dataesr/scientific-paragraphs-categorization huggingface dataset:
Compensation was performed with a CompBeads kit (BD Biosciences). Doublet cells were excluded using Forward Scatter Width and Forward Scatter Area and dead cells were excluded using Live/Dead staining. FACSDiva TM software version 6. 1. 2 (BD Biosciences) was used for the final analysis and graphical output. Cytokines and Chemokines Analysis. Supernatants were collected at various time points during inhibitory assays for analysis of cytokines and chemokines production. IFN-a was measured using Verikine Human Interferon Alpha MultiSubtype ELISA kit (PBL InterferonSource, NJ). MIP-1a, MIP-1b, IL-6 and TNF-a were measured with Cytometric Bead Array (CBA, BD Biosciences) using appropriate Flex sets and Human Soluble Protein Master Buffer kit (BD Biosciences), acquired on a LSRII SORP cytometer, and analyzed using FCAP Array software version 1.
0. 1 (Soft Flow, Hungary). Antibody Binding Assay. pDC were incubated with or without 10 mg/ml of purified anti-FccRII (CD32) Ab (BD Biosciences) for 30 minutes at 37uC, then labeled with anti-CD32 FITC (BD Biosciences) for 10 minutes at 4uC. The samples were fixed in Cytofix solution, acquired by LSRII SORP cytometer and analyzed using FACSDiva TM software version 6. 1. 2. Statistical Analysis. Groups were compared by one-way ANOVA (Kruskal-Wallis test). Values of P , 0. 05 were considered to be statistically significant. All statistical calculations were performed using GraphPad Prism 5. 04 software (GraphPad, CA).
Acknowledgments We thank  Dr. Hugo Mouquet  ( Institut Pasteur, Laboratory of Humoral Response to Pathogens, Paris ) for his support and the generous gifts of monoclonal anti-HIV-1  NAb  ( 10-1074 & 10E8 ). This work was supported by  EuroNeut41  [ FP7-HELTH-2007-A-201038 ],  EuroPrise  [ LSHP-CT-2006-037611 ], Dormeur Investment Service Ltd. , and  ANRS [French National Agency for Research on AIDS and Viral Hepatitis ] grants. A. L. and B. S. were supported by the grant from  ANRS. The authors are grateful to all the healthy blood donors from  EFS of Strasbourg  in this study.
Phylogenetic analyses revealed 7 haplotypes types corresponded to previously described species (Figure  3 ). No association was found between Rickettsia spp. and ity was observed (Figure  3 ). All A. loculosum ticks were infected with a Rickettsia with the R. africae strain infecting A. loculosum ticks in New Caledonia and with the ESF-5 strain isolated from the cattle tick, A. variegatum, in Ethiopia (GenBank accession no. CP001612. 1). Rickettsia spp. in C. capensis ticks had 3 well-supported genetic clusters and, thus, were more diverse. The most frequent Rickettsia sequences) was a probable strain of R. hoogstraalii. Other haplotypes corresponded to an unknown Rickettsia sp. that was closely related to R. hoogstraalii and to a probable strain of R. bellii haplotype of R. bellii was detected only on Réunion Island in 2 C.
Acknowledgments We thank  Sébastien Lefort ,  Aurélien Prudor ,  Chris Feare ,  Christine Larose ,  Gérard Rocamora , and  Sarah Temmam  for assistance with tick sampling and molecular work. This work was supported by the  Fond Européen de Développement Régional Pathogenes associés à la Faune Sauvage Océan Indien  ( Programme Opérationnel de Coopération Ter-Eparses PathOrnithoTiques project ). Postdoctoral fellowships (to M. D. , C. L. , and A. J. ) were funded by  RUN-Emerge European  project funded by  European Commission  under  FP7 program. C. LeR. and M. B. were supported by a training program fellowship from the  Federation Environnement-Biodiversité-Santé. Dr Dietrich  is a postdoctoral research associate in the  Center for Research on Emerging Infectious Diseases in the Indian Ocean on Réunion Island.
