dataesr/scientific-paragraphs-categorization huggingface dataset:
The disruption of SL4 structure in these cases correlated well with the abrogation of packaging observed in these mutants (Supplemental Figs. 2D  and 3 ). The only exception was SL4-06, which was as defective in packaging as the other mutants even though the native structure of SL4 was predicted to be maintained (Supplemental Figs. 2D  and 3 ). In order to address this inconsistency, the 2D structure of mutant SL4-06 RNA was studied by hSHAPE. Our experimental data showed that the substitutions introduced in this mutant prevented the formation of the wild-type bifurcated structure of SL4 (Figure  3 ). The native stem-loops II and III were observed, but an additional one was also present, and moreover, the internal loop was larger than in wild type DA024 RNA structure (Figure  3 ). Our experimental structural probing thus confirmed that disruption of the wild type bifurcated SL4 structure abolished RNA packaging, even if the pal II and ssPurines sequences were present and exposed in apical loops.
The mFold prediction and the hSHAPE analysis of the SL4-15 mutant revealed that indeed the bifurcated structure of SL4 had been compromised by the destabilizing mutation, correlating well with the loss of RNA packaging (Figures  2(d), 5,  and 6 ). The subsequent compensatory mutation in SL4-16 restored both the structure as well as RNA packaging using heterologous sequences (Figures  2(d), 5,  and 6 ). This was not the case with the second set of mutants (SL4-17 and SL4-18) in which the destabilizing mutant (SL4-17) did result in a loss of SL4 structure and corresponding RNA packaging, but the compensatory mutant (SL4-18) was unable   2 ) were introduced as constrains in the RNAstructure folding software (version 5. 3; 40). For the sake of clarity, only the SL4 domain of the resulting structures was drawn using the Assemble graphical tool  [69]  and presented here.
Briefly, RNAs were modified in a buffer favoring gRNA dimerization (50 mM sodium cacodylate (pH 7. 5), 300 mM KCl and 5 mM MgCl 2 ) in the presence of 2 µg total yeast tRNA (Sigma Aldrich). The modified RNAs were reverse transcribed using AS primers OTRs 9 and 10: (5ʹ AACAGATTTGGCTTCTGCGG 3ʹ; MMTV nt 614-633) labelled with VIC or NED, respectively. These primers allowed analysis of the RNA structure from approximately nucleotide 560 to 100. The primer extension products were loaded onto an Applied Biosystems 3130xl genetic analyzer and the electropherograms were analyzed with the QuShape software  [67]. The hSHAPE reactivities obtained with QuShape from three independent experiments were highly reproducible. These reactivities were averaged (Supplemental Table  2 ) before being used as constraints to fold the RNA secondary structure of the MMTV packaging signal with the RNAstructure software version 5.
Multiplex amplifications were conducted in the presence of primers/competimer for 18S ribosomal RNA. Panel IV shows RT-PCR of cytoplasmic cDNA using primers that amplify spliced β-actin mRNA. Owing to the large number of samples, gels have been spliced together as indicated by vertical white spaces in different panels to create part C for clarity. D) Cytoplasmic expression and relative packaging efficiency (RPE) of transfer vector RNAs, and hygromycin-resistance (Hyg r ) colony forming unit per ml (CFU/ml) for mutant transfer vector RNAs relative to the wild type DA024 construct. CFU/ml expressed for each mutant was normalized to the luciferase expression observed in the transfected cultures. The histograms represent data from multiple independent experiments (±SD). The RPE for each mutant was determined by dividing the packaged mutant viral RNA values by the luciferase-normalized cytoplasmic expression of the respective mutant (to take into account the varying transfection efficiencies).
Acknowledgments Authors would like to thank  Ms. Faiza Moureen ,  Dr. Jaleel Kizhakkayil , and  Dr. Suriya Aktar  ( Department of Microbiology & Immunology, CMHS, UAEU ) for their help in some of the cloning and experimental procedures at the initial stages of the project as a part of their training and  Dr Fabrice Jossinet  for his help in drawing the secondary structure models of Figures 3 and5 with Assemble. The authors would also like to thank the editorial assistance of  Akhil Chameettachal ,  Fathima Nuzra Nagoor Pitchai , and  Vineeta Pillai  ( Department of Microbiology & Immunology, CMHS, UAEU ) for reading the manuscript and providing their inputs.
