Recent discoveries concerning novel functions of RNA, such as RNA interference, have contributed towards the growing importance of the field. In this respect, a deeper knowledge of complex three-dimensional RNA structures is essential to understand their new biological functions. A number of bioinformatic tools have been proposed to explore two major structural databases  in order to analyze various aspects of RNA tertiary structures. One of these tools is RNA FRABASE 1.0, the first web-accessible database with an engine for automatic search of 3D fragments within PDB-derived RNA structures. This search is based upon the user-defined RNA secondary structure pattern. In this paper, we present and discuss RNA FRABASE 2.0. This second version of the system represents a major extension of this tool in terms of providing new data and a wide spectrum of novel functionalities. An intuitionally operated web server platform enables very fast user-tailored search of three-dimensional RNA fragments, their multi-parameter conformational analysis and visualization.http://rnafrabase.cs.put.poznan.pl.RNA FRABASE 2.0 has stored information on 1565 PDB-deposited RNA structures, including all NMR models. The RNA FRABASE 2.0 search engine algorithms operate on the database of the RNA sequences and the new library of RNA secondary structures, coded in the dot-bracket format extended to hold multi-stranded structures and to cover residues whose coordinates are missing in the PDB files. The library of RNA secondary structures (and their graphics) is made available. A high level of efficiency of the 3D search has been achieved by introducing novel tools to formulate advanced searching patterns and to screen highly populated tertiary structure elements. RNA FRABASE 2.0 also stores data and conformational parameters in order to provide "on the spot" structural filters to explore the three-dimensional RNA structures. An instant visualization of the 3D RNA structures is provided. RNA FRABASE 2.0 is freely available at RNA FRABASE 2.0 provides a novel database and powerful search engine which is equipped with new data and functionalities that are unavailable elsewhere. Our intention is that this advanced version of the RNA FRABASE will be of interest to all researchers working in the RNA field. A deep knowledge of complex three-dimensional folds of RNA structures is essential to understand the range of their biological functions. Both the Protein Data Bank (PDB)  and the Several dedicated programs, web-accessible servers and databases have been proposed for processing and analysis of the PDB files (c.f. ) to studOne of the most difficult issues in mining available structural data is the search of RNA structural motifs and fragments in a conformational space. Until now, only a small number of methods have been developed to cope with this kind of data exploration using various searching patterns. Initial approaches have been based on a representation of the RNA conformational space which reduces to pseudotorsional angles η and θ (PRIMOS ) or diheRecently, we have published RNA FRABASE 1.0 , a web-aOur approach has the advantage of interconnecting the search engine with the dedicated database. Although, PDB and NDB hold a complete collection of RNA structures and provide a wealth of structural data, several dedicated RNA databases have been proposed to facilitate the RNA tertiary structure analysis on different levels and from different points of view. The SCOR database , now disIn this paper, we describe RNA FRABASE 2.0 which has been equipped with powerful new search algorithms and data mining functionalities. It provides an intuitionally operated web server platform for proficient, very fast exploration of three-dimensional RNA structures and their multi-parameter conformational analysis. Due to frequent RNA FRABASE 1.0 user requests, the new library of RNA secondary structures (with graphics) has been made available. An instant visualization of the tertiary RNA structure is provided.The core of RNA FRABASE consists of the database, the search engine and the web interface. All the actions undertaken by the user involve these three key components of the system. In addition, a number of satellite programs and routines are responsible for computation and the processing of the structural information which is supplied to the database Figure . This neRNA FRABASE 2.0 stores a large collection of structural data about RNA molecules, in both their isolated and complexed form, all of which is derived from their PDB-deposited atom coordinates. It is automatically updated once a month. A complete set of models (not just the first one), collected in PDB for Nuclear Magnetic Resonance (NMR) and X-ray structures is processed and stored in the database. In total, as of December 2009, the database contains 1565 RNA structures. This represents about 88% of the 1770 deposited in the Protein Data Bank, including all the most important ones. Small structures  or those built from non-natural nucleotide residues are not collected. Key statistical information about the contents of the database with respect to the experimental method used are presented in Table RNA sequences, secondary structures, the PDB header data  and the NDB identification codes represent the core of RNA FRABASE 2.0. The first two of these are necessary to perform automatic pattern-based search of the 3D RNA fragments. A pattern within the database is composed of the sequence(s) and/or secondary structure(s) coded in dot-bracket format extended to hold multi-stranded structures. Since, the quality of the PDB-derived secondary structures affects the search results, it is important to hold the negative errors that occur in the structure description. Therefore, the dot-bracket convention used in RNA FRABASE 2.0 has been extended to cover RNA residues which are missing from the PDB coordinate section. Now, dot-bracket encoded RNA secondary structures contain a sign "-" to highlight these residues. This change has eliminated the search errors that were often induced by the omission of certain coordinates, mainly in X-ray structures.This modification has prompted us to prepare a new library of RNA secondary structures which is now available to the user under the "Secondary structures" menu. Although similar libraries are provided by some other databases, e.g. RNA STRAND , only RNFor the convenience of the user, each RNA secondary structure can be visualized on the spot  a collection of atom coordinates of unmodified and modified nucleotide residues occurring in RNA structures;(n-1)-P(n)-C4'(n)-P(n+1)) and θ (defined by atoms P(n)-C4'(n)-P(n+1)-C4'(n+1)) calculated by our own scripts;(ii) a description of torsion angles and sugar pucker parameters, including values for the pseudotorsion angles, η (defined by atoms C4'(iii) base-base and inter base pair parameters;(iv) base pair location, i.e. information specifying whether one or both bases of the pair belong to the resulting fragment;(v) a complete classification of canonical and non-canonical base pair types, in both Westhof's and Saenger's notation and information about multiplets;(vi) information about original residue and chain numbering.Although some of this information might be obtained from the other sources, we have collected it together to provide the user with the widest possible set of structural features of the 3D RNA fragments which are of interest and to use them as structural filters in the search.Moreover, in comparison to version 1.0, the RNA FRABASE 2.0 database has been extended to increase the effectiveness of searching for small 3D fragments. Now, the database stores additional information about basic structural elements, such as residues, base pairs, multiplets, dinucleotide steps, stems and loops . They are extracted from the sequence and secondary structure which is encoded in dot-bracket notation by our own scripts and loaded to the database records  are identified and extracted by our own scripts. The stem segments contain only canonical base pairs. The loop structure is composed of one- , two-  or n-strands (n-way junctions) closed, respectively, by one, two or n canonical base pairs .The RNA FRABASE 2.0 engine has been designed to search for user-specified single- or multi-stranded RNA fragments. The number of strands composing a fragment is unlimited. The user provides an input pattern describing those strands in the input box of the "Search" page or selects an appropriate tab sheet in the "Structural elements" page. To ensure effective performance, one should follow the RNA FRABASE format, which is described in detail within our "Help" pages. Input data validation procedures have been also implemented for user convenience.The search engine in version 2.0 is based upon two efficient algorithms. The first algorithm processes the queries inserted through the "Search" page. The basic underpinning idea is the same as for the algorithm implemented in RNA FRABASE 1.0, but it has been updated to hold the extended searching pattern and new filtering options. The second algorithm has been introduced to search for basic structural elements within the "Structural elements" page. Its application has allowed us to disable the threshold, set in RNA FRABASE 1.0 to block the search for small, highly populated, structural fragments. Consequently, the searching potential of RNA FRABASE has been significantly expanded.Wildcard characters are now allowed in the definition of the query pattern. They include: "^" (up arrow), which determines search for the 5'-end fragment only, "$" (dollar symbol) determining search for the 3'-end fragment only and "?" (question mark) which stands for any residue which might be paired, unpaired or missing. The "^" and "$" wildcards can be placed only at the beginning or at the end of the strand both within a single or multi-strand fragments of user interest  include all models of the structure;(ii) add through-space interactions;(iii) enable strand shift operation.These are available from both, the "Search" and "Structural elements" menus and improve the requested 3D RNA fragment specification. The first option, when checked, allows the search of the 3D fragment within all the models available basically for the NMR structures. When it is not checked, only the first model of each deposited structure is considered by the RNA FRABASE engine during the search process. The second option has been introduced to support the selection of fragments, such as bulges or loops, which interact through space with other RNA strands or fragments. The third option has been introduced to search for di- and multi-stranded fragments (c.f. ) and is Novel filtering options, resulting from the introduction of more conformational parameters to the database, have been implemented. This has allowed us to adjust the search engine so that it can address a variety of the information stored in RNA FRABASE 2.0.The RNA FRABASE 2.0 web page has a modern outlook Figure . HoweverRNA FRABASE 2.0 runs in the SUSE Linux environment. It has been developed in the relational management database system PostgreSQL. The search engine is served by the Apache http daemon along with PHP scripts. The web interface has been designed using HTML. The assistant programs have been encoded in AWK and PHP.RNA FRABASE 2.0 is the only web-accessible tool that enables fast automatic exploration of such a substantial selection of structural parameters available for user-defined 3D RNA fragments. Thus, we believe that our tool can be widely used in the study of RNA features and that it can significantly facilitate the processes for 3D RNA structure modelling. Its superiority over other tools has been demonstrated during the testing phase. The RNA FRABASE engine has been tested for a number of representative 3D RNA motifs, fragments and hypothetical structural elements. These have been selected because of their importance for RNA structure research . Its performance has been compared to the previous version  and alsoThe superiority of RNA FRABASE 2.0 in the number of results is clearly seen especially when the user searches for small structural elements, larger RNA structures with long sequences and fragments described by the secondary structure only, uses wildcard characters in the query or sets newly added advanced options. As for the processing time, it is comparable to that of FASTR3D and about ten times shorter than RNA FRABASE 1.0.In this paper, we have presented RNA FRABASE version 2.0, a web accessible database with an effective search engine, which allows for the automatic search of user-tailored, three dimensional fragments within a set of RNA structures. It provides the user with new structural data and functionalities that are unavailable elsewhere. Its versatility has resulted from the possibility to search for fragments of any size, starting from single residues to very big tertiary structure fragments. It is capable of a variety of major applications to underpin a broad spectrum of RNA research. One can envisage the following major applications of the tool: tailored search of 3D RNA fragments for comparative structure modelling using templates , motif shttp://rnafrabase.cs.put.poznan.pl, implemented in PHP, PostgreSQL and Apache, with all major browsers supported.RNA FRABASE 2.0 is freely available at MP, MS, JB and RWA conceived of the study and participated in the coordination of the project. MP, MS, JB and RWA participated in the design of the database and the web interface. All authors were involved in algorithm development. MB and SW implemented the search algorithms for small structural elements. MP, MS, MB and RWA contributed to analyse the results. MP, MS, EKB and RWA drafted the manuscript. All authors read and approved of the final draft.


