In this study, we employed real-time quantitative reverse transcription polymerase chain reaction to analyze the cis-acting elements on bovine coronavirus (BCoV) sgmRNA 7 required for the synthesis of its (−)-strand counterpart by deletion mutagenesis. The major findings are as follows. (1) Deletion of the 5'-terminal leader sequence on sgmRNA 7 decreased the synthesis of the (−)-strand sgmRNA complement. (2) Deletions of the 3' untranslated region (UTR) bulged stem-loop showed no effect on (−)-strand sgmRNA synthesis; however, deletion of the 3' UTR pseudoknot decreased the yield of (−)-strand sgmRNA. (3) Nucleotides positioned from −15 to −34 of the sgmRNA 7 3'-terminal region are required for efficient (−)-strand sgmRNA synthesis. (4) Nucleotide species at the 3'-most position (−1) of sgmRNA 7 is correlated to the efficiency of (−)-strand sgmRNA synthesis. These results together suggest, in principle, that the 5'- and 3'-terminal sequences on sgmRNA 7 harbor cis-acting elements are critical for efficient (−)-strand sgmRNA synthesis in BCoV.It has been demonstrated that, in addition to genomic RNA, sgmRNA is able to serve as a template for the synthesis of the negative-strand [(−)-strand] complement. However, the  During coronavirus replication, in addition to genomic RNA, a 3' coterminal nested set of subgenomic mRNAs (sgmRNAs), which are also 5' coterminal with the leader sequence of the genome, are synthesized via discontinuous transcription ,2,3,4. Acis-acting elements required for replication [interpreted as (+)-strand synthesis]  ,29,30,31terparts . Accordid sgmRNA .cis-acting elements important for the replication  at 3 × 10eviously ,38.NgoMIV and XbaI, and the digested fragment containing the sgmRNA 7 5' leader sequence was cloned into NgoMIV and XbaI-linearized pBM25A, creating BCoV sgmRNA 7 with the 301-nt 3' UTR and 25-nt poly(A) tail of MHV-A59. A previously described overlap PCR mutagenesis procedure was used to construct sNL in which the leader sequence of sBM25A was deleted [ NgoMIV and XbaI was cloned into NgoMIV and XbaI-linearized psBM25A to produce psNL. Constructs of psΔSL1 and psΔSL2 in which stem-loop I and stem-loop II, respectively, was deleted were similarly generated except for the corresponding oligonucleotides used in the first and second reactions, as described in SpeI and MluI was cloned into SpeI and MluI-linearized psBM25A to produce the mutant psΔB. Constructs psΔP (deletion of 3' UTR pseudoknot), ps3'Δ55 (deletion of 3'-termianl 55 nts), psΔ3'15 (deletion of 3'-termianl 15 nts), psΔ3'55–40 (deletion of nts from −40 to −55), psΔ3'55–35 (deletion of nts from −35 to −55), psΔ3'55–30 (deletion of nts from −30 to −55), psΔ3'30–15 (deletion of nts from −15 to −30), psgmA’ (substitution of the 3'-most nt with adenine), psgmG’ (substitution of the 3'-most nt with guanine), and psgmU’ (substitution of the 3'-most nt with uracil) were similarly constructed except the corresponding oligonucleotides (Construction of pBM25A in which the 288-nt 3' UTR of BCoV-Mebus DI RNA in pDrep1 was replaced with the 301-nt 3' UTR and 25-nt poly(A) tail of MHV-A59 has been described . To consleotides  used in MluI-linearized pBM25A and psBM25A were transcribed in vitro with RiboMAx T7 kit  in a total reaction volume of 50 μL. The reaction was performed at 37 °C for 90 min, treated with 5 μL DNase, and chromatographed through a Biospin 6 column . For transfection, 3 µg of transcript, which was quantitated by denaturing gel electrophoresis, was transfected into HRT-18 cells with lipofectin  in 35-mm dishes at ~80% confluency (~8 × 105 cells/dish), which were infected with BCoV at a multiplicity of infection of five PFU per cell 2 h prior to transfection [sfection  and virusfection ,39. For ® 480 instrument  using MHV3'UTR6(−) and BCV23-40(+) primers, and TaqMan®Universal PCR Master Mix (Applied Biosystems) according to the manufacturer’s recommendations. To assess the efficiency of (−)-strand sgmRNA synthesis from wt sBM25A and the mutants sNL, sΔSL1 and sΔSL2, 1 µg of RNA with decapping and head-to-tail ligation was used for RT reaction with oligonucleotide MHV3'UTR-DR(−) to synthesize cDNA with SuperScript III reverse transcriptase (Invitrogen). TaqMan®Universal PCR Master Mix (Applied Biosystems) with TaqMan probe-3 and primers MHV3UTR-DR(-) and BCVN(+) was used for RT-qPCR. To assess the synthesis of (−)-strand RNA from RNA samples without decapping and head-to-tail ligation, 1 µg of RNA was used for RT reaction with oligonucleotide MHV3'UTR-DR(−) to synthesize cDNA with SuperScript III reverse transcriptase (Invitrogen). TaqMan®Universal PCR Master Mix (Applied Biosystems) with TaqMan probe-3 and primers MHV3UTR-DR(−) and MHV3UTR-DR(+) (8–10 copies of each plasmid). The efficiency of (−)-strand RNA synthesis was normalized with the levels of internal controls, including 18S rRNA (with primers 18SrRNA(−) and 18SrRNA(+)), (+)-strand reporter-containing sgmRNA (with primers MHV3UTR-DR(−) and MHV3UTR-DR(+)) and helper virus M sgmRNA (with primers leader20(−) and M(+)) extracted at 8 hpt (VP0). The reactions were performed as an initial pre-incubation at 95 °C for 5 min, followed by 35 amplification cycles of 95 °C for 15 s and 60 °C for 60 s.To assess the efficiency of (−)-strand sgmRNA synthesis from wt sBM25A and the mutants except sNL, sΔSL1 and sΔSL2, 1 µg of decapped and ligated RNA collected from BCoV-infected sgmRNA-transfected HRT-18 cells at 8 hpt was used in an RT reaction with oligonucleotide MHV3'UTR3(−) and SuperScript III reverse transcriptase (Invitrogen). TaqMan probe-5  used forTR-DR(+) , which b5 cells/dish) were infected with BCoV at a multiplicity of infection of five PFU per cell. Two hours post-infection (hpi), 3 µg of transcript was transfected into the HRT-18 cells. To detect the replication of BCoV DI RNA and sgmRNAs, the supernatant was collected at 48 hpt and then used to infect fresh HRT-18 cells . Ten µg of TRIzol-extracted total cellular RNA at 48 hpi of VP1 was used for formaldehyde-agarose gel electrophoresis. To detect 18S rRNA, reporter-containing sgmRNA, and M sgmRNA, 10 µg of TRIzol-extracted total cellular RNA at 8 hpt (VP0) was used for formaldehyde-agarose gel electrophoresis. RNA was then transferred from the gel to Nytran membranes by vacuum blotting. The blots were probed with digoxigenin (DIG)-ddUTP labeled  oligonucleotides: TGEV 8(+) , BCVN(+) (for M sgmRNA), and 18SrRNA(+) (for 18S rRNA). The detected RNA was visualized according to the procedure of the manufacturer.A Northern blot assay was performed as described previously ,13. Briep values were determined with paired t test for RT-qPCR data.Statistical calculations were performed using GraphPad Prism v2.01 . p valuevs. 100%) under the similar experimental conditions.To study coronavirus replication, an ~ 2.2 kb BCoV DI RNA has been employed as a surrogate for the ~30 kb BCoV genome B have beConsidering that the inefficient head-to-tail ligation may decrease the numbers of (−)-strand RNA species to be quantitated and thus may affect the subsequent interpretation of the results, RT-qPCR was also performed using RNA without ligation reaction with primers annealing to the hypervariable region of MHV 3' UTR. Although the false positive results of (−)-strand RNA detection caused by copy-back (−)-transcripts generated by T7 RNA polymerase may occur, the levels of copy-back molecules determined by RT-qPCR from uninfected cells transfected with transcripts can be used as the background levels of (−)-strand RNA for BM25A and sBM25A. The relative quantitation of (−)-strand RNA synthesis from BM25A and sBM25A compared to the background levels would suggest that the increase of the (−)-strand RNA synthesis is due to the activity of viral RNA-dependent RNA polymerase. As shown in cis-acting elements required for coronavirus replication. Furthermore, the 3'-terminal 55-nt sequence has been shown to be critical for synthesis of (−)-strand MHV DI RNA [cis-acting elements required for (−)-strand sgmRNA synthesis.Structurally, sgmRNAs and genomic RNA in BCoV share common features at the termini, including a 5' leader sequence and 3' UTR. In addition to the leader sequence  within tV DI RNA . HoweverV DI RNA , this recis-acting elements within the leader sequence required for (−)-strand sgmRNA synthesis, we generated constructs sΔSL1 and sΔSL2 in which stem-loop I and stem-loop II, respectively, was deleted -strand sgmRNA , we foun deleted A and per deleted C, whereacis-acting element required for the efficient synthesis of (−)-strand sgmRNA.Using MHV DI RNA, it has been concluded that the 3'-terminal 55 nts are essential for (−)-strand DI RNA synthesis . We alsoThe last nucleotide of the 3'UTR in all coronavirus genomes and subgenomes sequenced to date is cytosine. Therefore, this conserved nucleotide may play an important role during coronavirus infection. Since this nucleotide positions at the 3'-terminus of genome and subgenome, we speculated that it may be involved in (−)-strand RNA synthesis. To test whether this nucleotide correlates with the efficiency of (−)-strand sgmRNA synthesis, the cytosine was substituted with adenine, uracil, and guanine to create constructs sgmA’, sgmU’, and sgmG’, respectively A. As sho et al. [cis-acting elements in the coronavirus genome required for replication have been identified in the past two decades [cis-acting elements on the (+)-strand sgmRNA that are essential for efficient (−)-strand sgmRNA synthesis, and these findings have expanded our knowledge of coronavirus replication.The previously published study has shown that (+)-strand sgmRNA can function as a template for the synthesis of (−)-strand counterpart ; however et al.  and thus et al.  may sugg et al. . As many decades ,49,50,51cis-acting proteins are not required for the replication of MHV DI RNA [cis-acting function for SL I and SL II in the context of subgenome and genome, these results may suggest that the individual SL is required for the template switching that is essential for the synthesis of (−)-strand sgmRNA from genome but the individual SL is not critical for the subsequent amplification of sgmRNA (−) strand using sgmRNA as a template.It appears that the main function of the BCoV DI RNA 5'-proximal 421 nts, which is missing in sgmRNA, is for (+)-strand DI RNA synthesis because (1) both DI RNA and sgmRNA are able to generate their (−)-strand counterparts and (2) only (+)-strand DI RNA can be synthesized from the template of DI RNA (−) strand. In addition, since the efficiency of (−)-strand sgmRNA synthesis from sgmRNA is ~50% less than that from BCoV DI RNA D and is V DI RNA . It has V DI RNA ,48, wher et al. [ et al. [ et al. [ et al. [Based on the secondary structure model of MHV 3' UTR proposed by Züst et al. , two hel[ et al.  with rev[ et al.  and