In the process of retrotransposition LINEs use their own machinery for copying and inserting themselves into new genomic locations, while SINEs are parasitic and require the machinery of LINEs. The exact mechanism of how a LINE-encoded reverse transcriptase (RT) recognizes its own and SINE RNA remains unclear. However it was shown for the stringent-type LINEs that recognition of a stem-loop at the 3′UTR by RT is essential for retrotransposition. For the relaxed-type LINEs it is believed that the poly-A tail is a common recognition element between LINE and SINE RNA. However polyadenylation is a property of any messenger RNA, and how the LINE RT recognizes transposon and non-transposon RNAs remains an open question. It is likely that RNA secondary structures play an important role in RNA recognition by LINE encoded proteins.Here we selected a set of L1 and Alu elements from the human genome and investigated their sequences for the presence of position-specific stem-loop structures. We found highly conserved stem-loop positions at the 3′UTR. Comparative structural analyses of a human L1 3′UTR stem-loop showed a similarity to 3′UTR stem-loops of the stringent-type LINEs, which were experimentally shown to be recognized by LINE RT. The consensus stem-loop structure consists of 5–7 bp loop, 8–10 bp stem with a bulge at a distance of 4–6 bp from the loop. The results show that a stem loop with a bulge exists at the 3′-end of Alu. We also found conserved stem-loop positions at 5′UTR and at the end of ORF2 and discuss their possible role.Here we presented an evidence for the presence of a highly conserved 3′UTR stem-loop structure in L1 and Alu retrotransposons in the human genome. Both stem-loops show structural similarity to the stem-loops of the stringent-type LINEs experimentally confirmed as essential for retrotransposition. Here we hypothesize that both L1 and Alu RNA are recognized by L1 RT via the 3′-end RNA stem-loop structure. Other conserved stem-loop positions in L1 suggest their possible functions in protein-RNA interactions but to date no experimental evidence has been reported.The online version of this article (doi:10.1186/s12864-016-3344-4) contains supplementary material, which is available to authorized users. L1 family is by its mass the largest family of Long INterspersed Elements (LINEs) in humans; L1s are present in more than 500 000 copies and occupy approximately 17% of the total human genome length . It is eL1 transcription is initiated by an RNA polymerase II promoter located at the 5′ UTR , 5. The The second family of retrotransposons by mass in the human genome is Alu, belonging to the class of Short Interspersed Elements (SINEs). By copy number Alu is the most ubiquitous retrotransposon with more than 1 mln copies present in the human genome and occupying approximately 11% of the total genome length , Alus arThe mechanism of how L1 RT recognizes L1 and Alu RNA remains an open question. Since SINEs borrow retrotransposition enzymatic machinery from LINEs, the more general question is how RT recognizes LINE and SINE RNAs. The similarity between LINE and SINE elements was first shown for the turtle genome: it was found that elements from the LINE CR1 family share the same 3′-end with tRNA-derived SINEs from the same genome . Later oIt was proposed to divide all LINEs in two groups: the stringent type and the relaxed type . TranspoCo-localization of L1 RNA, ORF1 and ORF2 suggests that upon translation of both ORFs from L1 RNA they immediately form ribonucleoprotein particle (RNP) . AdditioThe idea that secondary or tertiary RNA structure shared by L1 and Alu could be responsible for recognition and binding of ORF2, possibly along with a poly-A tail, was proposed by Boeke . An impoFor the stringent-type LINEs one of the important elements is a stem-loop structure located at the end of the 3′UTR –29. CurrNeisseria gonorrhoeae  is built as follows.Thus, a matrix M, of size  = min  = min  = 0.ij), and whose value does not exceed maxMismatch – M < = maxMismatch. If the corresponding coordinate is no less than minStemLen, the cell will be a starting point for the traceback processing. Let maxMismatch =2, and minStemLen =2, then the program searches for a maximum coordinate that has a value less or equal to 2. In the example this is the lowest right cell. The traceback processing is performed similar to the Needleman-Wunch algorithm with the only difference that the path is chosen according to minimum matrix values. The resulting stem structure is:After the matrix is filled the program searches for the cell with a maximum minimum of its coordinates – maxTo avoid cases where sequence pairing in stems starts with a gap or mismatch we add an additional requirement of M  = M .DNA Punctuation, implementing the aforementioned algorithm, is available at www.dnapunctuation.org.Source code in C and the program DNA Punctuation described above. We searched for structures with the stem length in the range of 15–50 bp, loop up to 10 bp, with 7 mismatches or gaps allowed. The stem-loop coverage profiles were constructed along the entire transposon length so that 1 is added if a base pair in the sequence is covered with a stem-loop and 0 otherwise. The total value for a given position was divided by the number of transposons, so that y-axis reflects a percentage of sequences having a base pair covered with the stem-loop. To adjust to a different length of the transposons we scaled the sequences to the normalized length of 100% with an average of 6 kb for L1 and 300 bp for Alu. In summary, the stem-loop coverage values reflect the percentage of analyzed sequences having sequence positions covered with stem-loop structures.Stem-loop structures were annotated with the program Secondary structures for the selected stem-loop sequences were reconstructed and visualized with RNA fold software (Vienna RNA Package 2.1.9) .To investigate position conservation for a bulge or an internal loop for a set of stem-loops we constructed pairing-unpairing profiles for a set of position-specific stem-loop sequences. If a base is unpaired then we add 1 to the profile or 0 otherwise. The values of the resulting profile reflect how many stem-loop structures will have unpaired bases at a given position. Two types of profiles were constructed: one with the length of a selected stem-loop normalized to 100%, and the other for a stem where only x-coordinates correspond to the exact position in a number of bases starting from the loop (x = 0) and towards the base of the stem.


In recent years, the role of RNA has expanded to the extent that protein-coding RNAs are now the minority with a variety of non-coding RNAs (ncRNAs) now comprising the majority of RNAs in higher organisms. A major contributor to this shift in understanding is RNA sequencing (RNA-seq), which allows a largely unconstrained method for monitoring the status of RNA from whole organisms down to a single cell. This observational power presents both challenges and new opportunities, which require specialized bioinformatics tools to extract knowledge from the data and the ability to reuse data for multiple studies. In this review, we summarize the current status of long non-coding RNA (lncRNA) research in endothelial biology. Then, we will cover computational methods for identifying, annotating, and characterizing lncRNAs in the heart, especially endothelial cells. C. elegans has more lncRNAs than any other organisms  and RNA sequencing (RNA-seq) has significantly improved the understanding of transcriptomes. For example, we now know that most of the human genome is transcribed ” in ECs -depletion. Both methods are aimed at removing rRNAs, which constitute ~80% of total RNA followed by 15% transfer RNAs (tRNAs) and only 5% for all other RNAs, including protein-coding genes and lncRNAs  to produce isoforms , which encodes a cysteine protease associated with angiogenesis and atherosclerosis, is highly edited (Stellos et al., CTSS transcript, thereby controlling CTSS mRNA stability and expression. The RNA editing enzyme ADAR1 levels and the extent of CTSS RNA editing are associated with changes in CTSS levels in patients with coronary artery diseases. Our study highlights the involvement of RNA editing in cardiovascular diseases, which has not yet been investigated (Uchida and Jones, In addition to studying lncRNAs, re-analysis of publicly-available RNA-seq data is also useful for studying RNA editing. RNA editing is a post-transcriptional modification to alter the sequence of RNA molecules (Keegan et al., In humans, RNA editing occurs mostly in repetitive Alu regions (Levanon et al., The one obvious usage of lncRNAs in medicine is using lncRNAs as diagnostic biomarkers as lncRNAs are more cell-type specifically expressed than protein-coding genes (Thurman et al., All authors made contributions to survey the current status of lncRNA research. All authors approved the final version of this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer KT and handling Editor declared their shared affiliation.


