dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, the silver-staining analysis showed co-immunoprecipitated host partners specific for each HCV protein, whereas only weak bands in the FLAG-MCS control condition were detected corresponding to nonspecifically bound material. Biological replicates of immunoprecipitated cell extracts were digested with trypsin, and the corresponding digests were subjected to LC-MS/MS analyses. Label-free quantitative proteomics was used to profile protein abundances across sample replicates and conditions. From three independent experiments, data were clustered to correlate the abundance of interacting proteins between immunoaffinity extracts. Triplicate LC-MS/MS analyses from all isolated protein complexes enabled the identification of 5,585 peptides from 1,254 proteins with a false discovery rate of less than 1% (supplemental Table  S1 ).
This experimental procedure allowed the identification of high-ranking enriched HCV interactors from the global analysis of LC-MS/MS data that correspond to a specific set of cellular proteins interacting predominantly with one virus-specific protein. Indeed, the overlap between this set of statistically enriched HCV interactors and those from previous largescale yeast two-hybrid or proteomics studies reported in the literature is significant, with 24. 5% of host interactors shared overall (24/98), demonstrating the reliability, robustness, and value of our approach. It also indicates that the interactome dataset is now close to completion. In fact, there is significant overlap between our data and data from a previously reported MS/MS analysis of Core and NS5A by Pichlmair et al. (32). Three hits with Core (DDX3X, NAP1L1, and NAP1L4) and eight hits with NS5A (USP19, HSPA5, SMYD3, BIN1, VAPA, MOBKL1A, VAPB, and FKBP8) were recovered in both studies.
Three days post-infection, cells were serum starved for 4 h and then stimulated with 5 ng/ml of IFN-␥ for 30 min. Cells were fixed, permeabilized, and stained with anti-STAT1 (green). Nuclei were stained with Hoechst. Cells were examined via laser-scanning confocal microscopy. family member HSD17B11), or as hit proteins retrieved in genome-wide RNAi studies (7, 18, 21) (DDX3X, PTCD1, calnexin, COPB1, RAN, VAPA, and VAPB; see supplemental Table  S5 ). This important overlapping of identified host binding partners from our work and previous studies reinforces the hits' representation of true HCV-associated host proteins with a role in the HCV life cycle. Additionally, the data identify common host proteins that likely play a role in the life cycle of divergent viruses. When we queried the set of 98 HCV partners for cellular proteins that were previously identified as binding partners of others viruses, remarkably, we identified a total of 624 distinct virus-host interactions involving 52 different virus species (supplemental Table  S6 ).
In conclusion, a set of 98 statistically enriched HCV partners was identified, including 74 novel and 24 previously described partners, as a result of an IP-MS/MS approach and global data analysis for the Core, NS2, NS3/4A, NS4B, NS5A, and NS5B proteins. Eleven novel virus-host PPIs were further confirmed by IP-Western blotting. The investigation of the HCV-associated proteins' roles in the viral life cycle through gene silencing emphasized 37 HCV cofactors in total, of which 11 among the enriched set of HCV partners were novel modulators of HCV replication. This study contributes to the HCV interactome through the robust identification of HCV partners and host factors that may enhance the selection of novel host therapeutic targets with fewer side effects, along with the knowledge of the human interactome.
Acknowledgments-We thank  Takaji Wakita  ( National Institute of Infectious Diseases, Tokyo ),  Charles Rice  ( Rockefeller University ), and  Apath LCC  for the use of JFH-1, Huh7. 5 cells, and  J6-JFH(p7-Rluc2A ) reporter virus;  Ralf Bartenschlager  ( University of Heidelberg ) for luciferase-encoding Con1b subgenomic replicon;  Didier Trono  ( E ´cole Polytechnique Federale de Lausanne ) for the lentiviral packaging plasmids;  Amelie Cessieux (Inserm U1111 ) for her technical assistance in bioinformatics;  Christine Vande Velde  for providing the anti-TARDBP antibody; and  Marie-Eve Racine  for cloning the Myc-tagged HCV protein constructs. We also acknowledge  IRIC's High Throughput Screening Core Facility ;  Jean Duchaine ,  Julie Lafontaine , and  Karine Audette  for lentivirus production and RNAi screening;  IRIC's Genomics Core Facility ; and  Raphae ¨lle Lambert ,  Caroline Paradis , and  Marianne Arteau  for sequencing.
