dataesr/scientific-paragraphs-categorization huggingface dataset:
Structure-Based Virtual Screening Discovery Studio Visualizer (DSV) [v17. 2. 0. 16349] software was used for protein visualization and preparation. MolProbity software (Manchester, UK) was used to assign the position of hydrogen atoms and the protonation state of histidine residues. Water molecules were removed before docking calculations. The docking calculations were performed with the open-source structure-based virtual screening docking program AutoDock Vina v1. 1, implemented with the Lamarckian Genetic search algorithm (exhaustiveness of 20). The optimized chemical structures of the ligands and the graphical interface AutoDockTools were used for the preparation of proteins and ligands into their lowest energy 3D conformations  [24]. The search space was included in a cubic grid of 24 × 24 × 24 Å, framing both ligand and flexible residues.
Flexible torsions of ligands were assigned with AutoTors, an auxiliary module of AutoDockTools. The receptor grid was designed around the following flexible residues: Ile149, Leu189, Ile236, Tyr258, and Thr265  [25]. Ligands were docked in their major microspecies structure at pH = 7. 2 (neutral or protonated). For each calculation, ten poses were ranked according to the scoring-function of Autodock Vina. For each ligand, the first pose, i. e. , with the lowest energy, was selected  [31, 56].
General Melting points were determined on Köfler melting point apparatus (Wagner & Munz GmbH, München, Germany) or Buchi melting point apparatus, model B-545. 1 H NMR and 13 C NMR spectra were obtained in CDCl 3 or DMSO-d 6 on a AV300 spectrometer (Brüker Biosciences, Billerica, MA, USA), and peak positions are given as s (singlet), d (doublet), t (triplet), q (quadruplet) or m (multiplet). Chemical shift (δ) values were given in ppm, and coupling constants (J) were recorded in Hz. The spectra were analyzed using MestReNova 14. 0. 1 software. High resolution mass spectrometry (HRMS) was performed by DCI/CH 4 using a Waters GCT-Premier mass spectrometer in positive mode, signals were given as m/z. Elemental analyses were carried out at the Laboratoire de Chimie de Coordination (Toulouse) with a Perkin Elmer 2400 series II analyzer.
Acknowledgments: The authors thank  Yannick Coppel  ( Laboratoire de Chimie de Coordination ) and  Marc Vedrenne  ( ICT-FR 2599 ) for their technical support in NMR analyses, and  Laure Latapie  (LGC) and  Eric Leroy  ( ICT-FR 2599 ) for their technical support in HR-MS experiments. The authors also thank  Ludovic Pilloux  and  Nicolas Salez  for their technical assistance in the evaluation of cytotoxic activity.
Funding: This research work was funded by the  "Ministère français de l'Enseignement Supérieur, de la Recherche et de l'Innovation" (PhD fellowship  for J. T. ), the  Regional Council of Occitanie and Toulouse Tech Transfer  (http://www. toulouse-tech-transfer. com, accessed on 18 March 2022) for valorization and transfer (Funding number:  ESR_PREMAT-00325 , PI:  F. El Garah ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Acknowledgments Authors are indebted to  Jérôme Mégret  from the ow cytometry platform and  Sophie Berissi  from the histology platform, SFR Necker, for technical help. They are grateful to  Dr Elke Schneider  for editorial assistance. Data in this manuscript have been partially presented in abstract form at Société Française d'Immunologie 2019 meeting. Part of this work was edited in  Viviane A. Agbogan 's PhD dissertation at the  Université de Paris, France ,  2021 , entitled " Thérapie cellulaire avec des progéniteurs hématopoïétiques dans des modèles de sclérose en plaques et de maladie du greffon contre  l'hôte » (unpublished). The manuscript has been posted as a preprint at Research Square DOI: https://doi. org/10. 21203/rs. 3. rs-944724/v1(52). Figure 9 was created using BioRender. com.
