo-trans has not yet been formally published, although it is available to the public (see above). Moreover, a comparison of large scaffolds (≥1000 bp) from our SOAPdenovo assemblies to GenBank’s non-redundant protein sequence database (nr) using the BLASTx sequence translation tool containing all vascular plants and algae showed that our assemblies reliably assembled known proteins; on average 88% ±1%  of scaffolds matched a known protein with high confidence (E<10−10).Transcriptomes were assembled using SOAPdenovo Our assembly methods followed Li et al. From each assembled transcriptome the response variable was the number of scaffolds ≥1000 bp, which ideally represents an estimate of the number of full length transcripts in a sample. There are potential pitfalls with defining genetic diversity of transcriptomes in this way. For example, if we focus on the number of scaffolds of all lengths as our response variable, then some genes will be represented by many small scaffolds. We clearly see this in our samples because the number of scaffolds ≥100 bp frequently exceeds 100,000, which is much larger than conventional estimates of the number of genes expressed in plant tissues r) between data sets that varied in the threshold size of scaffolds ranged between 1.00 (1000 bp vs. 900 bp) and 0.78 (1000 bp vs. 700 bp), with P<0.001 for all correlations , 600 bp, 700 bp, 800 bp, 900 bp and 1000 bp. The strength of Pearson product moment correlations  largWe tested the effect of tissue type on RNA quality, RNA yield, sequencing depth , and number of long scaffolds assembled using Proc Mixed in SAS 9.1 . We first used a likelihood ratio test (LRT) to determine whether an unequal variance model  provided a better fit for our data than a standard model that assumes homogeneity of variance. We then assessed whether our data met assumptions of normality, and we transformed data accordingly using either log or square root transformations. Pairwise differences between least-squares mean values of tissue types were compared using the Tukey-Kramer adjustment for multiple comparisons implemented using the ADJ option in SAS. Mean values were subsequently back-transformed to their original units for illustrative purposes.It is widely believed that harvesting the youngest tissue results in higher RNA quality and yield . To test this conventional wisdom, we systematically harvested young freshly expanding tissue and fully expanded mature tissue (but not “old” or senescing) from a phylogenetically diverse subset of 71 species as described above . The staWe attempted to understand the factors that provide the best predictors of the size of the assembled transcriptome, measured as the number of large scaffolds (≥1000 bp). We included four types of variables in the analyses explained below: 1) tissue type, 2) measures of RNA quality and yield, 3) factors associated with sequencing methodology, and 4) a covariate for sequencing depth (number of bases). Measures of RNA quality and yield included RNA concentration (ng/µl), total RNA yield or mass (µg), r26S/18S, RIN, OD 260/280 and OD 260/230. Sequencing methodology refers to factors that were under experimenter control; this included the mass of total RNA sequenced (µg) and the sequencing platform (Illumina GAIIx vs. HiSeq), which was also correlated with the length of sequence reads .We achieved on average 2 Gb of data across all samples . Nevertheless, there was variation in the number of bases sequenced  and we included this variable as a covariate in analyses.2) because this statistic is less sensitive to variation in sample size. The second analysis used the larger balanced data set of 512 samples, which lacked the OD ratios but contained all other variables. This data set considered all possible combinations of seven explanatory variables  and selected the best model according to the methods described above.We performed two sets of analyses because OD ratios were measured for a smaller number of samples than the other variables. In the first analysis, we included all nine variables described above, and we used maximum likelihood to compare the explanatory power of all possible linear combination of factors (511 possible models). The best model was identified according to the lowest Akaike information criterion (AIC) value; AIC measures the explanatory power of the model weighted by the number of parameters included, where lower AIC corresponds to greater explanatory power. As per convention, nested models with AIC values that differed by >2 (ΔAIC) were viewed as significantly different, whereas nested models with ΔAIC <2 were seen as statistically equivalent We focused our results on analyses that used scaffolds ≥1000 bp but we also performed analyses on smaller threshold cutoffs. Comparing results of the best-fitting models produced qualitatively identical results for all data sets with respect to the direction and statistical significance of explanatory variables. The only exception to this was that tissue type became a significant predictor of the number of scaffolds when the minimum threshold scaffold size was set to 600 bp, but this result was only found for the large data set without OD ratios.a priori guidance on the likelihood of success we asked: What is the success rate of a commonly employed commercial kit in extracting