supp[ et al. .It has been demonstrated that the 3'-most nucleotide species in BCoV DI RNA affects the efficiency of (−)-strand RNA synthesis . The samcis-acting requirements within the 5' and 3' termini and the nature of the assembled replication complexes leading to the initiation of (−)-strand RNA synthesis probably also vary between the two RNA species. Therefore, this variation in structure may explain why nts −55 to −35 and the last 15 nts within the 3'-terminal 55-nt sequence are required for efficient (−)-strand BCoV DI RNA synthesis [The terminal structures, including the 5'-terminal leader sequence and 3'-terminal 55 nts, on sgmRNA apparently play critical roles with regard to (−)-strand sgmRNA synthesis. It is logical that the initiation of (−)-strand sgmRNA synthesis is triggered from the 3' terminus of sgmRNA and that mutations in the 3'-terminal region may be detrimental to the synthesis of (−)-strand sgmRNA. However, the question remains as to how the sgmRNA 5'-terminal structures affect (−)-strand sgmRNA synthesis, as deletion of the 5' leader sequence impaired the synthesis of (−)-strand sgmRNA in this study. Since the interactions between the 5' and 3' termini of the virus genome have been suggested to be required for the initiation of replication in RNA viruses ,59,60,61ynthesis , but arecis-acting elements on sgmRNA and found that the 5'- and 3'-terminal sequences on sgmRNA 7 harbor cis-acting elements essential for efficient (−)-strand sgmRNA synthesis in BCoV. These findings have extended our knowledge of coronavirus replication. Furthermore, the reasons for the various observations discussed above regarding the requirements of 5'- and 3'-terminal cis-acting elements for the synthesis of (−)-strand RNA from genomic RNA and sgmRNA remain unclear; however, we speculate that the different experimental approaches may be largely responsible for the different outcomes. Therefore, further studies with similar systems are required in order to explain the various results.In this study, we systematically examined the


Unlike other small RNA classes like microRNAs (miRNAs) and small interfering RNAs (siRNAs), an exceptionally large number of factors are implicated in the biogenesis of piRNAs. Kai http://www.biomedcentral.com/1741-7007/12/61.See research article:  Drosophila ovarian environment  pathway has come to be recognized as the guardian of the germline genome, as it silences mobile genetic elements in animal gonads. Transposon mobility is potentially detrimental to genome integrity as it can lead to mutations, causing a block in germ cell development. Consistently, piRNA pathway mutant animals activate transposons in the gonads and are invariably sterile. A great deal of research - much of it in the t Figure a - has g pathway .Figure 1As with other small RNAs, piRNAs associate with Argonaute proteins, albeit a different class called Piwi (Box 1); however, early on it was realized that piRNA biogenesis is very different from that of other small RNAs. The most obvious distinguishable difference was the longer size (24 to 30 nucleotides) of piRNAs, which was inconsistent with a role for the Dicer endonuclease. Dicer acts as a molecular ruler to process double-stranded RNA precursors into approximately 21 nucleotide small RNAs, so structurally it would be impossible to produce longer small RNA species. Indeed, by the use of genetic mutants, Dicer was shown to be dispensable for piRNA biogenesis in fruitflies and fish.Another difference is the sheer complexity of piRNA sequences. Unlike the several hundred miRNA sequences that were initially catalogued through conventional cloning, high-throughput ′deep′ sequencing methodologies had to be employed to reveal the full spectrum of piRNA sequences. In fact, millions of individual piRNAs can be identified and their mapping to the genome led to the identification of a few hundred hotspots where they cluster together. These so-called piRNA clusters usually range from 50 to 100 kilobases (kb) and are the sources of most piRNAs .Drosophila ovarian environment, primary biogenesis is operational in both the somatic follicle cells and the germline, while secondary biogenesis - known as the ‘ping-pong’ cycle - is specific to the germline.Based on piRNA sequence signatures and biogenesis factor requirements, two distinct cytoplasmic piRNA biogenesis pathways have been recognized: primary and secondary biogenesis. In the O-methyl modification catalyzed by the RNA methyltransferase Hen1.A combination of transcriptome sequencing, mapping of transcription factor binding sites, and genetic analyses have revealed that piRNA clusters are transcribed into long single-stranded transcripts . Such prTo date, Zucchini is the only protein known to act as a nuclease during primary processing. Zucchini homodimerizes, presumably when anchored on the outer membrane of mitochondria, to form an endonuclease active on single-stranded RNAs , but itsflamenco cluster in the ovarian soma may not be enough to signal this, as Ci transcribes other protein-coding genes that do not enter the piRNA pathway. Alternatively, the information might be contained in the precursor RNA itself in the form of a specific sequence or an RNA structural element. The latter possibility is attractive given that the piRNA processing machinery is largely conserved across the animal kingdom, so it might identify conserved features in piRNA precursors.One major question to consider is how the primary processing machinery distinguishes Pol II-transcribed piRNA cluster transcripts from other capped and polyadenylated noncoding RNAs. One possibility is that the nuclear history of transcription from the cluster promoter is conveyed by a co-transcriptionally loaded RNA-binding protein. However, the transcription factor Cubitus interruptus (Ci) that transcribes the flamenco may transit through another perinuclear structure called Dot-COM/Flam-body that is in the proximity of Yb bodies [It is interesting to note that before reaching the nuage, cluster transcripts like that of b bodies . Once PIGermline piRNA biogenesis is more complex than in somatic cells as all three Piwi proteins  are involved, as opposed to PIWI alone. Most factors implicated in the follicle cells for primary processing also act in the germline, loading primary 1U-containing piRNAs into Aubergine (Aub) and PIWI. This results in the loading of essentially antisense repeat piRNAs into the two proteins [Bombyx mori (Silkworm) BmN4 cell culture have shed light on the molecular role of the conserved DEAD box RNA helicase Vasa in linking the two events [Secondary piRNA biogenesis links post-transcriptional gene silencing of transposon transcripts to biogenesis of a new piRNA, and this requires a number of factors unique to the germline environment Figure c. Recento events . Vasa iso events . The speKrimper belongs to one of the largest identifiable group of piRNA biogenesis factors: the Tudor domain-containing proteins. They are defined by the presence of a Tudor domain, which contains an aromatic cage that recognizes methylated arginine or lysine residues. Piwi proteins and Vasa are shown to be symmetrically dimethylated at arginines, and these are recognized by Tudor domains . Severaltap are fertile, they display mild activation of retrotransposons that are normally under control of germline piRNAs. In fact, germline piRNA levels and sequence signatures consistent with an active ping-pong cycle are slightly reduced in the mutant. The authors demonstrate biochemical interactions between the protein and other secondary piRNA biogenesis factors like Vasa, Spn-E, Aub and Ago3, indicating that Tap likely has a scaffolding role during assembly of complexes required for the ping-pong cycle.Tap is a multiple domain protein composed of a Lotus domain and three Tudor domains. It is a paralog of Tejas (Tej), which is known to be essential for secondary piRNA biogenesis and fertility in the fly ovarian germline . Like Tetap mutant has a mild phenotype, the authors examined the tej-tap double mutant flies, which turned out to be sterile. Indeed, the double mutants display stronger activation of transposons, changes in piRNA levels, and mislocalization of nuage components than any of the single mutants. Despite this apparent cooperative effect, the two proteins have distinct roles as they cannot functionally complement each other. So Tej and Tap are proposed to function synergistically in the ping-pong cycle. One strong display of this synergistic function is their role in nuclear accumulation of PIWI in the germline. While individual tej and tap mutants are able to support nuclear localization of PIWI, the protein remained in the cytoplasm in the double mutant, presumably due to lack of loaded piRNAs. This is likely a consequence of the strong disruption in localization of various nuage factors in the double mutant. This partnership between Tej and Tap is conserved, as mouse mutants lacking their orthologues Tdrd5 and Tdrd7 display infertility, with mice lacking Tdrd5 derepressing retrotransposons.Interestingly, the authors reveal a biochemical interaction between Tap and Tej. Given this, and the fact that Research so far has yielded a rich harvest of factors implicated in piRNA biogenesis, but key nucleases are still missing in the pathway. The use of available cell culture models should allow mechanistic study of these factors without the confounding effects of arrested germline development seen in mutant animals. Furthermore, application of a combination of biochemical, structural and genetic analyses of targeted point mutants should shed light on their molecular roles in the pathway.Small RNAs are bound by proteins belonging to the Argonaute family, which can be broadly classified into the ubiquitously expressed AGO clade that binds approximately 21 nucleotide microRNAs (miRNAs) and small interfering RNAs (siRNAs), and the animal-specific Piwi-clade, which is mainly restricted to the gonads. This mirrors the distribution of their respective associated small RNAs: miRNAs and siRNAs are detected in almost all organisms and tissues, whereas piRNAs are specific to animals and found in the gonads. Argonaute family proteins are built to bind small RNAs; they contain characteristic PAZ, MID and PIWI modules. The 5′ and 3′ ends of the bound RNA are contacted by the MID and PAZ modules, respectively, such that the RNA bases are available for base pairing with complementary sequences. Perfect match with a target leads to its endonucleolytic cleavage by the PIWI module. Thus, some Argonautes are small RNA-guided nucleases (slicers) that can post-transcriptionally control target RNA levels. Nuclear Argonautes function by recruiting complexes that promote either histone or DNA methylation at target genomic loci.


