dataesr/scientific-paragraphs-categorization huggingface dataset:
d RNA-seq, single-cell RNA-seq, and CITE-seq data have been deposited at GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. The mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium via the PRIDE partner repository and datasets identifier is listed in the key resources table. Original western blot images, flow cytometry data, mass spectrometry data, and microscopy data reported in this paper will be shared by the lead contact upon request. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
ACKNOWLEDGMENTS We thank the patients and their families, all members of the laboratory,  Stephen Elledge  (for the  PhIP-Seq phage display viral library ), and  Jose Mauricio Ayala ,  Veronique Cholette , and  Rebecca Bellworthy. The  Laboratory of Human Genetics of Infectious Diseases  is supported in part by grants from  Inserm ,  Paris Cite  ´University, the  St. Giles Foundation , The  Rockefeller University , the  Center for Clinical and Translational Science  ( UL1TR001866 ), the  National Center for Research Resources  and the  National Center for Advancing Sciences , the  National Institutes of Health (NIH) , ( R01AI095983 ,  R01AI088364 ,  R01AI163029 , and  U19AI162568 ), the  National Institute of Allergy and Infectious Diseases , the  French National Research Agency (ANR)  under the '' Investments for the future'' program  ( ANR-10-IAHU-01 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  French Foundation for Medical Research (FRM)  ( EQU201903007798 ), the  ANRS Nord-Sud  ( ANRS-COV05 ),  ANRS  ( ECTZ170784-ANRS0073 ),  GENVIR  ( ANR-20-CE93-003 ),  GENMSMD  ( ANR-16-CE17-0005-01 ),  AABIFNCOV  ( ANR-20-CO11-0001 ),  GenMIS-C  ( ANR-21-COVR-0039 ),  SUNLIVE  ( ANR-19-CE15-0012-01 ),  MAFMACRO  ( ANR-22-CE92-0008 ) grants,  Ecos-NORD  ( ECOS N C19S01 ), the  Fisher Center for Alzheimer's Research Foundation , the  Meyer Foundation , the  JPB Foundation , the  European Union 's  Horizon 2020 research and innovation program  ( 824110 ,  EASI-Genomics) , the  Square Foundation, Grandir-Fonds de solidarite´pour l'Enfance , the  Fondation du Souffle , the  SCOR Corporate Foundation for Science , the  French Ministry of Higher Education, Research, and Innovation  ( MESRI-COVID-19 ), and  REACTing-INSERM.
The  Laboratory of Virology  and  Infectious Disease  was supported in part by the  NIH  ( R01AI091707-10  to C. M. R. ). J. L. P. was supported by the  Francois Wallace Monahan Postdoctoral Fellowship at The Rockefeller University  and the  European Molecular Biology Organization Long-Term Fellowship  ( ALTF 380-2018 ). N. Marr  was supported by  Sidra Medicine  and the  Qatar National Research Fund  ( NPRP9-251-3-045 ). The  Yale Center for Mendelian Genomics  ( UM1HG006504 ) was funded by the  National Human Genome Research Institute , the  Yale GSP Coordinating Center  ( U24 HG008956 ), and the  Yale High-Performance Computing Center  ( S10OD018521 ). This research was partly supported by  Calcul Que´bec, Compute Canada Canadian Institutes of Health Research (CIHR)   Project Grant  to  D.
A. -L. N. was supported by the international PhD program of the  Imagine Institute , by the  Bettencourt Schueller Foundation  and the fin de the`se  FRM program  ( FDT202204015102 ). R. Y. was supported by the  Sackler Center for Biomedicine and Nutrition , the  Shapiro-Silverberg Fund for the Advancement of Translational Research at the Center for Clinical and Translational Science of the Rockefeller University , and the  Research Grant Program  from the  Immune Deficiency Foundation. D. Lee. was supported by  FRMfellowship  ( FDM202006011282 ). C. S. M was supported by an  Early-Mid Career Research Fellowship  from the  Department of Health of the New South Wales Government of Australia. S. G. T was supported by an  NHMRC   Leadership 3 Investigator Grant  ( 1176665 ) and  NHMRC  grant ( 1113904 ).
