dataesr/scientific-paragraphs-categorization huggingface dataset:
As our objective was to study FREPs-encoding transcripts, we retained all transcripts that contained contiguous immunoglobulin superfamily (IgSF) and fibrinogen (FBG) domains (full-length or partial). In total, we retained 69 FREP transcripts. The 69 retained transcripts were translated, and the corresponding amino acid sequences were aligned with the Bg FREP protein sequences contained within GenBank. None was an The monitored immune-relevant genes include immune recognition molecules, immune signaling proteins, and immune effectors. Within these categories, the genes were subdivided by shared protein domains (defined by Interproscan analysis of predicted protein exact match, but 30 of the 69 transcripts displayed only a few differences, and could thus be clearly assigned according to the GenBank FREP nomenclature  [37].
g. , C1 and C2). We identified seven loci that encode FREPs with one IgSF domain (grouped into classes A to F), and 16 loci corresponding to FREPs with two IgSF domains (grouped into classes H to M). Finally B. glabrata genome assembly did not allow us to establish the number of IgSF domains for a last FREP class named "O". After we characterized these 24 loci of FREPs, we used RNA-seq to analyze the representation of the corresponding transcripts in the four B. glabrata strains. We built a FREP transcriptome containing the 69 selected sequences, mapped the reads obtained from each snail strain to these sequences using the Bowtie2 software, and normalized the hit count values for each transcript using the upper quartile method. The normalized hit count values were then summed for transcripts belonging to the same class.
FREP polymorphism analysis As most of the recovered FREP transcripts were only partial sequences, we could not align and compare the sequences of the FREP variants. Thus, to estimate their degree of polymorphism, we selected the longest transcript sequence for each of the 24 FREP loci and performed Blastn alignments with each of the B. glabrata transcriptomes (before CD-Hit EST treatment; Blastn cutoff, 95%). The number of hits was further normalized by the total number of transcripts of each transcriptome. The results (Fig 8A ) showed that the variant numbers differed between sequences). Ig-fold: immunoglobulin-like fold, IPR013783. CTL: C-type lectin, IPR001304. FBG: fibrinogenrelated domains, IPR002181. GLECT: galectin, IPR001079. PGRP: animal peptidoglycan protein homolog, IPR006619. LRR: leucine-rich repeat, IPR001611.
SR: scavenger receptor cysteine-rich, IPR017448. BPI: lipopolysaccharide-binding protein, IPR001124 and IPR017942. TIR: toll-interleukin 1-receptor, IPR000157. C1q: complement component C1q domain, IPR001073. TNF: tumor necrosis factor family, IPR006052. CARD: caspase recruitment domain, IPR001315. MACPF: membrane-attack complex/perforin, IPR020864. DEATH: DEATH domain, found in proteins involved in cell death, IPR000488. (B) Genes previously described to have an immune function in B. glabrata species. The heat map represents the log2 fold change values for transcripts that are differentially represented between the snail strains. The table beside the heat map classifies the corresponding genes into three immune functions: effector, recognition, and cell migration. doi:10. 1371/journal. pntd.
Notably, although BgBAR had the fewest normalized FREP variants, it displayed the highest numbers of variants for the one-IgSF FREP A, C1 (corresponding to FREP 2 in GenBank), and D (corresponding to FREP 14 in Thus, our results show that BgBAR, which is the least susceptible to S. mansoni presents the highest level of FREP expression and the lowest number of total FREP variants among the studied strains. Moreover, it expresses a higher proportion of FREPs containing one IgSF domain, and has the highest ratio of one-IgSF variants/all FREP variants.
An initial denaturation at 95˚C for 12 min was followed by: 45 cycles of 11 sec denaturation at 95˚C, 11 sec annealing at 60˚C, and 19 sec elongation at 72˚C; a melting curve step from 65 to 97˚C with a heating rate of 0. 11˚C/sec and continuous fluorescence measurement; and a cooling step to 40˚C. For each reaction, the cycle threshold (Ct) was determined using the 2nd derivative method of the LightCycler 480 Software release 1. 5 (Roche). PCR experiments were performed in triplicate (technical replicates) from three biological replicates. The mean value of Ct was calculated. Corrected melting curves were checked using the Tm-calling method of the LightCycler 480 Software release 1. 5. Results were normalized with respect to the α-tubulin gene, as previously described  [22] , and ΔCt values were calculated.
