dataesr/scientific-paragraphs-categorization huggingface dataset:
There are no data on the persistence of maternal USUV antibodies in wild boar. Considering that maternal antibodies against most viruses disappear after six months in large mammals  [41] , our results would suggest that USUV circulated at least in summer and autumn of 2012 and 2013. Although flavivirus antibody persistence in artiodactyl species is unknown, a longer time span of exposure and antibody persistence could be associated with the higher seroprevalence found in adults, as suggested by field observations for WNV  [31, 34, 35, 37]. Given the potential long persistence of antibodies in artiodactyls, it is impossible to know from our results whether USUV circulated every year since 2009 or was re-introduced in 2012. Very few studies have been conducted to assess exposure of wild ungulates to USUV in infected areas in Europe.
Acknowledgments: The authors are very grateful to all hunters and local hunter's associations who helped to build the serum banking and who agreed to make samples available for our study. We are grateful to  Philippe Desprès  from  IPP (Paris, France ),  Davide Lelli  from  IZSLER (Brescia, Italy) , and  Nicholas Johnson  ( Weybridge, United Kingdom ) from  APHA  for providing reference flavivirus strains.
Methods of comparison These methods were used for the first (Time 1) and the second (Time 2) interobserver analysis. The evaluation of the variability of contours was benchmarked using a "reference" contour (delineated with the help of the three experts of the brainstem: HMD, DH, and ML). ARTISTRUCT™ software version 1. 0 (AQUILAB®, Lille, France) was used to evaluate and compare the contours, both qualitatively and quantitatively. For the first step, each volume submitted by each of the eight radiation oncologists in the WG (DVC, midbrain, pons, and medulla oblongata) was compared with the reference volume proposed by the three experts. In the second step, different indices reflecting the correlation of the volume with the "experts reference contour" for all volumes were calculated. Three of these indices are based on volume ratios: the Volume Ratio (VR) between the volume of one of the eight radiation oncologists and the volume of the experts, the Common Delineated Volume (CDV), and the Additional Delineated Volume (ADV).
Tunable resistive pulse sensing (TRPS). The size distribution of CD4 T cell-derived EVs and CEM-EVs was determined using the qNano instrument (Izon Science Ltd, Christchurch, New Zealand). The TRPS was previously described  48. We followed manufacturers' recommendations and NP400 nanopore membranes stretched between 43-47 nm were used. Voltage was set in 0. 3-0. 5 V to achieve a stable current 90-120 nA and the pressure at 0. 8 kPa, with the root mean square noise below 10 pA. Calibration beads used were CPC200B and CPC400 (mean diameters 210 nm and 350 nm respectively) (all from Izon). Running electrolyte was PBS filtered under 0. 1 µm. Measurement and analysis were performed with Izon Control Suite V3. 3. 3. 2001 Software. Flow cytometry. CD4 T cell-derived EVs and CEM-EVs were analyzed using high-sensitivity flow cytometry as previously described  49.
The determination of quantification cycle (Cq) and melting curve analysis was done using the Roche LC480 software and the 2 nd derivative method. Only molecules with a raw Cq ≤ 35 were considered to be expressed and included in subsequent data analyses. A first normalization was performed using interplate calibrators present in the 384-well microRNA Human panel I plate using the Exiqon GenEx qPCR analysis software. Normalization (ΔCq) was performed using the average of miRNAs expressed in all samples. Data were quantified using the ΔΔCq method. The change in miRNA expression was calculated as fold change relative to miRNA expression in the healthy control group using the 2 -ΔΔCq method. miRNAs were considered differentially expressed beyond a threshold of a 1. 5-fold change with a p value ≤ 0.
Quantification of individual miRNAs by quantitative RT-PCR. The expression level of specific miR-NAs was determined using the miRCURY LNA ™ Universal RT microRNA PCR system and ExiLENT SYBR ® Green master mix (Exiqon). Quantitative RT-PCR were performed in triplicate on a LightCycler 480 Real-Time PCR System (Roche). The expression of miR-146b-5p was normalized using the 2 -ΔΔCq method relative to the expression of miR-484 (hsa-miR-484 Product no. : 205636), which was defined by GeNorm and NormFinder softwares as the optimal reference (most stable) for CD4 T cells. Primers against the following targets were purchased from Exiqon: hsa-miR-146b-5p mimic (ID: 204553), hiv1-miR-TAR-5p mimic (ID: 204553) and hsa-miR-484 mimic (ID: 205636). In vitro EV uptake experiments. Syto RNA Select dyes (Molecular Probes) were used for RNA staining according to the manufacturer's protocol to produce Syto RNA-stained CEM-EVs (hence called Syto-EVs) prior to SEC as described above.