Synthesis of dsRNAs-Templates for dsRNA preparation were PCR-derived fragments between two T7 promoter sequences. Fragments for each gene were as follows: GFP (nt 26 -302, GenBank TM L29345), DMAP1 dsA and dsB region (nt 834 -1138 and 1150 -1419, National Center for Biotechnology Information (NCBI) NM_137659), IMD (nt 550 -919, NCBI NM_133166), dMyD88 (nt 929 -1240, NCBI NM_136635), and Akirin (nt 656 -1051, NCBI NM_139856). dsRNAs were synthesized with the MEGAscript T7 transcription kit (Ambion). Annealed dsRNAs were ethanol-precipitated and dissolved in distilled water for the transfection. Co-immunoprecipitation Assay-A total of 2 ϫ 10 6 S2 cells were co-transfected by calcium phosphate precipitation with 5 g of each expression vector. After the transfection in the presence of 500 M CuSO 4 for 48 h, a total of 4 -6 ϫ 10 6 cells were lysed in cell extraction buffer (Invitrogen) with protease inhibitors (Roche Diagnostics), and cleared cell lysates were incubated with anti-V5-agarose beads (Sigma) overnight under constant rotation at 4 °C.
We therefore explored whether DMAP1 interacts with Akirin. As shown in Fig. 2D , DMAP1 co-immunoprecipitated with Akirin. To further characterize the role of DMAP1, we looked for putative interactants in protein databases. Interestingly, both DPiM  (22)  and String 9. 05  (23)  databases pointed to the Brahma complex protein BAP55 as an interactant for DMAP1. Co-immunoprecipitation experiments in transfected S2 cells indeed revealed that DMAP1 physically interacts with BAP55 (Fig. 2E ).
* This work was supported, in whole or in part, by  National Institutes of Health  Grant  PO1 AI070167  (to J. L. I. and J. A. H. ), the  Agence Nationale de la Recherche  (Grant  ANR-13-BSV3-0009 ), the  Balzan Foundation  (to J. A. H. ), the  Investissement d'Avenir Programs  (Grants  ANR-10-LABX-0036  and  ANR-11-EQPX-0022 ),  CNRS , and INSERM. □ S This article contains supplemental Figs. S1 and  S2.
Introduction Effective immune defense in mammals relies on the detection of conserved pathogen structures by pattern recognition receptors (PRRs) of the innate immune system to prime immune responses  [1]. Several PRRs have been identified and extensively studied in the last decade. In particular, members of the NOD-like receptor (NLR)-family gained attention due to their intracellular localization  [2, 3]. One of the first NLRs shown to act as a PRR is NOD1. NOD1 is an intracellular protein that can be activated by diaminopimelic acid-containing peptides derived from bacterial peptidoglycan and acts as a sensor for invasive bacteria such as Shigella flexneri  [4] [5] [6]. A wealth of data suggest that NOD1 is an important PRR for a variety of bacteria in mammals, which also contributes to systemic activation of neutrophils, induction of adaptive immunity and immune tissue homeostasis (reviewed in  [2, 3] ).
doi:10. 1371/journal. ppat. 1004351. g001 times of NOD1 activation. As expected, the knock-down of SSH1 led to a strong increase in phosphorylation of its substrate cofilin at serine 3 (Figure  S5B ), proving that SSH1 is active in our cellular system. To rule out that the effects described above were related to the malignant origin of the cell lines, we assessed the phenotype of SSH1 depletion in primary human dermal fibroblasts. In line with the results obtained with cell lines, in primary human dermal fibroblasts NOD1-mediated IL-8 secretion was also significantly reduced upon SSH1 knock-down, whereas SSH1 depletion affected TNF-induced IL-8 secretion to a lesser extent (Figure  S5C ). Taken together, our data identified SSH1 as an essential component of NOD1-mediated activation of pro-inflammatory responses in human myeloid and epithelial cells.