The Universe of RNA Structures DataBase (URSDB) stores information obtained from all RNA-containing PDB entries (2935 entries in October 2015). The content of the database is updated regularly. The database consists of 51 tables containing indexed data on various elements of the RNA structures. The database provides a web interface allowing user to select a subset of structures with desired features and to obtain various statistical data for a selected subset of structures or for all structures. In particular, one can easily obtain statistics on geometric parameters of base pairs, on structural motifs  or on different types of pseudoknots. The user can also view and get information on an individual structure or its selected parts, e.g. RNA–protein hydrogen bonds. URSDB employs a new original definition of loops in RNA structures. That definition fits both pseudoknot-free and pseudoknotted secondary structures and coincides with the classical definition in case of pseudoknot-free structures. To our knowledge, URSDB is the first database supporting searches based on topological classification of pseudoknots and on extended loop classification.Database URL: http://server3.lpm.org.ru/urs/ RNA seems to be the least investigated class of irregular biopolymers. The role of the messenger, transfer and ribosomal RNA is well known, but the role of various non-coding RNA in the regulation of intracellular processes, including the regulation of gene expression, requires further study, see the review . RNA funFor classical secondary structures there is a common classification of structural elements , 16, howCurrently, there are several databases that contain information on RNA structures and structural motifs. RNA Frabase 2.0  stores RDespite the availability of a variety of databases dedicated to RNA structures at the moment, to our knowledge, there is no database that contains all available 3D structures of RNA and at the same time contains annotations of their main structural elements  and allows an annotation-based search. The vast majority of existing databases are limited to pseudoknot-free RNA structures.The presented database URSDB aims to fill the above gaps. Its main features are (i) detailed annotation of pseudoknot-related structural elements that naturally generalizes the annotation used for pseudoknot-free structures along with possibility to perform search using this annotation and (ii) a user-friendly web interface allowing, in particular, to select subsets of structures to be further analyzed.Our database is a set of thoroughly verified and uniformly annotated structures. Such dataset is necessary to develop and evaluate tools dealing with RNA structures, e.g. predicting RNA secondary structures, determining RNA-binding regions in proteins, comparing related RNA molecules, etc. For example, the database can help to create knowledge-based potentials for various classes of RNA–RNA or RNA–protein interactions. Another example is usage of the URS database within testing software for secondary structure prediction. A prediction program can fail on RNA having certain types of secondary structure. To reveal that, a developer needs training sets with different characteristics. At the same time a scientist studying RNA can use the database to extract the structures with desired features, e.g. containing pseudoknots with given signatures, and obtain the characteristics of the structures.RNA-containing structures were extracted from the PDB in mmCIF format; each file was divided into models. The base pairs  and dinucleotide steps were annotated using the DSSR program from 3DNA toolkit . We alsohttp://www.sencha.com/products/extjs/). Interactive 3D representations of structures and motifs are displayed using JSmol (http://sourceforge.net/projects/jsmol/).To create the database we designed a special program package; the package was implemented in Python 3 and consists of 28 independent modules. The package output is a set of text files in MySQL format. The database is powered by MySQL server 5.1. The database web interface is developed as a collection of Python 2.7 CGI scripts along with HTML pages and JavaScript code. Windows of individual structures and motifs use Sencha Ext JS framework , , …,  where k > 0 and each number denotes a nucleotide in the corresponding position of a chain and two nucleotides in a pair are connected with hydrogen bonds. We consider several types of stems. In case of standard stem all base pairs are supposed to be Watson–Crick pairs or Wobble (GU) pairs.A Stem is a sequence of base pairs of the form  are related to standard stems. However, they can be applied to stems of arbitrary type. Hereinafter ‘base pairs’ means complementary (i.e. Watson-Crick or Wobble G-U) base pairs.i, j) in the stem , ,…,  is called the external pair or the face of the stem. The last pair  is called the internal pair of the stem. The fragment  of an RNA chain is called the left wing of the stem, and the fragment  is called the right wing. We say that the base pair  crosses a stem with the face  if m < i < n < j or i < m < j < n, see The first pair  such that  or ;There is no base pairs  and 2);There is no t between i and j such that  such that  and  are faces of two stems.Either the base pair  is the face of a stem; otherwise the ECR is called a pseudoknotted ECR or a pseudoknot.An ECR  if i < k < l < j and there are no other ECR  such that i < m < k < l < n < j.An ECR  is a nested ECR of an ECR  such that:r < i < j < s;k, l) such that  or ;There is no base pair  and 2);There is no t between i and j such that  such that  and  are faces of two stems.Either the base pair  its internal pair.Let H be a stem and , if i < t < j. Fragment of chain is internal to stem H (synonym: lies inside H), if all its positions are internal to stem H. Stem H1 lies inside the stem H , if all the positions of its wings are internal to H.The position t belongs to the stem H, if it is internal to H and there is no stem H1, lying inside H, such that x < t < y, where  is the external pair (face) of H1.The position Loop of the stem H is the set of all positions that belong to stem H.r, s) is the fragment  with excluded H-ECRs. For technical reasons we consider faces of excluded ECRs as parts of the loop. See http://server3.lpm.org.ru/urs/struct.py?where=3&#def-stems-loops for further information.Our loops, as well as loops according to NNM, are in one to one correspondence with stems. Informally speaking the loop of a stem H with the internal pair  and vice versa, but our definitions also can be applied to pseudoknots. In the latter case the loop may contain wings and, along with ends of stems (‘faces’), may contain faces of pseudoknotted fragments, see stems 1 and 6, Our loops, as well as loops according to NNM, are in one to one correspondence with stems. The definition of pseudoknotted loop from the paper of B. Rastegari and A. Condon  does notWe divide a loop into sides separated by faces, see In our terms the loops from NNM classification can be described as follows:A loop is a hairpin if it does not contain faces and, therefore, has a single side. A loop is an internal loop if it contains exactly one face and therefore has two sides. A loop is a multiple junction if it contains more than one face and therefore more than two sides. An internal loop is a bulge if one of its sides is of zero length.We also consider an additional classification of loops. A loop is a classical loop if it does not contain faces of pseudoknots and wings. A loop is called isolated if it does not contain wings and is called pseudoknotted otherwise see . A stem pseudoknot signature of the ECR is a word composed of the letters assigned to the arcs’ ends; the order of the letters corresponds to order of positions of arcs’ ends. For example, the signature of an H-knot is abAB, signature of kissing hairpins is abAcBC and the signature of a triple knot is abcABC. For more details see http://server3.lpm.org.ru/urs/struct.py?where=3&#def-basic2.Definition of a pseudoknot or a pseudoknotted ECR was given in the Terminology section, see The signature definition coincides with the definition from ; descripThe database contains 2935 RNA-containing structures from PDB, 7718 RNA chains (only one model per PDB entry was considered), 1 314 360 base pairs of different types and 5130 pseudoknots.URSDB is a relational database powered by MySQL server. The database consists of 51 tables. The tables are divided into four groups:  tables oto select a set of PDB entries;to get numerical characteristics of structural elements for a selected subset of structures, for the entire database, for the non-redundant PDB list  or for ato analyze the structural elements of the chosen PDB entry or of the selected subset of entries.The home page contains the main menu and a brief description of the database; the menu in particular contains links to two query pages, ‘Structures’ and ‘Statistics’ see below.http://server3.lpm.org.ru/urs/struct.py?where=3#set-rest-pat for details. In general, a user’s query can be an arbitrary disjunction (‘or’ junction) of conjunctions (‘and’ junctions) of elementary queries.The interface supports a wide range of elementary queries related to general information on a PDB entry, molecules, RNA patterns, base pairs, etc. An RNA pattern can be described with a sequence fragment, dot-bracket notation, pseudoknot signature or ECR pattern. The latter contains descriptions of all stems within the ECR. Furthermore, the user can also request the presence or absence of loops of various types, pseudoknots, etc. See The user can edit previous queries, perform a search in previous search results or add new results to the results of a previous search. The search result is presented as a table; each line corresponds to a found structure. A user can specify structural information to be shown in a line and sort the table. By clicking an individual structure in the table, the user can activate a special window to analyze the structure, see below.http://server3.lpm.org.ru/urs/struct.py?where=3&#stat.The ‘Statistics’ page allows users to view statistics related to structural elements, e.g. chains, base pairs, links, stems, loops, pseudoknots, multiplets and RNA–Protein hydrogen bonds. The request may be carried out in four modes, for the entire database, for the