dataesr/scientific-paragraphs-categorization huggingface dataset:
Relative telomere length (RTL) DNA was extracted from isolated PBMC according to manufacturer's instructions (QIAMP DNA Blood mini kit reference 51,106, Qiagen, Courtaboeuf, France). A Nanodrop ND-1000 spectrophotometer (Labtech, Palaiseau, France) was used to quantify genomic DNA. Genomic DNA was stored in TE buffer (10 mM Tris-HCl, 0. 1 mM EDTA, pH 7. 5) at 4 °C at a concentration of 10 ng/μL. DNA samples were diluted into pure water before starting real-time quantitative multiplex PCR runs described in supplementary data Thermocycler was CFX96 Real Time System (Bio-Rad) with Bio-Rad CFX Manager software to generate standard curve and calculate T/S ratio. Two T/S results were obtained for each sample, and the final reported result for one sample in a given run is the average of the 2 T/S values.
Acknowledgments Prior presentation. Preliminary results were presented at the 19th Conference on AIDS and STIs in Africa (ICASA), December 4-9, 2017, Abidjan, Ivory Coast. Financial support. No grant was received for the study. Potential conflicts of interest. The authors declare that they have no competing interests. All authors have submitted the  ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
CAPN5 and CBLB are not required for Plasmodium uptake. Finally, we report a whole cell proteome expression dataset for human hepatoma cells and show that CAPN5 is strongly associated with CD81. Taken together, we used a combined quantitative proteomics-CRISPR/Cas9 strategy to map CD81 interactions in liver cells and identified CAPN5 and CBLB as HCV entry facilitators.
Of the final 42 CD81 network molecules defined in this study, ten proteins were previously recognized CD81 interactors including SCARB1, the integrins ITGA1, ITGA6, ITGB1, the G proteins GNA11, GNAI1, GNAI3, CD151, ApoE and prostaglandin F2 receptor negative regulator (PTGFRN)  [15]. Six of the CD81 liver cell interactors found here were previously reported HCV entry facilitators as described above (labeled with + in Table  1 ). In addition, the LFQ approach and extensive comparison of hepatoma and primary cell datasets elucidated 27 CD81 interactors without a previously reported role in HCV entry or CD81 binding. In summary, we defined a stringent set of 42 CD81 interactors in human liver cells (S3A Fig, Table  1 ).
The latter then activates its endocytosis together with influenza virus  [48]. It will be interesting to investigate whether CBL and CBLB affect influenza virus entry in a similar manner as HCV entry. Tetraspanins moreover play a role in entry and spread of HIV-1, human papillomaviruses and human cytomegalovirus  [49] [50] [51] [52]. Thus our study provides an important dataset of PPIs, which has implications beyond HCV infection. In fact we think that the unbiased interaction proteomics provides a different perspective on virus entry than functional knockout screens, as it opens avenues for diverse functional follow up studies and hypothesis generation. In the absence of infection CD81 is thought to regulate migration of liver cells  [39]. In confirmation of this notion, in silico analysis maps here identified CD81 network proteins to cellular movement pathways.
Data analysis was performed using FlowJo. For immunofluorescence analysis cells were plated on poly-lysine coated cover slips and fixed with 3% paraformaldehyde for 20 min. After three PBS washes, cells were permeabilized with 0. 05% TX-100 for 4 min. Anti-CAPN5 and anti-CBLB antibodies were per-adsorbed over night at 4˚C on CAPN5 and CBLB knockout cells, respectively. Scrambled knockout cells were then incubated o/n at 4˚C with these pre-adsorbed antibodies followed by staining for 1 h at RT with secondary Alexa-fluorophore conjugated antibodies. DAPI counterstained cells were mounted on glass slides with ProLong Gold antifade mountant (Molecular Probes, W32466) and analyzed by confocal microscopy using an inversed confocal laser-scanning microscope (Olympus Fluoview 1000), using a 60x or 100x magnification lens.
LC-MS/MS analysis Protein samples were reduced, alkylated and trypsinized as previously described  [19]. Tryptic peptides were separated on an EASY-nLC 1000 HPLC system coupled online to an orbitrap mass spectrometer (Q Exactive HF, Thermo Fisher Scientific) via nanoelectrospray source in single run experiments. Data was acquired using the Xcalibur software (Thermo Scientific). MS raw files were analyzed using MaxQuant algorithms against the human Uniprot FASTA database and a common contaminants database (247 entries) by the Andromeda search engine. with a false discovery rate (FDR) of 1% at peptide and protein level  [87, 88]. Inclusion criteria were set to at least one unique or razor peptide with a minimum length of 7 amino acids per protein group. MaxLFQ algorithms were used for protein quantification  [89].
