dataesr/scientific-paragraphs-categorization huggingface dataset:
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: The relative percent mortality (LDH) or viability (CCK-8) was calculated according to the following equations using absorbance without blanks: d KEY RESOURCES Mortalityð%Þ = Peptide À NC PC À NC Ã 100 Viabilityð%Þ = Peptide NC Ã 100 The LC 50 of each peptide was determined by using the sigmoid equation in GraphPad Prism 6 software: LC 50 = Bottom + À Top À Bottom Á 1 + 10 ððLogLC50 À XÞÃHillSlopeÞ Membrane permeabilization assay S2 cells were suspended and counted using trypan blue dye exclusion, then seeded in 96-well plates and incubated for 24hat 25 C. Then, cells were washed once with Phosphate Buffer Saline (PBS) 1X Ca 2+ /Mg 2+ , and once with culture medium before adding the peptides at the final concentrations (i.
NMR experiments Each synthesized peptide was dissolved in 50% 2,2,2-trifluoroethanol-d2 at a concentration of 0. 8 mM for U 9 and 1. 2 mM for M-Tb1a. Then, 2D 1H-NOESY (150 ms mixing time), 2D 1H-TOCSY (80 ms mixing time), sofast-HMQC 80 ( 15 N natural abundance) and  13  C HSQC ( 13 C natural abundance) spectra were performed at 298K on a BRUKER 700 MHz Avance III HD spectrometer equipped with a cryoprobe. 1 H chemical shifts were referenced to the water signal (4. 77ppmat 298K). NMR data were processed using the Bruker TopSpin software version 3. 6. 2 TM and analyzed with CCPNMR version 2. 2. 2. 81 Analysis of the 2D-TOCSY and NOESY spectra allowed for a complete assignment of 1 H chemical shifts using natural-abundance heteronuclear NMR spectra.
Structure calculations Structures were calculated using the Cristallography and NMR System (CNS)  82, 83  through the automatic assignment software ARIA2 version 2. 3  84  with NOE-derived distances, hydrogen bonds (consistent with the observation of a typical distance NOE cross-peak network for the a-helix) and backbone dihedral angle restraints determined with the DANGLE program  85  only for U 9. The ARIA2 protocol, with default parameters used simulated annealing with torsion angle and Cartesian space dynamics. The iterative process was repeated until the NOE cross-peak assignment was complete. The final run for each peptide was performed with 1000 initial structures, and 250 structures were refined in water. 15 structures were selected based on total energies and restraint violation statistics to represent the three dimensional structures of the U 9 and M-Tb1a peptides in solution.
QUANTIFICATION AND STATISTICAL ANALYSIS Data and statistical analysis Images (except for SEM, TEM, and immunocytochemistry experiments), absorbance and fluorescence data were acquired with the Biotek Cytation 1 microplate reader (Agilent Technologies, USA) using Gen5 software (Biotek, v3. 10. 06). Images were analyzed using ImageJ (1. 53e) or Gen5 software (Biotek, v3. 10. 06). Graphs and statistics were prepared and analyzed using GraphPad Prism 6 software (v6. 05). Normal distribution of data was determined by the Shapiro-Wilk test and homogeneity of variances by the Fisher test. If the data do not follow a normal distribution and/or do not have a homogeneous variance, a non-parametric test was performed. The statistical details and the replicate number of experiments can be found in the figure legends.
Immunofluorescence Cells were processed for immunofluorescent detection of viral and cellular proteins as previously described  (Rouillé et al. , 2006). Coverslips were mounted on glass slides using a Mowiol-based medium. For colocalization experiments, confocal microscopy was performed with an LSM880 confocal microscope (Zeiss) using a 63× /1. 4 numerical aperture oil immersion objective. Signals were sequentially collected by using single fluorescence excitation and acquisition settings to avoid crossover. For some experiments, high-resolution images (pixel 0. 035 × 0. 035 μm 2 ) were acquired using an Airyscan detector used in SR mode and processed for high-resolution reconstruction using Zen software version 2 (Carl Zeiss Microscopy). Images were assembled by using Adobe Photoshop software.