We selected the 15 GO terms that are most over-represented within the targets of CELF1 or ELAVL1. Figure  1B  shows the enrichment p-values of these 18 terms, as 12 are shared between these two datasets. The GO terms that are highly enriched upon depleting one protein are also highly enriched upon depletion of the other protein. A treemap generated by REVIGO summarizes these data  (Supek et al, 2011) , by removing redundant GO terms and grouping related terms into "superclusters" (Figures  S3A-B ). Of note, 6 out of 7 superclusters are shared between the targets of CELF1 and ELAVL1 (Figure  S3C ). Together, these data indicate that CELF1 and ELAVL1 regulate sets of genes with similar functional profiles. We next plotted for each of the 117,841 probes the splicing index measured upon ELAVL1 versus CELF1 depletion (Figure  1C ).
Data analysis The RNA maps were drawn as described  (Haberman et al, 2017). We retrieved the TCGA data from Firebrowse (http://firebrowse. org/) and TCGA splice seq  (Ryan et al, 2016). We analysed all the data with in-house R scripts, which are available as supplemental text. RT-PCR experiment with the quantification of at least 3 similar independent runs as in Figure  2. A, MICAL3 pre-mRNA includes a composite exon used either as a terminal or internal exon. The maturation pattern was revealed by RT-PCR with one forward primer (in exon "a") and two competing reverse primers (in exonic regions "c" and "d"). B, The last exon of JDP2 pre-mRNA contains two alternative cleavage-polyadenylation sites. The maturation pattern was revealed by separate RT-PCRs with one forward primer (in exon "a") and either a reverse primer in exonic region "b" for amplifications of both isoforms or a reverse primer in exonic region "c" for amplification of the longest isoform only.
-The same information for the 4 other datasets Table  S3 : 2951 genes (GeneNames) that produce transcripts that interact with CELF1 in HeLa cells in CLIP-seq experiments (ligands of CELF1) (Le  Tonquèze et al, 2016). Table  S4 : 4284 genes (GeneNames) that produce transcripts that interact with ELAVL1 in HeLa cells in CLIP-seq experiments (ligands of ELAVL1)  (Uren et al, 2011). All pixels from 30 nuclei resulting from 3 independent transfections were quantified (same data as Figure  5 ). The cumulative distribution (y value) is the fraction of EGFP fluorescence lifetimes that are less than or equal to the fluorescence lifetime (x value). A reduced EGFP fluorescence lifetime reveals that FRET occurs with mCherry, hence that the two fluorophores are in close proximity.
ACKNOWLEDGEMENTS We thank  Laure Le Calvez  for developing R scripts to draw RNA maps. The results shown here are in part based upon data generated by the  TCGA Research Network  http://cancergenome. nih. gov/. Microarray hybridizations were carried out on the  Plateforme génomique de Nantes. This research was funded by a grant from the  Ligue Régionale contre le Cancer  to LP. GD was supported by a  joint doctoral fellowship  from the  Ligue Régionale contre le Cancer  and the  Région Bretagne (ARED).
Bottom-Up Mass Spectrometry Analysis of Histones Several MS methods have been developed for use with histones; these methods analyze different sizes of protein fragments. This review will focus on the most widely adopted bottom-up approach; middle-down and top-down approaches are reviewed elsewhere  [30, 31]. The bottom-up analysis offers reliable comparisons between samples, but for the identification and quantification of PTMs, subtle pitfalls from histone preparation for proteomic analysis to MS data interpretation have to be considered. The bottom-up approach involves proteolysis of the histones into short peptides (8-30 amino acids), the sequences of which can then be determined by MS/MS fragmentation. Different proteolytic enzymes can be used, but trypsin remains the protease of choice for its efficiency and specificity of cleavage.
Data Interpretation Mass spectrometry fragmentation (MS/MS) data is interpreted using dedicated search engines which compare experimental MS/MS spectra to theoretical peptide fragmentation patterns and identify the best match. A number of search engines are now publicly available on the web (summarized here https://omictools. com/database-search-category) alongside the commercial solutions. These search engines rely on a "protein sequence database" which is explored to identify proteins and their PTMs from the experimentally acquired data. These databases should be comprehensive, non-redundant, and well annotated to allow optimal MS/MS data interpretation. Two major repositories exist, hosted by the National Center for Biotechnology Information (NCBI: http://www. ncbi. nlm. nih. gov/) and the Universal Protein Resource (UniProt: http://www.
