Consequently, HDV is dependent on host proteins to replicate its RNA genome. Recently, we reported that ASF/SF2 binds directly and specifically to an HDV-derived RNA fragment which has RNA polymerase II promoter activity. Here, we localized the binding site of ASF/SF2 on the HDV RNA fragment by performing binding experiments using purified recombinant ASF/SF2 combined with deletion analysis and site-directed mutagenesis. In addition, we investigated the requirement of ASF/SF2 for HDV RNA replication using RNAi-mediated knock-down of ASF/SF2 in 293 cells replicating HDV RNA. Overall, our results indicate that ASF/SF2 binds to a purine-rich region distant from both the previously published initiation site of HDV mRNA transcription and binding site of RNAP II, and suggest that this protein is not involved in HDV replication in the cellular system used.The hepatitis  These two proteins are mostly identical in sequence except that the large HDAg (HDAg-L) contains 19 additional amino acids at its C-terminus resulting from RNA editing of the antigenomic HDV RNA located on the termination codon of the small HDAg (HDAg-S) gene to a tryptophan codon The hepatitis entarity . The HDVdelta Rz, and ligated to produce circular molecules. Likewise, transcription of these circular antigenomes produces linear genomic multimers, which are processed into unit-length circular genomes.HDV requires the hepatitis B virus (HBV) envelope proteins for dissemination in vitroin vitroThe right terminal stem-loop domain of genomic HDV RNA is believed to contain RNA promoter elements for RNAP II HDV has a very limited coding capacity and uses numerous host proteins to ensure its replication. Several screening approaches have found that both HDAg and HDV RNA interact with various host factors at different stages of the viral life cycle ASF/SF2 is a member of a large family of proteins known as the serine arginine-rich (SR) proteins, which play essential roles in constitutive and alternative splicing Because ASF/SF2 is a very abundant host protein, and due to its specific binding to both R199G and the CTD of RNAP II, it was hypothesized that ASF/SF2 could play a role in HDV replication HinDIII-linearized pHDVd2. RNAs were synthesized by in vitro transcription from DNA templates (1 μg), using 200 U of T7 RNA polymerase  in 1X transcription buffer , with 5 mM of each NTP. Transcription reactions were carried out at 37°C for 4 h. DNA templates were removed by incubation with 10 U DNase I (Promega) at 37°C for 30 min. Products were resolved on either agarose gel or denaturing 5% polyacrylamide/7 M urea gel in 1X Tris-Borate-EDTA (TBE) buffer . Following electrophoresis, RNA bands were visualized by either SYBR green staining or UV shadowing, excised, and eluted in 500 mM ammonium acetate, 0.1% SDS, 10 mM EDTA overnight at room temperature. The eluted RNAs were then ethanol-precipitated in the presence of 0.3 M NaOAc, pH 5.0, resuspended in H2O, passed through a Sephadex G-50 column , and ethanol-precipitated for a second time. RNA was quantified by spectrophotometry at a wavelength of 260 nm.HDV RNA fragments, as well as the P11.60 RNA, were transcribed from DNA as previously described Escherichia coli strain BL21(DE3)pLysS (Novagen). Bacterial cultures were grown for 18 h at 37°C in Terrific Broth (Gibco) supplemented with 100 μg/μL of ampicillin, and cells were collected in 50-mL tubes following a 15-minute centrifugation at 6,000× g at 4°C. Pelleted cells were rotated in binding buffer  for 60 min at room temperature, and cell lysate was cleared by centrifugation at 14,000× g for 30 min. His-tagged ASF/SF2 was purified by immobilized metal affinity chromatography using the Ni-NTA Spin columns (Qiagen) according to manufacturer's instructions. The column-bound proteins were washed 5 times with binding buffer containing 40 mM of imidazole, and eluted twice with elution buffer . The protein extracts were dialyzed overnight at 4°C against 50 mM Tris-HCl, pH 7.9, 1 mM EDTA, 0.1 mM dithiothreitol, 50 mM NaCl, 50% glycerol, 1.5 M urea. Protein purity was evaluated by SDS-PAGE. To confirm the identity of the protein, following SDS-PAGE, the proteins were transferred to a nitrocellulose membrane (GenScript). Membrane was blocked in 5% non-fat dry milk in TBS buffer  +0.1% tween 20, and probed with a mouse monoclonal anti-ASF/SF2 antibody (Invitrogen). The membrane was then washed with TBS +0.1% tween 20, and incubated with a horse radish peroxidase-conjugated goat anti-mouse IgG antibody (Abcam). Protein was detected by incubation with the SuperSignal West Pico Chemiluminescent Substrate (Thermo Scientific), and visualized following exposure to X-ray film. Protein concentration was determined using the Bio-Rad Protein Assay Kit II (Bio-Rad), according to manufacturer's instructions. Protein aliquots were stored at −80°C.C-terminally hexahistidine (his) –tagged ASF/SF2 was expressed from plasmid pET-20b(+)-ASF transformed into o  =  Bmax*[RNA]/(appKD+[RNA])) by non-linear regression using GraphPad Prism 3.0.Intrinsic fluorescence was measured with a Cary Eclipse fluorescence spectrophotometer (Agilent Technologies) using the Cary Eclipse Scan Application software. All binding experiments were carried out at room temperature, using an excitation wavelength of 280 nm. Emission spectra were read between 300–400 nm. Binding experiments were carried out in 100 mM KCl, 20 mM Tris-HCl, pH 7.5. Background emission was eliminated by subtracting the signal of buffer alone, or buffer plus His-ASF/SF2. Binding assays were carried out at an His-ASF/SF2 concentration of 100 nM. Changes in His-ASF/SF2 fluorescence upon addition of RNA ligand were fitted to a one site binding equation -inducible promoter, and contains a mutated HDV RNA genome that is unable to produce HDAg Extraction of RNA and proteins from cultured cells was performed using the TRIzol Reagent (Invitrogen), following instructions given by the manufacturer. Total RNA concentration was estimated by spectrophotometry. RNA quality was verified by denaturing 1% agarose/2.2 M formaldehyde gel electrophoresis. RNA samples were used directly for cDNA synthesis, or stored in aliquots at −80°C. Protein concentration was determined using Bio-Rad Protein Assay Kit II (Bio-Rad), according to manufacturer's instructions, and the protein samples were stored in 1% SDS at −20°C.1541ACTGCTCGAGGATCTCTTCTCTCC1564-3′, R199G HDV reverse, 5′- 60ACATCCCCTCTCGGTGCTG41 -3′; β-actin forward, 5′- CCATGTTTGTGATGGGTGTGAACCA -3′, β-actin reverse, 5′- ACCAGTGGATGCAGGGATGATGTTC -3′. qPCR was performed by Chromo4 Real-Time Detector (Bio-Rad) using the iQ SYBR Green Supermix (Bio-Rad). The PCR reaction was set up as follows: initial denaturation at 95°C for 3 min, followed by 40 cycles of 30 s at 95°C, 20 s at 55°C and 30 s at 72°C. Following amplification, melting curve analysis was performed. The levels of HDV expression were quantified and normalized to β-actin expression using the 2-ΔΔCT method One μg of RNA extracted from 293-HDV cells was transcribed into cDNA using the iScript Select cDNA Synthesis Kit (Bio-Rad) and random primers. The cDNA samples were subjected to qPCR with the following primers: R199G HDV forward, 5′-GGAATTCTAATACGACTCACTATAGGG-E. coli and purified under denaturing conditions followed by renaturation by stepwise dialysis, as previously described To investigate the binding of ASF/SF2 to R199G, we performed binding experiments with purified recombinant histidine-tagged ASF/SF2 (His-ASF/SF2) on R199G-derived RNAs using intrinsic tryptophan fluorescence spectrophotometry. To obtain a pure sample of the protein for this assay, His-ASF/SF2 was overexpressed in An emission spectrum of purified His-ASF/SF2 is shown in D) between RNA fragments and His-ASF/SF2.To verify that the intrinsic protein fluorescence is directly proportional to protein concentration, the molar fluorescence of His-ASF/SF2 was analyzed . FluoresD) for the interaction, the concentration of the RNA at which 50% of the protein was bound, was calculated to be ∼0.64 μM under our experimental conditions. As a high appKD is often associated with a low binding specificity, we tested His-ASF/SF2 binding to P11.60 RNA , indicating that P11.60 binding to the protein is not specific and suggesting that P11.60 and R199G bind at distinct sites on the protein.The interaction of His-ASF/SF2 and R199G RNA was studied by observing the response of tryptophan fluorescence to the addition of RNA. The binding experiments were carried out by adding increasing amounts of RNA to a fixed amount of His-ASF/SF2 (i.e. 100 nM), and observing a change in the emission spectra. The inset of D to ∼0.87 μM, suggesting that this deletion did not greatly affect the ability of the RNA to bind His-ASF/SF2 (D ∼0.90 μM). However, no appKD could be calculated by reducing the stem further by testing either R156G or R47G. Based on these results, and previous findings that ASF/SF2 specifically binds polypurine stretches 5′-GGGAGGA-3′ . Both a non-target shRNA construct (shNT) and the empty vector were used as negative controls. Transfection efficiency was monitored by co-transfection with a plasmid encoding the green fluorescent protein, and estimated visually to be at least 70–80%. The expression of HDAg-S, and thus induction of HDV RNA replication, was induced with TET (1 μg/μL) 12 hours post-transfection to minimize cell death. Cells were collected 12 hours later, and ASF/SF2 knock-down was verified by Western blotting . When coThe effect of ASF/SF2 knock-down on the accumulation of HDV RNA was assessed by quantitative RT-PCR (qRT-PCR) using the level of β-actin mRNA to normalize the results. Upon TET induction, HDV RNA accumulation increased by ∼10-fold in non-transfected cells and in cells transfected with either a non-target shRNA construct or the empty vector . Unexpecin vitro and in cells replicating the HDV RNA genome Previous experiments have identified ASF/SF2 as a binding partner of HDV RNA, both D for the interaction by adding increasing concentrations of RNA to a fixed amount of protein and observing the resulting quenching of the intrinsic tryptophan fluorescence of the protein. Emission maxima of native proteins depend on the position of tryptophan residues in the folded protein. Typically, surface residues that are exposed to a polar solvent have an emission wavelength close to that of free tryptophan (i.e. emission maximum  = 348 nm for L-Trp in H2O; To localize the binding site of ASF/SF2 on R199G, we performed binding experiments using purified recombinant His-ASF/SF2 combined with deletion analysis and site-directed mutagenesis on the RNA fragment. The fluorescence binding assay used to characterize the His-ASF/SF2-HDV RNA interaction is a technique frequently used to investigate protein-ligand interactions in vitroin vivoAfter testing a number of truncation mutants, the His-ASF/SF2 binding site was localized to a short purine-rich region. Sequence-specific binding of ASF/SF2 to short, purine-rich regions of RNA has been demonstrated, both In summary, we have shown that ASF/SF2 is binding R199G at a distinct conserved location downstream of the proposed transcription initiation site and RNAP II binding region. In addition, our results indicate that additional ASF/SF2 binding sites exist on the HDV RNA genome. Although ASF/SF2 appeared to be dispensable for HDV accumulation in the cellular system used in our study, its direct and specific association with HDV RNA suggests that it is possible that it plays a role in HDV biology. Also, although not investigated, post-translational modifications on ASF/SF2, such as methylation and phosphorylation, have been reported to change its activity in cells


