dataesr/scientific-paragraphs-categorization huggingface dataset:
AV was in receipt of a  Contrat Hospitalier de Recherche Translationnelle (CHRP) INSERM-APHP , 2010. CW salary was funded in part by an  ERC grant  ( PGNfromSHAPEtoVIR 202283 , to  Ivo Boneca ,  Institut Pasteur, Paris, France ). ET was in receipt of a  French Research Ministry thesis grant , and a '' Bourse de fin de these'' FRM  grant. The work has been supported by  INSERM , and in part by grants from the  Agence de la Biome  ´decine 2007, 2009 (to AV), and from the  French National Agency  under reference  ANR-08-MIE-030  (to AV, ER, and CW). CC is in receipt of a 2011 ATIP-AVENIR grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
IgA is the predominant immunoglobulin isotype in mucosal tissues and external secretions, playing important roles both in defense against pathogens and in maintenance of commensal microbiota. Considering the complexity of its interactions with the surrounding environment, IgA is a likely target for diversifying or positive selection. To investigate this possibility, the action of natural selection on IgA was examined in depth with six different methods: CODEML from the PAML package and the SLAC, FEL, REL, MEME and FUBAR methods implemented in the Datamonkey webserver. In considering just primate IgA, these analyses show that diversifying selection targeted five positions of the Ca1 and Ca2 domains of IgA. Extending the analysis to include other mammals identified 18 positively selected sites: ten in Ca1, five in Ca2 and three in Ca3.
Primate and Mammalian IgA Sequences The complete sequences for primate IgAs used in a previous study  [10]  were obtained from GenBank (http://www. ncbi. nlm. nih. gov/genbank/); accession numbers are: Human IgA1 and IgA2 -J00220, J00221, M60192 and AJ012264; Chimpanzee IgA1 and IgA2 -X53702 and X53706; Gorilla IgA1 and IgA2 -X53703 and X53707; Gibbon IgA1 and IgA2 -X53708 and X53709; Orangutan IgA -X53704; Rhesus macaque IgA -AY039245 to AY039252, AY294614 and AY294615; Crab-eating macaque IgA -X53705 and Sooty mangabey IgA -AY544580 and AY544581. Complete sequences for non-primate mammalian IgAs were obtained from IMGT (http://www. imgt. org/IMGTrepertoire/), GenBank (http://www. ncbi. nlm. nih. gov/genbank/) and Ensembl (http://www. ensembl. org/index. htm). In total 64 sequences from 28 species were included in the analyses, representing marsupials, monotremes, and eight orders of placental mammals: primates, artiodactyls, perissodactyls, rodents, carnivores, lagomorphs, chiropters and cetaceans.
Accession numbers for the non-primate sequences used are: Cattle IgA -AF109167; Sheep IgA -AF024645; Pig IgA -U12594; Horse IgA -AY247966; Alpaca IgA -AM773729; Mouse IgA -J00475, AF175973 to AF175975, AH011154 to AH011156, and AY045750 to AY045752; Rat -ENSRNOT00000006888 and AY158661; Dog IgA -L36871; Panda IgA -AY818387; Rabbit IgA1 to 13 -X51647, X82108 to X82119; Big brown bat IgA -HM134938; Little brown bat IgA -HM134924; Short-nosed fruit bat IgA -HM134948; Black flying fox IgA -GQ427150; Dolphin IgA -AY621035; Possum IgA -AF091139 and AF027382; Opossum IgA -AF108225 and AF012110; Tasmanian devil -AFEY01402156; Platypus IgA1 and IgA2 -AY055778 and AY055779; Echidna IgA -AF416951. Excluded from the analysis was the recombinant human IgA2(n) allele  [25]  and the mouse IgA*3 allele for which the sequence has a nucleotide deletion in Ca1 that is presumably a sequencing or typographical error.