uniprot. org/). However, the current versions of these public databases contain many redundant entries (UniProtKB or NCBI). The NCBInr or UniRef100 databases contain only non-redundant proteins, but their histone nomenclature does not follow the current community standards  [36]. Recently, a proteomics-oriented and manually curated resource of mouse and human sequences, named MS_histoneDB, was released  [11]. Each histone entry in this database is annotated in accordance with the current nomenclature and unified with the "HistoneDB2. 0" database  [36, 37]. This resource is provided in a format that can be directly read by MS/MS data interpretation programs. Our team used MS_histoneDB to interpret proteomics data for histones extracted from mouse testis. Several histone variants that had so far only been inferred by homology or detected at the RNA level, were detected by MS analysis, thus confirming their existence at the protein level  [11].
Another critical parameter to specify before launching the interpretation of MS/MS data for histone peptides is the list of expected modifications. Close to 20 different structures of PTMs have thus far been described on histones  [38]. However, they cannot all be considered simultaneously by most search engines, which only accommodate a limited number of variable PTMs (e. g. , a maximum of nine for Mascot). The performance of eight programs was compared with respect to their capacity to identify PTMs on histone peptides, and pFind and Mascot appeared to yield good performances  [39]. As an alternative to matching experimental MS/MS spectra to theoretical spectra deduced from known protein sequences, programs designed to perform de novo sequencing of MS/MS data have been successfully used for sequences containing few modifiable residues.
Jaffe et al. [66]  conducted a large-scale analysis of histone modifications and implemented PRM to quantify histone H3 modifications from 115 cell lines contained in the Cancer Cell Line Encyclopedia. A few software tools have been developed to address the challenging process of quantifying differentially modified isobaric peptide forms. Garcia et al. [67]  developed the EpiProfile program to distinguish positional isomers, using discriminating fragment ions in their MS/MS spectra and extracting the chromatographic peak area under the curve based on information available on peptide retention times. The software successfully differentiated between H3 K9/K14 mono-acetylated peptides; H3K27me1 versus H3K36me1 peptides; and H4 K5/K8/K12/K16 mono-acetylated or di-acetylated peptides. Data-Independent Acquisition (DIA) has been effectively used to investigate histone PTMs  [68] [69] [70] , offering the combined benefits of SRM/PRM and DDA analysis modes.
The following primers were used for subsequent PCR amplification: the housekeeping gene CyclophilinA Fw primer 5′-ACCCCACCGTGTTCTTCGAC-3′ and Rev primer 5′-CATTTGC CATGGACAAGATG-3′ for normalisation; the Cobl Fw primer 5′-TGAGATCC AAGGACAAATGG-3′ and Rev primer 5′-CTCATCTCTGATTTGGGAGG-3′. CyclophilinA and Cobl were amplified simultaneously by PCR in the same tube and the products subjected to agarose gel electrophoresis. Band intensities were quantified with ImageJ software and normalised with respect to cyclophilinA. 3D-modelling. Imaris software was used for 3D-modelling. Data analysis. All analyses were performed on images of the rostro-dorsal part of the lateral walls of the lateral ventricles to maximise reproducibility (Fig. 1a ). Measurement of phalloidin fluorescence intensity. We compared actin enrichment (phalloidin fluorescence) in the centriolar patch (delineated by centriole staining) of mature (P15) ependymal cells in ciliary mutant mice, explants treated with SMIFH2 or DMSO and Lrrc6-depleted cells.
The mean intensity of phalloidin fluorescence was measured in the centriolar patch and on the surface of the rest of the cell; cell borders and actin linking the centriolar patch to the cell membrane were excluded. Apical actin was analysed on the sum projection of the 6 z-stacks encompassing Cep164 staining and subapical actin of the 6 z-stacks encompassing Akap450 staining with ImageJ software. The results are expressed as the ratio of the values in the centriolar patch over the remaining cell surface. Total mean actin (phalloidin fluorescence) levels were measured with ImageJ software in the apical and subapical networks (as defined above) at early (P4 and P6 mice) or late (P15 mice) stages of differentiation in ependymal cells transfected with sh1Cobl or shLrrc6 or shCtrl within cell borders, in the sum projection of transfected cells normalised with respect to the mean signal intensity in the three closest non-transfected ependymal cells at the same stage, using ImageJ software.