selected set of structures, for the non-redundant PDB list and for a selected PDB entry. When the request is processed and initial results are obtained one can continue using the ‘Filter’ field of the ‘Statistics’ page. The field allows setting various parameters of structural element to filter the results. Another possibility is to obtain the table containing the full list of structural elements meeting the given conditions. Clicking an element within the list one activates a window containing the detailed information on the element allowing viewing the element itself or the whole structure. For more information see the Help page at Analysis of an individual PDB entry or its structural elements is performed in a special window. The window consists of two parts; the right part contains a JSmol viewer, the left part contains detailed information on the entry and its structural elements, e.g. chains, base pairs, stems, loops and pseudoknots. The window may show a whole structure or a structural element. The structure window can be activated using the result table of the ‘Structures’ page. The structural element window can be activated from the result table of the ‘Statistics’ page.http://server3.lpm.org.ru/urs/struct.py?where=3#using-example for the example of a typical URS session.See In this section, we will discuss possible applications of the database and its web interface. In our opinion the main advantage of URSDB is a possibility to work with various types of structural elements, e.g. stems, single-stranded fragments, all types of loops, base pairs, pseudoknots, etc. This allows one to easily collect different types of statistical data. From the other hand our web interface may serve as a convenient tool for search for RNA structures and its motifs and for construction of its subsets, and to pick up needed individual cases.Below we consider two cases of using URSDB and its web interface named URS. The first example is related to a collection of statistics, and the second one demonstrates advantages of the URS interface.Escherichia coli 16S ribosomal RNA.We used URSDB to collect data on tertiary interactions with respect to their belonging to the secondary structure motifs (stems or loops). We were inspired by work  where auWe analyzed RNA tertiary interactions separately in pseudoknots and in classical structures. As the dataset we used a non-redundant PDB list without resolution cutoff . Each inAs one can see from http://www.rcsb.org/pdb/search/adv Search.do?search=new);RCSB PDB (http://ndbserver.rutgers.edu/ndbmodule/search/integrated.html);NDB (http://server3.lpm.org.ru/urs/struct.py).URS ANDat least one2+ MgionORat least one H-knot (H-type pseudoknot)ANDat least one2+ CaionPDB provides opportunity to compose a query only as an AND-junction or as an OR-junction and moreover it has no restrictions on RNA structural features. Despite these shortcomings its capabilities with respect to protein structures are performed at the highest level.The advanced search at NDB actually has a plenty of parameters related to RNA structures. Moreover at the moment it has more parameters than URS does. Also it allows composing OR-junctions along with AND-junctions. However its AND and OR clauses are placed in predefined order and it is hard to understand their mutual relations. Besides, the advanced search at NDB does not contain any parameters related to pseudoknots.As for the URS, its advanced search allows one to compose such a query in a couple of clicks. We hope that the search based on disjunctive normal form (i.e. disjunction of conjunctions) is both powerful and understandable.We plan to perform a comparative analysis of programs that annotate base pairs in RNA-containing PDB files. We will consider the four most popular programs, FR3D , MC-AnnoAnother direction in the development of the database is related to RNA–protein contacts. We will combine the data on RNA-protein interactions with the annotation of loops and stems and will add data on protein secondary structure.The list of structural element types available for statistics gathering and further analysis will be significantly enlarged. Along with computation of maximal and minimal value of parameters we will support the construction of histograms, etc. We also plan to implement a multi-step search, allowing user, for example, to form a set of PDB entries, as it is possible now, then form a set of RNA chains in the entries according to additional conditions, and then choose the desired stems from the chains and obtain base pairs statistics on the chosen stems. Such a search will allow the web interface to fully utilize all the features of the database.We also plan to add some new features to the web interface according to users’ requests. In particular, we plan to add the possibility of using 2D-visualization tools like VARNA , PseudoVURSDB is a powerful tool providing researchers with new instruments of RNA analysis compared to the existent tools. The database allows users to select a subset of structures with desired features, and to obtain various statistical data for a selected subset of structures or for all structures. For example, the user can easily get statistics on geometric parameters of base pairs, on structural motifs  or on types of pseudoknots. Users can also view and get information on an individual structure or its selected part, e.g. RNA–protein hydrogen bonds. URSDB employs an original definition of loops that fits both pseudoknot-free and pseudoknotted RNA secondary structures; in case of pseudoknot-free structures the definition coincides with the classical definition. To our knowledge, URSDB is the first database supporting search based on ‘topological’ classification of pseudoknots  and on e


Ostrinia furnacalis) and/or methyl jasmonate (MeJA). Altogether, 39, 636 genes were found to be differentially expressed. The sequencing data are available in the NCBI SRA database under accession number SRS965087. Our dataset will provide more scientific and valuable information for future work such as the study of the functions of important genes or proteins and develop new insect-resistant maize varieties.Corn is one of the most widely grown crops throughout the world. However, many corn fields develop pest problems such as corn borers every year that seriously affect its yield and quality. Corn′s response to initial insect damage involves a variety of changes to the levels of defensive enzymes, toxins, and communicative volatiles. Such a dramatic change secondary metabolism necessitates the regulation of gene expression at the transcript level. In this paper, we summarized the datasets of the transcriptome of corn plants in response to corn stalk borers ( Specifications TableValue of the data•The RNA-seq data allow the readers to access the transcriptomic profile of corn in response to Asian corn borer damage and MeJA stress.•The data evaluate the utility of illumina platform for RNA-sequencing.•Analyses of the significantly differentially expressed genes under Asian corn borer and MeJA treatment provide new targets for the development of insect-resistant maize varieties.•Studies reveal maize defensive mechanism to the feeding of Asian corn borer and MeJA treatment.1Over 50 million raw reads were generated for each experimental treatment  feeding; 62,189,006 for jasmonic acid exposure; 61,528,872 for exposure to both treatments; 53,089,614 for the control). Data is available at the NCBI SRA database under accession number SRS965087.22.1Ostrinia furnacalis (ACB) was maintained at College of Life Sciences, Heilongjiang University  on an artificial diet , 20 g agar powder, 1 g benzoic acid, 3 g sodium benzoate in 1000 mL ddH2O). The ACB were reared at 28 °C under a 14 h light: 10 h dark photoperiod. Corn seeds of variety Longdan46 were provided by the Maize Research Institute of Heilongjiang Academy of Agricultural Science and cultivated in a greenhouse at 25±1 °C, 60% humidity, and a 14 h light: 10 h dark photoperiod.A colony of 2.2st instar larvae of Ostrinia furnacalis (ACB) were evenly distributed on the leaves of each corn plant. The newly hatched ACB larvae from eggs are called the first instar larvae, and the 3 days old stages of 1st instar larvae of similar size were chosen for each treatment. MeJA treatment involved application of 5 ml of a 225 μM MeJA solution. Control plants were treated with 5 mL of 0.5% ethanol/water (v:v) solution. The third leaf counted from the bottom of each plant was selected and immediately frozen in liquid nitrogen and stored at −80 °C for later RNA extraction.Corn plants with four to five leaves were treated with ACB larvae (labeled as “CornOf2), MeJA (labeled as “CornJA1”), or both (labeled as “CornJAOf2”). The control group was treated with 5 mL of 0.5% ethanol/water (v:v) solution (labeled as “Corn1”). For each treatment, the entire experiment was repeated three times. Whole plants were covered with clear plastic bags. For insect feeding, twenty 13Total RNA was isolated and purified from leaf tissue using the TRIzol Plus RNA Purification Kit according to the manufacturer׳s instructions (Life Technologies). To remove any traces of genomic DNA from RNA extractions, the RNA was treated with RNase-Free DNase (Promega) as instructed by the manufacturer. Total RNA integrity and quantity were evaluated using the 2100 Bioanalyzer RNA 6000 Kit (Agilent Technologies) and the Invitrogen Qubit RNA Kit (Life Technologies). RNA samples had integrity values (RIN) between 7.6 and 9.0.4The TruSeq RNA Sample Prep Kit (Illumina) was used to isolate mRNA from about 5 μg of total RNA using oligo-d(T)25 magnetic beads. The mRNA was sheared with ions into ~200 nt fragments and was reverse-transcribed into cDNA. We then used the TruSeq PE Cluster Kit v3 (Illumina) to perform end repair, add an ‘A’-base to the blunt ends, and ligate the cDNA to paired-end adapters. The cDNA samples were amplified through 15 cycles of PCR. The amplification products were electrophoresed on a 2% Certified Low Range Ultra Agarose gel (Bio-Rad) and purified according to appropriate size of DNA fragments suitable for Illumina sequencing. The purified products were quantified using PicoGreen (Life Technologies) and a TBS-380 Mini-Fluorometer (Promega) and loaded on an Illumina cBot system for cluster generation by bridge PCR amplification. Sequencing was performed on an Illumina HiSeq 2500 platform.52 ratio ≥2 to screen more significantly expressed genes. Altogether, 76 genes were identified to be significantly co-expressed in the different treatments using VennDiagram http://cufflinks.cbcb.umd.edu/) Standard bioinformatics analyses about this work were reported by Yang et al. The scatter- and volcano-plots of differently expressed genes are as follows: CornOf2 vs CornJA1 A and Cor