Programmed cell death 5 (PDCD5) was first identified as an apoptosis-promoting protein and involved in some autoimmune diseases and inflammatory processes. Our previous study demonstrated greater expression of serum PDCD5 in asthmatic patients than controls. This study aimed to further explore the significance of PDCD5 in mice with induced allergic asthma.n = 8) and allergen -challenged mice (n = 8). The modified ovalbumin inhalation method was used to generate the allergic asthma mouse model, and the impact of OVA was assessed by histology of lung tissue and morphometry. The number of cells in bronchoalveolar lavage fluid (BALF) was detected. Pulmonary function was measured by pressure sensors. PDCD5 and active caspase-3 levels were detected.We divided 16 female mice into 2 groups: control (p < 0.05). PDCD5 level was correlated with number of inflammatory cells in BALF and lung function. Moreover, active caspase-3 level was increased in the OVA-challenged mice (p < 0.001) and correlated with PDCD5 level (p = 0.000).The expression of PDCD5 was higher with OVA challenge than for controls  contains supplementary material, which is available to authorized users. Allergic asthma is a chronic inflammatory disorder of the airways; many cells, such as lymphocytes, mast cells, eosinophils, smooth muscle cells, and cellular elements, contribute to its pathophysiological processes. The frequent occurrence of injury and repair initiated by chronic inflammation could lead to structural changes in the airway, collectively termed airway remodeling, which is characterized by epithelial injury, sub-epithelial fibrosis, enhanced deposition of extracellular matrix proteins, goblet cells and mucous gland hypertrophy and increased airway smooth muscle mass . Airway Programmed cell death or apoptosis is a form of cellular suicide widely observed in nature. Duncan et al. demonstrated a significant correlation of reduced apoptosis in eosinophils present in induced sputum with asthma severity . CaspaseProgrammed cell death 5 (PDCD5) was first identified as an apoptosis-promoting protein . It was Here we established a mouse model of allergic asthma and further explored the significance of PDCD5 in bronchial asthma.Chicken egg ovalbumin (OVA) and aluminium hydroxide powder were from Sigma-Aldrich . Periodic acid-Schiff staining (PAS) and Masson’s trichrome staining (Masson) kits were from Shanghai Yuanye Bio-Technology. Mouse anti-PDCD5 monoclonal antibody and the PDCD5 ELISA kit were gifts from Prof. Yingyu Chen . Anti-active caspase 3 antibody was from Abcam .This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the US National Institutes of Health. The protocol was approved by the Committee on the Ethics of Animal Experiments of Peking University Health Science Center (Permit No.: LA2011-062). All surgery was performed with animals under urethane anesthesia, and all efforts were made to minimize suffering.n = 8 each): control and allergen (OVA)-challenged group.BALB/c mice  were obtained from the Department of Laboratory Animal Science . They were kept under pathogen-free conditions and had free access to food and water during experiments. Mice were randomly divided into 2 groups  connected to a pressure sensor. A “Y”-type trachea tube connected to flow sensors and pressure sensors was inserted into the trachea. A ventilator was connected to a flow sensor. The tidal volume was 10 mL/kg and the respiratory rate was 60 times/min. The Powerlab multi-lead physical instrument was connected to flow and pressure sensors. The peak inspiratory flow (PIF), peak expiratory flow (PEF), intra-airway pressure (IP) and maximum rising slope of IP (IP slope) were measured, and data were analyzed by using Chart 4.1 .Mouse lungs were sequentially lavaged three times with 0.5 ml physiological saline. Recovered aliquots of BALF were pooled. BALF cells were pelleted by centrifugation at 2000 rpm for 5 min. Cell differentials were determined with cytospin preparations stained with Wright–Giemsa, and 200 cells were counted. The supernatant was stored at −80 °C.After BALF cytology, lungs were inflated with 10% formalin and immersed in 10% formalin fixation solution. Paraffin-embedded lung sections were stained with hematoxylin and eosin (H&E) for inflammatory cell infiltration and proliferation of smooth muscle, periodic acid-Schiff (PAS) for goblet cells and Masson trichrome for airway fibrosis and collagen deposition. An expert respiratory pathologist blinded to treatment groups graded the extent of inflammation in the lungs according to a semi-quantitative scoring system Scores for inflammatory cell infiltration were 0, no inflammatory infiltrations; 1, sporadic inflammatory cells; 2, more maldistributed inflammatory cells, not gathered into groups; 3, a large amount of inflammatory cells, uniformly distributed but few gathered into groups; and 4, large amount of inflammatory cells gathered into groups.2SO4 (50 μl) and absorbance was measured at OD 450 nm (OD450).The supernatant of BALF (1:10 diluted) was added to 96-well ELISA plates (100 μl/well) and incubated for 60 min at 37 °C. After washing, 1 μg/ml anti-PDCD5 antibody (100 μl/well) was added for incubation at 37 °C for 60 min. After multiple washes, TMB solution (100 μl/well) was added for incubation in the dark at room temperature for 15 min. Color development was stopped by adding 2 M HSections of 4 μm lung tissue were incubated with antibodies for PDCD5 (1:300) and active caspase-3 (1:100) at 4 °C overnight. IHC staining involved use of DAB . The signal was recorded in four grades by intensity of staining: 0, 1+, 2+ and 3+. The percentages of PDCD5-positive cells or active caspase-3 -positive cells were also recorded in four categories: 1 (0–25%), 2 (26–50%), 3 (51–75%) and 4 (76–100%). The sum of the intensity and percentage scores was used as the final staining score.t test to compare two groups. Pearson correlation coefficient (r) was calculated to assess correlations between nonparametric and parametric data. P < 0.05 was considered statistically significant. Data were analyzed by using SPSS 13.0 and GraphPad Prism 5.0.Data are presented as mean ± SD. One-way ANOVA was used to compare multiple samples and Student’s independent p < 0.05) .Compared with controls, OVA-challenged mice showed lower PIF and PEF  Fig. . The IP 4, p < 0.05). Moreover, the counts of macrophages, eosinophils, neutrophils and lymphocytes were higher (p < 0.05)  Table .Table 1Ip < 0.01; Table The allergic asthma model showed increased inflammatory cell infiltration in airways and pulmonary vasculature, goblet-cell hyperplasia, smooth muscle cell proliferation, peribronchial fibrosis and collagen level as compared with controls   and total number of macrophages , eosinophils , neutrophils  and lymphocytes . In lung tissue, PDCD5 staining intensity was negatively correlated with PIF  and PEF  but positively with IP slope  . Moreover, PDCD5 and active caspase-3 levels were positively correlated  (Fig. Active caspase-3 showed positive staining in airway epithelial cells of bronchioles and bronchium of OVA-challenged mice. Its protein was expressed mainly in macrophages and some B lymphocytes and plasmocytes. Active caspase-3 level was higher in OVA-challenged mice than controls (5.875 ± 0.354 vs. 2.375 ± 0.518, Our previous study demonstrated increased serum PDCD5 level in asthmatic patients, which was correlated with clinical manifestations and lung function . In the The asthmatic mouse model is commonly used to study human asthma because of the similarities. OVA-challenged mice show pathological and clinical features similar to that observed in human allergic asthma. The total number and proportion of eosinophils in BALF was increased in our asthmatic mice. Lung function, especially PEF, was decreased; as well, we found inflammatory cell infiltration, goblet cell hyperplasia, increased mucus secretion and collagen deposition around airways.PDCD5 was first characterized as upregulated in cells undergoing apoptosis . Later iAsthma in humans is due to not just allergy. Our previous study of PDCD5 in asthma patients did not specify whether asthmatic patients were allergic or not, whereas the mouse model in this study is an allergic asthma model. Considering the consistent correlation between PDCD5 expression and severity of asthma from clinical research to animal models, although we cannot regard PDCD5 as a potential biomarker for monitoring and controlling asthmatic severity, PDCD5 may play a role in allergic asthma.Moreover, we found increased PDCD5 protein level in airway epithelium and inflammatory cells around airways. PDCD5 could induce apoptosis of many kinds of cells , 35. ThuCaspases are a family of cysteine-dependent aspartate-directed proteases that play essential roles in apoptosis, necrosis, and inflammation . There aZn is an important regulator of caspase-3 . Truong-We found a correlation between PDCD5 expression and severity of asthma in OVA-induced asthmatic mice. PDCD5 may play a role in allergic asthma. Future studies are required to determine the underlying mechanism.


Rice blast disease caused by a fungus Magnaporthae oryzae is one of the most important biotic factors that severely damage the rice crop.Several molecular approaches are now being applied to tackle this issue in rice. It is of interest to study long non-coding RNA (lncRNA) inrice to control the disease. lncRNA, a non-coding transcript that does not encode protein, is known to play an important role in generegulation of various biological processes. Here we describe a computational pipeline to identify lncRNA from a resistant rice line. Thenumber of lncRNA found in resistant line was 1429, 1927 and 1981 in mock and M. oryzae (ZB13 and Zhong) inoculated samples,respectively. Functional classification of these lncRNA reveals a higher number of long intergenic non-coding RNA compared to antisenselncRNA in both mock and M. oryzae inoculated resistant rice lines. Many intergenic lncRNA candidates were identified from resistant riceline and their role to regulate the resistance mechanism in rice during M. oryzae invasion is implied. Rice (Oryza sativa) is one of the important cereal crops cultivatedand consumed throughout the world  and is aRNA transcribed from DNA does not have only to encode protein,but some RNAs that do not translated into proteins also haveability to regulate the gene expression. RNA molecules those arenot encoded proteins are called as non-coding RNA (ncRNA).Nowadays, the involvement of these ncRNA in the regulation ofgene expression has major attention of researchers to study them invarious biotic and abiotic imposed conditions. The ncRNA areclassified based on their size into small ncRNAs (sncRNAs) andlong ncRNAs (lncRNAs). The sncRNAs are generally smaller than200nt in length and further divided into microRNAs (mRNAs),small interfering RNAs (siRNAs), piwi-interacting RNAs, transactingsiRNAs and natural antisense transcript siRNAs , 4.However, lncRNAs are usually have more than 200 nt in length anddivided into 3 groups according to position of nearby proteincoding genes, i.e. long intergenic non-coding RNAs (lincRNAs),natural antisense transcripts (NATs) and intronic RNAs (incRNAs). In planThe RNA-seq data used for this study was downloaded from geneontology omnibus (GEO) via an accession no. GSE62911. Thisaccession GSE62911 contains 24 samples from resistant rice linesharbouring Pi9 blast resistance gene . The detA bioinformatics pipeline was developed in this study to extractlong non-coding RNA from RNA-seq data of rice . AlltheA perl script was used to fetch fasta sequence of transcriptencoding as long non-coding RNA (lncRNA). The fasta and gtf filesof each lncRNA were analyzed to determine the length of long noncodingRNA. The lncRNAs were categorized into differentcategories based on the position of protein coding genes, viznatural antisense lncRNA that overlap exon or intron in antisenseorientation, long intergenic lncRNA and lncRNA overlapping genein sense orientation using FEELnc . The genIn several species like Oryza sativa, Zea mays, Triticum aestivum,Actinidia chinensis, Gossypium arboreum and Brassica genomewidelncRNA has been identified and characterized ,24,25. TThe pipeline developed to retrieve long coding RNA (lncRNA) wasapplied on 24 RNAseq data derived from a blast resistant rice lineupon mock and M. oryzae (ZB13 and Zhong strain) inoculation.Three sets of each eight mock, eight ZB13 and eight Zhongreplicates of resistant rice line from different time-points weremapped against the reference genome O. sativa japonica group,cultivar Nipponbare; MSU release 7  with an So, the three fasta files had all the transcripts whether they haveprotein-coding potential or not. These three fasta files obtainedfrom mock, ZB13 and Zhong were contained total 93326, 95671 and94963 captured transcripts, respectively. After applying length(>200nt), blast  and CPC filter criteria, total 1589, 2101 and 2038transcripts were obtained from resistant line with mock, ZB13 andZhong dataset, respectively. Furthermore, filter out thehousekeeping RNA like tRNAs and rRNAs, the infernal  and tRNA scan were applied to get the final numbersof long non-coding RNA. The final numbers of lncRNAs were 1429,1927 and 1981 found in mock, ZB13 and Zhong dataset,respectively. These numbers clearly reflect that a higher lncRNAswere present in the datasets of resistant rice line challenged by M.oryzae strains compared to the dataset of mock inoculated. Theidentified lncRNA were further grouped into three categories,intergenic lncRNA, lnc overlapping exon or intron in senseorientation and lnc overlapping exon or intron in anti-senseorientation according to their relative locations from the nearestprotein-coding genes. FEELnc classifier module classifies lncRNAsby employing a sliding window strategy. The lncRNAs withoverlapping exon or intron in sense orientation were filtered fromresistant line because major role of intergenic lncRNA and naturalantisense lncRNA is known , 24, 27Similarly, total lncRNAs with overlapping intron in antisenseorientation were also identified from these three datasets; mock,ZB13 and Zhong, i.e. 22, 0 and 3, respectively. Total numbers ofintergenic lncRNAs (lincRNAs) were 437, 52 and 69 found in themock, ZB13 and Zhong datasets, respectively . AmongtThe length distribution of intergenic lncRNA and antisenselncRNA show that highest number of lncRNAs were found in arange of 200 to 1000 nucleotides in all the three datasets, mock,ZB13 and Zhong . A recenThe computational pipeline developed in this study is relevant tofind long non-coding RNAs (lncRNA) in different plants. Thehigher number of intergenic lncRNA found in resistant linecompared to antisense lncRNA indicates the importance ofintergenic lncRNA in resistant rice line upon biotic stress. Theuniform distribution of lncRNA across all the rice chromosomesshows that there is no bias in chromosomal distribution ofintergenic and antiense lncRNAs in mock and M. oryzaeinoculated resistant line.PJ, HD and PKS performed the complete analysis under guidanceof TRS and VS. All authors contributed to the writing of themanuscript.The authors confirmed that this research article content has noconflict of interest.