The monotreme and marsupial Ca1 sequences were not included because of uncertainty in their alignment with the Ca1 domain sequences of placental mammals and also to avoid the risk of saturation that could result from including these highly divergent sequences; likewise, sequences of the rapidly-evolving IgA hinge region were excluded from the analysis. For the analysis of the primate datasets and of the placental mammal Ca1 dataset, sequences were aligned using CLUSTAL W  [27]  as implemented in BioEdit  [28] , and corrected manually; notably, adjustments were made to follow the rigorous IMGT numbering system. For the mammalian Ca2 and Ca3 datasets, amino acid alignments were first generated using MUSCLE  [29]  and manual corrections, and these alignments were then used as a guide to prepare codon alignments for the same set of sequences.
We also used the five methods for detecting positive selection available from the DATAMONKEY web server  [35] : the Single Likelihood Ancestor Counting model (SLAC), the Fixed Effect Likelihood model (FEL), the Random Effect Likelihood model (REL), the Mixed Effects Model of Evolution (MEME) and the Fast Unbiased Bayesian Approximation (FUBAR). For these analyses, the best fitting nucleotide substitution model was determined through the automatic model selection tool available on the server. Because recombination can contribute to false inference of positive selection, causing a high rate of false positive detection  [36] ,  [37] ,  [38] , all datasets were screened for recombination using the GARD  [39]  method from the DATAMONKEY web server  [35]. No evidence of recombination was found.
Location of the PSC in Structural Models of IgA A molecular model of human IgA1 (MMDB ID: 10546, PDB ID:1iga  [40] ) and the three-dimensional X-ray crystal structure of human IgA1-Fc (PDB ID :1OW0  [9] ) were used to map the amino acids encoded by PSC onto 3D structures of the protein. To investigate their relation to putative sites of interest, the sites of interaction with host receptors (FcaRI, pIgR and Fca/mR  [3] ,  [8] ,  [9] ,  [12] ,  [13] ) and bacterial proteins (S. aureus SSL7 protein, streptococcal IgA binding proteins, N. meningiditis and H. influenzae type 2 IgA1 proteases  [3] ,  [14] ,  [19] ,  [41] ) were also mapped onto the 3D structure. For this purpose the NCBI application Cn3D 4. 1 (http://www. ncbi. nlm. nih. gov/Structure/CN3D/cn3d. shtml  [42] ) and iMol software  [43]  were used.
Because the goals of the two studies were different (sensitive detection in the earlier study versus detection of positions with the strongest signals for selection here), different cutoffs were applied. To reconcile the apparent discrepancies will require analysis of a much larger dataset of IgA sequences. To develop deeper understanding of IgA evolution, we compared IgA in a broad range of mammalian species. Of eighteen positions selected during mammalian evolution, only two are included in the five positions selected during primate evolution. This difference vividly illustrates the evolutionary plasticity of IgA. We find that diversifying selection has mainly targeted the Ca1 and Ca2 domains of IgA, and to lesser extent the Ca3 domain. Thus only three of the eighteen selected positions are in the Ca3 domain.
Association testing and meta-analysis Genome-wide genotype data were collected from 25 cohort studies and clinical centers (listed at the end of the paper and in Note S1 in Text S1). We obtained a data set of 11,860 HIV-1 infected individuals genotyped at multiple centers using several platforms (Table  S1  in Text S1). The present analysis focused on the subset of these individuals that are of European ancestry as assessed by principal components (PCs) analysis (see methods). For two of the genotyping centers, matched HIV-1 uninfected controls were available. For the remaining samples, large population-level control data sets were accessed from the Illumina Genotype Control Database (www. illumina. com) and the Myocardial Infarction Genetics (MIGen) Consortium (genotyped using the Affymetrix 6.