FUNDING YD is funded by the  EU Horizon 2020  project  COVIRNA  (Grant Agreement #  101016072 ), the  National Research Fund  (grants #  C14/BM/8225223 ,  C17/BM/11613033  and  COVID-19/2020-1/ 14719577/miRCOVID ), the  Ministry of Higher Education and Research , and the  Heart Foundation-Daniel Wagner of Luxembourg. FP has received support from the  "Fédération Française de Cardiologie", "Fondation de France" , the  French Government , managed by the  National Research Agency (ANR)  under the program " Investissements d'avenir " with the reference  ANR-16-RHUS-0003. FP is member of FHU-CARNAVAL. DdG-C has received financial support from  Instituto de Salud Carlos III  (Miguel Servet 2020:  CP20/00041 ), co-funded by the  European Social Fund (ESF) /" Investing in your future. "This work is supported by  Instituto de Salud Carlos III  ( PI20/ 00577 ), co-funded by  European Regional Development Fund (ERDF) /"A way to make Europe" and  Fundació La Marató  de  TV3  ( 202108-30/-31 ).
Statistical analyses We performed all statistical analyses with SAS Enterprise Guide 5. 1 (SAS Institute, Cary, NC, USA). Variables were described using mean, standard deviation, median and interquartile range (IQR) for quantitative variables, and frequencies and percentage for qualitative variables. Initially, Spearman's rank correlation coefficient generated correlations between variables of interest: change in CD4/CD8 ratio, time to ART initiation, neuropsychological domains, as well as blood neopterin, CSF NfL, and CSF WBC concentrations. These variables were selected based on their known relevance to the state of systemic inflammation and neuropsychological performance among HIV-infected individuals from the published literature. In a second step, linear regressions were performed to find variables associated with the trajectory of CD4:CD8 ratio.
The list of primers is presented at Supplementary Data, Table  1. PPIA, GAPDH, PGK1, HRPT1, TBP and HSP90AB1 were used as normalizer genes. Amplification was carried out on a CFX96 Real Time System (Bio-Rad) for 3 minutes at 95°C and 40 cycles of 2 steps consisting of 5 seconds at 95°C and 30 seconds at 60°C. The relative quantification of the mRNA levels of the target genes was determined using CFX Manager based on the DDCT-method  (Livak and Schmittgen, 2001). The six genes from RNAseq data were selected as potential normalizer genes according to their most stable expression. The expression stability of those genes was confirmed by using of the Gene Expression Module of CFX Manager (Bio-Rad). The amount of target was normalized to normalizer (housekeeping) genes. Relative quantification after normalization refers to the PCR signal of the target transcript in a treatment group divided by the values obtained from uninfected control cells, arbitrarily set to 1.
RNA-Seq Analysis Reads were quality trimmed with Sickle (v 1. 210) in "pe" (pairend) mode with default parameters. Paired sequences were then mapped to human reference genome (GhCR38. 80) with Tophat (v 2. 0. 14) (Trapnell et al. , 2009)  with default parameters. Genes were counted with htseq-count (v 0. 6. 1) (Anders et al. , 2015). DeSEQ2  (Love et al. , 2014) , an R package embedded in the package SARTools (v 1. 2. 0)  (Varet et al. , 2016)  was used to normalized the count table with 29,195 genomic features expressed and generate a list of differentially expressed genes (DEGs). A Benjamini-Hochberg p-value adjustment, a multiple testing correction, is performed to control the false positive rate. The threshold of statistical significance is set to 0. 05. Positive and control samples ranged from 41 to 85 million pair-end reads per sample and 51 to 94 million pair-end reads per sample, respectively (Supplementary Data, Table  2 ).
Functional Annotation Gene-set enrichment tests in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed with the R packages, GAGE  (Luo et al. , 2009). Gene sets with adjusted pvalue < 0. 05 were considered as being significantly enriched. A network of KEGG DEGs set was constructed using R package, FGnet  (Aibar et al. , 2015). A final network diagram was drawn with Cytoscape  (Shannon et al. , 2003). Gene-set enrichment tests in Reactome pathways  (Jassal et al. , 2020)  were performed with R package ReactomePA  (Yu and He, 2016)  with gsePathway function. Gene sets with adjusted p-value < 0. 05 were considered as being significantly enriched. We also used the Epifactor and DAVID databases  (Huang et al. , 2009; Medvedeva et al. , 2015). The datasets presented in this study can be found in online repositories.