Processing of read sequences obtained from RNA-seq of B. glabrata De novo transcriptomic assemblies were performed using the Velvet version 1. 2. 02 software implemented by the python script provided by Oases version 0. 2. 06, as previously described  [33, 44]. The resulting contigs were merged into unigene clusters using CD-HIT-EST version 4. 5. 4 A multiple k-mer assembly approach was applied to optimize the assembly. Four highquality reference transcriptomes were produced, comprising 117,269, 70,533, 82,500, and 79,664 transcripts for strains BgBRE, BgBAR, BgGUA, and BgVEN, respectively (for transcriptome details see S4 Table ). The transcripts were automatically annotated using Blast2GO version 2. 4. 2.
Differential expression of transcripts in B. glabrata Quality reads (Phred score >26) were aligned on the BgBRE transcriptome assembly using Bowtie2 (v2. 0. 2; mapping quality score 255) on the Galaxy server (http://bioinfo. univ-perp. fr)  [45]. The count of the reads mapped to each transcript was assessed using Hitcount to BAM (SAM tools v0. 1. 18. 0). The obtained values were normalized by the upper quartile method  [46] , which has been proposed to be more accurate than the RPKM method of normalization  [47]. The DESeq2 software (v2. 12; http://www. bioconductor. org/packages/ release/bioc/html/DESeq2. html)  [48]  was used under default settings to identify genes that were differentially expressed in the two biological snail sample duplicates (BRE1 and BRE2) versus the other snail strain samples (P<0.
Gene Ontology (GO) enrichment analysis To detect biological processes that were significantly over-or under-represented in the BgBAR strain, we examined the transcripts found to be differentially expressed between BgBAR and BgBRE. Functional enrichment was assessed using the GoStatsPlus package in R (https:// github. com/davfre/GOstatsPlus). The enrichment was calculated for the 3 categories including of biological process (BP), molecular function (MF), and cellular component (CC), and. The results were imported into REVIGO  [50]  for further analysis and visualization.
Sequence analysis of B. glabrata FREPs Transcripts corresponding to potential FREP sequences were selected from transcriptomes on the basis of Blast2Go annotations or the presence of immunoglobulin and/or fibrinogen domains (IPR013783 and IPR002181, respectively), as detected by Interproscan. As most of the obtained sequences were not full length, transcripts encoding complete sequences or partial sequences with contiguous Immunoglobulin superfamily (IgSF) and fibrinogen (FBG) domains were selected to be sure to have a transcript belonging to a real FREP family member and not to GREP, CREP, FREM or IgSF or FBG containing molecules. The retained transcripts were translated to predicted amino acid sequences and aligned with those of reference FREP proteins obtained from GenBank using BioEdit version 7.
1. 3. 0. and were also aligned on the B. glabrata genome draft using the Blast tool available on the Vector Base website (https://www. vectorbase. org/). For quantitative analysis of FREPs, the reads from each B. glabrata strain were mapped to a reference FREP transcriptome (FREP transcripts assembled from the transcriptomes of the four snail strains) using Bowtie2 (v2. 0. 2). The mapped reads were counted per transcript and the values were normalized by the upper quartile method, as described above. To estimate the FREP polymorphism in each strain, the longest transcript from each FREP class was aligned against the B. glabrata transcriptomes obtained before CD-Hit EST treatment, using Blastn (cutoff, 95% identity) on the Galaxy server. The number of mapped hits was further normalized to the total number of transcripts for each B.
Accession numbers The obtained SmPoMuc sequences were deposited in GenBank under the following accession numbers: KX645102-KX645377 for SmLE, KX645378-KX645624 for SmVEN, EU676447-EU676459/EU676503-EU676530/EU676572-EU676583/EU676595-EU676625 for SmBRE, and EU676460-EU676502/EU676531-EU676571/EU676584-EU676594/EU676556-EU676626 for SmGH2. The obtained FREP sequences were deposited in GenBank under the following accession numbers: KY024239 to KY024307.
Cytokine and chemokine secretion by autologous MDDC-T cells co-cultures The secretion of cytokines and chemokines induced during autologous MDDC-T cells co-cultures was measured in the culture supernatant using the Luminex technology (Cytokine Human 25-Plex Planel, Invitrogen), following the manufacturer's instructions. The following 25 mediators were tested: Eotaxin, GM-CSF, IL-1β, IL-1RA, IL-2, IL-2R, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12p40/p70, IL-13, IL-15, IL-17, IFN-α, IFN-γ, IP-10, MCP-1, MIG, MIP-1α, MIP-1β, RANTES and TNF-α. Data were analyzed using the GraphPad Prism Software version 5. 00 (San Diego, CA, USA). Comparisons for the different parameters were performed by Student's test. For the analysis a level of significance was set at p < 0. 05.