Exploration of top associations Since variation in the HLA region is well known to impact rate of HIV-1 disease progression and not acquisition, we sought to better understand the observed associations at this locus. Due to their shorter survival time, patients with rapid disease progression are underrepresented in seroprevalent cohorts, while individuals with prolonged disease-free survival times are more likely to be included, leading to an enrichment of factors that protect against disease progression in such populations. Additionally, some of the cohorts accessed for this analysis specifically recruited long-term non-progressors (LTNPs, Groups 2, 3 and 4). Inspection of the effect estimates at the top SNP (rs4418214) per data set showed that the majority of the association signal was driven by groups specifically enriched for LTNPs (Figure  2b ) suggesting a possible frailty bias in the overall results.
Analysis by transmission risk Since different modes of HIV-1 transmission may be influenced by different host factors, we further investigated if genetic variants may contribute to enhanced HIV-1 acquisition within transmission risk sub-groups. We stratified the study population by reported risk groups that were either primarily sexual (homosexual and heterosexual, n = 3,311) or parenteral (injection drug use and transfusion, n = 1,046). Association results in these sub-groups were consistent with those observed in the full set with no genomewide significant signals detected (data not shown).
Taken together these data suggest that genetic influences on HIV acquisition are either rare or have smaller effects than can be detected by this sample size. association of 22 variants previously reported to influence HIV-1 acquisition in this sample. All 22 of these variants could be measured in this sample either through direct genotyping or imputation. Of these, only one variant (rs1800872) showed nominal significance (p,0. 05, Table  1 ) although it did not survive correction for the number of variants tested (p. 2. 5610 23 ). Thus, none of the previously reported associations can be considered confirmed in this large sample.
Sample collection, genotyping and quality control The International Collaboration for the Genomics of HIV was established as a platform to combine all available genome-wide SNP data sets obtained on HIV-1 infected individuals worldwide. Patient material was collected at multiple clinical centers across North America, Europe, Australia and Africa (a list of contributing cohort studies and centers is given at the end of the paper). Genotypes for uninfected control individuals were obtained directly from three of the participating centers (GRIV, ACS, CHAVI) and from the Illumina genotype control database (www. illumina. com/icontroldb) and the Myocardial Infarction Genetics Consortium (MIGen) (NIH NCBI dbGaP Study Accession: phs000294. v1. p1)  [18]. Each data set was subject to quality control procedures performed prior to centralization of all data for the combined analysis.
However, to ensure consistency, all data were subject to further quality control once submitted. Per data set, samples with high missingness (,95% of sites successfully genotyped) and high heterozygosity (inbreeding coefficient. 0. 1) were removed. Ancestry was determined using EIGENSTRAT to project sample data onto the HapMap III reference panel. For the present analysis, only individuals clustering with the CEU/TSI subset were retained. To remove samples genotyped by multiple centers (and those with high relatedness) we performed identityby-state analysis taking the intersection of SNPs across all genotyping platforms, using PLINK version 1. 07  [27]. In the case of duplicates, the sample contributing the larger number of genotyped SNPs was retained. We further filtered out individuals with relatedness higher than 0.
Altogether, these results show that the type I IFN pathway controls CHIKV replication and pathogenesis in zebrafish. experiments. (D) Overlay of transmission and green fluorescence stereomicroscope images of a single representative wild-type CHIKV-GFP-infected larva, live imaged at different hours post infection (hpi). CHIKV infection is shown in the brain, liver, head mesenchyme, muscle, swim bladder and yolk (red, green, yellow, blue, white and magenta arrowheads, respectively). (E) Assessment using fluorescence stereomicroscopy of penetrance (% of infected larvae displaying infection) and severity (number of cells) of infection in specific organs at different time-points after CHIKV-GFP infection, following immunohistochemistry (IHC) with an anti-capsid antibody. Data pooled from 2 independent experiments, N = 20 larvae for each time-point.
