dataesr/scientific-paragraphs-categorization huggingface dataset:
5D ), indicating that IFN signaling in tumor cells is not needed for the antitumor effect upon administration of the BiSp-AFN. These data suggest that IFN signaling in immune cells rather than direct intrinsic and anti-proliferative effects on tumor cells is key for the observed effects. In contrast to IFNAR expression by tumor cells, PD-L1 expression by the tumor cells was needed for the antitumor efficacy of Bisp-AFN (Fig. 5E ). These results were confirmed in a tumor antigen-specific proliferation assay using gp100 in a B16 melanoma model. To that end, CFSE labeled CD8 + T cells carrying a TCR specifically recognizing the melanoma differentiation antigen gp100 were adoptively transferred into B16 melanoma bearing mice. After treatment, dilution of CFSE was analyzed in draining LN. Proliferation was significantly impaired in the B16-PD-L1 -/-tumor model following Bisp-AFN therapy (Fig.
Single cell RNA sequencing indicates key shifts in both lymphoid and myeloid cells in the tumor microenvironment To obtain a comprehensive understanding of the immune cell heterogeneity in the tumor microenvironment as well as effects induced by AcTaferon treatment, we performed single cell RNA sequencing (scRNAseq) on B16 tumor samples (Fig. 6A ). Uniform Manifold Approximation and Projection (UMAP) for dimension reduction visualization was performed on the CD45 + living immune cell population. Cells from the various treatment conditions were pooled in a single dataset (Fig. 6B ) whereupon the origin of each cell was visualized in a color-coded UMAP and linked to the different treatment conditions (Fig. 6C ). Clusters were identified based on detection of differentially expressed (DE) genes (Fig.
l. injected with PBS (grey) or Bisp-AFN (green) at days 7 and 9 after tumor inoculation. One day prior to immunization, gp100-specific CD8 + T cells (pMel) were adoptively transferred in B16 tumor-bearing mice. Data show percentages of gp100-specific CD8 + T cells that have undergone at least one division (A), stacked bar chart representing the percentage of cells per division peak (B) and representative flow cytometry proliferation profile for each treatment (C). Shown is a summary of two independent experiments (6/group/ experiment). D, E B16-bearing mice were p. l. injected at days 7 and 9 after tumor inoculation with the indicated conditions. Five days after the first delivery, the killing potency of the induced tumor-specific CD8 + T cells was analyzed in draining LN by in vivo cytotoxicity assay (D).
10x's CellRanger software was used for demultiplexing of the raw data, whereupon these reads were used as the input for CellRanger (10X Genomics), which align the reads to the mouse reference genome (mm10) using STAR and collapse to unique molecular identifier (UMI) counts. Scran and scatter R package was used for processing of the data in accordance to the workflow as described  [54, 55]. Outlier cells were identified based on three metrics i. e. library size, number of expressed genes and mitochondrial proportion. Cells from the control (PBS treated) sample were tagged as outliers when they were three median absolute deviations (MADs) away from the median value of library size and number of expressed genes, four MADs for the sample treated with Clec9A-PDL1-AFN. Likewise for both samples, cells 25 MADs away from the median proportion were identified as outlier.
These outlier cells were removed from the analysis. Genes expressed in less than 3 cells and cells expressing less than 200 genes were removed. The samples were aggregated using the merge function, counts were normalized and log2 transformed using the NormalizeData function, both from the Seurat R package (v4. 0. 2) using default parameters. Detecting highly variable genes, scaling, finding clusters, and creating UMAP plots was done using the Seurat pipeline. Clustering was performed using the first 37 principal components and a resolution of 1. 5.
In vivo proliferation assay Gp100-specific CD8 + T cells were isolated from spleens of naive pMel mice using MACS separation protocols (Miltenyi Biotec) and subsequently labeled with 0,5 μM carboxyfluorescein succinimidyl ester (CFSE, Life Technologies). Cells were adoptively transferred into B16tumor bearing mice. Mice were p. l. injected with PBS or the AcTakine condition twice, one day after adoptive transfer and an additional administration 2 days later. Proliferation of gp100-specific T cells, measured as a dilution of CFSE, was analyzed in draining LN three days after the last AcTakine delivery. Samples were acquired on an Attune Nxt Acoustic Focusing Cytometer (Life Technologies) and analyzed using FlowJo software.