P = 0.0003), intramural metastasis (P = 0.0051) and lymph node metastasis (P = 0.0026). According to a multivariate analysis, intramural metastasis , vessel invasion  and lymph node metastasis  were found to be independent prognostic factors. The prognosis of the patients with at least one of these factors was significantly poorer than that without. The five-year survival rate of the patients without these factors was as good as that with mucosal carcinoma of the oesophagus. Based on our findings, vessel invasion, intramural metastasis and lymph node metastasis are thus considered to be significant prognostic factors in patients with submucosal carcinoma of the oesophagus. © 2000 Cancer Research CampaignTo clarify the prognostic factors in patients with submucosal carcinoma of the oesophagus, we examined the results of surgical treatment for 78 cases over the last decade. The clinicopathological factors including age, sex, location of the tumour, length of the tumour, histological differentiation, subclassification of depth, lymphatic or blood vessel invasion, intramural metastasis and lymph node metastasis were all analysed. Then the correlation between these factors and prognosis was investigated. As a result, significant differences were observed in the survival rates between the groups regarding lymphatic vessel invasion (


The control of RNA stability is a key determinant in cellular gene expression. The stability of any transcript is modulated through the activity of cis- or trans-acting regulatory factors as well as cellular quality control systems that ensure the integrity of a transcript. As a result, invading viral pathogens must be able to subvert cellular RNA decay pathways capable of destroying viral transcripts. Here we report that the Kaposi's sarcoma-associated herpesvirus (KSHV) ORF57 protein binds to a unique KSHV polyadenylated nuclear RNA, called PAN RNA, and protects it from degradation by cellular factors. ORF57 increases PAN RNA levels and its effects are greatest on unstable alleles of PAN RNA. Kinetic analysis of transcription pulse assays shows that ORF57 protects PAN RNA from a rapid cellular RNA decay process, but ORF57 has little effect on transcription or PAN RNA localization based on chromatin immunoprecipitation and in situ hybridization experiments, respectively. Using a UV cross-linking technique, we further demonstrate that ORF57 binds PAN RNA directly in living cells and we show that binding correlates with function. In addition, we define an ORF57-responsive element (ORE) that is necessary for ORF57 binding to PAN RNA and sufficient to confer ORF57-response to a heterologous intronless β-globin mRNA, but not its spliced counterparts. We conclude that ORF57 binds to viral transcripts in the nucleus and protects them from a cellular RNA decay pathway. We propose that KSHV ORF57 protein functions to enhance the nuclear stability of intronless viral transcripts by protecting them from a cellular RNA quality control pathway. In order to replicate efficiently, a virus must ensure that its genes are properly expressed in the context of an infected host cell. Recent work has demonstrated that eukaryotic cells have RNA quality control pathways that degrade improperly processed, aberrant RNAs. Our published findings using an unusual Kaposi's sarcoma-associated herpesvirus (KSHV) nuclear RNA, called PAN RNA, have suggested that intronless polyadenylated transcripts are subject to such a quality control system. Because most KSHV genes lack introns, we hypothesized that KSHV must have evolved mechanisms that bypass this quality control system. In support of this idea, we show that the ORF57 protein, a multifunctional enhancer of KSHV gene expression, binds to and stabilizes PAN RNA. We further define an element called the ORF57-responsive element (ORE) in PAN RNA that is necessary for ORF57-binding and activity on PAN RNA. In addition, we show that the ORE is sufficient to confer ORF57-responsiveness to a heterologous intronless mRNA, but not its spliced counterpart. These observations substantiate the model that ORF57 enhances KSHV gene expression by protecting viral transcripts from host RNA surveillance pathways. More broadly, these data suggest that viruses producing intronless nuclear RNAs require mechanisms to evade host quality control mechanisms. Post-transcriptional events in mRNA biogenesis are of central importance to the fidelity and regulation of gene expression. Cellular factors regulate nearly every step of RNA metabolism including transcription elongation, RNA splicing, 3′ end formation, nuclear export, translation, etc. In fact, genome-wide profiling experiments demonstrate that a significant percent of the observed changes in RNA levels are dictated by regulation of the stability of a transcript rather than its transcription e.g. . RNA halThe Kaposi's sarcoma-associated herpesvirus (KSHV) is a member of the gammaherpesvirus family that causes Kaposi's sarcoma, a common AIDS-associated malignancy, as well as the lymphoproliferative disorders primary effusion lymphoma (PEL) and some cases of multicentric Castleman's disease (MCD) However, KSHV genes differ from canonical cellular genes in several relevant ways. Some transcripts are bicistronic, KSHV introns are smaller than the average size of a mammalian intron, and genes are more closely arranged in the genome than host genes. Most importantly for the present work, ∼70% of KSHV genes lack introns KSHV encodes a viral post-transcriptional regulator of gene expression called ORF57  that is essential for viral replication ORF57 is critical for the accumulation of the polyadenylated nuclear (PAN) RNA  ORF57-mediated enhancement of the exclusively nuclear PAN RNA suggests that it may be involved in inhibiting the proposed RNA surveillance mechanism. Here, we test this idea and find that ORF57 stabilizes PAN RNA, particularly those transcripts that lack the ENE. We see no ORF57-dependent effect on RNA polymerase II (pol II) density on the PAN RNA gene nor does ORF57 lead to PAN RNA export. Therefore, we conclude that the observed stability enhancement constitutes the major effect of ORF57 on PAN RNA accumulation. In addition, ORF57 binds PAN RNA directly in vivo and a deletion of the 5′ portion of PAN RNA, dubbed the ORF57-responsive element (ORE), reduces ORF57 binding and ORF57 response. We show that tethering of ORF57 to an ORE-deleted PAN RNA restores ORF57-mediated up-regulation. Finally, we show that the ORE is sufficient to confer increased ORF57-response to a heterologous intronless β-globin mRNA, but not its spliced counterpart. We conclude that ORF57 protects viral transcripts from the same cellular RNA decay pathway that the ENE protects from in cis and that its stabilization activity is dependent on ORF57 binding to target RNAs.If ORF57 protects transcripts from RNA decay pathways in vivo, we reasoned that the effects of ORF57 would be more pronounced on unstable ENE-lacking transcripts than on their ENE-containing counterparts. To test this idea, we compared the RNA levels of PAN RNA containing the ENE (PAN-WT) to PAN RNA lacking the ENE (PAN-Δ79) in the absence of ORF57 or in its presence. We transfected HEK293 cells with constructs that express PAN-WT or PAN-Δ79 and co-transfected ORF57-expression constructs at two concentrations or empty vector. After ∼18-24 hours, we extracted total RNA, and quantified relative RNA levels by northern blot . ConsistTet-off advanced (293TOA) cells with TRP-Δ79, a plasmid that expresses the ENE-lacking PAN-Δ79 transcript from a tetracycline-responsive promoter 1/2>24hr), impractical for use in decay assays. Overall, these data demonstrate that ORF57 increases the half-life of unstable PAN RNA transcripts.To directly examine the effects of ORF57 on PAN RNA half-life, we employed a transcription pulse strategy 1/2 ∼3-5hrs). The presence of the ENE appears to protect transcripts from the rapid decay system resulting in a decrease in the fraction of transcripts that are observed in this population. Consistent with these published findings, the decay profiles in Previous studies showed that PAN RNA is subject to two decay pathways with different kinetic properties 1/2 ∼7.8 min)  . In cont7.8 min) . In this7.8 min)  is incre7.8 min) . Taken tIE) promoter is responsive to ORF57 IE and EF1α promoters both show greater increases in steady-state levels in response to ORF57 . These data suggest that the effect of ORF57 on PAN RNA is not due to any promoter-specific element, but that magnitude of the ORF57 enhancement may be linked to a qualitative difference in these promoters.Previous reports suggested that ORF57 enhances transcription rates of specific promoters in certain cell types, including the PAN RNA promoter in 293 cells To further test the effects of ORF57 on PAN RNA transcription, we examined the pol II density on the PAN RNA gene by chromatin immunoprecipitation assays (ChIP). In these experiments, ChIP was performed using antibodies to RNA polymerase II and the relative levels of co-immunoprecipitating DNA were compared. We examined DNA from the 5′ and 3′ ends of PAN RNA  to assesORF57, like its homologs in other herpesviruses, has been implicated in the nuclear export of intron-lacking viral mRNAs ORF57 could stabilize PAN RNA by one of two non-mutually exclusive mechanisms. First, ORF57 may inhibit the activity of RNA decay enzymes, either by binding and inactivating them directly or by decreasing their expression. Second, ORF57 may interact with transcripts and protect the bound RNAs, directly or indirectly, from decay enzymes. The second model predicts that ORF57 is in a complex with PAN RNA and that this binding is necessary for protection by ORF57. Even though a previous report suggested that ORF57 did not bind to PAN RNA in vitro One difficulty in examining RNA-protein interactions using co-immunoprecipitation techniques is that RNA-protein complexes frequently reassort in cellular extract To control for ORF57-RNA reassortment in cell extract, we employed an ultraviolet light (UV) cross-linking protocol Little is known about the requirements for ORF57 recruitment or RNA-binding in vivo, so we tested whether cis-acting elements in PAN RNA are necessary for the ORF57-PAN RNA interactions. We utilized a series of four previously described PAN RNA expression constructs with 300-nt overlapping deletions in PAN RNA, called PANΔ1-Δ4 The model that ORF57-RNA interactions are required for activity predicts that diminished binding of ORF57 to PANΔ1 will result in decreased ORF57 activity. Therefore, we examined the accumulation of PANΔ1 in the presence of increasing amounts of ORF57 . Under tIE promoter (CMV-Δ1), the expression levels are reduced only slightly, ∼30% , correspond to the sequences deleted in PANΔ1-PANΔ4  tail in regulation of transcript stability. After transcription shut-off in the presence of ORF57, we found that some transcripts become hyperadenylated, while others are partially deadenylated  and 293A-TOA cells were grown in Dulbecco's Modified Eagle's Medium (Sigma) supplemented with 10% fetal bovine serum (FBS), 1X penicillin-streptomycin (Sigma), and 2 mM L-glutamate. TOA media utilized tetracycline-free FBS (Clontech) and was supplemented with 100 µg/mL G418 (Fisher Scientific). HH-B2 cells We have previously described the transcription pulse assay and the quantitation of PAN RNA decay kinetics GGATTCATGGTACAAGCAATGATAGACATTAGC 3′) and NC496 (5′ CTCGAGTTAAGAAAGTGGATAAAAGAATAAACCCAAAAGG 3′). Underlined sequence show relevant restriction sites. The product was digested with BamHI and XhoI and inserted into pcDNA-Flag  cut with the same. The sequence of all constructs using PCR-based methods was verified. Consistent with previous reports PAN-WT, PANΔ1, PANΔ2, PANΔ3, PANΔ4 and β-globin reporters were described in IE-WT (pcPAN) was made by PCR amplification of PAN transcribed region and downstream sequence using primers NC39 (5′ AAGCTTACTGGGACTGCCCAGTCACCTTGGCTGCCGCTTCACCATTTCC 3′) and NC42 (5′ GGGCCCCCATCCCAATCGACGCAATAAAGC 3′). The product was cut with ApaI, blunted, cut with HindIII and inserted into pcDNA3 digested with BbsI, blunted, and then digested with HindIII. The CMV-Δ1 derivative of this construct (pcPANΔ1) was generated by PCR amplification with primers NC494 (5′ AAGCTTACTGGGACTGCCATTCAATCCTCCGA 3′) and NC10 (5′ GGGGGCCCGTCACATTTAGGGCAAAGTGG 3′) using PANΔ1 as a template. The product was digested with XbaI and HindIII and inserted into pcPAN. The SV40 and