We used negative stain electron microscopy, size exclusion chromatography-multi-angle laser light scattering (SEC-MALLS) analysis, and fluorescence anisotropy measurements to show how the NP-RNA complexes evolve. Both proteins make small oligomers with 24-nt RNAs, trimers for A/NP, and dimers, tetramers, and larger complexes for B/NP. With shorter RNAs, the affinities of NP are all in the same range at 50 mM NaCl, showing that the RNAs bind on the same site. The affinity of B/NP for a 24-nt RNA does not change with salt. However, the affinity of A/NP for a 24-nt RNA is lower at 150 and 300 mM NaCl, suggesting that the RNA binds to another site, either on the same protomer or on a neighbour protomer. For our fluorescence anisotropy experiments, we used 6-fluorescein amidite (FAM)-labelled RNAs. By using a (UC) Influenza belongs to the group of negative strand RNA viruses (NSVs). These viruses have a RNA genome that is coated with nucleoprotein (NP); the nucleoprotein–RNA complex (N–RNA) is the template for both transcription and replication by the viral polymerase. NP, viral RNA (vRNA), and polymerase constitute the ribonucleoparticle (RNP). Within the group of negative-strain RNA viruses, two genome organizations have been described: viruses with their entire genome made up of a single RNA molecule (non-segmented NSVs) and viruses with segmented genomes. The influenza viruses belong to the latter, with eight segments for types A and B.To date, many X-ray structures of nucleoproteins bound to RNA have been published ,2. The fFor influenza virus, two structures of the nucleoprotein of influenza A (A/NP) and influenza B (B/NP) have been obtained: A/NP trimers ,27 and idK) were found to range from 4 to 70 nM, with experiments done at room temperature, 25 °C, and 37 °C. One test at 4 °C gave a 10-fold higher dK (380 nM). The monomeric mutants (R416A and E339A) bind RNA with much higher dK, between 1 and 10 µM [dK of around 40 nM, whereas the trimer of NP binds with an affinity of 24 nM. They also found that trimers remain trimeric when bound to RNA, whereas the monomer makes longer N–RNA complexes. This suggests that the trimers with RNA are blocked for making larger complexes.Several groups have measured the affinities between the influenza virus A/NP and B/NP towards RNA with different methods . Most stigher Kd 0 nM. Thend 10 µM ,39. TarudK measurements. We wanted to define a condition to obtain a homogeneous NP monomer bound to RNA for X-ray crystallization, but we did not find such condition. This paper describes the problems in making such a complex between NP and RNA. However, we have observed an interesting phenomenon between influenza A nucleoprotein and a 12-nucleotide RNA labelled with a fluorescein probe, which form very large protein–RNA complexes, similar to RNPs purified from the virus.In this paper, we show the effects of RNA binding on the oligomeric state of the NP from influenza viruses A and B, using size exclusion chromatography-multi-angle laser light scattering (SEC-MALLS), electron microscopy, and Escherichia coli BL21 (DE3) cells were transformed with the resulting plasmids. Expression of the recombinant protein was induced by adding 0.3 mM isopropyl-β-d-thiogalactopyranoside . Cultures were grown at 18 °C for 12 h and cells were collected by centrifugation at 4000 g, for 15 min at 4 °C. Pellets were resuspended and sonicated in lysis buffer , 5 mM β-mercaptoethanol  containing a cOmplete™ protease inhibitor cocktail . Purifications were performed at room temperature. Proteins were purified by Ni2+ affinity chromatography  followed by a heparin column . Heparin elution fractions were dialyzed against 20 mM Tris-HCl pH 7.5 at 50 mM, 150 mM or 300 mM NaCl, and 5 mM β-ME. The last purification step was a size-exclusion chromatography using a Superdex increase S200 10/300 GL column . The proteins were eluted in 50, 150, or 300 mM NaCl according to the experimental needs. Peak fractions were concentrated using a 10 kDa concentrator . The protein concentrations were determined using the extinction coefficients at 280 nm for A/NP ε = 55,537 M−1·cm−1 and for B/NP ε = 27,975 M−1·cm−1.The full-length NP genes of the H1N1 strain A/WSN/1933 and of the strain B/Memphis/13/03 were used. A/NP was cloned in pET22b  and B/NP in pETM11 (EMBL). 5′phosphate RNA labelled in 3′ with 6-fluorescein amidite (FAM). Five to 24 nt polyUC RNA  were used. After subtracting the polarization values obtained for RNA alone, the mean value of three independent experiments were fitted to the standard binding equation:y.Fluorescence anisotropy assays were performed on a Clariostar  microplate reader, fitted with polarization filters to measure fluorescence anisotropy. The binding assays were done in 384-well plates at room temperature (60 µL reaction volume) in 20 mM Tris-HCl pH 7.5, 50, 150, or 300 mM NaCl and 5 mM β-ME. Concentrations ranging from 0 to 3 µM of A/NP or B/NP were titrated into 5 nM orescein . Bmax is−1) were applied between a carbon and a mica layer. The carbon was then floated on the top of a heavy atom salt drop (2% (w/v) sodium silicotungstate, pH 7.0). The carbon film was covered by a copper grid. Both were fished using a small piece of paper and air dried before insertion in the electron microscope [5′phosphate-polyUC-FAM3′-NP complex was incubated overnight at room temperature using 100 µM of proteins in 20 mM Tris-HCl pH 7.5, 50, 150 or 300 mM NaCl and 5 mM β-ME and with a final ratio NP:RNA of 1:1. The dilutions for EM were performed with a buffer with the same salt concentration.Samples  using a laser emitting at 690 nm and protein concentration was measured on-line with the use of differential refractive-index measurements, with an Optilab T-rEX detector (Wyatt Technology) and a refractive-index increment (dn/dc) of 0.185 mL·g−1. Weight-average molar masses (Mw) were calculated with ASTRA (Wyatt Technology) as previously described [SEC was performed with a Superdex increase S200 10/300 GL column  equilibrated with 20 mM Tris-HCl, pH 7.5, 50, 150, or 300 mM NaCl, and 5 mM β-ME. Analytical runs were performed at 20 °C with a flow rate of 0.5 mL·minescribed .The nucleoproteins of influenza viruses A and B were purified as monomers with only 5% of the protein forming larger oligomers b. The UV5′phosphate-polyUC-OH3′ was used for the MALLS experiments and 5′phosphate-polyUC-FAM3′ for the negative staining EM and for the fluorescence anisotropy experiments.In order to assess the influence of both salt and RNA size on the binding of monomeric NP, a series of salt conditions  were tested against size-incremented polyUC RNA from 5 to 24 nt. The experiments were performed using SEC-MALLS , negativAll the SEC-MALLS curves of A/NP and B/NP at 50, 150, and 300 mM NaCl are shown in For these experiments different RNA molecules were used with a different time of incubation, overnight for EM instead of 1 h as for SEC-MALLS . For A/N5′phosphate-(UC)6-FAM3′, we can observe oligomeric entities extremely different from all the other conditions. 5′phosphate-(UC)6 the sample shows mainly trimers and a few larger complexes but no rods or worm-like structures. This means that the 6-fluorescein amidite at the 3′-end of the RNA provokes the reorganization of the NP–RNA complexes to these RNP-like entities. We found that the worm-like structures appear only at 150 mM NaCl, but not at 50 mM or 300 mM NaCl. This phenomenon is observed with fluorescein-labelled RNA ranging from 10 to 13 nt but not with 8 or 16 nt 6. 5′phosphate-(UC)6-FAM3′ can form worm-like structures. However, it is still to be determined why the labelled RNA makes these interesting entities that are not observed with the unlabelled RNA. However, interestingly, we also show by MALLS that A/NP and B/NP, in complex with labelled RNA, have a tendency to form higher oligomeric species at 150 mM NaCl compared to RNA without FAM (Many studies use “FAM” for fluorescence anisotropy to measure affinities between proteins and RNAs. Zheng and co-workers  have usehout FAM . Fluoreshout FAM .In this paper, we tried to understand the oligomerization process of A/NP and B/NP with respect to the salt concentration and the RNA length. Our results show many differences between the two proteins but it remains difficult to explain these without crystal structures of NP with RNA. However, we show that to crystallise the protein in the presence of RNA, it is necessary to find a compromise between the salt concentration and the RNA length. An unlabelled 12-nt RNA could be a good compromise between these two criteria because it binds strongly to the protein and is not too long to interfere with the crystallisation process. We also show that it is possible to reconstitute an RNP-like structure using recombinant NP in the absence of the RNA polymerase. We cannot yet explain why we can only have this species with a labelled RNA but it is a very interesting tool to start further investigations towards better understanding the interaction between NP and RNA.