5), while 208 genes were three-fold less abundant (log 2 FC ≤ -1. 5) in infected cells. Among them, 153 genes were eight-fold enriched (log 2 FC ≥ 3) and 41 genes were eight-fold less abundant (log 2 FC >-3 (Table  S1 ). To validate RNA-seq gene expression profiles, the expression of 30 genes involved in different processes was assessed by RT-qPCR. Results were in agreement with RNA-seq data (Table  1 ). We then examined whether RNA level alterations corresponded to changes in protein abundance, focusing on several secreted factors. We chose periostin, an osteoblastspecific factor involved in the regulation of cell adhesion and organization of extracellular matrix  (Sugiura et al. , 1995; Kudo and Kii, 2018) , as representative of the product of a down-regulated gene, and as four cytokines/chemokines, CSF-G, CXCL6, IL-1b, and IL-6, as representative products of up-regulated genes.
Gene-Set Enrichment Analysis (GSEA) Using KEGG Database In order to interpret gene expression data in association with biological processes or molecular functions, we performed a GSEA with the GAGE tool on all expressed genes (29,195 genes) (Table  S2 ). This analysis revealed 33 significantly enriched KEGG pathways (adj. p-val < 0. 05) (Figure  3  and Table  S3 ), organized into five functional families: cellular processes (11 pathways), organismal systems (10 pathways), environmental information processing (7 pathways), metabolism (3 pathways), and genetic information processing (2 pathways). More than half of the enriched pathways belonged to functional categories related to signal transduction, immune system and cell growth and death (Figure  3  and Table  S3 ). The signal transduction category included the PI3K-Akt signaling pathway (with the highest number of DEGs: 23 DEGs), which negatively mediates NF-kB-dependent inflammation  (Lv et al.
GSEA Using Reactome Database To complete functional gene analysis, GSEA was also carried out on the 29,195 expressed genes with the Reactome database, which is more detailed and largest than KEGG database with up to six hierarchical levels  (Chowdhury and Sarkar, 2015). The number of Reactome enriched pathways was much higher compared to those identified by KEGG, 106 vs 33, respectively. Reactome GSAE overestimates enriched pathways by considering that all pathway levels are enriched at the same time. To facilitate and clarify further analyses, we removed the redundancy of enriched pathways by considering only the deepest. This resulted in 70 Reactome enriched pathways: 61 upregulated and 9 downregulated pathways (Figure  4  and Table  S4 ). This analysis confirmed the deregulation of the pathways identified according to the KEGG database, but with a more precise categorization and a higher number of cellular processes.
, 2001)  and in the development of infection  (Kim et al. , 2018). Other downregulated pathways belonging to Synthesis of DNA, collagen formation, degradation of extracellular matrix (ECM), integration of energy metabolism and signaling by receptor tyrosine kinase (PTK2) categories were identified. Immune System and Signal Transduction Genes Are Among the Top Highly Induced DEGs GSEA using both KEGG and Reactome databases pointed to the importance of changes in signal transduction and immune system-associated gene networks during the long-term S. aureus intracellular infection. Genes belonging to these functional categories represented 22% of DEGs associated to the KEGG hierarchy (504/2850) (Table  S2 ). Moreover, they also accounted for 58% of the top highly regulated genes associated to the KEGG hierarchy (n = 64/194), with a clear bias toward upregulation (i.
S. aureus Infection Triggers Transcriptional Reprogramming of Genes Involved in Metabolism The second group of the top highly regulated genes and enriched pathways highlighted with both KEGG and Reactome databases belongs to the metabolism category (291 genes including 22 tophighly upregulated genes, Table  S2 ) and signal transduction and immune system categories genes that also play an important role in metabolism. Among them the APP SAA2 (FC = 49, as mentioned above), is involved in high density lipoproteins metabolism and cholesterol homeostasis,  (Ely et al. , 2001; Krishnan et al. , 2015) , the phospholipases A2 (FC = 75) is implicated in lipid metabolism, and the hydroxysteroid (11beta) dehydrogenase 1 (FC = 53) is involved in hormone metabolism (Table  3 ). The expression of SOD2 that protects the host against reactive oxygen and reactive nitrogen species  (Eisenreich et al.
