Arabidopsis thaliana RBPs at the systems level, we have employed interactome capture techniques using cells from different ecotypes grown in cultures and leaves. In vivo UV-crosslinking of RNA to RBPs, oligo(dT) capture and mass spectrometry yielded 1,145 different proteins including 550 RBPs that either belong to the functional category ‘RNA-binding’, have known RNA-binding domains or have orthologs identified in mammals, C. elegans, or S. cerevisiae in addition to 595 novel candidate RBPs. We noted specific subsets of RBPs in cultured cells and leaves and a comparison of Arabidopsis, mammalian, C. elegans, and S. cerevisiae RBPs reveals a common set of proteins with a role in intermediate metabolism, as well as distinct differences suggesting that RBPs are also species and tissue specific. This study provides a foundation for studies that will advance our understanding of the biological significance of RBPs in plant developmental and stimulus specific responses.RNA-binding proteins (RBPs) have essential roles in determining the fate of RNA from synthesis to decay and have been studied on a protein-by-protein basis, or computationally based on a number of well-characterised RNA-binding domains. Recently, high-throughput methods enabled the capture of mammalian RNA-binding proteomes. To gain insight into the role of  S. cerevisiae) have shown, that the total amount of transcribed RNA that will eventually be translated is only about 0.5%13cis-acting regulatory functions, it is likely that the repertoire of the expressed RBPs may be highly specific and informative about developmental and physiological states of cellular systemsAlthough transcription is the first and main target of gene expression control, transcripts are also subject to post-transcriptional control including RNA processing, modification and localization. In addition, translational and post-translational regulations as well as the turnover rate of specific proteins add to the complexity of the system. Perhaps surprisingly, previous studies in yeast  and Landsberg erecta (Ler-0) as well as 4-week old Arabidopsis leaves. We applied the interactome capture technique according to established protocols303233In order to identify the RNA-binding proteome of bona fide RBPs from non-specifically bound proteins. We applied a fold increase of >1.5 as threshold and a statistical confidence of p ≤ 0.05 for proteins that were detected in both UV and nUV data sets to classify proteins as UV enriched. These criteria largely exclude false positives but cannot exclude false negatives. Examples of such false negatives include RNA-binding plectin (AT4G25740) that contains the Plectin/S10, N-terminal domain that has been proposed to bind RNA as part of its role in the ribosomal subunit assembly and translationIn these analyses, three biological replicates were tested and a protein was considered present only if it was detected in at least two of the replicates. Further, a stringent statistical criterion was applied to proteins identified in both UV-crosslinked and nUV samples in order to differentiate http://prosite.expasy.org). The classification of RNA-binding domains (RBDs) was carried out as detailed previously82+-binding aspartate motifs Oryza sativa) with 322 proteins. It is also noteworthy that multiple RBDs were detected in some RBPs identified in this study, while other proteins contain a single domain repeated multiple times much like previously reported18A search for classical, non-classical and unknown RBP domains in the RB-proteome was performed using prosite (https://www.arabidopsis.org/) and AGRIGO (http://bioinfo.cau.edu.cn/agriGO/) facilitated classification of the identified proteins. A molecular function analysis looking at the RB-proteome in terms of functional categories reveals that over 43% of the proteins are annotated as involved in ‘binding’ with about 50% of the proteins in this category involved in ‘DNA or RNA-binding’  networks may direct post-transcriptional gene regulation. Thus, the dual functional role of proteins acting as enzymes and RBPs can be tuned by metabolites and/or cofactors42Escherichia coli, aconitase B has been shown to be a moonlighting protein that can switch to its apo-aconitase form that binds to its own mRNA 3′UTR and stabilising it when intracellular levels of iron are low45Bacillus subtilis aconitase was reported to bind specifically to sequences resembling eukaryotic mRNA iron response elements and this binding is dependent on the availability of ironin vivo evidence remains limitedTo learn if the identified proteins are part of particular metabolic pathways, we performed a metabolic pathway analysis using Kyoto encyclopedia of genes and genomes (KEGG) database. Here, 665 proteins (~55% of the RB-proteome) were assigned to pathways and the ‘genetic information processing’, ‘carbohydrate metabolism’ and ‘energy metabolism’ were the most represented , Table 1C. elegans4950C. elegans, yeast and Arabidopsis RB-proteomes contains both classical and non-classical RBDs. However, the number of proteins containing classical RBDs are generally greater in mammalian than in Arabidopsis, yeast and C. elegans RB-proteomes  mammalian systems comprising mouse embryonic stem cellsroteomes . For exaical RBD . The preogenesis . Proteinproteome . Interesproteome , 16 of tproteome  and 11 o 30314849. This sein vivo and performed oligo(dT) capture. The RNA-RBP complex, and isolated RBPs and RNA samples were analyzed by western blotting using antibodies against DEAD box-6 helicase (DDX6) as a positive control, and clathrin heavy chain and catalase, a peroxisomal protein, as novel candidate RBPs for validation. The blots showed distinct positional band shifts between the RNA-RBP complex sample and the RBP sample for all the antibodies tested  comprises four different domains, namely clathrin heavy chain or VPS 7-fold repeat, clathrin heavy chain linker or propeller domain, armadillo-type fold and tetratricopeptide-like helical domain, none of which have yet been directly associated with mRNA binding. However, the presence of the tetratricopeptide-like helical domain, that comprises superhelical structures that are well suited to binding large molecules including nucleic acids and proteinsCatalase 3 (AT1G20620), a peroxisomal protein containing only catalase-related and haem-dependent domains, catalyzes the breakdown of hydrogen peroxide into water and oxygen making it a part of the plant antioxidative system. Since catalases are highly expressed enzymes and have a fast turnover rateThe interactome capture technique which combines UV-crosslinking and oligo(dT) capture and mass spectrometry has provided a platform to identify RBPs in plants, at the systems level. Although novel RBP candidates have been identified, not all previously annotated RBP were identified in this study, suggesting that some of the RBPs may be stimulus specifically induced and/or tissue- and developmental stage-specific. This conjecture is partly supported by enriched GO categories where RBPs could be clustered into specific biological process and molecular function categories.In summary, this study provides a platform for systems scale studies aiming at identifying and characterizing novel RBPs and their role in response to stresses such as salinity, drought and cold. Future investigations will include, firstly, further validation of more novel candidate RBPs, secondly, the identification of novel specific RNA-peptide interacting regions in plants as well as the elucidation of the role of posttranslational modifications in the regulating RNA-protein interactions. Thirdly, future work also aim to identify the RNA targets of novel RBPs.Arabidopsis thaliana, ecotypes Columbia-0 and Landsberg erecta (Ler-0) were grown in liquid culture as previously described565758® filter units  and cells were rinsed with 1× phosphate buffered saline immediately before UV-crosslinking. For whole plant work, Arabidopsis plants (ecotype Col-0) were grown under controlled conditions: 22 °C, 100 μmol m−2 s−1, and relative humidity of 70%. Four week-old plants were subjected to UV-crosslinking, as described previouslyCells derived from roots of In vivo crosslinking and isolation of Arabidopsis RBPs was performed, as detailed described2 UV light at 254 nm for ~90 s, lysed in lysis buffer (20 mM Tris (pH 7.5), 500 mM LiCl, 0.5% (w/v) lithium dodecyl sulfate, 1 mM EDTA, 5 mM DTT) using a PowerGen 125 grinder , vortexed for 1 min and incubated on ice for 10 min. Debris were pelleted by using an Allegra® X-22R centrifuge  at 400 × g for 10 min at 4 °C and the supernatant was carefully collected. Oligo(dT)25 magnetic beads  were used to capture poly(A)+ RNAs, undergo several washes prior to elution of mRNA-protein complexes, as described elsewhereg for 30 min at 20 °C. Then 100 μl of 50 mM iodoacetamide was added, the samples were mixed at 600 r.p.m for 1 min using a benchtop mixer (ThermoFisher Scientific) and incubated for 30 min at room temperature in the dark and then concentrated. Buffer exchange was performed by adding 100 μl of 8 M urea in 0.1 M Tris-HCl (pH 8.0) and concentrated, then 100 μL 50 mM NH4HCO3 was added and the sample was concentrated again in three successive rounds. A 1 μg of trypsin in 120 μl of 50 mM NH4HCO3 was added and the filter units were incubated at room temperature overnight. The derived peptides were collected by centrifugation of the filter units. The filters were washed with 50 μL of 0.5 M NaCl and centrifuged again. The collected peptides were acidified with TFA and desalted using Sep-Pak cartridges   as described elsewhereProtein samples were reduced, alkylated, buffer exchanged and digested, as described previously30Arabidopsis thaliana in the Arabidopsis information resource  with precursor mass tolerance of 20 ppm, a fragment ion mass tolerance of ±0.5 Da and strict trypsin specificity allowing up to one missed cleavage, peptide charges of +1, +2 and +3, carbamidomethyl modification on cysteine residues as fixed modification. Further stringency was applied to the protein identification by considering only proteins with a molecular weight search (MOWSE) score ≥ 32 (95% confidence limit). Data was further analyzed and validated with Scaffold v4.0.4  allowing for 0.1% FDRDiscover v1.2.0.208 (Thermo Scientific) and submitted to a local MASCOT  server and searched against p-value (using Student’s T-test) were calculated. UV-crosslinking enrichment was determined by employing a fold increase of ≥1.5 and a p-value of ≤0.05.Proteins that were detected in both the UV-crosslinked samples and the control (non-UV crosslinked samples) were quantitatively analyzed to assess UV-crosslinking enrichment. Spectral counts were normalized, and a fold change and http://prosite.expasy.org/scanprosite/; October 2015). Gene ontology (GO) enrichments were performed using AGRIGO (http://bioinfo.cau.edu.cn/agriGO/) and tair . The pathway analysis was done with the KEGG mapper .Classical and non-classical RNA-binding domains were detected from all proteins using prosite  the mammalian systems comprising mouse embryonic stem cellsIn vivo UV-crosslinking and isolation of Arabidopsis Col 0 RNA-RBPs was performed, as described earlier. Following the capture poly(A)+ RNAs using Oligo(dT)25 magnetic beads, the eluted sample was split into three fractions. One fraction was directly concentrated using a freeze drier to concentrate RNA-RBP complexes, the second fraction was used for RBP isolation as described previously3030In order to validate some of the novel candidate RBPs identified in this study, a western blot-based assay was performed. How to cite this article: Marondedze, C. et al. The RNA-binding protein repertoire of Arabidopsis thalianaSci. Rep.6, 29766; doi: 10.1038/srep29766 (2016).