Whole-body in vivo imaging Larvae were anesthetized and laid on the bottom of an agarosecoated, sealed Petri dish, and imaged as described  [34]. To assess efficiency of depletion strategies, Z-stacks with 22 mm steps of anesthetized larvae were taken with a Leica Z16 APO A macroscope and quantification performed using ImageJ software. For Figures S6A and S6B in Text S1 quantification was performed as described before  [76].
Time-lapse in vivo imaging For in vivo time-lapse imaging, 4-6 larvae, anaesthetized with 112 mg /ml tricaine, were laterally positioned and immobilized in ,1% low melting point agarose in the center of a 54-mm plastic bottom Petri dish, then covered with 2 ml water containing tricaine. Multiple field transmission and fluorescence imaging was performed using a Nikon Biostation IMQ, using a 106 objective (NA 0. 5) and a DSQi camera. Imaging was typically performed at 26uC and Z-stacks with 10 mm steps were acquired at least every 30 minutes. Imaging sessions typically lasted 6-24 hours; control uninfected larvae were always included. Cell emergence and death data were concatenated from multiple imaging sessions covering the 0 to 72 hpi time frame.
Imaging of fixed embryos Fixed embryos were progressively transferred into 80% glycerol before imaging. Confocal images of IHC-processed fish were taken with a Leica SPE inverted confocal microscope equipped with a 166 (NA 0. 5), 636 (NA 1. 30) oil immersion objectives and a 106 (NA 0. 30) dry objective. Images of larvae stained by WISH or Sudan Black B were taken with a Leica MZ16 stereomicroscope using illumination from above. Whole-body images of IHCtreated larvae were taken with a Leica Z16 APO A macroscope. Images were processed with the LAS-AF (Leica), ImageJ and Adobe Photoshop softwares. Cells with amoeboid morphology were scored as ''leukocytes''.
Acknowledgments We thank  Maxence Fre  ´taud for technical assistance,  Pierre Boudinot  and  Matthew Albert  for critical reading of the manuscript,  Stephen Higgs  for providing the CHIKV-GFP strain,  Cathy Gonzalez  and  Nadine Peyrieras  for helping to generate the ifnw1:mCherry transgene,  Annemarie Meijer  for trans-shipping mpeg1:Gal4 fish,  Marie Nguyen  and  Emmanuelle Perret  of the Flow Cytometry and Dynamic Imaging platforms of the  Institut Pasteur, respectively , for assistance.
This work was financed by the  Agence Nationale de la Recherche  ( Zebraflam  grant  ANR-10-MIDI-009 , and  CHIK-HOST-PATH2  grant),  Region Ile-de-France  ( DIM-Malinf ), and institutional grants from the  Institut Pasteur  and  CNRS. NP is endowed with a fellowship from Fundaca ˜o para a Ciencia e a Tecnologia ( SFRH/BD/60678/2009 ). GJL is supported by the  National Health and Medical Research Council  (grants  461208  and  637394 ). The Australian Regenerative Medicine Institute is supported by grants from the  State Government of Victoria  and the  Australian Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Methods Seventy-seven centres from 33 countries have been contributing to an international secured web-based registry for autoinflammatory diseases (EUROFEVER), hosted by the PRINTO website (Paediatric Rheumatology International Trial Organisation, http://www. printo. it). The registry collects anonymised demographic, clinical, laboratory and molecular genetic data on patients with autoinflammatory diseases. Complete clinical information on 1868 consecutive children was available.
S. Typhimurium The standard streptomycin model for murine S. Typhimurium diarrhea was used to assess Cybb expression in the infected mucosa of wild type C57BL/6 mice. Streptomycin pretreated animals were infected with wild type S. Typhimurium (S. Tm wt ; 5610 7 cfu by gavage) for 12 or 24 h. Samples of the cecum tissue (the site of the initial and most pronounced enteropathy  [18, 19] ) were recovered to analyze Cybb expression by reverse transcription quantitative real-time PCR (RT-qPCR). In line with earlier data  [28] , the abundance of Cybb mRNA in the cecum increased by about 3-fold after 12 h and about 8-fold after 24 h of infection compared to streptomycin-treated animals (Fig. S2A ). This went along with mucosal inflammation and infiltration of neutrophils and monocytic phagocytes into the cecal mucosa as observed by histopathology and flow cytometry analysis (Fig.