Corrections were made manually. The centriolar patch boundaries were estimated on the basis of the spatial density of the centrins and corrected manually. The pilot experiment was also quantified manually, blind to the experimental conditions; the results were equivalent to those obtained in the experiments counted automatically. Quantification of centriolar patch size. We also determined, in each cell, the size of the centriolar patch, defined as the area of the minimum convex xy euclidean space that contains all the centrioles in the cell. Centriolar patch size, on all cells, was calculated with Matlab software. Patch size was very sensitive to the presence of scattered centrioles, because all of the centrioles were taken into consideration. Distance to the nearest neighbour. For each patch or cell, we also determined the nearest neighbour xy euclidean distance between centrioles.
Inside each cell, centrins were assigned to their corresponding Cep164 (unit vector) using the Hungarian algorithm  69  that minimises the sum of the distances between all pairs of points. The angle between the unit vectors and their mean direction were calculated. The centriolar patch boundaries were estimated from the spatial density of the centrins and corrected manually. Circular plots with a 20°b inning interval were obtained from the resulting angles. The in-house Matlab toolbox for generating vector plots and the corresponding analyses can be downloaded from https://github. com/biocompibens/centriole_analysis. git. Statistics. Means of two conditions were compared with the Mann-Whitney test. Means of more than two conditions were analysed by one-way ANOVA followed by Dunn's multiple comparison tests (GraphPad Prism 6.
0). The Kolmogorov-Smirnov or Waston-U2 tests were used to compare whole angle distributions shown as circular plots (MatLab). The χ 2 test for trends was used to compare centriole neighbour distances (GraphPad Prism 6. 0). Scatter plots are presented as the mean±sem. ROUT methods were used to remove outliers generated by plugins analysis of automatic counts, with a Q value of 1% (GraphPad Prism 6. 0). All results were obtained in at least three independent experiments on three different animals or cell cultures.
Acknowledgements We thank the  IBENS Imaging Facility , member of  France-BioImaging , supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02 PSL ) and by the  "Région Ile-de-France " (NERF No. 2011-45 ), the  "Fondation pour la Recherche Médicale " ( DGE 20111123023 ) and the  "Fédération pour la Recherche sur le Cerveau-Rotary International France " (2011). We thank the " Fondation Imagine " for supporting the acquisition of the Stimulated emission depletion microscope. We are grateful to  L. Goldstein ,  B. Yoder  and  J. Gleeson  for providing the Kif3a-cKO, Ift88-cKO and centrin2-GFP mice. We thank  X. Morin  for the CAAGS-mCherry plasmid,  M. Bornens  for the CTR453/AKAP450 antibody and  I. Caillé  and  S. Scotto  for the shRNA backbone.
We are grateful to  C. Auger ,  A. Correia , and  D. Varela  for their help with the animal husbandry. We greatly appreciated the helpful discussions with  P. Bastin ,  A. Benmerah ,  J. F. Joanny  and all the members of the  Spassky laboratory. This study was supported by the  CNRS , the  ENS , the  INSERM , the  Fondation Pierre-Gilles de Gennes (FPGG03) , the  Agence Nationale de la Recherche  ( ANR-12-BSV4-0006  CILIASTEM),  La Ligue contre le Cancer-Comité de Paris  ( RS14/75-88 ), the program ≪Investissements d' Avenir≫  ( ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02 PSL ), the  Fondation pour la Recherche Médicale  (20140329547), the  INCA  grant  2014-1-PL BIO-11  and the  European Research Council  ( Consolidator grant  "EDeN" # 647466  to N. S. , Senior grant "forcefulactin" # 249982  to M.
Statistical Analysis Continuous variables are presented as means±SD or mean±SE and discrete variables as frequencies or percentages. Pairwise comparisons were performed using the Mann-Whitney and χ 2 tests, as appropriate. Telomere values are presented and compared with or without adjustment to age and sex. Statistical analyses were performed using the Number Crunching Statistical System (NCSS) 9 statistical software package (NCSS, Kaysville, UT) and JMP (v. 9) software (Cary, NC). A paired t test was used for the intrapair comparison of matched subjects by age (±2 years) and sex. Bivariate relationships between continuous variables were determined using Pearson correlation coefficients. More complex analyses were performed using general linear models, assuming a normal error distribution for the analyses of variation in LTL and MTL and the derived telomere estimates below.