EF1a-driven constructs were also derivatives of pcPAN. In this case, pcPAN was digested with NruI and HindIII to remove the CMVIE promoter and SV40 or EF1a promoters were inserted using the same restriction sites. Promoter sequences for SV40 and EF1a were amplified using primers NC418 (5′ TCGCGACTCCCCAGGCAGGCAGAAGTGATC 3′) and NC419 (5′ AAGCTTTGGATATACAAGCTCCCGGGGATC 3′), and NC416 (5′ TCGCGAGGCTCCGGTGCCCGTCAGTGGATC 3′) and NC417 (5′ AAGCTTGAACGTTCACGGCGACTACTGATC 3′), respectively.The CMVAAGCTTGCCACCATGGCTTCTAGACCC 3′) and NC449 (5′ AAGCTTAGATCCACCCTTGTCATCGTCGTCCTTGTAGTCTACCTTTCTCTTCTTTTTTGGTCCACCTCCACCTCCGTAGATGGATCC 3′) and pcNMS2 TCTAGAAACTACCAAACTGGGTCTAGAAATGC 3′) and NC134 (5′ GACCCTAGATCTACTATAGAATAGGGCCCTCT 3′), digestion of the resulting product with XbaI, and insertion into pcPANΔ1 digested with XbaI.The NMS2-NLS-Fl expression vector (pcNMS2-NLS-Fl) was constructed by amplifying the MS2 coat protein coding sequence using primers NC448 (5′ 7 cells) per sample. Twenty-four hours post-transfection (plus/minus dox as indicated), methanol-free formaldehyde was added to the media at final concentration of 0.75%. Plates were incubated for 10 min and formaldehyde was quenched with 125 mM glycine for 5 min. After washing three times in ice-cold 1X phosphate buffered saline (Sigma), cells were harvested with a rubber policeman and collected by centrifugation at 3500×g for 3 min. Pellets were resuspended in 500 ml RIPA buffer  with 1X protease inhibitors , and 1 mM phenylmethanesulphonylfluoride (PMSF), sonicated 6 times for 5 seconds using a Branson Sonifier 450 with a 4.8 mm diameter micro tip producing an average DNA size of ∼250–1000 bp. The extracts were then centrifuged at 800×g for 5 minutes at 4° and the supernatant was pre-cleared for one hour with 20 µl of Protein-A agarose (Pierce). After centrifugation to remove the beads, absorbance 260 was determined. While this does not accurately reflect DNA concentration due to the complexity of the extract, the value can be used to equilibrate extract concentrations for immunoprecipitation (5% was placed at −20° as “input”. Approximately 8 µg of 8WG16 antibody (Abcam) was added to extract (except no antibody control) and the mixture was nutated overnight at 4°. In addition, 20 µl of Protein-A agarose were blocked overnight at 4° with 0.5 mg/ml sheared salmon sperm DNA and 0.1 mg/ml bovine serum albumin (BSA) in RIPA. The next day, the beads were added to the antibody-extract mixture and the nutated for 1.5 hr at 4°. The beads were then washed a total of six times by nutating at room temperature for 3 minutes in 1 mL of the following solutions: 1) RIPA, 2) low salt wash , 150 mM NaCl), 3) high salt wash , 500 mM NaCl), 4) LiCl wash , 5) TE , 6) TE. After washing, the beads were nutated in 150 mL of elution buffer  for 15 min. The elution step was repeated, the eluted fractions were combined, 60 µl 1M Tris-HCl (pH 6.8) was added to the eluted complexes, proteinase K was added to 0.2 mg/ml and the samples were incubated at 37° for 60 min. The crosslinks were subsequently reversed at 65° for 5–18 hr. The samples were extracted with phenol-chloroform isoamyl alcohol (25∶24∶1), ethanol precipitated in the presence of 0.3M sodium acetate and 20 µg GlycoBlue (Ambion). The pellets washed with 70% ethanol and resuspended in 20 µl of water.For ChIP assays, one 10 cm plate of HEK293 cells was used (∼10CGCCGATTGTGGGTTGA 3′) and NC528 (5′CGAAAGCCAGGATGGGTATATT 3′); PAN 3′, NC550 (5′ TGTTTTAATGTGTATGTTGTGTTGGAAGT 3′) and NC551 (5′ TTCACCTACAAGAAAACATCGTTAGTC 3′); GAPDH, NC533 (5′ ATGGAAATCCCATCACCATCTT 3′) and NC534 (5′ CTAGTTGCCTCCCCAAAGCA 3′). Efficiency of the PAN 5′, PAN 3′ and GAPDH amplification was determined to be 80%, 63%, and 72%, respectively Input and pellet DNA was diluted 1∶100 and 1∶5, respectively and 2 ml of this was used as template for a 20 µl real-time PCR reaction containing iTAQ fast SYBR green supermix (Bio-Rad) with a final concentration of 100 nM primers. Real-time PCR parameters were 40 cycles of 95° for 3 sec and 60° for 30 sec using a 7500 Fast Real-time PCR system (Applied Biosystems). Primers used were: PAN 5′, NC527 , NC30 (CCAATGTTCTTACACGACTTTGAAACTTCTGACAAATGCC), and NC31 (GCACGTTAAATTGTCAAAAGTATAACATGTTTTTCCAATA). In situ hybridization was performed as previously described UV cross-linking and cell-mixing experiments were performed as previously described Table S1e−bx+ce−dx, where a + c = 100, b<10, b>0, d>0. These data correspond to the decay curves shown in Kinetic constants from PAN RNA decay experiments. These constants were derived from nonlinear regressions of the average values of each data point using the equation y = a(0.04 MB DOC)Click here for additional data file.Figure S1Kinetic parameters of PAN RNA decay data. (A) Percentage of PAN RNA transcripts in the “slow” decay pathway is shown with standard deviation. These values are effectively the inverse of those displayed in (0.99 MB TIF)Click here for additional data file.Figure S2n = 3). It should be noted that the PAN RNA promoter construct is pBluescript-based while the others are pcDNA3 derivatives, so it is possible that some of the basal accumulation differences are due to this difference. All transcripts utilize the PAN RNA polyadenylation signal.RNA abundance in the absence of ORF57 from multiple promoters. Quantitation of northern blot data showing PAN RNA levels driven by each of the indicated promoters. All values are relative to the PAN RNA promoter; error bars are standard deviation ((0.29 MB TIF)Click here for additional data file.Figure S3TRP-Δ79 RNA remains nuclear in the presence of ORF57. TRP-Δ79 was transfected into 293A-TOA cells and the transfected cells were used for in situ hybridization with PAN RNA probes (middle). PAN RNA signal is shown in the presence and absence (vector) of ORF57 as indicated. Nuclei are stained with DAPI (left) and merged images are shown (right panels).(6.40 MB TIF)Click here for additional data file.


What kind of strategies subjects follow in various behavioral circumstances has been a central issue in decision making. In particular, which behavioral strategy, maximizing or matching, is more fundamental to animal's decision behavior has been a matter of debate. Here, we prove that any algorithm to achieve the stationary condition for maximizing the average reward should lead to matching when it ignores the dependence of the expected outcome on subject's past choices. We may term this strategy of partial reward maximization “matching strategy”. Then, this strategy is applied to the case where the subject's decision system updates the information for making a decision. Such information includes subject's past actions or sensory stimuli, and the internal storage of this information is often called “state variables”. We demonstrate that the matching strategy provides an easy way to maximize reward when combined with the exploration of the state variables that correctly represent the crucial information for reward maximization. Our results reveal for the first time how a strategy to achieve matching behavior is beneficial to reward maximization, achieving a novel insight into the relationship between maximizing and matching. How do animals, including humans, determine appropriate behavioral responses when their behavioral outcomes are uncertain? Decision-making is a fundamental process of the brain for organizing behaviors, and depends crucially on how subjects have been rewarded in their past behavioral responses. Mechanism of reward-driven learning has extensively been studied theoretically and experimentally. A well-known example includes the reinforcement learning theory based on the temporal difference (TD) error algorithmaN/(N1+N2+…+nN) = aI/(I1+N2+…+nN), where aN  represents the times option a has been chosen and aI the total amount of income obtained at the option. A typical example showing this law is the alternative choice task, in which subjects have to choose one from the two options that may be rewarded at different average rates. Matching and maximizing are mathematically equivalent in simple tasksNevertheless, animals often exhibit matching behavior in a variety of decision-making tasksDecision-making models to reproduce the matching behavior have been proposedWe first prove that partial maximization of reward leads to matching behavior irrespective of the mathematical algorithm used for this computation. A crucial step is to define “the matching strategy” that plays a central role in the present study. We then demonstrate how the matching strategy substitutes for the maximizing strategy in a decision-making task that is difficult to solve, when matching is combined with an appropriate utilization of available information sources.r〉 is the average reward per choice from all options and 〈r|a〉 the average reward conditioned on choice of option a. We can derive the above expression from the relationship aI≅〈r|a〉aN. Thus, the matching law equalizes the expected returns on all the options that are chosen sufficiently many times. Note that the matching law should not be confused with “probability matching”a is proportional to 〈r|a〉 rather than aI. Probability matching is typically observed in a task in which each expected return 〈r|a〉 is fixed and independent of subject's behavior . In such a simple task, the maximizing behavior satisfies the matching law, but not the probability matching. Hereafter, we focus on the matching law. Moreover, we consider the case where subjects make choices at fixed intervals. We can employ the discrete time steps without much loss of generality, since the framework describes a free-response task on continuous time if the interval is sufficiently short and choosing nothing is an available option.The analysis is easier if we express the matching law as followsw =  that determines the behavioral policy to make decision. These variables are often called “policy parameters” in mathematical models of decision making. Then, the probability of choosing option a is given as a function ap(w) of the synaptic weights. To ensure a smooth search for an optimal set of choice probabilities, we require that arbitrary infinitesimal changes of {ap(w)} allowed in the space of choice probabilities can be caused by some set of infinitesimal changes {jdw}.We analyze the outcome of the decision process without specifying the detail of neural decision system. To this end, we assume a set of ‘synapses’ r〉 by modifying behavioral outputs. Whatever algorithm is used, the synaptic weights to maximize 〈r〉 should satisfy the stationary condition ∂〈r〉/∂jw = 0 for arbitrary j, i.e.,With the above definitions, we can describe the average reward per choice as jw, whereas the second term the possible change in 〈r|a〉 generated implicitly by the change in subject's behavioral policy. The conditional expectation value 〈r|a〉 is obtained by taking an average over all possible patterns of past choices in which the newest choice is option a. In general, the reward probability depends not only on the current choice, but also on the history of the past choicesr|a〉 depends on the choice probabilities that produced the past choices, and hence depends on jw.The first term contains the explicit dependence of the choice probability on In order to maximize reward, the brain has to explore the correct dependence of the reward probability on the past choices. It seems, however, difficult to infer this dependency correctly with little knowledge on an accurate model of the environment. In such a difficult situation, the brain may simply omit the second term in Eq. 2 in its practical attempt to maximize reward,jdw} and taking a summation over j gives adp(w)≡Σj(∂ap/∂jw)jdw represents the infinitesimal change caused by {jdw}, and R≡ and dp(w)≡(dp1(w), dp2(w), …, ndp(w)) are vectors in the space of multiple options. If all options have non-vanishing stationary choice probabilities, the probability changes dp(w) may occur in an arbitrary direction that satisfies the probability conservation 1≡ is an n-dimensional identity vector. Therefore, the conditions R·dp(w) = 0 and 1·dp(w) = 0 can simultaneously be satisfied only by such R that is parallel to 1. If the stationary choice probability vanishes for some option, ap = 0, we can forbid the changes in this direction (adp = 0), and R should have identical components for all the options exhibiting non-zero choice probabilities. These results and Eq. 1 imply that the truncated stationary condition given by Eq. 3 is