Image stacks were then processed using the Imaris software to model a 3D-image of peroxisomes. Objects smaller than 0. 1 μm were filtered out and the volume of each object was measured, enabling the volume of each peroxisome and the average number of peroxisomes per cell to be calculated. For each measurement, 30 cells from 3 independent infections were analyzed. Statistical differences were determined with a Kruskall-Wallis test.
ROS measurements Plasmids expressing roGFP2 sensors localized in the cytosol, mitochondria or peroxisomes were transfected into control, SGR2aand SGR3a-containing Huh-7 cells grown in glass-bottom 4 well μ-slide (Ibidi). At 18 h post-transfection, cells were rinsed three times with sterile PBS and placed in phenol red-free DMEM. As a positive control, H 2 O 2 was added to naïve Huh-7 cells at a final concentration of 0. 4 mM. Two hours later, cells were imaged at 37°C and 5% CO 2 with an LSM880 confocal microscope (Zeiss) using a 63× /1. 4 numerical aperture oil immersion objective. Once the acquisition conditions had been set for each roGFP2 sensor, all cells expressing this sensor were acquired using the same parameters. Images were acquired by sequentially exciting with 405 nm and a 488 nm lasers, and then processed using ImageJ software.
0E+04 counts, and the maximum allowed ion accumulation times were 250 ms for full MS scans and 100 ms for tandem mass spectra. The dynamic exclusion was set to 30 s. The full proteome analysis was performed in same conditions with a column of 500 mm length, heated at 55°C and a 3 to 20% B gradient separation of 170 min. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Perez-Riverol et al. , 2022)  partner repository with the dataset identifier PXD044994 and 10. 6019/PXD044994.
Proteomic data analysis Raw data collected during nano LC-MS/MS analyses were processed and converted into an *. mgf peak list format with Proteome Discoverer 1. 4 (Thermo Fisher Scientific). MS/MS data were analyzed using the search engine Mascot (version 2. 4. 0, Matrix Science, London, United Kingdom) installed on a local server. Searches were performed with a tolerance on mass measurement of 0. 02 Da for precursor and 10 ppm for fragment ions against a composite targetdecoy database built with a Human Swissprot database (taxonomy 9,606) fused with the sequences of recombinant trypsin, NS3, NS4A, NS4B, NS5A, NS5B, and a list of classical contaminants. Cysteine carbamidomethylation, methionine oxidation, protein N-terminal acetylation, and cysteine propionamidation were searched as variable modifications.
Up to one missed trypsin cleavage was allowed. For the APEX2-biotinylated protein samples, the identification results were imported into Scaffold software 4. 5. 3 and peptides were filtered out according to the cutoff set for proteins hits with 1 or more peptides taller than 9 residues, ion score > 20, identity score > -7, corresponding to a 1% false positive rate. For the full proteome analysis, the identification results were imported into Proline software 1   (Bouyssié et al. , 2020). Peptide spectrum matches taller than 9 residues and ion scores >10 were retained. The false discovery rate was then optimized to be below 1% at the protein level using the Mascot Modified Mudpit score. Extracted Ion Current based quantification was performed with Proline 2. 0. At peptide level, a single charge level per peptide was selected.
Then, for peptides with 0 spectra, those with abundance above the upper bound of the 99% confidence interval of the distribution were filtered out. The abundance of a protein was obtained by summing the abundances of its component peptides. Statistical analysis was performed by using R (version 4. 1. 2). Data were log2 transformed and distributions were centered on the upper quartile. Only proteins with at least three measurements (non missing values) in at least one group were kept. Following recommendations of  Tyanova et al. (2016) , missing values were then imputed using a normal distribution X ~ (μ-1. 8σ, 0. 3σ). Differential expression analysis between control Huh-7 and SGR-JFH1 and between control Huh-7 and SGR-DBN3a was performed using limma R package  (Ritchie et al. , 2015) , which uses an empirical Bayesian approach to estimate variances in moderated t tests.