E coli K12, thus it is unknown whether or not the bacterium possesses similar complex transcriptomes. Furthermore, although RNA-seq becomes the major method for analyzing the complexity of prokaryotic transcriptome, it is still a challenging task to accurately assemble full length transcripts using short RNA-seq reads.Although prokaryotic gene transcription has been studied over decades, many aspects of the process remain poorly understood. Particularly, recent studies have revealed that transcriptomes in many prokaryotes are far more complex than previously thought. Genes in an operon are often alternatively and dynamically transcribed under different conditions, and a large portion of genes and intergenic regions have antisense RNA (asRNA) and non-coding RNA (ncRNA) transcripts, respectively. Ironically, similar studies have not been conducted in the model bacterium E. coli K12 under different culture conditions and growth phases using a highly specific directional RNA-seq technique that can capture various types of transcripts in the bacterial cells, combined with a highly accurate and robust algorithm and tool TruHMM (http://bioinfolab.uncc.edu/TruHmm_package/) for assembling full length transcripts. We found that 46.9 ~ 63.4% of expressed operons were utilized in their putative alternative forms, 72.23 ~ 89.54% genes had putative asRNA transcripts and 51.37 ~ 72.74% intergenic regions had putative ncRNA transcripts under different culture conditions and growth phases.To fill these gaps, we have profiled the transcriptomes of E. coli K12 also has a highly complex and dynamic transcriptomes under different culture conditions and growth phases. Such complex and dynamic transcriptomes might play important roles in the physiology of the bacterium. TruHMM is a highly accurate and robust algorithm for assembling full-length transcripts in prokaryotes using directional RNA-seq short reads.As has been demonstrated in many other prokaryotes,  E. coli K12 and B. subtilis, only 3,409  of 0.87, they were spun down at 3,200 g for 25 min. For heat shock treatment (HS), the cell pellets were resuspended in the same volume of MOPS medium  KH2PO4 and 10 ml of 20% glucose, Teknova, Hollister, CA), and incubated at 48°C and 250 rpm. For phosphorus-starvation treatment (M-P), the cell pellets were resuspended in the MOPS medium without KH2PO4. Three milliliter cell suspension were collected in a tube containing 1.5 ml RNA Later (Invitrogen) immediately after the cell pellets were resuspended in the indicated medium (0 min) and at the indicated time points thereafter . Cells were spun down at 6,000 g, 8 min and -4°C, and the pellets were resuspended in 1.5 ml of RNAlater. The samples were stored at -80°C until use.A frozen stock of crp gene . To enrich mRNAs and other transcripts, majority of rRNAs were removed from the DNase-treated total RNA using a MICROBExpress kit (Ambion) following the manufacturer’s instructions.Total RNA was isolated using a RiboPure™ -Bacteria Kit (Ambion) following the manufacturer’s instructions. Once isolated, ~10 μg total RNA was treated with 8 units DNase (Invitrogen) twice to remove genomic DNA, and the complete removal of DNA was confirmed bythe absence of the product of 35 cycles PCR amplification of a 196 bp fragment of the In our early stage of experiments, sequencing was done on an Illumina GAII platform at the sequencing core facility of the University of North Carolina at Chapel Hill, and the directional RNA-seq libraries were constructed by following an Illumina’s instruction using their Small RNA Sample Prep Kit with some modifications. Briefly, after the purified mRNA was fragmented using a RNA fragmentation kit (Ambion), the fragmented RNA was treated with Antarctic phosphatase (NEB) to remove the 5’-tri-phosphate groups of RNAs with an intact 5’-end. A mono-phosphate group was then added back to the 5’-end of fragmented RNAs by polynucleotide kinase  in the presence of 10 mM ATP. The v1.5 sRNA 3’ Adaptor (5’/5rApp/ATCTCGTATGCCGTCTTCTGCTTG/3ddC/) was ligated to the 3’-end of fragmented RNAs using truncated T4 ligase 2 (NEB), and the SRA 5’ RNA adaptor (5’GUUCAGAGUUCUACAGUCCGACGAUC) was ligated to the 5’-end of fragmented RNAs using T4 ligase. To preserve short inserts from small RNAs we omitted the size selection step after PCR application of inserts. In our later experiments, sequencing was done on an Illumina HiSeq 2000 platform at David H. Murdock Research Institute of the North Carolina Research Campus , and we constructed the directional RNA-seq libraries using Illumina’s TruSeq Small RNA Sample Prep Kit, so that multiplex sequencing can be achieved by using the barcoded PCR primers. The details of the method will be described elsewhere . Briefly, after similar treatments as described above, the 5’ Adapter (RA5: 5’ GUUCAGAGUUCUACAGUCCGACGAUC), and 3’ Adapter (RA3: 5’ TGGAATTCTCGGGTGCCAAGG) were ligated to 5’- and 3’-end of fragmented RNAs, respectively. Reverse transcription-PCR (RT-PCR) was performed using SuperScript II Reverse Transcriptase Kit using the SRA RT primer, followed by 16 cycles of PCR amplification. Again, the size selection was omitted on PCR products to preserve short inserts from possible small RNAs. Single-end sequencing on the Illumina GA II platform was done with 76 cycles, while that on the HiSeq 2000 platform was done with 100 cycles. Some samples (HS15 min and M-P4 h) were sequenced on both platforms.E. coli K12 substr. MG1655 were obtained from NCBI (http://ftp.ncbi.nlm.nih.gov/genomes/Bacteria/Escherichia_coli_K_12_substr__MG1655_uid57779/), and the experimentally verified operons in the bacterium were downloaded from RegulonDB  interval around the predicted TSS by the scoring function ) we developed before  centred by the predicted TSSs with a p-value ≤0.05 in E. coli.Click here for fileThe TSSs detected and not detected by TruHMM in the H. pylori samples.Click here for fileThe reconstructed operons with alternative TSSs in the E. coli samples.Click here for fileThe consistent longest possible alternative operons across all the E. coli samples.Click here for fileThe combined known small RNAs from Storz’s group [ and RegulonDB [ and those we reconstructed in E. coli samples.’s group ,56 and RegulonDB  and thosClick here for fileThe predicted antisense RNAs in the E. coli samples.Click here for fileThe predicted non-coding RNAs in the E. coli samples.Click here for fileThe hypothetical proteins expressed in the E. coli samples.Click here for file


The high-throughput genomics communities have been successfully using standardized spreadsheet-based formats to capture and share data within labs and among public repositories. The nanomedicine community has yet to adopt similar standards to share the diverse and multi-dimensional types of data (including metadata) pertaining to the description and characterization of nanomaterials. Owing to the lack of standardization in representing and sharing nanomaterial data, most of the data currently shared via publications and data resources are incomplete, poorly-integrated, and not suitable for meaningful interpretation and re-use of the data. Specifically, in its current state, data cannot be effectively utilized for the development of predictive models that will inform the rational design of nanomaterials.de novo to more readily describe the complexity of nanomaterials and associated small molecules. In this paper, we have discussed the main features of each file format and how to use them for sharing nanomaterial descriptions and assay metadata.We have developed a specification called ISA-TAB-Nano, which comprises four spreadsheet-based file formats for representing and integrating various types of nanomaterial data. Three file formats  have been adapted from the established ISA-TAB specification; while the Material file format was developed The ISA-TAB-Nano file formats provide a general and flexible framework to record and integrate nanomaterial descriptions, assay data (metadata and endpoint measurements) and protocol information. Like ISA-TAB, ISA-TAB-Nano supports the use of ontology terms to promote standardized descriptions and to facilitate search and integration of the data. The ISA-TAB-Nano specification has been submitted as an ASTM work item to obtain community feedback and to provide a nanotechnology data-sharing standard for public development and adoption. As we enter the National Nanotechnology Initiative’s second decade , the earA cross-cutting need in the nanotechnology community is the development of standards to support both meaningful data submission to public repositories and information exchange among investigators . In ordeThe ability to share data