dataesr/scientific-paragraphs-categorization huggingface dataset:
Population genetic structure was also inferred using a Bayesian clustering approach implemented in the program STRUCTURE 2. 3. 4  (Pritchard et al. , 2000). The program was run with five independent runs, using the admixture model with correlated allelic frequencies and the LOCPRIOR model developed by  Hubisz et al. (2009)  to take into account the sampling location of each individual. All simulations used 100,000 iterations in the burn-in phase and 100,000 generations in the data collection phase. Selection of the number of distinct clusters (k) was based on Evanno's DeltaK  (Evanno et al. , 2005)  using the Harvester software  (Earl & vonHoldt, 2012). STRUCTURE analyses were conducted on two partitions of the data: (i) the entire data set to identify those populations that were most related to the tick population on Spitsbergen (n = 418 ticks from 15 populations, testing k = 1-15) and (ii) the three most closely related populations to examine finer scale structure (n = 135 ticks from four populations, testing k = 1-5).
We used the method described by  Garnier et al. (2017)  to determine negative and positive exposure thresholds (for details, see Figure  S2  and https://doi. org/10. 5281/zenodo. 7538438). As anti-Bbsl antibodies are known to persist for several years in seabirds  (Staszewski et al. , 2007) , a seropositive status reflects an exposure event that occurred at an unknown time prior to sampling.
| Borrelia detection Of the 37 tick DNA extracts tested by qPCR, one was found positive for Bbsl infection. After reamplification and Sanger sequencing, we obtained a 379 bp fragment that was used in the blastn search (Genbank accession number ON979421). The top 100 sequences identified in the blast search all corresponded to B. garinii (or a nonidentified Borrelia) with more than 98. 42% identity.
DATA AVA I L A B I L I T Y S TAT E M E N T The data that support the findings of this study are openly available. Microsatellie genotype data are provided in the Supporting Information files (Table  S3 ) and at https://doi. org/10. 5281/zenodo. 7547277. Sequence data has been deposited in the NCBI database (accession number: ON979421). Host serology data and code is available at https://doi. org/10. 5281/zenodo. 7538438.
Cell polarity is an essential and highly conserved process governing cell function. Cell polarization is generally triggered by an external signal that induces the relocation of the centrosome, thus defining the polarity axis of the cell. Here, we took advantage of B cells as a model to study cell polarity and perform a medium-throughput siRNa-based imaging screen to identify new molecular regulators of polarization. We first identified candidates based on a quantitative proteomic analysis of proteins differentially associated with the centrosome of resting non-polarized and stimulated polarized B cells. We then targeted 233 candidates in a siRNA screen and identified hits regulating the polarization of the centrosome and/or lysosomes in B cells upon stimulation. Our dataset of proteomics, images, and polarity indexes provides a valuable source of information for a broad community of scientists interested in the molecular mechanisms regulating cell polarity.
To do so, we individually silenced these proteins with SmartPool siRNA, stimulated B cells with antigen-coated beads for 90 min, fixed and stained the cells for automated fluorescence imaging, and finally assessed centrosome and lysosome polarity using an in-house developed Fiji macro. Some of the selected hits that affected centrosome and/or lysosome polarization were already known regulators of cell polarity from the literature, confirming the robustness of our approach and dataset (Fig. 1 ).
05), we formally identified 1,253 proteins out of a total of 2,915 quantified proteins (Fig. 2a ), among which 1,129 proteins showed an absolute variation ≥10%. 852 of them were found in at least two replicates (Fig. 2b ). We next used an in-silico permutation test to estimate whether this overlap was one could anticipate by chance. We simulated 3 replicates by uniformly sampling 721; 1,092; and 710 proteins (corresponding to the numbers of selected proteins in replicate #1, #2, and #3, respectively, Fig. 2b ) among the 16,547 proteins found in the SwissProt mus musculus (version 20120905) database used to identified proteins following the mass spectrometry analysis. Such a simulation was repeated 100,000 times and the distribution of the numbers of shared proteins between replicates (one-by-one, and between the three) was then computed.
Strikingly, this procedure showed that the numbers of shared proteins observed experimentally (Fig. 2b ) fall into low probability areas (Fig. 2c ), strongly arguing for a non-random overlap between replicates in our dataset. Similar conclusions were obtained using the SuperExactTest function of the R package  13. The final selection of proteins differentially associated with the centrosome for the phenotypic screen was based on the number of replicates in which they were quantified and their extent of variation (See below).