To do this, we intersected either the 1514 upregulated DEGs or the 1336 downregulated DEGs associated with S. aureus intracellular infection with the Epifactor database  (Medvedeva et al. , 2015). This database includes 720 epifactors classified according to their function: (i) enzymes that "write" epigenetic marks, such as DNA methyltransferases (DNMTs) and histone acetyltransferases (HATs) and methyltransferases (HMTs), (ii) enzymes that "erase" epigenetic marks, such as histone deacetylases (HDACs) and demethylases (HDMs); (iii) proteins that "read" these marks, (iv) chromatin-remodeling enzymes that displace nucleosomes, (v) scaffold proteins that assemble macromolecular chromatin-regulatory complexes, and (v) diverse cofactors. This analysis identified an important number of DEGs encoding epifactors (117 DEGs), of which 92 were downregulated (Table  S6 ) and 25 upregulated (Table  S7 ).
The strikingly important number of epifactor genes which were downregulated by infection (7% of all of the downregulated DEGs) prompted us to examine their functions in detail, using the Gene Ontology of Biological Processes (GO-BP) enrichment analysis of the DAVID software. This analysis showed that 30 of these genes encoded epifactors with a negative effect on transcription (i. e. , repressors): BAHD1, BRCA1, CBX1, CBX2, CBX6, CBX5, CHD4, CTCF, DNMT1, DNMT3A, EHMT1, GATAD2A, HDAC4, HDAC6, HDAC10, KDM5C, MBD1, MBD3, NSD1, PARP1, PRMT6, RCOR1, SCMH1, SIN3B, SMARCA4, TRIM24, UHRF1, WHSC1, ZGPAT, ZMYND11 (Table  S7 ). Interestingly, several of these components belong to chromatin-repressive complexes, in particular, the BAHD1 (4 genes), NurD (4 genes), Polycomb PRC1 (5 genes), mSin3A (1 gene) and CoREST (1 gene) complexes (Table  5  and Table S5 ).
ACKNOWLEDGMENTS The authors thank  Pr. Friedrich Götz , ( Laboratory of Microbial Genetics, University of Tübingen, Germany ) for providing S. aureus mCherrySA113 strain. The authors thank  Mary Bret  for revising the English. The authors thank  Dr. Tarek Msadek  ( Institute Pasteur, Paris ) for providing laboratory facilities for some experiments. The authors thank the genotoul bioinformatics platform  Toulouse Occitanie  ( Bioinfo Genotoul ) for providing computing and storage resources.
MHC I internalization Marchf1 -/- WT Ubl3 -/- Marchf1 -/- WT Ubl3 -/- Marchf1 -/- WT Ubl3 -/- W T U b l 3 -/- M a r c h f 1 -/- UBL3 is not required for MARCH8 function in nonhematopoietic cells. In contrast to hematopoietic immune cells, MARCH1 is not expressed in TEC, which instead use the closely related ligase MARCH8 to regulate MHC II expression (Fig. 4h )  5, 6. Surprisingly, MHC II was not altered in Ubl3 -/- medullary or cortical TEC (Fig. 4h ), even though medullary TEC express UBL3 at levels comparable to DCs or B cells according to the Immunological Genome Project (ImmGen) database  39. Similarly, we found no changes in MHC II surface expression on type II lung alveolar epithelial cells (AECII), bronchial epithelial cells (BEC) or endothelial cells (EC) of Ubl3 -/-mice compared to WT, even though these cell types also employ MARCH8 to regulate MHC II expression  10  (Fig.