equivalent to the matching law.Multiplying Eq. 3 by arbitrary variations {Thus, the steady choice behavior exhibits matching when the decision system ignores the influence of subject's past choices on the expected outcome in aiming for the stationary condition of reward maximization. Hereafter, we call this suboptimal maximization strategy to achieve Eq. 3 “matching strategy”. By contrast, we call the strategy to directly solve Eq. 2 “the maximizing strategy”.n = 2), in which the expectation value of return on each choice pattern is specified completely by the subject's current (ta) and most recent choices (ta−1) as p2(w) = 1−p1(w), the subject's decision system controls only the choice probability p1(w) through w, and makes every choice with probability p1(w). Then the average return on the current choice 〈tr|ta〉 is obtained by averaging ta−1 = 1,2 as w through the choice probability p1(w). Since ∂〈tr|ta〉/∂jw≠0, the matching strategy does not maximize reward in this task. Actually, it gives 〈r〉 = 0.25 whereas the maximizing strategy yields 〈r〉 = 0.45 /2 = 0.6) than the best independent choice behavior in that task. Thus, to produce a better outcome in some situation, the subject is required to make each choice depending on the past choices or other available information. Below, we investigate the relationship between the matching and maximizing strategies, taking all possible choice behaviors into account.The quantitative analysis conducted in σt before making a choice ta at time t. Then, in a given task, the external and internal information available for the subject at time t consists of the histories of sensory signals, subject's past choices and the past returns: Ht = . A decision-making task specifies the conditional probability distribution P. In contrast, the general rule to determine subject's choice behavior is described by the conditional probability distribution P(ta|Ht). The problem is how to explore an optimal behavioral policy Pˆ(ta|Ht) to maximize the average reward 〈r〉 in a given task.To make the argument as general as possible, we include the case where the subject may receive sensory signals P(ta|Ht) on the whole history Ht. Hence, subject's decision system may extract partial information st from Ht, and restrict the behavioral policy asIn practice, however, it is difficult to optimize the dependence of st “state variables”. We assume that the decision system controls the definition of state st, Ht↦st, and P(ta|st). In order to maximize the average reward, the decision system has to adopt an appropriate definition of state with which an optimal behavioral policy Pˆ(ta|Ht) satisfies Eq 4. It has been proved Ht↦st satisfiesP that satisfies Eq. 5 and does not depend on the variables that are not reflected in s. Therefore, state s that satisfies Eq. 5 represents crucial information about reward delivery in that task. The above theorem means that the optimal policy Pˆ(ta|Ht) depends on only the crucial information. Hereafter, we may say that a definition of state variables, Ht↦st, is correct if and only if st satisfies Eq. 5. Note that the selection of the correct definition may not be unique.We may call the above Ht↦st. Let sap = P(ta = a|st = s) be the choice probability with which the decision system in state s chooses option a. Each state-dependent choice probability is determined as a function of the synaptic weights sap(w). In order to explore all possible patterns of state-dependent choice probabilities smoothly, we assume that an arbitrary pattern of {sap} and an arbitrary direction of infinitesimal changes {sadp} allowed in the space of probabilities can be expressed by some pattern of w and some direction of infinitesimal changes dw, respectively P(s), where 〈r|a, s〉 is the average reward conditioned on choice of option a in state s, and P(s) is the distribution of the states that the subject has visited over sufficiently many decision trials with fixed {sap(w)}. The stationary condition for reward maximization ∂〈r〉/∂jw = 0 is written as Taking the state dependence into account, the average reward is written as 〈w into account. In contrast, as in the previous case, the matching strategy ignores the dependence of the expected outcome of the current choice on w in aiming for the stationary condition. The outcome in the present case consists of the return tr and the next state st+1. Therefore, the matching strategy ignores the dependence of P on w, and hence ignores ∂〈r|a, s〉/∂jw and ∂P/∂jw, where P≡P. By transforming the second term repetitively with the recursive relation P(s′) = Σsa,Pasp(w)P(s) and by setting ∂〈r|a, s〉/∂jw = ∂P/∂jw = 0, we obtain the stationary condition of the matching strategy  takes a fixed value specified by the task, which is actually independent of w. Therefore, the matching strategy coincides with the maximizing strategy, which indeed earns the globally maximal average reward (red triangle) unless the choice behavior is trapped by a local stationary point. In contrast, if an incorrect definition of state variables is chosen, the set of generable choice behaviors (blue curve) does not necessarily include the optimal choice behavior. Therefore, the maximizing strategy can lead to only the best choice behavior (blue triangle) within the restricted set. The conditional probability P depends on the past choices that are not reflected in state st, and hence depends on w. Therefore, the matching strategy (blue cross) in general deviates from the maximizing one (blue triangle).We schematically illustrate the relationships between the maximizing and matching strategies with correct and incorrect definitions of the state variables . The horta, ta−1, ta−2) . An actor-critic system  are smaller than the globally maximal one, and the average rewards earned by the actor-critic systems operating on the incorrect state variables (magenta and blue curves) are still smaller.To explain the above results, we conduct numerical simulations of a simple alternative task in which the reward probability is given as a function of the current and most recent two choices (−2) see . A correstem see  operatinstem see . In contThus, the matching strategy is as efficient as the maximizing one if they are combined with a mechanism to explore and select a correct definition of state variables. However, the matching strategy in general deviates from the maximizing one for the choice behaviors restricted by an incorrect definition of state variables.How subjects decide behavioral responses based on their experience and reward expectancy is a current topic in neuroscience. In particular, which choice behavior, matching or maximizing, is more fundamental in decision making has long been debated. The relationship between matching and maximizing behaviors has been often discussed in the restricted case where every choice is independent of the past choices. For instance, Loewenstein and Seung Most importantly, we have clarified the general relationship between matching and maximizing strategies among all the possible choice behaviors. We have proved that the matching strategy can lead to the optimal choice behavior when the subject's decision system correctly discovers the information sources sufficient to specify the expected outcome, and can utilize the information through state variables. Differences between the matching and maximizing strategies can arise when the decision system assigns incorrect information sources to the state variables. Our results for the first time revealed how a strategy to achieve the matching behavior is beneficial to reward maximization, and how the ignorance of the relevant information leads to the matching behavior.a priori knowledge about the probabilistic rule of the outcomes of their behavioral responses. It seems unlikely that the brain easily identifies the relevant information sources from infinitely many combinations of the histories of past sensory inputs, returns and choices. This might explain why the matching law appears so robustly in various animal species and in various decision-making tasks as a result of ignorance of the relevant information sources. In contrast, the matching strategy with the incorrect selection of information sources may replicate various deviations from the matching behavior, such as the under/over-matching observed in various situations The information sources relevant to the expected outcome are task-dependent. In realistic situations, the subject would have no t at which the subject is required to make decision, 2) a finite number of fixed options  available for the subject at every time step, and 3) a scalar amount of reward given to the subject at every time step. For the decision system, we required the following assumptions: 4) the decision system can control the definition of state st and the state-dependent choice probabilities {asp} through a set of synapses w = , 5) it adopts a definition of state st with which the number of possible states is finite (l), and 6) on a certain definition of state, an arbitrary pattern of possible {asp} and an arbitrary direction of possible infinitesimal changes {asdp} can be expressed by some w and dw, respectively. The assumption 6 requires the following condition:q(w) represents the ln-dimensional vector function consisting of the state-dependent choice probabilities {asp(w)}, and J(w) is the Jacobian matrix of q(w): ijJ(w) = ∂iq(w)/∂jw. Equation 9 requires m≥l(n−1). Independent choice behaviors are generated in the case l = 1.Our proof of matching law (Eq. 3) is valid for a wide class of natural learning rules, including those employing a widely-used soft-max function for choice probabilities (see below). In the following, however, we explicitly describe the assumptions necessary to make our proof mathematically rigorous. For decision-making tasks, we assumed 1) discrete time step tr = 1) or not given (tr = 0) to the subject according to the probability determined by the subject's current (ta) and most recent one or two choices (ta−1 and ta−2). Each choice should be taken from one of two options , although it is straightforward to extend the present results to more general tasks with more than two options. The conditional expectation value of return on each choice pattern is given as a task parameter: r〉 = Σaa′,a″,aaga″′aapa″′P, where aa′a″p is the conditional choice probability aapa″′≡P, and P is the probability distribution P≡P obtained as a solution of equation P = Σa″aapa″′P. The best average rewards obtainable by the restricted choice behaviors with state-definition ts≡ta−1 and no state variable can be calculated by restricting aapa″′ as aap′1 = aap′2 = aap′ and ap1 = ap2 = ap, respectively.To examine the performance of the matching and maximizing strategies, we introduced a decision-making task in which reward is given  = jw(t)+Δjw(t). Meliorationp1(w) = w0, p2(w) = 1−w0, Δw0 = α(w1−w2) and α is a positive constant, and ta = a, and r|1〉 and 〈r|2〉 are estimated as w1 and w2, and the choice probabilities are determined by w0 updated by the estimated average returns. Local matchingβ is a positive constant. Individual updating rules are described as u = α(tr−u) (actor-critic), Synapse-updating rules can be described by change Δasw} and defining the choice probabilities in each state as asw〉 = λβP(s)asp(asQ−sV), where λ is a positive constant, and Vs≡ΣaQasaps represent the relative values of choosing a in state s  and of state s , respectively. Using the relations st = s, and u estimates the average reward and the variable sν represents the state-value of s estimated with the temporal difference (TD) error algorithm. While the actor-critic system is usually designed for maximizing a discounted sum of future rewardsAn iterative method to achieve Eq. 7 was shown in α = λβ = 0.05, and the initial values of all dynamical variables were set to 1. The value of β was set as β = 4 by default, while it was varied for the Q-learning simulations (y according to Δy = (tr−y)/200 from an initial value of 0.64, which is the average reward obtained with even choice probabilities: p1 = p2 = 0.5.In the simulations shown in ulations . To showText S1Strategies of different learning rules. Several well-known learning algorithms are categorized into the matching, maximizing and other strategies.(0.19 MB DOC)Click here for additional data file.Text S2Matching strategy in state-dependent choice behaviors. The extensions of the stationary condition and the matching law are derived.(0.19 MB DOC)Click here for additional data file.


