dataesr/scientific-paragraphs-categorization huggingface dataset:
Treg To explore possible mechanisms that could explain the enrichment of Penk mRNA in Treg cells, we examined the Immuno-Navigator dataset to extract the genes that are most correlated with Penk and with each other (Figure  2A ). Among these genes, we observe several members of the TNFR superfamily (Tnfrsf1b (TNFR2), Tnfrsf4 (OX40), Tnfrsf9 (4-1BB), Tnfrsf18 (GITR)) as well as the transcription factor Batf. Furthermore, Batf expression is highly correlated with Penk in Treg (R=0. 599, Figure  2B ). Thus, TNFR and BATF could be involved in the regulation of Penk in Treg. To examine this possibility, we reanalyzed our previously published dataset establishing the transcriptome of Treg stimulated with anti-CD3, anti-CD28 antibodies and TNFR agonists in vitro  18. We observe that addition of TNFR2, OX40 or 4-1BB agonists increases Penk expression at 36h post-stimulation relative to controls (Figure  2C ).
Extraction of Treg meta-signatures The datasets used were selected based on a "Treg* AND (Tconv* OR Teff*) AND Mus Musculus" search in the GEO dataset web site (https://www. ncbi. nlm. nih. gov/gds). GEO datasets were manually inspected for inclusion of studies comparing fresh Treg with fresh Tconv from lymphoid organs. Characteristics of selected datasets are summarized in Table  1. For each dataset, we generated a list of differentially expressed genes with a cutoff based on a false discovery rate (FDR) inferior to 0. 05 and a log2 fold change superior to 1 with the GEO2R embedded algorithm.
Acknowledgments The authors would like to thank  Olivier Brégerie ,  Flora Issert , and  Doriane Foret  for taking care of our mice;  Dr Martina Lubrano di Ricco  and  Morgane Hilaire  for technical help,  Dr Benoit Salomon  for providing Foxp3 Cre-ERT2 and ROSA tdTomato mice;  Francois-Xavier Lejeune  ( ICM, Paris ) for help with biostatistics analysis and  Dr Jean-Luc Teillaud  for critical review of the manuscript.
Funding This study was funded by a research grant from  Sorbonne University (SU)  (Emergence 2021) to GM, and by the  National Institute for Health and Medical Research (INSERM). NA was funded by a doctoral fellowship from  Fondation ARC  and by a post-doctoral fellowhip from  EGLE  TX, MF by a doctoral fellowship from the  French Ministère de l'Enseignement Supérieur et de la Recherche , MP and MN by pregraduates fellowships from SU and  EGLE TX  respectively. The funders had no role on the supervision of the research, nor in the analysis of the results.
Bioinformatics analyses Sequences were downloaded from the UniProt database (UniProt Consortium, 2021). AlphaFold2 (AF2)  (Jumper et al. 2021 ) models were extracted from the AFDB database  (Varadi et al. 2022). Sequence similarities were initially searched against UniProt using PSI-BLAST  (Altschul et al. 1997). The jackhammer software  (Finn et al. 2015)  was also used to query the initial REMIND domain alignment and target the Uniprot reference proteomes with default parameters. HH-PRED  (Zimmermann et al. 2018 ) was used to search protein domain and structure databases. A comparison of 3D structures against those included in the Protein Data Bank (PDB) was performed using DALI  (Holm 2022). 3D structures were visualized with the UCSF Chimera package  (Pettersen et al. 2004 ). Sequence alignment was rendered using ESPript  (Robert and Gouet 2014).
ACKNOWLEDMENTS We thank our colleagues who generously provided invaluable antibodies for this study. In addition, we are grateful to  Omar Ndao  and  Scott Thomas  for technical assistance, members of the  Gif Imaging Facility (I2BC, CNRS) :  Cynthias Dupas  for electron microscopy,  Romain Le Bars  and  Sandrine Lecart  for the training and help on confocal microscopy; and other members of the Lab:  Ariane Honfozo  and  Mahendra Jamdhade  for their fruitful discussions. This work was supported by a  Ph. D. fellowship from the  Campus France, I2BC (CNRS)  and  WAEMU  to R. H, and by financial support from the  Agence Nationale de la Recherche  grant  N°ANR-19-CE44-0006  to ST.