* This work was supported by an operating grant from the  Canadian Institutes of Health Research  ( MOP-115058 ) awarded to D. L. and L. C. C. and by the  Novartis/Canadian Liver Foundation Hepatology Research Chair  awarded to D. L. L. M. , B. d. C. , F. P. , and V. L. are supported by  ANR ,  ANRS ,  Inserm , and the  European Union 's  7th program  ( FP7/2007-2013 ) under Grant Agreement No. 267429  ( Sys-Patho ). IRIC is supported in part by the  Canadian Center of Excellence in Commercialization and Research , the  Canada Foundation for Innovation (CFI) , and the  Fonds de Recherche du Quebec en Sante (FRQS). □ S This article contains supplemental material.
Stereospecific assignments of pro-chiral methyl groups of valine and leucine were obtained using 10% 13 C-labeled protein samples  (24). Structure calculations with torsion angle dynamics were performed by using the program CYANA 3. 0  (25). For every assigned peak, the distances between the protons (for a range between 2. 4 and 6 Å) were calibrated using the calibration algorithm of CYANA. The set of NOE distance restraints together with and backbone dihedral angle restraints derived from TALOSϩ  (26)  based on the chemical shifts were used for water refinement  (27)  using CNS  (28). The structure was validated using iCing. Molecular images were generated with PyMol (Schrödinger). Isothermal Titration Calorimetry-Calorimetric measurements were performed with a VP-ITC (MicroCal Inc. ) instrument at 25 °C using samples in NMR buffer.
* This work was supported, in whole or in part, by the  European Social Fund  and state budget of the  Czech Republic Grant   CZ. 1. 07/2. 3. 00/20. 0042 (to K. T. ), the  Centre National pour la Recherche Scientifique  (to B. S. ), Grant  ANR-10-LABX-0030-INRT  managed under the  program Investissements d'Avenir   ANR-10-IDEX-0002-02  (to B. S. ), the  European Union   Sixth Framework program  " 3D-Repertoire" Grant LSHG-CT-2005-512028  (to B. S. and M. S. ) and EURASNET Grant  LSHG-CT-2005-518238  (to B. S. ),  Ligue Contre le Cancer  (Equipe Labellisee 2014) (to B. S. ), the  CERBM-IGBMC  (to B. S. ), and  Deutsche Forschungsgemeinschaft  Grant  SFB1035  (to M. S. ). Author's Choice-Final version full access. The atomic coordinates and structure factors (code  4UQT ) have been deposited in the Protein Data Bank (http://wwpdb.
Mouse lung histology and morphometry Lung was fixed by intratracheal instillation of 4% PFA at a pressure of 25 cm H 2 O and embedded in paraffin. Sections (3 μm) were stained with haematoxylin and eosin (H&E) or with Victoria Blue. Stained lung sections were scanned using a Slide Scanner (Mirax, Zeiss) and the mean linear intercept (L m ) of the airspace was measured using Image J software on ten randomly selected fields at a magnification of × 100 in a blind manner.
Western blot Lung protein extracts were prepared as previously described  [10]. Proteins blotted on nitrocellulose membrane were incubated with anti-SIRT1 (1 : 1000; Cell Signaling, Schaffhausen, Switzerland) or anti-actin (1 : 1000; Sigma, Buchs, Switzerland), followed by incubation with peroxidase-conjugated anti-mouse or rabbit. Proteins were detected with ECL reagents (Amersham Pharmacia Biotech, Basel, Switzerland). Densitometric evaluation was performed using Quantity One software (Bio-Rad Laboratories, Hercules, CA, USA).