ORF57 possesses numerous activities that promote the expression of viral genes, including the three major functions of enhancement of RNA stability, promotion of RNA splicing, and stimulation of protein translation. The multifunctional nature of ORF57 is driven by its ability to interact with an array of cellular cofactors. These interactions are required for the formation of ORF57-containing ribonucleoprotein complexes at specific binding sites in the target transcripts, referred as Mta-responsive elements (MREs). Understanding of the ORF57 protein conformation has led to the identification of two structurally-distinct domains within the ORF57 polypeptide: an unstructured intrinsically disordered N-terminal domain and a structured α-helix-rich C-terminal domain. The distinct structures of the domains serve as the foundation for their unique binding affinities: the N-terminal domain mediates ORF57 interactions with cellular cofactors and target RNAs, and the C-terminal domain mediates ORF57 homodimerization. In addition, each domain has been found to contribute to the stability of ORF57 protein in infected cells by counteracting caspase- and proteasome-mediated degradation pathways. Together, these new findings provide insight into the function and biological properties of ORF57 in the KSHV life cycle and pathogenesis.Kaposi’s sarcoma-associated herpesvirus (KSHV) ORF57 protein (also known as  In general, most putative motifs in the ORF57 protein are highly degenerated from the consensus aa sequences found in the cellular counterparts and are often missing residues essential for their function. For example, the described AT hook lacks a core “PRGRP” sequence, and functional RGG boxes require at least 2 or 3 repeats of an RGG or RG motif separated by 0–4 residues to mediate RNA binding ,18. The Early in KSHV lytic infection, ORF57 is expressed as an abundant monocistronic RNA ,22. ORF5In experimental systems, a second intron has been detected. For example, a second intron was noticed in the 3' end of ORF57 RNA in iSLK-219 cells, a stable cell line harboring a recombinant KSHV.219 virus , and couThe expression of KSHV ORF57 is also regulated by cellular RNA export factors and cofactors at the posttranscriptional level. RNA export factors UAP56 and URH49, and RNA export cofactors RBM15 and OTT3, modulate KSHV ORF57 expression. Knockdown of each factor with RNAi (RNA interference) decreases ORF57 expression. The reduced expression of these factors causes a deficiency in nuclear export of ORF57 RNA and consequently decreased expression of ORF57 and its targets in the context of the KSHV genome .N-terminus [ORF57 protein exhibits predominantly nuclear localization and its concentration is increased in SRSF2 (SC35)-positive nuclear speckles that are also enriched in other splicing factors ,20. The terminus  . Inhibition of RNA splicing impairs the export of unspliced, intron-containing RNAs from the nucleus to the cytoplasm. In contrast to the majority of mammalian pre-mRNA transcripts, which contain introns, approximately two-thirds of KSHV gene transcripts are intronless. They are therefore expected to be exported from the nucleus to the cytoplasm inefficiently, and to therefore be poorly expressed. However, the KSHV genome robustly expresses these intronless genes during lytic infection for viral multiplication and pathogenesis. KSHV ORF57 protein was initially thought to be responsible for this robust expression of viral intronless genes in infected cells because its homologues in other members of the herpesvirus family promote the export of viral intronless transcripts ,33,34,35The involvement of ORF57 in RNA export was first tested using a pMD138-derived pCMV128 system expressing a chimeric RNA containing a chloramphenicol acetylase (CAT) ORF as a reporter within its suboptimal, inefficiently spliced intron. Because CAT expression requires the RNA export of the unspliced RNA, the observed increase in CAT activity in the presence of ORF57 expression was interpreted as promotion of RNA export by ORF57 . Howevertranscription and export) complex that links the majority of spliced and unspliced RNAs to the cellular export machinery [ORF57 interacts with Aly/REF, a cellular cofactor originally considered to be essential for RNA export , as do oachinery ,43 throuachinery . Thus, tachinery . Howeverachinery  and lateachinery . An ORF5achinery . These sachinery ,47 and fachinery . In Aly/achinery ,50. Agaiachinery . Consistachinery .Cytoplasmic accumulation of the targeted RNAs could be a result of ORF57-mediated RNA stability, rather than ORF57-mediated export, initially proposed by our group  and lateRNA stability is one of the fundamental mechanisms in the regulation of gene expression that determines the final RNA concentration at the posttranscriptional level ,60,61. TN-terminus of ORF57 interacts with the RNA-recognition motif (RRM) of PAPBC1 and alleviates the negative effect of PAPBC1 on PAN [Binding of PABPC1 to the 9-nt core of PAN MRE is important for ORF57 interactions with PAN . However1 on PAN . ORF57, 1 on PAN , similar1 on PAN  and colo1 on PAN .in vivo UV-crosslinking and anti-ORF57 CLIP in 2006 [ORF57 enhances the stability of protein-encoding ORF59 RNA by increasing its half-life in the cells . ORF57 b in 2006 . ORF59 R in 2006 . Althoug in 2006 , thereby in 2006 ,66. ORF5 in 2006 ,40,58. HMultiple pathways have been identified to regulate RNA stability at all stages of RNA biogenesis, both in the nucleus and in the cytoplasm . To dateBased on the characteristics of HSV-1 ICP27, ORF57 was originally proposed to inhibit RNA splicing. However, the KSHV genome encodes at least one-third of its genes with one or more introns that require RNA splicing for their expression and productive infection . It seemetc.) and other components of the spliceosome, including SM proteins and U snRNPs  [Although ORF57’s ability to promote RNA splicing is independent of other viral factors, it requires the interaction of ORF57 with several cellular splicing factors  . Togetheroteins) . ORF57 aroteins) . SRSF3, roteins) , binds troteins) ,71,72,73Despite its predominantly nuclear localization, a small fraction of ORF57 protein remains in the cytoplasm, where ORF57 associates with the translating ribosomes and cellular factors related to protein translation. These observations suggest that ORF57 might play a role in protein translation. ORF57 directly interacts with PCBP1 (poly(rC)-binding protein 1), a multifunctional cellular RNA-binding protein involved in activating protein translation from an internal ribosome entry site (IRES) , and proORF57 interacts with PYM protein , a celluBy using an anti-ORF57 CLIP assay to search for genome-wide RNA targets of ORF57 in JSC-1 B cells, we recently identified 11 viral transcripts as ORF57 targets and found that the coding region of vIL-6 contains an MRE composed of two motifs for ORF57 binding (MRE-A and MRE-B). ORF57 binds MRE-A to regulate vIL-6 RNA stability. MRE-B has a binding site for cellular miR-1293 . MutatioORF57 was proposed to contribute to transcriptional control partially based on its interaction with KSHV ORF50 (Rta) and k-bZIP (K8) ,84,85,86After the discovery of a functional MRE in the 5' PAN region that increases PAN stability, we compared wt PAN and its mutant with point mutations in the 9-nt MRE core for their expression in HEK293 cells either in the context of a CMV promoter or the native PAN promoter . AlthougORF57 may have a role in the induction of genome instability during KSHV lytic infection. Genome instability is manifested by increased numbers of DNA double-strand breaks due to activation of cellular DNA damage. ORF57 sequesters the TREX complex from the site of transcription and causes a double-strand break response and significant DNA damage. Overexpression of ORF57 results in the formation of RNA:DNA hybrids (R-loops) vulnerable to DNA breaks .in silico analysis to predict the secondary structure of ORF57 demonstrated several fundamental features of the protein conformation: (1) ORF57 exhibits overall low structural complexity, with only one third of all residues being in a secondary structure; (2) ORF57 consists almost exclusively of α-helixes, with only one β-sheet; and (3) the identified structural elements are unevenly distributed along the ORF57 polypeptides, with the majority clustered in the ORF57 C-terminal half  and an α-helix-rich C-terminal domain (aa 153–455). The intrinsically disordered region (IDR) in the unstructured ORF57 N-terminus encompasses both acidic and basic regions  does not significantly affect ORF57 activity [C-termini could simply reflect the conservation of protein structure rather than function.In contrast to the -helixes . Due to of ORF57 ,114, butactivity ,20,56,58C-terminus of ORF57 mediates ORF57 self-interaction, a feature that has been found in several homologues of the ICP27 family [in vitro and homodimers in vivo via its C-terminus [C-terminus, we have recently identified the C-terminal α-helixes 7–9 as being responsible for ORF57 homodimerization. Introduction of point mutations in α-helix 7 (aa 280–299) prevents ORF57 from homodimerizing. Interestingly, these regions exhibit high conservation in both protein structure and amino acid sequence among ORF57 homologues [The 7 family ,105,115.terminus . By scremologues .N-terminal acidic region of ORF57  of the ORF57-interacting protein(s). Third, mutation and truncation experiments are important tools for dissecting the sequence motifs involved in ORF57 functions, but mutation or truncation may change the protein folding and thereby expose other motif(s) that are covered during normal folding. This could affect the activities of ORF57 by changing its interactions with other cellular proteins. Thus, future studies to correlate ORF57 protein structure, folding, and function are urgently needed in order to understand the biochemical and biophysical properties of individual ORF57 protein domains and their contribution to each of the ORF57 functions.