( A) Cells involved in the production of and response to IFN-g. Proteins for which a mutation of the corresponding gene has been recognized to cause isolated MSMD are depicted in blue, those responsible for syndromic MSMD are shown in red, and those that can cause either isolated MSMD or syndromic MSMD are depicted with crossed lines. (B) Relative clinical severity of isolated MSMD forms vs. relative residual IFN-g activity. (C) Principal component analysis of WES data from the two patients and our in-house WES database. (D) Consensus negative selection (CoNeS) of IRF1. (E) Diagram of the various IRF1 cDNA constructs used for transient overexpression experiments. (F) RT-qPCR on HEK293T cells without (NT) and with transfection with empty vector (EV) or various IRF1 cDNAs. The bars represent the mean.
M85A-DDK-F Eurofins CAACTTTCGCTGTGCCGCGAACTCCCTG CCAGAT IRF1-p. M85A-DDK-R Eurofins ATCTGGCAGGGAGTTCGCGGCACAGCG AAAGTTG IRF1-p. M111A-DDK-F Eurofins GCGAGTGTACCGGGCGCTTCCACCTCTC IRF1-p. M111A-DDK-R Eurofins GAGAGGTGGAAGCGCCCGGTACACTCGC IRF1-p. Q35*-F Eurofins GAGGAGATGATCTTCTAGATCCCATGGAAGC IRF1-p. Q35*-R Eurofins GCTTCCATGGGATCTAGAAGATCATCTCCTC IRF1-p. R129*-F Thermo Fisher Scientific GTCGAAGTCCAGCTGAGATGCTAAG IRF1-p. R129*-R Thermo Fisher Scientific CTTAGCATCTCAGCTGGACTTCGAC IRF1-p. R129-DDK-F Thermo Fisher Scientific GCTGGACTTCGACTTTCTTTCTTTTCTCTG IRF1-p. R129-DDK-R Thermo Fisher Scientific ACGCGTACGCGGCCGCTCGA TaqMan Fast Universal PCR Master Mix (2X), no IRF1-p. W11R-DDK-F Thermo Fisher Scientific Thermo Fisher Scientific Cat# 4352042, GCATGAGACCCCGGCTAGAGATG AmpErase UNG IRF1-p. W11R-DDK-R Thermo Fisher Scientific CATCTCTAGCCGGGGTCTCATGC Deposited data IRF1-D7-8-F Thermo Fisher Scientific CTGGAGTCAGGGCCTGCTCC RNA sequencing on primary fibroblasts IRF1-D7-8-R This manuscript Thermo Fisher Scientific gene expression omnibus (GEO: GSE218033) CTCTTGGAGCAGTCGGAGTGGC RNA sequencing on iPSC-derived macrophages IRF1-D7-8-DDK-F This manuscript Thermo Fisher Scientific gene expression omnibus (GEO: GSE218033) TTGAGTAGGTACCCCTTCCCATCCACGTTTG RNA sequencing on monocyte-derived macrophages IRF1-D7-8-DDK-R This manuscript Thermo Fisher Scientific gene expression omnibus (GEO: GSE218033) ACGCGTACGCGGCCGCTCGA Mass spectrometry on primary fibroblasts IRF1-p.