In this study, we determined the crystal structure of N-terminal importin-β-binding domain (IBB)-truncated human importin-α1 (ΔIBB-h-importin-α1) at 2.63 Å resolution. The crystal structure of ΔIBB-h-importin-α1 reveals a novel closed homodimer. The homodimer exists in an autoinhibited state in which both the major and minor nuclear localization signal (NLS) binding sites are completely buried in the homodimerization interface, an arrangement that restricts NLS binding. Analytical ultracentrifugation studies revealed that ΔIBB-h-importin-α1 is in equilibrium between monomers and dimers and that NLS peptides shifted the equilibrium toward the monomer side. This finding suggests that the NLS binding sites are also involved in the dimer interface in solution. These results show that when the IBB domain dissociates from the internal NLS binding sites, e.g., by binding to importin-β, homodimerization possibly occurs as an autoinhibition state. In eukaryotic cells, the cytoplasm and the nucleus are divided by the nuclear membrane. The nuclear pore complex (NPC), situated on the nuclear membrane, acts as a gate that allows free diffusion of particles, such as small molecules and ions of less than ~9 nm –3. PartiXenopus importin-α seems to form homodimers or multimers during purification . T. T19]. TProcheck  and MolPProcheck  programsE. coli strain BL21 (DE3) Codonplus-RIL (Stratagene). h-importin-α1 and ΔIBB-h-importin-α1 were expressed by the ΔIBB-h-importin-α1. The AUC-SV for ΔIBB-h-importin-α1 shows that it is in a concentration-dependent monomer–dimer equilibrium (C(s) distributions are characterized by two peaks, one at 3.1–3.3 S and one at 3.9 S; the relative sizes of the peaks vary with protein concentration, and they are interpreted as corresponding to monomer and dimer, respectively. The C(s) distributions were used to create a weight average S value (sw) isotherm. We calculated the sw by integration of the C(s) curves between 2.5 S and 5.0 S for each protein concentration, and then plotted the sw vs protein concentration using SEDPHAT  provided the first crystal structure of an importin-α that was shown to dimerize not only in the crystal but also in solution, as revealed by dynamic light scattering (DLS) . I. I38]. IIt is reported that ΔIBB-h-importin-α1 (70–529) complexes with the cap-binding complex (CBC) . In the e.g., by the competitive binding of NLS peptides to the NLS binding sites and/or the binding of the IBB domain to importin-β  h-importin-α1. Each number assigned to the resulting elution position coincides with the lane of 12.5% SDS-PAGE. The blue line depicts the affinity column chromatography elution profile and the green line depicts the gradient of the NaCl concentration of the elution buffer. Fractions 39–44 were merged and concentrated. (B) ΔIBB-h-importin-α1. Fractions 40–45 were merged and concentrated.(EPS)Click here for additional data file.S2 FigXenopus importin-α1 (P52171). The position of K108 of importin-α1 is represented by a blue triangle. The residues  involved in the P1′-binding pocket around the minor NLS binding site are conserved among the importin-αs. Thus, the P1′-binding pocket should be conserved.Sequences of human importin-α1 (UniProtKB: P52292), mouse importin-α2 (P52293), yeast importin-α (Q02821), and (EPS)Click here for additional data file.


It is now popularly accepted that there was an “RNA world” in early evolution of life. This idea has a direct consequence that later on there should have been a takeover of genetic material – RNA by DNA. However, since genetic material carries genetic information, the “source code” of all living activities, it is actually reasonable to question the plausibility of such a “revolutionary” transition. Due to our inability to model relevant “primitive living systems” in reality, it is as yet impossible to explore the plausibility and mechanisms of the “genetic takeover” by experiments.Here we investigated this issue by computer simulation using a Monte-Carlo method. It shows that an RNA-by-DNA genetic takeover may be triggered by the emergence of a nucleotide reductase ribozyme with a moderate activity in a pure RNA system. The transition is unstable and limited in scale , but can get strengthened and globalized if certain parameters are changed against RNA . In relation to the subsequent evolution, an advanced system with a larger genome, which uses DNA as genetic material and RNA as functional material, is modeled – the system cannot sustain if the nucleotide reductase ribozyme is “turned off” . Moreover, the advanced system cannot sustain if only DNA’s stability, template suitability or replication fidelity (any of the three) is turned down to the level of RNA’s.Genetic takeover should be plausible. In the RNA world, such a takeover may have been triggered by the emergence of some ribozyme favoring the formation of deoxynucleotides. The transition may initially have been “weak”, but could have been reinforced by environmental changes unfavorable to RNA (such as temperature or pH rise), and would have ultimately become irreversible accompanying the genome’s enlargement. Several virtues of DNA (versus RNA) – higher stability against hydrolysis, greater suitability as template and higher fidelity in replication, should have, each in its own way, all been significant for the genetic takeover in evolution. This study enhances our understandings of the relationship between information and material in the living world.The online version of this article (doi:10.1186/s12862-015-0548-1) contains supplementary material, which is available to authorized users. In modern organisms, DNA is the major genetic material and protein is the major functional material, both seeming indispensible. Thus, there is a dilemma for the evolution of life: “Which came first, DNA or proteins?” The RNA world hypothesis provided a possible solution to this problem: in some early stage of life, there is neither DNA nor protein but only RNA, acting as both genetic material and functional material . The RNAAs a clear consequence of the scenario concerning the RNA world, DNA and proteins (encoded) should have appeared afterwards. For the emergence of DNA, there would be a problem of “genetic takeover”; for the emergence of proteins, there would be a problem of “functional takeover”. Indeed, “after RNA, which came first, DNA or proteins?” is still a question . WhiThe present computer simulation study focuses on the genetic takeover and follows the idea of DNA emerging before proteins. Perhaps the authors’ own opinions about the emerging order of DNA and proteins are not important here, and the choice could simply be technical: the genetic takeover is simpler in principle than the functional takeover given that RNA and DNA share the same mechanism in their synthesis, i.e., by base pairing (unlike that in proteins’ synthesis). In particular, we have conducted a series of computer simulation studies concerning the scenario of early development of the RNA world, including the emergence of several important ribozymes –13, the Indeed, for this transition to occur, additional functions concerning new kinds of template-directed copying should be required, i.e., the cross copying between DNA and RNA as well as DNA’s replication. However, these new kinds of copying may have been initially catalyzed by that “old” ribozyme, i.e., the ribozyme that catalyzed RNA’s own replication (the “RNA replicase” – “Rep” for short) , 16, givPractically, the present model was derived directly from the one used in our recent study , in whicPNRR = 0, see Tables 6 in the case shown here), the chromosome (black stars) begins to decrease while the chromosome lacking the Nrr gene (black x-shapes) begins to increase, which, taken together, mean the “losing” of the Nrr gene. A similar trend can be observed at the cellular level (see the bottom panel). Indeed, the ribozyme Nrr (blue points) also decreases. Finally, the Nrr gene goes into extinction . Second, DNA is easier to act as a template . Third, DNA is less error-prone in replication , where n is the quantity of inner impermeable ions, including nucleotides/deoxynucleotides and RNA/DNA (measured by the number of nucleotide/deoxynucleotide residues), and b is the quantity of amphiphiles within the membrane. Wherein, b/2 (there are two layers in the membrane) is a “scale” representation of the surface area of the membrane. Consequently, (b/2)3/2 is a scale representation of the cellular space. Thus, n/(b/2)3/2 is a representation of the concentration of the ions. FOP × n/(b/2)3/2 represents the consideration for the “osmotic pressure effect”; a higher concentration of the inner impermeable ions would cause the protocell to be more swollen, and thus amphiphiles within the membrane are less likely to leave [The probability of an amphiphile leaving the membrane is assumed to be to leave .PNPP)y] / [1 + FDE × n/(b/2)3/2], where n is the quantity of inner impermeable ions and b is the quantity of amphiphiles within the membrane. The index y equals to (b/LAM)3/2, which represents the consideration of the limiting effect of the cellular space on the influx of nucleotide (or deoxynucleotide) precursors. When b equals to LAM (the lower limit of the number of amphiphiles to form a protocell membrane), y equals to 1. When the b increases, meaning that the cellular space increases correspondingly, the probability of a nucleotide (or deoxynucleotide) precursor permeating into the protocell would become greater. FDE × n/(b/2)3/2 represents the consideration of the effect of Donnan’s equilibrium – see [PNPPP)y] / [1 + FDE × n/(b/2)3/2]. However, the probability of an amphiphile precursor permeating into a protocell is assumed to be 1-(1-PAPP)y, wherein the effect of Donnan’s equilibrium is not considered because amphiphile precursors are assumed to be uncharged molecules.The probability