Bioinformatics, hierarchical clustering, network analysis and hit scoring Statistical analysis of proteomics data was conducted using a nonparametric two samples test correcting for multiple hypothesis testing in Perseus  [90]. Proteins significantly enriched in CD81 co-IPs from hepatoma cells (FDR 0. 05, s0 = 1; s0: the minimal log 10 fold change) and primary hepatocytes (intensity difference!4) were considered for further analysis. 33 interaction partners of CD81 fulfilled this criterion. For functional follow up of CD81 binding proteins, we additionally included the nine closest nodes from a STRING network analysis based on experimental data and database search with a confidence of 0. 4  [91]  of the 33 proteins, resulting in 42 CD81 binding partners to be tested for their role in HCV infection.
RNAi screen for HCV host factors Lunet N hCD81 cells stably expressing Firefly luciferase (Lunet N hCD81 FLuc) were transfected with pools of three siRNAs targeting the 42 selected CD81 interaction partners (Ambion Silencer Select) and infected with the Renilla luciferase reporter virus JcR-2A as detailed in  [19]. The screen was conducted nine times on cells of three independent passages. Normalization and statistical analysis was performed on a set of 45 targets including CD81, CLDN1 and SCARB1 positive controls in R using the Bioconductor package RNAither  [95]. Briefly, normalization for plate effects was done by subtracting the plate mean and dividing by the plate standard deviation.
Statistical analyses Experiments were performed at least in three biological replicates with three technical replicates per experiment unless otherwise stated. Results are presented as mean plus the standard deviation (SD) of three biological replicates unless otherwise indicated. The 50% tissue culture infectious dose (TCID50) was calculated based on Kaerber and Spearman  [101, 102]. Statistical significance was determined by one-way Analysis of Variance (ANOVA) followed by Dunnett's multiple comparison test or by unpaired t test in GraphPad Prism 5 (GraphPad Software, Inc. , San Diego). A p value of less than 0. 05 was considered statistically significant.
Acknowledgments We thank all individuals, who donated primary liver material. We thank  Christine Goffinet  and all members of the  Institute for Experimental Virology  for critical discussions,  Takaji Wakita  for the JFH1 isolate,  Jens Bukh  for intergenotypic chimeric HCV strains,  Dirk Lindemann  for the pc. Z. VSV-G plasmid,  Gert Zimmer  for VSV Ã M Q ,  Volker Thiel  for coronavirus 229E-Luc,  Didier Trono  for pWPI and pCMVΔR8. 74constructs,  Marc Schmidt-Supprian  and  Klaus Heger  for the pLenti CRISPR v2 ccdB plasmid,  Jean-Francois Franetich ,  Maurel Tefit  and  Thierry Houpert  for rearing mosquitoes.
This work was supported by a fellowship from the  Hannover Biomedical Research School  and the  Centre for Infection Biology  (ZIB, https:// www. mh-hannover. de/zib_zib. html)to JB, grants and provides new insight into the distinct cell invasion mechanisms of HCV and malaria parasites. from the  Deutsche Forschungsgemeinschaft  ( SFB 900 , project  A6 , www. sfb900. de)and the  Helmholtz Association   SO-024  to TP (www. helmholtz. de),grants from the  European Union  ( FP7 PathCo Collaborative Project   HEALTH-F3-2012-305578 , www. pathco. org)and the  Laboratoire d'Excellence ParaFrap  ( ANR-11-LABX-0024 , www. labex-parafrap. fr) to OS, and grants from the  Human Frontier Science Program  ( LT-000048-2009 , www. hfsp. org/), the  German Academy of Science Leopoldina  (LPDS 2009-9, www.
The integrated transcriptome and proteome analysis was based on the four different approaches described by Haider and Pal  [21] : (1) intersection of transcriptome and proteome data, (2) identification of the common biological processes altered in the two datasets (3) identification of the common functional pathways altered in the two datasets, (4) topological network methods, with the analysis of the transcription factor binding sites (TFBSs) present upstream from the open reading frames of the altered proteins identified by ICPL method.