In vivo cytotoxicity assay To analyze the potency of the induced antigen-specific T cells, an in vivo cytotoxicity assay was performed. To that end, B16 tumor-bearing mice were p. l. injected twice with a one-day interval with PBS or the Bisp-AFN. Three days after the last delivery, the killing assay was performed according to current Protocols of Immunology  [56]. In brief, spleen cells from syngeneic mice were pulsed or not with 5 μM EGSRNQDWL peptide (mouse gp100  [25] [26] [27] [28] [29] [30] [31] [32] [33]. Peptide pulsed cells were labeled with 2,5 μM CFSE after which they were mixed at a 1:1 ratio with 0,25 μM CFSE labeled, non-peptide pulsed cells. At least 10 7 cells were injected intravenously (i. v. ). Spleen and draining LN were isolated 24 h later. Specific lysis was analyzed in draining LN using an Attune Nxt Acoustic Focusing Cytometer (Life Technologies) to acquire the samples and FlowJo software to analyze the data.
All original cDNA molecules were ligated to a 12-base random adaptor sequence used as a Unique Molecular Identifier (UMI). Enrichment for target sequences was performed with a first PCR reaction using a primer against the TCR constant region and a primer against the adaptor sequence. A second PCR was used to further amplify the library, add platform-specific adaptor sequences and introduce additional sample indices. Samples were next pooled and sequenced in two Illumina MiSeq v2 500 runs. All raw read data was merged and analyzed using the CLC Genomics Workbench (GWB) 21 (https:// digit alins ights. qiagen. com/)  [57]  via the mouse "Immune Repertoire Analysis" workflow. We used default parameters for this analysis, except for "min UMI group size" (set to 1 to increase sensitivity).
Acknowledgements We thank the  VIB Nanobody Core  ( Gholamreza   Hassanzadeh Gassabeh)  for the sdAb selection; the  VIB Flow Core  ( Gert Van Isterdael  and  Julie Van Duyse ) for the cell sorting experiments and the  VIB Nucleomics Core  ( Stefaan Derveaux  and  Ruth Maes ) for their help in the scRNAseq and TCR sequencing study. Additional acknowledgements are attributed to  Kevin Verstaen  for his help with processing and visualization of the scRNAseq data.
mm10. Known Gene database was counted, reported, and annotated using the Genomic Features, Genomic Alignments and org. Mm. eg. db packages. Raw datasets were submitted to NCBI GEO database (GSE165285). To identify genes differentially expressed in WT versus Lgals7 -/- and Tg46 tumors, we used the DESeq2-test on the basis of the normalized number of reads mapped to each gene  [42]. Functional enrichment analysis was performed using the Enrichr resource  [43]  and Cytoscape´s plugins ClueGo/CluePedia (http://www. cytoscape. org/) based on the list of dysregulated transcripts between samples (p < 0. 05; FC > ±2). Quantification of the absolute abundance of immune and stromal cell populations was estimated using MCPcounter algorithm (https://github. com/ebecht/MCPcounter) based on RNA-seq data.
Data mining analysis of LGALS7 gene in human neoplastic diseases For a comparative analysis of LGALS7 gene expression and copy number variation in different human cancers, data from 9,186 primary tumors and their associated normal adjacent tissue samples obtained from The Cancer Genome Atlas (TCGA) Pan-Cancer Analysis Project were analyzed. Clinical and pre-processed data (RNA-Seq RSEM normalized counts, CNV profiles) were retrieved from the UCSC Xena Browser (http://xena. ucsc. edu/). Data were integrated and visualized using the R software. A panel of NMSC lesions and normal skin samples (E-MTAB-5678;  [46] ) was also analyzed using the EdgeR R/Bioconductor package for data pre-processing of the RNA-Seq transcriptome provided as the number of counts mapped to each gene. Average LGALS7 and LGALS7B mRNA expression profiles (log2 count per millions) were employed for further statistical analysis with R software.
Notably, Cxcl1 secretion may be controlled by activation of HGF receptor c-Met  [49, 50] , which aberrant signaling has been associated with cancer progression and invasion  [51, 52]. Notably, exome-seq analysis of DNA from WT, Lgals7 -/-and Tg46 mouse papillomas (Supplementary Dataset 2 Mutational Profiles) showed high variability of genomic missense mutations particularly localized in the c-Met gene in lesions from Tg46 mice (Fig. 4C ). Indeed, Tg46 lesions presented the highest number of non-synonymous mutations on target genes described either as cancer-driver genes or as potential drivers, as defined by the IntOGene database (Fig. 4D  and Supplementary Fig. S2A ). Mutations in Tg46 tumors impinged on cancer driver genes mostly related to phosphoinositide 3-kinases (PI3K)-Akt signaling (e.