None of the peroxisomal membrane and matrix proteins was dysregulated in a significant manner. These data indicate that the variation of expression of peroxisomal proteins in SGR-containing cells is too moderate to be appreciated by differential analysis with only four replicates per group. In particular, some proteins, such as PECR for example, show a fold change comparable to differentially expressed proteins (Figure  5B ) but the variability between replicates is too high to observe an adjusted value of p below 0. 05. Moreover, these differential analyses were performed protein by protein. We observed that multivariate analyses separated Huh-7 from DBN3a and JFH1 when looking at only peroxisome proteins (Figure  5C ). This suggests that even if individual peroxisome proteins present a moderate change of expression levels, HCV replication has a significant effect on their expression when being taken together.
Acknowledgments We thank  M. Schrader ,  M. Cormont ,  M. Fransen , and  F. Zhang  for providing plasmids,  Takaji Wakita  for JFH1 virus and replicon,  Charles M. Rice  and  Jean-François Delagneau  for antibodies,  Hervé Drobecq  for help in BP synthesis,  Sophie Salomé-Desnoulez  and  Elizabeth Werkmeister  for help with image acquisition and analysis, and  Sophana Ung  for help in assembling figures. Immunofluorescence and electron microscopy analyses were performed with the help of the imaging core facility of the  BioImaging Center Lille (BiCeL). Lipidomic analyses were performed in MetaToul facility (Toulouse metabolomics and fluxomics facilities, www. metatoul. fr), which is part of the  French National Infrastructure for Metabolomics  and Fluxomics MetaboHUB- ANR-11-INBS-0010. We are grateful to the  Institut Français de Bioinformatique (IFB)  ( ANR-11-INBS-0013 ) and the  Bilille platform  ( UAR 2014 -US 41 -PLBS ) for providing help in statistical analysis of proteomics data.
ACKNOWLEDGMENTS This work was supported by a project on Deciphering the Origin of the Eukaryotic Cell funded by  Gordon and Betty Moore Foundation  and  Simons Foundation  grant  GBMF9738  and by  Czech Science Foundation  grant  22-29633S. The work on T2SS in the O. F. lab is funded by  Institut Pasteur ,  CNRS , and the  Synergy   T2SS ANR-19-CE11-0020-01  grant. We declare no conflict of interest.
Molecular investigation Genomic DNA was extracted with a QIAsymphony (QIAGEN) and the DSP DNA Mini Kit. Whole-genome sequencing and bioinformatic analysis were performed as previously described  [9] , and the sequencing reads obtained are available from the European Nucleotide Archive (ENA)/GenBank (Accession number: PRJNA998219). We aligned the genomes of F93 and F94, ceftriaxone-resistant strains previously isolated in France (WHOY (F89), F90, F91 and F92) and Japan (WHOX) and strains with high-level resistance to azithromycin and resistance to ceftriaxone recently described in the UK (WHOQ and H22-494 (Case 10)), Austria (AT158) and Australia (A2735 and A2543)  [3, 4, [9] [10] [11] [12] [13] [14]. We corrected the alignments for recombination with Parsnp software version 1. 2  [15]  and constructed a maximum-likelihood phylogeny for core-genome comparisons, visualised with iTol, as previously described  [9].
To this end, previously published RNAseq data for MDP-treated vs untreated Ly6C hi mouse monocytes (GEO accession number GSE101496) were mined for candidate genes encoding for enzymes involved in glycolysis. We observed an MDP-induced upregulation of several glycolytic genes such as the enzyme Gapdh that regulates the conversion of D-glyceraldehyde 3-phosphate into 1,3bisphosphoglycerate (Supplementary Figure  7A )  (26). To get further insights on how NOD2 may provide energy for monocytes, we next quantified the glycolytic capacity and reserve of THP1 cells that were deficient or not for NOD2. As THP1 cells mainly rely on glycolysis as a source of ATP for survival  (78) , these cells represent a suitable model. The oxygen consumption rate (OCR) was measured in NOD2-deficient and WT THP1 cells.