These functionalities are achieved via the establishment of a complex network of long-distance RNA–RNA contacts involving (at least as known to date) the highly conserved 3′X tail, the apical loop of domain IIId in the internal ribosome entry site, and/or the so-called Alt region upstream of the CRE. Changing contacts promotes the execution of different stages of the viral cycle. The 5BSL3.2 domain thus operates at the core of a system that governs the progression of HCV infection. This review summarizes our knowledge of the long-range RNA–RNA interaction network in the HCV genome, with special attention paid to the structural and functional consequences derived from the establishment of different contacts. The potential implications of such interactions in switching between the different stages of the viral cycle are discussed.Viral genomes are complexly folded entities that carry all the information required for the infective cycle. The nucleotide sequence of the RNA virus genome encodes proteins and functional information contained in discrete, highly conserved structural units. These so-called functional RNA domains play essential roles in the progression of infection, which requires their preservation from one generation to the next. Numerous functional RNA domains exist in the genome of the hepatitis C virus (HCV). Among them, the 5BSL3.2 domain in the  Predicting how RNA virus populations might evolve – a major public health goal – is a challenging task. RNA viruses, such as hepatitis C virus (HCV), replicate by virtue of a relatively low fidelity viral genome-encoded RNA-dependent RNA polymerase. It has been calculated that for each round of replication the mutation rate in HCV reaches a maximum of 1.3 nucleotides (nt) . The varcis-functional RNA domains that operate in an interconnected manner to perform functions essential to the execution of the viral cycle     . Viral pigure 1) . It is nigure 1) , but onligure 1) . The affigure 1) . The 5′ igure 1) . These oigure 1) . Some ofFigure 2). This is one of the most conserved genomic regions, with ∼85% of sequence identity preserved across viral isolates , in the absence of any other factor, can direct the recruitment of the 40S ribosomal subunit . The 48Synthesis . In addiynthesis , or perhynthesis  or 2D . However, several questions remained unclear during past years. In a recent work , new rolFigure 2)    . Under pigure 2) . Far froigure 2) . Domainsigure 2) , which gigure 2) . Domain igure 2) .Figure 2), which operate as recruiting platforms for the binding of eIF3 (in the JIIIabc junction)    or the 4igure 2) .In addition to the well-known IRES-protein interactions, three GGG residues within the essential apical loop of subdomain IIId specifically recognize  the highly conserved CCC triplet in structural element helix 26 of the rRNA 18S . This inThe molecular and functional organization of the HCV 5′UTR provides clear evidence of how information contained in genomic RNA domains governs the progress of the infective cycle.Figure 3):Further proof exists of the essential nature of genomic functional domains in HCV RNA. The HCV 3′UTR is a ∼200–250 nt-long sequence that contains essential functional domains required for viral replication and translational control . Three w(i)Figure 3) ( The hypervariable region (HV); this is around 40 nts long and shows low-level sequence conservation among HCV isolates . Howeverigure 3) . Though igure 3) .(ii)Figure 3; Figure 1) have been shown to preferentially bind poly(U) sequences in vitro Figure 3)  have been identified by chemical modification assays , which exposes the highly conserved k sequence motif complementary to the apical loop of the upstream 5BSL3.2 domain (see below) . Nevertheless, recent NMR and SAXS studies have led to the proposal of the existence of a predominant, relatively rigid conformation defined by two stem-loops exposing the palindromic sequence motif DLS   . This conformation favors the initiation of primer-independent RNA synthesis via the viral RNA-dependent RNA polymerase NS5B protein  . This 98n assays  and NMR n assays : a threee below) , and a tigure 3) . The DLSigure 3)  allowing protein . All thi protein , suggest protein . Dimer g protein , helpingAs well as its essential role in RNA replication, the 3′UTR functions as a translation enhancer . This isThe HCV 3′UTR thus plays various roles during the viral cycle, controlling the progress of infection via a collection of functional elements that can switch between different structural states.cis-acting signals have been identified in the ORFs of RNA viruses , expanding the functional repertoire of the viral genome via their participation in viral translation, replication, and infectivity   . One of Figures 1, 4; cis-acting replication elements (CRE). The central domain 5BSL3.2 was shown indispensable for HCV propagation . The stem is interrupted at its 3′ end by an 8 nt-long bulge. Interestingly, both unpaired regions are phylogenetically conserved across different genotypes and show low synonymous site sequence variation    that canigure 4) , 2015. Iigure 4) . This obFigure 4)  . These pFigure 5) . RNase and chemical mapping have confirmed this secondary structure and support the proposed large cruciform structure  . Domainsigure 5)  that foltructure . HoweverThe remarkable structural features of the 5BSL3.2 domain, as well as its strong conservation, point to its critical involvement in the HCV infective cycle. As mentioned above, the observation that both the apical loop and the bulge of the 5BSL3.2 domain are highly preserved among different genotypes and viral isolates suggests their participation in interactions with other viral RNA elements. Several reports have provided substantial proof of the existence of long-distance RNA–RNA contacts, together forming a complex network of interactions that appears to be governed by 5BSL3.2 . Such a Figure 5)  . Importaigure 5) . This coigure 5) . HoweverFigure 5) . Such conformational rearrangements would affect IRES function due to their interference with the efficient recruitment of the 40S ribosomal subunit mediated by the subdomain IIId  . The aroain IIId ,b. This ain IIId . The 5BSk, located in the apical loop of the downstream 3′SL2 element within the 3′X tail   . The bioes 3, 5) . These aes 3, 5) . Recent  motif k . Hence, Figure 5)  . This coigure 5) . ChoosinFigures 3, 5). As mentioned above, this is a favored folding state for virus replication   . Intereses 3, 5) . These ses 3, 5) .The creation of this complex network of RNA–RNA interactions promotes the structural remodeling of the viral genome, not only in the directly involved domains, but also in distant regions. This phenomenon achieves complex and interconnected genomic-RNA-folding-dependent regulatory pathways. Indeed, the conformational rearrangement mediated by the 5BSL3.2 domain at the 3′ end is regulated by the IRES region, most likely as a consequence of the interaction IIId-5BSL3.2 . Using cThe folding of the IRES region is also influenced by the presence of the 3′ end of the HCV genome. The 3′UTR and the CRE region have been shown to effect remarkable changes in subdomains IIIe and IIIf  – essentThe 5BSL3.2 domain thus participates in the establishment of different long-range RNA–RNA interactions in the absence of proteins. This helps create a complex network of contacts, the careful regulation of which allows the HCV genome to acquire different functional conformational states, facilitating proper switching between the different stages of the viral cycle.The recruitment of both host and viral components is important in the regulatory activities of the CRE region . The apiIn recent years, exhaustive lists of cellular proteins susceptible to recruitment by the CRE region have been produced . InteresSubsequent proteomic analyses have identified a collection of RNA-binding proteins with highly conserved RNA recognition motifs able to bind to the CRE region . hnRNPA1∗ represses RNA replication (MicroRNAs (miRNAs) produced by the host cell have been identified as agents that regulate viral infection . Via a llication . Let-7b lication . Let-7b lication . This, aThe HCV cycle is extensively compartmentalized, both spatially and temporally: viral protein synthesis, replication, and encapsidation occur in different cellular localizations and do not overlap in time . The swiFigure 6; for a review see Figure 6). The emerging HCV polyprotein is co- and post-translationally cleaved by cellular proteases and viral NS2-NS3 and NS3-NS4A proteases to release 10 HCV proteins . This contact promotes the viral genome’s acquisition of a circular form. In addition, the interaction IIId-5BSL3.2 blocks the recruitment of 40S ribosomal particles by the IRES, contributing to the creation of a translationally repressed-state . At this time, the IRES region is occluded by the translational machinery, favoring the interaction Alt-5BSL3.2. Because of the high concentration of viral RNAs, genomic dimerization is thermodynamically favored . This results in viral RNA storage, but HCV dimeric genomes are also excellent templates for the initiation of replication .During early infection, HCV virions are endocytosed and their genomes released into the cytosol by a little-understood mechanism  . Viral Rigure 6) . In a lalication . AccordiThe model proposed above recapitulates many of the findings made regarding the molecular biology of HCV. More importantly, it provides an overview of the critical role of long-distance RNA–RNA contacts in the progression of the infective cycle, and points to 5BSL3.2 as a multifunctional component encoded in the viral genome.The contrast between the high structural conservation and sequence variability of the HCV RNA genome reflects an efficient mechanism for retaining essential functionalities in certain structural elements while favoring the acquisition of novel capabilities. Whereas many genomic domains govern essential steps in infection, the control exerted by 5BSL3.2 at multiple levels reflects the functional versatility and fine regulation that can be achieved by a single stem-loop. This domain is thus an interesting target for novel therapeutic agents. Exploring the structural dynamics of functional RNA domains offers a powerful, informative methodology for future drug design. Extending the knowledge acquired in HCV to related viruses, such as flaviviruses, will allow conformational maps of common structural domains to be produced, help reveal similar viral mechanisms involved in the infective cycle, and perhaps contribute to the development of novel therapeutic and diagnostic strategies.CR-L and AB-H designed and wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


During the last ten years, next generation sequencing methods, technologies and platforms have revolutionized genomics and transcriptomics research fields and advanced their applications in agriculture and biomedicine –3. To daBoth DNA and RNA sequencing projects have been largely carried out using these platforms, which require construction of relevant libraries. Depending on the project goals, genomic DNA sequencing can be classified into 1) whole genome sequencing/re-sequencing and 2) reduced genome complexity sequencing. However, transcriptome analysis methods can be classified in different ways. When each transcript is considered as a unit, transcriptome sequencing can be grouped into two categories: 1) end to end (5′ – 3′ end) sequencing and 2) either 5′ end or 3′ end sequencing only. Alternatively, transcriptome analysis can be conducted by sequencing specific RNA sub-populations, such as polyA+ RNA sequencing, polyA- RNA sequencing, microRNA sequencing, circular RNA sequencing, isoform sequencing or others [Nevertheless, the original DNA and RNA molecules are manipulated during library preparation. Fragmentation with chemical treatment, enzyme digestion or physical shearing is often used, breaking DNA/RNA molecules into pieces. Enrichment is a process to develop libraries for specific sub-groups or targets/signatures of DNA/RNA for sequencing. Both 5′ and 3′ end adaptors are ligated to fragmented DNA/RNA molecules, forming “5′ adaptor – DNA/RNA inserts – 3′ end adaptor” sandwiches that fit into sequencing platforms. To date, direct RNA sequencing  is the oIt is well known that RNA-seq data analyses possesses three biases: fragmentation bias, length bias and transcriptome composition bias ,6. When Zea mays genome reference, frequency of enzyme ApeKI recognition sites in the reference genome and size selection of 70 – 318 bp in library construction, Beissinger and colleagues [Genotyping by sequencing leads to biases as well, particularly in number of reads per sample, number of sites sequenced per sample and number of reads per site. For example, Byrne and colleagues  reportedlleagues  predicteEnd-to-end sequencing of transcripts by RNA-seq is not cost-effective for conventional transcriptome analyses. As such, the community has developed more than 15 methods and technologies to carry out whole transcriptome tag/target sequencing . These mIL1A) were present in the library. Generally speaking, the 3′ most tag is expressed most abundantly, but the numbers of tags per million (TPM) for other tags decrease significantly depending on their distances from poly(A) end of the mRNA. Nevertheless, the results indicate that SAGE-based tools generate noisy data for transcriptome analysis. In fact, only the 3′ most cut site tag is the only tag that should be used in analysis. If other tags were wrongly chosen for analysis, expression patterns/trends of a given gene might be dramatically different.The SAGE  technique was designed to catch one tag per transcript only . HoweverLibrary preparation often uses ligases, either T4 ligase for DNA ligation or T4 RNA ligase for RNA ligation. As discussed above, ligation adds adaptors/linkers to 5′ end and/or 3′ end of DNA/RNA inserts, allowing them to fit in specific sequencing platforms. In reality, not only 5′ adaptors – DNA/RNA inserts – 3′ adaptors, but other combinations within inserts or between adaptors and inserts can occur randomly, especially when DNA/cDNA molecules are digested with restriction enzymes. Our recent experience shows that even a 120 bp read can contain three digested fragments that are located on different chromosomes. For example, after “unchaining” of the ligated artifacts, 106 million reads were increased up to 132 million reads with ≥ 36 bp in length. As a consequence, the unique mapping rate was also improved from ~45% to ~65%. In my opinion, the community needs to pay more attention to the artifact issues that occur during the preparation of next generation sequencing libraries.Here I just listed a few examples about biases, noisy data and artifacts due to DNA/RNA manipulation during preparation of the next generation sequencing libraries. Therefore, bioinformaticians should get to know how the libraries are prepared before they can develop programs and software to handle these challenges sufficiently and carefully. In particular, we need to be cautious if we would develop tools to explore polyadenylation events using RNA-seq data, because the 3′ UTR reads are biased and incomplete. We would not collect reliable information if we simply use the tag counts derived from a SAGE library or its derivatives to determine alternative polyadenylation events or proximal to distal/distal to proximal site shifts. Furthermore, ignoring artifact reads is not cost-effect. Next generation sequencing technologies generate high quality reads since none of the platforms use Sanger sequencing. However, data analysis errors will occur if we do not pay attention to biases, noisy data and artifacts.