Acknowledgements The authors thank  Pr. R Desrosiers  for the gift of SIVmac  239 ,  Stéphane Huguet  for support,  Juliette Nguyen ,  Roxane Beauvoir  and  Armelle Walton  for assistance in virology, and the  NIH  AIDS research repository for reagents. Celine Amadori  is a recipient of a  CIFRE doctoral fellowship  from the  Association National Recherche Technologie (ANRT). We also thank all the patients who gave their informed consent for this study.
Funding This work was supported by  Biodim Mutabilis  under the Authorization Number  DUO 2145 , assigned by the  French Ministry of Research  for work with genetically modified organisms, and by  Grants  from  EU   FP7  to Biodim, BB, SE and AZ under the  HIVINNOV consortium , Grant Agreement No. 305137 , from the  Eurostar Grant ResistAids to Biodim and BB  (Grant Agreement No. E!10239 ) and by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , and Instruct as part of the  European Strategy Forum on Research Infrastructures (ESFRI). Felipe García team was supported by Grants  SAF2015-66193-R ,  SAF2012-39075 ,  Montserrat Plana  by Grant  FIS PI15/00480  and  Nikki van Bel by NWO-CW (TOP Grant ).
Immune phenotyping Whole blood, LN cells and cells from FRT compartments were analyzed by flow cytometry. The cells were incubated with the antibodies listed in Table  S1 , then washed and fixed with FACS lysing buffer or BD Cell Fix solution. A Fortessa 2-UV 6-Violet 2-Blue 5-Yelgr 3-Red laser configuration was used (BD biosciences), with Diva (BD) and FlowJo 9. 8. 3 (Tristar, USA) softwares. At least 500 events for rare cell populations (i. e. pDC) were recorded. The gating strategies are illustrated in supplemental figure  S1a-d.
The antibodies listed in Table  S2  were used for intracellular staining. At least 5,000 events in the CD8 + T cell gate were recorded. The gating strategy was as described elsewhere  (12). Briefly, expression of cytokines and activation markers was evaluated in CD4 + and CD8 + T cells, and Boolean gate analyses were performed with FlowJo software. The percentages of cells positive for cytokines and activation markers were then compared between unstimulated and MVA-or GAG peptide poolstimulated cells. Immune response was considering positive against the antigen when the percentage of cells positive for cytokines and activation markers were at least twice superior to the percentage under unstimulated condition.
The plates were then washed 5 times and incubated for 1 h with a 1:20,000 dilution of horseradish peroxidase-conjugated goat antimonkey H+L chain IgG (Bio-Rad, Marne-la-Coquette, France) or with a 1:5,000 dilution of horseradish peroxidase-conjugated goat anti-monkey IgA (Alpha Diagnostic international, San Antonio, TX). The plates were washed five times, then 100 µL of o-phenylenediamine dihydrochloride (OPD) (Sigma Aldrich) was added and incubated for 30 mins at RT in the dark. The reaction was stopped by adding 2N H2SO4. Absorbance was measured at 492 nm with spectrophotometer (Tecan, Lyon, France), and data were analyzed with Magellan software (Tecan). Antibody titers were calculated by extrapolation from the OD as a function of a serum dilution curve and were defined as the dilution of the test serum reaching 2 OD of the corresponding preimmune serum or vaginal fluid, tested at 1:50 and 1:30, respectively.
Transcriptome analysis Microarray data were analyzed with R/Bioconductor software. Gene expression values were quantile normalized. Differentially expressed genes were identified with a paired nonparametric t-test (q-value<0. 05), based on a fold-change cutoff of 1. 2. Functional enrichment analysis used QIAGEN's Ingenuity Pathway Analysis (IPA, QIAGEN, Redwood City, ww. qiagen. com/ingenuity). Hierarchical clustering presented in the heatmaps were generated with the Euclidian metric and complete linkage methods. Microarray raw data are available from the EBI-ArrayExpress database (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-5663. The transcriptomic and cellular co-expression network was generated by using the Spearman correlation coefficients, based on the abundance of cell populations and normalized gene expression values across the whole dataset.