meaningfully among investigators from separate biomedical disciplines is a challenge -11. In oCompared to XML-based formats, spreadsheet-based formats offer the advantage that they can be created, viewed, and edited by the investigator using software  that is broadly available and familiar to most researchers. The most widely adopted spreadsheet-based formats of biological data are MAGE-TAB  and ISA-Information describing the chemical composition and three-dimensional (3D) structure of nanomaterials is often represented in an unstructured fashion, with no standardized way to associate this information with the assay data from characterization studies. Nanomaterials are diverse: chemical composition, size, geometry, morphology and surface chemistry all play important roles in determining the biological properties of resulting nanomaterials. Complex nanomaterials are often multi-component systems formulated with small molecules for specific biomedical applications such as drug delivery, imaging, and detection of diseases. Such complex formulations make the representation of exact chemical composition, physical structure, and properties very challenging and very necessary.in vitro and in vivo analysis of biological activities and abiotic studies of physicochemical properties. Each characterization itself involves a wide range of variables using different types of assays, which can also vary depending on the endpoint measured, technology used, and the protocol applied. Furthermore, even minor variations in the chemical composition, assay protocols and conditions, and time point of analysis , can give rise to different characterization results. Currently, most of the measurements on nanomaterial properties depend on non-standardized protocols and diverse instrumentation technology types. Experimental results are dependent on protocol details that are not consistently captured or reported [Nanomaterial analysis requires knowledge of the characterization experiment details so that the results can be accurately and interpreted. Nanomaterial interactions with biological systems are complex and, accordingly, associated data are often generated at multiple levels of detail. The properties of nanomaterials are analyzed based on the data available from different characterization studies, such as reported . For exaTo address the challenges associated with the sharing and re-use of nanomaterial data, we have developed the ISA-TAB-Nano specification, a standard format for representing and sharing data pertaining to the description and characterization of nanomaterials. ISA-TAB-Nano is intended to facilitate the submission and exchange of assay metadata and nanomaterial descriptions, along with other information , among individual researchers and to/from nanotechnology online resources. We expect that ISA-TAB-Nano will provide organizations with a standard approach to representing nanomaterial data that will enable researchers to share nanomaterial information effectively and to make cross-material comparisons.®) program, to support the informatics needs of nanomedicine researchers. Today, the group has over 20 active participants who represent a broad range of expertise and come from academic, industrial, government agency and other institutions. Beginning in March 2010, a sub-group of the Nano WG began developing a specification to facilitate the exchange of nanomaterial data amongst research labs and with public repositories of nanoparticle information. An important goal of this effort from the outset was to use and extend concepts from the caBIG® Life Sciences Domain Analysis Model [, in vitro, and in vivo assays.In 2008, the Nanotechnology Working Group (Nano WG) was established as part of National Cancer Institute (NCI) cancer Biomedical Informatics Grid   and indiThe ISA-TAB-Nano specification is based on the ISA-TAB specification -18,25. IThe ISA-TAB Study file is used to record information about the source and characteristics of a subject (sample) under study as well as the different treatment protocols that are applied to prepare the samples for the analysis (assay). The subject under study could be an organism, a sample taken out of the organism, or any other sample. The Assay files are used to record the assay metadata along with references to the names (or URIs) of the raw/derived data and image files. In the ISA-TAB framework, the type of assay is determined by the endpoint of measurement and by the technology (technique) used to measure that endpoint. Therefore, a single study (of a given sample) can comprise several types of assays. The Investigation file serves to relate the different types of assays to their corresponding studies. In particular, the Investigation file is used to record reference information about each Study file and Assay file; point of contact; publications; the parameters, instruments, software, and reagents of a protocol; the factors of the study (a factor is defined as any independent variable that is varied in an experiment to investigate its effect on the subject under study) and the terminology sources referred to in the Investigation, Study and Assay files.biological specimen or biospecimen material type, while referencing samples derived from non-biological sources as material samples.An important extension to ISA-TAB required to support nanomaterials came from the need to distinguish samples derived from biological sources  and samples derived from non-biological sources . Hence, for the ISA-TAB-Nano specification, we consider a sample derived from biological sources as a The ISA-TAB Study file can be used to describe the source and characteristics of biological specimens, however, it is not suitable for describing nanomaterials. In this work, we have designed a “Material” file format for describing nanomaterials. In general, this file format is applicable for describing any material sample, nanoscale or otherwise, as discussed later. Hence, in ISA-TAB-Nano, the concept of a sample (as used in ISA-TAB specification) is extended to distinguish between biological specimens and material samples by incorporating information about the latter into the Material file.The Material file is designed to capture information related to the description of nanomaterials. Typically, the description of a nanomaterial is complex and includes different types of information  related to the whole nanomaterial and its individual chemical components. A typical example is that of a nanoparticle formulation ,28, whicmaterial samples . To represent this information, we have introduced a fourth ISA-TAB-Nano file called the Material file. We use the Material file to describe material samples while using the ISA-TAB Study file to describe biospecimens. We have modified the definitions of some of the field names in the ISA-TAB Investigation, Study, and Assay files and added new fields names to expand the scope of information that needs to be shared using the ISA-TAB-Nano specification. Detailed information of the field names and definitions of each file format can be found in Supporting Information. For more detailed information about the structure and contents of the ISA-TAB files, the reader may refer to the ISA-TAB specification [The ISA-TAB-Nano specification makes use of the ISA-TAB Investigation, Study, and Assay files to record and share the assay metadata. In addition to sharing the assay metadata, we also want to share the endpoint measurements and description  of both nanomaterials and small molecules . To accommodate this additional information, we have made extensions to ISA-TAB. In particular, the ISA-TAB Study file is suited for recording the source and characteristics of biological specimens and for specifying any treatments performed on them. However, the Study file is not designed to represent information about the chemical composition and characteristics of fication . The rulThe following sections describe the different components of the ISA-TAB-Nano files. Supporting Information provides additional materials to supplement the manuscript discussion, including a summary of extensions to the ISA-TAB format, the standard specification, and examples.The types of information shared using ISA-TAB-Nano pertain to the material samples, biospecimens, assays, and protocols. As listed in Table In the ISA-TAB-Nano framework, all material samples are viewed as formulations having one or more chemical components . The ISA-TAB-Nano material file has been designed for recording descriptions of various types of formulations, ranging from a simple chemical compound to a complex multi-component nanoparticle formulation.The layout of the ISA-TAB-Nano Material file is same as that of the Study and Assay files, wherein the field names are arranged as columns and material sample information is entered as rows. Columns in the ISA-TAB-Nano Material file provide information on the material and its components, the relationship between materials and components, and any associated data files, as mentioned in Table The Material file is used to describe a material sample and its structural and chemical components. There are different ways to identify the components, depending on how the sample is viewed at its various levels of molecular and structural granularities. Figure Term Source REF labels) and their identification numbers (defined by Term Accession Number labels) used within the ontology. Thus, we use row 2 to describe the material sample as a whole, and subsequent rows to describe each material sample component. If a material sample that is described in a Material file is also a component of another material sample, then the components of the former sample need not be specified in the Material file of the latter sample. To illustrate this scenario, we consider another hypothetical sample called LAB-2 that contains both LAB-1 and an image contrast agent called Magnevist® (LAB-3). Figure ®, a small molecule gadolinium-based image contrast agent. The dendrimer is conjugated to the Magnevist® and is intended to be used as a delivery vehicle for image contrast agents like Magnevist® in MRI applications.In Figure ist® LAB-. Figure ® of LAB-2 sample. The term “covalent linkage” is used here as a generic term. Normally, if the specific type of covalent linkage is known , then one should use the appropriate term identifying that linkage. The NPO [Material Constituent field  that contain detailed descriptions of the sample. The example given in Figure Material Source Name field of the sample’s Material file. For example, we would name the Material file of samples LAB-1 and LAB-2 as m_LAB-1.txt and m_LAB-2.txt, respectively.In practice, one ISA-TAB-Nano Material file should be created for each material sample in a study. When using the format “m_xxxx.txt” to name the Material files, the string “xxxx” should exactly match the string used in the in vitro, and in vivo properties of material samples. We can identify the type of each assay based on the measured endpoint, measurand, and technique used. For example, if an assay is related to a size characterization study, the endpoint could be “size” and the technique could be “dynamic light scattering (DLS)”, or “scanning electron microscopy (SEM)”, or other relevant technique. The corresponding assay types are “size by DLS assay”, “size by SEM”, etc. Assays of the same type may be performed using different protocols, by different people, at different dates, and under different experimental conditions. To interpret the characterization results of a material sample meaningfully, we need to have information about the assay type; biospecimen information (if it is a bioassay); protocol descriptions; dates of synthesis and analysis of the material sample; the measured endpoint variables; as well as the protocol variables of the experiment. Based on the ISA-TAB specification, variables that are kept constant in an assay experiment are termed “parameters”, while variables that are changed for studying their effects on the measured endpoint values are termed “study factors”.Different types of assays are performed to measure the physicochemical, Material Source Name).In a physicochemical characterization study, one measures the properties of a material sample and studies how these properties are affected by the study factors. Typically, one prepares several dilutions of same material sample and may even suspend each sample in different test media to achieve solution conditions suitable for performing a specific assay. Therefore, each analyzed sample may have its own identifier while its (source) material sample to be characterized has a different identifier  represents the identifier of the material sample that is to be characterized. The second column contains the name of the protocol applied to prepare each individual biospecimen or material sample for the analysis. After the protocol column, we create columns to capture the attributes of the protocol including the name of the person who performed the protocol, the date when the protocol was applied, and the values of each parameter in the protocol. After these, we add two more columns to record the sample name (identifier) and its material type.In the ISA-TAB Study file, a column Sample Name  using column headers The Investigation file is a reference for the different assays and protocols that have been conducted in a single study. The Investigation file is divided into several sections  and Study Assay File Name (Study Assays section), respectively. As a result, the Investigation, Study and Assay files are linked. The data in the Study and Assay files for each sample are integrated through the Sample Name fields found in both of the files. This is illustrated in Figure Material Source Name REF entered in the Study file matches the string entered in the Material Source Name of its Material file  is that it can be readily created and edited with any spreadsheet software, without the need for specialized bioinformatics support. However, this also creates challenges because such software does not enforce validation rules for ISA-TAB-Nano files. One possible solution to this problem is to adapt the suite of open source tools provided by the ISA-TAB community -18, suchXML-based validation is also of interest and would involve developing an ISA-TAB-Nano XML schema based on the ISA-TAB-Nano specification such that any ISA-TAB-Nano XML file converted from the ISA-TAB-Nano files can be formally validated against the XML schema. Having an XML-based validation service would also enable software systems to import and export validated ISA-TAB-Nano files to and from distributed nanotechnology resources.In this paper, we have provided an overview of ISA-TAB-Nano, a spreadsheet-based exchange format for information pertaining to the description and characterization of nanomaterials. The ISA-TAB-Nano specification extends the ISA-TAB specification that was designed for sharing all types of ‘omics data. Specifically, ISA-TAB-Nano leverages the three primary ISA-TAB file formats - Investigation file, the Study file, and the Assay file - to represent the metadata related to the characterization of nanomaterials. To capture the measurement data, ISA-TAB-Nano specifies new fields in the Investigation file and in the Assay file. In addition, ISA-TAB-Nano includes a new file format, the Material file, to capture the description of nanomaterials and chemical substances  that are characterized in an assay. The ISA-TAB-Nano specification was recently submitted as an ASTM work item to obtain community feedback and to promote ISA-TAB-Nano as a voluntary consensus standard for the nanotechnology community. Additionally, we hope that the ISA-TAB-Nano standard will be adopted by journals and other organizations to ensure a uniform representation of information that is too often “lost” in a prose Methods section and cannot be easily extracted for re-use.Many of the ISA-TAB extensions developed for ISA-TAB-Nano could also be used for studies of other types of complex materials or formulations. In particular, the “Material” file addition is likely to be useful outside of the nanotechnology realm for a broader range of samples with complex composition or structural characteristics.Key to promoting the successful adoption of ISA-TAB-Nano will be the availability of resources to facilitate the creation and validation of these files. Our adherence to the parent ISA-TAB specification will allow the ready adaptation of the ISA Tools -18 to suThe authors have no competing interests to declare.All authors contributed to the design and testing of the ISA-TAB-Nano standard. DGT, SG, SLH, JDK, and NAB were primarily responsible for the preparation of the manuscript with contributions and revisions from the other authors. All authors read and approved the final manuscript.This Supporting Information provides a summary of ISA-TAB-Nano extensions to the ISA-TAB format, glossaries for the terms used in the ISA-TAB-Nano specification, and an overview of the worked examples and templates provided in the other supporting files.Click here for fileThis file contains the examples described in Supporting File 1.Click here for fileThis file contains ISA-TAB-Nano templates for common assays.Click here for file


Eukaryotic cells express a complex layer of noncoding RNAs. An intriguing family of regulatory RNAs includes transcripts from the opposite strand of protein coding genes, so called natural antisense transcripts (NATs). Here, we test the hypothesis that antisense transcription triggers RNA interference and gives rise to endogenous short RNAs (endo-siRNAs).We used cloned human embryonic kidney cells (HEK293) followed by short RNAseq to investigate the small genic RNA transcriptome. 378 genes gave rise to short RNA reads that mapped to exons of RefSeq genes. The length profile of short RNAs showed a broad peak of 20-24 nucleotides, indicative of endo-siRNAs. Collapsed reads mapped predominantly to the first and the last exon of genes (74%). RNAs reads were intersected with sequences occupied by RNAPII or bound to Argonaute . In the first exon, 94% of the reads correlated with RNAPII occupancy with an average density of 130 (relative units); this decreased to 65%/20 in middle exons and 54%/12 in the last exon. CLASH reads mapping to multi-exon genes showed little distribution bias with an average of about 5 CLASH reads overlapping with 60% of the endo-siRNA reads. However, endo-siRNAs (21-25 nt) intersecting with CLASH reads were enriched at the 5′end and decreased towards the 3′end.We then investigated the 378 genes with particular focus on features indicative for short RNA production; however, found that endo-siRNA numbers did not correlate with gene structures that favor convergent transcription. In contrast, our gene set was found notably over-represented in the NATsDB sense/antisense group as compared to non-overlapping and non-bidirectional groups. Moreover, read counts showed no correlation with the steady-state levels of the related mRNAs and the pattern of endo-siRNAs proved reproducible after an induced mutagenic insult.Our results suggest that antisense transcripts contribute to low levels of endo-siRNAs in fully differentiated human cells. A characteristic endo-siRNA footprint is being produced at sites of RNAPII transcription which is also related to AGO1. This endo-siRNA signature represents an intriguing finding and its reproducibility suggests that the production of endo-siRNAs is a regulated process with potential homoeostatic impact. The full nature of the human transcriptome is taking shape thanks to highly efficient sequencing strategies that reach unprecedented depth . Layers The potential of NATs to form RNA-RNA hybrids with the sense transcript can trigger various mechanisms and regulatory cascades. The three best supported ones are RNA masking, the establishment of chromatin marks and RNA interference. RNA masking describes a process where the antisense transcript occludes a regulatory motif in the sense RNA by direct base pairing. Depending on the nature of these motifs the interactions stabilize or de-stabilize the mRNA . Well-doLUC7) into close vicinity to the HBA2 gene. The resulting antisense transcript was shown to induce methylation of a GC island in the promoter of HBA2 and silence the gene [p15) was shown to be epigenetically silenced by antisense