Notably, no obvious change in p-cofilin levels was observed after stimulation of HEK293T cells with TNF, although TNF induced high IL-8 release (Figure  4F  and S6D ). Moreover, in cells in which NOD1 was depleted by siRNA TriDAP treatment did not robustly influence p-cofilin levels over time (Figure  4F ). Accordingly, TriDAP failed to induce IL-8 secretion form these cells (Figure  S6D ). Moreover, expression of SSH1 did not result in increased basal NF-kB activity (Figure  S8B ). Collectively, these data provide evidence that NOD1 signaling relies on the presence of SSH1 that acts downstream of NOD1 and involves the activation of cofilin.
Data analysis of siRNA screen Data was processed using the CellHTS2 package  [42] , Bioconductor/R, and Excel (Microsoft). By dividing the luciferase signal (relative light units; RLU) by the b-gal signal (ABS405), normalized relative light units (RLU/ABS405 = nRLU) were achieved. To exclude experimental artifacts, all data from a given plate was excluded, if the average b-gal signal of the non-targeting controls (ABS405) was. 2. 5, ,0. 2, or had a standard deviation of. 50%. Next, all wells showing a b-gal signal of ,40% of the noncoding controls, supposedly due to low plasmid transfection efficiency or siRNA toxicity, were excluded from further analysis. Subsequently the nRLU where normalized relative to the inhibitory effect of the p65-control-siRNAs compared to the non-targeting controls (normalized percent inhibition; NPI) and median z-scores of the 4 biological replicates were calculated (centered to the median of non-targeting controls), using CellHTS2.
In the next step, the median z-scores of individual siRNAs were used to calculate a ranked gene list, using the redundant siRNA analysis algorithm; genes with less than two hit-siRNAs ('OPI-hits') were excluded (RSA)  [17]. This list comprises genes leading to a decreased p65 activity, when knocked down (termed ''inhibiting hits''). For TNF-counter screening in HEK293T cells, as well as for hit validation in HEK293T and THP1 cells, the top 435 inhibiting hits were selected. For each of these genes, the two siRNAs showing the strongest effect in the screen were re-synthezised and assembled on 384-well plates ('validation plates'). To validate the results of the primary screen, the experiments were repeated as described above. Data analysis using CellHTS2 was done as described above; siRNAs were selected as 'inhibiting Tri-DAP-hits', if their median Z-score exceeded the median of non-targeting controls by more than two standard deviations.
To exclude unspecific hits, all siRNAs selected for validation were screened for their influence on TNF-induced p65 activation. Data analysis was done analogous to the Tri-DAP validation screen. 'Inhibiting TNF-hits' were excluded from further analysis. Data from the THP1-blue screen consists of two parameters (QUANTI-Blue absorption at 632 nm for Tri-DAP response [QB], and XTT absorption at 485 nm for cell viability [XTT]) and was processed similar as described above: QB-signal of each well was normalized to cell viability (XTT), yielding nQB (normalized QUANTI-Blue absorption; QB/XTT = nQB). After quality control and outlier flagging, the three best experimental replicates were NPI-normalized to non-targeting and NOD1control-siRNAs using CellHTS2, and median Z-scores were used for hit identification.
Gene accession numbers SSH1: Gene ID 54434; NOD1: Gene ID 10392; NOD2: Gene ID 64127; ROCK1: Gene ID 6093; ROCK2: Gene ID 9475. Ranked presentation of all hits. (B) Gene ontology (GO) enrichment of the 435 preliminary hit genes retrieved by the primary screen compared to the druggable-genome library (background). Number of hit genes associated with enriched terms according to the GO of biological processes. Enrichment factors and negative log(P-values) are indicated in brackets. (C) Known regulators of NF-kB retrieved by the screens. Ingenuity Pathway analysis of 56 (out of 435) preliminary inhibiting screen hits already known to be involved in NF-kB regulation. Among them, 28 could be validated in the HEK validation screen (dark red: strong hit; light red: weaker hit; white: not validated).