In all compartments, CD154 + cells represented a large percentage of CD4 + T cells after stimulation with PMA and ionomycin (data not shown). The anti-HIV-1 response was also analyzed after in vitro stimulation with gag peptide pools and co-stimulatory antibodies (anti-CD28 and anti-CD49d mAbs). The addition of co-stimulatory antibodies induced non-specific activation of T cells and thus increased background CD154 expression, even in non-stimulated conditions. As this could have masked weak responses, we measured the HIV-1-specific CD4 + T cell response by analyzing the percentage of CD4 + T cells that expressed CD154 and produced IFN-γ (i. e. only specific T cells). The Gag-specific CD4 + T cell response was very weak and only detected in PBMC (Figure  3c ). Thus, MVA-specific CD4 + T cells were mainly found in blood and in lymph nodes draining the vaccine inoculation site.
Systemic and mucosal polyfunctional vaccine-specific CD8 + T cell responses Like the CD4 + T cell response, the vaccine response mediated by CD8 + T cells was monitored by in vitro re-stimulation assays in blood over time, and in all compartments at euthanasia. The percentage of MVA-specific CD8 + T cells that produced IFN-γ increased in blood after the first vaccine inoculation and rose strongly after the second injection (respectively 1. 79% and 5. 38% of total CD8 + T cells; mean) (Figure  4a ). A similar profile was observed for MIP-1β and TNFα production by antigen-specific CD8 + T cells over time (data not shown). MVA-specific CD8 + T cells were detected in PBMC and LNs of all animals three weeks after the second vaccine injection. They represented from 0. 15% to 7. 16% of total CD8 + T cells, depending on the animal (Figure  4b-c ).
Acknowledgment The authors thank  Drs Anne-Sophie Beignon ,  Antonio Cosma ,  Mireille Centlivre  and the Bmuc division members for scientific discussions,  D. Young  for critical editing of the manuscript,  Transgene  for providing the wild type MVA strain and  ANRS/INSERM  and the  Vaccine Research Institute the MVA-HIV-B vaccine , the IDMIT core facilities for animal interventions and sampling,  Rahima Yousfi  for technical assistance and  Dr Bernard Verrier  for providing the p24 HIV-1 antigen. Revised manuscript.
Footnotes This work was supported by  French government  " Programme d'Investissements d'Avenir" (PIA)  under Grant  ANR-11-INBS-0008  funding the  Infectious Disease Models and Innovative Therapies  ( IDMIT, Fontenay-aux-Roses, France) infrastructure and PIA  grants  ANR-10-LABX-77  and  ANR-10-EQPX-02-01  funding the  Vaccine Research Institute (VRI, Créteil, France) , the FlowCyTech facility, respectively. The authors declared no conflict of interest. Corresponding author  Elisabeth Menu Center for Immunology of Viral Infections and Autoimmune diseases (ImVA) Building  02-4th floor 18 route du Panorama 92265 Fontenay-aux-Roses, France  +33 1 46 54 83 14  Elisabeth. menu@pasteur. fr.
Discussion The present case is the first report of a relapsing fever responsive to treatment with imidocarb dipropionate associated with Cytauxzoon sp. infection in a cat in France. It describes the course of the infection and brings new information about clinical expression of suspected cytauxzoonosis in the domestic cat in Europe. The clinical signs reported here correspond to the signs previously described during infection with Cytauxzoon sp. (and usually described in case of piroplasms infection in other species). The facts that (i) no other cause (infectious in particular) could be evidenced, that (ii) intra-erythrocytic inclusions were systematically Consensus sequence from the infected cat described in the present case report is indicated by an arrow. Identity and Genbank® accession numbers are indicated for each sample.
detected in this clinical case presented a high homology (99%) with Cytauxzoon sp. and Cytauxzoon manul previously detected in wild and domestic felids from Europe and Mongolia  [7, 9, 11, 19]. In contrast, the sequence revealed an identity of only 95% with C. felis sequences deposited in GenBank®. The parasites described from Europe as Cytauxzoon sp. are closely related and may belong to the same species. They are distinct from Cytauxzoon felis, the agent of feline cytauxzoonosis in the New World. Epidemiological and clinical data on Cytauxzoon sp. infection in domestic and wild felids in Europe have rarely been reported and originate from Spain, Italy, France, Portugal and Romania  [7, 9-13, 15, 16]. In the domestic cat, single occasional molecular detections of Cytauxzoon sp. have been described in France  [11]  and Spain  [11]  with no associated clinical finding.
fragments obtained from each laboratory were 100% homologous to each other in the common fragments. A consensus sequence of 944 bp was deposited in Genbank under accession number KX881967. Comparison of this consensus sequence to sequences deposited in GenBank® using the basin local alignment search tool (BLAST) (https://blast. ncbi. nlm. nih. gov/Blast. cgi) as well as phylogenetic analyses (Fig. 2) affiliated the organism found in the cat's blood to Cytauxzoon sp. with 99% identity with sequences of Cytauxzoon sp. previously detected from domestic cats in France (EU622908) and Spain (AY309956) and from wild lynxes in Spain (EF094468 to EF094473). It also had a 99% identity with Cytauxzoon manul from Mongolian wild cats (AY485690 and AY485691).