Many methods have been developed for comparing RNAs using either                    sequence or three-dimensional (3D) structure (backbone geometry) information.                    Sequences and 3D structures contain non-overlapping sets of information that                    both determine RNA functions. When comparing RNA 3D structures, both types of                    information need to be taken into account. However, few methods compare RNA                    structures using both sequence and 3D structure information. Recently, we have                    developed a new method based on elastic shape analysis (ESA) that compares RNA                    molecules by combining both sequence and 3D structure information. ESA treats                    RNA structures as 3D curves with sequence information encoded on additional                    coordinates so that the alignment can be performed in the joint                    sequence-structure space. The similarity between two RNA molecules is quantified                    by a formal distance, geodesic distance. In this study, we implement a web                    server for the method, called RASS, to make it publicly available to research                    community. The web server is located at                              Comparison of ribonucleic acid (RNA) structures is an effective tool for studying the                functions of RNA molecules and their evolutionary relationships. There have been a                number of methods developed for RNA structure alignment/comparison . Databas            Sequence alignment and structure alignment use different sets of information: the                former uses information of side chains, which are reduced to single letters, and the                latter uses information of the backbone geometry. Both types of information play                important roles in determining RNA functions. In principle, structure alignment                methods should utilize sequence information since such information is almost always                available. Recently, we have developed a RNA structure alignment method ,24                                 We use ESA to compare two RNA structures or structural fragments. ESA treats RNA                structures as 3D curves and utilizes a geometric framework that has been developed                originally in image analysis and computer vision for shape analysis of parameterized                curves and surfaces . The               The implementation is done using Matlab (with some embedded C functions). Due to                Matlab initialization, RNA structure comparison at the web server takes more time                than running the programs locally.                            The user interface of RASS is shown in Figure                                             Starting (for each structure A and B): the index of                                the starting residue from the RNA chains. The default value is                                1.Ending (for each structure A and B):                                the index of the ending residue from the RNA chains. The default                                value is 10 000. That means for RNA chains with more than 10 000                                residues, without specifying the starting and ending positions of                                the residues, the first 10 000 residues will be used in comparison.                                If the length of the RNA chain is less than 10 000, then the actual                                chain end position will be used. This value is large enough for the                                RNA structures currently in PDB. Being able to select starting and                                ending positions from the RNA chains to be compared is a feature                                most of the existing web servers do not provide. With this option                                users can select a partial structure within a larger RNA molecule                                for comparison, which can be interesting either structurally or                                functionally. Since our method is a global alignment method, this                                option can be especially useful if a user wants to compare a partial                                structure based on some prior                                knowledge/information.Lambda: weight                                for the nucleotide sequence . The valThe parameters are preset at default values. Users have the flexibility to                    specify values for the following:                                         P-value:                                geodesic distance calculated by ESA between two RNA chains is                                displayed under the Distance and P-value tab.                                    P-value is obtained by comparing the geodesic                                distance with an empirical Gaussian distribution derived from a set                                of pairwise distances computed using a large number of                                non-homologous RNA structures taken from PDB (P-value indicates that the chains are                                related/similar statistically in the joint sequence-structure                                space.Distance and from PDB . A discuGeodesic path: this tab displays                                the optimal structural deformation from RNA molecule A to RNA                                molecule B viewed from three different angles Figure . These vSequence                                alignment: this tab shows the global sequence alignment between two                                RNA molecules Figure . The   Structural Alignment: the 3D                                optimal superposition is displayed with Jmol Figure . Users cThe output is displayed in a series of drop-down tabs (see Figures 2–                                                            In this study, a web server is implemented to provide a tool for users to compare and                align two RNA molecules in the joint sequence-structure space. A typical scenario                for a user when using our server is as follows: The user will provide two RNA                molecules as input where one may be a RNA molecule with known function and the other                is a RNA molecule the user may want to know more about. After alignment of the two                molecules, the user can look at the structure alignment using Jmol to identify the                structural similar regions and dissimilar regions to infer how the two molecules may                share similar function while differ in some substrate specificities. From the                sequence alignment, the user can identify conserved nucleotides. Here, those                nucleotides that align well in sequence space are also spatially close on structure                space since RASS aligns both sequence and structure simultaneously. From the aligned                nucleotides, the user can gain more insight on the functional and/or evolutionary                relationship of the two molecules.            http://stat.fsu.edu/∼jinfeng/ESA.html.For pairwise alignment of a large set of RNA structures, users can download the                freely available programs provided at                             Supplementary Data are available at NAR Online.


These include methods which can compare read profiles, e.g., from direct (non-sequence based) alignments to classification of patterns into functional groups. In this review, we highlight the emerging applications of read profiles for the annotation of non-coding RNA and cis-regulatory elements (CREs) such as enhancers and promoters. We also discuss the biological rationale behind their formation.Functional annotation of the genome is important to understand the phenotypic complexity of various species. The road toward functional annotation involves several challenges ranging from experiments on individual molecules to large-scale analysis of high-throughput sequencing (HTS) data. HTS data is typically a result of the protocol designed to address specific research questions. The sequencing results in reads, which when mapped to a reference genome often leads to the formation of distinct patterns (read profiles). Interpretation of these read profiles is essential for their analysis in relation to the research question addressed. Several strategies have been employed at varying levels of abstraction ranging from a somewhat  RNA sequencing (RNA-seq); and, (b) Chromatin immunoprecipitation coupled with DNA sequencing (ChIP-seq) reflecting proteins interacting with DNA. Both of these protocols are designed to sequence a biological molecule, which in case of RNA-seq is RNA and in case of ChIP-seq is DNA, extracted from a sample of interest  technologies have revolutionized the field of molecular biology. Two widely used experimental protocols derived from this technology are: (a) KEY CONCEPT 1. RNA Sequencing (RNA-seq)Method based on high-throughput sequencing technology that is used to determine the nucleotide sequence of all the RNAs transcribed within a given sample .KEY CONCEPT 2. Chromatin immunoprecipitation coupled with DNA sequencing (ChIP-seq)Method based on high-throughput sequencing technology that is used to determine the nucleotide sequence of all the DNA segments in the genome where a protein interacts.reads.” These reads carry the nucleotide sequence information of various RNA and DNA molecules captured during the RNA-seq and ChIP-seq experiments, respectively. To determine the genomic location of these reads, they are mapped back to the reference genome using mapping tools , such as enhancers and promoters. Specifically, we review how different computational methods exploit read profiles obtained from RNA-seq and ChIP-seq data for the functional annotation of ncRNA and CREs, respectively . This is in part due to the large potential for ncRNAs, which was first realized with the sequencing of the human genome which revealed that only ~1.2% of genome encodes for proteins  RNA molecules transcribed from their respective genes, but not translated into proteins.An important step toward uncovering the function of non-coding transcripts includes the study of their read profiles. The read profiles can be linked with RNA secondary structure, in particular for miRNAs and sometimes also for tRNAs and snoRNAs  have proved ideal to identify novel miRNAs and also to robustly quantify their expression across different physiological conditions Figures , 2. Due *, offset miRNA) toward the other end; and, (b) identifying a viable hairpin secondary structure corresponding to the defined potential miRNA locus using an RNA secondary stucture prediction method, in this case RNAfold  to the biological model of miRNA biogenesis. Specifically, they start by mapping reads to known precursor miRNAs and assigning them to corresponding miRNA annotations. Next, they analyse the genomic regions where remaining reads align for their potential as precursor miRNA. This analysis includes: (a) assigning a read with highest expression at a potential miRNA locus as the predicted mature miRNA. This is followed by extending the read by 20 bp (offset miRNA) at one end and by 