NOX2 is expressed in lung macrophages of emphysematous lungs, whereas NOX1 is detected in alveolar epithelial cells We first studied the expression of NOX1 and NOX2 in lung sections of control and emphysematous patients by immunohistochemistry (Figure  1 ). In control lungs, NOX1 was only detected in alveolar endothelial cells (data not shown) and macrophages (Figure  1A , asterisk), whereas epithelial cells (Figure  1A , arrow) were negative for NOX1, as previously demonstrated  [33]. In emphysematous lungs, NOX1 was highly expressed in bronchial (Figure  1A , ampersand) and type II epithelial cells (Figure  1A , arrows), whereas NOX2 was mainly detected in alveolar (Figure  1B , arrow) and interstitial macrophages (Figure  1B , arrowhead) in both control and emphysematous lungs. The cell-type-specific localizations were confirmed by serial immunostaining of emphysematous lung sections with an anti-NOX1 antibody and a specific marker for type II epithelial cells [anti-pro-surfactant C (SPC)], and with an anti-NOX2 antibody and a pan-macrophage marker (anti-CD68).
Acknowledgments This study was supported by the  Swiss National Foundation, Marie-Heim Vögltin (SC) ,  Ligue Pulmonaire Suisse  (SC), and  Ligue Pulmonaire Genevoise  (SC), and also by the  European Community Framework Programme  under grant agreements NEURINOX (Health- F2-2011-278611 ) and the  KA Wallenberg Foundation (RH). We would like to thank  S Startchik ,  O Plastre ,  T Cagarelli ,  L Beer ,  Y Hourier , and  P Henchoz  for technical assistance and help, and  JD Lambeth  for the anti-NOX1 antibody.
Lack of Swd2. 2 has a minor effect on the expression of protein-coding genes To establish the impact of Swd2. 2 on gene expression, we used strand-specific tiling arrays hybridization to compare the transcriptomes of swd2. 2D and swd2. 2+ strains (raw data accessible at GEO GSE38005). Both strains were grown in triplicates for one generation at 34uC, the temperature at which lack of Swd2. 2 improves chromosome segregation in cut3-477 (Figure  1A ). Results showed that 47 genes were significantly under-expressed in the absence of Swd2. 2 (Fold change FC#21,9, p,0,01), whilst 14 genes were over-expressed (FC$+1,9, p,0,01) (Table  S2 ). Note that of the 62 tRNA genes detected in our microarray analysis, only 6 were under-expressed (Fold change FC#21,9, p#0,03). An in-depth statistical analysis established that the mis-regulated genes were all placed within a very specific genomic context (see Text S1 and Figure  S7  in the supplementary data).
Mass-spectrometry analysis All chemicals were purchased from Sigma-Aldrich (UK) unless otherwise stated. Acetonitrile and water for LC-MS/MS were HPLC grade (Fisher, UK). Formic acid was Suprapure 98-100% (Merck, Darmstadt, Germany) and trifluoroacetic acid was 99% purity sequencing grade. All HPLC-MS connector fittings were from Upchurch Scientific or Valco (Hichrom and RESTEK, UK). Gel-free samples were prepared and loaded onto strong cation exchange columns, reduced with DTT, alkylated with iodoacetamide, and digested with trypsin as described in  [47]. Samples were dried under low pressure and reconstituted in 10 ml of 0. 1% (v/v) formic acid 2. 5% (v/v) acetonitrile for nano-LC-MSMS. Capillary-HPLC-MSMS data were acquired on an on-line system consisting of a micro-pump (1200 binary HPLC system, Agilent, UK) coupled to a hybrid LTQ-Orbitrap XL instrument (Thermo-Fisher, UK).