A67P-DDK-F This manuscript Thermo Fisher Scientific ProteomeXchange (PXD037759) GCCGATACAAACCAGGGGAAAAG scRNAseq on cryopreserved PBMCs IRF1-p. A67P-DDK-R This manuscript Thermo Fisher Scientific gene expression omnibus (GEO: GSE216489) CTTTTCCCCTGGTTTGTATCGGC Experimental models: Cell lines IRF1-p. M1_A84del-F Eurofins ATGAACTCCCTGCCAGATAT HEK293T cells IRF1-p. M1_A84del-R ATCC Eurofins Cat# CRL-11268, RRID:CVCL_1926 GGCGATCGCGGCGGCAGATC THP1 WT IRF1 retrovirus F Song et al. 87 Thermo Fisher Scientific ATCC Cat# TIB-202, RRID:CVCL_0006 GATCCATTTAAATTCGAATTCATGCCCATC THP1 IRF1 KO Song et al. 87 N/A ACTCGGATGCGC THP1 IFN-gR1 KO clone 30 IRF1 retrovirus R This manuscript Thermo Fisher Scientific N/A ATCGATACCGTCGACCTCGAGTTAAACCT THP1 STAT1 KO clone 13 This manuscript N/A TATCGTCGTCATC iPSC healthy control clone 11 sgRNA IFNGR1 exon 3F Ackermann et al.
d RNA-seq, single-cell RNA-seq, and CITE-seq data have been deposited at GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. The mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium via the PRIDE partner repository and datasets identifier is listed in the key resources table. Original western blot images, flow cytometry data, mass spectrometry data, and microscopy data reported in this paper will be shared by the lead contact upon request. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. d This paper does not report original code. d.
1% Tween20 and 10% milk, before being washed twice in 1x TBS with 0. 1% Tween20 and probed with primary antibodies (1/10,000) in 1x TBS with 0. 1% Tween20 and 5% milk overnight at 4˚C. After a further four washes in 1x TBS with 0. 1% Tween20, membranes were incubated with anti-rabbit secondary antibody (1/10,000) for 90 min, then washed four times in 1x TBS with 0. 1% Tween20. Clarity Western ECL Substrate (BIO-RAD) was used to detect the proteins by chemiluminescence using Fujifilm LAAS 4000 and software Image Reader (Fuji).
Methods PsABio (NCT02627768) was a multinational, prospective, real-world, observational cohort study of patients with PsA who started ustekinumab (an IL-12/IL-23 inhibitor) or a TNFi as first-, second-or third-line bDMARD treatment. The study was designed to evaluate the persistence, effectiveness and tolerability of ustekinumab and TNFi. The study design, patient population and evaluations have been described elsewhere  [21, 22]. Data were collected at baseline, then every 6 months up to 3 years, with a window of ±3 months to align with standard clinical practice. In addition to the main statistical analysis, exploratory analyses were performed on various patient subgroups. In the analysis presented here, male and female patients were compared for disease activity, patientreported outcomes and treatment persistence.
The Baseline set included all eligible patients with baseline data and without major protocol deviations. The Safety set included all patients with baseline data and an additional 3 female and 2 male patients excluded from the baseline set (no valid baseline assessment [within 62 days prior to bDMARD start]) and any available follow-up data included in the 36-month data analysis. The effectiveness set-1 included all eligible patients from the Baseline set with any effectiveness follow-up data up to 12[+3] months (hereafter referred to as 12 months). The 'remainer' data analysis is based on a previously obtained effectiveness set that included two patients less, referred to as effectiveness set-2. Data on these two patients were not yet available at the time of previous analyses  [21, 22]. 'Remainer' patient groups included all patients who remained on the initial treatment (ustekinumab or TNFi) and had a selected Month 12 visit (defined as a visit that took place within the Month 12±3 visit window).
A rating of 0 (none/no difficulty/very well) to 10 (extreme/extreme difficulty/very poorly) is given for each question. Data were also collected for the following variables: the presence of dactylitis, enthesitis, nail psoriasis and psoriasis skin involvement (BSA) according to four categories (clear/almost clear skin, 10%) and the EuroQol-5 Dimension (EQ-5D; descriptive across five dimensions [mobility, self-care, usual activities, pain/discomfort, anxiety/depression]) and Fibromyalgia Rapid Screening Tool (FiRST; 6 fibromyalgia items, 5/6 indicates fibromyalgia presence) Partially missing dates were imputed for analysis. These included start and stop days of previous treatments or of treatments within the study, laboratory sample dates and other dates (if incompletely known, day and/or month were imputed), the BASDAI and the PSAID-12 missing item scores were imputed according the recommendations of the developers of the scales.