Background: Genital psoriasis can be stigmatizing, is highly prevalent among patients with psoriasis, and has limited treatment options. Apremilast is a unique oral immunomodulating phosphodiesterase 4 inhibitor approved for psoriasis treatment. Objective: To assess the efficacy and safety of apremilast 30 mg twice daily in patients with genital psoriasis. Methods: DISCREET, a phase 3, placebo-controlled trial (NCT03777436), randomized patients with moderate-to-severe genital psoriasis (stratified by affected body surface area \10% or $10%) to apremilast or placebo for a 16-week period, followed by an apremilast extension period. Week 16 results are presented. Results: Patients were randomized to apremilast (n = 143) or placebo (n = 146). At Week 16, 39. 6% and 19. 5% of apremilast and placebo patients, respectively, achieved a modified static Physician Global Assessment of Genitalia response (primary endpoint; score of 0/1, $2-point reduction); treatment difference was significant (20.
At the time of this research, there were no other ongoing studies involving oral systemic treatments for G-PsO. Apremilast, an oral, immunomodulating phosphodiesterase 4 inhibitor, has demonstrated efficacy in and is approved by the US Food and Drug Administration for psoriasis across all disease severity levels. [12] [13] [14] [15]  It is the first oral medication studied specifically in patients with G-PsO using clinical measures of G-PsO. DISCREET (NCT03777436) was a phase 3 multicenter, randomized, placebocontrolled, double-blind study of apremilast that evaluated the clinical efficacy, impact on QoL, and safety of apremilast 30 mg BID in patients with moderate-to-severe G-PsO. Based on results from DISCREET, US Food and Drug Administration-approved product labeling now specifically mentions the efficacy of apremilast for the treatment of G-PsO.
Study design Patients were randomized 1:1 to receive apremilast 30 mg BID or placebo for 16 weeks (Supplementary Fig (moderate-to-severe psoriasis) and plaque psoriasis BSA $1% in a nongenital area at screening and baseline; and a diagnosis of chronic plaque psoriasis $6 months before the study. Exclusion criteria are described in the Supplementary Methods, available via Mendeley at https://data. mendeley. com/ datasets/dhtrp945bn/1. Patients were permitted to use unmedicated emollients and nonmedicated shampoos for psoriatic lesions during the study.
6% in the placebo group. All individual components of the GPSS had statistically significant treatment differences favoring apremilast. In patients with BSA \10%, the treatment difference in modified genital PGA response was 16. 6% (95% CI: 2. 7, 30. 5) favoring apremilast; among patients with a BSA $10%, the treatment difference was 24. 8% (95% CI: 7. 7, 41. 9) favoring apremilast (Supplementary Fig  3 , available via Mendeley at https://data. mendeley. com/datasets/dhtrp945bn/1). Overall sPGA response, GPI-NRS response, change from baseline in affected BSA, and change from baseline in DLQI and GPSS scores were comparable for patients in the BSA \10% and BSA $10% subgroups.
Acknowledgments This work was funded by grants from the  Agence Nationale de la Recherche  ( ANR-16-CE11-0005-01 ), the  Centre National de la Recherche Scientifique  and the  Aix-Marseille Université. The authors thank the participation of the  Protein-Glycan Interaction Resource of the CFG  (supporting grant  R24 GM098791 ) and the  National Center for Functional Glycomics (NCFG)  at  Beth Israel Deaconess Medical Center ,  Harvard Medical School  (supporting grant  P41 GM103694 ). This work has benefited from the facilities and expertise of the.