6b ), recapitulating observations made in mice that cannot ubiquitinate MHC II molecules  14. This was not the case for all antigen forms, as the presentation of untargeted d Spleen cDCs labeled with FIP-conjugated mAb for MHC II, CD86, or MHC I were incubated for 30 mins at 37°C. Fluorescence was quenched (+Q) and internalization was calculated as described in Methods. CD86 internalization not shown for cDC2 (low expression). Data representative of two independent experiments with similar results (MHC II) or pooled from two (CD86) or three (MHC I) experiments performed in triplicate, mean ± SD, one-way ANOVA with Bonferroni's test. e MHC II was immunoprecipitated (IP) from spleen cDCs using anti-MHC II antibody, analyzed by non-denaturing SDS-PAGE, and immunoblotted (IB) for ubiquitin and MHC II β-chain.
Third, the accumulation of MHC II-C3dg complexes on splenic cDC1 made them highly susceptible to trogocytosis by marginal zone B cells  13  , causing a reduction in cDC1 numbers, a phenomenon also   8  UBL3 function is conserved in humans. Human CD14 + monocytes were infected with non-targeting control shRNA or shRNA against UBL3 (sh1 UBL3 or sh2 UBL3) and analyzed after five days in culture. Top left: Cell lysates were probed with antibodies against UBL3 (LS-C661402) and actin, and quantification of gene silencing was determined by UBL3 signal intensity. Immunoblot depicts a scenario of approximately 95% UBL3 knockdown. Data from n = 6 donor samples examined in a single experiment, with symbols connected with a line representing individual donors. Top right: Surface MHC II, CD86, and MHC I on monocyte-derived DCs (CD16 -CD1a + ) and monocyte-derived macrophages (CD16 + CD1a -) were analyzed by flow cytometry.
Cells were expanded for 10 days and genomic DNA from MHC II hi or unsorted cells (reference library) was isolated using a previously described salt precipitation protocol  53. For each sample, 100 µg DNA was used to amplify the gRNA cassette by two-step PCR described previously  54  using Hot Start Taq Polymerase (New England Biolabs), however in the second PCR we incorporated the 8 bp barcode into the reverse as well as a forward primer. Barcoded amplicons were pooled and purified using Nucleo Mag NGS beads (Macherey-Nagel), then sequenced using a NextSeq 500 (Illumina). CRISPR/Cas9 library data processing and differential representation analysis were performed using an established bioinformatics pipeline  55. Negative binomial linear models of gRNA count data incorporated blocking terms to account for any batch effects introduced by independent library infections.
077 g/cm3 Nycodenz (Nycomed Pharma). For surface marker staining, cells were washed in EDTA-BSS with 2% (v/v) FCS and stained with specific antibodies (see Supplementary Table  3  for a full list of antibodies, suppliers, and dilutions). For C3 staining, cells were incubated in Fcreceptor block (Milteny Biotec), incubated with biotinylated anti-C3 mAb (clone 11H9, Novus Biological), and incubated with DyLight 488-conjugated streptavidin (BioLegend). Stained cells were acquired on the LSRFortessa using FACSDIVA 9. 0. 1 (BD Biosciences) or CytoFLEX LX (Beckman Coulter) flow cytometer and analyzed with FlowJo software (Tree Star) with the exclusion of cell doublets and dead cells in all cases, identified based on forward and side scatters (FSC and SSC), as well as staining with a fixable viability dye, propidium iodide, or DAPI.
Supplementary Fig. 2  shows the gating strategies to identify specific immune cell populations. Statistical analysis was performed with Prism software (Graphpad). Real-time quantitative PCR. Total RNA was extracted from DCs isolated from WT or Ubl3 -/-mice with >90% purity, using the RNeasy Plus Mini Kit (Qiagen). RNA was quantified by UV spectrophotometry (Eppendorf) and reverse transcription of RNA using the SensiFAST cDNA Synthesis Kit (Bioline) was performed according to the manufacturers' instructions. cDNAs were used as templates for qPCR assay using TaqMan Fast Advanced Master Mix (Life Technologies). Each sample was analyzed in triplicates, and HPRT was used as an internal control. A control without cDNA was also included for each primer set (Thermo Fisher Scientific). Relative gene expression was determined with the ΔΔCq method, with ΔΔCq = (Cq(sample) -(Cq(HPRT)))-(Cq(reference)-(Cq(HPRT))), and fold change = 2 -ΔΔCq.