A sub group of 14 were not influencing TNF-a-induced NF-kB activation (blue borders). (D) Distribution of the siRNAs in the THP1-blue validation screen. Each dot represents the median Z-score of the experimental replicates after NPI-normalisation (Normalized Percent Inhibition) to the plate internal NOD1-controls. A positive value represents an inhibitory effect of the respective siRNA on NF-kB activation. THP1 hit-siRNAs are defined as exceeding 1. 5 S. D. of CTRL from the median of the CTRL siRNAs. Z-scores were normalized to control siRNAs set to 0. CTRL: non-targeting controls. (E) STRING database analysis of the 28 hits from the THP1 screen. Genes, where the NPI values of both siRNAs exceeded 1.
Ethics statement Approval by Ethics Committee was deemed unnecessary because, under Italian law, such an approval is required only in the hypothesis of clinical trials on medicinal products for clinical use (art. 6 and art. 9, leg. decree 211/2003). This research was conducted on [samples and] data already available, and not collected for this study. CD4 cell counts were previously determined for each patient only for clinical reasons and not for research. All samples and data used in the study were previously anonymized, according to the requirements set by Italian Data Protection Code (leg. decree 196/2003) and by the General authorizations issued by the Data Protection Authority.
6%]) in combination with either an NNRTI (N = 112 patients, 94. 5% of them treated with efavirenz) or a ritonavir-boosted PI (N = 161 patients, mainly treated with darunavir  [35. 4%]  or lopinavir  [29. 8%]  or atazanavir [29. 2%]). Thirty-two patients (10. 5%) were treated with raltegravir, mainly administered in combination with 2 NRTIs +1 ritonavir-boosted PI (25 patients, 78%). No significant correlation between the third drug used and the different FPR levels was observed (data not shown). The majority of patients attended a high compliance level (87. 2%). Transmitted drug resistance was found in around 8% of patients. Nearly all patients (99%) have been treated with effective therapy with GSS $3. Prevalence of patients infected with X4-tropic viruses at first-line HAART start V3 genotypic tropism test was performed in a median (Interquartile Range, IQR) time of 20  days before HAART start.
Acknowledgments We gratefully thank  Andrea Biddittu ,  Massimiliano Bruni ,  Luca Carioti ,  Fabio Continenza ,  Alberto Giannetti ,  Anna Pacifici ,  Daniele Pizzi  and  Marzia Romani  for sequencing and data management. The manuscript was presented in part at the following meetings: 9th European Workshop on HIV and  Hepatitis Treatment Strategies and Antiviral Drug Resistance ,  23-25 March 2011 , Paphos, Cyprus (Reviews in Antiviral Therapies, Vol 2, pag 30, Abstract  O_27 ); 10th European Meeting on HIV & Hepatitis Treatment Strategies & Antiviral Drug Resistance, March  28-30 2012 ,  Barcelona, Spain  (Reviews in Antiviral Therapy & Infectious Diseases 2/2012 #  P_31  pp  52-53 ).
Although it is used in ACVs, filamentous hemagglutinin was not included, as accurate assembly and assignment of SNPs was not possible due to the presence of repeats and paralogs. As previous studies suggest that variation in the Ptx promoter, ptxP, was linked to clonal sweeps  (15, 21, 33) , we also included ptxP alleles in our analyses. S1 ). Red circles indicate the major changes in antigen gene alleles in proteins used in current ACVs (from ptxA2 to ptxA1, fim2-1 to fim2-2, ptxP1 to ptxP3, and fim3-1 to fim3-2). With the exception of ptxA10, prn16, and prn17, all alleles have been described before, and references and accession numbers are given in Text S1 in the supplemental material. The major changes in antigen gene alleles (from ptxA2 to ptxA1, fim2-1 to fim2-2, ptxP1 to ptxP3, and fim3-1 to fim3-2) are marked on the nodes in the phylogenetic tree in Fig.