Selection of candidates for the phenotypic screen. In order to maximize the potential discovery of unexpected regulators of centrosome repositioning, differential criteria with relatively low stringency were applied to select candidate proteins. Therefore, we first selected 106 proteins showing consistent variation in the three replicates and a variation of at least ± 10% in one of them. We completed with 72 proteins harboring consistent variation of at least ± 20% in two replicates. To these 178 proteins, 109 proteins that displayed non-consistent variation between replicates or identified only in one replicate were added according to their described function in the literature and a minimal variation of ± 20%, bringing the number of selected proteins to 287 (Fig. 2d ). The functional classification of these 287 proteins was then performed using the UniProtKB database (Fig.
Hit selection. After the calculation of raw polarity indexes for individual cells in each well (as illustrated for plate #3 from replicate #1 in Fig. 4a ), the corresponding normalized polarity indexes were calculated by dividing them by the median value of the control well (B01) per plate and replicate (Norm. PI B01 = 1, Fig. 4b ). Figure  4c  provides an overview of the median of the normalized polarity indexes of all wells in the 4 plates of replicate #1. Normalization per plate and per replicate allowed us to pool, for each candidate, the individual normalized polarity indexes of all cell-bead couples from replicates #1 and #2 before performing hit selection. The median was then recalculated for each candidate on the pooled dataset (Fig. 4d ). Proteins were considered as hits when their silencing significantly decreased or increased centrosome or lysosome repositioning more than one standard deviation (1 SD) from the control condition in the pooled data (Fig.
Data Records The mass spectrometry proteomics raw data files (. raw), the Mascot merged result files (. msf) used for data analysis in myProMS  18  and a table summarizing the full list of proteins differentially associated with centrosome between resting non-polarized and stimulated polarized B cells identified by SILAC-based quantitative proteomics (absolute fold change ≥ 1 AND p-value ≤ 0,05 AND peptide used ≥ 3) have been deposited to the ProteomeXchange Consortium  19  via the PRIDE partner repository  20  with the dataset identifier PXD038725  21. The dataset is organized as such: • 3 excel files (Design PRIDE 20 fractions R1; Design PRIDE 17 fractions R2; and Design PRIDE 30 fractions R3) summarizing the correspondence between the gel slices and the associated raw files per replicate.
Images of the siRNA screen are publicly available in the BioStudies database under the accession number S-BSST897  22  (https://identifiers. org/biostudies:S-BSST897). The data is organized as such: • An excel file (Submission-File_list) providing all the information on each image: Localization in the zip file, Name of the image, Replicate, Plate, Well, Field (position of imaging), Target gene name, NCBI gene ID, Description (control or target), QC info, Channels identity. • A zip file containing all the images, named as listed in the excel file Submission-File_list. Images are organized in folders per replicate and per plate. The identity of each image file is specified in its name, according to the Submission_File-list. For example, Rep3-Plate4-Well_B01-Pos8 corresponds to the Replicate #3, Plate 4, Well B01, position 8, i.
Code availability Annotated macros used to analyze the screen are available on GitHub: https://github. com/Mathieu-Maurin/ screening_B_cell_polarization. The files are organized as such: -README. md: This file contains brief instructions on how to use the macros. -Macro_1_cell_bead_couple_extraction: The purpose of this macro is to extract bead/cell couples for analysis. It works on composite images reconstructed with the following channels: Channel 1: gamma-Tubulin; Channel 2: Dapi; Channel 3: Lamp1; Channel 4: Fluorescent beads. -Macro_2_polarity_analysis: The purpose of this macro is to determine centrosome and lysosome raw polarity indexes for each bead/cell couple extracted by Macro_1. -Macro_3_normalise_and_concatenate_tables. The purpose of this macro is to normalize data and concatenate tables.
acknowledgements The authors thank Adrian Isaacs (UK Dementia Research Institute at University College London) for kindly providing the Chmp2b -/-mice, Emeline Perthame (Bioinformatics and Biostatistics HUB at Institut Pasteur) for having performed the permutation test, and Michael Richard (CurieCoreTech Mass Spectrometry Proteomics at Institut Curie) and Patrick Poullet (INSERM U900, Institut Curie) for having performed the analysis using the R package SuperExactTest. We acknowledge the members of the Lennon-Duménil team and Elaine Del Nery for fruitful discussions.