5D, E ). Accordingly, when we used Fig. 3  Enhanced Gal-7 expression shapes the immune landscape in murine skin papillomas. Volcano-plots from analysis of RNAseq data from Lgals7 -/-, Tg46 and WT papillomas, showing 240 and 264 genes differentially expressed in Lgals7 -/-(A) and Tg46 (B) lesions, respectively, compared with WT tumors (p < 0. 05, FC > 2). C Functional enrichment analysis of RNAseq data. Bioprocesses found enriched in Lgals7 -/-(left panel) and Tg46 (right panel) tumors, compared with their WT counterparts, are shown. D Genes found to be dysregulated in Lgals7 -/-or Tg46 lesions compared with WT tumors, and network-based pathway enrichment analysis. E Heat map analysis of tumor-infiltrating immune cells. F Analysis of CD11b + cells in papillomas from Tg46, WT and Lgals7 -/-mice, evaluated by immunofluorescence.
S4A  and S4B ), we then investigated its relevance in human NMSC. RNAseq data analysis from a cohort of NMSC patients (E-MTAB-5678 dataset) revealed increased LGALS7 expression in neoplastic (intraepidermal carcinoma; IEC and squamous cell carcinoma; SCC) and pre-neoplastic (actinic keratosis; AK) lesions compared to normal tissue (Fig. 8A ). Immunohistochemistry of tissue samples from a cohort of NMSC patients showed a substantial increase in GAL-7 protein expression in pre-malignant and malignant lesions compared with healthy skin tissue (Fig. 8B ), substantiating transcriptional data. Notably, no significant variations were found in the transcriptional profile of other members of the galectin family commonly involved in tumorigenesis, between neoplastic, pre-neoplastic and healthy skin (Supplementary Fig.
S4C ). Thus, heightened GAL-7 expression may represent an early hallmark of carcinogenesis in human NMSC. In line with our findings in murine carcinogenesis, NMSC malignant lesions showed enrichment in myeloid cell components as evidenced by RNAseq data analysis (E-MTAB-5678) (Fig. 8C ). Indeed, IEC, SCC and AK lesions exhibited up-regulation of genes associated with monocytic myeloid cells (CD11b and CD14) and down-regulation of granulocytic markers (CD66b and AQP9) compared to healthy skin tissue (Fig. 8D ). Regarding myeloidrecruiting chemokines, higher expression of CXCL1, S100A8, CCL7 and CSF1 was observed in lesions from AK, IEC and SCC patients compared with normal skin tissue (Fig. 8E-F ). We found a strong positive correlation between LGALS7 mRNA expression and transcription of CXCL1, S1008A, CCL7 and CSF1 in neoplasic skin lesions (Fig.
ACKNOWLEDGEMENTS This work was supported by grants from  Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación  (PICT V  2014-3687  and  PICT 2017-0494  to  GAR  and  PICT 2012-0164  and  PICT 2015-0122  to JPC), as well as funding from  Bunge & Born Foundation ,  Sales Foundation ,  René Baron Foundation  and  Richard Lounsbery Foundation  to GAR. LJR was supported by  Nordea-fonden and Olav Thon Foundation  and PS was supported by a  Chinese Scholarship Council. We thank  Ferioli, Ostry  and  Caraballo  families for kind donations.
Data collection procedures (socio-demographic, socio-economic, paraclinical and clinical data collection procedures) A total of 1087 children were sampled. Children were randomly selected from the list of children in each class until the required number of samples were obtained. Sociodemographic data, including village, gender, age, were obtained using a structured questionnaire. Socioeconomic information was collected through questionnaires administered by a sociologist. The questions were close ended to facilitate the children's responses. For the parents' level of education (yes or no), type of toilet (modern or traditional), frequency of use of streams (No: Never frequents the river; Yes: I frequent the river several times a week), type of water used (drilling, river, rain, drinking), and reason for frequenting streams (swimming, fetching water, taking a bath).
Statistical analysis Socio-demographic, clinical and parasitological data were recorded on survey forms with identifiers for each child. The data was recorded in Excel. Calculations of prevalence, and intensity of infection were made using SPSS (IBM, version 23. 0, Armonk,USA). Binomial analysis was performed on R software (Lucent Technologies, Jasmine Mountain, USA) to assess the relationship between clinical signs and urogenital schistosomiasis infection. Participants were divided into two age groups (6-10 and 11-14 years) for each sex. We performed statistical analyses to assess the relationship between parasitic infections and demographic, sociocultural, socioeconomic, clinical signs, and sonographic factors using a multivariate logistic regression. The differences in proportions were tested using the chi-square test or the exact Fisher test depending on the data.