of a nucleotide (or deoxynucleotide) precursor permeating into a protocell is assumed to be [1-(1-um – see  for a dePCD × (1-2 × LAM/b), where b is the quantity of amphiphiles within the membrane. When b is no more than twice that of LAM (the lower limit of the number of amphiphiles to form a protocell membrane), the probability is no more than 0, i.e., the protocell could not divide. This assumption represents the consideration that the larger the protocell, the more the probability that it would divide.The probability of protocell division is assumed to be PMV/ m1/2, where m is the polymer’s mass relative to a monomer . This assumption represents the consideration of the effect of the molecular size on the molecular movement. The square root was adopted here according to the prediction of the Zimm model concerning the diffusion coefficient of polymer molecules in solution [m1/3 was used. We have not found any apparent difference concerning our results that this change may bring about, but now that we have relevant data coming from experiments, we would like to modify our model according to such knowledge.The probability of the movement of an RNA (or DNA) molecule is assumed to be solution , in agreFBBD should be significantly smaller than 1, Second, because DNA is less likely to self-fold , FSFD sch other , DNA repPTLR> > PTL, PNPFR> > PNPF, PNFR> > PNF, PNRR> > PNR, and PAFR> > PAF. Those non-enzymatic “inter-transforming” rates between RNA’s and DNA’s precursors, i.e., PNR, PDNO, PNPR, PDNPO, PNPPR, and PDNPPO, should be quite low and of the same order. “Template-directed ligation” should be significantly more efficient than “random ligation”, so PTL> > PRL. The end-to-end ligation of an RNA (or DNA) chain  should be similar in efficiency to the random ligation of two different RNA (or DNA) chains, so PEL should be of the same order as PRL. Nucleotide (or deoxynucleotide) residues in an RNA (or DNA) chain should be protected. And so here, nucleotide (or deoxynucleotide) residues within the chain are assumed to be unable to decay, whereas a nucleotide (or deoxynucleotide) residue at the end of a chain, which is only “half protected”, may decay, but at a rate lower than that of free nucleotides (or deoxynucleotides), i.e., PNDE < PND and PDNDE < PDND. Amphiphiles within a membrane should be protected, so PADM < PAD. Because of the self-assembly feature of amphiphilic molecules, PMF> > PCB and PAJM> > PALM. The movement of molecules should be easier than protocells, so PMV > PMC. A nucleotide (or deoxynucleotide) precursor’s precursor should be easier to permeate through the membrane than a nucleotide (or deoxynucleotide) precursor, so PNPPP > PNPP. Other considerations may include: PBB and PRL may be of the same order, PND ≥ PNF, PNPD ≥ PNPF, PDND ≥ PDNF, PDNPD ≥ PDNPF, PAD ≥ PAF, PAPP ≥ PNPP, and so on. To set parameters “appropriately”, according to logic and our knowledge, represents an effort to make the “behavior” of our model system relevant to that of its target system in reality.Certainly, there should be quite a few other constrains for an appropriate parameter setting. Ribozymatic reactions should be much more efficient than corresponding non-enzymatic reactions, so All the supporting data are included as additional files.


Bunyaviridae. Studies have found that humans, domestic and wildlife animals can be infected by SFTSV. However, the viral ecology, circulation, and transmission remain largely unknown.Severe fever with thrombocytopenia syndrome (SFTS) is caused by SFTS virus (SFTSV), a tick-borne phlebovirus in family Haemaphysalis longicornis and H. flava. Viral RNA was identified in larvae of H. longicornis prior to a first blood meal, which has never been confirmed previously in nature. SFTSV-RNA and antibodies were also detected by RT-PCR and ELISA, respectively, in wild mammals including Erinaceus europaeus and Sorex araneus. A live SFTSV was isolated from Erinaceus europaeus captured during the off tick-feeding season and with a high SFTSV antibody titer. Furthermore, SFTSV antibodies were detected in the migratory birds Anser cygnoides and Streptopelia chinensis using ELISA.Sixty seven human SFTS cases were reported and confirmed by virus isolation or immunofluorescence assay between 2011 and 2014. In 2013–2014 we collected 9,984 ticks from either vegetation or small wild mammals in the endemic area in Jiangsu, China, and detected SFTSV-RNA by real-time RT-PCR in both questing and feeding H. longicornis might occur in nature, which suggests that H. longicornis is a putative reservoir host of SFTSV. Small wild mammals such as Erinaceus europaeus and Sorex araneus could be infected by SFTSV and may serve as natural amplifying hosts. Our data unveiled that wild birds could be infected with SFTSV or carry SFTSV-infected ticks and thus might contribute to the long-distance spread of SFTSV via migratory flyways. These findings provide novel insights for understanding SFTSV ecology, reservoir hosts, and transmission in nature and will help develop new measures in preventing its rapid spread both regionally and globally.The detection of SFTSV-RNA in non-engorged larvae indicated that vertical transmission of SFTSV in  Haemaphysalis longicornis, collected in vegetation without being blood fed, indicating a possibility of a vertical transmission of SFTSV in H. longicornisis in nature. We also detected SFTSV-RNA in four mammal species which may serve as natural amplifying hosts for SFTSV. In addition, we identified antibodies against the virus in two migratory bird species, suggesting wild birds, exposed to infected ticks, could spread the virus through flyways for long-distance transmission. These findings provide novel insights for understanding SFTSV ecology and transmission mechanism and help develop new measures to halt its rapid spread.Severe fever with thrombocytopenia syndrome (SFTS) is an emerging hemorrhagic fever, caused by a tick-borne phlebovirus. Studies have found that a variety of domestic and wildlife animals can be infected by SFTS virus (SFTSV), but the natural reservoir host for the virus remains unclear. Although the SFTSV-RNA was identified in certain species of ticks or their larvae, contamination from their host animals cannot be excluded to be the source. We analyzed 9,984 ticks collected from vegetation or feeding mammals in 2013–2014 in Jiangsu province, an endemic area in China, and detected SFTSV-RNA in both parasitic and questing ticks. Interestingly, SFTSV-RNA was identified in larvae of  Bunyaviridae 038). However, sample collection from the SFTS patients was not specifically for this study; instead the collection and the Certificate were for an existing project aiming at isolating the virus from the human patients.Small wild mammals and wild birds were captured and handled in strict accordance with good animal practice according to the Animal Ethics Procedures and Guidelines of the People’s Republic of China . Handling and sampling including tick and blood collection were also approved by the Animal Ethics Committee, Jiangsu Provincial Center for Disease Control and Prevention, with the certificates No. SCXK[su] 2012–0021 and No. JSCDCLL[2012]039. The certificates also provided the permission on the protocol for sampling wild birds including Sixty-seven SFTS cases were reported in the region of Jiangsu province  between Haemaphysalis longicornis, H. flava, H. concinna, and H. doenitzi. Of the identified ticks, H. longicornis  was the most abundant species collected, followed by H. flava , H. concinna , and H. doenitzi .A total of 9,984 ticks  were collected from 2013 to 2014  from vegetation or wild mammals or birds in 24 localities . The ticQuesting ticks started to appear in March, reached peak density in June and declined afterwards through September. A total of 6,360 questing ticks were collected , including 886 larvae, 3302 nymphs, 1,042 male adults, and 1,130 female adults Tables .Mus musculus (21.0%), Sorex araneus (14.3%), Rattus flavipectus (13.5%), Rattus norvegicus (11.5%), and Erinaceus europaeus (12.4%)  . A totalH. longicornis, and one pool of nymphs (1.4%) of H. flava; in 2014, we detected positive viral RNA in two, ten, six, and five pools of larvae (0.4%), nymphs (0.5%), male adults (0.9%), and female adults (0.7%), respectively, of H. longicornis, and one pool in nymphs (0.5%) of H. flava , 0.2%] and male adults (0.9%) of H. flava . RemarkaH. longicornis, and one pool of nymphs (1.1%) of H. flava; in 2014, we detected viral RNA positive in two, seven, two, and two pools of larvae (0.9%), nymphs (0.6%), male adults (1.0%), and female adults (0.5%), respectively, of H. longicornis, and one pool of nymphs (0.8%) of H. flava  and nymphs (0.3%) of H. flava . Comparid Tables  AlthoughErinaceus europaeus , Sorex araneus , R. fulvescens , and Mus musculus ; viral antibodies were detected in all species sampled with the highest prevalence in Erinaceus europaeus . Viral antibodies were also detected in A. cygnoides  and S. chinensis , two migratory birds captured during the tick-feeding season , 823 small wild animals, representing eight species of small mammals and two species of wild birds, were captured. Animal blood and tissues were prepared for viral RNA extraction with nested PCR as described earlier, and sera were examined for viral antibodies by a specific sandwich ELISA kit. As shown in g season . HoweverErinaceus europaeus  and Sorex araneus  with at least one organ positive, while viral antibodies were detected in Mus musculus , Rattus norvegicus , Sorex araneus , and Erinaceus europaeus . We noted that there were fewer species of animals detected positive for either viral RNA or antibodies in animals captured during the period off the tick-feeding season compared to those during the tick-feeding season. No wild birds tested positive for viral RNA . Our data indicated that viral RNA of SFTSV was only detected in iral RNA .M. musculus, R. fulvescens, S. araneus, and Erinaceus enropaeus. Spleens were positive for viral RNA in all animals, while other organs or blood varied in their presence of viral RNA. In addition, we showed the viral antibody titers in these animals, captured either during the tick-feeding or off-tick-feeding season, and also showed the collection of the ticks at their various stages on the skin of the captured