70 bp , stem length and loop length to train a random-forest classifier  toward one side and subsequent extensions of 15 bp (loop region) and read length (miRNA*) and 22 bp (offset miRNA) at the other end  and subsquent discovery of novel miRNAs, e.g., in  are distinct positions in the genome that are actively bound by various transcription factors resulting in an increase or decrease in the expression of, mostly, proximal located genes KEY CONCEPT 7. Distinct positions in the genome actively bound by various transcription factors resulting in an increase or decrease in the expression of, mostly, proximal located genes. These include enhancers, promoters, silencers and others.The recently developed ChIP-seq technology allows us to address both these shortcomings by exploiting the second characteristic of CRE, which is the marked absence of nucleosomes in these regions   the NFRs bound by a specific transcription factor (TF ChIP-seq), or (b) the region flanking NFRs where the nucleosome undergoes specific histone modifications (Histone ChIP-seq)  for the functional annotation of ncRNAs and CREs Table . Various*  and mRNAs. Read profiles from these transcripts can include patterns, for example originating from alternative splicing mechanisms (both coding as well as non-coding). Furthermore, with growing amount of new applications of sequencing (such as CLIP-seq and PAR-CLIP), we anticipate that the need for comparing read profiles would increase. Indeed, two recent methods  utilized the patterns obtained after mapping short reads from PAR-CLIP to determine the miRNA target sites (Corcoran et al., The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Knowledge of the distribution and diversity of RNA viruses is still limited in spite of their possible environmental and epidemiological impacts because RNA virus-specific metagenomic methods have not yet been developed. We herein constructed an effective metagenomic method for RNA viruses by targeting long double-stranded (ds)RNA in cellular organisms, which is a hallmark of infection, or the replication of dsRNA and single-stranded (ss)RNA viruses, except for retroviruses. This novel dsRNA targeting metagenomic method is characterized by an extremely high recovery rate of viral RNA sequences, the retrieval of terminal sequences, and uniform read coverage, which has not previously been reported in other metagenomic methods targeting RNA viruses. This method revealed a previously unidentified viral RNA diversity of more than 20 complete RNA viral genomes including dsRNA and ssRNA viruses associated with an environmental diatom colony. Our approach will be a powerful tool for cataloging RNA viruses associated with organisms of interest. Viruses are the universal genetic elements associated with all three domains of life , and virRNA sequencing (RNA-seq) is a popular method in RNA virus metagenomics and is widely used for RNA virus identification . PurificIn an attempt to resolve these issues, an environmental viral metagenomic approach targeting intracellular long double-stranded RNA (dsRNA) has recently been examined , 9, 37. We herein established a novel strategy to obtain full-length RNA virus sequences with extremely high efficiency by applying a short dsRNA full-length cloning method  for physMagnaporthe oryzae strain S-0412-II 1a, naturally infected with Magnaporthe oryzae chrysovirus 1 strain A (MoCV1-A)   were incet al. with a few modifications  and was then incubated at 37°C for 2 h. The final concentrations of CH3COONa, MgCl2, ZnSO4, and NaCl were adjusted to 90 mM, 15 mM, 3 mM, and 300 mM, respectively. DsRNA was purified using an RNeasy Mini Kit . A one-tenth volume of 10 × ShortCut buffer and 10 × MnCl provided with ShortCut RNase III  was added to the dsRNA solution and fragmented by ultrasound at 4°C in Snap-Cap microTUBEs using a Covaris S220 . The fragmentation conditions were as follows; run time 35 s, peak power 140.0 W, duty factor 2.0%, and 200 cycles/burst. Fragmented dsRNA was divided into two equal volumes, and maintained at 37°C with or without ShortCut RNase III (NEB). DsRNAs were then purified using a ZymoClean Gel RNA Recovery Kit . Note that dsRNA purification from M. oryzae was carried out in the laboratory of Prof. Teraoka.DsRNA was purified as described by Okada ications , 46. Briet al. (The PC3-T7 loop primer (5′-p-GGA TCC CGG GAA TTC GGT AAT ACG ACT CAC TAT ATT TTT ATA GTG AGT CGT ATT A-OH-3′) was ligated to fragmented dsRNA as described by Potgieter et al. , and dsRet al. , cDNA waet al. . Second et al. . After hTotal RNA was isolated from a diatom colony using the TRIzol Plus RNA Purification Kit (Invitrogen) according to the manufacturer’s protocol. The RNA fraction was treated with DNase I . Double-stranded cDNA was synthesized from 2 μg of total RNA with random primers (9-mers) using a PrimeScript Double Strand cDNA Synthesis Kit (Takara). The resultant cDNA was quantified using a Qubit dsDNA HS Kit.Ultrasound was used to fragment cDNA in Snap-Cap microTUBEs at 4°C using a Covaris S220 . The fragmentation conditions were as follows; run time 55 s, peak power 175.0 W, duty factor 5.0% and 200 cycles/burst. The Illumina library was constructed with KAPA Hyper Prep Kit Illumina platforms . The quantity of the library was evaluated using the KAPA library quantification kit (Kapa Biosystems). Each 300 bp of the paired-end sequences of each fragment were determined with the Illumina MiSeq platform .de novo exclusively with the CLC Genomics Workbench (CLC Bio), and assemblies were manually examined and extended using the Tablet viewer . Adaptor and primer sequences were trimmed, and low quality sequence regions were removed with default parameters. PhiX sequences derived from control libraries and experimentally contaminated sequences  were also removed using a mapping tool. The consensus sequences of viral contigs were obtained t viewer . Using tt viewer . A sequet viewer .Multiple alignments based on the deduced aa sequences of putative RNA-dependent RNA polymerase (RdRp) genes in dsRNA contigs were obtained using ClustalX 2.0  and MEGAThe data sets supporting the results of this study are available in the GenBank database repository (accession nos. DDBJ: AP014890–AP014920) and Short Read Archive database (accession no. DDBJ: DRA003723 and DRA003724).The novel dsRNA purification and library construction method, named FLDS, consists of cellulose column chromatography, the physical fragmentation of dsRNA, cDNA synthesis using a loop primer, and the PCR amplification of cDNA . The purde novo assembly were identical to the entire region of the MoCV1-A genome segments attained using a conventional cloning and sequencing method  were likely to have been derived from dsRNA . The resg method , 45 withg method . Read mag method  showed tg method . These rde novo assembly and manual extension, we obtained 42 composite viral contigs  in the predicted protein-encoding sequences (CDSs), the number of genome segments in related viruses, and terminal conserved sequences in each segment of a single virus, we identified 22 viral putative composite genomes out of 31 full-length viral segments. Sequence similarities between the 22 putative viral composite genomes were used to classify them into 19 putative viral species, and each of the two genome types was identified in three species . In general, −1 ribosomal frameshift signals  were lo viruses . Picobir viruses . The genaviridae . DCADSRVsequence  29). To. ToTotivdetected . The CDS−15), a member of the genus Pestivirus of the family Flaviviridae, which consists of the arthropodborne pathogens of humans and other animals. The genome size and CDS structure of DCASSRV-1 (11.4 kb) were similar to those of Flaviviridae (9.6–12.3 kb)  , and thee genera . A phyloarticles . The prearticles .This study revealed the presence of novel RNA viruses associated with a diatom colony and inferred the unexpected evolutionary relationship between environmental viruses and pathogenic animal viruses. Among the RNA viral genomes obtained in this study, some dominant populations showed greater similarities to fungal viruses than to known diatom viruses; however, several ssDNA and ssRNA viruses have already been identified from marine diatoms , 43. We de novo assembly. These were classified into five dsRNA  and four ssRNA  virus families. To the best of our knowledge, this is the largest number of full-length genome sequences of novel RNA viruses identified in one metagenomic library. The viral RNA community successfully detected in this study consisted of dsRNA viruses with or without virion formation and ssRNA viruses detected as replicative intermediates. Our results suggest that FLDS has the potential to detect a wide range of RNA viruses, excluding retroviruses.FLDS revealed 22 full-length and some partial composite viral RNA genomes associated with a diatom colony by Several studies have been performed using metagenomic analyses targeting dsRNA with Next-Generation Sequencing technology. In these studies, viral read abundance reached a maximum of 52.7% . The comTotal RNA-seq is considered to be a less-biased method for identifying RNA viruses despite the very low abundance of viral reads in general. In the present study, FLDS enriched the viral RNA reads by > 300-fold that with total RNA-seq . Notably


Tapping panel dryness (TPD) is a serious threat to natural rubber yields from rubber trees, but the molecular mechanisms underlying TPD remain poorly understood. To identify TPD-related genes and reveal these molecular mechanisms, we sequenced and compared the transcriptomes of bark between healthy and TPD trees. In total, 57,760 assembled genes were obtained and analyzed in details. In contrast to healthy rubber trees, 5652 and 2485 genes were up- or downregulated, respectively, in TPD trees. The TPD-related genes were significantly enriched in eight GO terms and five KEGG pathways and were closely associated with ROS metabolism, programmed cell death and rubber biosynthesis. Our results suggest that rubber tree TPD is a complex process involving many genes. The observed lower rubber yield from TPD trees might result from lower isopentenyl diphosphate (IPP) available for rubber biosynthesis and from downregulation of the genes in post-IPP steps of rubber biosynthesis pathway. Our results not only extend our understanding of the complex molecular events involved in TPD but also will be useful for developing effective measures to control TPD of rubber trees. Although indigenous to the Amazonian rainforest, the rubber tree is widely cultivated in Southeast Asia. With the fast development of the world economy, the demand for natural rubber has increased continuously. Rubber tree plantations must be located in sub-tropical to tropical zones, and therefore increasing rubber yield per hectare is an effective means of meeting world demand. Rubber yield has increased substantially in recent years owing to the cultivation of high-yield clones and the utilization of ethephon (an ethylene generator), but yield can be negatively affected by tapping panel dryness (TPD). It is estimated that TPD-related losses account for 12–14% of natural rubber yieldNatural rubber  in TPD trees might alter mitochondrial metabolism and result in impaired latex biosynthesiset al. first proposed that the production and scavenging of reactive oxygen species (ROS), the