Results A total of 90 grey literature reports and 22 publications were included out of 342 reports and 1753 publications initially screened (Fig. 1 ). The final data set included information for 77 countries. Grey literature data came from subnational, national or multinational reports, the last of which included the Surveillance Atlas of Infectious Diseases published by the European Centre for Disease Prevention and Control (ECDC), Sistema de Redes de Vigilancia de los Agentes Responsables de Neumonias y Meningitis Bacterianas (SIREVA II) and World Health Organization (WHO) Meningitis Weekly Bulletins: Inter country Support Team -West Africa. Publicly available data varied substantially across countries and global regions. Data are reported across the decade and for the final 3 years analysed (2017-2019) to summarise recent trends.
5. 1b. Gene expression quantification was computed with RSEM and Kallisto (version 0. 46. 0) in transcript per million (TPM) and count per million (cpm), respectively. We also performed analyses on previously published TEC RNA-seq data (GEO: GSE 138494, (15)) and publicly available single-cell TEC RNA-seq data (ArrayExpress: E-MTAB-8560, (31)). Reads were pseudo-aligned using Kallisto on the murine reference genome (mm10). Read count normalization, log transformation, dimensionality reduction, and data visualization were performed using the scatter (version 1. 18. 16) and scran (version 1. 18. 7) packages.
Immunohistology and immunofluorescence Thymi were fixed in 10% formalin, embedded in paraffin, and sliced into 4-mm-thick sections. Central sections were stained with H&E and scanned with a NanoZoomer Digital Pathology system (Hamamatsu) with a magnification of 40X. We analyzed the images using the NDP. view software (version 2. 7. 52, Hamamatsu). For immunostaining, sections were de-paraffinized, rehydrated, and treated with BOND Epitope Retrieval Solution 2 (an EDTA-based pH 9 epitope retrieval solution) in Bond RX Stainer (Leica Biosystems, Buffalo Grove, IL, USA). Sections were stained with primary antibodies, followed by staining with secondary antibodies and DAPI (Life Technologies). A wash step with PBS preceded all staining steps. The omission of primary antibodies and their replacement by isotype were used as controls.
Specifications of primary and secondary antibodies are described in Supplementary Table  1. Klf4 images were acquired with a Leica SP8 confocal microscope (Leica Microsystems, Wetzlar, Germany) with 40X and 63X objectives and analyzed using LAS-X (Leica Microsystems, Wetzlar, Germany) software. Cell populations defined by K8, K5, VIM, and PDPN staining were quantified using the software QuPath (version 0. 3. 2; Queen's University Belfast; Northern Ireland). Eighteen images with x20 magnification of the cortex, the cortico-medullary junction (CMJ), and the medulla were acquired from two KO and LOX mice. 8-bit channel-based images were exported and stacked in Fiji [(http://fiji. sc/ (32) ]. VIM-K8 and PDPN-K8-K5 co-expression was assessed by performing the QuPath cell detection algorithm based on the DAPI channel, and automated quantification of the underlying staining intensity of VIM, PDPN, K5, and K8.
Identification and analysis of differentially expressed genes (DEGs) DEGs between KO and LOX females were identified using the limma package with the voom function and the treat method in R software with thresholds of FC ≥ 1. 5 and p-value ≤ 0. 05. We only kept genes with expression higher than one cpm in at least three samples. Gene ontology (GO) enrichment analysis was performed using g:Profiler (version e106_eg53_p16_65fcd97) with g:SCS multiple testing correction method applying a significance threshold of 0. 05 [https://biit. cs. ut. ee/gprofiler/gost,  (33) ]. Gene Set Enrichment Analysis (GSEA) of DEGs was performed with the fgsea package in R. Genes were ranked based on the fold changes obtained with the treat method from the limma package. The enrichment score was calculated from the genesets of Epithelial cell differentiation markers and Hallmark Epithelial-Mesenchymal Transition from GSEA (http://www.
Furthermore, Klf4 transcripts showed the highest expression of the five top-ranked TFs for both cTECs and mTECs (Figure  1B ; Supplementary Table  2 ). Expression of Klf4 showed the most considerable variations between the end of gestation and D28 post-delivery in cTECs (Supplementary Table  2 ). As TECs are a highly heterogeneous population, we asked whether a single subtype of TECs highly expresses Klf4. Using publicly available scRNA-seq data (31) of TECs from 16 weeks aged females, we analyzed Klf4 expression in different TEC subtypes. Although very few cells were available for rare TEC subsets (e. g. , 11 cells for tuft-like mTEC), we found that a proportion of at least 35% of cells expressed Klf4 (Supplementary Figure  1A ). Previous reports have emphasized the importance of Klf4 for epithelial homeostasis in many tissues like the lung, skin, and intestine using a conditional deletion  (19, 20, 22).