transcription. P15 is repressed in a variety of cancers and an inverse expression of sense and antisense transcripts was discovered in leukemic cells [Chromatin modification as a result of ectopic expression of NATs has been linked to human disease ,17. A rathe gene . In addiThe involvement of NAT-triggered RNA interference in gene regulation is supported by research focusing on the Slc34A gene (encoding a Na-phosphate cotransport protein). In this case, endo-siRNAs derived from sense/antisense overlaps were found in mouse testis and kidneys ,18. MoreRNA interference involves two key enzymatic components, an endo-ribonuclease and an effector protein complex. The endonucleases, Dicer or Drosha, process double-stranded RNA precursors into short RNA duplexes of about 22 base pairs. These oligonucleotides are integrated into effector complexes that include an Argonaute protein ,20. One A putative link between antisense transcription and the synthesis of endo-siRNAs has not been comprehensively investigated. We used total RNA from cloned of HEK293 cells to determine the short RNA transcriptome. The analysis focused on reads that mapped to exons of protein-coding genes. Our findings suggest that sense/antisense transcripts can feed into an RNA interference related pathway. The resulting low levels of endo-siRNAs contribute to a stable short RNA signature in differentiated somatic cells.To avoid cell heterogeneity due to the accumulation of stochastic mutations we first cloned HEK-293 cells by serial dilution. A single clone was selected and expanded for short RNA sequencing. Total RNA was isolated, size selected and then used to generate a directional cDNA library. The material was amplified and sequenced on an Illumina HiSeq 2000. A total of 30.9 million parsed reads were obtained and annotated to the human genome hg18). The most prevalent of the genic short RNAs were microRNAs (31%) and short structural RNAs (9%). Genic short RNAs comprised 5% of the reads of which 1% (282319) were between 16 and 30 bases long  [First, we investigated putative sources of genic endo-siRNAs; stalled RNA polymerase II (RNAPII) and the RNA interference pathway both being linked to the genesis of these RNA species. To assess the involvement of RNAPII we downloaded the publicly available RNAPII ChIP-Seq data (chromatin imunoprecipitation-sequencing) from the GEO database , middle exons (22.5%), last 3′ exon (37.8%) and single exon genes 10.6%) using IGV  foll.3%, midd0.6% usinNext, the collapsed and binned reads were intersected with a CLASH  dataset that was generated using an antibody against AGO1 and UV treated HEK cells, kindly provided by A. Helwak and D. Tollervey ,28. We fIn order to test the hypothesized link between convergent transcription and endo-siRNAs we compiled characteristic parameters of each individual gene associated with endo-siRNAs. The argument was that convergent transcription of genes in close vicinity will favor endo-siRNA production. First, the distance to both neighboring genes was determined by subtracting the end coordinate of the last exon from the start coordinate of the first exon of the following gene. The parameters collected of the 378 genes included orientation, length and exon-number, distance to neighboring genes and their orientation as well as the number and orientation of reads. The finding, that 30.6% of the genic reads were oriented in antisense to the transcript of origin may suggest a role for antisense transcription in generating endo-siRNAs.2 values suggest that there is no direct correlation between the convergent transcription of closely located genes and endo-siRNAs. Other parameters such as the length of the endo-siRNA producing genes and their exon count had no influence on the number of endo-siRNA reads (not shown).We sorted the 176 genes with a near neighbor in tail-to-tail configuration according to the distance between the two genes to assess if the potential antisense transcript promotes the formation of endo-siRNAs. As Figure http://natsdb.cbi.pku.edu.cn/). We found the sense/antisense (SA) category over-represented in our dataset as compared to the overall distribution (53% versus 42.2%). The non-bidirectional gene category was reduced (41.7% versus 52.9%) whereas the bi-directionally transcribed but non-overlapping loci were represented equally in both datasets  or they were located at chromosome ends in clusters with relaxed imprinting. This finding concurs with the documented reduced expression of antisense transcripts from the X chromosome and suggests that in a biologically relevant cellular context convergent transcription may be linked to epigenetic gene silencing.http://bioinformatics.sdstate.edu/datasets/2012-NAT/) [We also assessed the expression levels of endo-siRNA related genes using the natural antisense database (12-NAT/) . Since t12-NAT/) , we founSo far, we identified and characterized short RNAs in differentiated HEK293 cells which map to exons of protein coding genes. A significant number of endo-siRNAs correlate with RNAPII occupancy and AGO1 CLASH signals. Because SA loci are enriched in our dataset it is conceivable that convergent transcription contributes to the synthesis of these endo-siRNAs.In HEK cells more than 12’000 genes are expressed , only 37The most prevalent of the genic short RNAs in both control and mutagenized samples were microRNAs, which is in agreement with published short RNAseq data. Hsa-mirs 10a/b, 182 and 92a-1/92a-2 made up more than 60% of all miRNA reads. In total, 238 different miRNAs scored higher than 100 reads in the control HEK293 cells and these were further examined. The miRNA expression pattern proved resistant towards mutagenic insults showing a pooled variance of 3.68 (expression change compared to wild type) , 2.26 (C5) and 1.99 (C12) million reads mapped to 261 different structural RNAs, predominantly snoRNAs. In contrast to the stable miRNA transcriptome, the reads mapping to small structural RNAs showed considerable pooled variance of 29.06 in the mutagenized samples. Interestingly, the vast majority of the structural genes were down regulated (63.6 and 67.05%), only 27.59 and 30.27% remained stably expressed , showed no correlation between mRNA levels and short RNA reads. On the other hand, we identified genes where a very low number of randomly distributed reads could well be explained by RNA breakdown or sequencing errors, these were not studied in further detail. The reproducibility of the endo-siRNA signature after mutagenesis, however, suggests that such random errors do not contribute significantly to the sequencing output.Third, the short RNAs identified in this screen could be synthesized by components of the RNAi machinery. Nuclear localization of key components such as Dicer, Drosha and Argonaute proteins and shuttling of related RNA protein complexes between the cytoplasm and the nucleus has been reported ,36. Our C. elegans, Drosophila and also in mouse [One particular focus of the presented work was to investigate a potential link between convergent transcription, i.e. the co-expression of sense and antisense transcripts, and the formation of endo-siRNAs. Genic endo-siRNAs have been documented in several systems, predominantly in in mouse ,37-40. Hin mouse . Our resin vitro and applied at high concentrations indicating that the process was inefficient. Our findings also suggests that the cell culture model used may not express balanced levels of all the components essential for the processing of NATs into endo-siRNAs and transcriptional silencing. For example, Hela cells transfected with vectors expressing both thymidylate synthase (TS) sense and antisense transcripts failed to generate TS related siRNAs [trans[There is a substantial body of evidence that siRNAs can promote both transcriptional silencing and activation through chromatin modifications ,43. The d siRNAs . On the As [trans. Our finAs [trans,45.In HEK293 cells convergent transcription may trigger the production of endo-siRNAs; however, the process appears to be rather inefficient and only relevant in specialized cell types. Moreover, in depth analysis of genic short RNAs revealed two interesting and novel features of the transcriptome: Firstly, we identified a distinct endo-siRNA signature that maps to a restricted number of genes and remains largely stable after a mutagenic insult. Secondly, read numbers of endo-sRNAs do not reflect steady-state mRNA levels of their parent genes.Cell culture, cloning and mutagenesis: The human embryonic kidney cell line HEK293 was maintained and passaged according to established cell culture conditions. Cloning was performed in a 96 well cell culture dish by serial dilution starting with 60-120,000 cells. Cells were grown until single colonies could be identified. Individual clones were transferred to 24 well plates and grown to confluency. At this stage a single clone was chosen and passaged into two wells of a 6 well plate. When the cells were about 80% confluent, the medium in one well was replaced and 100 μg/ml ethyl methanesulfonate  was added. Cells were grown in the presence of EMS for 24 hours followed by a 24 hour recovery period in fresh medium. At that point the mutagenized cells were re-cloned by serial dilution. Thereafter, single mutagenized clones were expanded and two clones, C5 and C12 were randomly selected for further experiments. The non-mutagenized original clone was propagated to yield enough cells for RNA extraction and long term storage.Nucleic acid isolation: RNA and DNA were isolated using Trizol according to standard methodology.Short RNAseq: Short RNA purification was performed by GATC Biotech in Konstanz, Germany. In brief, the RNA was quality tested and size selected on a denaturing polyacrylamide gel (approximately 19-29 bases). Tagged 3′ adaptors and 5′ adaptors were ligated to the recovered RNA to ensure strand specificity. The material was used for cDNA synthesis. The three samples were pooled and sequenced on an Illumina HiSeq 2000. The sequencing data has been submitted to the European Nucleotide Archive, accession number GSE52996.Data analysis: The reads were processed using the fastx toolkit to remove low quality