ubiquitin proteasome pathway (UPP), PCD, and rubber biosynthesis (RB) pathways might play important roles in rubber tree TPDet al. found that the target genes of TPD-related microRNAs are associated with RB, ROS-scavenging systems, and PCD16et al. characterized a novel translationally controlled tumor protein of rubber tree (HbTCTP1). The TCTP is a multi-functioning protein that carries out vita roles in various life processes, and we propose that HbTCTP1 may carry out antioxidant function during TPDet al. suggested that the candidate TPD-related genes in latex and phloem tissues are associated with ROS-scavenging, ethylene biosynthesis, and signalinget al. identified and analyzed TPD-related genes by comparing transcriptomes between healthy and TPD trees. They proposed that genes involved in RB and jasmonate synthesis are associated with TPD occurrenceRecent studies on TPD have focused mainly on identifying and characterizing TPD-related genes. Chen Although some progress has been made with regard to understanding TPD, the underlying molecular mechanisms TPD remain largely unknown; in this respect, a prerequisite is to identify TPD-related genes and analyze their expression patterns and functions. It is well established that TPD has a long-term course comprising initial and advanced stages. The susceptibility to TPD depends on the nature of rubber tree variety. The rubber tree clone RY 7-33-97, cultivated by Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, has been widely planted in China, and its susceptibility to TPD is higher than that of PR 107, which is planted on a small scale in China. The TPD-related genes identified to date are insufficient to completely elucidate the molecular mechanisms underlying TPD. With these considerations, we sequenced, assembled and compared the bark transcriptomes between healthy and TPD trees to elucidate the expression profiles of TPD-related genes. Our study provides new insights into the potential molecular mechanisms involved in rubber tree TPD.http://estscan.sourceforge.net/)  .To facilitate the global analysis of the bark transcriptome, all predicted unigenes were assigned to different functional categories with Blast2GO. The annotations were manually verified and integrated by gene ontology (GO) classification. Among the 57,760 assembled unigenes, 22,479 were categorized into 42 GO terms based on nucleotide sequence identity. Three main categories, namely biological process, molecular function and cellular component, contained 21, 10, and 11 GO terms, respectively . MetabolThe Clusters of Orthologous Groups (COG) protein database facilitates the phylogenetic classification of all proteins encoded in a complete genome, and it reflects one-to-many and many-to-many orthologous relationships as well as simple one-to-one relationships. As 2 ratio ≥2 as thresholds, a total of 8,137 TPD-related genes were identified as DEGs between healthy and TPD trees, among which 5652 and 2485 were up- or downregulated, respectively, in TPD tree bark compared with healthy tree bark  in pre-IPP steps of the RB pathway were upregulated in TPD trees, whereas 3 genes  involved in post-IPP steps of the RB pathway were downregulated in TPD trees  is utilized for the synthesis of different classes of isoprenoids including rubber. Interestingly, 62 TPD-related genes identified in this study were found to be involved in 9 IPP-requiring KEGG pathways and were enriched in monoterpenoid biosynthesis . Of thesPD trees . As showet al. also sequenced, assembled and analyzed rubber tree transcriptomes. In their study, “general function prediction only” was the most highly represented COG group, followed by “translation, ribosomal structure and biogenesis”, and “posttranslational modification, protein turnover, chaperones”et al.RNA-seq is a cost-effective approach for transcriptome analysis. Several studies have reported transcriptome profiling in different tissues of rubber tree20222324252627et al. identified 22,577 DEGs associated with TPD using a similar methodet al.et al. were 1320 and 789, respectively. The inconsistencies may be attributable to the differences in rubber tree variety (RY 7–33–97 vs. PR 107) and/or the TPD stages analyzed  in the two studies. Our results further underscore the fact that rubber tree TPD is a complex process involving many genes. Hence, future work should focus on the key genes in important TPD-related regulatory pathways  to elucidate the molecular mechanisms involved in rubber tree TPD. To understand the functional implications of TPD-related genes, 8137 TPD-related genes identified in this study were analyzed for GO terms and KEGG pathways. These TPD-related genes were classified into 41 GO terms including antioxidant activity, response to stimulus, metabolic process, transcription regulator activity, etc. Further analyses indicated that TPD-related genes were significantly enriched in 8 GO terms including binding, catalytic activity, growth, cellular process, cellular component organization, molecular transducer activity, death, and biological regulation. Whereas in Liu et al.’s study, the TPD-related genes were mainly associated with GO terms including cell, cell part, binding, catalytic activity, metabolic process, and cellular processRNA-seq analysis generated a comprehensive view of TPD-related genes that might participate in regulating TPD occurrence and development. In our study, 5652 and 2485 genes were up- or downregulated, respectively, in TPD tree bark. Liu Of 115 KEGG pathways associated with the TPD-related genes, metabolic pathway was the most dominant, followed by plant-pathogen interaction, biosynthesis of secondary metabolites, spliceosome, endocytosis, ubiquitin mediated proteolysis, starch and sucrose metabolism, and RNA degradation. These predominant 8 KEGG pathways mentioned above were mainly associated with metabolism, PCD, protein degradation, and RNA processing. In addition, the TPD-related genes were significantly enriched in 5 KEGG pathways including plant-pathogen interaction, ABC transporters, monoterpenoid biosynthesis, base excision repair, and caffeine metabolism. Importantly, the TPD-related genes enriched in 5 KEGG pathways above are also associated with the GO terms such as metabolic process, immune system process, antioxidant activity, response to stimulus, death, etc.et al. reported that tapping and stimulation with ethylene raises the activity and content of ACC synthetase in rubber tree latexet al. reported that the candidate genes related to TPD occurrence are associated with ROS-scavenging, ethylene biosynthesis and signalingAs a stimulant of latex production in rubber tree, ethylene has been widely utilized in latex production. Chrestin suggested that rubber tree TPD is associated with ethylene overstimulation2+ concentration regulates both ROS production and the localized PCD/hypersensitive response32Some researchers have suggested that PCD might occur in TPD rubber trees9101112et al. suggested that subcellular compartmentalization of IPP for RB correlates with carotenoid synthesis in plastidsDXS and CMK) were upregulated in TPD trees compared with healthy trees. Lois et al. suggested that DXS might catalyze the first potential regulatory step in carotenoid biosynthesisIn rubber tree, IPP is generated by the methylerythritol phosphate and mevalonate pathways. Chow GPPS, REF, SRPP) involved in post-IPP steps of RB were downregulated in TPD trees. Li et al. also found that REF and SRPP were downregulated in the latex of TPD treeset al. reported that REF expression in latex of high-yield rubber clones was significantly higher than that in a low-yield cloneet al. reported that genes involved in RB (as well as those for jasmonate biosynthesis) were associated with TPD occurrenceThe first symptom of TPD is partial dry zones along the tapping panel. In the advanced stage, the tapping panel is completely dry and lacks latex flow32Our results as well as those from previous studies related to rubber tree TPD suggest that TPD is a complex process involving many genes. The TPD-related genes we identified are mostly associated with ROS metabolism, jasmonate and ethylene biosyntheses, UPP, PCD, and RB. Based on all these results, we speculate that overexploitation (excessive tapping as well as overstimulation with ethylene) induces excessive ROS production, which alters the balance between ROS production and scavenging and results in ROS accumulation. As toxic molecules, the excessive ROS further results in PCD and UPP, which necessarily affect RB. To protect itself against further damage, the rubber tree initiates PCD, and latex yield decreases (or even stops), resulting in TPD.et al.The high-yield clone RY 7–33–97 was planted at the experimental farm of Chinese Academy of Tropical Agricultural Sciences in 1992. During the most recent 11 years, the plants were tapped for every third tapping, 1.5% ethephon was applied 2 days before tapping to stimulate latex yield. The rubber trees affected by TPD were also tapped along with healthy trees to maintain uniform conditions until sample collection. In this study, the trees with normal latex flow were considered as “healthy” trees , whereasde novo transcriptome assembly was performed according to Li et al.RNA-seq library construction and Illumina sequencing were performed at the Beijing Genomics Institute-Shenzhen, China, according to the manufacturer’s instructions . Paired-end sequencing was performed on an Illumina HiSeq 2000 platform, and RNA-seq was performed with two independent biological replicates. The http://www.ncbi.nlm.nih.gov/), Swiss-Prot (http://www.expasy.ch/sprot/), COG (http://www.ncbi.nlm.nih.gov/cog/), and KEGG pathway (http://www.genome.jp/kegg/) using the BLASTX program (2.2.26+) with an e-value < 1E-5, and the best aligning results were selected to annotate the unigenes. If the alignment results differed among the databases, the results from the NCBI nr database were preferentially selected, followed by Swiss-Prot, KEGG, and COG .All unigenes were searched against protein databases such as NCBI nr (Blast2GO (version 2.3.5) was subsequently used to facilitate GO annotation according to the molecular function, biological process and cellular component ontologies2 ratio ≥ 2 as thresholds. With a q-value ≤ 0.05 as a threshold, GO enrichment analysis was carried out using Blast2GO (version 2.3.5). The calculated p-values were subjected to Bonferroni correction, taking a corrected q-value ≤ 0.05 as a threshold. KEGG pathways of the DEGs were analyzed using Cytoscape software (version 2.6.2) (http://www.cytoscape.org/) with ClueGO plugin (http://www.ici.upmc.fr/cluego)The expression level of each gene was calculated via the RPKM method (reads per kb per million reads) using RSEM software (v1.1.17)42In each real-time RT-PCR sample, gene-specific primers were designed and used, and the internal reference, the 18S rRNA gene (GenBank acc. No.: AB268099), was amplified along with the target genes. The primers for real-time RT-PCR are shown in Accession codes: The assembled sequences and sequencing reads were submitted to the NCBI TSA project, and the accession number was SAMN03704954.How to cite this article: Li, D. et al. Transcriptome analyses reveal molecular mechanism underlying tapping panel dryness of rubber tree (Hevea brasiliensis). Sci. Rep.6, 23540; doi: 10.1038/srep23540 (2016).