Protein identification and quantification MS/MS data were searched using MASCOT Versions 2. 3 (Matrix Science Ltd, UK) against a Schizosaccharomyces pombe database downloaded from the Sanger Institute (http://www. sanger. ac. uk/) with 5022 sequences. Variable methionine oxidation, STY phosphorylation, protein N-terminal acetylation and fixed cysteine carbamidomethylation were used in all searches. Precursor mass tolerance was set to 7 ppm and MS/MS tolerance to 0. 4 amu. The significance threshold (p) was set below 0. 05 (MudPIT scoring in Mascot). All LC-MS runs were combined using MaxQuant (version 1. 0. 13. 8), assuming a false positive rate of 0. 01  [49].
Strand-specific tiling arrays ProfilXpert (http://www. profilexpert. fr/) performed the strandspecific tiling arrays and their initial analysis. The data were established in triplicates. RNAs extracted in the lab were purified using the miRNeasy mini kit (Qiagen), dosed using a nanodrop and their quality was assessed using a Bioanalyzer 2100 (Agilent). To prepare cDNAs, 250 ng of RNA were then amplified using the WT Expression Kit (Ambion). The resulting cDNAs were then dosed (Nanodrop) and their quality assessed using a Bioanalyzer 2100 (Agilent). 5. 5 mg of cDNAs were then fragmented and labelled using the GeneChIP WT Terminal Labelling Kit (Affymetrix). 5 mg of cDNAs were then hybridized on a GeneChip S. pombe Tiling 1. 0 Array (Affymetrix). The data were normalized using Quantile Normalization  [52]  and RMA background correction  [53]  using the software Partek Genomics Suite 6.
5. To establish which genes were deregulated in swd2. 2D, the median value of the signals recorded for each gene in each replicate was first calculated. These median values were then averaged for the three replicates. For each gene, The Fold-change (FC) and the corresponding p-value were calculated to express variation in expression between swd2. 2D and swd2. 2+ cells. To identify genes with potential transcription termination defects, we proceeded as follows: for every gene whose 39UTR is annotated in the fission yeast database, we calculated the ratio between the average signal recorded in the 39UTR and the average signal recorded in the ORF in swd2. 2+ and swd2. 2D cells. We considered that genes had a transcription termination defect in cells lacking Swd2. 2 when the ratio obtained for swd2.
Acknowledgments Most microscope observations were made using the PLATIM platform of  SFR BioSciences Gerland -Lyon Sud  ( UMS3444/US8 ). We are very grateful to  Marie Delattre  ( LBMC, ENS-Lyon ) for the extensive use of her microscope facility and to  Karen May  for sending strains from Edinburgh to Lyon. We are very obliged to Severine Croze and  Nicolas Nazaret  from  ProfileXpert  for the strand-specific tiling arrays analysis. We acknowledge the contribution of  Esther Mollereau  to  Figure 6D. We thank  Ken Noma ,  Chris Norbury ,  Jo Murray ,  Luis Rokeach ,  Sylvie Tournier ,  Jean-Paul Javerzat  and  Jonathan Millar  for strains and materials, and developers of R and Ubuntu for their software. We are grateful to  Keith Gull  for the anti tubulin Tat1 antibody and to  Michael Keogh  for the use of his anti acetyl-Pht1 antibody and for communicating unpublished results.
This work was supported by a  CNRS   ATIP2008  grant to PB and a  Programme Grant  from the  Wellcome Trust  [ 083610 ] and the  Wellcome Trust   Centre for Cell Biology core grant  [ 077707 ] to KH. GY was supported by the  European Research Council  ( FP7/2007-2013 Grant Agreement nu 281359 ). TLB was funded by  SynthSys Edinburgh , a  Centre for Integrative Systems Biology (CISB)  funded by  BBSRC  and  EPSRC , reference  BB/D019621/1. PL was supported by a  CNRS   core grant  to the  LBMC  and an  ANR ''Chaire d'excellence' grant  ( CHX2011 TRACC ) attributed to VV. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data and phenotypes We used the pedigree data set provided for the Genetic Analysis Workshop 18 (GAW18) with knowledge of the simulated model. We focused on the simulated quantitative trait diastolic blood pressure measured at exam 1 (DBP_1). We used the trait Q1 to control for type I error. In the simulated model, there were 1457 SNPs (in 288 genes) contributing to DBP and/or systolic blood pressure variability. Their individual contribution ranged from as low as 0. 001% for gene ZZEF1 to as high as 6. 5% for gene MAP4 (for DBP). Part of the total heritability was generated using polygenic alleles from 1000 SNPs that were randomly selected in each replicate. The trait Q1 was uninfluenced by any of the provided genotyped SNPs. There were 200 replicates but the genotypes were the same across the replicates.