Furthermore, mice injected with MDP were protected against TNBS-or DSS-induced colitis by the suppression of multiple TLR pathways (29) suggesting a cross-tolerization of TLRs by chronic NOD2 stimulation  (83). To evaluate the LPS responsiveness of MDP-treated cells, THP1 and THP1 cells that do not express NOD2 (THP1 NOD2 -/-) were incubated sequentially with MDP and then LPS or LPS alone. As expected, the cells pre-treated with MDP enhanced their subsequent cytokine response upon LPS treatment (Figure  5A , left upper part). This synergistic effect on the production of TNF-a to subsequent treatment with LPS was blunted with THP1 NOD2 -/-(Figure  5A , lower part). In order to confirm our data with primary cells, mouse BM monocytes were cultured in the presence of MDP. The responsiveness to LPS was next analyzed by measuring the release of Tnf-a by specific ELISA.
NOD2 loss-of-function mutation impairs the phenotypic switch of monocytes in CD patients in a TNF-a dependent manner To evaluate the importance of a monocyte phenotypic switch in CD patients with NOD2 mutations, we used a published RNA-seq data set from an exploratory cohort that is deposited in the GEO database (GSE69446)  (84). Given that CD14-expressing cells are obligate precursors of discrete subsets of phagocytes that play a role in CD pathogenesis, the monocytes were isolated from peripheral blood of healthy controls (n=2) and CD patients in complete remission for at least 4 weeks prior to inclusion. Among those five patients, three carried the loss-of-function mutation in the NOD2 gene, referred to as 1007fs mutation. As depicted in the Venn-diagram (Figure  6A ), MDP treatment of monocytes from healthy donors and CD patients significantly modified the expression of up to 362 and 1,660 genes, respectively.
Pathway enrichment analysis identified a set of genes among the 17 that were implicated in TNF-a signaling via NF-kB (5/200)(adjusted p-value 2. 53E-7) (Supplementary Data Set 5) and TNF-a effects on cytokine activity, cell motility, and apoptosis (4/135) (adjusted p-value 1. 75E-4) (Supplementary Data Set 6). Accordingly, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis of the 941 up-regulated genes in CD cells which are not present in control cells after MDP treatment identified a set of genes related to "TNF signaling pathway" (28/112)(adjusted p-value 5. 6E-11) (Supplementary Data  Set 7). These results extracted from transcriptomic data suggest that MDP might regulate and affect the differentiation of monocytes into the macrophage pathway by the induction of DCinducing soluble factors, such as TNF-a.
Among the common 156 up-regulated genes induced by MDP either in control and CD monocytes as compared to untreated, analysis of MSigDB database indicated also the pathway "mTORC1 signaling" and containing SLC7A5, XBP1, HSPA5, PPA1, AK4, and CFP (Supplementary Data Set 1). Further studies will be needed to better understand the MDP-induced regulation of glycolysis enzymes and their regulation of mTORC1 signaling  (98). Additionally, among the genes having a loss of MDP-induced expression in monocytes from SNP13 CD patients, AK4 is involved in the positive regulation of mouse myeloid cells glycolysis and inflammatory cytokine production such as Tnf-a and Il-6 (99). In addition, we observed a decline of oxidative phosphorylation and glycolytic activity in NOD2deficient THP1 cells suggesting a metabolic switch after NOD2 stimulation.
Analysis of Azimuth Cell Type database showed enrichment of up-regulated genes that are related to "Myeloid Dendritic Type 1" among which CCR7 is a hallmark of DC as it is critically required for their migration to lymph nodes (118). Equally of importance, the transcription factor NRA43, which is involved in the proper differentiation of mo-DCs  (85) , is less induced by MDP treatment in monocytes from CD patients than in cells from healthy controls (FC 1. 6 and FC 1. 71, respectively) as compared to untreated. Thus, by using BM chimera and fecal transplantation models, our study highlights the role of NOD2 and the microbiota in the reconstitution of monocyte-derived cells in the colon of mice at steady state while not affecting mo-Macs during colitis. NOD2 promotes mo-DCs development by interfering with mouse BM and human monocyte differentiation in macrophages.