Gene expression analysis by RNA sequencing is now widely used in a number of applications surveying the whole transcriptomes of cells and tissues. The recent introduction of ribosomal RNA depletion protocols, such as RiboZero, has extended the view of the polyadenylated transcriptome to the poly(A)- fraction of the RNA. However, substantial amounts of intronic transcriptional activity has been reported in RiboZero protocols, raising issues regarding their potential nuclear origin and the impact on the actual sequence depth in exonic regions.Using HEK293 human cells as source material, we assessed here the impact of the two commonly used RNA extraction methods and of the library construction protocols (rRNA depletion versus mRNA) on 1) the relative abundance of intronic reads and 2) on the estimation of gene expression values. We benchmarked the rRNA depletion-based sequencing with a specific analysis of the cytoplasmic and nuclear transcriptome fractions, suggesting that the large majority of the intronic reads correspond to unprocessed nuclear transcripts rather than to independent transcriptional units. We show that Qiagen or TRIzol extraction methods retain differentially nuclear RNA species, and that consequently, rRNA depletion-based RNA sequencing protocols are particularly sensitive to the extraction methods.We could show that the combination of Trizol-based RNA extraction with rRNA depletion sequencing protocols led to the largest fraction of intronic reads, after the sequencing of the nuclear transcriptome. We discuss here the impact of the various strategies on gene expression and alternative splicing estimation measures. Further, we propose guidelines and a double selection strategy for minimizing the expression biases, without loss of information.The online version of this article (doi:10.1186/1471-2164-15-675) contains supplementary material, which is available to authorized users. Next generation sequencing (NGS) has become the gold standard for in depth transcriptome analysis, since RNA sequencing (RNA-seq) provides a high dynamic range and a virtually unbiased view of the transcriptome landscape , 2 althoHere, using HEK293 human cells as source material, we set out to assess the influence of the RNA extraction methods (TRIzol versus silica gel) and of the library construction protocols (rRNA depletion versus poly(A)+ selection) on 1) the relative abundance of intronic reads and 2) on the estimation of gene expression values. Further, in order to benchmark this information, we sequenced both the cytoplasmic and nuclear RNA fractions of HEK293 cells to investigate the origin of the intronic reads observed in rRNA-depleted RNA-seq procedures. Based on the data generated, we discuss the respective performances of the different protocols in detecting the non-polyadenylated and non-coding fractions of the transcriptome, and their impact for analyzing the transcriptome landscape in general.We carried out a comparative sequence analysis of the total, nuclear, poly(A)+ and cytoplasmic RNA fractions of HEK293 cells, extracted by either organic or non-organic methods, respectively with the purpose of investigating the influence of RNA extraction and library preparation protocols on RNA-seq data analysis Figure . All libHere, we used the RNeasy preparation method (Qiagen), the phenol-chloroform based TRIzol procedure, and we separated the nuclear and the cytoplasmic fraction using the non-organic extraction kit PARIS (Life Technologies). All RNA extractions were performed in duplicates. We detected nuclear unspliced RNAs  in TRIzol RNA, Qiagen RNA and nuclear RNA, whereas none could be seen in the cytoplasmic RNA fractions + selected libraries, without significant difference in the exonic coverage between the procedures using Qiagen or TRIzol RNA extractions . In contrast, the data obtained with RiboZero RNA-seq were highly sensitive to the RNA extraction methodology. In fact, the RiboZero procedure only generated data comparable to that of the poly(A)+ RNA-seq when using cytoplasmic-fractionated RNAs (87% of exonic and 10% intronic reads), whereas nuclear-fractionated RNA processed with RiboZero led to 31% of exonic and 61% intronic sequences reads Figure . ResultsThe qualitative variations observed between protocols, raised issues regarding the estimation of expression levels of coding genes. We calculated the expression values in reads per kilobase per million (rpkm)  for eachThe rpkm expression values for the cumulative intronic sequences of each gene were the highest for TRIZol-RiboZero RNA-seq, after the nuclear RNAs Figure b. WithinComparative analysis of TRIzol and Qiagen RNAs sequenced by either poly(A)+ or RiboZero protocols, revealed clear differences in the coding sequences sequencing depth, which was inversely proportional to that of intronic sequences, an observation, which can be logically explained by the sampling factor.We investigated how those differences between protocols impacted gene expression values. We applied principal component analysis (PCA) and pairwise correlations on the various dataset obtained for HEK293 + and RiboZero RNA-seq, Table . In totaTo detect specifically the non-polyadenylated protein coding genes in our dataset, we applied arbitrary and restrictive filters, which included the average difference in rpkm between RiboZero and poly(A)+ >5 rpkm) and the fold change >2). In total, 74 protein coding genes met these criteria, which included 43 out of the 46 replication-dependant histone cluster genes that were identified as differentially expressed above  and exonic  sequence length than those genes with higher expression in RiboZero RNA-seq . These observations corroborate the reads sampling factor contribution to the apparent different expression. For any given gene whose pre-mRNAs are sequenced along with the matured RNAs, the overall expression of the coding parts will appear lower if the protocol favors the presence of nuclear RNAs. However, we did not see a correlation between the size of the coding sequences and the number of genes seen with varying levels of expression between protocols, in contrast to previously reported results [On the other hand, 1,075 unique genes were expressed at higher levels in poly(A)+ RNA-seq than in RiboZero RNA-seq, of which more than half showed exonic values greater than 10 rpkm  and which can be polyadenylated or not . Non-codThe overall distribution of lncRNA expression levels was similar across methods median = 1 rpkm) and was lower than for protein coding genes (median = 8.2 rpkm) in all methods, confirming previous results + selection. However, in the bulk of transcripts obtained from RiboZero data, it remains unclear how to differentiate between transcripts that are polyadenylated, not polyadenylated and/or bimorphic . Under tFinally, lncRNAs expressed from intergenic regions of the genome (lincRNAs) have been the focus of increasing attention in the last years as they are emerging as key regulators of diverse cellular processes and several thousands have been described in human and mouse –31. The RNA-seq can be instrumental for detecting alternative splicing events , 38, andIn summary, the procedure of choice for identifying alternative splicing events by NGS remains the poly(A)+ RNA-seq strategy , whereas the analysis is more challenging with RiboZero sequencing, for which the sequencing depth should also be increased to compensate for the large fraction of reads localized outside of coding regions.The majority of NGS-based transcriptome analysis had interrogated the polyadenylated fraction of RNAs extracted from tissues or whole cells, assuming that most known mature mRNAs are polyadenylated and located in the cytoplasm , 41. NonStarting from the same source of HEK293 cells, we compared two commonly used RNA extractions protocols (Qiagen and TRIzol) coupled with two RNA-seq approaches (poly(A)+ and RiboZero) and described the qualitative and quantitative differences in the respective data output. In turn, the fraction of reads mapping to intronic regions was higher in RNA extracted with TRIzol than in RNA extracted with a Qiagen protocol, and was mostly attributed to hnRNA localized in the nucleus. These differences were particularly pronounced after using rRNA depleted total RNA sequencing, while being less dramatic with poly(A)+ RNA-seq. The relative differences observed in the proportion of exonic, intronic and intergenic sequences were protocol dependent. These observations argue for processing of RNA in a highly controlled and standardized manner. For example, cells or tissue should not be stored in varying volumes of e.g. TRIzol for varying periods of time, which will consequently release variable amounts of nuclear material, introducing differences in the exonic/intronic read distribution. Whenever possible, it is preferable to keep the samples as fresh frozen material (in liquid nitrogen), and perform the nucleic acid extraction in one step.The expression levels of protein coding genes quantified in rpkm tended to be lower in RiboZero RNA-seq than in poly(A)+ RNA-seq, resulting in a reduced sequencing depth in exonic regions when the number of reads in intronic sequences was high (e.g. Trizol extractions). This issue might need particular attention when conducting metadata analysis exploiting RNA-seq data generated by different extraction and selection protocols, especially if the expected expression changes between samples are of small amplitude (e.g. trisomic versus disomic chromosome configurations in aneuploidies).a priori between polyadenylated and not polyadenylated, or bimorphic transcripts. The gain of information resulting from RiboZero RNA-seq might be dimmed, if one cannot discriminate between the RNA sub-populations, if expression data are biased and if the power of detecting alternative splicing is reduced. It is therefore advisable to benchmark the RiboZero method with a poly(A)+ selection, when high resolution analysis of the transcriptome is required. It is possible to prepare and index these two fractions sequentially from the same source of starting material, with the advantage of capturing both polyadenylated and non-polyadenylated fractions and to finally sequence those in one experiment.Previous studies have reported an impact of the presence of nuclear RNA on steady state mRNA expression analysis , 43. How® (#CRL-11268). The cells were grown in parallel in 75 cm2 flasks  for 3 days from a P16 passage in D-MEM medium (GIBCO #31885-049) supplemented with 1% Penicillin/Streptomycin (GIBCO #15140-122) and 5-10% FCS (Sigma #7524). After two passages all cells were pooled together and washed with two times with PBS and splitted into aliquots of 1 million cells. Cell pellets were frozen in liquid nitrogen and further used for RNA extraction.The commercially available HEK 293 T cell line was purchased from ATCCFrozen cell pellets were re-suspended in 500 μl TRIzol (Life Technologies #15596-018), briefly vortexed and 200 μl Chroroform (Merck #102445) was added. Heavy MaXtrack Tubes (Qiagen #1038988) were used the phase separation. The RNA precipitation was done with 10 μg RNase free Glycogen and 500 μl Isopropanol (Merck #109634). The RNA pellets were washed with 1 ml of 70% ice cold Ethanol.Total RNA from cell pellets was purified using the RNeasy Mini Kit  and frozen cells were re-suspended in 350 μl of buffer RLT and the lysates were passed 5 times through a 20-gauge needle, and processed following the manufacturer’s instructions.