Correlation between S. haematobium infection and socio-economic status and environmental factors Socio-economic data collected show that almost all schools (99. 6%) had a traditional toilet. Most children had various reasons for visiting pools or river (98. 1%). The main reason for going was swimming (80. 7%). Domestic water sources were variables, but the river was the main source of water (50. 2%). Most children lived near the river/pond (94. 4%) and used to urinate in the water source (92. 5%). Concerning parents, agriculture was the primary activity (29. 0%). Multivariate analysis of socio-economic parameters associated with urogenital schistosomiasis showed that children with a modern toilet were all affected (P = 0. 083) with a medium burden (75%) (P = 0. 008). Affected children indicated swimming as the main reason for river use and had high parasite loads (P < 0.
Acknowledgements The authors would like to thank mainly  Amadou Dabo called Boua  for his tireless efforts, the transport of the team and the equipment;  Boubacar Fofana , for the realization of the ultrasounds;  Mathias Dolo  and  Kadra Koné , the geographer; all the school and health authorities and the population of the six sites; the staff of the  MRTC (Maria Research and Training Center) ; the staff of the faculty of Medicine and Dentistry and the  Faculty of Pharmacy. We thank the Malian government for the availability of funding and the  IHPE laboratory ,  Univ. Montpellier, CNRS, Ifremer ,  Univ. Perpignan Via Domitia, Perpignan, France  for the welcome.
The amplification ended with a final cooling step for 30 s at 40 • C. Rickettsia positive pools that were negative for R. helvetica were identified by a previously described conventional PCR method targeting the 23S-5S intergenic spacer region (Chitimia--Dobler et al. , 2018) and subsequent sequencing of the PCR product. Positive pools were partly tested by conventional PCRs targeting partial ompA  (Fournier et al. , 1998)  and ompB  (Roux and Raoult, 2000). Subsequent Sanger sequencing was conducted by an external contractor (GATC Biotech, Konstanz, Germany). Sequences were analyzed using BioEdit Alignment Editor Version 7. 1. 1  (Hall, 1999)  and compared with sequences deposited in the GenBank database of the National Centre for Biotechnology Information (NCBI) using the Basic Local Alignment Search Tool (BLAST)  (Altschul et al.
helvetica ranged from 0. 0 % to 9. 09 %; R. monacensis ranged from 0. 26 % to 0. 28 % at the two positive collection sites. Basically, no variability of the 23S-5S intergenic spacer region, OmpA or OmpB nucleotide sequences was observed between the samples and in comparison with sequences from rickettsiae sampled in Germany, besides a minor variability in the 23S-5S intergenic spacer region from a German sample and the OmpB gene in a Danish sample, both R. helvetica (figure included as supplementary material 1). Rickettsia monacensis nucleotide sequences, have been submitted to GenBank (accession numbers: OQ645342, OQ645343, OQ645344, OQ645345).
Flow cytometry analysis. MAbs panels, staining. Immunophenotypic studies were performed on frozen samples, using up to 23-colours flow cytometry panels. See extender data Table  3  for antibody panel information. The list of mAbs used are detailed in gating strategy we used to identify the immune cell subtypes and their respective subsets are represented in extended data Fig. 3. Antibody titration was performed to choose the concentration that provided the maximal brightness of the positive cell population and the lowest signal for the negative cell population. Approximately 1 × 10 6 to 5 × 10 6 frozen PBMCs were used per patient per stain. Staining was performed as described previously  (28)  except for the panel using HLA-E-or HLA-A*02-pentamers (Pro-immune). Pre-incubation with a blocking anti-CD94 mAb (clone HP-3D9, 5 μg/mL, BD Biosciences) was performed to completely abrogate the non-specific staining of CD94 + /NKG2 + T cells by HLA-E-pentamers before the staining with the other antibodies.
Whole-Genome Sequencing (WGS) WGS was performed by the Commissariat à l'énergie atomique et aux énergies alternatives (CEA), as part of a collaboration between CEA-IBFJ/CNRGH, Institut Imagine, INSERM and Université Paris Descartes. One microgram of genomic DNA was used to prepare a library for WGS using the Illumina TruSeq DNA PCR-free library preparation kit, according to the manufacturer's instructions. After normalization and quality control, qualified libraries were sequenced on a HiSeq X Five platform (Illumina), as paired-end 150 base pair reads. One lane of the HiSeq X Five flow cell was used for each sample to reach an average sequencing depth of 30 ×. The sequence quality parameters were assessed throughout the sequencing run, and standard bioinformatics analysis of sequencing data was based on the Illumina pipeline to generate FASTQ files for each sample.