Alternatively, this might be indicative of a Myd88-dependent, but Cybb-independent defense mechanism which may contribute to limiting bacterial growth in the enterocytes. Such mechanisms could be an interesting topic for future research. To this end, the data verified the Myd88dependency of enteropathy in S. Tm avir infected Cybb 2/2 mice. Finally, we have assessed the dependency on mucosal monocytic phagocytes. Using transgenic mice expressing the diphtheria-toxin receptor under control of the CD11c promoter (DTR +/2 ;  [30] ) and diphtheria toxin-mediated (DTX) cell depletion, it has been previously established that S. Tm invG relies on mucosal CD11c + monocytic phagocytes for traversing the gut epithelium and colonizing the cecal LP  [21]. Thus, Cybb 2/2 DTR +/2 mice or Cybb 2/2 littermates were treated with DTX and infected for 4 days with S.
S. Tm avir Induces Intermediate Levels of Enteropathy in Mice Reconstituted with a Mix of Cybb 2/2 and Wild Type Bone Marrow To further analyze how Cybb restricts S. Tm avir infection of the cecal mucosa, we performed an experiment with mixed bone marrow chimeric mice. The chimeras were generated by reconstituting irradiated Cybb 2/2 mice (congenic marker Ly5. 2) with a mix of 50% Cybb 2/2 (congenic marker Ly5. 2) and 50% C57BL/6 (congenic marker Ly5. 1) bone marrow. After 8 weeks, these chimeras displayed 69% Cybb 2/2 (congenic marker Ly5. 2) and 31% C57BL/6 (congenic marker Ly5. 1) cells in the cecal LP as tested by flow cytometry at the end of the experiment (data not shown). In these mice, the stromal cells and all CD45. 2 + cells (i. e. phagocytes, B-cells, T-cells, etc. ) were Cybb-deficient, while the CD45.
Flow Cytometry Cecum and mLNs were chopped and digested in RPMI (Invitrogen) and Liberase TL (Roche) for 45 min at 37uC under vigorous shaking. The resulting cell suspension was filtered through a 100 mm nylon cell-strainer (Milian) and stained in buffer containing PBS, 5 mM EDTA, 10% FCS and 50 mg/ml streptomycin. All fluorophore-labeled monoclonal antibodies were purchased from BD Biosciences or Biolegend. The LP cells were analyzed on a LSR II cytometer (Becton Dickinson) and graphs were produced with FlowJo software (Tree Star, Inc. ).
RT-qPCR The excised cecum tissue was washed in cold PBS, placed in 600 ml RNAlater (Qiagen) and subsequently frozen at 280uC. Total RNA extraction was done using the RNeasy mini kit (Qiagen) with RNase-free DNase digest (Qiagen). For reversetranscription of 1 mg mRNA aliquots, the RT 2 HT First Strand cDNA Kit (Qiagen) was used. Custom RT 2 Profiler PCR Arrays (Qiagen) were run with RT 2 SYBR Green ROX FAST (QIAGEN) on an Applied Biosystems 7900 HT Fast Real-Time PCR System to amplify the resulting cDNA. Relative mRNA levels (2 2DCq ) were determined by comparing the PCR quantification cycle (Cq, determined with the Software SDS 2. 2. 1) for 27 genes related to inflammation and defense against S. Typhimurium infection (the selection is based on Songhet et al. , 2010) with the reference gene Actb.