™ Kit  was used to separately isolate nuclear and cytoplasmic RNA from actively grown cells, following the manufacturer instructions.PARISfree™  following the manufacturer instructions. In brief, digestions were performed at 37°C for 20 minutes and using 2U of TURBO DNase in 90 μl reaction. Ethanol precipitation was done with 3 M NaOAc and 10 μg Glycogen. All concentrations were evaluated with the Qubit 2.0 Fluorometer (Invitrogen) and RNA quality was monitored by Bioanalyzer.All RNAs were DNase treated using TURBO DNA-poly(A)+ selected RNA libraries were prepared following a protocol published recently and preserving the strand information . The staTM Magnetic Gold kit  following the manufacturer instructions. The rRNA depleted RNA pellets obtained after the ethanol precipitation step was re-suspended in 0.5 μl of RNase free water. The samples were than further processed as described in [rRNA was removed from 200 ng of total RNA using the RiboZeroribed in , but staSequencing was carried out on the HiSeq2500 with 2 × 51 cycles and using version 3 of the Illumina sequencing chemistry. All reads were aligned to the NCBI37/hg19 assembly of the human reference genome using BWA (v.0.5.9-r16) . Only reCoordinates for protein coding genes and corresponding exons were downloaded from the Ensembl Genes (v70) database using BioMart tool and Homo sapiens genes (GRCh37.p3) dataset. Only the intervals on chromosomes 1–22, X, Y, M with merged overlapping exons belonging to the same gene were used to calculate exons hits. Non-overlaping introns and intergenic regions were generated using BEDTools  and UCSChttp://www.gencodegenes.org/). The definition and classification of each lncRNA subtype can be found at http://www.gencodegenes.org/gencode_biotypes.html. RPKM values were calculated as described above.The analysis of long non-coding RNAs was based on Ensembl (v70), which is based on the annotation and curation within the Gencode project  of each rpkm expression value (e.g. for scatter plots) was calculated using following formula: The analysis of differential expression Analysis was conducted with the Bioconductor Package NOIseq, Version 1.3.0 . The inpThe paired reads were aligned with tophat 2.0.3 using Bowtie 1 and junction coordinates based on Ensembl (v62). The resulting spliced alignments were used to count reads that span known junctions with a 5 base seed on the donor and acceptor side. The junctions were defined by unique genome position.https://www.ebi.ac.uk/ena).The sequence data set supporting the results of this article are available in the European Nucleotide Archive under the accession number PRJEB4197 (Supplementary data file. The supplementary data files contains the supplementary figures and legends S1-S8. (PDF 2 MB)Additional file 1:Additional file 2: Table S1: Mean exonic and intronic RPKM values of protein coding genes. The cumulated length of the exonic and intronic sequence used for the rpkm calculation is given in columns 7 and 8, respectively. The standard deviation (SD) of the is shown in the columns next to each mean rpkm values.The gene annotation is based on the Ensembl (v70). (XLS 8 MB)http://www.bioconductor.org/packages/2.13/bioc/vignettes/NOISeq/inst/doc/NOISeq.pdf. (ZIP 5 MB)Additional file 3: Table S2: List of differentially expressed protein coding genes (1). The table lists each gene detected as differentially expressed by the NOISeq algorithm , for each pairwise comparison listed in the first column. The gene annotation used as input is based on the Ensembl (v62). The values given in the tables correspond to the NOISeq output , ranking) and are described at: Additional file 4: Table S3: Mean RPKM expression of lncRNAs. The table lists for each annotated lncRNA it’s expression values across the different methods. The lncRNA annotation is based on the Ensembl version 70. (XLSX 1 MB)Additional file 5: Table S4: Splice Junctions statistics. The table lists mapping statistics of exon junction reads of protein coding genes derived from the TopHat alignment. (XLSX 58 KB)


RNA Modification Base, http://mirlab.sysu.edu.cn/rmbase/), to decode the genome-wide landscape of RNA modifications identified from high-throughput modification data generated by 18 independent studies. The current release of RMBase includes ∼9500 pseudouridine (Ψ) modifications generated from Pseudo-seq and CeU-seq sequencing data, ∼1000 5-methylcytosines (m5C) predicted from Aza-IP data, ∼124 200 N6-Methyladenosine (m6A) modifications discovered from m6A-seq and ∼1210 2′-O-methylations (2′-O-Me) identified from RiboMeth-seq data and public resources. Moreover, RMBase provides a comprehensive listing of other experimentally supported types of RNA modifications by integrating various resources. It provides web interfaces to show thousands of relationships between RNA modification sites and microRNA target sites. It can also be used to illustrate the disease-related SNPs residing in the modification sites/regions. RMBase provides a genome browser and a web-based modTool to query, annotate and visualize various RNA modifications. This database will help expand our understanding of potential functions of RNA modifications.Although more than 100 different types of RNA modifications have been characterized across all living organisms, surprisingly little is known about the modified positions and their functions. Recently, various high-throughput modification sequencing methods have been developed to identify diverse post-transcriptional modifications of RNA molecules. In this study, we developed a novel resource, RMBase ( Post-transcriptional modification of RNA molecules occurs in all living organisms, and is one of the most evolutionarily conserved properties of RNAs –5. It ca6A, m5C, 2′-O-Me) within coding and non-coding sequences at single nucleotide or very high resolution . To determine the transcriptome-wide landscape of RNA modifications, recently many studies have developed high-throughput modification sequencing methods to identify diverse post-transcriptional modifications of RNA molecules . Applicasolution . With th6A has been predicted to affect protein translation and localization  and the supplementary data of the original references  motifs as described by previous study  genotype data of the HapMap project (release 28)  . For eacease 28) ,4, such ease 28) .All miRNA-target interactions for human and mouse were downloaded from our starBase platform ,42. All All data sets were processed and stored in a MySQL Database Management System. The database query and user interface were developed using PHP and JavaScript. The query result table is based on jQueryUI and DataTables, which is a highly flexible tool for sorting and filtering the search result.We constructed RMBase Genome Browser to provide an integrated view of reference sequences, modification sequencing data, aligned sequencing reads, RNA modification sites, protein-coding genes, ncRNA genes and transcripts. RMBase Browser is built on JBrowse  which isWe integrated 139 025 RNA modification sites generated by 18 independent studies to profile the genome-wide modification landscape of more than 100 types of RNA modifications  which may be used to display RNA modification sites from various modification types. For each type of the RNA modification, users can select species in the query page. In the result page, the basic information of modification sites was displayed in a data table which includes 10 distinct fields to describe the details of modification sites. For each interface, the numbers of RNA modification sites are indicated in bottom-left corner of table. The user can also click on the title of the table to sort RNA modification sites according to various features, such as chromosome, genome positions, the number of supporting experiments, modId, the gene names or the gene types. User can input the keyword in search box to filter the results. The users can click on a modId within the table to launch a detailed page that provides further information about the RNA modification site in question. The detailed information for a modification site includes a description of the modification site, the list of supporting experiments and sequence that was extended by an additional 20 nt in both the 5′- and 3′-directions for the modification site. The ‘PubMed ID’ section enabled the retrieval of the primary articles yielding the modification data. Click the ID link to visit the NCBI PUBMED website.We provided five web interfaces  format. After the user has completed the data submission, a typical iteration of the modTool program may require several seconds or minutes to finish. The output of this program mainly consisted of a data table that included 10 distinct fields to describe the details of hits. The results include the query name, modification positions on genomes, modification type, the number of supporting experiments or studies, gene name, gene type (e.g. protein-coding or ncRNA) and regions  on genes. Users can reorder any columns in the result table. Thus, it is convenient for data view and comparison in the user-defined vision style. Moreover, the keyword search was supported to scale down the results. Only 200 entries of hit information are displayed in the table, and users can obtain all results in text format by clicking on the ‘export’ button.6A-seq, RiboMeth-seq) and public resources, RMBase reveals extensive post-transcriptional modification of RNA in mammalian and yeast.By integrating a large set of RNA modification sites derived from all available high-throughput modification sequencing methods  RMBase provides the genome-wide landscape of pseudouridine (Ψ), m5C and 2′-O-Me modifications. (iii) RMBase provides genomic coordinates of all modification sites. This will facilitate computational or experimental biologists to correlate their results with all modification sites deposited in RMBase. (iv) RMBase allows combined analysis of RNA modification data and GWAS data, which identify hundreds of disease-related SNPs resided in the modification sites. These results will help to reveal the real causations and mechanisms for diseases or phenotypes identified from GWAS studies. (v) RMBase also illustrates relationships between RNA modification sites and miRNA target sites. (vi) In RMBase, we provided RMBase genome browser to provide a quick overview of a particular region in the genome and for visually correlating various types of features (Figure In comparison with the other databases related to RNA modifications, including MODOMICS , RNAMDB s Figure  and a neOverall, RMBase allows an integrative analysis of various high-throughput modification data that provide insights into the epigenetic regulation of the transcriptome. As genome-wide high-throughput sequencing data for RNA modifications become more and more available, RMBase will help researchers further investigate these data and discover potential functional roles of RNA modifications hidden in these data.With the development of new high-throughput modification sequencing method, there will be more and more single nucleotide resolution modification sequencing data. We have built an automatic pipeline which is run in our high-performance computer servers to automatically annotate, analyze and merge all high-throughput modification data sets, and then import these data into our local MySQL database. We will continually maintain and update the database every two months or whenever new high-throughput modification data sets are released in public databases. RMBase will continue to expand the storage space and improve the computer server performance for storing and analyzing these new data, and we will develop or integrate new tools to decode the landscape of RNA modifications.http://mirlab.sysu.edu.cn/rmbase/. The RMBase data files can be downloaded and used in accordance with the GNU Public License and the license of primary data sources.RMBase is freely available at


Objective. To determine the impact of coal mining, measured as the number of coal mining-related facilities nearby one's residence or employment in an occupation directly related to coal mining, on self-rated health in Appalachia. Methods. Unadjusted and adjusted ordinal logistic regression models calculated odds ratio estimates and associated 95% confidence intervals for the probability of having an excellent self-rated health response versus another response. Covariates considered in the analyses included number of coal mining-related facilities nearby one's residence and employment in an occupation directly related to coal mining, as well as potential confounders age, sex, BMI, smoking status, income, and education. Results. The number of coal mining facilities near the respondent's residence was not a statistically significant predictor of self-rated health. Employment in a coal-related occupation was a statistically significant predictor of self-rated health univariably; however, after adjusting for potential confounders, it was no longer a significant predictor. Conclusions. Self-rated health does not seem to be associated with residential proximity to coal mining facilities or employment in the coal industry. Future research should consider additional measures for the impact of coal mining. The Appalachian Region is a 205,000-square-mile area that follows the spine of the Appalachian Mountains and includes all of West Virginia and parts of 12 other states , chemicaCoal mining is one of the major economic industries for eight Appalachian states  . The areThe use of self-rated health (SRH) as a predictor of mortality is well-established . Twenty-http://www.m-s-g.com/web/index.aspx). Numbers were prescreened by Genesys MSG to remove nonworking or nonresidential numbers; 5054 numbers were available to be called, with a target sample size of 400–500, chosen as the target sample size because this was intended as a demonstration project.The sample was drawn primarily from 10 counties of interest : coal: Boone, WV; Logan, WV; Mingo, WV; Monongalia, WV; and Raleigh, WV; noncoal: Berkeley, WV; Calhoun, WV; Tyler, WV; Yancey County, NC; and Cocke County, TN. A total sample of 9000 telephone numbers, drawn from a pool of all landline and cellular telephone phone numbers in the relevant area codes, was purchased from Genesys Marketing Systems Group (MSG)  and loaded into Win-Cati 5.0, a computer assisted telephone interviewing (CATI) program using the Windows platform. The Win-Cati system enabled supervisors to ensure that phone numbers were called on schedule and allowed for direct data entry, reducing the likelihood of data entry errors. Numbers were called at different times during the day, evening, and weekend call periods to increase the likelihood of contact, with an average of 2.2 call attempts to each number.SRH was measured using responses from the survey question, “Would you say that in general your health is…,” rated on a 5-point Likert scale from 1 (excellent) to 5 (poor) (question 53). We independently compared SRH responses for groups expected to have different levels of exposure to coal mining. The first comparison was based on the self-reported number of coal mining facilities nearby that person's home or the number of “yes” responses to questions which asked participants whether or not they lived near the following facilities (questions 44–49): underground continuous coal mine, underground longwall coal mine, mountaintop removal coal mine, surface coal mine, impoundment pond, or