dataesr/scientific-paragraphs-categorization huggingface dataset:
Yeast Two-Hybrid analysis The coding sequence for full-length YU2 VPR (nt5557 to nt 5850) was PCR-amplified and cloned into pB27 as a C-terminal fusion to LexA. The constructs were used as baits to screen at saturation a highly complex dT-primed human CEMC7 library. 60. 3 million clones (6-fold the complexity of the library) were screened using a mating approach with Y187 (mata) and L40DGal4 (mata) yeast strains as previously described  [61]. His+ LacZ+ colonies were grown on a medium lacking tryptophan, leucine, and histidine. 265 colonies were selected. The prey fragments of the positive clones were amplified by PCR and sequenced at their 5p junction. The resulting sequences were used to identify the corresponding interacting proteins in the GenBank database (NCBI), using a fully automated procedure.
Western blot procedure and antibodies Cells were lyzed in 500µl SB buffer (60mM Tris pH 8, 10% Glycerol, 2% SDS, 1% bromophenol blue, 100mM DTT) using a 27G needle and protein extracts were separated by SDS-PAGE electrophoresis. Following transfer onto PVDF membranes, proteins were revealed by immunoblot analysis using a chemiluminescent procedure (CDPStar®, Applied Biosystems). Signals were acquired by a LAS 3000 apparatus (Fujifilm) for further quantification, using the Multigauge software (Fujifilm). Monoclonal antibody directed against the HA (16B12) tag was purchased from Covance Research Products; anti-GFP from Roche; anti-FLAG M2 and anti-Tubulin monoclonal antibodies from Sigma, anti-Cullin4A and RbAp46 rabbit polyclonal antibodies and anti-MTA2 mouse monoclonal antibody from Abcam; anti-Histone H4 and anti-HDAC1 rabbit polyclonal antibodies were purchased from Cell Signaling; anti-DCAF1 rabbit polyclonal antibody was obtained from Gentaur; anti-DDB1 mouse monoclonal antibody was purchased from Zymed and anti-HAT1 mouse monoclonal antibody was purchased from Santa Cruz.
Acknowledgements We thank  Diana Ayinde  for her comments on the manuscript. We thank  Richard Benarous  for fruitful discussions. We thank  Yongfeng Shang  for his precious contribution with the gift of the ZIP and sZIP constructs,  Nathaniel Landau  for his gift of the pNL4. 3deltaEnv-deltaVpr HIV-1 construct and  Edward Barker  for his gift of the DHIV constructs. We thank the Cochin.
Acknowledgments We are thankful to the State TB Officer of  Andhra Pradesh , District TB Officer of Visakhapatnam and  RNTCP Key  staff for providing the necessary support for the study. The study was conducted as a part of the ' TB Operations Research Training  Project' aimed to build operational research capacity within the  Government of India 's RNTCP. This training project was conceived and implemented jointly by  Central TB Division (Directorate General of Health Services, Ministry of Health and Family Welfare, Government of India) ,  The National TB Institute (Directorate General of Health Services, Ministry of Health and Family Welfare, Government of India Bangalore, India) ,  World Health Organization (India Country Office) , The  International Union Against Tuberculosis and Lung Diseases (The Union, South-East Asia Regional Office, New Delhi, India)  and  U.
However, we cannot exclude that residual DRAM is enough to induce LMP. Because LMP is upstream from MOMP and cell death, we evaluated the consequence of DRAM inhibition on viral infection. Thus, we evaluated the numbers of infected cells (Gag + ) in the culture as previously described  [10]. The siRNAs targeting p53 and DRAM clearly resulted in the presence of greater number of HIV-infected CD4 + T cells than control siRNA (mock) (Figure  6E ). This is consistent with the levels of HIV DNA measured (data not shown). Thus, the knock-down of DRAM and p53 protein levels by specific siRNAs resulted in numbers of infected cells three to four times greater than the control. We also measured by ELISA in the supernatants the p24 production in the presence or absence of DRAM. Our results revealed that viral production is increased following DRAM knock-down as compared to the control (Figure  6F ).