Conclusion These real-world data from PsABio on gender differences suggest that, at the start of biologic treatment, females have a worse clinical picture of PsA than males. Although treatment improvements were seen in both genders, a lower percentage of women reached a favourable disease state of low or minimal disease activity at 1 year, and more women stopped/switched biologic due to both lower effectiveness of the treatment and AEs. A better understanding of the mechanisms underlying these differences may improve therapeutic management in females with PsA. a Safety set included all patients who had baseline data and an additional 3 female and 2 male patients excluded from baseline set (no valid baseline assessment [within 62 days prior to bDMARD start]); b AEs do not include neoplasms unless stated.
After incubation, the dye 7-Aminoactinomycin D (7-AAD, Sigma) was added to the cells to quantify the % of K562 dead cells by flow cytometry (BD LSRFortessa). To quantify NK cell cytotoxic activity, the percentage of lysis of K562 cells by NK cells was obtained by the percentage of 7-AAD positive cells among CFSE positive K562 cells. CFSE-unstained K562 cells and CFSE-stained K562 cells, both without NK cells, in the presence or not of 7-AAD addition, were used as non-NK cell containing controls. Data were analyzed with the FlowJo (BDBiosciences) software.
CD107a degranulation assay NK cells were mixed with K562 cells at a 1:1 ratio for a coculture of 18h at 37°C. Thereafter, the cells were spun down and stained for 30 min on ice with fluorochrome-conjugated monoclonal antibodies anti-CD56-APC (BD Biosciences) and anti-CD107a-FITC (BD Biosciences) in staining buffer composed of phosphate-buffered saline (PBS) supplemented with 0. 5% of bovine serum albumin (BSA) and 2mM of ethylene diamine tetraacetic acid (EDTA) (Invitrogen). Cells were washed twice with PBS and fixed in 4% paraformaldehyde (PFA). To quantify degranulation by NK cells, the percentage of CD107a-positive cells among CD56 + cells was measured by flow cytometry. Data were analyzed with the FlowJo (BD Biosciences) software.
CD107a surface expression on NK cells NK cell degranulation following stimulation can be quantified by determining their surface expression of the lysosomeassociated membrane protein-1 (LAMP-1/CD107a) (25). Herein, the effects of 3mM CaCl 2 , ALG-CM, or BIA-CM were studied on CD107a expression by NK cells after incubation with K562 at a 1:1 ratio during 18h. Compared to the control medium, CaCl 2 solution and BIA-CM had no effect on CD107a expression among NK CD56 + , whereas CD107a expression was significantly increased in the presence of ALG-CM, especially on CD56 dim subpopulation, as shown in Figure  2. These results demonstrate that the conditioned medium with the pure alginate dressing ALG induces the activation of NK cells by increasing their degranulation. To note, no effect of CMC addition to ALG-CM or control CM was detected on CD107a expression (data not shown).
Lymphocyte activation marker CD69 on NK cells is up-regulated in the presence of exogenous Ca 2+ and ALG-CM Having observed an increase in NK cell-mediated cytotoxic activity in the presence of exogenous CaCl 2 solution and ALG-CM, the expression of NK cell-activating or inhibitory receptors was further studied on NK cells. Both CD56 dim and CD56 bright NK cell subsets were analyzed for their membrane phenotype. The main modification observed under exogenous CaCl 2 solution and ALG-CM was the increase in the expression of the CD69 activator receptor within the CD56 dim NK cells, as shown in Figure  3 (A-B , upper and lower left side). The results did not show any significant changes in the expression of the epitopes NKG2D and NKp30 (Figure  3 , A-B, upper and lower middle, and right side) or of the other membrane receptors analyzed (data not shown).