Interferon Signature Testing The analysis of 24 genes and 3 housekeeping genes was conducted using the NanoString customer designed Code-Sets according to the manufacturer's recommendations (NanoString Technologies, Seattle, WA). One hundred nanograms of total RNA was loaded for each sample. Agilent Tapestation was used to assess the quality of the RNA. Data were processed with nSolver software (NanoString Technologies Seattle, WA). The data were normalized relative to the internal positive and negative calibrators, the three reference probes, and healthy control samples. The median of the 24 probes for each of 29 healthy control samples was calculated. The mean NanoString score of 29 healthy controls + 2 SD of the mean was calculated. Scores above this value (2. 75) were designated as positive.
IRdye-conjugated anti-mouse (926-68,070, LI-COR) or anti-rabbit (925-32,211, LI-COR) secondary antibodies diluted in intercept (TBS) blocking buffer plus TBS supplemented with 0. 1% Tween 20 (TBS-T) (intercept (TBS) blocking buffer: TBS-T = 1:2 v/v) were used to detect targeted proteins. Membranes were scanned using the Odyssey CLx System (LI-COR). Densitometry quantification and analyses were performed using the Image Studio Lite software v. 5. 2 (LI-COR).
Structure Modelling of STAT2-USP18 Interaction Deletion mutagenesis in combination with coimmunoprecipitation assays has previously identified the amino acid region 138-572 of STAT2 and 51-112 of USP18 to be important for their interaction  [8]. More recently, arginine 148 in STAT2 has been suggested to be directly involved in USP18 binding  [9]. Based upon these criteria, molecular docking was performed to obtain a potential model of the interaction. The structure of STAT2 was obtained from the AlphaFold database removing residues 709-851 of the highly disordered C terminus. Then, a homology model of USP18 was generated with Phyre2  [12] , using the available experimental structures in the Protein Data Bank  [13]. ClusPro was adopted  [14]  to dock the two proteins by deriving loose restraints in the following manner: leucine 103 was deemed to be the residue closest to the centroid of the region 51-112 in the structure of USP18.
Homozygous Missense Substitution in STAT2 Identified Through Whole-Genome Sequencing (WGS) Taking a candidate gene approach to the WGS data, and concentrating on homozygous variants given the recorded parental consanguinity, we noted a very rare STAT2 variant in the homozygous state in our patient, where a single nucleotide transition c. 656C > T (transcript: NM_005419. 4) results in an alanine to valine substitution at residue 219. The patient's asymptomatic parents were heterozygous for this variant, which is present only once on gnomAD and is not present in the Greater Middle East Variome database. In silico analysis using SIFT and Polyphen predicted this STAT2 p. (A219V) substitution as "Deleterious" and "Possibly damaging" (score 0. 698), with a CADD score of 17. 81. STAT2 protein sequence alignment across different species showed that the A219 residue is well conserved (Fig.
Statistics Quantitative data are presented as mean ± standard deviation (SD) or as median and interquartile (IQ) 25th-75th percentile when analyzed using non-parametric assays. Receiver operating characteristic (ROC) curves were generated to determine the area under the curve (AUC), the optimal cut-off values were chosen using Youden's index, and the Cohen's kappa was used to compare techniques. Categorical data were analyzed using Fisher's exact test. Statistics were conducted using GraphPad Prism 9. 2 (La Jolla, CA) software, and p-values<0. 05 were considered significant.
Acknowledgements We are thankful to  Dr. Wesley H. Brooks  ( University of South Florida, USA ) and  Gisèle Touzanne  for editorial assistance, to  Michèle Tiraby  and  Daniel Drocourt  ( INVIVOGEN®, Toulouse, France ) for their precious advice, and to  Elizabeth Argentin ,  Lorie Estrada ,  Fabienne Haudrechy  and  Elodie Martin  for technical assistance.
Diversity database: DNA barcode, OTU identification and good practice in vouchers deposition Central to moving forward research on Acanthocephala is the enhancement of DNA barcode data and museum collections. Twenty years after barcoding was proposed as a basis for molecular classification of operational taxonomic units (OTUs)  [138, 139] , an increased effort to broaden taxonomic coverage for acanthocephalans in COI barcoding is needed to catalyze taxonomic investigation. Difficulties in OTU identification in metabarcoding approaches and ecological studies occur mostly because currently, only a minority of described acanthocephalan species has been sequenced: as for today, 116 acanthocephalan species and 186 Barcode Index Numbers (BINs) are registered in the publicly available database (BOLD system, Feb 26, 2022), i.