Self-reported health (SRH) is a measure widely used in health research and population studies. Differences in SRH have been observed between countries and cultural values have been hypothesized to partly explain such differences. Cultural values can be operationalized by two cultural dimensions using the World Values Survey (WVS), namely the traditional/rational–secular and the survival/self-expression dimension. We investigate whether there is an association between the WVS cultural dimensions and SRH, both within and between countries.Data from 51 countries in the WVS is used and combined with macroeconomic data from the Worldbank database. The association between SRH and the WVS cultural dimensions is tested within each of the 51 countries and multilevel mixed models are used to test differences between these countries. Socio-demographic and macroeconomic variables are used to correct for non-cultural variables related to SRH.Within countries, the survival/self-expression dimension was positively associated with SRH, while in most countries there was a negative association for the traditional/rational–secular dimension. Values range between 4 and 17% within countries. Further analyses show that the associations within countries and between countries are similar. Controlling for macroeconomic and socio-demographic factors did not change our results.The WVS cultural dimensions predict SRH within and between countries. Contrary to our expectations, traditional/rational–secular values were negatively associated with SRH. As SRH is associated with cultural values between countries, cultural values could be considered when interpreting SRH between countries. Self-reported health (SRH) is one of the most widely used health measures in academic research and is often included in population surveys, such as the European SHARE , the worCulture has been described as “The rich complex of meanings, beliefs, practices, symbols, norms and values prevalent among people in a society” . AlternaHaving introduced cultural values, we consider their possible role in SRH within and between countries. Within countries, there is evidence that cultural values play a role in SRH. Zola has compared symptoms reported by Italian-American and Irish-American patients with an identical diagnosis , 21. TheBetween countries, several studies have found evidence for a role of cultural values on SRH. Jürges found differences in mean SRH between countries and hypothesizes that cultural values may explain those differences . MackenbThe above findings give rise to the hypothesis that cultural values are related to SRH. The aim of this study is then to determine such a relation exists, both within and between countries. This leads to the following two research questions:Is there an association between the WVS cultural dimensions and self-reported health within countries?Is there an association between the WVS cultural dimensions and self-reported health between countries?We formulate the following hypotheses. First, findings within countries suggest cultural values determine how people perceive their health –22. ThisThe association found between countries, the ecological level, may not be representative for the associations within countries, the individual level, which troubles the interpretation of associations between countries. For instance, a positive association for cultural values and SRH may exist between countries, while a negative association exists within countries. Extrapolating the between country level to the individual level would then lead to a false inference. This problem is called the ecological fallacy. To avoid it, we assess the associations both between and within countries. Thus between country associations can be interpreted in the light of within country associations. Socio-demographic factors will be used as control variables, as their importance for SRH has been shown in previous studies. Macroeconomic variables are also included as control variables, as they can account for non-cultural differences between countries and are shown to be correlated with happiness .To fulfill the aims of this research paper, cultural values need to be operationalized into a quantifiable concept that discriminates between countries. The World Values Survey (WVS) Association has done so and their data is used here. The World Values Survey Longitudal Data  and the Figure WVS researchers Ronald Inglehart and Christian Welzel have been able to create the Inglehart–Welzel cultural map , 19, whiThe WVS and EVS databases contain a variety of socio-demographic variables, such as gender, education, income, self-perceived social class and age. Gender is coded as 0 for female and 1 for male. Education, income  and self-reported social class are categorical variables that have 1 as lowest value and 5 or 10 as highest values. Self-reported health was a categorical variable coded as (1) Very good, (2) Good, (3) Fair, (4) Poor or (5) Very poor. SRH was reversed to (5) very good health to (1) very poor health. Macroeconomic data is obtained from the World Bank . The macFor research question 1, information is needed on the coefficients of the association between cultural values and SRH, which will be provided by models 1 and 2. Regressions are performed for each country separately. In both models, SRH is the dependent variable and the two cultural dimensions are the independent variables. Model 2 also includes socio-demographic variables.A subscript To test whether the intercepts and slopes from model 1 and 2 differ between countries, models 3, 4 and 5 are constructed. These multilevel mixed effects models include random slopes for the cultural variables and random country dependent intercepts. SRH is the dependent variable, the WVS cultural dimensions are the independent variables and country is the level variable.The mixed models can be interpreted as following: For research question 2, information is needed about the association between countries. As multilevel models do not generate such an association between countries, a simple regression analysis with country level values for the cultural values and SRH was performed, to determine an association for the cultural dimensions and SRH between countries. Model 6 provides a mathematical representation of this regression analysis.As in the earlier models, A sample from the IVS was obtained, including 506,268 respondents. Some of the macroeconomic data was not available for the first two waves of the WVS, which led to the exclusion of the first two waves. The unavailable macroeconomic data concerns mostly health-related variables, while variables such as GDP per capita were available and led to the exclusion of 92,456 cases. Furthermore, not all questions were asked in each country and wave, which led to the exclusion of 142,468 more cases. Missing macroeconomic data and unasked questions reduced the dataset to 271,344 cases. An additional 113,761 cases contained missing values for SRH, the cultural values or socio-demographic data, leaving 157,583 cases from 51 countries to be used for our within-country analyses. The mixed models contained a minimum of 45 countries, totaling 100,590 respondents. The difference between the sample sizes of the regression models and the mixed models is caused by missing macroeconomic data, mainly in wave 3 of the WVS. No data estimation for missing data was used.Means of SRH differ per country; all countries in the sample have a mean between 3.1 and 4.4. Figure Regressions for SRH and cultural values were performed for 51 countries, using models 1 and 2. The coefficients for each cultural dimension per country are presented in histograms in Figs. The results of the mixed models, Eqs. , 4) and and4) anThe between country regression coefficients from model 6 are reported in Table entclass1pt{minimaentclass1pt{minimaOur main finding is that the associations between the two cultural dimensions and SRH are similar both within and between countries. Within countries, there is a positive association between the survival/self-expression dimension and SRH and a negative association between the traditional/rational–secular variable and SRH. The amount of variance explained by cultural values varied between 4 and 17% for each country. Adding socio-demographic and macroeconomic variables left the association between the two cultural dimensions and SRH unchanged, while doubling the amount of variance explained. Between countries, similar associations were found as within countries. Cultural values can result in a 0.75 change on the 5 point SRH scale in the extreme case. The associations within and between countries were similar, although the slopes and average SRH differ between countries.Our results show a similar association between cultural values and SRH within and between countries. This similarity is relevant, as within and between country associations are not necessarily the same. It is possible that individual associations do not hold on a country level or vice versa. Our results present evidence that these associations are similar, which allows us to extrapolate the association between cultural values and SRH from one level to the other. Thus we avoid the ecological fallacy of making inferences at the ecological level, while the association at the individual level is unknown. This is an important result of our research, as we can now justify claims about the association between countries by similar findings at the individual level.From our Western European point of view, we hypothesized that both cultural dimensions would be positively associated with SRH. Western countries score relatively high on rational–secular and self-expression values and are usually considered to have sophisticated health care. In agreement with this, the survival/self-expression variable was positively associated with SRH, confirming a finding by Inglehart and Baker . This isContrary to our hypothesis, the traditional/rational–secular dimension was negatively associated to SRH. This implies that traditional values are related to higher SRH and rational–secular values to lower SRH. We can only provide ad hoc explanations for this relation. Traditional values are related to a high importance of authority, intolerance for abortion and homosexuality, religion and family ties, while rational–secular values imply the opposite. It is well known that family ties and social support play a role in the well-being of individuals, shown by high correlates of social functioning and mental and physical health in quality of life measures . This suBetween countries, differences in SRH, and also differences in the associations between cultural values and SRH have been found. These differences, shown by the significant random effect in slopes in the mixed models, will not be further explored. A purely methodological explanation could be, for example, measurement error. Another explanation might be that cultural concepts or health differ between countries. The significant random intercepts in the mixed models show that SRH differs between countries, confirming earlier findings. The variance in slopes and intercepts suggests that SRH is not only explained by cultural values, socio-demographic and macroeconomic variables, but also by other unaccounted differences between countries.One of the limitations of this study is that the IVS and Worldbank databases contain many missing values. The original IVS database included some 500,000 respondents, while our final dataset contains only 157,583 respondents for the within country regressions and 100,590 respondents for the mixed models. This reduction is mainly caused by missing macroeconomic data and unasked survey questions in a substantial amount of the waves in the IVS. This accounts for around 230,000 missing values, while around 110,000 missing values arise for other reasons. Additional analyses revealed no substantial differences between the final dataset and the data that was excluded from analysis and the remaining dataset is still very large and contains more than 1000 respondents per wave for each country in most cases. Therefore, we assume that there is no systematic selection bias. Another limitation is that we make the assumption that the association between cultural values and SRH does not change over time. WVS data were collected in waves, but these waves were collapsed into a single wave because our focus is on differences between countries and not on temporal effects. Furthermore, another limitation of our study is that cultural subgroups within countries may have a different relation between cultural values and SRH than the relation at the aggregate group level (that is country level), for which we cannot control. Furthermore, other literature suggests that differences in response styles might cause differences in SRH between countries . HoweverThe main strength of this study is that we avoid the ecological fallacy. Another strength of this study is that it confirms the associations between SRH and socio-demographic and macroeconomic variables found in earlier studies –9. LastlSRH is widely used in academic research and population studies as a measure of health. Large cross-country studies such as the Survey of Health, Aging and Retirement in Europe (SHARE) , the OEC