high-quality RNA for NGS , with flowers, buds and tissue mixtures yielding the most RNA and fruits producing the least , descriptors of RNA quality were not highly correlated with one another. For example, although the mass of RNA isolated from flowers was 3.5× greater than RNA mass from fruits (P = 0.03), average RIN was 37% lower in flowers than in fruits, although not significantly (P = 0.21). Means and statistical comparisons among all tissues are available online  in length, 7444 scaffolds ≥800 bp  and 4997 scaffolds ≥1000 bp . As described in the methods, 88%  of long scaffolds (≥1000 bp) matched a known protein in the GenBank nr protein database for algae and plants with high confidence (<E−10). All measures of sequencing depth and transcriptome size varied significantly among plant tissues , which was 13% higher in young tissues  than in mature tissues . There was no clear effect of tissue age on any metric of transcriptome size including the number of large scaffolds .Unexpectedly, age of tissue  had only a weak effect on RNA quality and no clear effect on sequencing results . Althougcaffolds . The onlRNA quality and covariates associated with sequencing methodology both predicted the number of large scaffolds assembled from individual samples. For the data set that included OD ratios, we compared all 511 possible linear models  using maP<0.001, r2 = 0.02), but including the number of bases in the model led to a weaker overall fit of the data (ΔAIC = 15.9) compared to models that excluded this covariate.We also used a larger data set (see Methods) that lacked OD ratios to predict the number of large scaffolds. Among all possible linear models , the modF1,167 = 6.29, P = 0.013), and this effect explained 3.6% of the total variation in the number of scaffolds among samples.Finally, we evaluated the optimal method of shipping RNA using a subset of samples (see Methods). Samples were either sent as frozen RNA extract on dry ice (N = 100) or as dehydrated total RNA (N = 68) using Genvault’s GenTegra RNA kit . Genvault samples had 17% more scaffolds on average (5675±265 scaffolds) than samples sent as frozen extracts (4842±211 scaffolds)  had a larger impact on the number of scaffolds than metrics of RNA quality. Third, Illumina sequencing and assembly of scaffolds were fairly robust to variation in RNA quality. Based on these results, we recommend researchers and genomic facilities optimize RNA quality and sequencing methodology as much as possible, but still sequence samples of low to moderate quality as these samples can yield large transcriptomes sequenced by Illumina.As expected, quality of RNA and size of transcriptomes varied among tissue types Our results identify specific metrics of RNA quality that predict the size of assembled transcriptomes. Specifically, measures of RNA integrity (RIN) and purity (OD 260/230) predicted the number of large scaffolds assembled from samples. Since RIN quantifies RNA degradation Although we identify significant predictors of transcriptome sequence/assembly quality, it is noteworthy that these predictors account for less than 10% of the total variation in the number of large scaffolds. There are two principal explanations for this weak predictive value. First, transcriptomes are by their very nature complex mixtures of many types of RNA that dynamically change in space and time P = 0.17) when we restrict our analysis to just HiSeq samples.The most counter-intuitive result was the relationship between sequencing platform and number of large scaffolds. Contrary to our expectation, Illumina’s HiSeq platform, which produced predominantly 90 bp reads, resulted in 34% fewer large scaffolds than samples sequenced on Illumina’s GAIIx platform, which produced 74 bp reads. This result is surprising because the HiSeq platform was designed to increase sequencing depth and produce longer high-quality reads that should facilitate the assembly of longer scaffolds and larger transcriptomes. Why then do we find the opposite to be true in our large and phylogenetically diverse data set? One possibility is that the sequencing chemistries of the HiSeq and GAIIx platforms are biased in their ability to sequence GC-rich templates, which could lead to systematic biases when sequencing inherently GC-rich mRNA. This does not seem to explain our result because a comparison of HiSeq and GAIIx platforms finds no clear bias in %GC content of scaffolds . A seconA third possible explanation relates to differences in the true quality of sequenced bases on the two platforms. Sequence quality is quantified as Q-scores, where higher Q indicates lower sequencing error. Unfortunately, no direct comparison of Q-scores can be made between samples sequenced on the GAIIx and HiSeq platforms because the software used to estimate Q-scores on the GAIIx platform systematically underestimates Q-scores and thus over-estimates error rates Given the results of this study and the collective experience of the many collaborators involved in 1KP, we are able to provide plant researchers with practical recommendations for maximizing success of isolating high-quality total RNA from plants for NGS also see . First, Our experience in sequencing and assembling over six hundred transcriptomes also allows us to make conclusions and recommendations that promote implementation