coal preparation facility. For example, if someone lived near all of the possible coal mining facilities, they were considered to live near six coal facilities. The second comparison was based on the survey question, which asked participants, “Are you or have you been employed in an occupation directly related to coal mining?” (question 96). Graphical representations were created to compare and contrast SRH status for these questions. Because we were interested in SRH as related to coal mining, a predominantly male occupation, we examined the descriptive characteristics by gender and ran gender-stratified and not stratified models.p < 0.15. Due to the small number of females with work in coal mines, we restricted those analyses to males only. Score tests were used to assess the proportional odds assumption for all models. For the male only models, SRH was collapsed into three categories  in order to satisfy the assumption of proportional odds. Statistical significance for all multivariable models was assessed as p < 0.05. This study was approved by the University of Pittsburgh Institutional Review Board. Respondents were not provided an incentive for their participation.We used ordinal logistic regression to investigate whether the number of coal facilities nearby and/or having an occupation directly related to coal mining are predictors of SRH after adjusting for age, sex, and other potential confounders. Other potential confounders were included in adjusted multivariable models for SRH if the potential confounder had univariate statistical significance of Each telephone number in the sample was called at least once. Numbers called more than once were called back at different times during the day, evening, and weekend call periods to increase the likelihood of contact. An average of 2.2 call attempts were made to each number. A total of 415 interviews were completed. Using American Association for Public Opinion Research (AAPOR) standard definitions , the coop = 0.04). This relationship was more pronounced in males (p = 0.01).As shown in p = 0.08).The number of coal mining facilities near the respondent's residence was not a statistically significant predictor of SRH. Adjustment by the covariates age, sex, BMI, smoking status, income, and level of education did not change the association between SRH and number of mining facilities nearby . Howeverp < 0.05); in the females only model, age, BMI, smoking, income, and education were all statistically significant predictors of SRH (p < 0.05).In the stratified models, the number of nearby coal facilities was not statistically significant for males or females (data not shown). In the males only model, BMI and smoking were statistically significant predictors of SRH (p = 0.98). Higher BMI, smoking every day, and having a high school or less education were statistically significant predictors of poorer SRH.As shown in Compared to WV overall, the respondents of this survey were less likely to report their health as fair or poor (20% survey versus 25.2% WV) ; in factIn the univariable ordinal logistic models, SRH is also associated with the demographic/socioeconomic factors of age, income, and level of education, as well as the personal risk factors of obesity and smoking every day. The number of coal facilities near the respondent's residence was not associated with SRH. Employment in coal-related occupations was found to be negatively associated with SRH.As shown in p = 0.92, data not shown). Age, BMI, smoking, income, and education were all highly statistically significantly associated with poorer SRH in the multivariable model. These factors seemed to affect SRH in females more than males.Zullig and Hendryx (2011) found that residents of mountaintop mining communities generally had poor self-reported health . In our We found no evidence of an association between SRH and employment in an occupation related to coal mining. However, the percentage of participants reporting a “poor” health status was 9% higher for those who are or have been employed in an occupation related to coal mining versus those who are/have not. In addition, approximately 62% of men with mining employment have an education level corresponding to “high school or less,” whereas the education levels of men without such employment are more evenly distributed. Thus, it is possible that, by including both of these variables in the model, the effect of coal-related employment on SRH is being masked by education level.This study has some limitations. First, survey data can be prone to self-reporting bias, although SRH is a validated outcome . The numThis study found no associations between SRH and residential proximity to coal mining facilities or employment in the coal industry. It did, however, provide support for the body of literature associating lower SRH with individual-level factors age, smoking status, income, and education.


Recent work from Peter Nagy group demonstrates that replication of a model positive strand RNA virus can be reconstituted in the in vitro reaction with liposomes of chemically defined composition and reveals an exclusive role of phosphatidylethanolamine in sustaining efficient viral RNA replication. This study opens new possibilities for investigation of membrane contribution in the replication process that may ultimately lead to development of novel broad spectrum antiviral compounds targeting the membrane-dependent elements of the replication cycle conserved among diverse groups of viruses.All positive strand RNA viruses of eukaryotes replicate their genomes in association with membranes. These viruses actively change cellular lipid metabolism to build replication membranes enriched in specific lipids. The ubiquitous use of membranes by positive strand RNA viruses apparently holds major evolutionary advantages; however our understanding of the mechanistic role of membranes, let alone of specific lipid components of the membrane bilayer, in the viral replication cycle is minimal. The replication complexes that can be isolated from infected cells, or reconstituted  Positive strand RNA ((+)RNA) viruses are arguably the most numerous viruses of eukaryotes . They inIt is believed that membranes may promote recruitment of viral and cellular factors required for replication, facilitate proper topological assembly of multicomponent replication machinery, and hide dsRNA replication intermediates from cellular sensors of infection. However, the mechanistic details of how exactly the membranes are involved in the replication process remain elusive.The main structural elements of biological membranes are phospholipids. These molecules consist of two functionally different parts. Two long chain fatty acid moieties attached to a glycerol backbone constitute the hydrophobic portion of the molecule and, when phospholipids form the lipid bilayer, the fatty acid composition determines membrane properties such as thickness, permeability, and fluidity, ultimately influencing membrane function as a biological barrier. The other part of a phospholipid molecule is a polar headgroup which faces the aqueous environment. Different headgroups, which possess different shapes and charges, determine specific recruitment and interactions of membrane-associated proteins and are an important part of membrane shaping mechanisms supporting positive (convex) or negative (concave) membrane curvature.Xu and Nagy in their recent report in PNAS  providedin vitro with purified recombinant p33 and p92, membrane-free fraction of yeast cytosolic proteins, and artificial liposomes. By providing liposomes of different composition it was possible for the first time to directly investigate requirements of the viral replication reaction for specific phospholipids. Liposomes made from phosphatidylethanolamine (PE), a phospholipid with a positively charged conically shaped headgroup, supported replication of the template RNA up to half of the control level achieved when the system was supplemented with the membranous fraction from yeast cells. The requirement for PE was very specific, as liposomes prepared from lysophosphatidylethanolamine (LPE), a molecule similar to PE but without one of the fatty acid chains, supported only a barely detectable level of replication. The same background replication was detected with phosphatidylcholine (PC) liposomes, and no replication at all was observed when liposomes prepared from several other lipid species were tested. However, if the PE liposomes also contained about 10%–15% of a lipid mix mimicking the composition of intracellular membranes from Nicotiana benthamiana, a natural plant host for TBSV, the replication activity was significantly higher than that achieved with pure PE liposomes. The stimulatory effect could be partially recapitulated by addition of 10% of PC or LPE to PE liposomes, while at a concentration of more than 20% these lipids were inhibitory for replication.In this new study, Xu and Nagy could reconstitute TBSV replication in vitro observations suggesting the important role of PE in TBSV replication were corroborated by several in vivo experiments. First, the authors showed that the total amount of PE is increased in yeast and N. benthamiana cells where TBSV RNA is replicating, and that PE content is significantly enriched at the replication sites. Second, replication of TBSV in yeast mutants with deletion of the PE methyltransferase gene resulting in accumulation of excess PE was up to 10 times more efficient than in wild type (wt) cells. Moreover, when the in vitro system with the same amount of recombinant TBSV proteins was reconstituted with the membrane fraction isolated from either wt or PE methyltransferase-deficient yeast, the latter could also support higher replication level of TBSV RNA replication. Finally, TBSV replication was significantly inhibited in yeast dependent on exogenous source of ethanolamine for PE synthesis when they were incubated in ethanolamine-free medium, and the replication could be rescued up to the wt level when ethanolamine was provided. Interestingly, while TBSV replication organelles normally develop on peroxisomal membranes, disruption of peroxisome biogenesis results in redistribution of the replication sites to the ER membranes [The embranes . Here Xude novo synthesis of the lipid and/or its active transport from the other cellular locations. Expression of only the p33 protein was sufficient to induce PE redistribution in the yeast cells. Since the exogenously added fluorescently labeled PE was found to be recruited to peroxisomal membranes in a p33-dependent manner, the active lipid transport must be involved in generation of the TBSV replication organelles, but the molecular mechanisms of how p33 controls the cellular lipid trafficking pathways remain to be elucidated.In principle, enrichment of the replication sites in PE could be achieved by localized in vivo represent membrane invaginations connected to the cytoplasm with narrow necks and the viral replication machinery is associated with the inner, negatively curved membrane surface. Enrichment of the membrane leaflet in PE with its conically shaped headgroup may promote development of the negative membrane curvature facilitating invagination development. Such spatial enclosure contributes to protection of the replicating RNA which becomes resistant to RNAse treatment [in vitro system since replicating RNA was easily degraded by micrococcal nuclease in the presence of PE liposomes. The membrane floatation assay also demonstrated that liposomes made from other phospholipids that were not effective in supporting replication could recruit viral replication proteins and template RNA just as good or even better than PE liposomes, excluding the possibility that high PE content is required simply for binding of the viral replication complexes to membranes. However, these experiments do not discriminate non-specific binding from assembly of the components of the replication machinery in the correct arrangement, leaving the possibility that PE promotes proper topological organization of the replication complex. Since the same amount of recombinant p33 and p92 and the yeast soluble protein fraction was added to the in vitro reactions containing different liposomes, it is possible that PE directly stimulates activity of a viral protein or a host factor required for replication. It was previously shown that phospholipids may stimulate specific steps in the replication cycle. Replication of (+)RNA viruses proceeds through synthesis of a −RNA strand on a genome RNA template, and the –strand further serves as a template for multiple rounds of synthesis of progeny +RNA strands. Exogenously added phospholipids activated productive synthesis of + strand RNAs, but were dispensable for the first −RNA strand synthesis by the isolated replication complexes of flockhouse virus, an insect (+)RNA virus [How does specific lipid composition of the replication membranes, and in particular their enrichment in PE, stimulate the replication reaction? The TBSV replication sites reatment . HoweverNA virus . In thisNA virus ,8 that iNA virus ; as wellNA virus ,12. It wThe active manipulation of the cellular lipid synthesis and trafficking pathways by diverse (+)RNA viruses of plants and animals has been documented, showing that these viruses actively build their replication membranes with an optimal lipid microenvironment rather than just remodel pre-existing cellular organelles ,19,20,21