Identification of acute or recent hepatitis C virus (HCV) infections is important for detecting outbreaks and devising timely public health interventions for interruption of transmission. Epidemiological investigations and chemistry-based laboratory tests are 2 main approaches that are available for identification of acute HCV infection. However, owing to complexity, both approaches are not efficient. Here, we describe a new sequence alignment-free method to discriminate between recent (R) and chronic (C) HCV infection using next-generation sequencing (NGS) data derived from the HCV hypervariable region 1 (HVR1).p < 9.58 × 10−4) differences in the means and frequency distributions of PhyChem features were found between HVR1 variants sampled from patients with recent vs chronic (R/C) infection. Moreover, the R-associated variants were found to occupy distinct and discrete PhyChem spaces. A radial basis function neural network classifier trained on the PhyChem features of intra-host HVR1 variants accurately classified R/C-HVR1 variants (classification accuracy (CA) = 94.85%; area under the ROC curve, AUROC = 0.979), in 10-fold cross-validation). The classifier was accurate in assigning individual HVR1 variants to R/C-classes in the testing set  and in detection of infection duration (R/C-class) in patients (CA = 88.45%). Statistical tests and evaluation of the classifier on randomly-labeled datasets indicate that classifiers’ CA is robust (p < 0.001) and unlikely due to random correlations (CA = 59.04% and AUROC = 0.50).Using dinucleotide auto correlation (DAC), we identified physical-chemical (PhyChem) features of HVR1 variants. Significant  for further testing and validation.The PhyChem features of intra-host HVR1 variants are strongly associated with the duration of HCV infection. Application of the PhyChem biomarkers to models for detection of the R/C-state of HCV infection in patients offers a new opportunity for detection of outbreaks and for molecular surveillance. The method will be available at  Hepatitis C is a liver inflammation caused by HCV. Approximately 80% of HCV-infected individuals develop a life-long (chronic) infection, while the other experience a short-term infection and clear the virus . AccuratWe have recently shown that genetic diversity of intra-host HVR1 variants is associated with duration of HCV infection and can be applied for the detection of recent (R) or chronic (C) infections . The stuThere are myriads of ways for transforming DNA/RNA sequence data into numerical representations. One of the most informative representations is based on using scads of PhyChem properties for individual nucleotides or various combinations of nucleotides . The aimn = 15,041) sampled from 301 HCV-infected patients diagnosed with chronic (n = 123) or recent (n = 178) infection –patients infected for more than 1 year or less than a year, respectively– were described in our previous study , was generated using the following equations:FPR is the false positive rate and TPR is the true positive rate. Computation of AUROC values was done by computing the probability that the RBFNN classifier ranks a randomly chosen positive instance above a randomly chosen negative instance, which was accomplished by calculating the ρ statistic from the U statistic. Equations and description of the method can be found in [Four metrics used to evaluate the RBFNN classifier(s) are reported herein: classification accuracy (CA), Fmentclasspt{miniman = 124) and C (n = 98) patients. Small (range of mean differences: 0.003–0.019) but significant (p < 2.20X10−16) variance in nt frequencies was observed between the R- and C-associated HVR1 sequence variants (Table p < 2.2 × 10−16) correlation to R/C classes by Pearson’s product-moment correlation tests . Differential distribution of the R/C-associated HVR1 PhyChem variants was observed in equal-width binning plots  and t-values > 3.30 for the remaining 145 PhyChem indexes of DNA dimers used to represent PhyChem variants of HVR1. Differences in the means of such indexes between the R- and C-associated HVR1 PhyChem variants (range of mean differences: 0.003 to 0.068) were found statistically significant  and statistical significance (p < 2.2 × 10−16). The HVR1 DNA PhyChem features (n = 15) with statistically significant (p < 2.2 × 10−16) medium correlation  to R/C classes are shown in Table n = 10,927) by a merit scoring method [Merit ≤ 0.416) to the R/C classes could be observed for feature subsets comprised of only 22 DNA PhyChem features of HVR1. Moreover, such feature-class associations were not found in the randomly-labeled datasets .Among all tested, 145 DNA PhyChem features of HVR1 were found to have small-to-medium correlation with the R/C classes at the statistical significance level of Similar analyses on the HVR1 QS data from 25 C- and 54 R-patients indicated no major differences between the training/test data in terms of the minimum/maximum range of values for the 148 DNA PhyChem features investigated here, as well as in terms of the differential R/C-association in the distributions of the PhyChem variants in binned plots (data not shown).n = 124) and C (n = 98) patients. In a MDS plot, the R-associated PhyChem variants of HVR1 were observed to occupy a more central and restricted PhyChem space than the C-associated variants, which displayed a much broader distribution  differences with the training set used to initialize the RBFNN classifier prior to validation with the test dataset  differ between the R- and C-associated HCV strains examined in this study. However, differences between the R/C HVR1 variants became markedly appreciable after applying the DNA DAC transformation method to the NGS data (Table p < 2.20X10−16) correlation to R/C, with R-values ranging between 0.137–0.539, represents a 96.2% increase of correlative features over the original 4 nt-based information . In addition, in binned plots  the DNA DAC-based features provide a better discrimination for differentiation between the R/C classes than the nt diversity alone; and (ii) more importantly, there are substantial differences in the PhyChem structure between HVR1 variants from the R and C classes.Welch’s t-test indicated that the nt frequency and PhyChem property distributions along the HVR1 genomic region significantly  for further testing and validation.Unlike the laboratory diagnostic methods for identification of acute HCV infection, our approach is based on extracting PhyChem features from NGS data and using an RBFNN classifier for identification of the R/C-infections, and, thus, suitable for being hosted by Global Hepatitis Outbreak and Surveillance Technology (GHOST) – a web-based virtual diagnostic system for extraction of public health information from sequence data (see paper in this issue). In addition, our study highlights the importance of considering genomic regions that encode IDPs or IDPRs as potential sources of predictive biomarkers, as well as relevance of the examination of HVR1 in biomarker discovery projects for detection of HCV-related traits. We are currently expanding investigation into DNA PhyChem features expressing higher tiers of interaction between nt-dimers  and finalizing a python-based script, which will be made available to authenticated users of GHOST (


Aplysia given long-term sensitization (LTS) training induced sensitization when injected into untrained animals; furthermore, the RNA-induced sensitization, like training-induced sensitization, required DNA methylation. In cellular experiments, treatment with RNA extracted from trained animals was found to increase excitability in sensory neurons, but not in motor neurons, dissociated from naïve animals. Thus, the behavioral, and a subset of the cellular, modifications characteristic of a form of nonassociative long-term memory (LTM) in Aplysia can be transferred by RNA. These results indicate that RNA is sufficient to generate an engram for LTS in Aplysia and are consistent with the hypothesis that RNA-induced epigenetic changes underlie memory storage in Aplysia.The precise nature of the engram, the physical substrate of memory, remains uncertain. Here, it is reported that RNA extracted from the central nervous system of  Aplysia can be successfully transferred by injecting RNA from sensitized into naïve animals. Moreover, a specific cellular alteration that underlies sensitization in Aplysia, sensory neuron hyperexcitability, can be reproduced by exposing sensory neurons in vitro to RNA from trained animals. The results provide support for a nonsynaptic, epigenetic model of memory storage in Aplysia.It is generally accepted that long-term memory (LTM) is encoded as alterations in synaptic strength. An alternative model, however, proposes that LTM is encoded by epigenetic changes. Noncoding RNAs (ncRNAs) can mediate epigenetic modifications. Therefore, RNA from a trained animal might be capable of producing learning-like behavioral change in an untrained animal. Here, it is demonstrated that the memory for long-term sensitization (LTS) in the marine mollusk Aplysia  were obtained from Alacrity Marine Biological Services and initially housed in a 50-gallon aquarium filled with cooled (12–14°C), aerated seawater. For the experiments, the animals were placed individually into custom-built Plexiglas chambers that were continuously perfused with cooled (14°C) seawater. One day before training, each animal was implanted bilaterally with Teflon-coated platinum wires . For this procedure, the animal was anesthetized by cooling in cold seawater (4°C) for 13 min. Wires, prepared by removing the Teflon from the ends with forceps, were threaded through a 20-gauge needle, which was used to insert the wire into the animal’s tail. Following this procedure, the animal was placed into the experimental chamber, where it was given 24 h to recover and acclimate to the chamber. The siphon-withdrawal reflex (SWR) was tested as follows: The siphon was lightly stimulated with a soft, flexible probe and the duration of the resulting SWR was timed. Timing of the SWR began once the siphon had retracted completely beneath the parapodia and ended as soon as the siphon reappeared. Responses were given a score of 1.0 s if the siphon did not withdraw completely