PECF594-conjugated anti-CD11c (HL3) was from BD Horizon. Allophycocyanin-Cy7conjugated anti-CD11b (M1/70), Brilliant violet 510-conjugated anti-MHC Class II (I-A/I-E) (M5/114. 15. 2), Brilliant violet 650conjugated anti-CD45. 2 (104), Brilliant violet 711-conjugated anti-CD45. 1 (A20), PE-conjugated anti-CD135 (A2F10), APCconjugated anti-CD115 (AFS98), PE-conjugated anti-CD226 (10E5), FITC-conjugated anti-CD102 (3C4) were all from Biolegend. PE-conjugated anti-CCR2 (475301) was from R&D systems. The data were analyzed with Flowjo software V10. 1 (TreeStar). For human cells, a similar procedure was used with anti-HLA-DR FITC (eBioscience, clone LN3), anti-CD1a APC (Biolegend, clone HI149), anti-CD16 PE-Cy7 (BD Pharmingen, clone 3G8), anti-CD14 PE (Miltenyi Biotec, clone REA599).
Statistics Data were analyzed using Prism6. 0 (GraphPad Software, San Diego, CA). Statistical significance was assessed by non-parametric Mann-Whitney test or two-way ANOVA for multiple comparisons. Values represent the mean of normalized data ± SEM. *, P<0. 05; **, P<0. 01; ***, P<0. 001; ****, P<0. 0001. accession number GSE101496). (B) Mitochondrial respiration calculated as OCR (B, upper part), glycolysis activity calculated as ECAR (B, lower part), before and after Glc (glucose) administration. (Oligo, oligomycin; 2DG, 2deoxyglucose; AA, antimycin A; Rot, rotenone). Extracellular acidification rate (ECAR) was measured in the MDP-treated Nod2 +/+ (blue) and Nod2 -/-THP1 (red) cells. Data are representative of 2 independent experiments with at least four biological replicates. (C) Measure of the ECAR non glycolytic, ECAR basal and ECAR maximal between the THP1 NOD2 +/+ and NOD2 -/-.
SUPPLEMENTARY FIGURE 8 Mouse BM monocytes from WT (A) and Nod2 -/-mice (B) were treated with MDP or LPS for 24h (1 st Tx), and washed before a second treatment with MDP or LPS (2 nd Tx). mTNF-a was measured 24h after the last treatment in the supernatant. Data are representative of 2 independent experiments with at least three biological replicates. Bars indicate mean ± SEM. Statistical significance was assessed by the non-parametric Mann-Whitney test. **, P<0. 01.
Funding This work was funded by the  French government 's  ATIP-Avenir program  and by the  Fondation Recherche Médicale"  (grant number  EQU202103012718 ). LP also received a fellowship from the  ATIP-Avenir program , funding support by the  French national IBD patients' association (Association François Aupetit (AFA)  and by " Comité du Nord de La Ligue contre le cancer  ". CC received a fellowship funded by the cancer charity " La Ligue contre le cancer ". ES received funding support from the  Agence Nationale de la Recherche  (grant number  ANR-17-CE15-0011-01 ). This work is supported by a grant from  Contrat de Plan Etat-Région CPER Cancer   2015-2020.