In the ASE/AIE-mediated genetic mechanism for age-related gene regulation, a recently identified age-related homeostasis mechanism, two genetic elements, ASE (age-related stability element) and AIE (age-related increase element as a stem-loop forming RNA), play critical roles in producing specific age-related expression patterns of genes.359, while hnRNP A3 in the mouse liver nuclear extracts was a mixture of phosphorylated and unphosphorylated Ser359. HepG2 cells engineered to express recombinant hFIX transduced with adenoviral vectors harboring an effective siRNA against hnRNP A3 resulted in a substantial reduction in hFIX expression only in the cells carrying a hFIX expression vector with AIE, but not in the cells carrying a hFIX expression vector without AIE. The nuclear hnRNP A3 protein level in the mouse liver gradually increased with age, while its mRNA level stayed age-stable.We successfully identified heterogeneous nuclear ribonucleoprotein A3 (hnRNP A3) as a major mouse liver nuclear protein binding to the AIE-derived RNAs of human factor IX (hFIX) as well as mouse factor IX (mFIX) genes. HnRNP A3 bound to the AIE RNA was not phosphorylated at its SerWe identified hnRNP A3 as a major liver nuclear protein binding to FIX-AIE RNA. This protein plays a critical role in age-related gene expression, likely through an as yet unidentified epigenetic mechanism. The present study assigned a novel functional role to hnRNP A3 in age-related regulation of gene expression, opening up a new avenue for studying age-related homeostasis and underlying molecular mechanisms. We previously reported the first molecular mechanism of age-related homeostasis, the ASE/AIE-mediated genetic mechanism for age-related gene expression AIE originally identified in the hFIX gene has a 102 base pair (bp) stretch of dinucleotide repeats  in the middle of 3′-untranslated region (3′-UTR), which potentially forms and functions as a stem-loop RNA structure (hereafter referred as hFIX-AIE RNA) after the gene is transcribed In this report, we describe identification of hnRNP A3 as the mouse liver nuclear protein, which specifically bind to hFIX-AIE RNA as well as to mFIX-AIE RNA, its phosphorylation status in binding to AIE RNA, functional characterization analyzed by siRNA and age-related expression profiles of its gene and protein, thus assigning a novel functional role to this protein.32P-hFIX-AIE RNA resulted in two major mobility shifted bands with increasing amounts of NEs (lanes 5–7), suggesting that hFIX-AIE RNA specifically bind at least two nuclear proteins. Intensities of these bands drastically reduced with addition of excess amounts of non-radiolabeled hFIX-AIE RNA , and their intensities again efficiently reduced by addition of non-radiolabled mFIX-AIE RNA probe (lanes 5 and 6) as well as of non-radiolabled hFIX-AIE RNA (lane 7). The shifted bands generated with 32P-hFIX-AIE RNA (lane 8) were also efficiently reduced with addition of non-radiolabled mFIX-AIE RNA (lane 9), indicating that both hFIX-AIE RNA and mFIX-AIE RNA likely bind same nuclear proteins. Reflecting the smaller size of mFIX-AIE RNA (50 nucleoside residues) than that of hFIX-AIE RNA (149 nucleoside residues) used, the band shifts produced with 32P-mFIX-AIE RNA (32P-hFIX-AIE RNA (lane 8). When hFIX-AIE RNA probe/protein complexes were UV cross-linked, two bands of approximate 44 kDa (major) and 50 kDa (minor) shown with bracket a were observed in sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE), and with subsequent RNase treatments, their sizes lowered to about 40 and 45 kDa shown with bracket b, respectively, presumably due to a loss of the RNase-accessible portions of RNA fragment (32P-mFIX-AIE RNA/nuclear protein complex (data not shown). Two major mobility shifted bands observed for the UV cross-linked and RNase-treated hFIX-AIE RNA increased their band intensities with increasing amounts of NEs used , showed no significant competition in RNA EMSA, while homo-poly(U) and homo-poly(A) showed significant competitions on formation of -AIE RNA .32P-hFIX-AIE RNA/protein complex required for preparative solution-phase isoelectric focusing (IEF) was obtained by repeating the procedures of RNA EMSA with the 32P-hFIX-AIE RNA probe, UV-irradiation and RNase digestion of the 32P-hFIX-AIE RNA probe/protein complex extracted, SDS-PAGE separation, subsequent excision of the radio-active gel area corresponding to 30 kDa–60 kDa and extraction of the treated complex by electro-elution. Two dimensional electrophoresis (2DE) analyses of the complex samples showed that the solution-phase IEF efficiently concentrated the most 32P-hFIX-AIE RNA/protein complex to pH 4.6–5.4 zone . Subsequent 2DE , although not at very significant level  in addition to hnRNP A3. Since no hnRNP A2/B1 as well as other proteins were detected in all other three independent experiments with both hFIX-AIE RNA and mFIX-AIE RNA, hnRNP A3 was determined to be a major AIE RNA binding nuclear protein. In 2DE, the FIX-AIE RNA/hnRNP A3 complex migrated to pI3-5.4 shifting from the expected pI7-9 for the free form of hnRNP A3 A sufficient amount of 5.4 zone , b, a mu5.4 zone , a, and uent 2DE  and autouent 2DE  of the puent 2DE . Similare of 117 . In thes359  356–377  is in a mixed state of unphosphorylated and phosphorylated. Liver nuclear proteins prepared from 6 month-old mice had the same number of protein spots of hnRNP A3 with their Ser359 residues phosphorylated in a similar 2DE separation pattern found for 3 month-old animals, while liver nuclear proteins prepared from animals at 18 and 21 months of age showed two more hnRNP A3 protein spots with their Ser359 phoshorylated (data not shown). Both mass-spectra m/z 1910.9 and m/z 1990.8 were always observed with hnRNP A3 protein obtained from these spots. In the present study, Ser359 was most candidate phosphorylation site  analyses of liver NEs obtained from mice at 3 months of age identified at least 19 protein spots to contain hnRNP A3 (data not shown). By definition of Mascot scores ≥53 being significant, 14 and 5 of these spots were found to contain only hnRNP A3 protein and mixtures of proteins including hnRNP A3, respectively. Four of the 14 hnRNP A3 single protein spots contained hnRNP A3 with its Serorylated , while oion site  and no e5′-GTATGGCAAGATTGAAACCATAGAG-3′ and 5′-GCCCATTAATAGTGTGGTATTTCTG -3′, and 5′-AAATGGTGAAGGTCGGTGTG-3′ and 5′-TGAAGGGGTCGTTGATGG-3′, respectively. Among the six siRNA vectors against hnRNP A3 prepared, one (siRNA 6) designed to the region, nucleotide (nt) 113 through 132, of the hnRNP A3 gene , reproducibly reduced the hnRNP A3 mRNA level by approximately 78% in 293T cells, the largest suppression of hnRNP A3 expression . In a similar experiment, no such reductions were observed with adenoviral vectors harboring the scrambled siRNA 6.Effects of transduction with adenoviral vectors harboring the most effective siRNA, siRNA 6, and its target sequence scrambled siRNA on hFIX expressions from HepG2 cells transfected in advance with -416FIX m1/1.4 and -416FIX m1, hFIX minigene expression vectors with and without AIE, respectively, are shown in An expression profile of the hnRNP A3 gene (mRNA level) observed for a period of 1 through 21 months of age is shown in Two major hnRNP A3 protein bands of approximately 38 kDa and 41 kDa identified by Western blot analysis of the liver NEs apparently corresponded to hnRNP A3a and A3b isoforms, respectively As we previously reported 32P-hFIX-AIE RNA/nuclear protein complex identified well-defined radioactive protein spots  and homo-poly(A) show significant competitive effects on hnRNP A3 binding to AIE RNAs, while homo-poly (C) does not, suggesting a preference of AT-rich nucleoside sequences by hnRNP A3 in its binding to AIE RNA . AlthougMALDI-TOF/MS analyses of the UV cross-linked hnRNP A3/hFIX-AIE RNA complex never detected specific peptides encompassing the region aa 100–126 , while sm/z 1910.9, which corresponds to the peptide aa 356–377 with its Ser359 unphosphorylated, was detected, while m/z 1990.8 corresponding to the same peptide with its Ser359 phosphorylated was always absent  increased with temperature. HnRNP K, one of the hnRNP family proteins, is known to bind to the differentiation control element (DICE), a CU-rich stretch of sequence in the 3′-UTR of 15-lipoxygenase (LOX) mRNA, inducing translational repression of the LOX gene 396, it frees the bound mRNA, allowing it to be used for translation In MALDI-TOF/MS analyses of the nuclear hnRNP A3 in complex with the hFIX-AIE RNA, a mass-spectrum s absent , A and Borylated . Here, mn Ser359 . YamaokaOur efforts for constructing effective adenoviral vectors harboring siRNAs against hnRNP A3 resulted in identification of a highly effective siRNA, siRNA 6, which was capable to reduce the hnRNP A3 mRNA level by 78% in 293T cells . This siThe hnRNP A3 mRNA level in the mouse liver stays stable throughout the tested period of age (1 through 21 months) . In contHnRNP A3 is a recently found member of the hnRNP family protein, currently composed of more than 20 different proteins including hnRNP A1, A2, B1 and B0a/b, which are known to have highly diverse functions such as precursor mRNA processing, transcriptional regulation and DNA recombination This study has successfully assigned a novel functional role to hnRNP A3 in age-related regulation of gene expression, opening up a new avenue for studying age-related homeostasis and underlying molecular mechanisms, and for developing new preventive methods and treatments for relevant age-associated diseases.32P]-UTP, and poly(U), poly(A) and poly(C) homoribonucleoside oligomers were obtained from GE Healthcare . Antibodies used for hFIX-specific enzyme-linked immunosorbent assay (ELISA) and HepG2 cell line were obtained as previously described Radiolabeled nucleoside, α UTP , 1 µg hFIX-AIE or mFIX-AIE template DNA and 2 µl of T7 MEGA shortscript enzyme mix. The reaction mixture was then added with 1 µl of RNase-free DNase solution, incubated for 15 min at 37°C and subjected to electrophoresis using a 6% polyacrylamide urea gel. The gel was then exposed to an X-ray film for 10 sec, and gel areas containing radioactivity were precisely located by matching with the autoradiogram and excised. The gel pieces recovered were then incubated in a probe elution buffer (Ambion) at 37°C overnight. Non-radioactive AIE RNA