It is made The copyright holder for this preprint (which this version posted July 18, 2023. ; https://doi. org  /10. 1101/2023. 07. 18. 549442 doi: bioRxiv preprint 6 Tick primers and a TaqMan probe were designed from 347 Ixodida 18S rRNA sequences, including 17 genera and 111 species (Supplementary material 1). Sequences were aligned with the software Muscle (version 3. 8. 31)  (Edgar, 2004) , and oligonucleotides were designed to target conserved regions using the find primer tool of the software AliView (version 1. 26)  (Larsson, 2014). As explained above, we used these oligonucleotides targeting the 18S rRNA gene of ticks as a control in parallel with B. burgdorferi s. l. detection  (Camicas, 1998). Eleven strains assigned to five species of the B. burgdorferi s. l. species complex that are publicly available through sequencing projects were chosen to represent the diversity of the targeted genomes based on available genomic datasets (Table  2 ).
Borrelia bavariensis BgVir SAMN02603389 Borrelia bavariensis NMJW1 SAMN02603884 The chromosomes of these strains were obtained either from NCBI RefSeq corresponding accessions when the sequencing project was complete, or from de novo assemblies of raw reads obtained from the SRA archive using SPAdes 3. 13. 0  (Nurk et al. , 2013). Mauve 2. 3. 1  (Darling et al. , 2010)  was used to identify chromosomal sequences in de novo assemblies and to obtain a multiple-alignment of all the available chromosomes. Sequences. CC-BY 4. 0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted July 18, 2023. ; https://doi.
, 2012). Parameter settings included: PCR product length between 70 and 100 pb, melting temperatures of the oligos between 55 and 60°C, and checking the specificity of primers and probes against Ixodes sp. sequences stored in the nr database. We identified polymorphisms that could affect the efficiency of the qPCR in priming site by blasting targeted loci, and introduced degeneracy in oligos based on the most problematic sites.
3t) was utilized to predict the potential impacts of these identified SNPs. As well as plotting the coverage ratios of the genomic bins which allows researchers to visualize the distribution of coverage across the genome and identify regions with higher or lower coverage. All results presented in this study were generated using the Genome Instability Pipeline (GIP,  [20] ) version 1. 0. 9. The GIP code is maintained and freely distributed at the GitHub page under https://github. com/giovannibussotti/GIP. All GIP outputs were used as input for giptools, a suite of thirteen modules that allows comparing GIPprocessed samples and visualizing changes in chromosomal copy number, gene copy number, and SNPs. Complete documentation for GIP and giptools, including a description of all options, is available at https://gip.
Comparing the genomes of our 14 Moroccan isolates with 8 publicly available L. tropica genomes from Lebanon, Jordan, and Saudi Arabia (NCBI BioProjects: PRJNA438080, PRJNA453461, and PRJNA589179) again resulted in clustering of the 12 isolates representing the core isolates from the Azilal province. In contrast, Ltr_16 clustered closer to two Lebanese isolates, while M3015 groups closest to Jordanian and Israeli isolates (Figure  6 ). The large divergence observed between all isolates (except within the core group) suggests that the L. tropica genome seems to evolve in a local fashion in response to ecological constraints that still remain to be elucidated.
ACKNOWLEDGEMENTS The authors would like to express their gratitude to  Dr Jean-Claude Dujardin  for taking the time to read our paper and providing valuable constructive criticism. We also wish to thank "the  Parasitology, Mycology Department, CHU de Montpellier"  for providing us with some L. tropica isolates. Additionally, we extend our deep appreciation to the local team of  Foum-Jamaa's Health Centers  and the  Health Delegation of the Moroccan Ministry of Health  for their assistance.