Downregulated DEGs showed enrichment for motifs specific to FOXN1, TP63, and ZNF750 (Figures  4F, G ; Supplementary Table  4 ). ZNF750 controls epithelial homeostasis by inhibiting progenitor genes while inducing differentiation genes  (35). FOXN1 is a well-known key transcription factor for TEC differentiation  (36, 62). TP63 blocks TEC proliferation and is essential for the development of the thymic epithelium, and the p63-FoxN1 regulatory axis regulates postnatal TEC homeostasis  (63) (64) (65) (66). Besides, both ZNF750 and FOXN1 are TP63 gene targets based on ChEA3 databases, and Klf4 is a ZNF750 target gene in skin ECs  (35). Hence, genes under expressed in KO TECs show a strong enrichment for binding motifs of TF regulating both cTEC and mTEC differentiation. Lastly, we performed a gene set enrichment analysis (GSEA) on genes under expressed in TEC from KO females using a list of epithelial cell differentiation markers.
Acknowledgments We thank  Isabelle Caron ,  Sebastien Harton ,  E ́ric Bouchard  ( Animal care facility ),  Annie Gosselin ,  Angeĺique Bellemare-Pelletier  (Flow cytometry),  Jennifer Huber ,  Raphaelle Lambert  ( Genomics core facility ),  Meĺina Narlis ,  Nicole Charbonneau  (Histology core facility), and  Christian Charbonneau  (Microscopy images aquisition) for their technical assistance. We thank  Dr. Yusuke Takahama  and the  Mutant Mouse Resource & Research Centers (MMRRC)  for kindly providing the Psmb11-Cre knock-in mice and the floxed Klf4 mice, respectively. Finally, we thank  Anca Apavaloaei  and  Nandita Noronha  for their help with the GSEA analyses.
1 2 12 FIGURE 1 Klf4 is highly expressed and regulated in TECs. (A) Pipeline selection for identifying TFs with binding motifs enrichments among genes differentially expressed at maximal thymic involution during late pregnancy (LP) compared to day 6 (D6) post-partum in both cTECs and mTECs ( (15), GSE138494). (B) Expression of five top-ranked TFs expression (TPM, transcripts per million) from TFEA analysis in both cTEC and mTEC during late pregnancy. TFs are ordered from the lowest to the highest MeanRank score on the barplot. Mean TPM was retrieved from previously published RNAseq data (accession number GSE 138494). (C) Klf4 expression (TPM) in cTECs and mTECs from non-pregnant controls (NPC) and during late pregnancy (LP). (D) Klf4 expression (TPM) in cTECs and mTECs from delivery (D0) to D28 post-delivery.
Statistical analysis The continuous variables were expressed as median with its interquartile range (IQR). For continuous variables, the Mann-Whitney test was used to compare unpaired data set-points and the Wilcoxon matched-pairs signed-rank test for paired values. For categorical variables, Fischer's exact test was used. To assess the viral decay of total HIV-1 DNA, the kinetics was evaluated by calculating the difference between the viral loads at the different set points for each patient. For the early group, the difference was calculated between treatment initiation (at the time of diagnosis in PHI) and at one year after. For the deferred group, the difference was calculated between diagnosis and treatment initiation and between treatment initiation and at one to two years after for each patient.
Genetic association analysis The association study was performed using all SNPs, considered independent loci, or using alleles, considering the gene loci from which SNPs are derived, as independent loci. For the latter, the most likely haplotypes were reconstructed using DNAsp (v6. 12. 03)  (Rozas et al. , 2017) , using default parameters and 1,000 Markov chain Monte Carlo iterations. Then, we obtained two genotype datasets using either the SNPs or the deduced alleles. The basic genetic diversity indices were obtained using the online version of Genepop  (Rousset, 2008) software (v4. 7. 5). This software was also used to assess eventual deviations from Hardy-Weinberg equilibrium (F IS and F ST ) and highlight the most contributing SNPs or alleles to the population/group structure. Further statistical analyses were performed.
Genotyping and genetic basis indices A total of 20 candidate SNPs linked to the immune system were investigated in 36 standard rats and 33 DCS-resistant rats. In these 69 Wistar rats, 16 primers gave rise to amplifications and eight sequences were heterozygous. The only six primers associated to MyD88 and NFKB1 revealed a diversity between resistant and standard rats (Supplementary Data S2) and deserved to be studied in this work. Considering the low number of individuals in these populations but faced with the need to characterize them, we only briefly carried out population genetic statistics. Therefore, rs1074255550 (MyD88_50), rs106151549 (MyD88_49), rs198397997 (MyD88_97), rs19813385 (NFK_85), rs197284969 (NFK_69), and rs197247545 (NFK_45) Fis estimates were calculated, independently or not, i.