To check the absence of extravesicular dye, equivalent quantities of Syto RNASelect dyes were subjected to SEC in parallel. Fractions 7 and 8 were collected and used as experimental controls (called "Syto Control"). For flow cytometric analysis, HUVEC were seeded in 0. 2% gelatin-coated (Sigma-Aldrich, St. Quentin Fallavier, France) 24-well plates at a concentration of 75,000-cells/mL for 24 hours to enable cell adhesion and then co-incubated with 2 × 10 6 Syto-EVs for 2 hours, 6 hours, 12 hours, 24 hours and 48 hours. Then, the HUVEC were washed three times and harvested with trypsin. Cell suspensions were then analyzed by flow cytometry. The data were analyzed with the Kaluza 1. 5a software after the target events were gated according to the forward and side scatter signals. HUVEC co-cultured with Syto-Control or without Syto-EVs served as controls.
cDNA from lungs, aorta and hearts were analyzed for the expression of miR-146b-5p as described above, and miR-146b-5p levels were normalized to U6 snRNA levels (Product no: 203907) (Exiqon). cDNA from lungs was analyzed for expression of IRAK-1, TRAF-6, ICAM-1 and VCAM-1 transcripts using LightCycler 480 Probes Master (Roche Applied Science), and the corresponding gene expression was normalized to the expression of the housekeeping gene GAPDH. The following oligonucleotide primers were purchased from Thermo Fisher Scientific: IRAK-1 (Mm01193538_m1), TRAF-6 (Mm00493836_m1), ICAM-1 (Mm00516023_m1), VCAM-1 (Mm01320970_m1) and GAPDH (Mm99999915_g1). Immunohistochemical analyses. Lungs were fixed in 4% PFA for 2 hours and placed in a 30% PBS sucrose solution overnight at 4 °C before being embedded in a cutting medium and frozen at -80 °C.
Serial 8-µm transversal cryo-sections of lungs were obtained. VCAM-1 expression was analyzed using rabbit polyclonal anti-VCAM-1 antibody (sc-8304) from Santa Cruz Biotechnology. For detection, horseradish peroxidase (HRP)-conjugated goat anti-rabbit antibody was used (Thermo Fisher Scientific). Sections stained with secondary antibodies alone were used as controls of non-specific staining. The slides were examined under a light microscope. Lung sections were further counterstained with hematoxylin and eosin and examined at 20× or 63× magnification using a DMi8 advanced fluorescence microscope (Leica Microsystems, Germany). NIH ImageJ software version 1. 45 was used for quantification.
, 2018). Both studies further showed that Vpx interacts with HUSH and induces its proteasomal degradation through the hijacking of DCAF1, using the same mechanism as the one described for SAMHD1 antagonism. Though, HUSH antagonism by Vpx is genetically separable from SAMHD1 antagonism as revealed by a Vpx mutant defective for HUSH degradation, but still able to inactivate SAMHD1  (Chougui et al. , 2018). Mimicking HUSH depletion, Vpx induces the reactivation of a latent HIV-1 provirus through H3K9me3 marks depletion in the J-Lat A1 model of latency  (Chougui et al. , 2018). Furthermore, HUSH depletion gives an advantage to replication-competent HIV-1 or SIVmac239-Vpx viruses  (Yurkovetskiy et al. , 2018). Altogether, these data report a new role of Vpx that counteracts HUSH, a complex active at the post-integration level (Figure  5 ).
All these findings may explain the fact that, to this day, no RORγt inhibitor has yet reached Phase III clinical trials. A Phase II trial in psoriasis of the oral inhibitor VTP-43742 was terminated early due to unspecified safety concerns. Several new compounds (see Figure  2 ) have still been tested in Phase I trials, mostly for psoriasis, with only one compound tested for MS. The most advanced compound is the inverse agonist GSK-2981278 tested for topical use in psoriasis. The results from the Phase II trial have been submitted but are not yet publically available (ClinicalTrials. gov NCT03004846). The IL-23 cytokine has also been targeted directly for the treatment of chronic inflammatory diseases, and has shown remarkable results for the treatment of psoriasis. The efficacy and safety of ustekinumab, a fully human monoclonal antibody that blocks the p40 subunit of IL-23 and IL-12, were tested in a phase 2 clinical study  69.
Live cell imaging Images were captured using a 63x oil immersion objective on a Zeiss AxioImager M2 microscope fitted with an AxioCam ICc1 digital camera and analysed with the AxioVision 4. 8. 2 software. Ookinetes and zygotes were stained with a Cy3-conjugated mouse monoclonal antibody 13. 1 which recognises the P28 protein on the parasite surface  (Tewari, Dorin, Moon, Doerig, & Billker, 2005).