reads and trim low quality bases. The parsed reads were mapped to the hg18 build of the human genome using Bowtie . SamtoolReverse transcription- quantitative PCR: RNA from the original extraction, stored at -80°C, was used for expression analysis by RT-qPCR. Reverse transcription of approximately 0.5 μg of total RNA was performed using the Omniscript kit from Qiagen following the supplier’s instructions. In brief, RNA and 2.5 μM random hexamers were denatured for 3 minutes at 70°C and cooled to 37°C. The reaction mix including polymerase, dNTPs, buffer and RNase inhibitor were added. After one hour the reaction was denatured for 2 minutes at 95°C and stored at -20°C. 0.5 μl of the RT product was amplified in 1x Lightcycler 480 Probes Master mix (Roche) and gene specific PrimeTime® qPCR primers and probes (Integrated DNA Technologies). The cycling protocol included a denaturation step (95°C for 10 minutes) and 45 cycles of 95°C for 5 seconds, 55°C for 20 seconds and 72°C for 1 second when fluorescence was determined. The RT was performed twice with each RNA and the qPCRs were repeated and run in duplicates. qPCR reactions that resulted in a Ct difference of >1 between duplicates were repeated. The sequence of all the primers and the details about the PrimeTime® Assays are provided in Additional file The authors declare that they have no competing interests.AW planned the study and wrote the manuscript. SC analyzed and mapped the RNAseq data, MC compared the data to deposited data sets. JF cloned and mutagenized the cells, SA performed the expression studies. JHR conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.Compilation of genes producing short RNAs.Click here for fileGenes with short reads compared to the total number of genes.Click here for fileExpression of endo-siRNA related genes.Click here for fileExpression data of miRNAs and structural RNAs.Click here for fileLength distribution of the short RNAs in the three samples control, clone 5 and clone 12.Click here for fileSummary of qPCR results.Click here for fileDetails and sequences of primers and probes for qPCR.Click here for file


Archived tissues from previously completed prospective trials represent invaluable resource for biomarker development. However, such specimens are often stored as sections on glass slides, in which RNA is severely degraded due to prolonged air exposure. We evaluated whether a proportion of archived sectioned formalin-fixed paraffin-embedded (AS-FFPE) tissues yield transcriptome profiles comparable to freshly cut (FC) FFPE tissues, which can be used for retrospective class prediction analysis.Genome-wide transcriptome profiles of 6 to 7-year-old AS-FFPE tissue sections (generated from 5 to 16-year-old blocks) of 83 hepatocellular carcinoma (HCC) and 47 liver cirrhosis samples were generated by using whole-genome DASL assay (Illumina) and digital transcript counting (nCounter) assay (NanoString), and gene signature-based prediction of HCC subclasses and prognosis was compared with previously generated FC-FFPE profiles from the same tissue blocks.RNA quality and assay reproducibility of AS-FFPE RNA were comparable to intermediate to poor quality FC-FFPE samples . Analyzable transcriptome profiles were obtained in 64 (77%) HCC and 36 (77%) cirrhosis samples. Statistically more confident predictions based on random resampling-based method (nearest template prediction) were obtained in 37 (58%) HCC and 13 (36%) cirrhosis samples. Predictions made in FC-FFPE profiles were reproduced in 36 (97%) HCC and 11 (85%) cirrhosis AS-FFPE profiles. nCounter assay was tested in 24 cirrhosis samples, which yielded confident prediction in 15 samples (63%), of which 10 samples (67%) showed concordant predictions with FC-FFPE profiles.AS-FFPE tissues yielded poorer quality RNA and transcriptome profiles compared to FC-FFPE tissues. Statistically more confident class prediction was feasible in 37 of 83 HCC samples and 13 of 47 cirrhosis samples. These results suggest that AS-FFPE tissues can be regarded as a resource for retrospective transcriptome-based class prediction analysis when they are the only available materials. Clinical deployment of transcriptome-based biomarker has been a challenging task due to multiple reasons We have successfully utilized the cDNA-mediated annealing, selection, extension and ligation (DASL) assay We analyzed AS-FFPE tissue sections (one to three 10 micron-thick slices for each sample) sectioned from 5 to 16-year-old FFPE tissue blocks and archived for additional 6 to 7 years on glass slides at room temperature with drying reagent in sealed bags. These are subsets of samples analyzed in our previous studies, in which RNA was isolated from FC-FFPE tissues: 83 out of 118 hepatocellular carcinoma (HCC) tissues used to determine molecular subclasses Total RNA was isolated as previously described Whole-transcriptome profiling was performed by using the HumanHT-12 whole-genome DASL beadarray ver. 4.0 (Illumina). Total RNA was converted to cDNA using biotinylated oligo-dT18 and random nonamer primers, followed by immobilization to a streptavidin-coated solid support. The biotinylated cDNAs were then simultaneously annealed to a set of assay-specific oligonucleotides based on content derived from the National Center for Biotechnology Information (NCBI) Reference Sequence Database (release 98). The extension and ligation of the annealed oligonucleotides generate PCR templates that are then amplified using fluorescently-labeled (P1) and biotinylated (P2) universal primers. The labeled PCR products are captured on streptavidin paramagnetic beads, to yield single-stranded fluorescent molecules which are then hybridized, via gene-specific complementarity, to the HumanHT-12 BeadChip, whereafter fluorescence intensity is measured for each bead. Hybridized chips were scanned by using iScan (Illumina) and raw measurements were extracted by Genome Studio software ver. 3.0 (Illumina).www.broadinstitute.org/genepattern) As a quality measure of the expression profiles, we calculated proportion of gene probes with a “present” signal computed based on built-in negative control probes by the Genome Studio software . We also computed inter-sample correlation to detect outlier poor quality profiles as previously described The prognostic 186-gene signature was implemented in the digital transcript counting (nCounter) assay (NanoString) www.ncbi.nlm.nih.gov/geo) with accession numbers GSE10186 and GSE10140 for the HCC and liver datasets, respectively. AS-FFPE profiles newly generated in the current study are available at GEO with accession number GSE46444.Prediction of HCC subclasses  The quality of RNA extracted from AS-FFPE tissues was compared with FC-FFPE tissues. 2 values of 0.91–0.93 suggest that the amount of assayable RNA molecule was reduced to approximately 1/40 in AS-FFPE compared to FC-FFPE tissues. We next assessed how microarray signal detection is affected by archiving FFPE sections by comparing the proportion of probes with detected signal namely % present call . It is expected that %P-call is similar and inter-sample correlation is high within adult tissues sharing the same tissue lineage Consistent with the Bioanalyzer result, whole-transcriptome profiling of technical replicates of AS-FFPE RNA showed reproducibility comparable to the intermediate to poor quality FC-FFPE RNA samples Figure 2We next evaluated gene signature-based class prediction in the AS-FFPE profiles in comparison to the FC-FFPE profiles. In the 64 HCC samples, a meta-analysis-based signature of three HCC subclasses , which has been well-validated in multiple independent cohorts and assay platforms In the profiles of 36 liver cirrhosis samples, we performed class prediction using a prognostic 186-gene-expression signature, which has been extensively validated in multiple independent cohorts and assay platforms  certification Recently developed genomic profiling technologies such as DASL have enabled analysis of archived FFPE tissue specimens with rich clinical annotations and follow-up necessary for validation of predictive/prognostic molecular signatures. However, these assays are optimized for FC-FFPE materials, and AS-FFPE is often the only available archived materials in many pathology labs and clinical trial study coordination centers. In addition, FC-FFPE tissue processing requires scheduling of sectioning and RNA isolation within a certain timeframe, which is limited by sample processing capability of each pathology lab. Therefore, analysis of AS-FFPE tissues will substantially increase the opportunities of retrospective assessment of predictive/prognostic molecular signatures.Our results clearly demonstrated that the quality of AS-FFPE RNA is distinctly inferior to that of FC-FFPE RNA. Approximately three-fourths of AS-FFPE tissues yield analyzable transcriptome profiles for the purpose of retrospective class prediction analysis, and statistically more confident prediction was feasible in approximately one-third of the samples. The relatively good prediction confidence p-values and comparable prediction consistency in the nCounter assay suggest that the assay is another option to retrospectively analyze AS-FFPE tissues. The lack of target amplification may contribute to the higher prediction confidence. This may also indicate that high-throughput sequencing-based transcript counting could be an option once the assay is optimized for AS-FFPE specimens. A recent study showed minimal lab-to-lab and day-to-day experimental variations in the gene expression measurements in nCounter assay, further supporting its utility in the analysis of FFPE tissues The sites of degradation in FFPE RNA was observed roughly in random fashion in our previous study In summary, RNA isolated form AS-FFPE tissues was more degraded than that from FC-FFPE tissues, and yielded poorer quality transcriptome profiles. However, our results suggest that transcriptome signature-based disease classification is still feasible in a subset of AS-FFPE tissues, which will help increase the opportunities of retrospective validation and facilitate eventual clinical development of predictive/prognostic molecular signatures.