The differences in their Cq cycles were calculated (DCq). In all experiments, the upper limit of Cq was fixed to 35 cycles. Then, the fold-increase over streptomycintreated C57BL/6 mice was calculated and plotted. Each sample was controlled for mouse genomic DNA contamination. All DNApositive data were excluded from further analysis. Lastly, RNA quality was monitored with the Agilent RNA 6000 Nano Kit (Agilent Technologies) on a 2100 Bioanalyzer (Agilent Technologies) and only samples with a RNA integrity number (RIN). 9. 90 were included.
Acknowledgments We are grateful to  Hardt lab members  for discussions, to  Manja Barthel  for technical support, to  Balamurugan Periaswamy  and  Anja Bu  ¨rkli for statistical advice, to  Lisa Maier  and  Carmen Dolores Cordova  for critical reading of the manuscript, to  Michael Detmar  for access to the RT-qPCR machine and to the RCHCI team, in particular  K. Holzinger ,  S. Egger ,  J. Fehr ,  S. Freedrich  and  T. C. Weber  for excellent support.
3 or NLAD8 strains). FarRed-loaded autologous target CD4 + T cells were added, and Gag + target cells were measured 48 h later by flow cytometry. One representative experiment (out of four) is shown. The reverse transcription inhibitor NVP was used as a positive control. NI, noninfected cells. (b) Cell-associated inhibition assays were performed as in panel a. bNAbs were used at the indicated doses, and the percentage of inhibition of infection was calculated. For cell-free infections, viruses (NL4. 3 or NLAD8 strains) were incubated with the indicated bNAbs for 1 h and then added to HeLa-derived P4C5 target cells, which carry an HIV-1 LTR--gal reported cassette. After 36 h, infection was quantified by measuring -gal activity. Data are mean ± SD from four independent experiments. Lines represent fitted results.
Cells were infected in triplicate with 1 or 5 ng Gag p24. Viruses were incubated with the indicated bNAbs for 1 h before infection. After 36 h, cells were lysed in PBS, 0. 1% NP-40, and 5 mM MgCl 2 and incubated with the -gal substrate CPRG (Roche), before measurement of 570-nm OD. Calculation of IC50. Dose-response inhibition curves were drawn by fitting data from three to six independent experiments to sigmoid dose-response curves (variable slope) using Prism software (GraphPad Software). Percentage of inhibition was defined as (percent signal in nontreated target cells  percent signal in bNAb-treated cells)/(percent signal in nontreated target cells) × 100. The IC50 was calculated with Prism.
We thank members of the  Virus and Immunity Unit  for help and critical reading of the manuscript. We thank the  National Institutes of Health AIDS reagents program  for the gift of reagents. O. Schwartz was supported by grants from the  Agence Nationale de Recherche sur le Sida ,  Sidaction, AREVA Foundation , the  Vaccine Research Institute , the  Labex IBEID program , the  FP7 program HIT Hidden HIV  ( Health-F3-2012-305762 ), and  Institut Pasteur. M. Malbec  was supported by fellowships from  Sidaction ,  Fonds de dotation Pierre Bergé , and  Fondation pour la Recherche Médicale. H. Mouquet was supported by the  Milieu Intérieur program  ( ANR-10-LABX-69-01 ). M. C. Nussenzweig was supported by grants from the  Bill and Melinda Gates Foundation  ( Collaboration for AIDS Vaccine Discovery grant   38619s ) and the  Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery  grant  AI 100663-01.
DNA sequencing and analysis All of the amplicons were ligated into pMD-18 T vector (Takara) and transformed to Escherichia coli DH5α competent cells. The positive colonies were sequenced with ABI PRISM 3730 Genetic Analyzer, and sequences were analyzed with DNAtools software. Further comparison with taeniid cox1 gene sequences available in GenBank was made using the BLAST and Clustal W software. Phylogenetic analyses were performed using the neighbor joining (NJ), maximum parsimony (MP), and minimum evolution (ME) methods, and the Mega 5. 05 program. The bootstrap value was set as 1,000 replications with a cutoff value of 50%.