probes were prepared in a similar manner by using non-radioactive nucleosides in transcription reaction.32P-AIE RNA probe with a specific activity of approximately 1.9×109 cpm were incubated for 50 min on ice with 2.5, 5 or 10 µg of NEs in the presence of 0.2 µg of yeast RNA and 15 U RNase inhibitor in EMSA binding buffer . The reaction mixture was then added with 8 µl loading buffer, and subjected to EMSA on a 10% non-denaturing polyacrylamide gel. The gel was scanned with a Storm 860 PhosphorImager . For competitive EMSAs, non-radiolabeled RNA probe in various molar excesses over that of 32P-labeled RNA probe was added to the reaction mixture containing NEs 5 min prior to addition of the radiolabeled probe. The rest of EMSA was performed as described EMSA with an AIE RNA probe was carried out as described by Cok et al. 32P-hFIX-AIE RNA/nuclear proteins complex were performed as follows. Liver NEs (10 µg) was incubated with 32P-hFIX-AIE RNA probe (approximately 1×106 cpm/10 µl) in a similar condition as described above. The reaction mixture was then exposed to UV light for 30 min on ice , followed by incubation with 2 µl of RNase T1 (5 U/µl) (Ambion) for 20 min at 25° C. The reaction mixture was then added with 8 µl SDS loading buffer , and subjected to SDS-PAGE using a 10% polyacrylamide gel in the presence of 0.1% SDS. SDS-PAGE gels were scanned with a Storm 860 . For competitive RNA EMSAs, non-radiolabeled hFIX-AIE or mFIX-AIE RNA probe in various molar excesses over that of 32P-labeled hFIX-AIE or mFIX-AIE RNA was added to the reaction mixture containing NEs 5 min prior to addition of the radiolabeled AIE RNA probe. The rest of conditions including UV cross-linking, RNase T1 treatment and SDS-PAGE was same as above. Competition assays with 10 µg each of homo-poly nucleoside fragments, homo-poly(U), homo-poly(A) and homo-poly (C), were done in a similar manner as for non-radiolabeled AIE RNA competitors.UV-crosslinking, RNase treatment and analysis by SDS-PAGE of 32P-AIE RNA complex were excised and subjected to electro-elution using an Amicon Centriruter microelectroeluter . The solution recovered was concentrated with a Centricon apparatus (Millipore). This procedure was repeated to obtain a sufficient amount of nuclear protein/32P-labeled hFIX-AIE RNA complex for further analyses. The pooled sample solution was then subjected to IEF using a ZOOM® IEF Fractionator (Invitrogen), and solution fractions with radioactivity were recovered, concentrated with a Centricon apparatus and subjected to analytical 2DE. Its IEF (the first dimension) was performed by using an IPG Runner system with a pre-casted ZOOM strip of an appropriate pH range gradient, followed by SDS-PAGE using ZOOM gels. The gel was then silver stained and exposed to an X-ray film at 4°C for 1 to 6 days depending on the radioactivity strength. For identification of a protein(s) bound to the AIE RNA probe used, the central gel area of radioactive spots visualized in autoradiogram was excised and in-gel digested with trypsin . Peptides generated were then recovered by extracting with a solution of acetonitrile:water:trifluoroacetic acid (66:33:0.1 by volume), vacuum-concentrated and cleaned with a Zip Tip C18 (Millipore). The peptide containing fractions collected and dried were mixed with CHCA matrix before applying to an Axima® CFR MALDI-TOF/MS . PMF analysis was done with a MASCOT server version 2.1  taking the UniProtKB mouse protein database (taxonomy: mus musculus).For identification of the hFIX-AIE RNA binding protein(s), preparative RNA EMSA using 20 µg NEs was repeated in a similar condition as for analytical scale RNA EMSA. Following the UV cross-linking step, the reaction mixture was added with 2 µl RNase T1 (5 U/µl) and incubated for 20 min at 25° C. The reaction mixture was subjected to a preparative SDS-PAGE using NuPAGE® Novex Bis-Tris 4–12% ZOOM® Gel . The gel was exposed to an X-ray film at 4°C for 1 day. Radioactive areas of the gel containing the nuclear protein/Mouse liver NEs (300 µg) were subjected to IEF using a 24 cm immobiline DryStrip (pH 6–11) , followed by SDS-PAGE using a pre-casted 10–16% gradient polyacrylamide gels (BIO-RAD). Gels were then stained with Colloidal Coomassie Brilliant Blue (CBB) and scanned by GS-800 scanner (BIO-RAD). Protein spots were analyzed with a PDQuest software , and excised, destained in 50 mM ammonium bicarbonate/50% acetonitrile and dried in a vacuum concentrator . Dried gel pieces were then rehydrated with 5 µl of 20 ng/L trypsin in 10 mM ammonium bicarbonate, and incubated at 30°C overnight. Mass-spectrometric analyses of the tryptic peptide samples were carried out as described above except by using an AXIMA® MALDI-TOF2 (Shimadzu Biotech).. The most effective siRNA identified in suppressing the target protein mRNA level was then cloned into pBAsi-mU6 Neo vector, and digested with EcoRV, thus releasing a siRNA-encoding fragment. The fragment was then inserted into a cosmid pAxcwit vector (Takara Bio) at a SwaI site, and the cosmid vector generated was used for producing recombinant adenoviral particles harboring the highly effective siRNA expression unit with a Takara adenovirus expression vector kit. Adenoviral particles produced were purified by CsCl gradient centrifugation and stored at −80°C until use. Control recombinant adenoviral particles harboring a scrambled siRNA of the above selected siRNA or a siRNA against GFP gene were similarly constructed.Six separate candidate sequence sites of the AIE RNA binding protein gene were selected by utilizing the siRNA design support system R3 program  for constructing an optimal siRNA expression unit against the gene. Double stranded (ds) 19 or 20 nt-long sequences corresponding to these sites were synthesized by mixing complementary oligonucleotides containing a loop sequence (ctgtgaagccacagatggg) with BamH1 and Hindzш sticky ends at 5′ and 3′ ends, respectively, thus forming a short hairpin RNA against the protein RNA. The expression units encoding siRNAs against to the target protein were cloned into pBAsi-mU6 Neo DNA vector (Takara). The efficacy of each siRNA was assessed in 293 cells co-transfected with the target binding protein expressing plasmid (0.1 µg) and its siRNA expressing plasmid (0.3 µg). An expression vector unit for the target protein, was prepared by RT-PCR with the total mouse liver RNA as a template, followed by insertion into the Gateway® pDESTTM 26 Vector using the Mammalian Expression System with Gateway® Technology kit (Invitrogen). All measurements of each siRNA test sample were done in duplicated RT-PCR. After subtracting the PBS-saline control background, the results were normalized to that of GAPDH (control), and averaged9 pfu in 100 µl PBS were administered into tail veins of mice (C57BL6/J) at 12–17 month of age . Animals were then sacrificed on day 4 and 14 post-viral injection, their liver tissues removed and quick frozen on dry ice, and stored at –80°C until use. Total RNAs were prepared from these tissues by a procedure of acid guanidium phenol chloroform. PCR primers and TaqMan probe used for the target protein in these quantitative real-time RT-PCR assays were 5′-TGTCAGGAAAGCTGCAGGTTACT-3′ and 5′- CATCGTTCCTTCTGTGGCAGATT-3′, and 5′-FAM-CATCGTGCACCGCAA-MGB-3′, respectively. Similarly, for β–actin (control), 5′-TCCACCTTCCAGCAGATGTG-3′ and 5′-CAGTAACAGTCCGCCTAGAAGCA, and 5′-FAM-CATCGTGCACCGCAA-MGB-3′ were used, respectively. RT-PCR Results were normalized to that of β–actin mRNA.Recombinant adenoviral particles harboring siRNA expression unit against the target protein ranging from 8.4×107 pfu/µl in PBS-saline) at 2, 20 and 100 MOI, and incubated at 37°C for 1 hr under gentle rocking every 10 min. The medium was then switched to fresh DMEM and incubation continued for 48 hr. Human FIX produced to the medium was then quantified by hFIX-specific ELISA as previously described HepG2 cells were cultured in Dulbecco's modified eagle medium (DMEM) as previously described. The cells were transfected with a mixture of hFIX minigene expression vector, -416FIXm1/1.4 (hFIX expression vector containing AIE) or with -416FIXm1 (hFIX expression vector without AIE) 32P-labeled cRNA probes were generated using a MEGAscript in vitro transcription kit (Ambion) and α [32P]-UTP. Hybridization was performed by incubating an aliquot of total liver RNAs (10 µg) with 32P-labeled probe (approximately 1×106 cpm/10 µl) for overnight at 42°C. The resulting hybridized mixture was digested with a mixture of RNase A (2.5 units/ml) and RNase T1 (100 units/ml) in RPAш kit. The final precipitates obtained were then dissolved in gel loading buffer, and subjected to SDS-PAGE using a 6% polyacrylamide gel with 8M urea. The gel was transferred onto a Whatmann 3 MM paper, dried and exposed to an imaging plate . The plate was then scanned and quantified for radioactivity with a Storm 860  using a NIH imaging program.RPA for quantifying the mRNA level of hFIX-AIE RNA binding nuclear protein was performed using a RPAш kit (Ambion) according to the manufacturer′s instruction. Total RNAs of liver tissues of mice (C57BL6/J) at various ages (n = 4 per age point) were prepared by an acid guanidium phenol chloroform procedure Liver NEs (10 µg/assay) prepared as described above from C57BL/6J mice at various ages (n = 10 per age point) were added with 20 µl SDS loading buffer, and its protein concentration was measured by the BCA method Figure S1MS/MS analysis of the peptide fragment from a mass peak of m/z 1990.8. The fragmentation spectra of the peptide  of mouse hnRNP A3 are shown at the lower panel. Annotated ions as nominal m/z values and results from fragmentation of an amide bond with charge retention on the carboxyl end (y series ion) or amino terminus (b series ion) are shown. Mascot scores for possible phosphorylation site at Ser 359, Ser 357, Ser 356, Ser 367 are 138.6 , 125.8 (2.1 e-11), 123.4 (3.7 e-11), 38.2 (0.012), respectively. These data indicated that Ser 359 is the most likely phosphorylation site. Fragment ions, y19-98, y20-98, b4-98, b5-98, b9-98, b10-98, b13-98, b19-98 and b21-98 showed a loss of 98-Da in mass corresponding to a phosphate group. Fragment ions b2 and y18 indicated that a phosphate group is not present in the SS dipeptide sequence (Ser 356 through Ser 357) and PYGGGYGSGGGGSGGYGSR (Pro 360 through Arg 377).(1.30 MB EPS)Click here for additional data file.