Involvement of SNPs at the transcript level We researched the Ensembl database  (Cunningham et al. , 2021)  for rats and found that a permutation on MyD88_50 (C>T) or MyD88_49 (C>A) has consequences on the 3′ untranslated transcribed region (3′ UTR) regulating the expression of MyD88, found behind the STOP codon. Concerning the Myd88_97 variant, a nucleotide change (T>C) affects the regulatory region 5′ UTR, which is a segment of the messenger RNA (mRNA) placed upstream of the initiation codon. Please note that this SNP is only described in the context of the ENSRNOT00000096753. 1 transcript of the MyD88-204 isoform containing 119 amino acids. The structure of the A0A8I5ZSW8_ RAT protein remains poorly described at present. NFKB1 has two protein forms, namely, NFKB1-201 of 970 amino acids (aa) and another shorter NFKB1-202 of 1,011 aa, both of which will undergo modification in their expression level if the 3′UTR zone undergoes a permutation in NFKB_45 (C>T).
Concerning the SNP NFKB_69, the substitution (missense) of the nucleotide C by T leads to the replacement of an aspartic acid by an asparagine at position 824/970 (or 865/1,011), and this is in the folding between two alpha helices (Figure  7B ) according to the model of AlphaFold  (Consortium, 2022)  F1LGH2 of 970 aa. This substitution is located in the p105 death domain (DD)  (Park et al. , 2007)  according to the protein summary of the Ensembl database. After uploading the sequence to the AlphaFold website  (Jumper et al. , 2021) , the variation was submitted in Missense3D  (Ittisoponpisan et al. , 2019)  which did not detect structural damage on the general model (af-f1lqh2-f1-model_v4; residue ID:827; Variant Asp > Asn) nor on a more specific p105 model focused on the death domain (af-q63369-f1-model_v4; residue ID:376; Variant Asp > Asn).
This finding highlights the major need for rapid seeding of fresh tissues on an agar media to enable the growth of this fastidious bacterium from human samples  (34). Serologic testing, when performed, identified mostly high titers in MAT, ELISA, or IFA, confirming Francisella infection, but was performed after the initial diagnosis. The identification of 1 strain was suspected on the basis of results of the Vitek 2 GN assay. MALDI-TOF mass spectrometry enabled identification of 2 strains but did not provide subspecies distinction  (35) (36) (37). F. tularensis has been identified using the in vitro diagnosis database of the Biotyper mass spectrometer since 2017 (partial integration of 5 species of the security-relevant library as a library extension) only if the extension was added to the database by the user.
In contrast, the Vitek MS database does not contain any F. tularensis spectra. In 7 of 8 cases, the strain was identified or confirmed by molecular methods (Table  1 ). Because of the high level of 16S rDNA similarity among the Francisella species (98. 5%-99. 9%), 16S rDNA amplification and sequencing enables identifying the bacterium only to the genus level  (38). Species or subspecies determination requires PCR targeting specific genes, such as ISFtu2, 23kDa, tul4, or fopA  (39). Some of those targets, however, might cross-react with F. novicida, F. philomiragia, or Francisella-like endosymbionts. Additional techniques are necessary to identify F. tularensis subspecies, such as PCR targeting a junction between ISFtu2 and a flanking 3′ region  (18) , identification on the basis of size differences of amplified DNA products (Ft-M19, ISFtu2, RD1, and pdpD-2 assay)  (17, 40) , and amplification and sequencing of the 16S-23S rRNA intergenic spacer region  (16).
gondii marker gene, BAG1, exclusively expressed in bradyzoite stages  (43) , showed no upregulation in our data. PRF, essential for gliding motility, invasion and exit from host cells  (44) , shows increased expression in the late stages of T. gondii tachyzoites. MIC2 and SAG1, two other T. gondii genes expressing proteins essential for invasion, virulence and host cell binding  (45, 46) , showed expression across all infection conditions, peaking at early time points (Figure  1C ). The observed expression of these established host and T. gondii marker genes in all cells of their respective condition justifies the number of cells used here, to investigate host-pathogen interactions. For each infected cell, we retrieved expression data from the host and the parasite. We then isolated expression values of the host and the parasite to analyze them individually.