Background: Vitiligo is an autoimmune depigmenting disorder with no effective and safe treatments. Its pathogenesis is not fully elucidated. Objective: This substudy of a randomized, double-blind, placebo-controlled phase 2b trial (NCT03715829) evaluated effects of ritlecitinib, an oral JAK3/TEC family kinase inhibitor, on skin and blood biomarkers in participants with nonsegmental vitiligo (NSV). Methods: Sixty-five adults with NSV participated in the substudy and received daily treatment for 24 weeks with placebo (n 5 14) or ritlecitinib with or without a 4-week loading dose: 200 (loading dose)/50 mg (n 5 13), 100/50 mg (n 5 12), 50 mg (n 5 11), 30 mg (n 5 8), or 10 mg (n 5 6). Skin (lesional and nonlesional) biopsy samples were obtained at baseline and at 4 and 24 weeks. Changes from baseline to weeks 4 and 24 in skin and blood molecular and cellular biomarkers were evaluated by RNA sequencing, quantitative real-time PCR, proteomic analysis, and flow cytometry.
RNA sequencing RNA extraction, library preparation, and sequencing for both blood and skin samples were conducted at BGI Americas (Cambridge, Mass Sequencing data quality control and preprocessing. Clean RNA-Seq raw reads from BGI Americas were processed using the QuickRNASeq pipeline. 36  Raw sequence reads were mapped to human reference genome GRCh38 and GENCODE reference transcriptome release 30. Gene ontology. Gene ontology was performed using canonical KEGG, Reactome, and BioCarta pathways with XGR software (xgr. r-forge. r-project. org/). Enrichr's publicly available biological process gene ontology database was used to perform gene ontology analysis on differentially expressed genes (DEGs). 37  Quantitative real-time PCR TaqMan Low Density Array cards (Thermo Fisher Scientific) were used for RT-qPCR.
Statistical analysis RNA-Seq data were log 2 transformed with 'voom transform'  42  and modeled using a mixed-effect model with time, treatment, and tissue interaction as a fixed effect and a random effect for each participant (using the R 'limma' package). The Benjamini-Hochberg procedure was used to adjust P values for multiple hypotheses by controlling the false discovery rate (FDR). Normalized PCR values were also modeled using a mixedeffect model with time, treatment, and tissue interaction as a fixed effect and a random effect for each participant (using the R 'nlme' package). Normalized protein expression values quantified by Olink proteomic analysis in blood were also modeled by a similar approach using a mixed-effect model with time and treatment interaction as a fixed effect and a random effect for each participant (using the R 'nlme' package).
The insurgence of vitiligo in melanoma patients with cancer immunotherapy including immune checkpoint inhibitors has been shown to be an index of better prognosis, possibly because the melanocyte and melanoma-shared antigen-specific CD8 1 T cells infiltrate both in the tumor and vitiligo lesion microenvironments. 52  While ritlecitinib is an irreversible inhibitor of JAK3 and the TEC kinase family, the half-life of JAK3 and TEC family kinases ranges from 2-3 hours (for JAK3 and IL-2-inducible T-cell kinase) to 12-24 hours (for Bruton tyrosine kinase), indicating that irreversible covalent inhibition does not lead to an extended effect. 33, 34  Ritlecitinib is under investigation as a treatment for alopecia areata (NCT03732807, NCT02974868),  53, 54  NSV (NCT05583526, NCT03715829),  35  rheumatoid arthritis (NCT02969044),  55  and inflammatory bowel  This study has some limitations.
001. (C) Common or most enriched pathways in 50 mg ritlecitinib-treated groups at week 24 compared to BL, using DEGs analyzed with publicly available function-based pathway databases. Vertical dashed line denotes significance threshold (FDR < 0. 05). ***P <. 001 compared to placebo group on respective treatment weeks. Bars atop bar plot represent means 6 SEMs. BL, Baseline; LS, lesional; NL, nonlesional; W, week.