C. elegans have provided evidence as to the physiological mRNA targets of QUAKING and GLD-1 proteins, two other members of the STAR protein family. The GLD-1 binding site is defined as a hexanucleotide sequence (NACUCA) that is found in many, but not all, physiological GLD-1 mRNA targets. Previously by using Systematic Evolution of Ligands by EXponential enrichment (SELEX), we defined the QUAKING binding site as a hexanucleotide sequence with an additional half-site (UAAY). This sequence was identified in QKI mRNA targets including the mRNAs for myelin basic proteins.SAM68, SAM68-like mammalian protein 1 (SLM-1) and 2 (SLM-2) are members of the K homology (KH) and STAR  protein family. The function of these RNA binding proteins has been difficult to elucidate mainly because of lack of genetic data providing insights about their physiological RNA targets. In comparison, genetic studies in mice and in vitro with a short RNA sequence from its tra-2 physiological mRNA target.Herein we report using SELEX the identification of the SLM-2 RNA binding site as direct U(U/A)AA repeats. The bipartite nature of the consensus sequence was essential for SLM-2 high affinity RNA binding. The identification of a bipartite mRNA binding site for QKI and now SLM-2 prompted us to determine whether SAM68 and GLD-1 also bind bipartite direct repeats. Indeed SAM68 bound the SLM-2 consensus and required both U(U/A)AA motifs. We also confirmed that GLD-1 also binds a bipartite RNA sequence These data demonstrate that the STAR proteins QKI, GLD-1, SAM68 and SLM-2 recognize RNA with direct repeats as bipartite motifs. This information should help identify binding sites within physiological RNA targets. The K homology (KH domain) is a prevalent RNA binding domain that is an evolutionarily conserved domain initially identified as a repeated sequence in the heteronuclear ribonucleoprotein particle (hnRNP) K . The KH 1α1α2β2β3 topology with an additional C-terminal α helix (α3) for maxi-KH domains [1/α2 that provides close contact with the phosphate groups such that the neighboring nucleotides can form Watson and Crick base pairing with conserved amino acids within the KH domain [The KH domain makes direct protein-RNA interactions with a three-dimensional βH domain ,6. The sH domain -9.C. elegans GLD-1, Drosophila How, KEP1, Sam50 and Artemia Salina GRP33 [There exists a subfamily of KH domains that contain extended loops between β1/α1 and β2/β3 and that contain an additional C-terminal helix in their topography . These mna GRP33 . STAR prna GRP33 -18, mRNAna GRP33 -21, mRNAna GRP33 ,23 and pna GRP33 -28. Genena GRP33 , GLD-1 ina GRP33 -32, How na GRP33 , Kep1 inna GRP33  and motona GRP33 . GeneticSystematic Evolution of Ligands by EXponential enrichment (SELEX) [SF1 or branch point binding protein (BBP) was shown to recognize the branchpoint site RNA sequence (UACUAAC) ,36 and s (SELEX) , we defi (SELEX) . In the  (SELEX)  and now 14 and we randomly sequenced 20 RNA molecules from the initial library and noted, as expected, that each sequence was unique [32P-α-UTP such that the amount of specific SLM-2 bound RNAs could be measured after each round. After six cycles of selection, we observed an approximately 10% of binding of the initial input (not shown), demonstrating that we indeed had enriched specific sequences. To confirm the SELEX amplification of the SLM-2 specific RNA ligands, we performed a gel electromobility shift assays (EMSA) with purified pools of RNA transcripts isolated from rounds 2, 4 and 6. The RNAs were 32P-labelled and incubated with buffer or increasing concentration of His-SLM-2. The SLM-2/RNA complexes were observed as slow migrating complexes on native gel electrophoresis  as well as downstream perfect half-site (UAAC)  and as the perfect core (underlined). To define the requirements of QRE-2, we performed EMSA with various combinations of region A and B. The QRE-2 sequences with regions A and B bound QKI with high affinity  and the substitution of the UAAC half-site in region A or region B diminished considerably the RNA binding affinity  are known QKI targets ,43. The ity Fig. . These fC. elegans GLD-1 that consists of a hexanucleotide (NACU(C/A)A) [tra2 and gli repeated element (TGE) containing the hexanucleotide (UACUCAU) and its neighboring half site (UAAU)A) . To examAAU)Fig. , TGE-wt.AAU)Fig. . These dAAU)Fig. , TGE-m1,AAU)Fig. . The TGEAAU)Fig. . In summIn the present study, we identified a SLM-2 consensus sequence as direct U(U/A)AA repeats using SELEX. The bipartite nature of the consensus sequence was essential for SLM-2 high affinity RNA binding. The identification of a bipartite mRNA binding site for QKI  and now We identified SLM-2 in 1999 by searching databases with SAM68 sequences . IndepenPreviously we showed that SLM-2 had a preference for poly (G) rich homopolymeric RNA , therefoSam68 is known to bind cellular RNA as well as DNA . Sam68 iquaking viable (qkv) mice represent an animal model of dysmyelination [KIP1, confirming the role for the QKI isoforms during myelination [The lination . The deflination . QKI nullination . By usinlination . The QKIlination  with a nlination ,44. Thislination ,44. QRE-The MBP mRNAs are localized at the distal processes of oligodendrocytes in intact tissue . The facC. elegans homolog of QKI is GLD-1, a known protein translation inhibitor required for germ-line differentiation [The ntiation ,65. Manyntiation -28 and antiation . Most, bntiation , contain1A2C3U4A5A6C7, the CK region makes important contacts with the RNA. All STAR domain containing proteins have the most important GXXG sequence located in a loop between the two first alpha helices of the KH domain. This sequence of residues is absolutely conserved among the STAR domain proteins and makes the contact with the RNA especially with the bases U4A5A6C7. By looking closely at the residues in the CK region that make important association with the RNA bases, we find that two residues  was performed as previously described [15, were synthesized by (Invitrogen). The oligonucleotides were amplified by PCR using corresponding forward and reverse primers as previously described [13 molecules) was transcribed in vitro using the T7 RNA polymerase (Promega) and (α-32P)-UTP. RNA was purified from denaturing TBE-acrylamide gels, heated to 65°C fro 5 min, and precleared using TALON Metal Affinity Resin (BD Bioscience) to absorb non-specifically bound RNAs. Unbound RNAs were incubated in binding buffer (50 mM Tris-HCl (pH 8.0), 590 mM KCl) with the recombinant His-SLM-2 for 30 min, then with TALON Metal Affinity Resin for another 30 min. After four washes with binding buffer, the RNAs were eluted by TRIzol extraction (Invitrogen). The purified RNAs were ethanol precipitated and resuspended in water with RNase-free DNase for a 15 min reaction. The DNase reaction was quenched for 10 min at 65°C. Reverse transcriptions were performed using M-MLV reverse transcriptase (Promega) and a reverse oligonucleotide annealing to the 3' primer binding site. cDNAs were then generated by PCR amplification with the reverse oligonucleotide and the forward oligonucleotide annealing to the 5' primer binding site containing the T7 promoter. After round 6, the cDNAs were amplified with the reverse primer and a forward primer containing the EcoRI restriction site. The DNA fragments were digested with EcoRI and BamHI and subcloned into pBluescript SK+ (Stratagene) for blue/white selection. Forty-three random white colonies were selected, their plasmid were purified and the SELEX sequence was identified by DNA sequencing (Genome Quebec).escribed . Essentiescribed . After Pin vitro transcription of oligonucleotides harboring a T7 binding site in the presence of 32P- UTP, using T7 MegaShortscript (Ambion) according to the manufacturer's protocols. RNAs were purified on TBE-acrylamide gels before use. For EMSAs, a constant concentration of 32P-labeled RNA (100 pmol) was incubated alone with buffer or with increasing or decreasing concentrations of the corresponding tested proteins in the following buffer: 20 mM HEPES (pH7.4), 330 mM KCl, 10 mM MgCl2, 0.1 mM EDTA, 0.1 mg/ml heparin and 0.01% IGEPAL CA630 (Sigma). The 30 μl reaction were incubated at room temperature for 1 h, and 3.3 μl of RNA loading dye (glycerol containing 0.25% (w/v) bromophenol blue, 0.25% (w/v) xylene cyanol) was added to each. A portion (15 μl) of each sample was separated on native Tris-glycine 8%-acrylamide gels. The gels were dried and the bound and unbound RNAs were quantified using a Storm Phosphorimager (Amersham). The fraction of bound RNA was determined and plotted using the software program Prism 3.0 (GraphPad Software).RNAs were prepared by run-off BamH1 and XhoI directionnal cloning. His-GSG(SAM68) was prepared by subcloning the GSG domain of mouse Sam68 into pET-18c. Protein purification was performed as per the manufacturer's instructions.Recombinant GST-QKI-5 was described previously . MaltoseAG performed the experiments and analyzed the data. AG and SR conceived the experiments and wrote the manuscript.


The “RNA revolution” that started at the end of the 20th century with the discovery of post-transcriptional gene silencing and its mechanism via RNA interference (RNAi) placed tiny 21-24 nucleotide long noncoding RNAs (ncRNAs) in the forefront of biology as one of the most important regulatory elements in a host of physiologic processes. The discovery of new classes of ncRNAs including endogenous small interfering RNAs, microRNAs, and PIWI-interacting RNAs is a hallmark in the understanding of RNA-dependent gene regulation. New generation high-throughput sequencing technologies further accelerated the studies of this “tiny world” and provided their global characterization and validation in many biological systems with sequenced genomes. Nevertheless, for the many “yet-unsequenced” plant genomes, the discovery of small RNA world requires in vitro cloning from purified cellular RNAs. Thus, reproducible methods for in vitro small RNA cloning are of paramount importance and will remain so into the foreseeable future. In this paper, we present a description of existing small RNA cloning methods as well as next-generation sequencing methods that have accelerated this research along with a description of the application of one in vitro cloning method in an initial small RNA survey in the “still unsequenced” allotetraploid cotton genome. In the 1990s two independent discoveries opened up the previously unsuspected world of noncoding RNAs (ncRNAs). The phenomenon of RNA interference (RNAi) was being uncovered as cosuppression in plants , quellinOver the past several years RNAi has become a powerful tool for understanding the role played by dozens of plant and animal genes in a wide range of cellular processes, both normal and pathogenic . MoreoveIn addition, the universe of small RNAs has expanded to include not only miRNAs but new classes including endogenous small interfering RNAs (siRNAs), 21U RNAs, and Piwi-interacting RNAs (piRNAs) . Of thesThere are a number of strategies that have been proposed for cloning small RNAs. Before discussing these, however, there is one factor common to all of them that is essential to be aware of. Small RNAs, whether from plant cells, animal cells, or other sources, represent a small fraction of the total RNA mass present. Agilent Technologies quantifies the quality of cellular RNA in the form of their RNA Integrity Number (RIN). Very high quality intact RNA has a RIN of 10.0 and the lower the RIN, the more degraded the RNA. RIN values between 6.5 and 10.0 represent a continuum of acceptable to excellent RNAs. Using RIN as the point of departure, Agilent assessed the relative fraction of total RNA that is within the small RNA size range in forty tissues from human, mouse, and rat . r = −0.58; P < .01, df = 38) between overall RNA quality as assessed by RIN value and relative small RNA mass. Clearly, increasing amounts of RNA degradation will introduce a greater mass of small fragments that lie in the true small RNA zone. This will result in a greater mass of competing RNA that will make it more and more difficult to see the real small RNAs that are the targets of interest even if the majority of the degraded RNAs are themselves unclonable by some of the methods discussed below. While there will be variation from RNA source to RNA source, it is clear that larger RNA components like mRNAs, rRNAs, and tRNAs, comprise by far the bulk of the total RNA and that the relative mass of the true small RNA fraction should and will be the smallest in very high quality RNA. A generalized RNA mass profile for high RIN RNA is presented in The results, summarized in Small RNA enrichment can be accomplished in a number of ways. One of the simplest ways is to simply run a sample of total RNA on a denaturing polyacrylamide gel (dPAGE) and excise the area of the gel containing the small RNA fraction (see the appendix). The problem with this method is that the enriched small RNAs must be removed from the gel and purified for further manipulations and this routinely results in a substantial loss of what is already a small amount of mass to begin with. There are ways to minimize this loss of material and we will discuss one of these in the next section. Other methods for enriching the small RNA fraction have been developed including column capture and release methods like the mirVana protocol from Ambion and the timed size exclusion method, represented by the flashPAGE fractionator system, also from Ambion. The point is