Strains and sequencing. The clinical isolates used in this study are listed in Table  S1  in the supplemental material. DNA was isolated by the participants and sequenced using Illumina technology  (69). Nineteen isolates were sequenced using the Genome Analyzer II and resulting in single reads of 37 bp (sequencing method 1). Thirty-eight isolates were sequenced using the Genome Analyzer II and resulting in paired-end reads of 50 bp (sequencing method 2). The remaining isolates were sequenced using 12 multiplexed tags on the Genome Analyzer II, producing pairedend reads of 54 bp (sequencing method 3). The accession numbers of the raw sequence data are listed in Table  S1. SNP detection. Reads for all sequenced samples were mapped against the complete Tohama I reference genome sequence (accession number BX470248) using SMALT (http://www.
sanger. ac. uk/resources/software /smalt/). Reads mapping with identical matches to two regions of the reference genome were left unmapped. The alignment of reads around insertions and deletions (indels) was improved using a combination of pindel  (70)  to identify short indels and dindel  (71)  to realign the reads. SNPs were identified using samtools mpileup (http://samtools. sourceforge. net) and filtered as described previously  (72)  Information about promoters, genes, and proteins was retrieved from the sequenced genome of B. pertussis Tohama I. The annotation was updated using BLAST  (73) , and domain information was recovered from SMART  (74)  and Conserved Domain Database  (75). Homoplasic SNPs were identified by reconstructing base changes for each variable site onto the phylogenetic tree under the parsimony criterion.
Any site for which the observed number of base changes for the maximum parsimony reconstruction on the tree was greater than the minimum possible number of changes for that site is homoplasic. Phylogeny. The phylogenetic relationships of the entire data set were inferred under a maximum likelihood framework using PHYML  (76)  with an HKY85 model of evolution. The global phylogeny was rooted using B. bronchiseptica MO149 (sequence type 15 [ST15]), which was previously shown to be most closely related to B. pertussis  (77, 78). Mutation rates and ancestral node dates for lineage IIb were estimated using Bayesian analysis in the BEAST version 1. 6. 2 package  (79). Analyses using the variable sites within lineage IIb isolates with isolation dates available were run under a general time reversible (GTR) model of evolution, with all combinations of constant, expansion, logistic and skyline population size models, and strict, relaxed exponential, and relaxed lognormal clock models.
ACKNOWLEDGMENTS This effort was initiated during the Bordetella Workshop in Cambridge on 22 to 24 July 2008. Therefore, we are very grateful to the attendees and especially to Olivier Restif, who organized this meeting. We thank Gwendolyn L. Gilbert (Westmead Hospital, Australia) and Margaret Ip (the Chinese University of Hong Kong) for supplying strains. This work was supported by the Wellcome Trust (grant number 098051), the RIVM (SOR project S/230446/01/BV), and the National Health and Medical Research Council of Australia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data collection and structure solution X-ray diffraction data were collected at SLS (Vilingen, Switzerland) and processed using iMosflm  [24]. The structure was determined by molecular replacement using the BALBES  [25]  server with the DGC structure from Methylococcus capcsulatus (PDB code: 3ICL), which has 40% amino-acid sequence identity with tDGC, as a search probe. The structure was built by iterative cycles of model building at the computer graphics using COOT  [26]  and refinement using Autobuster  [27]. The geometrical parameters for c-di-GMP were generated using the c-di-GMP coordinates 2V0N from the PDB (www. rcsb. org). Buried solvent accessible surface areas upon dimer formation were calculated using the PDBePISA server  [28]. The quality of the structures was assessed using the MOLPROBITY  [29]  server and the figures were generated using the Pymol software  [30].