Imaging of each channel was done sequentially using three angles and five phase shifts of the illumination pattern as described previously  (Gustafsson et al. , 2008). The refractive index of the immersion oil (Cargille) was adjusted to 1. 516 to minimize spherical aberrations. Sections were acquired at 0. 125 μm z steps. Raw OMX data were reconstructed and channel registered in SoftWoRx software version 6. 1. 3 (Applied Precision).
Liver stage imaging HeLa cells (1 x 10 5 cells) were seeded in glass-bottomed imaging dishes. Salivary glands of female A. stephensi mosquitoes infected with myosin-GFP parasites were isolated and disrupted using a pestle to release sporozoites, which were pipetted gently onto the seeded HeLa cells and incubated at 37 °C in complete minimum Eagle's medium containing 2. 5 μg/ml amphotericin B (PAA), and gassed with 5% CO 2 in air. Medium was changed 3 hrs after initial infection and once a day thereafter. For live cell imaging, Hoechst 33342 (Molecular Probes) was added to a final concentration of 1 μg/ml, and parasites were imaged at 24, 48, 55 hrs post-infection using a Leica TCS SP8 confocal microscope with the HC PL APO 63x/1. 40 oil objective and the Leica Application Suite X software.
Sporozoite motility assays Sporozoites were isolated from midgut and salivary glands from mosquitoes infected with WT-GFP and ∆MyoE parasites between days 21 and 24 post infection. Isolated sporozoites in RPMI 1640 containing 3% bovine serum albumin (BSA, Fisher Scientific) were pelleted (5 min, 5000 rpm, 4° C). The motility assay was performed as described previously  (Moreau et al. , 2017). Briefly, a drop (6 µl) of sporozoites was transferred onto a microscope glass slide with a cover slip. Time-lapse videos of sporozoites (1 frame every 1 sec for 100 cycles) were taken using the differential interference contrast settings with a 63X objective lens on a Zeiss AxioImager M2 microscope fitted with an AxioCam ICc1 digital camera and analysed with the AxioVision 4. 8. 2 software. The assay using matrigel was performed as described previously  (Volkmann et al.
, 2012). A small volume (20 µl) of sporozoites, isolated as above for WT-GFP and ∆MyoE parasites were mixed with Matrigel (Corning). The mixture (6 µl) was transferred on a microscope slide with a cover slip and sealed with nail polish. After identifying a field containing sporozoites, time-lapse videos (1 frame every 2 sec for 100 cycles) were taken using the differential interference contrast settings with a 63X objective lens on a Zeiss AxioImager M2 microscope fitted with an AxioCam ICc1 digital camera and analysed with the AxioVision 4. 8. 2 software. shows successful gene deletion using primers 11 and 12. WT-GFP was used as a control. Figure Legends.
Acknowledgements We thank  Judith Green  for very useful discussions and  Julie Rodgers  for technical assistance. This project was funded by  MRC   Investigator Award  and  MRC  project grants to RT [ G0900109 ,  G0900278 ,  MR/K011782/1 ]. RFW was supported by an  MRC  project grant [ MR/M011690/1 ]. AAH was supported by the  Francis Crick Institute  which receives its core funding from  Cancer Research UK  ( FC001097 ), the  UK Medical Research Council  ( FC001097 ), and the  Wellcome Trust  ( FC001097 ).
Furthermore, cells were washed in PBS, stained with anti-CD45 PE-Texas Red (Life Technologies), anti-CD3 A700 (EXBIO), anti-CD4 PE-Cy7 (eBioscience), anti-CD8 HV500 (BD Biosciences), anti-PD-L1 BV421 (BioLegend), anti-PD-1 FITC (BioLegend), anti-CTLA4 APC (BioLegend) monoclonal antibodies. The percentage of CD45 + CD3 + CD8 + PD-L1 + , CD45 + CD3 + CD8 + PD-1 + and CD45 + CD3 + CD8 + CTLA-4 + were determined by flow cytometry. The data were analyzed with the FlowJo software package (Tree Star, Inc. ).
Regulatory T cells (Treg) are crucial in the proper balance of the immune system. A better characterization of Treg-specific genes should extend our knowledge on their complex biology. However, to date there is no consensual Treg signature in the literature. Here, we extracted a molecular Treg meta-signature relative to CD4 + conventional T cell from 8 different but comparable publicly available microarray datasets. We confirmed the validity of our result using the much larger but less stringent Immuno-Navigator database. However, many genes of the Treg meta-signature were also expressed at the protein level by other immune cell subsets, as assessed by mass cytometry, with the noticeable exceptions of Il2ra, Ctla4, and Tnfrsf9. Surprisingly, the proenkephalin (Penk) gene was a prominent member of this restricted Treg meta-signature.