Acknowledgements This work was funded by the  French government 's  ATIP-Avenir program. LFP also received a fellowship from the  ATIP-Avenir program. CL received of a PhD fellowship funded by the  INSERM , the  Nord-Pas de Calais Regional Council , and the  Association pour la Recherche sur le Cancer " cancer charity. We thank the staff at the animal and cytometry facility at the  Pasteur Institute of Lille.
Abiotic factors Abiotic factors such as temperature and relative humidity not only have a direct effect on tick development, questing activity and longevity, but temperature may also modulate pathogen development and survival in ticks  (Shih et al. , 1995; Estrada-Peña et al. , 2011). interactions that affect vector competence is essential for the identification of molecular drivers for tick-borne diseases, and exposes paradigms for controlling and preventing these diseases. Although our understanding of tick-pathogen interactions is still limited, advances in this field are facilitated by the increasing number of available genomic resources, including metabolomics, transcriptomics, and proteomics datasets of various ticks and tick-borne pathogens (TBPs)  (Nene et al. , 2004; Ayllón et al.
FUNDING Part of the research included in this was supported by the  Ministerio de Economia y Competitividad (Spain)  grant  BFU2016-79892-P  and the  European Union  ( EU) Seventh Framework Programme  ( FP7 )  ANTIGONE  project number  278976. SA and AD would like to acknowledge FCT for funds to GHTM - UID/Multi/04413/2013. MV was supported by the  Research Plan of the University of Castilla-La Mancha  ( UCLM ),  Spain. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements The authors thank  Kristin Hartmann  ( Core facility mouse pathology/UKE ) and  Dr. Thomas Meyer  ( Department of Psychosomatic Medicine/UMG ) for assistance on animal experimentation and  Silja Koechy  and  Nadine Gotzmann  ( UMG/DZNE ) for technical assistance. We also thank  Benjamín Torrejón Escribano  ( CCiT-UB ) for the technical support. We are indebted to the  Neurological Tissue Bank of the Biobanc-Hospital Clinic-IDIBAPS  for sample procurement.
Methods EuroSIDA is a large, on-going prospective cohort study of 18 931 individuals living with HIV. The study collects data from 111 hospitals in 34 different countries across Europe, as well as Israel and Argentina  [17, 18]. Recruitment started in 1994, and data are collected 6-monthly on standardized case report forms (CRFs). Variables collected include demographic information, CD4 counts, viral load (VL) measurements and start and stop dates for all antiretroviral drugs used. All patients gave informed consent to be included in EuroSIDA at enrolment. Non-AIDS-related events are collected in EuroSIDA following the standardized HIV Cohorts Data Exchange Protocol (HICDEP) code for data collection (http://www. hicdep. org/ wiki/Hicdep_1. 90). Clinicians at EuroSIDA participating sites were asked 6-monthly to report any event not previously reported to EuroSIDA, including all events since the last follow-up.
Patients were included in the CONC cohort if (1) they started a new antiretroviral drug other than RAL as part of a cART regimen on or after 21 December 2007, and had no previous exposure to the new drug, including as part of a different co-formulation; (2) they had at least 1 month's prospective follow-up in this cohort, and (3) they had a CD4 count and a VL measured within 6 months prior to the start date of the new drug. Baseline for the analysis in the RAL cohort was defined as the date on which the patient first received RAL. In the HIST and CONC cohorts, baseline was the date on which the patient first received the new antiretroviral drug (not RAL). Patients were allowed to contribute data to more than one cohort, but with no overlap in the follow-up time (Fig. 1 ). Those who contributed data to the HIST cohort were also eligible for inclusion in the RAL cohort upon initiation of RAL or in the CONC cohort upon initiation of a new antiretroviral drug after 21 December 2007.
Patients who contributed data to the CONC cohort were also allowed to 'switch' into the RAL cohort upon initiation of RAL. However, in order to be able to evaluate the risk associated with currently being exposed to RAL, patients in the RAL cohort could not switch to the CONC cohort upon initiation of a new antiretroviral drug. Analyses were performed on the data set including cumulated data collected from patient journals until December 2014. The data were collected, keyed and quality assured at the Coordinating Centre between January 2014 and December 2014.