Patient recruitment The entire project was approved by the local institutional review board (CPP Ouest II) and the Direction Générale de la Santé (DGS). All patients included in this study were diagnosed for de novo GB (WHO 2007 classification) by a central committee of neuropathologists and gave their written informed consent prior to their enrolment. Five patients (Table  1 ) with both proteome and transcriptome analysis of their TZ and PBZ tissues were selected from the databank of the "Grand Ouest Glioma Project". More detailed information on tissue samples characteristics can be found in our previous publications  [11, 15, 16].
Transcriptome analysis Transcriptome analyses of the tumor samples were performed as previously described in one of our previous publication on the transcriptomic platform Biosit, Rennes, France  [16, 22]. In brief, total RNA was isolated from the GB samples using the NucleoSpin RNAII kit (Macherey-Nagel, Hoerdt, France) and RNA integrity (RNA integrity NC8) was assessed with an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Extraction of RNA microarray data was performed with an Agilent Whole Human Genome 4 × 44 K Microarray 15 Kit (Agilent technologies), according to the manufacturer's recommendations. Raw RNA data were then log2-transformed and normalized (quantile normalization and baseline transformation) using R v3. 1. 0. (http://www. r-proje ct. org). For the transcriptomic profile identification, we used a non-parametric rank product method to account for hybridization bias, allowing the identification up-or down-regulated genes in pooled GB tumor tissue by comparison to the pooled peritumoral brain samples, using the RankProd R package.
Proteome analysis The protocol for proteome analyses has already been described in our previous publications  [11, 12]. TZ samples were analyzed using ICPL, that allows a highthroughput identification and quantification of a sample's protein profile  [23, 24]. Intact proteins were labeled with isotopic derivatives of nicotinic acid of different molecular weight, then subject to gel liquid chromatography and tandem mass spectrometry with an Esquire HCT Ultra PTM Discovery mass spectrometer, to identify and quantify proteins. Peptides were identified by querying the human Swiss-Prot database with the Mascot search engine (v. 2. 2. 07) applying a score above the identity threshold and a FDR < 1%. Differentially expressed proteins were identified in the TZ by comparison to the PBZ samples with a threshold > 1.
ncifc rf. gov). The probability of alteration of the biological process was calculated using one-sided Fisher exact P-value and the False Discovery Rate (FDR) was calculated using onesided Fisher exact P-value corrected for multiple comparisons. Biological and functional processes were considered significantly altered in each dataset with P and FDR < 0. 05. (3) The functional pathways identified by both transcriptome and proteome data as being altered were identified using KEGG database (http://www. genom e. jp/kegg/pathw ay. html). Functional pathways were considered significantly altered in each dataset with P < 0. 05 corrected for multiple comparisons using Benjamini-Hochberg method, and a fold-change > 2. (4) We looked for the presence of direct edges between transcripts and proteins with the identification of TFBSs in the regulatory region upstream from the open reading frames of the proteins identified as deregulated in GB.
Comparison of altered biological processes The transcriptomic data indicated that 149 biological processes were altered in the tumor samples, and the proteome analysis indicated that 23 biological processes were altered (list in Additional file 3: Table  S2 ). Twelve biological processes were found to be altered in both datasets, all repressed in the transcriptome analysis but enriched in the proteome analysis, except for the "regulation of biological quality" process, which was enriched in both datasets (Table  3 ).
These findings are consistent with previous reports  [11, 16, 38]. No biological processes linked to tumorigenesis, for example cell cycle regulation, cell metabolism or cell motility, were found in both transcriptome and proteome analysis to be altered in GB. Some such processes were found in one dataset to be altered in GB, for example d-glutamine metabolism in the transcriptome and glycolysis/glyconeogenesis in the proteome  [39, 40]. We observed the "pathogenic Escherichia coli infection" enrichment in GB through the proteome analysis. Its relation with the GB is presently unknown. Considering the involvement of genes in multiple biological processes, this enrichment could be an artefact. However, recent studies highlight the role of microbiota in gastric and breast cancer developments and may be present in GB  [25, 26].
Further studies are needed to reply to this observation. Although NEFL, SYN1 and twelve biological processes were in common between the two omic analyses, they did not show the same deregulation (except for the "regulation of biological quality" process): transcriptome analysis indicated that they are under-expressed in TZ whereas proteome analysis indicated that they are overexpressed. Mismatches of this type between such pairs of datasets has already been described  [21, 25, 26] ; there are several possible explanations including translation.