The formation of a protective protein container is an essential step in the life-cycle of most viruses. In the case of single-stranded (ss)RNA viruses, this step occurs in parallel with genome packaging in a co-assembly process. Previously, it had been thought that this process can be explained entirely by electrostatics. Inspired by recent single-molecule fluorescence experiments that recapitulate the RNA packaging specificity seen in vivo for two model viruses, we present an alternative theory, which recognizes the important cooperative roles played by RNA–coat protein interactions, at sites we have termed packaging signals. The hypothesis is that multiple copies of packaging signals, repeated according to capsid symmetry, aid formation of the required capsid protein conformers at defined positions, resulting in significantly enhanced assembly efficiency. The precise mechanistic roles of packaging signal interactions may vary between viruses, as we have demonstrated for MS2 and STNV. We quantify the impact of packaging signals on capsid assembly efficiency using a dodecahedral model system, showing that heterogeneous affinity distributions of packaging signals for capsid protein out-compete those of homogeneous affinities. These insights pave the way to a new anti-viral therapy, reducing capsid assembly efficiency by targeting of the vital roles of the packaging signals, and opens up new avenues for the efficient construction of protein nanocontainers in bionanotechnology. Viruses are major pathogens in all kingdoms of life. Single-stranded (ss)RNA viruses make up a significant fraction of these pathogens with detrimental impacts on human health. Their control via vaccination will only ever be possible for a limited subset of examples, so innovative routes to antiviral therapy are urgently required. Virion formation and uncoating are highly cooperative aspects of the viral lifecycle that are potential drug targets, but these have largely not been exploited thus far. One reason for this, with respect to assembly, is the apparent lack of specificity for RNA in in vitro assays of spontaneous co-assembly to form virus-like particles. Many viral coat proteins (CPs) also seem able to assemble correctly in the absence of RNA. The lack of sequence specificity has been interpreted to mean that assembly is driven largely by electrostatics –4. RNAs h). The assays are sensitive in the nanomolar concentrations range (~1 nM for RNA & ~100 nM for CP), which are much more reflective of early concentrations within infected cells than most in vitro assays where CP concentrations of ≥10 hs derived from smFCS similar to those determined by other techniques, such as X-ray crystallography and mass spectrometry. Furthermore, the technique showed good discrimination between the starting materials (dis-assembled RNAs and CPs) and the end products (capsids) in in vitro reassembly reactions.We have recently established single-molecule fluorescence correlation spectroscopy (smFCS) assays to monitor the fates of dye-labeled CPs or RNAs during in vitro reassembly at the low CP concentrations typical of in vivo scenarios . These ah values, consistent with an ensemble of differing conformers in equilibrium  [The most interesting information from these assays comes from dye-labeled genomic RNAs to which a full complement of CPs is added, allowing each RNA to form a capsid . Initialium Fig. . This ist (W82R)  that binThe results described above suggest that there should be multiple PSs in the genomic sequences that play important functional roles via contacts with CP at defined positions in the capsid. For example, we have shown previously that contacts between characteristic stem-loop motifs in the MS2 sequence and CP subunits result in a conformational change of the symmetric, RNA-free CP dimer –11, the T = 1 capsid help overcome an electrostatic barrier between the CP monomers that prevents assembly in the absence of RNA , where up to 80% of the genomic RNA is ordered and has been modeled as a series of stem-loops positioned along the particle two-fold axes . FurtherHigh-affinity PSs have been identified in many viral systems, and their roles in initiating capsid assembly and conferring packaging selectivity have been discussed. In MS2, there is a single copy, high-affinity, stem-loop CP-binding site (TR)  that plaT = 1 capsids in vivo package a genomic RNA consisting only of mRNA for the CP. There are three known viral strains. Unusually in this class of virus, our recombinant mRNA in E. coli cells is expressed and the recombinant CP assembles to T = 1 particles that encapsidate that mRNA, suggestive of packaging specificity [T = 1 capsids with STNV CP, but with dramatically different efficiencies, the –A.X.X.A- version being much more efficient. X-ray structure determination of the virus-like particle (VLP) created by B3-induced assembly reveals repeated RNA-CP contacts around the helices that occur at the virion three-fold axes. Compared to the virion structure, these helices have become more ordered allowing a cluster of positively charged side-chains to come into close contact. These aptamer sequences appear to overcome the potential electrostatic repulsion between CP monomers and are obvious candidates to be the PSs in the STNV RNA inferred from the smFCS experiment.For example, STNV cificity . We testcificity . We testcificity . The STNSimilarly, RNA SELEX and known structures for CP-binding RNA stem-loops suggested a characteristic motif for PSs in MS2 –27. UsinA similar analysis of the PSs in an evolutionarily related phage, GA, revealed a different PS motif and different PS distributions in the secondary structure of that packaged genome, but the same contact pattern between PS in the RNA organization in proximity of the capsid, pointing to an evolutionarily conserved packaging arrangement in this family of RNA viruses.The tight link between the structure of the packaged genome and capsid assembly implies a conserved assembly mechanism for these RNA viruses. This is because the positions of the PSs and the way in which the genomic RNA is organized between them define the assembly pathways and intermediates. For example, for MS2 assembly is assumed to nucleate at the highest affinity PS TR, Fig. , which iThese results pave the way for a better understanding of how the PSs contribute to making capsid assembly both efficient and accurate. The existence of multiple PSs suggests that they fulfil a regulatory role during capsid assembly, which is perhaps surprising, given that the largest fraction of them ) have relatively weak affinity for their cognate CP. In order to understand this phenomenon, we have analyzed capsid assembly for a dodecahedral model system consisting of 12 pentagonal building blocks that can interact with 12 packaging signals on a hypothetical RNA Fig. a assume The analysis revealed that heterogeneous PS affinity distributions  performed consistently better than RNAs with identical PSs, such as homogeneous polymers . An example of each scenario is shown in Fig. Inspired by the remarkable insights into RNA virus assembly provided by the single-molecule experiments for MS2 and STNV , we havevice versa , there is clear discrimination in favor of the cognate CP-genomic RNA complex against MS2 genomic RNA and its fragments, and 00 nM, thument}M) . As our These insights suggest a novel approach to anti-viral drug design, i.e., targeting the formation of the vital PS-CP contacts, and/or their consequences. Indeed, we have shown that blocking the CP-induced collapse of MS2 RNA ablates capsid assembly and others have demonstrated that a clinically approved alkaloid that binds to extra helical bases in RNA blocks assembly of TMV in vitro from its known PS. The packaging signal hypothesis may hence pave the way to a novel antiviral therapy.


The Gadd45 proteins play important roles in growth control, maintenance of genomic stability, DNA repair, and apoptosis. Recently, Gadd45 proteins have also been implicated in epigenetic gene regulation by promoting active DNA demethylation. Gadd45 proteins have sequence homology with the L7Ae/L30e/S12e RNA binding superfamily of ribosomal proteins, which raises the question if they may interact directly with nucleic acids.in vitro. Sucrose density gradient centrifugation experiments demonstrate that Gadd45a is present in high molecular weight particles, which are RNase sensitive. Gadd45a displays RNase-sensitive colocalization in nuclear speckles with the RNA helicase p68 and the RNA binding protein SC35. A K45A point mutation defective in RNA binding was still active in DNA demethylation. This suggests that RNA binding is not absolutely essential for demethylation of an artificial substrate. A point mutation at G39 impared RNA binding, nuclear speckle localization and DNA demethylation, emphasizing its relevance for Gadd45a function.Here we show that Gadd45a binds RNA but not single- or double stranded DNA or methylated DNA The results implicate RNA in Gadd45a function and suggest that Gadd45a is associated with a ribonucleoprotein particle. Gadd45 genes are a family of stress response genes, which are involved in diverse processes, including cell growth, DNA repair, and apoptosis, and function as tumor- and autoimmune suppressors Gadd45 genes encode multifunctional, 18 kDa acidic proteins, which can homo- and heterodimerize and which are predominantly localized in the nucleus The Xenopus, Zebrafish, and mammalian cells More recently Gadd45 proteins have been implicated in epigenetic gene regulation, promoting active DNA demethylation via a DNA repair mechanism. Gadd45a binds to the repair endonuclease XPG and initiates excision repair at methylated CpG motifs in Gadd45 proteins exhibit sequence homology to the L7Ae/L30e/S12e superfamily Arabidopsis. ROS3 resides in nuclear speckle-like structures and binds small RNAs. It was suggested that these RNAs may guide the DNA demethylase towards their substrate The fact that Gadd45 proteins belong to the L7Ae/L30e/S12e superfamily raises the question whether they may also bind RNA. Importantly, RNAs have been repeatedly implicated in active DNA demethylation although their history in this process is confusing Gadd45a has been shown to associate with chromatin Xenopus tropicalis (xt) Gadd45a overexpression in human cells and E.coli we used constructs containing xtGadd45a ORF in vectors pRKW2 and pET28a as well as N-EGFP tagged xtGadd45a in pCS2 For HEK293T cells (ATCC CRL 11268) and RKO cells (ATCC CRL 2577) were grown at 37°C in 10% CO2 for 293T cells and 5% for RKO cells in Dulbecco's Modified Eagle's Medium (DMEM), 10% fetal calf serum, 2 mM L-Glutamine, 100 U/ml penicillin and 100 µg/ml streptomycin. Transient DNA transfections were carried out using FuGENE6 (Roche), TurboFect™ (Fermentas) in case of HEK293T, and for RKO cells a combination of Lipofectamine and Plus reagents (Invitrogen) was used following the manufacturer's instructions.Cell detergent extraction and RNase treatment was essentially as described 2, 0.5 mM EDTA, and 25% glycerol; with or without subsequent RNase treatment as described above. Cells were fixed with 2% formaldehyde and antigen retrieval was performed as described For detection of endogenous Gadd45a HEK293T cells grown on coverslips and subjected to UV irradiation (40 mJ/cm2) were permeabilized with 0.2% Triton X-100 in 20 mM Tris-HCl (pH 7.4), 5 mM MgCl2, 2.5 mM Magnesium acetate, 0.25% Triton X-100, 0.1 mM EDTA, 2 mM DTT, Complete™ proteinase inhibitors (Roche)) in a douncer homogeniser. Homogenates were mixed 1∶1 with buffer B ), and centrifuged through a buffer B cushion for 20 min at 13,000 g in a Beckman SW41 Ti rotor. The pellet was resuspended in nuclear extraction buffer (50 mM Hepes-NaOH (pH 7.8), 140 mM NaCl, 1 mM MgCl2, 1 mM DTT, 0.1 mM Vanadyl ribonucleosides (Sigma), proteinase