of NGS for the study of transcriptomes. Optimizing RNA integrity (RIN) and purity (OD 260/230) will improve size of assembled transcriptomes. However, we show that NGS is robust to variation in RNA quality, and therefore researchers and genome facilities should consider sequencing samples that show significant deviation from optimal levels of RIN scores, OD 260/230 ratios and certain other metrics of RNA quality. Implementing this recommendation could save researchers and sequencing facilities considerable time and money presently allocated for optimizing samples. Also, shipping dehydrated RNA to genome centers, instead of frozen extract, can further increase sequencing success.NGS is a powerful method for studying plant transcriptomes, and it is now possible to sequence virtually all expressed genes in a given tissue. Our study developed and evaluated many protocols that can facilitate the isolation of total RNA from most green plants. We have also identified several factors that affect the sequencing and assembly of transcriptomes. As a whole, these results provide a resource to increase success and efficiency of NGS to tackle the next generation of questions in plant biology.Figure S1A comparison of the frequency of scaffolds according to variation in % GC content between samples sequenced on HiSeq versus GA II platforms. Distributions are broadly overlapping. The long right-tail from the HiSeq samples is caused by a disproportionate number of algae samples sequenced on that platform. These algae exhibited especially rich GC transcripts, which is consistent with the results of published whole genome sequences of green algae (PDF)Click here for additional data file.Table S1Raw data used to analyze RNA quality, sequencing depth and number of scaffolds. For each sample we provide the species and family name, the tissue collected , the contributor of the sample, and the RNA isolation method (numbers correspond to Supplementary Protocols). Metrics of RNA quality and yield include the mass of total RNA isolated, RNA concentration, the ratio of the large RNA ribosomal subunit to the small RNA ribosomal subunit (r28S/18S), the RNA Integrity Number (RIN), OD 260/280, and OD 260/230. Sequencing data includes the sequencing platform, read length (averaged between the paired-ends), mass of total RNA sequenced, number of reads, number of bases, number of bases that surpassed the Q20 threshold and the number of scaffolds (c) greater than 100 bp (c100), greater than 200 bp (c200), and so on and so forth. We also include the number of scaffolds that fall within bins (b) of different sizes, ranging from b100  to b1000 (number of scaffolds greater 1000).(XLS)Click here for additional data file.Table S2The success/failure of RNA isolations using Qiagen’s RNeasy Plant Minikit Click here for additional data file.Table S3Correlations in the number of scaffolds with different minimum threshold size cutoffs used during assemblies. For example, the column labeled 500 bp contains all scaffolds that are 500 bp or larger. Pearson product moment correlation coefficients (r) are shown in the upper triangular matrix, and P-values are shown in the lower triangular matrix.(PDF)Click here for additional data file.Table S4Least-squares means of descriptors of RNA quality among different plant tissues types. The least-squares mean ±1 SE and sample size are provided for each tissue type.(PDF)Click here for additional data file.Table S5P-values for a posteriori pairwise contrasts of RNA quality among tissue types. P-values are adjusted for multiple comparisons within each variable using the Tukey-Kramer correction method. P-values <0.05 are bolded.(PDF)Click here for additional data file.Table S6Least-squares means of descriptors of sequencing success among plant tissue types. The least-squares mean ±1 SE and sample size are provided for each tissue type.(PDF)Click here for additional data file.Table S7P-values for a posteriori pairwise contrasts of various sequencing metrics among different tissue types. P-values are adjusted for multiple comparisons within each variable using the Tukey-Kramer correction method. P-values <0.05 are bolded.(PDF)Click here for additional data file.Table S8The statistical fit of all possible combinations of explanatory factors in the data set that included OD ratios. Fit of models was statistically compared using maximum likelihood statistics according to the Akaike information criterion (AIC). Models are arranged in the order of best-fitting models (lowest AIC) to poorer fitting models (higher AIC). Inclusion or absence of explanatory variables from models is shown by 1 and 0, respectively.(PDF)Click here for additional data file.Table S9The statistical fit of all possible linear combinations of factors in the large data set that excluded OD ratios. The fit of models was statistically compared using maximum likelihood statistics according to the Akaike information criterion (AIC). Models are arranged in the order of best-fitting models (lowest AIC) to poorer fitting models (higher AIC). Inclusion or absence of explanatory variables from models is shown by 1 and 0, respectively.(PDF)Click here for additional data file.Appendix S1Eighteen protocols used to isolate total RNA from plant tissue included in this study.(PDF)Click here for additional data file.