This result highlights the importance to have large cohort of patients to limit the misinterpretation of the overall transcriptomic and proteomic data. The low correlation between transcriptome and proteome data is not new and not restricted to cancer tissue  [28] [29] [30] [31] [32] [33]. For example, a correlation of only 17% was found between mRNAs and proteins in lung adenocarcinoma  [28, 34] ; in prostate cancer, the correlation between gene expression and protein levels is also poor to moderate  [35]. Song et al. [36]  performed proteomic profiling of eight GBs and their paired normal brain tissues and afterwards assessed overlap with RNA gene expression profiling from GEO and TCGA datasets of GBs. They found a correlation of only 2% between the differentially expressed proteins and genes from microarray.
The low overlap between transcriptome and proteome data observed in our study can be explained by the general biological phenomena described above but also by analytical bias and GB-specific alterations (Fig. 2 ). On the analytical side, the choice of the analysis technique is crucial in omic studies and directly affects both the results and the feasibility of comparison between different datasets  [37, 38]. RNA microarray techniques are the most widely used methods, allowing fast and accurate identification of mRNAs  [39] ; however, the number of probes on the microarray chip limits the extent of mRNA detection and probe set identification is a source of error in mRNA identification  [40]. The ICPL method used here for proteome analysis allows assessment of protein levels but only a fraction of the proteome corresponding to the abundant proteins is analyzed  [24].
The proteomic analysis generated a specific dataset of proteins for which a gradient of over-expression was observed from the periphery to the core of the GB  [31]. At the transcriptome level, we observed that the molecular heterogeneity was much more important within tumors than between patients  [16, 41]. The molecular definition of this intratumoral heterogeneity of GB is still incomplete, but the data are rapidly growing. For example, Nobusawa et al. [42]  observed numerous tumor area-specific genomic imbalances, and our previous study as others reported inherent intratumor molecular subtype heterogeneity in GBs  [7, 16, 43]. Other studies showed that RTK amplifications as well as MGMT status are heterogeneously distributed in GB  [44] [45] [46]. More recently, next-generation sequencing techniques were able to highlight this intratumoral heterogeneity, to identify different clonal population of GB cells and understand their role in the recurrence  [47, 48].
Pathway enrichment analysis on this methylation signature of inflammaging included IL-6 and IL-9 signalling and several of the identified CpG sites mapped on genes involved in the regulation of inflammation, such as AIM2 and SOCS3. AIM2 encodes for an IFN-γ inducible protein that recognizes altered or mislocalized DNA and initiates the assembly of the inflammasome by promoting caspase-1 activation and pro-IL1 pro-IL18 cleavage  [126]. Methylation of AIM2 was negatively associated with the expression of the gene and with CRP levels; interestingly, we found that in publicly available datasets AIM2 methylation decreases with aging (data not shown) in agreement with age-associated increase in CRP levels. Finally, SOCS3 is induced by various cytokines, including IL-6, IL-10, and IFN-γ and it encodes for a negative regulator of STAT3 signalling.
Acknowledgments This work was supported by the  Seventh Framework Programme  (grant number  602757 , HUMAN); by the  Horizon 2020 Framework Programme  (grant number  634821 ,  PROPAG-AGING ); by  JPco-fuND  (ADAGE); by  Fondazione Cassa di Risparmio delle Province Lombarde (CARIPLO)  ((Rif. 2015-0564 ). This project has received funding from the  H2020 Marie Skłodowska-Curie Actions , grant agreement No  675003. http://www. birmingham. ac. uk/panini.
Different superscripts indicated significant differences between treatments. the western blots. All the antibodies have been shown to cross react successfully with rainbow trout proteins of interest  (Dai et al. , 2013; Jin et al. , 2014). After washing, membranes were incubated with an IRDye infrared secondary antibody (LI-COR Biosciences, Lincoln, USA). The bands were visualized by infrared fluorescence using the odyssey Imaging System (LI-COR Biosciences) and quantified by odyssey infrared Imaging System software (version 3. 0, LI-COR Biosciences).
Statistical analysis Normality of distributions was assessed by Shapiro-Wilk test. Data were analysed by Two-way ANOVA to assess the differences between lines, diets and interactions. If interactions between diets and lines were statistically significant, post-hoc Tukey test would be used to compare all the groups. Data were analysed with R software (v. 3. 3. 3)/R Commander Package. Treatment effects and interactions were considered statistically significant at P<0. 05. Results were presented as means±s. d. (n=6 samples per treatment based on non-significant differences between tanks per group).
Acknowledgements We thank  E. Plagnes-Juan ,  V. Veŕon ,  A. Surget  and  K. Dias  for technical assistance in the laboratory. We also acknowledge the technical staff of the  INRA  experimental facilities ( F. Terrier ,  F. Sandres ,  A. Lanuque ) at  Donzacq, Landes, France  for fish rearing and feed producing.