ML was supported by a fellowship from  ANRS. RS was supported by  Programa Ciencia -financed by Programa OperacionalPotencial Humano POPH -QREN -Tipologia 4. 2 -Promoca ˜o do Emprego Cientı ´fico, co-funded by  Fundo Social Europeu and National funding from Ministry of Science, Technology and Higher Education (MCTES). '' Fondation pour la Recherche Medicale ,'' to JE and from  CNRS  and  ARC  to FH. VR is supported by fellowship from  FCT  code  SFRH/BD/ 64064/2009. JE thanks the  Canada Research Chair program  for financial assistance. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Gene expression normalization Background adjustment and normalization of all raw probe intensity were performed using the Robust Multi-array Average (RMA) algorithm  [25]  implemented in Affymetrix Expression Console. The expression values were reported in arbitrary units. Moreover, the MicroArray Suite 5 (MAS5) algorithm  [26]  was used on raw data to identify probe sets which were out of the limit of detection for the system and to flag the transcript as Present (P) or Absent (A). P-and A-flagged information was used to filter data in order to remove false positive after statistical analysis.
Statistical analysis of microarray data Statistical analysis was performed using the TIGR Multi-Experiment Viewer version 4. 5. 1 software (TMeV, http://www. tm4. org/mev/). Significant differentially expressed genes between the two tumor groups and between the two contralateral tissue groups (MRT treated or not) obtained 6 h after irradiation were assessed using an unpaired Significance Analysis of Microarrays (SAM)  [27]. A False-Discovery Rate (FDR) lower than 5% was fixed to generate significant genes list. Hierarchical clustering was directly generated thanks to TMeV from this list. For all other analyses, probes were only considered when at least n-1 values were P-flagged (MAS5) in any of both compared groups.
Functional analysis The molecular & cellular functions and canonical pathways associated with differentially expressed genes identified by SAM test (FDR 5%) and P/A flag filtered were identified thanks to Ingenuity Pathways Analysis (IPA) software (IngenuityH Systems, www. ingenuity. com). The data set was restricted to mammal species. Molecules from the data set were associated with the most relevant ''molecular & cellular functions'' and ''canonical pathways'' in the Ingenuity Knowledge Base. The significance of the association between the data set and the bio-function or the canonical pathways was measured in 2 ways: 1) a ratio between the number of molecules for a given function or pathway issued from the data set and the total number of molecules for the same function or pathway is displayed.
RT-qPCR transcriptional validation for genes regulated 6 h after MRT Some selected significantly up-and down-regulated genes were validated using reverse transcription and quantitative polymerase chain reaction (RT-qPCR) on untreated and MRT-treated tumor samples (7,n,10, depending on sample availability). Two micrograms of total RNA were transcribed into cDNA using Promega Reverse Transcription reagents with random dN6 primers. Specific gene primers (Table  1 ) were designed using software (https://www. roche-applied-science. com/sis/rtpcr/upl/ ezhome. html). Real-time PCR were performed according to the SYBR Green method on an Mx3000 TM apparatus (Stratagene, La Jolla, CA) using Quantitect SYBH reagents (Qiagen, France). Data were normalized using two housekeeping genes, Atp5b and Arpc1a, selected according to Affymetrix data because of both their suitable range of quantification and very low variation in expression levels across all samples (8% and 7%, respectively).
Identification of biological functions modulated in 9 L tumor and contralateral brain tissues 6 hours after MRT Genes identified by SAM analysis with a 5% FDR were classified into different bio-function categories based on IPA database (Figure  5 ). The response to MRT irradiation involved 22 and 18 molecular and cellular functions in tumor and normal brain tissues, respectively. All functions found in contralateral tissue were also represented in tumor tissue. Some bio-function categories such as cell death, growth, proliferation, cell cycle, cellular function and maintenance can contribute to cell response to radiation-induced damages. Inflammation and immunity reaction appeared as a common point of cellular movement, antigen presentation and cell-to-cell signaling/interaction categories.
Cells were stained with optimally diluted fluorochromeconjugated antibodies at room temperature for 10 minutes. Then the cells were acquired on CyAn (Beckman Coulter) and analyzed by Flowjo software (Tree Star) with computed compensation. At least 10 4 pDCs were gated for analysis. To measure the survival rate of stimulated pDC, purified pDCs stimulated for 24 hrs were washed twice with PBS and stained with Annexin V Apoptosis detection Kit (BD Biosciences) according to the manufacture's instructions. Cells were acquired on CyAn and analyzed as above.
Intracellular cytokine analyses MNCs were stimulated as indicated for 6 hrs, GolgiPlug (BD Biosciences, 1:1000 dilution) was added in the culture for the last 3 hrs. Cells were harvested, fixed and permeabilized by using the Cytofix/Cytoperm kit (BD Biosciences). Briefly, cells were first fixed in fixation/permeabilization solution for 20 min at 4uC. Following 2 wahsing steps with the permeabilization/washing solution, cells were stained with optimal concentrations of fluorochrome-conjugated antibodies against IFN-a or IL-12 p40 in the permeabilization/washing solution for 30 min at 4uC. After washing 3 times, cells were resuspended in PBS containing 1% fetal calf serum and acquired on CyAn (Beckman Coulter) and analyzed by Flowjo software (Tree Star). At leat 10 4 targeted cells (for instance, at lease 10 4 pDCs were acquried from MNCs if pDCs were the target cells.