into the parapodia. Three pretests were delivered once every 10 min, beginning 25 min before the start of training . Sensitization training comprised two rounds of training separated by 24 h. Each round of training consisted of five bouts of tail shocks delivered at 20-min intervals. During each bout of training, the animal received three trains; the intertrain interval was 2 s. Each train was 1 s in duration and consisted of shocks  delivered to the animal’s tail via a Grass stimulator  connected to the platinum wires. A single posttest of the SWR, performed exactly as the pretests, was made at 48 h after the start of training. The testing and training were conducted by different experimenters, and the tester was blind to the experimental treatment of the animal.Adult training A, 2A. SeC, In the experiments involving RNA injections (see Results), naïve animals were given three pretests, identical to those that preceded the sensitization training, at 30, 20, and 10 min before the injection C, 2C. A g for 15 min. The upper aqueous phase was transferred into a new tube. The sample was then centrifuged for 10 min at 4°C after addition of 500 μl isopropanol to precipitate the RNA. The resulting RNA pellets were washed with 70% ethanol and centrifuged for 2 min at 4°C. After being air-dried for 10 min, the RNA pellet from each tube was dissolved in 30 μl DIH2O; then the RNA from ganglia dissected from trained animals  was combined, or the RNA from ganglia dissected from untrained animals was combined, into a single tube, and the RNA concentration was measured using Nano Drop (Thermo Fisher ND-1000). After the RNA concentration had been determined, 70 μg of the combined RNA was aliquoted and ASW was added to this aliquot to attain a volume of 100 μl; this solution was then injected into the hemocoel of an animal via its neck. Each recipient animal therefore received 70 µg of either RNA from trained animals or RNA from control animals.To prepare a single RNA injection, the pleural-pedal and abdominal ganglia were removed from four to five sensitization-trained animals, or from four to five untrained controls, immediately after the 48-h posttest. The total RNA was then extracted from the dissected ganglia. The ganglia were initially homogenized in TRIzol reagent for 30 s; typically, 1 ml TRIzol was used to homogenize the central ganglia from two animals. For every 1 ml TRIzol reagent, 200 μl chloroform was added and mixed by vortexing for 15 s. After incubation at room temperature for 5–10 min, the sample was centrifuged at 12,000 × The DNA methyltransferase (DNMT) inhibitor RG108 (Sigma) was dissolved in DMSO to a concentration of 25 mM. To inhibit DNMT, a volume of 100 μl/100 g of body weight of RG108 was injected intrahemocoelically into each animal C.Aplysia sterile hemolymph and 50% Leibowitz-15 . During electrophysiological recording the cell cultures were perfused with 50% ASW and 50% L-15 (recording medium). The recordings from isolated neurons were made using dissociated neurons that had been in culture for 5 d at the start of the experiments. For the experiments on synaptically coupled pairs of neurons (sensorimotor cocultures), the neurons were in culture for 3 d before the initial recordings. The neurons were impaled with sharp micropipettes (20–30 MΩ) filled with 1.5 M potassium acetate, 0.5 M potassium chloride, and 0.01 M HEPES (pH 7.2). The recorded voltage signals were amplified with an Axoclamp 2B amplifier (Molecular Devices), digitalized with an ITC-18 (Instrutech), and acquired and stored using Axograph software.Pleural sensory neurons and small siphon (LFS) motor neurons were individually dissociated from adult animals and placed into cell culture . Some ofDuring the measurements of the biophysical properties of isolated sensory and motor neurons, the cell membrane potential was current clamped at –50 mV. The action potential (AP) firing threshold was determined by injecting 2-s current pulses of incremental intensity (0.1 nA for the sensory neurons and 0.01 nA for the motor neurons). Cells were injected with a 2-s steady pulse of suprathreshold positive current for the measurements of neuronal excitability . In the In the experiments involving sensorimotor cocultures, the amplitude of the monosynaptic EPSP evoked by a single presynaptic AP was assessed on day 1 of the experiment. For this purpose, the presynaptic sensory neuron and postsynaptic motor neuron in the coculture were impaled with sharp microelectrodes. To prevent the motor neuron from spontaneously firing during testing, the neuron’s membrane potential was held at –80 to –85 mV by passing negative current (0.3–0.8 nA) into the cell via the recording microelectrode using the bridge circuit of the amplifier. An initial EPSP was elicited through brief intracellular stimulation of the sensory neuron using a positive current pulse . After the pretest, the microelectrodes were removed from the sensory and motor neurons, and the recording medium was replaced with cell culture medium. Then the coculture was treated either with RNA-containing medium or control medium (see Results). The sensory and motor neurons were reimpaled with microelectrodes and the amplitude of the monosynaptic EPSP reassessed 24 h later.Following the initial electrophysiological measurements on day 1, the recording medium was washed out with normal cell culture medium. The cultures were then randomly assigned to treatment with RNA from trained animals (trained RNA group), RNA from untrained animals (control RNA group), or vehicle. For the RNA treatments, 1 μg of RNA was added to each cell culture dish, yielding a concentration of 0.5 μg of RNA per 1 ml of cell culture medium. The RNA from the trained animals, the RNA from the control animals, or the vehicle was added to the cell culture dish and left in the dish for 24 h, after which it was washed out with the recording medium for 30 min, and the posttest electrophysiological measurements made.U tests were used for comparisons of two independent groups. A paired t test or a Wilcoxon rank-sum test was used to compare two dependent groups. When three independent groups were involved, the significance of the overall group differences was initially assessed with a one-way ANOVA or a Kruskal–Wallis test. Given that the group differences were significant, Dunn’s post hoc tests were used for pairwise comparisons. Normality of the distribution were tested with a Shapiro–Wilk test. Levene's test centered to the mean (car package) was used with R software to test for homogeneity of variance in the synaptic experiments. All reported levels of significance represent two-tailed values. The statistical analyses are summarized in The statistical analyses of the data were performed using SigmaStat (Systat Software). Nonparametric tests were used to assess the statistical significance of differences whenever necessitated due to non-normality of the data or to the violation of the assumption of homogeneity of variance among experimental groups. Mann–Whitney Aplysia were given sensitization training consisting of spaced bouts of tail shocks for two consecutive days  The duration of the SWR in the recipients was measured 24 h after the RNA injection. The SWR was significantly enhanced in the trained RNA group of animals compared to the control RNA group  motor neurons in dissociated cell culture. A previous study of LTS in  current . Thus, t current C,D. ThisAplysia involves long-term facilitation (LTF) of the monosynaptic connection between the sensory and motor neurons of the withdrawal circuit  Which specific RNA(s) mediate(s) the memory transfer? and (2) How does the naked RNA get from the hemolymph/cell culture medium into Aplysia neurons? Regarding the first question, although we do not know the identity of the memory-bearing molecules at present, we believe it is likely that they are ncRNAs. Note that previous results have implicated ncRNAs, notably microRNAs (miRNAs) and Piwi-interacting RNAs  represent other potential candidate memory transfer molecules (Aplysia neurons, perhaps via extracellular vesicles, they likely caused one or more epigenetic effects that contributed to the induction and maintenance of LTM (Our data indicate that essential components of the engram for LTM in  piRNAs; , in LTM olecules . Regardiolecules . Moreoveolecules . And it olecules . If, as e of LTM .There have been prior reports of the successful transfer of LTM from trained donor animals to naïve recipients via cannibalism  or RNA iAplysia, the cellular and molecular basis of which is exceptionally well characterized (Aplysia enabled us to show that the RNA from sensitized donors not only produced sensitization-like behavioral change in the naïve recipients, but also caused specific electrophysiological alterations of cultured neurons that mimic those observed in sensitized animals. The cellular changes observed after exposure of cultured neurons to RNA from trained animals significantly strengthens the case for positive memory transfer in our study.A major advantage of our study over earlier studies of memory transfer is that we used a type of learning, sensitization of the defensive withdrawal reflex in cterized . The extAplysia in our study are mediated, in part, by DNA methylation (Another difference between our study and earlier attempts at memory transfer via RNA is that there is now at hand a mechanism, unknown 40 years ago, whereby RNA can powerfully influence the function of neurons: epigenetic modifications . In facthylation .Aplysia offers dramatic support for the idea that memory can be stored nonsynaptically (The discovery that RNA from trained animals can transfer the engram for LTS in ptically , and indptically . In addi


Human parechoviruses (HPeV) are picornaviruses with a highly-ordered RNA genome contained within icosahedrally-symmetric capsids. Ordered RNA structures have recently been shown to interact with capsid proteins VP1 and VP3 and facilitate virus assembly in HPeV1. Using an assay that combines reversible cross-linking, RNA affinity purification and peptide mass fingerprinting (RCAP), we mapped the RNA-interacting regions of the capsid proteins from the whole HPeV1 virion in solution. The intrinsically-disordered N-termini of capsid proteins VP1 and VP3, and unexpectedly, VP0, were identified to interact with RNA. Comparing these results to those obtained using recombinantly-expressed VP0 and VP1 confirmed the virion binding regions, and revealed unique RNA binding regions in the isolated VP0 not previously observed in the crystal structure of HPeV1. We used RNA fluorescence anisotropy to confirm the RNA-binding competency of each of the capsid proteins’ N-termini. These findings suggests that dynamic interactions between the viral RNA and the capsid proteins modulate virus assembly, and suggest a novel role for VP0. Picornaviridae family. The mature virion is icosahedrally-symmetric with a triangulation number of T = 1 (pseudo T = 3) and is composed of capsid proteins VP0, VP1 and VP34. Unlike in other picornaviruses, the parechovirus VP0 is not proteolytically cleaved in the final maturation of the virions5. There is also an extensive network of VP0 N-termini on the inner capsid surface that enhance inter-pentamer stability, along with an annulus of VP3 termini under the vertex3.Human parechoviruses (HPeV) are important human pathogens for which we lack antivirals or vaccines. They have a positive-sense, single-stranded RNA genome and belong to the 4. Upon interaction with viral pentameric assembly intermediates, these PSs drive capsid assembly. Multiple VP1 and VP3 residues were found to contact the viral RNA in the atomic models of HPeV1 (PDB: 4Z92 & 5MJV)4. When these residues were mutated to alanine, virus assembly was