animals (E. europaeus (JSM-12), with an antibody titer up to 1:512 and captured during the off-tick-feeding season  of the SFTSV genome were amplified from SFTSV-RNA positive pools of ticks and small animals, and sequences were analyzed with DNA sequencing, which showed 93.9%-99.5% similarity with those from SFTSV strains from humans, goats, and ticks reported previously in China, Korea, and Japan ,6,7,18.7 bp of tH. longicornis, including one isolate from questing ticks, and two were from spleens of two Erinaceus europaeus (one captured during the tick-feeding season and the other off the tick-feeding season).H. longicornis, and two from Erinaceus europaeus. Sequence data have been submitted into the NCBI Genbank (accession numbers KF356517 through KF356552). Using the neighbor joining method in MEGA software, a phylogenetic tree was constructed using the L segment sequences of 42 isolates from Jiangsu province and 35 reference SFTSV strains isolated from other places in China, Korea, and Japan, and showed that all the isolates fall into five genotypes (designated clades 1–5). Two SFTSV strains, one from human (Jiangsu/China/2011/JS2011-69) and the other from ticks (Jiangsu/China/2014/2014-H.longicornis-01) isolated from Yixing County of Jiangsu province, geographically adjacent to Zhejiang province, were clustered, based on the L segment, into the Japanese clade (J3) [Whole genome sequencing was performed on 20 SFTSV isolates, including 15 from human serum samples, three from ade (J3)  togetherH. longicornis from vegetation or patients and domestic animals in the villages. Moreover, SFTSV-RNA was detected in H. longicornis and additional tick species such as Amblyomma studinarium and Ixodes sp. collected in the endemic region [H. longicornis, indicating a possibility of a vertical transmission of the virus from infected adult H. longicornis to their progeny through eggs in nature. Experimentally it was shown recently that viral RNA could be identified in larvae, hatched from eggs laid by infected female adult H. longicornis [The first confirmed SFTS in human was reported in Jiangsu in 2007 . Since tc region ,12. In tgicornis , indicatgicornis .H. longicornis and H. flava. Moreover, one viral strain (2014-H. longicornis-02) was isolated from viral RNA-positive tick pools of questing H. longicornis, which falls in clade 1 . These results suggest that Erinaceus europaeus and Sorex araneus may act as vertebrate amplifying hosts for the virus. We also noted that viral RNA was detected in the mammals that actually had viral antibodies, suggesting that in these wild animals SFTSV may maintain and replicate in the presence of the specific antibodies. In fact one virus strain was isolated from an animal (Erinaceus europaeus) captured during the off-tick-feeding season Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Leishmania major transcriptome we found a Putative ATP dependent DNA helicase (Lmjf_09_0590) 3’ UTR which has a structural signature similar to well-known HDV hammerhead ribozyme, even though they have variable sequence motifs. Henceforth, to determine their structural stability and sustainability we analyzed our predicted structural model of this 3’UTR with a 30ns MD simulation, further confirmed with 100ns MD simulation in presence of 5mM MgCl2 ionic environment. In this environment, structural stability was significantly improved by bonded interactions between a RNA backbone and Mg2+ ions. These predictions were further validated in silico using RNA normal mode analysis and anisotropic network modelling (ANM) studies. The study may be significantly imparted to know the functional importance of many such 3’UTRs to predict their role in a mechanistic manner.In the modern era of post genomics and transcriptomics, non-coding RNAs and non-coding regions of many RNAs are a big puzzle when we try deciphering their role in specific gene function. Gene function assessment is a main task wherein high throughput technologies provide an impressive body of data that enables the design of hypotheses linking genes to phenotypes. Gene knockdown technologies and RNA-dependent gene silencing are the most frequent approaches to assess the role of key effectors in a particular scenario. Ribozymes are effective modulators of gene expression because of their simple structure, site-specific cleavage activity, and catalytic potential. In our study, after an extensive transcriptomic search of  Regulatory non-Coding RNAs have gained a lot more attention in the field of modern molecular biology in general and gene regulation either at pre transcriptional or post translational level in particular. As most of the well-studied organisms such as model organisms and infectious pathogens, genome wide analysis studies are increasing at fast pace with the advent of modern genomics and genome sequencing studies. These studies helps to unravel the underlying mechanisms of various biological processes such as the unknown mysteries behind certain pathogens pathogenicity, their survival even after the availability of the advanced chemotherapy and may help us to design effective vaccination.Leishmania. Various species of this parasite causes various forms of leishmaniasis, which is the third most prevalent infectious disease in the world. As per 2010 statistics of World Health Organization (WHO) almost 2 million people suffer from various forms of this disease. The typical lifecycle of this parasite contains two hosts one is a vertebrate mostly human and other is an invertebrate host mostly a sand fly known as Phlebotomus. The major incident of leishmaniasis caused by Leishmania major is its cutaneous form. As these parasites are eukaryotes, they have a complex genomic structure with 36 chromosomes and most of these genes are arranged in head to head and tail to tail manner. Even though the genome of L.major was sequenced back in 2005[One such kind of protozoan parasite which is highly prevalent in case of sub-tropical regions is Gene regulation at either the post transcriptional level or pre transcriptional level is a vital biological process. Controlling specific gene expression at various stages of the pathogen life cycle ultimately decides the fate of the pathogen. Biochemical and metabolomics  studies Leishmania parasite regulates its gene expression in response to changes in their environment. The size of L. major haploid genome[L. major genome predicts that protein coding genes are organized as large polycistronic units in a head to head or tail to tail manner. We all know that RNA polymerase II transcribes long polycistronic messages in absence of defined RNA polymerase II promoters, as a result of polycistronic transcriptions. mRNA synthesis requires host transcriptional control which involves 5’trans-splicing of a 39 nucleotide capped leader RNA and 3’cleavage of poly adenylation tail. Several examples of Leishmania support the notion that the developmental regulation of mRNA level is determined post transcriptionally by the sequence located in 3’UTR which usually controls mRNA stability and translation.id genome is 32.8 The post translational regulation is generally associated with stage specific gene association. Lack of promoter elements for RNA Pol II and unusually long 3’ UTR sequence provides the molecular basis of this type of control. Stage specific post transcriptional regulation is complex and involves the coordination of different mechanisms that can be independently triggered by environmental signals including the differentiation of promastigotes to amastigotes within mammalian macrophages.Leishmania are not encoded by any intron with a single exception of gene encoding poly A polymerase which has reported the grouping of genes together into long strand specific poly cistronic clusters that are co- transcribed to yield poly cistronic mRNA. Leishmania seems to have lost the ability to regulate transcription initiation by RNA polymerase II and despite considerable efforts no canonical parameters for RNA pol II transcribed protein coding genes have been identified so far. Moreover the general transcriptional factors such as TATA box and TFIIB are highly divergent in Trypanosomatidae family. Recent reports suggests that Leishmania induces some non-coding RNAs which plays a vital role in its development and survival inside the host macrophage[Leishmania induced the repression of certain non-coding RNA elements which helped the host macrophages to polarize differently when compared with the normal ones[Protein coding regions in acrophage. While irmal ones, in anotrmal onesLeishmania genome, consistently with an active post transcriptional regulation of gene expression. The question is how the Leishmania RNA polymerase II transcribes genes in the absence of well-defined promoter elements[The lack of control at level of transcription initiation might be explained by both the mode of poly cistronic transcription and the considerable need of these parasites during the complex life cycle, which may be more efficiently accomplished through downstream mechanism that involves RNA and protein regulatory processes. Numerous RNA binding proteins are encoded in the  elements. It migh elements. But the elements,9 plays Xenopus embryos and they help in the maturation [Xenopus and helps in mRNA maturation [In our study we found a 3’ untranslated regions (UTR) present in the putative ATP dependent DNA Helicase (Lmjf_09_0590). This UTR is classified as Musashi Binding Element (MBE). It has specific RNA motif (ACCUUCUG) and helps in the formation of stem loop like secondary structure. This element is typically found in 3’ regions of the mature transcripts. It was initially discovered in turation  of thoseturation . In pursLeishmania major genome[L. major and were searched for total RNA sequences using BLASTN (NCBI) tool. RNA sequences of predicted and hypothetical protein genes were removed. Later, all these RNA sequences retrieved were checked for the regulatory elements like Riboswitches and 3’ and 5’ UTR sequences using RegRNA 2.0.