inhibitors (Roche) and sonicated. Nuclear extract was centrifuged at 15000 g for 30 min, and the supernatant was used for further procedures.HEK293T or RKO cells were harvested, washed twice with DPBS buffer, and homogenized in buffer A  or 40 U/ml of DNase I (MBI) for 30 min at room temperature. Soluble nuclear proteins were applied to the top of a 8–40% sucrose gradient and centrifuged for 26 h at 50000 g at 4°C. Samples containing sedimentation markers thyroglobulin (19S), β-galactosidase (16.4 S), catalase (11S) or a cytoplasmic fraction containing ribosomal subunits were run separately. Proteins from gradient fractions were precipitated and analyzed on immunoblots.Sucrose gradient sedimentation analysis was performed using nuclear extract from 2×102). In reactions without competitor, 2.5 or 1.3 µM recombinant proteins were preincubated for 20 min with 10,000 cpm (approximately 2 ng) of 32P-labelled multiple cloning site (MCS) RNA of pCS2 and pXT1 plasmids. In competition assays, unlabeled competitor nucleic acids were preincubated for 10 min with recombinant proteins before addition of labeled RNA. Reactions were applied to nitrocellulose filters that were pre-blocked with 50 µg/ml BSA in RNA binding buffer, washed with RNA binding buffer and quantified by scintillation counting.Filter binding assays were performed essentially as described Haloarcula marismortui ribosomal protein L7Ae-rRNA complex  online servers. Structure analysis was carried out using SwissPBD Viewer www.pymol.org). PDB2PQR Two close homologues of xtGadd45a were used as templates for homology modeling: human , and mouse  according to the manufacturer's instruction except that lysis buffer contained 1 mM of MgCl2 and 0.04% of NP40. Step elution with 60–100 mM imidazole was performed. Fractions were analyzed by SDS-PAGE with Coomassie staining (Thermo Scientific), and fractions eluting at 100 mM imidazole were dialyzed against lysis buffer and used for further experiments.pET28a vectors containing ORF of xtGadd45a and mutants were transformed into BL21DE3 Dual-Luciferase reporter assays (Promega) were performed as described in in vitro methylated pOctTK-EGFP and 1.2 µg pBl-KS or xtGadd45a. Transfected plasmid DNA was recovered 72 h after transfection, digested with NotI and either HpaII or MspI and analyzed by Southern blot using a GFP probe. The expression of EGFP was additionally analyzed by SDS-PAGE and Western blot using anti-GFP antibody.HEK293T cells were transiently transfected in 10 cm dishes with 5 µg HpaII To test whether Gadd45a binds RNA, we carried out filter binding assays using recombinant Gadd45a and radiolabeled synthetic vector RNA, which indicated significant RNA binding compared with M-MLV reverse transcriptase . To furtin vivo, we analyzed its sedimentation behaviour in sucrose density gradients  particles The RNase sensitive sedimentaion profile of Gadd45a supports it being associated with RNA endogenously.To further examine whether Gadd45a is a RNA binding protein we analyzed the RNase sensitivity of its localization. Cells were transfected with Gadd45a and soluble proteins were detergent extracted with or without RNase treatment and analyzed by Western blot or immunofluorescence (IF) microscopy . In WestIn HEK293T cells endogenous Gadd45a showed only a weak, homogeneous nuclear signal. However, following UV-irradiation, which induces Gadd45a expression, the protein was colocalized with SC35, but was also found in SC35 negative punctae and in the nuclear periphery . Once agThe nuclear speckle co-localization with RNP proteins SC35 and p68 and its RNase sensitivity support that Gadd45a is an RNA binding protein and may be part of an RNP.To gain insight into the structural basis of Gadd45a-RNA interactions we inspected the three dimensional structures of Gadd45 as well as of structures of other L7Ae members, which were solved in complex with RNA.Xenopus tropicalis Gadd45a by employing the available crystal structures of Gadd45g Haloarcula marismortui ribosomal protein L7Ae-rRNA complex. All three L7Ae family proteins are bound to the so-called kink-turn RNA motif. Analyzing general rules of recognition of this type of RNA, we identified two main patches on the RNA-binding surface of these proteins. In patch 1  specific RNA binding is not absolutely essential for DNA demethylation and b) that G39 is a critical amino acid for the function and localization of Gadd45a.The main findings of this study are that Gadd45a is an RNA binding protein and that it appears to be part of an RNP particle. This is in line with the function of other members of the L7Ae/L30e/S12e superfamily, which are either ribosomal components or associated with RNP particles. Gel filtration and cross linking analysis of recombinant Gadd45b,g indicates that the protein forms a dimer of 35 kDa The conclusion that Gadd45a is in an RNP complex is supported by sucrose density gradient centrifugation and its localization in nuclear speckles. It is interesting that nuclear speckles are a site of active transcription, RNA splicing and processing. This raises the possibility that Gadd45a RNPs are associated with genes undergoing active DNA demethylation and transcriptional activation. Of note, p68/Ddx5, which colocalizes with Gadd45a in nuclear speckles, was previously described as a component of a DNA demethylase complex Drosophila as mutation of squid, which encodes an hnRNP. In both cases the chorion of fly eggs is dorsalized due to defects in grk mRNA localization and translation RNP complexes play prominent roles in RNA processing, RNA transport and RNA translation Click here for additional data file.Figure S2 Possible H-bonding networks in patch 2 and G-binding region in human hsp15.5 kDa protein (A), yeast ribosomal scL30e protein (B), Haloarcula marismortui ribosomal hmL7Ae protein (C) and the model of xtGadd45a (D). Residues colored in light and dark blue form patch 1 and those colored in light and dark green form patch 2, respectively Click here for additional data file.Figure S3Modeling the Gly to Ala mutation on the basis of the hsp15.5-RNA complex. Panel A represents possible the hydrogen bonding network formed by the guanine base which is properly bound and oriented in the G-binding region. Panel B shows surface representations of the same structure illustrating that the guanine base perfectly fits into the G-binding region without any sterical clashes. Exchange of glycine residue for alanine leads would lead to considerable sterical clashes (C). To resolve these clashes we propose that the guanine base moves out from the G-binding site (D) leading to a loss of most hydrogen bonds (E).(1.08 MB JPG)Click here for additional data file.Figure S4Induction of EGFP expression from HpaII methylated promoter by xtGadd45a wild type and mutants. Western blot analysis of EGFP induction from HpaII methylated pOctTK-GFP reporter, as well as of xtGadd45a wild type and mutants expression. Loading was controlled using histone H3. A representative experiment out of three independent experiments is shown.(0.49 MB JPG)Click here for additional data file.Figure S5A, crystal structure of a yeast spliceosomal protein scSnu13p dimer complex with bound RNA molecules modeled on the hsp15.5-RNA complex crystal structure by fitting corresponding scSnu13p and hsp15.5 proteins in SwissPDB Viewer. B, model of xtGadd45a dimer complex obtained by GRAMM docking in hydrophobic mode with RNA molecules superimposed from hsp15.5-RNA complex crystal structure by fitting corresponding xtGadd45a and hsp15.5 proteins in SwissPDB Viewer. In both cases the red area on the protein surfaces represents the conserved glycine of the G-binding region. From these structures it is evident that dimerization and RNA-binding interfaces (patches 1 and 2) do not overlap.(0.69 MB JPG)Click here for additional data file.


Escherichia coli the mean and cell-to-cell diversity in RNA numbers of different genes vary widely. This is likely due to different kinetics of transcription initiation, a complex process with multiple rate-limiting steps that affect RNA production.In in vivo kinetics of production of individual RNA molecules under the control of the lar promoter in E. coli. From the analysis of the distributions of intervals between transcription events in the regimes of weak and medium induction, we find that the process of transcription initiation of this promoter involves a sequential mechanism with two main rate-limiting steps, each lasting hundreds of seconds. Both steps become faster with increasing induction by IPTG and Arabinose.We measured the in vivo dynamics of transcription may be applicable at a genome-wide scale and provide valuable insight into the dynamics of prokaryotic genetic networks.The two rate-limiting steps in initiation are found to be important regulators of the dynamics of RNA production under the control of the lar promoter in the regimes of weak and medium induction. Variability in the intervals between consecutive RNA productions is much lower than if there was only one rate-limiting step with a duration following an exponential distribution. The methodology proposed here to analyze the  Escherichia coli , and given N measured intervals between transcription events, Δtk, where k goes from 1 to N, the log-likelihood is:Given the distribution of time intervals between consecutive transcription events, obtained from multiple cells subject to the same induction, it is possible to determine the maximum likelihood fit of a model with d is the probability density function for a sum of d exponential random variables with means μd. The probability density function for the sum can be found by the convolution of the probability density functions of the individual exponential random variables. The density functions for d = 1,..3 are:where π1, μ2 ... μd] are the expected means and standard deviations of the durations of each of the steps composing the intervals between production events. We use this procedure to find the values of μ that provide the highest log-likelihood for d = 1,...,4. No significant improvement of fit was observed for values of d > 2 , so that π2 equals the density function of the gamma distribution with parameters k = 2 and θ = μ1 = μ2.We note that the singularities of the probability density functions, formulas (5) and (6), were not problematic since the maximum likelihood estimate of the μ's differed from the second decimal onward. Furthermore, the singularities can be removed. For example, in (5), if μThe goodness of fit of the models can be assessed by comparison. For that, we perform a likelihood-ratio test between pairs of models to reject a null model in favour of the alternative. Finally, we verified that the method reliably distinguishes the duration of each step, when they differ by ~25% in duration, from 200 intervals sampled from a model of gene expression.ASR conceived the manuscript. MK executed the experiments. HM, AH, JLP and AG executed the analytical studies. OYH participated in the planning with ASR. ASR wrote most of the manuscript. All authors contributed in the writing, read and approved the final manuscript.Example of a movie generated from temporal images of a cell. Images were taken approximately 7 min after induction, one every minute, for approximately 2 hours. The cell was induced with 0.01 mM of IPTG and 0.067 mM of Arabinose. The cell identification number and the time (s) when the frame was captured are shown in the top right and left corners, respectively.Click here for fileSupplementary information. Supplementary information: qPCR analysis of the target RNA; image analysis and cell segmentation, detection and counting of mRNA in cells; analyses of the intervals between production events assuming an ON-OFF mechanism of RNA production; measurements of RNA numbers under full induction.Click here for file