Results for Matched Pairs Because participants with ASCVD were older, more likely to be males, we also analyzed data for matched pairs. Online Table  IV  shows the results of the t paired analyses for the 79 age-and sex-matched pairs. Individuals with ASCVD had shorter LTL (P=0. 023), wider LTL-MTL A (P=0. 022), and lower LTL/MTL A (P=0. 0097). MTL was not different in the 2 groups (P=0. 59). Additional analyses separately in men and women of the Discovery Cohort showed similar impact of ASCVD in both sexes (Online Table  V ; Online Figures  IV  and V ).
Sources of Funding This study has been supported by the  French National Research Agency (ANR) , Translationnelle: N°ID RCB: 2014-A00298-39: 2014 to 2017 and the  Investments for the Future program  under grant agreement No. ANR-15-RHU-0004. A. Aviv  research is supported by  National Institutes of Health  grants  R01HD071180 ,  R01HL116446 , and  R01HL13840.
Hazard ratios (HR) or Odds ratios (OR) with their 95% confidence intervals (CI) were computed in these analyses. The association of plasma copeptin with the risk of cardiovascular events was tested in two sets of analyses. In the first set, data from the cohorts were analyzed separately and two regression models were tested in each cohort. Model 1 included as independent covariates baseline parameters with p < 0. 10 (in at least one of the cohorts) in the comparison between incident cases and participants with no events (data from Table  1 ), except UAC and eGFR, while model 2 also included these markers of kidney function. The second set of analyses was performed with pooled data from the cohorts to increase sample size and the number of events during follow-up, and thus the statistical power of the analyses.
Plasma copeptin was shown to be positively associated with insulin resistance, the metabolic syndrome and with the incidence or the prevalence of type 2 diabetes in the general population  [32, 33, 35, [59] [60] [61] [62]. Taken together, the available data suggest that vasopressin has several potential dysmetabolic and pro-atherogenic effects that could explain the association of copeptin with CVD. There are limitations of our study to acknowledge. We have measured copeptin as a surrogate of vasopressin. However, plasma concentrations of vasopressin and copeptin correlate over a wide range of plasma and/or urine osmolalities  [34] , and the correlation seems relatively stable for eGFR > 28 ml/min/1. 73 m 2  [63]. Only 78 SURDIAGENE participants had baseline eGFR below this threshold, and their exclusion had only a minimal impact on the results (data not shown).
Funding The authors received no funding for the present investigation. The original DIABHYCAR trial and cohort recruitment was supported by grants from  Sanofi-Aventis (Paris) , the  French Ministry of Health  ( PHRC -Angers 1996 ) and the  "Association Française des Diabétiques" (AFD ;  Research Grant   2004 ). The SURDIAGENE study was supported by grants from the  French Ministry of Health  (PHRC-Poitiers 2004, PHRC Interregional 2008),  Association Française des Diabétiques  ( Research Grant   2003 ),  Groupement pour l'Etude des Maladies Métaboliques et Systémiques  (GEMMS Poitiers, France). Analysis and interpretation of data were done without the participation of these organizations and companies.
Gall) were used at 1:5000 and 1 μg/ ml dilution, respectively. The mouse anti-SR antibody (1H4) raised against X. laevis SR proteins (Santa Cruz Biotechnology) was used at 1:200 dilution. The secondary antibodies Alexa fluor 488, Alexa fluor 568 IgG, goat anti-rabbit, Alexa fluor 568 IgG goat anti-mouse (Invitrogen Corp. , Carlsbad, CA) or Alexa 488 goat anti-mouse IgM (Life technlogies) were used at 1:1000 dilution. Standard transmitted light and fluorescence microscopy were performed using a wide field Leica microscope with a plan Apo 100X oil objective (NA = 1. 4). Images were captured using a CoolSnap HQ, Photometrics camera driven by the software Metamorph6 (Universal imaging). In order to probe accurately the fluorescence, series of confocal Z-planes (0. 7 µm distance) were collected.
Quantification of fluorescence density and colocalization. A specific algorithm for image processing was developed to quantify fluorescence density of each chromosome using the ImageJ software. Chromatin areas were first isolated on the Hoechst images using an automatic thresholding with the Otsu's method after removing noise with a filter and background correction. The resulting region of interest (R. O. I) designated as MASK was then applied to the GFP images. Fluorescence density was quantified by measuring the average intensity of the GFP images inside masks previously built (Fig. S6 ). Colocalization analyses were carried out using the ImageJ plugin "Coloc2".