Melting temperature assay Circular dichroism (CD) measurements were performed using a Jasco spectropolarimeter (J-810) with a 1 mm path length quartz cuvette. To determine the melting temperature, 200 ml protein samples at a concentration of 0. 8 mg/ml, in 20 mM Tris-HCl (pH 8. 5), 250 mM NaCl, 5% (v/v) glycerol, 10 mM MgCl 2 were used. The temperature was gradually increased from 25 to 95uC at the rate of 1uC/min and ellipticity was recorded at a wavelength of 220 nm for every 0. 2uC increment in temperature. The melting temperature was determined by plotting the CD data at 220 nm against temperature and fitting to a Boltzmann sigmoidal curve (between 60 and 95uC) using the curve fitting software XL Fit4.
The arrows indicate the nucleophilic attack of the 39 oxygen atom on the a-phosphate of the adjacent GTP. (d) Superposition of the ''optimized'' active dimer (this work, yellow and grey) and the c-diGMP cross-linked YdeH dimer (PDB code: 3TVK, Zaehringer and Schirmer) (colored in pink and teal). doi:10. 1371/journal. pone. 0110912. g004.
Background: Numerous Plasmodium falciparum antigens elicit humoral responses in humans living in endemic areas. Use of multiplex assays is a convenient approach to monitor the antibody response against multiple antigens, but to integrate multiplex assay-derived data with datasets, generated previously using ELISA, comparative studies are needed. This work compares antibody responses to two P. falciparum antigens monitored using both technologies. Methods: The IgG response against the merozoite surface protein-1 PfMSP1p19 and the PF13-DBL1α1 domain of the P. falciparum Erythrocyte Membrane Protein1, expressed by the rosette-forming parasite 3D7/PF13 (PF13), was investigated using ELISA and a MAGPIX®-Luminex duplex assay. Archived plasma samples collected before the rainy season from 217 villagers living in Ndiop, a Senegalese meso-endemic setting, were studied.
Louis, MO, USA) in PBSB was then added to each well and incubated in the dark, with shaking at 350 rpm for 45 min. This step was followed by two careful washes of the plates as above, with 100 μL/well of PBSB. The beads were then resuspended in 120 μL PBSB and analysed on a Multiplex MAGPIX system (Millipore, MA, USA) using the xPONENT 4. 1 software for acquisition and assay design. The reader was set to read a minimum of 50 beads of unique fluorescent signature per region with the output of median fluorescence intensity (MFI) per sample as stated by manufacturer's instructions. Antibody response was considered positive for MFI values if the signal was greater than twice the background signal [mean of 6 determinations +2 SD of the negative pool of nonimmune control sera]. The MFI ratio was calculated as MFI sample/MFI naïve (MFI naïve being the mean MFI signal +2 SD of naïve controls), and as for ELISA, responders were those with MFI ratio > 2.
Statistical analysis Comparisons for categorical variables were done using the Fisher exact test, continuous variables of antibody responses were analysed using the Kruskal Wallis and the Spearman rank correlation test for non-normally distributed data. The ROC (Receiver Operating Characteristic) curve analysis was used to determine the best threshold point for optimal positivity according to the two methods. P values <0. 05 were considered significant. A Poisson regression model was used to analyse the relationship between antibody responses and the incidence of malaria attacks during the follow-up period. Antibody responses to MSP1 and PF13, expressed as OD or MFI, OD or MFI ratios or categorized into responders vs. nonresponders, were analysed as monoplex and duplex responses. For analysis, the age stratification was based on the age-dependent distribution of the parasite and clinical data determined for this site  [20].
Malaria attacks were considered independent if separated by more than 15 days. For each villager, the follow-up time was calculated as the number of days actually spent in the village during the 5. 5 months of follow-up. Villagers who were away from the village for more than 30 days during the follow-up period were excluded from the malaria incidence analysis. Follow-up time was also adjusted for individuals who received anti-malarial treatment, by excluding a period of eight to 15 days after the first day of treatment (eight days for quinine, ten days for chloroquine and 15 days for sulphadoxine-pyrimethamine treatments, respectively). Statistical analyses were performed with Egret 3. 01®, R and Statview 5. 0® softwares.