Because human subjects are constantly exposed to environmental airborne antigens, it is impossible to obtain truly naive tonsils. Therefore, we turned to a model of non-human primates and compared a naive animal with macaques infected intra-nasally with influenza A virus (H3N2 strain). Tonsils were sampled on day 5 or 14 after infection. We profiled tonsil mononuclear phagocytes using scRNA-seq after cell enrichment (Fig. 3  A and Fig. S3 A ). Unsupervised clustering of the integrated dataset identified 11 clusters (Fig. 3  B and Fig. S3  B) that we annotated as monocytes (expressing VCAN, S100A8, and CLEC4A), S100A8 + macrophages (VCAN, S100A8, CD163, FCN1, CTSD, CD68), C1QC + macrophages (C1QC, CTSD, CD68, LGMN, DNASE2), macrophages (CTSD, CD68, LGMN, DNASE2), cDC1 (CPVL, FLT3, CLEC9A, C1orf54), three clusters of cDC2 (CPVL, CSF1R, FLT3, CD1C, FCER1A), transitional dendritic cells (DC; CPVL, LTB), and two clusters of activated DC (LAMP3, FSCN1, CCR7; Fig.
3  C and Table  S4 ). Cells from the different samples (naive, day 5 after infection and day 14 after infection) had a significantly different expression of IFNstimulated genes (CXCL10, ISG15, IFIT2, IFIT3, IFI44L, OAS2, MX1; Fig. S3 C ), with virtually undetectable expression in the naïve condition, high expression at day 5 after infection, and reduced to undetectable expression at day 14 depending on clusters. These results confirm the naive state of the uninfected macaque and suggest that inflammatory responses are in a resolving phase 14 d after influenza A infection. To examine the homology between macaque macrophage clusters and clusters from human tonsils, we generated gene signatures of the macaque macrophages and converted to human homologue genes (Table  S5 ). We calculated enrichment scores for these signatures in the human scRNA-seq dataset (Fig.
S3 D ). We found that the C1QC + macaque macrophages signature was enriched in the C1QC + human macrophages, while the S100A8 + macaque macrophages signature was enriched in S100A9 + human macrophages, and the signature for the third cluster of macaque macrophages was slightly enriched in S100A9 + human macrophages (Fig. S3 D ). Of note, FOLR2 expression was detected only in C1QC + macaque macrophages (Fig. 3 C ). To further explore the correspondence between macaque and human macrophage clusters, we used a referencemapping approach using the two scRNA-seq datasets. Macaque cells displaying high-scoring correspondence were projected onto the human macrophage clusters (Fig. 3 D ). Cells projecting onto the S100A9 + human macrophages belonged mainly to the macaque monocytes and S100A8 + macrophages clusters.
The third cluster of macaque macrophages showed mixed features and could represent the equivalent of human "intermediate" macrophages. Finally, to address the dynamics of these populations during immune responses, we examined the sample origin of cells in each cluster (naive or influenza-infected animals). We found that the cluster of monocytes was virtually absent from naive tonsils, and the proportion of cells belonging to the S100A8 + macrophages cluster was minimal in naive tonsils (Fig. 3, G  and H ; and Fig. S3 E ). We conclude that recruitment into the tonsils of short-lived monocyte-derived macrophages is not constitutive and correlates with on-going immune responses. Tonsil CD36 hi macrophages are specialized for the stimulation of Tfh cells To gain more insight into the function of human tonsil macrophage subsets, we performed enrichment analysis for biological pathways of human cells (BioPlanet database;  Huang et al.
, 2019)  using the RNA-seq data from human tonsils (Fig. 4 A ). We found that CD36 hi macrophages were enriched for cytokine activity (TNF-α, leptin, IL-5) and receptor signaling pathways (phagosome, extracellular matrix-receptor, G-protein coupled receptor). CD36 lo macrophages were enriched for the control of mitosis and microtubules dynamics (phosphorylation of Emi1, expressed genes between S100A9 + , C1QC + , and intermediate macrophages. (C) Average expression of selected genes for S100A9 + , C1QC + , and intermediate macrophages. Genes of special interest are highlighted by asterisks. (D-G) Macrophage populations were identified by the surface expression of CD36 and CD81. (D) Intracellular staining for C1QC and S100A8/9. Proportion of C1QC + or S100A8/9 + populations among CD36 lo , CD36 int , and CD36 hi macrophages (n = 8 in three independent experiments).