Single-marker mixed linear model (analysis limited to the SNPs and genes associated with DBP) We first estimated the power to detect association of Y with any of these associated variants using the measured genotype test  [4]  (mixed-linear regression model), as implemented in QTDT software (http://www. sph. umich. edu/csg/abecasis/QTDT/). The single-marker measured genotype test was conducted for each SNP, using all 200 replicates. We found that MAP4 was the only gene detectable (power = 96%) at the genome-wide significance level (α<1 × 10 -8 ), which accounts for the largest percentage of the variance of DBP and contains SNPs with very strong individual effects. Any of the remaining SNPs or genes were unlikely to be detected at stringent significance criteria (power <50%). We estimated that it would take approximately 40 days to analyze the whole-genome data (>8 million SNPs) by replicate and by phenotype using the measured genotype test.
Because of these computational constraints, we derived a new trait adjusted for family relatedness using GRAMMAR  [5]  as implemented in the GenABEL add-on package developed for the R statistical software  [6] , which allowed us to use the single-marker linear model. Lastly, because our goal was to evaluate whether power to detect association with SNPs with weak effects could be enhanced by pooling their effects, we further adjusted in a linear regression framework the decorrelated trait DBP_1 for the strong effects of MAP4 (SNPs3_48040283 and 3_48064367).
Acknowledgements This project was partially supported by the  platform bioinformatics Toulouse Midi-Pyrenees. NB has received funding from the  European Community 's  Seventh Framework Programme  ([FP7/2007-2013] under grant agreement no  212877  ( UEPHA*MS) ) and from  La Fondation pour l'Aide à la Recherchesur la Sclérose En Plaques. MS has received funding from  France PARKINSON Association. The GAW18 whole genome sequence data were provided by the  T2D-GENES Consortium , which is supported by  NIH  grants  U01 DK085524 ,  U01 DK085584 ,  U01 DK085501 ,  U01 DK085526 , and  U01 DK085545. The other genetic and phenotypic data for GAW18 were provided by the  San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder Study , which are supported by  NIH  grants  P01 HL045222 ,  R01 DK047482 , and  R01 DK053889.
The Genetic Analysis Workshop is supported by  NIH  grant  R01 GM031575. This article has been published as part of  BMC Proceedings Volume 8 Supplement 1 , 2014: Genetic Analysis Workshop 18. The full contents of the supplement are available online at http://www. biomedcentral. com/bmcproc/supplements/8/S1. Publication charges for this supplement were funded by the  Texas Biomedical Research Institute.
Quantification Lipids were quantified using calibration curves specific for the nine individual lipid classes and up to 12 fatty acid moieties. Twenty-three calibration curves were generated in non-diluted and 10-fold diluted matrices to correct for matrix-induced ion suppression effects. More abundant lipid species which displayed nonlinear response in non-diluted extracts were quantified from a 10or 100-fold diluted sample. An in-house developed Excel Macro was used to compile the data from the three injections. Limits of detection (LOD) and limits of quantitation (LOQ) were assessed on at least 2 lipid species of the 9 lipid classes quantified and was 1. 5 pg for ceramides, 0. 5 pg for LPC, between 140 and 550 pg for PA, between 0. 2 and 0. 8 pg for PC, between 15 and 30 pg for PE, 3 pg for PG, 35 pg for PI, between 3 and 7 pg for PS, 0.