Acknowledgments We are very grateful to  F. Goffinet  ( CIC Mere-Enfant ) for the cord blood sample collections. This work was supported by grants from  Agence Nationale pour la Recherche  ( ANR 09-MIEN-017 ) and has received funding from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence ''Integrative Biology of Emerging Infectious Diseases' ' (grant nuANR- 10-LABX-62-IBEID) ). Work in the O. S. lab is also supported by  ANRS ,  Sidaction  and  Areva Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials. We acknowledge the  Center for Human Immunology at Institut Pasteur  for support in conducting these studies.
This work was supported by grants from  Agence Nationale pour la Recherche  ( ANR 09-MIEN-017 ) and has received funding from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence ''Integrative Biology of Emerging Infectious Diseases' ' (grant nuANR- 10-LABX-62-IBEID) ). Work in the O. S. lab is also supported by  ANRS ,  Sidaction  and  Areva Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Statistical analysis Difference in positive and negative serology for schistosomiasis and toxoplasmosis between eumycetoma patients and endemic controls were calculated with the Chi square test (GraphPad Instat 3. 00) by determining both the two-sided p-value and the Odds Ratio using Yates correction. The 95% confidence interval of the Odds Ratio was calculated using the approximation of Woolf. The Mann-Whitney U test was used to compare differences between IgG levels raised against the MmTCTP antigen in the study populations (GraphPad Instat 3. 00). The Kruskal-Wallis test (SPSS Inc 17) was used to test if concentrations of antibodies against Schistosoma spp. differed significantly between patients with larger lesions compared to patients with smaller lesions, by including size (small, moderate, large) as the grouping variable.
Spectratyping data of each TRBV-TRBC and TRBV-TRBJ combinations were extracted using GeneMapper software (Applied BioSystems) and analysed with ISEApeaks software  [34]   [35]. Each spectratype or profile is composed of several peaks (typically 4 to 10) separated according to their corresponding length of run-off products, spaced by 3 nucleotides as expected for in-frame transcripts. Each peak corresponds to a given CDR3 length. For each profile associated with a TRBV-TRBC or TRBV-TRBJ combination, the area under each peak was calculated and stored in a peak database. These values were then computed to quantify the differences between CDR3 length distributions. (1) In a given context, the ''perturbation score'' for a given TRBV-TRBC or TRBV-TRBJ profile was calculated as follows: perturbation score~1 2 X n i~1 Dp i,an {p i,ref D where p i,an and p i,ref are the relative areas of the peak #i from the analysed and the reference profiles, respectively; n is the number of peaks detected in the reference profile.
An average perturbation was then computed for all the TRBV-TRBC or TRBV-TRBJ of a given sample, and/or for the different individuals of a dataset. As the intensity of CDR3 peaks was usually not comparable between different amplifications, we considered the percentage of use of each CDR3 length (i. e. the ''relative area''), obtained by dividing the area of CDR3 peaks by the total area of all peaks within the profile. Profile perturbations range from 0 (analysed profile is identical to the reference), to 100 (no overlap at all). (2) The repertoire diversity can be assessed by diversity measures based on the concept of Shannon entropy that provides a measure of the quantity of information encompassed in the repertoire. Given P = (p 1 ,…,p S ): a CDR3 profile where p i is the relative proportion of the peak #i within S peaks and X S i~1 p i ~1.
01 (type 1 error) on the perturbation scores and diversity index. Statistical significances of the differences for different group pairs were determined using empirical Bayes test from the limma (Linear Models for Microarray) packages (R/Bioconductor). We chose this test because it outperforms the classical Student t-test or Mann-Whitney-Wilcoxon non parametric test for small samples  [36]. In addition, repertoire perturbation scores were used to perform Principal Component Analysis (PCA) analysis in order to compare the statistical dispersion of the samples on a multidimensional plan. Statistical and multivariate analyses were performed using R software (http://www. r-project. org/).