7 2742. 1 22063. 7, 579. 5. 5 (0. 3) (0. 3) (1784. 0) (1784. 0) 30 mg BL 7 - 0. 7 20. 1, 1. 4 - - - - 2585. 7 22726. 0, 3454. 2 - - - - (1. 0) (4171. 3) W4 6 6 0. 4 20. 1, 0. 9. 8 20. 2 20. 7, 0. 3. 6 1166. 1 22701. 6, 3185. 8. 4 21267. 5 21676. 3, 4211. 2. 3 (0. 6) (0. 6) (3678. 9) (3678. 9) W24 5 4 0. 2 (1. 2) 21. 0, 1. 3 1. 0 20. 7 21. 8, 0. 4. 4 629. 5 24793. 2, 5042. 4 1. 0 22911. 6 27829. 4, 2006. 2. 4 (1. 2) (5018. 2) (5018. 2) 10 mg BL 5 - 0. 3 0. 1, 0. 5 - - - 1440. 2 21130. 8, 1580. 6 - - - - (0. 3) (1546. 6) (Continued) disease (NCT02958865 [ulcerative colitis], NCT03395184 [Crohn disease], NCT05636193 [celiac disease]). A total of 1562 patients and 362 healthy volunteers have been exposed to ritlecitinib, excluding currently ongoing studies (data not shown). There has been no increased risk of melanoma, nonmelanoma skin cancer, or other malignancies associated with ritlecitinib usage compared to the general population.
ACKNOWLEDGMENTS The authors thank the  University of Wisconsin Translational Research Initiatives in Pathology (TRIP) laboratory , supported by the  UW Department of Pathology and Laboratory Medicine, UWCCC  ( P30 CA014520 ) and the  Office of The Director-NIH  ( S10OD023526 ) for use of its facilities and services. We thank  Amanda Loke  for help in processing samples. We thank  Dr. Chris Buck , ( National Cancer Insitute, Bethesda, MD ) and  Professor John Doorbar  ( University of Cambridge, UK ) for providing antibodies. We thank  Jing Zhang  for her guidance on radiation treatment for mouse bone marrow transplant studies. This research was supported by the  2020   AACR-Genentech Immuno-oncology Research Fellowship , Grant Number  20-40-18-WANG  to W. W. , the  University of Wisconsin-Madison Carbone Cancer Center Transdisciplinary Cancer   Immunology-Immunotherapy Pilot Grant  to P.
Image acquisition and analysis Two channel images were acquired in a fully automated and unbiased manner using the Opera Phenix™ High-Content Screening System (Revvity, Fr) and a 10x air objective (NA = 0. 3). Thirty-ve images per channel per condition were collected, transferred to the Columbus Conductor TM Database and analyzed with Acapella software (Revvity, Fr), using a customized algorithm for cell segmentation. Approximately 32000 cells were counted per well.
Funding This work was funded by the  French National Research Institute for Agriculture, Food and Environment (INRAE) , the  Ecole Nationale Vétérinaire d'Alfort (Enva) , the  Fonds Eperons  and the  Institut Français du Cheval et de l'Equitation (IFCE)  and the  DIM1Health (Ile de France , NB). The  Roslin Institute  received funding from the  Biotechnology and Biological Sciences Research Council (UK). The  UtechS PBI , part of the  France-BioImaging infrastructure network  ( ANR-10-INSB-04 ;  Investments for the Future ) received funding from the  Région Île-de-France (DIM1Health)  and the  Institut Pasteur. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
RNA extraction and RT-qPCR Total RNA and RNA enriched upon specific protein immunoprecipitation (RIP) were extracted using TriReagent (Invitrogen, Thermo Fisher Scientific) according to the manufacturer's instructions. For total RNA analysis, 1 μg of RNA was reverse transcribed with the SuperScript VILO Master Mix (Invitrogen, Thermo Fisher Scientific) according to the manufacturer's instructions. For the RIP experiment, 1 µL of samples before and after IP was used for retro-transcription using the same protocol. RT-qPCR was performed on cDNA using the Maxima SYBR Green qPCR Master Mix K0253; Thermo Fisher Scientific) on the CFX96 touch Real-Time PCR machine (Bio-Rad) using the following primers at an annealing temperature of 60°C:.