Acknowledgements This work was supported by two  Eric Guiler Tasmanian devil Research Grants through the Save the Tasmanian devil appeal of the University of Tasmania Foundation , an  Australian Academy of Science ,  FASIC Early Career Fellowship  to BU, an  International Associated Laboratory Project France/Australia  to  FT & BU , and a  Deakin Faculty of Science   Engineering & Built Environment Research Grant Scheme 2017  ( SEBE-RGS17-UJVARI ). FT is also supported by the  ANR EVOCAN, Andr e Hoffmann and la Fondation MAVA. RH is supported by a  National Science Foundation  grant ( NSF DEB-1316549 ) and an  Australian Research Council   DECRA Fellowship  ( DE170101116 ). We are grateful to two anonymous reviewers and to A/Prof. Athena Aktipis  for their valuable comments on our manuscript.
Both in vitro and in vivo data support the specific inhibition of PI3Kδ as a promising therapy. Indeed, the selective p110δ inhibitor IC87114 is able to dampen the activity of the mutated PI3Kδ in vitro in APDS1 patients' T cells  (52) , and both p110δ (APDS1) and p85α (APDS2) are strongly inhibited in vitro by the PI3Kδ-specific inhibitor idelalisib (GS-1101 or CAL-101), which is currently approved by the US-Food and Drug Administration for the treatment of chronic lymphocytic leukemia  (50, 75). In addition, the first clinical trial (#NCT02435173) that has been conducted by Novartis with the PI3Kδ-specific inhibitor leniolisib (CDZ173) in six APDS patients produced encouraging results  (76). Oral administration of leniolisib during 12 weeks was well tolerated and was associated with an improvement of both laboratory and clinical parameters (reduction of peripheral transitional B cells, naive B cells, and senescent T cells; decrease of IgM and inflammatory cytokines levels; reduction of splenomegaly and lymphadenopathy)  (76).
2a -f)  31, 32. The 4. 9 Å resolution map of the inner filament part enabled us to build a pseudo atomic model of BCL10 residues 10-115 and MALT1 residues (30-121) by flexible fitting of a BCL10 homology model and a related crystal structure of MALT1 DD into the EM density (Fig. 1h, i ). Interestingly, the structural comparison with the MALT1 DD X-ray structure (pdb ID: 2G7R) reveals that in the presented EM density helix α6 is kinked and not forming an extended helix as previously proposed  37. The observed fold of the kinked MALT1 helix is similar to other CARD family members (Fig. 1i ). BCL10 filament assembly is critical for CARD11 recruitment. The BCL10 CARD structure is considerably stabilized in comparison to the NMR structure due to the extensive network of interactions within the BCL10-MALT1 filament assembly (Fig.
The MALT1 protease activity is represented in relative fluorescence units and data from at least three independent experiments were used. BCL10-MALT1 complex formation analysis. The BCL10-MALT1 complex fractions were pooled and concentrated to 0. 5 μM. Filament formation was initialized by addition of PreScission protease (GE Healthcare) in equimolar amounts. Polymerization of BCL10-MALT1 species was monitored by dynamic light scattering (Viscotek 802 DLS) as a function of time over 100 min. Higher order species (above 10 4 nm) were excluded from data evaluation. Data was analyzed by the OmniSIZE 3. 0 software package (Viscotek). BCL10-MALT1 complex preparation for cryo electron microscopy. BCL10-MALT1 complex fractions were concentrated stepwise to 1 mg/ml. Higher molecular species formation was monitored for each concentration step by dynamic light scattering (Viscotek 802 DLS).
Filament formation was initialized by addition of equimolar amounts of PreScission Protease (GE Healthcare) and performed overnight at 12 °C. Analytical size exclusion chromatography (S200 5/150 GL) was performed to separate BCL10-MALT1 filaments from non-oligomerized protein. Directly before blotting Octyl-beta-Glucoside was added to the selected size exclusion fraction with a final concentration of 0. 001%. 4. 5 µl of the BCL10-MALT1 sample was applied on glow-discharged R1/2 grids (Quantifoil Cu R1/2, 300 mesh). The grids were blotted for 3 s at 95 % humidity and 15 °C and plungefrozen in liquid ethane using a Leica Plunger (FEI). Cryo electron microscopy data collection. Cryo-EM images of the BCL10-MALT1 complex were collected on a 200 keV Talos Arctica microscope equipped with a Falcon III detector (FEI).