Further analysis of public datasets and of our own RNA sequencing experiments confirms that Penk was over expressed by Treg in various murine tissues, including thymic Treg. Interestingly, Penk expression was increased in intra tumoral Treg whereas it was down modulated in the central nervous system of mice suffering from EAE. Finally, we propose a mechanistic model linking TNFR signaling and the transcription factor Batf in the regulation of Penk expression in Treg. Altogether, our results provide the first Treg meta-signature in mice and identifies Penk as a novel and unexpected Treg marker.
Safety and efficacy of Treg-based therapies will surely benefit from targeting molecules and/or pathways truly specific to Treg. In an attempt to resolve some of these issues, we reasoned that digging out a Treg meta-signature (TMS) from available datasets comparing well-defined Treg and Tconv should lead to a more robust signature than studies taken separately. As a starting point, we decided to focus our investigation in Mus Musculus, because many databases and tools are available in mice. We also focused our analysis on resting Treg freshly isolated ex vivo, taken from non inflammatory lymphoid organs, to avoid bias due to T cell activation and/or tissue localization. This naive approach led to the first description of a "universal" Treg signature, which includes several known Treg specific markers but also new ones, such as the pro-enkephalin gene Penk, indicating a function for the endogenous opioid pathway in immunoregulation.
Extraction of Treg meta-signatures The datasets used were selected based on a "Treg* AND (Tconv* OR Teff*) AND Mus Musculus" search in the GEO dataset web site (https://www. ncbi. nlm. nih. gov/gds). GEO datasets were manually inspected for inclusion of studies comparing fresh Treg with fresh Tconv from lymphoid organs. Only GSE17580  (15) , GSE24210  (16) , GSE37532  (17, 18) , GSE40685  (19) , GSE42021  (20) , GSE7852  (21) , GSE50096  (22) , and GSE15907 (ImmGen Project  (23) ) were selected and a list of genes significantly up regulated in Treg compared to Tconv was determined for each dataset using GEO2R with an adjusted p. value cutoff of 0. 05 (False Discovery Rate). The commonality within gene sets was determined using the online tool from the Bioinformatics Evolutionary Genomics department form the Ghent University (http://bioinformatics.
psb. ugent. be/webtools/Venn/). The Treg signature from Immuno Navigator (https://genomics. virus. kyoto-u. ac. jp/immuno-navigator/?) was generated by extracting 634 CD4 T cell samples and 240 Treg samples from the database. Differential gene expression was determined using Qlucore v3. 4 with optimal variance set at 0. 5 and False Discovery Rate <0. 05. All but one dataset (GSE37532) where the Affymetrix Mouse Gene 1. 0 ST Array was used, were generated using the Affymetrix Mouse Genome 430 2. 0 Array.
Analysis of the TMS All network analysis and visual representations were performed with Cytoscape v3. 7  (24)  (https://cytoscape. org). For enrichment analysis, we used ClueGO v2. 5. 5 and CluePedia 1. 5. 5 that integrates several ontology and pathway databases and creates a functionally organized network of GO/pathway terms  (25, 26). To represent PPI networks, we used the GeneMania  (27)  or STRING (28) Cytoscape applications, with embedded enrichment analysis. Expression of Penk in various tissues is represented by the graphical tool embedded in GEO2R, the Jamovi software  (29)  or Prism v8. 3 (Graphpad). Putative regulators of Penk mRNA in mice was generated with Ingenuity Pathway Analysis (Qiagen).
Finally, cells were incubated with Cell-ID Intercalator-Ir (Fluidigm) in 2% PFA for 16 hours before freezing at -80°C until acquisition on Helios cytometer and CyTOF software version 6. 0. 626 (Fluidigm) at the Cytometry Pitié-Salpétrière core (CyPS). Dual count calibration, noise reduction, cell length threshold between 10 and 150 pushes, and a lower convolution threshold equal to 10 were applied during acquisition. Data files were normalized with the MatLab Compiler software normalizer using the signal from the 4-Element EQ beads (Fluidigm) as recommended by the software developers. To normalize the variability between mice for supervised (i. e 2D plots) and unsupervised (i. e tSNE) analysis, cell samples from 3 mice were pooled before stainings.