[Entire or genome was obtagRNA 2.0.Among the detected 3’UTR sequences, ATP dependent DNA helicase (Lmjf_09_0590) was predicted for secondary structures using Mfold and RNAfFrom Eqs  and 2)2)INF=PPredicted putative 3’ UTR structures were subjected to Deformation Profile (DP) analysisIf we compare between 2 given RNA structures, in which one is known and the other is a predicted one then RMSD and INF of both can be calculated using the above Eqs  and (2).OL3 [After structure prediction and statistical validation of ATP dependent DNA helicase, the most favorable 3D structure model was of this sequence which was further checked for their stability by performing long range MD simulations. MD simulations were performed using Desmond 3.2 (D.E. Shaw Research). Updated AMBERf99 force field ,19 (whicOL3 ) was incNormal mode is a pattern of motion in which all parts of the system move sinusoidally with the same frequency and with a fixed phase relation. In biological macromolecules such as proteins and RNA this can be calculated using the phase planes of the given system,23. In o+2 ion in its active state in its active conformation. To check whether the predicted RNA regulatory element has similar property and shows an interaction with Mg+2 ion in its minimum energy confirmation and how this Mg+2 ion helps in its stabilizing the folding kinetics, we performed 100ns MD simulation in presence of 5mM, 10mM and 25mM MgCl2 concentrations using Desmond (D.E. Shaw Research) with same parameters mentioned earlier. All MD simulations were performed on HP ProLiant SL390s workstation equipped with 2 Intel Quad core processors and 1 Nvidia tesla M2090 GPUs with 4190 Mb memory. After successful completion of MD simulations all the essential quality parameters were analyzed and RMSD, RMSF were calculated along with the interactions of putative ribozyme and Mg+2 ions during the entire length of MD simulations. All the plots were generated using the Desmond 3.2 analysis tools. Structural analysis, images and movies were generated using Pymol (Delano Scientific).It is a known phenomenon from earlier reports ,27 that L.major  were used to extract the transcriptome using NCBI BLASTN tool. Resulting transcriptome was searched for regulatory elements such as Riboswitches and UTR sequences using RegRNA 2.0. 340 regulatory elements were extracted from 3’UTR regions of many RNAs.Entire Genome of -1. After tertiary structure prediction δG was further reduced to -14.2 Kcal.mol-1 and the best predicted 3d structure has mean RMSD of 1.28 Å with the other set of predicted models. This model has an overall INF of 95.1% suggesting the model to have the highest accuracy. ATP dependent DNA helicase putative ribozyme 3’ UTR was similar to the Hammerhead Ribozyme structure of Hepatitis Delta Virus (HDV) trans acting ribozyme[340 regulatory sequences were used for 2D structure predictions using M-fold and RNAfold algorithms. After secondary and tertiary structure predictions, 3’ UTR ATP dependent DNA Helicase (Lmjf_09_0590) has a similar structural signature of a typical Hammerhead Ribozyme and Hairpin loop Ribozymes. The 3’ UTR has nearly 60–70 bases with a signature of Musashi Binding element (MBE). The structural predictions were statistically validated using Interactive Network Fidelity (INF). All structural models of 3’UTR elements have more than 90% of INF which is considered desirable for the accurate RNA structural predictions. Values are tabulated and provided in  ribozyme,27. The MD simulation for ATP dependent DNA helicase was performed for 100ns and the trajectory was stabilized after 6ns of MD production run. Simulation event analysis indicated that all the parameters are maintained constantly throughout the simulation time length visualized in While performing the deformation profile analysis a standard hepatitis delta virus (HDV) ribozyme (PDB Id: 4PR6) is used as a reference structure and after obtaining their deformation indices (DI), their Di’s were plotted against each other. We found most of the nucleosides are with proper Di’s with lower or optimized energies. Some of the terminal bases have the higher DI with higher energy. Detailed DI values of the top 30, 3d structural models are presented in the th base to 28th base and the second rigid domain is observed from 29th to 58th bases. Rest of the structure is shown as flexible. NMA indicates that rigid domains impart structural rigidity and firmness. Flexible regions may help in structural dynamics important for their activity. NMA is in corroboration with earlier MD simulation analysis performed on putative ribozyme, wherein most of the flexible regions show high degree of movements and rigid domains shows lesser fluctuations.After successful NMA, two rigid normal mode domains are identified from the putative ribozyme shown in 2. 10mM and 25mM MgCl2 system RMSD analysis shows an unstable RNA conformations, whereas 5mM MgCl2 salt concentration shows a stable RMSD, observed in 2 system is shown in 2 RMSD analysis is illustrated in same 2 MD simulation is further extended till 100ns time length. After successful completion of the simulation, trajectories are analyzed for their quality parameters such as pressure, temperature and volume apart from energy and potential energies of the system illustrated in 2.In order to identify the conformational changes during the flexible state of the putative ribozyme, we performed MD simulations at various concentrations  of MgCl2, MgCl2 has stabilized the movements of the flexible domains identified from earlier RNA normal mode analysis and trajectory analysis. It also indicates that some of the Mg+2 ions interact with the active ‘Adenosine’ base which has stabilized the flexible domains as well as the less fluctuating site of MBE Box 37–42. RMSD and RMSF plots are shown in +2 ions. Their interaction with the RNA backbone suggests that Mg+2 ions play a vital role in stabilization of catalytically active regulatory RNAs such as ribozymes. Trajectory analysis of 100ns MD simulation showed the improved stability and sustainability of the 3’ UTR during the entire MDS. Apart from the RMSD and RMSF analysis, we have also calculated the intra molecular hydrogen bonds which were formed during the entire time scale of MD simulation, represented in 2 some of the Mg 2+ ions form hydrogen bonds with the backbone of certain of RNA bases (C 26) shown in 2+ ions (Blue spheres in animation) and RNA backbone represented as yellow color dashes. Hydrogen bonds between Mg 2+ and RNA backbone may be the main cause of its improved stability and better dynamics suggesting Mg 2+ ions play a vital role in its catalytic activity. One of the hydrogen bond interaction between Mg+2ion and a nucleoside C26 of 3’ Helicase UTR was mapped using LigPlot+[RMSD and RMSF analysis shows that in presence of MgCl LigPlot+ program  bases C  shown in LigPlot+.Apart from MD simulation analysis, structural analysis of Helicase 3’ UTR minimum structure obtained from MD trajectory analysis depicts that it has many canonical and non-canonical interactions, such as base paring of G25-C18, A23-U20 are some of the canonical Watson-crick base paring and A35-G47, U34-U48, U33-C49 are non-canonical base parings represented in . Moreove2 adds strengths to our initial predictions. These simulation studies help to understand the mechanistic behavior of these putative regulatory elements. Hammerhead like ribozyme structures suggested that these RNA regulatory elements might also act like autoregulatory ribozymes.Gene specific regulation in parasites can be used extensively for the study of various metabolic processes under the user defined conditions which may enable the researchers to understand function of specific gene in presence or absence of another specific gene. This kind of scenario is very hard in case of the eukaryotic parasites such as Leishmania. We aim to create synthetic RNA mediated specific auto regulatory element which may be used for the above mentioned purpose. Artificial RNA regulatory elements were found during extensive analysis of entire leishmanial transcriptome. Further structural predictions and statistical validation suggested ATP dependent DNA Helicase 3’ UTR to be the putative ribozyme. Extensive structural analysis which includes MD simulations and MD simulations in presence of MgClAlthough structural studies of regulatory RNAs are still in its infancy, one can foresee it to play a pivotal role in the regulation of gene expression involving long range structurally complex interactions. Elucidating the importance of structural states in RNA may help to discuss the mechanistic role of non-coding RNAs in disease, thus aiding in the creation of structural framework necessary for RNA-based therapeutics.in vitro transcription assays, functional characterization using ribozyme cleavage assay and RNA EMSA. Further biophysical characterization would help us to create artificial gene specific autoregulatory elements and may even be used to deliver into Leishmania parasites using the liposomal formulations for therapeutic intervention. Development of ribozymes as innovative nucleic acid-based enzymes that have the capability of coupling a catalytic function to a molecular switch may certainly open the way to the development of artificial RNA—based networking systems which will be quite fascinating.In future we would like to further validate the initial predictions using S1 Fig Energy of the system and potential energy of the molecule indicate that during the simulation potential energy is decreased. (c-e) pressure, temperature and volume are constantly maintained in the entire 100ns MD simulation.(TIF)Click here for additional data file.S2 Fig2 concentrations.  RMSD analysis of the same at 10mM and 25mM salt concentrations.(a) RMSD analysis of 3’ Helicase UTR at 5mM MgCl(TIF)Click here for additional data file.S3 FigThis hydrogen bond was observed from the one of the snapshots of MD trajectory and H-bond interaction was plotted using Ligplot+ program.(TIF)Click here for additional data file.S1 MovieIn this movie we can visualize the fluctuations observed at both 3’ and 5’ end, but the rigid domains identified from normal mode analysis shows constant 2D and 3D structures.(MP4)Click here for additional data file.S2 Movie+2 ions are forming hydrogen bonds with some of the nitrogen bases. One such example is illustrated in +2 ions and their interaction.During the MD run we have observed some of the Mg(MP4)Click here for additional data file.S1 SpreadsheetThese values are calculated from the automated python scripts.(XLSX)Click here for additional data file.