Statistical analysis Power analysis using G*Power software (Dusseldorf University, Dusseldorf, Germany) was performed to estimate the sample size required to detect differences between groups in accordance with previous studies performed in patients with Metabolic Syndrome and Type 2 diabetes  [18, 31]. The main outcome was defined as impaired HDL3c antioxidative activity. The number of patients with a defined power of 0. 80 and alpha error level of 0. 05 was 10 subjects in each group. Power analysis was performed by a statistician taking into account mean values, standard deviations and between groups differences previously observed in antioxidative activity of HDL across several populations  [17e19]. Distributions of all variables were analysed for normality using the ShapiroeWilks test.
Normally distributed variables are expressed as means ± standard deviation (SD); non-Gaussian distributed variables are expressed as median (interquartile range). Between-group differences in normally-distributed variables were analysed using Student's t-test. For non-Gaussian distributed variables, the ManneWhitney U-test was used, or they were log transformed to ensure normality before statistical analysis. Differences in dichotomous variables were analysed by Fisher's exact test. Spearman's correlation coefficients were calculated to evaluate relationships between variables. Differences were considered significant at p < 0. 05 using two-tailed statistics. The statistical software INFOSTAT (National University of C ordoba, Argentina) and SPSS 17. 0 (Chicago, Ill, USA) were used.
Acknowledgements These studies were primarily supported by a collaborative grant from  CONICET  ( Buenos Aires, Argentina ) and  National Institute for Health and Medical Research (INSERM, Paris, France). We gratefully acknowledge further support from the  CODDIM Ile-de-France, University of Pierre and Marie Curie (UPMC) , the  Fondation de France  and the  Fondation pour la Recherche M edicale (Paris, France) ,  University of Buenos Aires (UBACyT   CB23 ),  Craveri Foundation  and from  CONICET  ( PIP 516 ). A. K. acknowledges the award of a Contrat d'Interface from  Assistance Publique-Hôpitaux de Paris/ INSERM (France).
Flow cytometry assays Flow analyses for B16F10wt cells (wild type) and msVEGFR2 expressing clones 13. 3, 16. 4, and MLuMEC,FVB endothelial cells, were performed on FACS-LSR flow cytometer (BD Biosciences) and analyzed using CellQuest software (BD Biosciences) after labeling with anti VEGFR1,2,3 antibodies. Detailed procedure is described in Supplementary data.
Soluble mVEGFR2 binds to its ligand We anticipated that the soluble VEGF receptor-2 (msVEGFR2) encoded by the pHRE-msVEGFR2 vector would recognize and bind to VEGF. This activity was assessed and quantified by a surface plasmon resonance assay with murine and human VEGF-A using hypoxia-conditioned supernatant from HEK-IFP-msVEGFR2 cells compared to the HEK-IFP control cell line. The data clearly demonstrated that the recombinant msVEGFR2 was able to bind to either murine or human VEGF-A (Figure  2A ). Clear interspecies cross recognition was illustrated by the affinity constant values: KD =4,5 nM for msVEGFR2 to mVEGF (Figure  2A , left) and KD =7,8 nM to hVEGF (Figure  2A , right).
4 and 13. 3 clones as compared to the B16F10 melanoma tumors (supplementary Figure  S5, D ) and in the serum (supplementary Figure  S5 B ). Although the level of VEGF-A inside the msVEGFR2 expressing tumors is higher (supplementary Figure  S5 C ), it seems to be retained locally since its level is lower in the corresponding sera (supplementary Figure  S5 A ). Such data corroborate the in vitro results reported on Figure  4  and on supplementary Figure  S3. Moreover this effect is confirmed by the in vivo kinetics measurement of tumor growth, which is clearly slower when B16F10 cells express the msVEGFR2 (supplementary Figure  S5 E ). These data suggest an effect on tumor angiogenesis. Thus vessels structure was observed and function assessed. Consequently, pericyte recruitment and vessel structure were studied by histochemical staining of tumors frozen and fixed sections.