that, whatever method is employed, the small RNA fraction of total cellular RNA must be enriched to increase the likelihood of successfully cloning small RNAs.Once the small RNA fraction is enriched and purified, there are several ways to proceed to clone the individual small RNAs contained in the fraction. Berezikov et al.  reviewedThe next phase of cloning is to join a second adaptor to the small RNA population. This time, the adaptor is joined to the 5′ end. As shown in PCR amplicons can be directly cloned using any one of several PCR cloning vectors or the amplicons can be processed to form concatamers which are then cloned. Concatamer formation from amplicons is a direct descendant of the Serial Analysis of Gene Expression (SAGE) methodology developed in the 1990s by Velculscu and colleagues , 28. TheC. elegans called Cel-1 repeatedly failed even though there was ample evidence that it existed. Their persistence in uncovering the reason for Cel-1 being refractory to conventional small RNA cloning methods paid off in their discovery that Cel-1, and, now, other small interfering RNAs, was tri-phosphorylated on its 5′ end [two 3′ ligations with the reverse transcription step in between the two ligations. This alternative method, named by them 5′ Ligation Independent Cloning, is completely indifferent to the state of the 5′ end of the target RNAs. The reverse transcription step following the initial 3′ adaptor ligation makes the initial 5′ end the new 3′ end with a hydroxyl group ready for a second 3′ ligation step regardless of what may or may not have been present on that initial 5′ end. The 5′ Ligation Independent Cloning option revealed that a secondary pool of small RNAs was being produced in C. elegans via a completely different pathway from conventional miRNAs [One aspect of the cloning methods shown in s 5′ end . They del miRNAs .While each small RNA cloning strategy has its own strengths and weaknesses, the method employing a preactivated, adenylated 3′ linker sequence, pioneered by David Bartel , has proOnce purified, the linker, with the form rApp-(dNTP)n-ddC, will react with the free 3′ hydroxyl of an RNA in the presence of T4 RNA Ligase and the absence of ATP to create a 3′-linkered RNA plus AMP. This reaction is quite efficient so long as a relatively small mass of T4 RNA Ligase is used. Aravin and Tuschl  showed tOnce the target small RNAs are 3′ ligated, any unligated linkers are removed by a denaturing polyacrylamide gel electrophoresis (dPAGE) purification of the ligated material. As with initial small RNA enrichment, gel purification of the ligated RNAs is subject to substantial loss of material. One way to significantly reduce this loss is to process the acrylamide gel slice containing the RNAs using a column originally developed by Edge Biosystems for cleaning up Sanger dye terminator cycle sequencing reactions. Called Performa Columns, these spin columns will retain the acrylamide gel, salts, and urea while passing as much as 95% of the RNA into the collection tube (see the appendix). The 3′-linkered RNAs so recovered will have a 3′ end block courtesy of the linker but will retain their 5′ phosphate groups. This provides a coupling group for ligation of an oligonucleotide composed of a few 5′ DNA bases and a run of 3′ RNA bases that will ligate to the target RNAs in the presence of T4 RNA Ligase and ATP. Again, a commercial 5′ linker, called 5′ MRS, is available from IDT that is compatible with each of their 3′ linkers as well as the NEB 3′ linker.Doubly-ligated RNAs are converted into an all DNA substrate by reverse transcription using an RT primer complementary to the 3′ linker. These cDNAs are then amplified in a PCR reaction that uses the RT primer as the reverse PCR primer and a forward PCR primer compatible with the 5′ linker. Thus, all target RNAs can be amplified for subsequent cloning using a universal PCR primer pair. Following PCR amplification the target-containing amplicons can be cloned with any one the vector systems designed for PCR cloning.The generally accepted criteria for adding a new miRNA to the ever growing catalog being ably curated in miRBase , 31 are C. elegans [http://www.454.com/products-solutions/system-features.asp; [The first of the massively parallel sequencing systems to arrive on the scene was the Roche pyrosequencing platform originally developed at 454 Life Sciences . This pl elegans . Accordires.asp; ). This mres.asp; .http://www.solexa.com/), also incorporates the use of oligonucleotide adaptor ligations to produce millions of short, ligated nucleic acid fragments that are then covalently bound to a solid surface and ultimately interrogated by reversible fluorescent terminator synthesis reactions [Another of the next generation sequencing platforms, based on a four-color DNA sequencing-by-synthesis (SBS), introduced by Illumina/Solexa . However, similar to the Illumina/Solexa system, there is a potential for incorporating substitution errors and with the shorter read lengths these can be misleading when sequencing small RNAs [Also in the small RNA size range of read lengths is the Applied Biosystems' Sequencing by Oligo Ligation and Detection (SOLiD) platform. SOLiD is the combination of MSSP and polymerase colony (polony) sequencing , 44, 45 he color , 42, 45.he color , 42. Thiall RNAs . Arabidopsis thaliana, yielding over 75 thousand distinct sequence signatures [Although yet-unavailable for many small scale molecular biology laboratories with limited funding constraints, these new generation sequencing platforms are already being widely used by plant researchers to characterize plant small RNAs. A pioneer MPSS effort has revealed more than 2 million small RNAs from flower and seedling tissues of model plant gnatures . The smagnatures , 48 and gnatures  mutant bgnatures , 51. Chegnatures  publisheIn general, all of the next generation sequencing technologies offer unprecedented sequencing depth in a very short time. The power of these platforms is that they are only capable of finding all or nearly all of the small RNAs expressed in a particular tissue but they can do so in a quasiquantitative manner due to the enormous number of sequence reads generated, dramatically reducing the cost. However, since next generation sequencing platforms are still under development and most likely will be improved for higher throughput and accuracy at reduced cost, at present, the suitability of any particular platform for small RNA sequencing comes down to study objectives and the availability of the platforms.G. hirsutum L. These results are initial surveys, but the first effort of “wet-bench” works toward studying the small RNA world for a complex “still unsequenced” allotetraploid cotton genome. There are many excellent methods available that utilize known microRNA sequences for the purpose of determining both absolute and relative expression levels in various tissues and under various conditions. These methods primarily focus upon either quantitative, or real-time, PCR or microarray hybridizations. However, as noted above, the primary objective of small RNA cloning is different, it is discovery of both new miRNAs and new classes of small RNA. In this final section, we will briefly present results that we have obtained using an adenylated cloning linker strategy  species [n = 4X = 52, a haploid genome size of 2200–3000 Mb DNA, and a total recombination length of approximately 5200 cM (an average of 400 kb per cM) [The genus c groups  and 5 al species . The gen per cM) . Accordi per cM) . Further per cM) –59. Gossypium revealed a total of 452, 634 nucleotide sequences, corresponding to an 8, 239 core subset of nucleotide, 375, 447 Expressed Sequence Tag (EST), and 68948 Genome Survey sequence (GSS) records . Efforts toward sequencing entire cotton genome(s) are in progress [G. raimondii  (D5), will soon be completely sequenced and available for researchers [in silico bioinformatics analysis [As of February 2009, a search of the GenBank nucleotide database for progress  and the earchers . Neverthearchers . This isanalysis –63. Thisanalysis . G. hirsutum standard line) and sequenced ~ 300 individual colonies with the 3′ and 5′ specific linker ligated small RNA inserts [Using the adenylated cloning linker strategy outlined above, we have conducted an initial survey of small RNA content in the 3–5 days old root tip tissue of Texas-Marker-1 ( inserts . Our seq inserts . In addiin silico using ovule-derived small RNA sequences putatively indicated their involvement in numerous important biological processes including processes involving previously reported fiber-associated proteins (Recently, using the same size-directed small RNA cloning strategy with adenylated linkers, we have characterized  the smalproteins . ResultsThe discovery of the world of small, regulatory RNAs has provided geneticists with a phenomenal array of opportunities as well as questions. This discovery has also led to the development of a powerful set of new molecular tools that can be used to answer those questions and take full advantage of those opportunities. The techniques built around RNA interference, real-time PCR, and microarrays allow an unprecedented level of precision in unraveling the mechanisms of gene expression and regulation. So, too, have the developments in small RNA cloning and next generation DNA sequencing discussed here opened previously barred windows on genome organization that will continue to feed into the functional genomics pipeline. The size-directed small RNA cloning strategy using adenylated linkers, highlighted with its application for the “yet-unsequenced” cotton genome small RNA characterization, is an efficient methodology for studying these tiny molecules in various plant genomes, especially suitable for the “small-scale” plant genome laboratories worldwide, that lack access to the still-expensive next generation sequencing platforms.


Prochlorococcus/Synechococcus group of marine cyanobacteria.Non-coding RNAs (ncRNA) are regulators of gene expression in all domains of life. They control growth and differentiation, virulence, motility and various stress responses. The identification of ncRNAs can be a tedious process due to the heterogeneous nature of this molecule class and the missing sequence similarity of orthologs, even among closely related species. The small ncRNA Yfr1 has previously been found in the Prochlorococcus, MIT 9211 and SS120, in which it must have been lost secondarily. Yfr1 consists of two predicted stem-loop elements separated by an unpaired sequence of 16–20 nucleotides containing the ultraconserved undecanucleotide 5'-ACUCCUCACAC-3'.Here we show that screening available genome sequences based on an RNA motif and followed by experimental analysis works successfully in detecting this RNA in all lineages of cyanobacteria. Yfr1 is an abundant ncRNA between 54 and 69 nt in size that is ubiquitous for cyanobacteria except for two low light-adapted strains of E. coli and of 7 other eubacteria belonging to the alpha-proteobacteria, chlorobiaceae and spirochaete. The integration of RNA motif prediction into computational pipelines for the detection of ncRNAs in bacteria appears as a promising step to improve the quality of such predictions.Starting with an ncRNA previously found in a narrow group of cyanobacteria only, we show here the highly specific and sensitive identification of its homologs within all lineages of cyanobacteria, whereas it was not detected within the genome sequences of  However, a small number of ncRNAs is transcribed from the reverse complementary strand of the respective target and hence these are fully or partially overlapping with their target RNAs. This latter class of ncRNAs is called cis-encoded or antisense RNAs.Non-coding RNAs (ncRNAs) are sequence-specific regulators of gene expression, mediating a plethora of cellular responses to changing environmental conditions and morphological differentiation. In bacteria, ncRNAs are a heterogeneous group of functional RNA molecules normally  lacking a protein-coding function. They are frequently smaller than 200 nt in size, and act to regulate mRNA translation/decay but can also bind to proteins and thereby modify protein function . In mE. coli, most of which had been overlooked by traditional genome analysis. Many, or possibly most major stress responses in E. coli include at least one small regulatory RNA as part of the regulon UU$")score   { gcnt = 0;      len = length(h5 [5]);      loop = length(ss [6]);      for{         j = len - i + 1;         b1 = h5 ;      b2 = h3 ;         if((b1 == "g" && b2 == "c") || (b1 == "c" && b2 == "g"))         gcnt++;      }   # require 65% GC in the stem!      SCORE = 1.0 * gcnt/len;      if(SCORE < .65)      REJECT;   }Yfr1_multi_2 5'-GTGTGGTGTGAGGAGTGAACGGAA-3'Yfr1_gloeo_2 5'-CATGGTGTGAGGAGTGAACGGAAAC-3'SsA_Thermorev 5'-GCGACGCCGTTTTACCT-3'SsaA_6803rev 5'-CACCACGCCGTTTTACCT-3'SsaA_7120rev 5'-CGCAACGCCGTTTTACCT-3'4), pH 7.2).Hybridization was performed at 54°C for Yfr1 and 50°C for 6S RNA in hybridization buffer (50% deionized formamide, 7% SDS, 250 mM NaCl, 120 mM Na(POncRNA, non-coding RNA; sRNA, small RNAGG and IMA performed RNA analyses in the laboratory, BV designed and carried out bioinformatic analyses and participated in drafting the manuscript. WRH designed research and wrote the manuscript. All authors read and approved the final manuscript.Prochlorococcus sp. SS120 and MIT9211 with relaxed conditions.Results and conditions of an RNA motif-based search for Yfr1 homologs in Click here for file