Flow cytometry and Cytometric Bead Array (CBA) Immunophenotyping of cells was performed with fluorochrome-conjugated antibodies, and intracellular staining was performed after the fixation and permeabilization (ThermoFisher). Cells were labelled with fixable viability dye prior to fixation, followed by at least 3 hours of intracellular staining with the following anti-transcription factor antibodies; Foxp3-PeCy7, T-Bet-PE, Rorɣt-BV650 and Gata3-APC. Cytokine production (IFN , IL-4, IL-17A, IL-10) was also assessed by Cytometric Bead Array (CBA) Kit (BD Biosciences) on supernatants collected at day 3 of polarization. Data analysis was performed using FlowJo (Tree Star software) and FCAPArray Software (CBA analysis).
Detection of differential methylation Data processing and statistical analyses were performed using R/Bioconductor (R version 4. 0. 3). Bed files containing modified cytosine information were transformed into a BSseq object for differential methylation analysis with dispersion shrinkage for sequencing data (DSS) as previously described  4, 16. DSS tests for differential methylation at single CpG-sites were evaluated using a Wald test on the coefficients of a beta-binomial regression of count data with an 'arcsine' link function. Differentially methylated regions (DMRs) were defined as those loci with at least 3 CpG sites within a distance of less than 50 bp, and with changes in > 50% of all CpG sites exhibiting p value < 0. 05. Differentially methylated loci (DML) and DMR locations are available via GitHub (https://github.
com/hernandezvargash/Tcell. 5mC. ID ). DMRs were plotted using the plotDMRs function of the dmrseq R package  17, 18. Off-targets were analysed for transcription factor (TF) activity using the cistrome information associated with the MIRA R package  19. After extracting all genomic binding regions corresponding to selected TFs, MIRA functions were used for aggregation of DNA methylation data and visualization of TF activity.
Classification of Th cell subsets using DNA methylation (5mC) To basecall raw reads and detect modified bases, we used the latest high performing tool, Megalodon  15  (https://github. com/nanoporetech/megalodon) combined with methylation models of the "Remora" repository. Megalodon is a command line tool that works by extracting high accuracy modified base calls from raw nanopore reads and anchoring the information rich basecalling neural network output to a reference genome (mm10). Global patterns of methylation were compared between different Nanopore sequencing runs and no differences were detected (Supplementary figure  2A ). Conversely, there were significant differences between global 5mC distribution of CD4 Th cell subsets (Supplementary figure  2C ); Th0 v Th2 (p<2. 2e-16), Th0 v Th17 (p = 0.
00086) and Th1 v Th2 (p<2. 2e-16), Th1 v Th17 (p=0. 0083) and Th17 and Th2 (<2. 2e-16). Interestingly, the only cell subsets where no global difference was detected were Th0 v Th1 (p=0. 45). These data could be explained in part by the genetic background of the mouse line which are known to be prone to develop certain Th cells. Nevertheless, these data highlight the potential specificity of methylation landscapes for different Th cell types. Differentially methylated loci (DMLs) between each subset were determined, resulting in the identification of 573 significant (P<0. 01) DMLs with greater than 10% difference (Supplementary Table  4 ). The number of distinct DMLs identified in Th1, Th2, Th17, and Treg cells when comparing to Th0 cells, were 59, 169, 103, and 79, respectively. Furthermore, a comparison of Th0 cells with Th1/17 cells identified 74 DMLs while 89 DMLs were identified between Th17 and Th17-noTGFβ cells, highlighting a potential effect of TGFβ alone on Th epigenetic identity.
DNA methylation in transcription factor binding sites reveals their activity in Th cells In addition to targeted regions, Nanopore sequencing captures around 1GB of 'off' target reads in each sequencing run. To strengthen the identification of Th cell subsets using DNA methylation, we determined the DNA methylation status of transcription factor (TF) binding sites in 'on' and 'off' target reads with Bioconductor package MIRA (Methylation-based inference of regulation activity). MIRA aggregates the DNA methylation in all known binding sites for chosen transcription factors. TF's were chosen matching the known transcriptome orchestrators of Th cell subtypes Tbx21 and Stat1 (Th1); Gata3 (Th2), Stat3 and Runx3 (Th17), Foxp3 (Treg)  25, 26. Importantly, Bcl6 is a TF for T follicular helper cells  26  and was used as a negative control for target Th cells.