RNA-Seq analysis RNA was extracted from sorted cells using the NucleoSpin RNA XS kit from Macherey-Nagel, quantified using a ND-1000 NanoDrop spectrophotometer (NanoDrop Technologies) and purity/integrity was assessed using disposable RNA chips (Agilent High Sensitivity RNA ScreenTape) and an Agilent 2200 Tapestation (Agilent Technologies, Waldbrunn, Germany). mRNA library preparation was performed following manufacturer's recommendations (SMART-Seq v4 Ultra Low Input RNA Kit, TAKARA Bio). Final samples pooled library prep was sequenced on Nextseq 500 ILLUMINA with HighOutPut cartridge (2x400Millions of 75 bases reads), corresponding to 2 times 23 x 10 6 reads per sample after demultiplexing. Poor quality sequences were trimmed or removed with Trimmomatic software  (31)  to retain only good quality paired reads.
Analysis of the GEO Treg meta-signature As a starting point, we searched for a common Treg signature (that is a list of genes differentially expressed in Treg compared to conventional T cells (Tconv)) across publicly available datasets retrieved from the GEO web site. We manually selected 8 datasets based on subjective criteria explicited below, and availability of the GEO2R analytical tool for the dataset. We deliberately limited the search to cells isolated from peripheral lymphoid tissues at steady state. Among the 8 datasets, 4 had been generated from lymph node cells, 3 from spleens and 1 from the bone marrow, 7 had been generated in C57BL/6 and one in BALB/c mice. Various ages and sexes were present in the datasets (range: 6-36 weeks-old). Various Treg sorting strategies were also used, from classical CD25 + sorting to isolation of GFP + cells from Foxp3-GFP transgenic mice.
So we believe that the 8 chosen datasets were representative of typical Treg profiles found in many studies. For each dataset, we generated a list of differentially expressed genes with a cutoff based on a false discovery rate inferior to 0. 05 and a log2 fold change superior to 1. We then established a list of differentially expressed genes common to the 8 datasets: 58 genes were found to be common to all 8 datasets. To gain biological insights from this list of genes, we generated a network of putative protein-protein interactions (PPI), protein-DNA and genetic interactions, pathways, reactions, gene and protein expression data, protein domains and phenotypic screening profiles from the Treg meta-signature (TMS) using the Genemania Cytoscape application, linking nodes of the network based on a score aggregating experimental and literature-based interactions  (27)  (Figure  1A ).
We represent the mean fold-change as a color code and the numbers of neighbors connected to each node (degree) as the size of the node to highlight some noticeable facts: Foxp3 and Il2ra were the most differentially expressed genes and were also among the most connected genes to other members of the network, as expected from the sorting Treg strategy used to generate these datasets. The well described Treg marker Ctla-4 was also highly differentially expressed and connected to other members of the network. Only Tnfrsf4 (OX40) was more connected to the network but had a slightly lower fold change, much like Tnfrsfr9 (4-1BB) and Tnfrsf1b (TNFR2). Genes with high fold-change but lower degree were Itgae (CD103), Rgs1, Klrg1, Ikzf2 (HELIOS), Nrp1 and surprisingly Penk, a gene coding for the proenkephalin, precursor of Met-enkephalin, an important mediator of peripheral nociception (pain perception).
This in silico isolation of many known Treg markers confirms the validity of our approach and suggest that novel molecules isolated by this method, such as Penk, could be considered as reliable Treg "markers". To gain further biological insights from the TMS, we performed an enrichment analysis using the ClueGO plugin in Cytoscape which interrogates several ontology/pathways databases simultaneously and produce a network of ontologies/pathways terms according to the statistical significance of the enrichment (26) (Figure  1B ). As expected, most of the significant GO/pathway terms revolved around T cell proliferation and its regulation. Among the TMS, only few genes belong to several pathways at the same time and Foxp3 was the most prominent one. Surprisingly, Slamf1 (CD150) and Prnp (CD230) were also connected to several related ontologies.
Analysis of the Immuno-Navigator Treg meta-signature. To confirm and extend the findings, we then turned to the Immuno-Navigator database, a batch-corrected collection of RNA quantification across numerous studies and many samples and cell types in mice and humans  (32). We compare 634 samples of CD4 T cells, that include many types of CD4+ T cell subsets with 240 samples of purified Treg, also defined in various ways and from various origins, including gene-deficient animals. Once variables with low variances were filtered out, a Principal Component Analysis (PCA) showed that the CD4 and Treg samples segregated well apart, with PC1 recapitulating 72% of the variance (Figure  2A ), showing that the remaining variables in the datasets could discriminate the two subsets with high accuracy. This was also visible in the clustered heatmap, where Treg samples clearly segregated from CD4 samples (Figure  2B ).