1% of formic acid in acetonitrile). Qexactive data were searched with Mascot algorithm (version 2. 6. 2, Matrix Science) against the Swissprot database with H. sapiens taxonomy (release 2020_05) as well as the sequences of GFP and of SINV virus (20 394 sequences) with a decoy strategy. The resulting. dat Mascot files were then imported into Proline v2. 0 software  (Bouyssié et al. , 2020)  to align the identified proteins. Proteins were then validated on Mascot pretty rank equal to 1, 1% FDR on both peptide spectrum matches (PSM) and protein sets (based on Mascot score). For statistical analyses of the mass spectrometry data, spectral counts (SpC) of the identified proteins were stored in a local MongoDB database and subsequently analysed through a Shiny Application built upon the R packages msmsEDA (Gregori J, Sanchez A, Villanueva J (2014).
Acknowledgements We thank  Béatrice Chane-Woon-Ming  for her help with the proteomic analyses and  Dr. Paula Lopez  for her initial support with plaque assays. We also thank  Dr. Carla Saleh  for providing the SINV WT and GFP infectious clones and  Dr. Diane Griffin  for providing the CAPSID antibody. We thank  Dr. Cyril Bourgeois ,  Dr. Patryk Ngondo  and members of the  Pfeffer labs  for critical reading of the manuscript. This work received financial support from the  Agence Nationale de la Recherche  ( EndoDRAI  project  ANR-22-CE15-0011-01  (to E. G. ) and was supported by the  Idex 2022 Attractivité  grant under the framework of the  IdeX University of Strasbourg  (to E. G. ) and from the  French Minister for Higher Education, Research and Innovation  (PhD contract to C. P. ). This work of the  Interdisciplinary Thematic Institute IMCbio+ , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), by  SFRI-STRAT'US  project ( ANR-20-SFRI-0012 ),  EUR IMCBio  ( IMCBio   ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program.
The supernatant was discarded, and the cells were washed twice with PBA. The stained cells were resuspended in 200 μL of PBS before running on a BD FACSCanto (BD Biosciences, San Jose, CA). A minimum of 50,000 events for PBMC and 10,000 events for IEL were collected per sample. Flow cytometry data were analyzed with FlowJo (FlowJo LLC, Ashland, OR), using fluorescent minus one samples to establish appropriate gating. Each antibody and conjugating fluorochrome was titrated to determine the optimal concentration for staining, and compensation beads (UltraComp eBeads Plus microspheres, ThermoFisher) were used to calculate spillover values and set appropriate voltages and gating parameters. Data were analyzed on live and single cell events using FlowJo software (version 10. 0. 7, Treestar, Palo Alto, CA) according to the gating strategy depicted in Figures  1  and 2.
Bioinformatic and Statistical Analysis For flow cytometry data from both PBMC and IEL, Kruskal-Wallis nonparametric analysis was used for comparing the receptor expression of γδTCR + , WC1. 1 + , and WC1. 2 + in the γδ T cell panel, and IgM +, CD32 + , and CD21 + in the B cell panel on d 2, d28, and 42 of life as indicated by the log expression of median fluorescence intensity (MFI). In addition, proportions of lymphocytes, γδ T cells, and B cell populations were calculated based on receptor expression in FlowJo, and data were analyzed using a generalized linear mixedeffect model (SAS version 9. 4, SAS Institute Inc. , Cary, NC). Initially, we included the effects of the tyndalized Lactobacillus Helveticus postbiotic supplementation in the model, but because no differences were observed for any of the variables measured in this study, we removed the treatment effect from the analysis and focused solely on the changes over time.
The final model included the fixed effects of day and the random effect of calf nested within day. The covariance structure with the lowest Akaike's information criterion was selected for each variable. We used the first-order autoregressive structure for equally spaced measurements or spatial power for unequally spaced measurements. The Kenward-Roger method was used to calculate the approximate denominator degrees of freedom for the F tests in the statistical models. Continuous data were examined for normal distribution of residuals after fitting the statistical models using Shapiro-Wilk and homogeneity of variance by plotting residuals against predicted values. Non-normally distributed data were subjected to Box-Cox transformation using the TRANS-REG procedure of SAS to achieve normality before analyses.