Using the most recent genome assembles for A. albopictus 54 (AalbFP1. 0) and A. aegypti  55  (AaegL5. 0) (A. malayensis genome has not been assembled), we mapped reads from A. albopictus and A. malayensis and calculated DEGs between infected samples and time-matched non-infected controls. To select the best genome approach and validate DEGs, we quantified expression levels of several genes at 4 dpi by RT-qPCR in midguts of separate mosquito batches for both species. For both A. albopictus and A. malayensis, DEGs obtained by mapping on the A. albopictus genome correlated best with qPCR data (Fig. S2  and S3 ) and were further analyzed. Of note, A. malayensis genes do not have IDs and hereafter, are identified from A. albopictus orthologs. To identify similarities in the transcriptomes between our different conditions, we performed a principal component analysis (PCA) on normalized gene expression values (Fig.
54  (Table  1 ; Table  S3 ). A majority of the immune genes identified in the A. albopictus genome were detected in our RNA-seq dataset (463 out of 664; not including JNK-related genes). Interestingly, most of the PRRs (GNBPs and PGRPs), all the components of the cytoplasmic signaling of the immune path- To identify patterns in the immune response to CHIKV infection at 1 and 4 dpi in A. albopictus, we clustered and plotted the expressions of the 50 most variable immune genes in a heatmap (Fig. 2 ; Table  S3 ). We detected 5 clusters. Cluster 1 contained only one C-type lysozyme that also belonged to the significant DEGs identified above and was highly upregulated (> 77 fold) at both time points. Cluster 2 was made of five genes only downregulated at 4 dpi and included two PRRs (one PGRP and one GNBP), two proteases involved in the regulation of the immune signaling (one CLIP and one SRPN), and one LYS.
malayensis midguts. Activation of the TOLL, IMD, JAK/STAT and JNK pathways occurs through transcriptional regulation of their components in response to infection  34, 43, 73, 74. Interestingly, the JNK pathway appeared to be induced in both mosquito species and upon both viral infections. Two Kayak-like homologs were significantly upregulated (i. e. , DEG) at 1 dpi with CHIKV in both mosquito species and at 4 dpi with DENV in A. malayensis. In A. aegypti salivary glands, Kayak was previously shown to be induced by both CHIKV and DENV and to be co-regulated with Basket, one of the upstream components of the JNK pathway  34. Supporting the activation of JNK, we also  TEPs. Although our transcriptomic data indicate an activation of the JNK pathway, other immune pathways appeared induced as well.
For instance, the induction of a Relish homolog together with the reduction of its inhibitor Cactus among the most regulated immune genes in A. malayensis at 1 dpi with CHIKV suggest the activation of the Toll pathway. The induction of Ikk-β in DENV-infected A. malayensis at 1 dpi show activation of the IMD pathway  75. However, while components of several immune pathways were regulated, the activation of the JNK pathway was consistent in both A. albopictus and A. malayensis upon infection by CHIKV and DENV. In conclusion, we conducted a high-throughput transcriptomic analysis in two Aedes vectors with DENV and CHIKV at two early time points in midguts. Our dataset provides a multidimensional picture of the transcriptomic and more specifically immune regulations in these conditions. In A. albopictus, we identified different gene regulation clusters, which may represent different immune pathway responses to CHIKV infection in midguts.
Acknowledgements We thank the  Genome Biology Facility at Duke-NUS Medical School  for the RNA sequencing. Support for this research came from a  Ministry of Education (Singapore)   Tier3 grant  ( MOE2015-T3-1-003 ) to RMK and JP, a  National Medical Research Council (Singapore)   ZRRF grant  ( ZRRF/007/2017 ) to JP, an  Agence Nationale de la Recherche (France)  grant ( ANR-20-CE15-0006 ) to JP, a  Singapore Infectious Disease Initiative grant  ( SIDI/2017/008 ) to IHM, and the  Duke-NUS Medical   School Signature Research Programme  funded by the  Agency for Science Technology and Research (A*Star Singapore).