%internalization ¼ ðQ n À Q 0 Þ=ðF 0 À Q 0 Þ 100 Data were acquired using a BD LSRFortessa (BD Biosciences) and analyzed with FlowJo software (Tree Star). In vivo antigen presentation assay. Single-cell suspensions generated from the lymph nodes of OT-I × Ly5. 1 or OT-II × Ly5. 1 mice were stained with rat antimouse mAbs specific for: CD11b (M1/70), F4/80 (F4/80), red blood cells (TER119), Ly6G/-Ly6C (RB68C5), MHC II (M5/114), CD45R (RA36B2) and CD4 (GK1. 5) for OT-I T cells or CD8 (53-6. 7) for OT-II cells. Cells were washed and incubated with BioMag anti-rat IgG-coupled magnetic beads (Qiagen). After magnetic depletion, the CD4 + T-cell-enriched supernatant was recovered. Cells were washed with PBS 0. 1% BSA and labeled with CellTrace Violet. Single-cell suspensions were treated with red cell removal buffer, washed with PBS 0.
For flow cytometry analysis, cells were stained in PBS containing 0. 5% human serum and 2 mM EDTA with antibodies against CD16 (clone 3G8), CD1a (HI149), CD86 (IT2. 2), HLA-A,B,C (W6/32) (all BioLegend), and HLA-DR (LN3, eBioscience). Dead cells were identified using LIVE/DEAD fixable Aqua dye (Thermo Fischer Scientific). Cells were fixed using Intracellular Fixation & Permeabilisation Buffer Set (eBioscience) and analyzed on a FACSVerse (BD Biosciences) instrument. Data were analyzed with FlowJo (Tree Star). Western blot. For western blot analysis of human monocytes, cells were lysed in radioimmunoprecipitation assay buffer (Thermo Scientific) supplemented with cOmplete Mini EDTA-free protease inhibitor cocktail (Roche), and post-nuclear lysates were resolved by SDS-PAGE using 4-12% Bis-Tris NuPAGE gels (Invitrogen).
Signal intensity was measured using a ChemiDoc MP imaging system (Bio-Rad), analyzed with FIJI software, and statistical analysis was performed with Prism (GraphPad). shRNA interference. shRNA (all from Sigma) against human UBL3 (sh1: Clone NM_007106. 2-591s1c1; sh2: Clone NM_007106. 2-757s1c1) or non-targeting control shRNA (MISSION shRNA SHC002) were in the LKO. 1-puro vector (Sigma). Gene silencing was performed following an established protocol  62. In brief, viral particles were produced by transfection of 293FT cells in six-well plates with DNA and TransIT-293 (Mirus Bio) per well: for VSV-G pseudotyped SIVmac VLPs, CMV-VSV-G and pSIV3+; for shRNA vectors, CMV-VSV-G, psPAX2 and LKO1puro-derived shRNA vector. One day after 293FT cell transfection, the culture medium was replaced by a fresh medium.
Acknowledgements We thank the  Antibody Services Facility and Genomics Hub (Walter and Eliza Hall Institute)  for the provision of reagents and expert assistance. We acknowledge the  Melbourne Cytometry Platform  from the  Bio21 Institute node (University of Melbourne ) for the provision of flow cytometry services. We acknowledge the  Melbourne Mass Spectrometry and Proteomics Facility  of the  Bio21 Molecular Science and Biotechnology Institute at the University of Melbourne  for the support of mass spectrometry analysis. We thank  Satoshi Ishido  ( Hyogo College of Medicine, Nishinomiya, Japan ) for providing the Marchf1 -/-and Marchf8 -/-mice. The generation of Ubl3 -/-mice by CRISPR/Cas9 was performed by  Andrew Kueh  and  Marco Herold  ( WEHI MAGEC laboratory ). This work was supported by  National Health and Medical Research Council , Australia grants and fellowships ( 1058193 ,  1154502 ,  1113293 , and  1163090  [to J.