prevented4. The atomic models do not cover the complete sequences of the capsid proteins or the full genome. The N-terminal regions of all three capsid proteins were apparently disordered4. The virion population may contain multiple states of both the RNA and the capsid, as was recently observed for bacteriophage MS28. Hence, we expect that there are more RNA-protein interactions to be discovered in the virion. More direct methods could be utilized to identify the regions of the HPeV1 capsid that interact with the encapsidated RNA. One such method is reversible cross-linking, affinity purification, and peptide-mass fingerprinting (RCAP) which has previously been used to map protein-nucleic acid interaction sites. RCAP has been successfully used to map regions of the capsid protein that interact with the virion RNA in brome mosaic virus, adenovirus, and bacteriophage MS212. The MS2 protein-RNA interactions identified by the RCAP assay have since been confirmed in asymmetric cryoEM reconstructions of MS213.Regions of structured RNA were recently identified as packaging signals (PSs) that interact with VP1 and VP3 in the HPeV virionHere we mapped regions within the HPeV1 capsid proteins that interact with the encapsidated RNA using RCAP. Several regions within VP1 and VP3 were found to interact with the RNA. Surprisingly, VP0 was also identified to contact the genomic RNA within the HPeV virion. The N-terminal regions of all capsid proteins not visualized in the HPeV1 atomic model apparently contact viral RNA. Recombinantly-expressed VP0 and VP1 protein were shown to bind both the full genome as well as several sub-genome length RNAs. Curiously, the RNA binding profile of VP0 differed dramatically when analyzed in the context of the virion or with the recombinant VP0. RNA fluorescence anisotropy was used to confirm that the disordered capsid N-termini indeed contact the genome. We propose that these highly disordered regions are important for both RNA binding and the co-assembly of the virion genome and capsid proteins.14. Within these peptides, several lysines and arginines were not cleaved, likely due to their crosslinking to RNA15. These missed cleavages increase our confidence in the assignment of the protein sequences that contact the RNA. These RCAP peptides were mapped primarily to the inner surface of the capsid from the HPeV1 atomic model, or to disordered regions likely to be in the inner cavity of the capsid. Of the peptides that appear on the outer surface of the capsid, they all actually spanned the capsid and thus were exposed to the encapsidated RNA, with the exception of one short peptide 4. Residues 19 and 21 in VP3 and 203 and 205 in VP1 were found to be in RNA-interacting regions in the atomic models, but were not directly identified with high confidence by RCAP. Rather, these residues were found in close proximity to residues which were identified. In light of this confirmation of previous data, we now went on to consider the other RCAP results for which there was no a priori data. Strikingly, several regions which are disordered in the HPeV1 atomic model were identified by RCAP to contact RNA, specifically: residues 6–31 of VP0, 1–23 of VP1; and 11–14 for VP3  spectroscopy  and assayed as wet drops to obtain spectra of denatured protein. TCA treatment caused the two amide I peaks to collapse into a single broad peak at 1654 cm−1 characteristic of disordered peptide, and the complete ablation of the amide II peak , which was recovered consistently and with associated missed cleavages in protein-RCAP experiments, but was missing in whole-virion RCAP. In the atomic model of HPeV1, this region is unavailable to the encapsidated RNA. It stands to reason that this region could have a role early in assembly, but these contacts are lost later on, presumably after the pentamer has been formed and VP1 and VP3 are available to help stabilize pentamer-RNA binding. The N-terminal disordered region of VP0 is present in both whole-virion and protein-RCAP, again in contrast to VP1 where it only appears in whole-virion RCAP. A role for the N- and C-terminal regions of VP0 early in capsid assembly is attractive because VP0 is the first sequence synthesized during polyprotein translation, and would have the earliest and least-encumbered access to the viral mRNA. Such a transient interaction might help in the initial nucleation of capsid assembly. Subsequently, the VP3 N-terminus interacts with four other VP3 N-termini to form the VP3 annulus stabilizing the pentamer  to 20 mM Hepes, pH 8.0, 150 mM NaCl and 2 mM MgCl2.HPeV1 Harris strain was cultured in HT29 cells (SIGMA) and purified by cesium chloride gradient as described previouslyVP0 was cloned in pCOLD-I (Takara Bio) which has an N-terminal His-tag. The primers used for cloning were:ctcgagATGGAGACAATTAAGAGTATTG-3′; and5′-gaagaaaagcttTCAATTATCATATATGTTGAC-3′5′-gccgccThe restriction sites for enzymes XhoI and HindIII are underlined in the primer sequence, respectively, and gene-specific regions in uppercase. The PCR running conditions were 98 °C (3 min), 80 °C (1 min), 55 °C (10 s), 72 °C (1 min), [98 °C (10 s) + 55 °C (10 s) + 72 °C (35 s)] × 29, 72 °C (10 min), 4 °C. The expression was performed in BL21 (DE3) by induction with 1 mM IPTG at 15 °C for 18 h. The cell pellet was resuspended in 20 mM bicine, pH 8.0, 150 mM NaCl, 2 mM dithiothreitol, 1 EDTA-free protease inhibitor tablet (ThermoFisher Scientific), 0.1 mg/mL lysozyme, ~25 units of benzonase (Merck Millipore) and kept on ice for 30 min. The cells were lysed at 15000 psi for 6 min in an Emulsiflex C3 (Avestin) high pressure homogenizer. The supernatant was added to nickel-bound agarose beads and VP0 was eluted from the beads with 1 M imidazole in 20 mM bicine pH 8.0 and 150 ml NaCl. The eluted VP0 was further purified by gel filtration on a superdex 75 10/300 GL column  in 20 mM bicine pH 8.0 with 150 mM NaCl.32. This plasmid has a His-patch thioredoxin on its N-terminus and 6 histidines on its C-terminus. The plasmid was transformed in E coli BL21 (DE3) for expression and induced with 1 mM IPTG at 18 °C for 18 h. The cells were lysed in 20 mM Tris-HCl pH 7.5, 150 mM NaCl, 1 EDTA-free protease inhibitor tablet (ThermoFisher Scientific), 0.1 mg/mL lysozyme, ~25 units of benzonase (Merck Millipore) by keeping on ice for 30 min followed by sonication for 10 min in pulse mode. The lysed cells were centrifuged and the pellet was washed twice in 20 mM Tris-HCl pH 7.5, 0.5 M NaCl, 5 mM imidazole, 0.5% Triton X-100 and resuspended in solubilization buffer: 20 mM Tris-HCl pH 8.0, 0.5 M NaCl, 8 M urea. The resuspended pellet was sonicated for 10 min in pulse mode. The soluble protein was recovered by centrifugation in rotor A-4-62 (Eppendorf 5810 R centrifuge) at 4000 rpm, 30 min and 4 °C. The chromatography and on-column folding was performed on a 1 mL HisTrap column . The solubilized protein was loaded onto the solubilization-buffer equilibrated column using a P1 pump. The column was washed with 5 column volumes of 20 mM Tris-HCl pH 8.0, 0.5 M NaCl, 8 M urea, 20 mM imidazole and returned to solubilization buffer. The column was then installed onto an Äkta FPLC system and a 20 ml linear gradient was run from 8 M urea  to 0 M urea . A step gradient of 5 mM, 10 mM, 20 mM, 40 mM, 60 mM, 300 mM and 500 mM imidazole in 20 mM Bicine pH 8.0, 500 mM NaCl was used to elute the on-column folded VP1 protein. Protein purity was assessed by SDS-polyacrylamide gel electrophoresis  was a kind gift from Katja C. Wolther, Amsterdam Medical CentreThe refolding of VP0 and VP1 was confirmed by Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy (Smiths Detection DuraSamplIR II and Bruker IFS 66/S). The FPLC fractions containing protein eluting at 1 or 0.5 M imidazole  were assayed by pipetting 2 µL onto the ATR crystal surface and drying with forced air. Fractions that did not contain protein served as blanks. To obtain a reference spectrum of denatured peptide, aliquots of VP1 fractions and non-protein containing fractions were mixed 1:1 with 100% trichloroacetic acid (TCA) and assayed as wet drops to obtain spectra of denatured protein.33 with T7 promoter region present in the forward primer. Sub-genomic lengths of 1576, 2335 and 3037 nucleotides correspond to the 5′UTR-VP0, 5′UTR-VP3 and 5′UTR-VP1 regions of the HPeV1 genome. The forward primer used for amplifying all the three sub-genomic regions is:The HPeV1 sub-genomic RNAs used in RCAP and RNA fluorescence anisotropy experiments were PCR amplified from an HPeV1 infectious cDNA clone5′-taatacgactcactatagggTTTGAAAGGGGTCTCCTAGAGAG-3′.The reverse primers are:5′-gccgccaagcttTCAATTATCATATATGTTGAC-3′ (for 1576 n.t.);5′-gccgccaagcttTCAATATGGACTCTGATTTG-3′ (for 2335 n.t.);5′-gaagccaagcttTCACTGGAATGTAACAACAG-3′ (for 3037 n.t.).in vitro transcription of RNA using MEGAscript T7 transcription kit  was added to quench the formaldehyde and incubated for 10 min at room temperature, then crosslinked protein-RNA complexes were subjected to trypsin digestion using sequencing-grade trypsin  for 16 hours using a 1:20 w/w ratio of enzyme to capsid. RNA-peptide complexes were then selectively precipitated using lithium chloride, and to facilitate mass spectrometric analysis peptide-RNA conjugates were reversed by supplementing the buffer with 150 mM sodium chloride and heating at 65 οC for 1 h. Control reactions lacking formaldehyde, and when using the recombinant protein lacking RNA, were performed in parallel.The RCAP assay was done as described previously18. The crosslinked peptide-RNAs were digested with either 1 μg trypsin, chymotrypsin, or Asp-N as specified (Pierce MS-grade protease) overnight at 37 °C. Controls included RNAs and peptides without formaldehyde, and proteolyzed samples.RCAP assays performed with chemically synthesized peptides with an N-terminal carboxyfluorescein . Each peptide was purified using HPLC to at least 95% purity and the molecular mass was confirmed by mass spectrometry to be accurate to within 1 Dalton. The peptides were present at two molar excess to the HPeV1 RNA and crosslinked as described above. To facilitate ionization and proteolysis, cysteine-containing peptides were supplemented with 200 mM Tris-HCl (pH 9.0), 5 mM TCEP, and alkylated with 20 mM bromoethylamine prior to digestion as described previouslyFor the virion RCAP data, peptides were analyzed using an LTQ Velos Pro dual-pressure linear ion trap mass spectrometer (ThermoFisher Scientific). Tryptic digests were injected into a C18 column (Dionex UltiMate 3000 HPLC) and eluted using a linear gradient of 2–45% acetonitrile in water with 0.1% formic acid, over a 90 minute gradient. The flow rate was 50 uL/min, and effluent was electro-sprayed into the LTQ. Tandem mass spectra were obtained using collision-induced dissociation. Experiments using recombinant protein were analyzed on an LTQ Orbitrap mass spectrometer (ThermoFisher scientific), and separated similarly using an Accela HPLC. Peptide RCAP data was generated on a Bruker Autoflex III MALDI-ToF and analyzed using GPMAW  using the VL-XT algorithm37.Database searches used searchGUI and the peptide sequences were compiled with peptideshakerPeptide binding to HPeV1 RNA used peptides containing an N-terminal carboxyfluorescein. HPeV1 RNA was titrated into 100 µL solution of peptide (0.1 µM) to final concentrations 25–100 nM and the results were normalized to the anisotropy of the peptide alone. Fluorescence anisotropy data was generated on a Synergy H1 microplate reader (BioTek) and calculations were performed in Gene 5 software (Biotek). Pairwise T-Tests were performed using Microsoft Excel.The datasets generated and analyzed during the current study are available from the corresponding authors on request.Supplementary informationSupplementary Movie 1