Since all CD8a Since all CD8 + there was no population of CD8a single positive cells (Figure  1A ). Three cell populations were sorted from the lymphocyte gate (FSC low /SSC): a CD8 + D11 + subset (S1) containing CD8 + T cells only, a CD8 2 D11 + subset containing other lymphocytes among those most likely T helper cells (S2), and a CD8 2 D11 2 subset containing other cells with lymphocyte scatter characteristics such as B cells and thrombocytes (S3). To validate the sorting conditions sorted subpopulations were checked by subsequent flow cytometry were purities of more than 97% were recorded (data not shown). Moreover, RT-QPCR experiments showed that the CD8a/D11 double positive lymphocytes (S1) expressed cd3cd at a high level, while cells from S2 expressed about twice less, and S3 cells were cd3cd 2 (Figure  1B ).
Importantly, spectratyping was repeated from independent PCR amplifications and produced similar results, excluding that the expanded peaks observed in irregular profiles from S2 could be due to a random amplification of a few sequences, as frequently observed in experiments performed from cDNAs containing rare templates. To obtain a statistical validation of the differences observed between S1 and S2, we performed a quantitative analysis of all profiles using the ISEApeaks software  [35]. ISEApeaks calculates the perturbation score of a given TRBV-TRBJ spectratype in comparison with a reference, summing the differences of areas calculated for each peak between the given and the reference profile  [35]. Taking the average of the whole T cell population (i. e. both S1 and S2 groups), we computed the global perturbation score from all spectratypes for both S1 and S2 fractions of each individual.
Detection of plasminogen bound to GBS cell surface Plasminogen conjugation with FITC was performed by incubation for 1 h at 30uC in darkness of 400 mg of human plasminogen with 40 mg of FITC (Sigma-Aldrich) in 500 mL of 1 M sodium carbonate buffer, pH 9. 2. GBS cells were cultured to log-phase, washed twice in PBS and then fixed in PBS containing 1% paraformaldehyde. After an incubation period of 20 min at 4uC, fixed bacteria were washed twice with PBS and incubated for 30 min at 37uC with different amounts of FITC-conjugated human plasminogen (10, 20 and 50 mg) in 100 mL (final volume). Thereafter, the cells were washed, resuspended in PBS, and analyzed by fluorescence-activated cell sorter (FACS) on an Epics XL cytometer (Beckman Coulter). Data were analyzed using the Expo32 software.
GBS invasion of the hBMECs was significantly greater at all time points analyzed when the bacteria were pretreated with human plasma compared to untreated cells (Figure  3A ). This difference was not due to increased bacterial growth since untreated GBS or human plasma-treated GBS displayed similar growth rates (data not shown). We next determined whether the interaction of GBS with human plasma in the presence of hBMECs can lead to acquisition of cell surface plasmin activity. The proteolytic activity on the GBS cell surface was assessed using the chromogenic substrate S2251 at different time points (60, 120, 180, 240, and 300 min) of co-culture. As shown in Figure  3B , GBS co-cultured with hBMECs in human plasma acquired an increasing surface plasmin-like activity over the incubation period that reflects bacterial growth.
To ensure that the observed GBS cell surface plasmin activity was due to binding and activation of the plasminogen present in human plasma, we determined at 300 min post-infection the proteolytic activity of GBS cells previously incubated with eACA prior to hBMECs infection. As shown in Figure  3C , pre-incubation of GBS with eACA significantly (P = 0. 0231) inhibited the acquisition of cell surface plasmin activity by GBS. The inhibition was not complete, presumably because plasmin(ogen)-coated GBS can arise after bacterial multiplication in the plasma. No difference between the bacterial CFU at this time point was observed between eACAtreated and untreated GBS indicating that the eACA treatment did not modify the bacterial growth (data not shown). Moreover, as shown in Figure  3D , infection of hBMECs with GBS in human plasma leads to a significant cell detachment, as observed at 240 and 300 min post-infection.
Briefly, 1. 10 6 cells were harvested and non-specific binding prevented by blocking in PBS-5% FBS-1% BSA-10% FcR Blocking Reagent (Myltenyi Biotec) for 30 minutes on ice. Cell suspension was incubated with specific antibodies for 45 minutes on ice. Filtered, single-cell suspension was analyzed by Fluorescence Activated Cell Sorting (FACS) on a SORP FACSAria2 (BD Biosciences). Data were processed with FACSDiva 6. 3 software (BD Biosciences). Doublets were excluded by FSC-W × FSC-H and SSC-W × SSC-H analysis, single stained channels were used for compensation, and fluorophore minus one (FMO) controls were used for gating, 500 000 events were acquired per sample.
Confocal analysis Tumors were washed two times with PBS and fixed with paraformaldehyde 3. 7% (Sigma) before staining. The antibodies used were the mouse anti human CD73_APC (Biolegend, #344006, clone AD2) and CD90_PerCP-Cy5. 5 (Biolegend, #328118, clone 5E10). The non-specific sites were blocked with PBS with 0. 3% bovine serum albumin and 0. 5% HS during one hour. Tumors were incubated with the antibodies and FcR bloking reagent (Miltenyi, #120-000-442) overnight and washed third in PBS. Slides were mounted with the Fluoromount Kit containing 4-, 6-diamidino-2-phenylindole (Invitrogen) to counterstain the nuclei. The slides were analyzed with a Zeiss confocal microscope Laser Scanning Microscope 710 (Carl Zeiss). Pictures were analyzed with Zen 2008 V5,0,0228 software (Carl Zeiss).
Cytokine array Cell culture supernatant was collected and proteins quantified based on Bradford assay. 200 μg of protein was loaded on R&D system W Human Cytokine Antibody Array panel A (R&D system W ) according to manufacturer's instructions. Arrays were revealed using HorseRadish Peroxidase (HRP) and SuperSignal West Pico Chemiluminescent Substrate (Thermo Scientific). Data were collected using Geliance CCD camera (Perkin Elmer), and extracted using ImageJ software (NIH). Briefly pictures were inverted and background subtracted. We defined a120 micron diameter area signal capture for all spots. We defined our signal as the median pixel density value. For comparison, independent array values were normalized on their positive control intensity values.
Our results strongly suggest that NFATc1 controls Nod1 at the transcriptional level. In silico analysis (Genomatix Software GmbH) has identified putative NFAT binding sites in human and murine Nod1 and Nod2 promoter regions. Downexpression of NFATc1 inhibited Nod1 more efficiently than Nod2. However, the in silico analysis did not permit us to predict differences in the number of putative binding sites for NFATc1 on the promoter regions of Nod1 and/or Nod2. We have no direct explanation for the preferential inhibitory effect of silencing NFATc1 on Nod1 expression. The fact that much less Nod1 than Nod2 is present in immune cells may account, at least in part, for the greater reduction in Nod1 mRNA expression induced by NFATc1 silencing. Nevertheless our findings strongly suggest that CsA, through NFATc1 inhibition, alters Nod1-mediated phagocytic functions.
Real-time PCR and reverse transcription PCR Total RNA from mouse kidneys, neutrophils, or macrophages was purified with RNAble (Eurobio laboratories) and reverse transcribed using Moloney Murine Leukemia Virus reverse transcriptase (Invitrogen). cDNA was subjected to quantitative real-time PCR using a Chromo IV sequence detector (MJ Research). The mouse Tlr2, Tlr4, Nod1, Nod2 and ß-actin primers used and the corresponding Taqman probes are listed in Table  S2. PCR data were reported as the relative increase in mRNA transcripts versus that found in uninfected kidneys or vehicle-treated neutrophils or macrophages cells and corrected using the respective levels of ß-actin mRNA. Quantitative real-time PCR was also performed on RNA extracted from blood samples of renal transplant recipients using human TLR2, TLR4, NOD1, NOD2, and ß-ACTIN primers and corresponding Taqman probes (listed in Table  S2 ).
Accession numbers The mouse gene accession numbers (GenBank) are as follows: ßactin, NM_007393. 3; GAPDH, AK144690; NFATc1/NFAT2, NM_016791. 4; Nod1, NM_172729; Nod2, NM_145857; RipK2, NM_138952. 3; Tlr2, NM_011905. 3; Tlr4, NM_021297, Tlr5, AF186107. 1; Tlr9, AF314224. The human gene accession numbers (GenBank) are as follows: GAPDH, NM_002046. 3; NOD1, NM_006092. 2; NOD2, NM_022162. 1; TLR2, NM_003264. 3; TLR4, NM_138554. 3. The mouse protein accession number (UniProtKB/Swiss-Prot) are as follows: ß-actin, P60710; CCL5, P30882; CD11b/integrin alpha-M/beta-2; P05555; CXCL1; P12850; CXCL2, P10889; NFATc1/NFAT2, 088942; Nod1, Q8BHB0; Nod2, Q8K3Z0; RipK2, P58801; Tlr2, Q9QUN7; Tlr4, Q9QUK6, Tlr5, Q9JLF7; Tlr9, Q9EQU3. The human protein accession numbers (UniProtKB/Swiss-Prot) are as follows: IL-8, P10145; NOD1, Q9Y239.