dataesr/scientific-paragraphs-categorization huggingface dataset:
pneumoniae, murB was proved to be co-transcribed with the potABCD genes  (Ware et al. , 2005; Liu, W. , 2018). In S. pneumoniae, the murB-potABCD operon is induced by environmental stresses and involved in fitness and pathogenicity  (Shah et al. , 2011). Insertion of IS1548 into the folK-murB intergenic region, toward the murB gene, might thus lead to a modification of the expression of murB and downstream genes, and could have some effects on the physiology of S. agalactiae and on its capacity of adaption to environmental stresses. In this work, we analyzed the prevalence of IS1548 in the folK-murB intergenic region in a collection of nine hundred and eleven S. agalactiae strains. The genomic sequences of these strains were available as whole genome contigs or as complete genome sequences at the National Center for Biotechnology Information (NCBI) database.
Identification of strains with IS1548 genomic insertion The genome of 911 S. agalactiae strains were analysed in this study. The sequences of these genomes were available as whole genome contigs or as complete genome sequences at the NCBI database on the 19 th of January 2018 (Supplemental Table  S1 , https://www. ncbi. nlm. nih. gov/genome/genomes/186?). To identify strains with an IS1548 genomic insertion, the complete genome sequence or all of the contigs sequences of each of these strains were first blasted (https://blast. ncbi. nlm. nih. gov/) with the IS1548 complete DNA sequence of strain Mc1 (GenBank Acc. n°Y14270). To identify strains with an IS1548 insertion in the folK-murB intergenic region, the genome of these strains was then blasted with a 239 bp sequence of strain L29 comprising the 3'-end of folK and the beginning of IS1548 (GenBank Acc.
Multi Locus Sequence Typing of Streptococcus agalactiae strains S. agalactie strains with an IS1548 genomic insertion were typed by MLST. Seven housekeeping genes were analyzed: alcohol dehydrogenase gene (adhP), phenylalanine tRNA synthetase gene (pheS), amino acid transporter gene (atr), glutamine synthetase gene (glnA), serine dehydratase gene (sdhA), glucose kinase gene (glcK) and transketolase gene (tkt). A sequence type (ST), based on the allelic profile of these housekeeping genes, was assigned to each strain by submitting the complete genome sequence or all of the contigs sequences of each strain to the Streptococcus agalactiae MLST databases (http://pubmlst. org/sagalactiae/; Jolley and Maiden, 2010). Strains were then grouped into clonal complexes (CCs) with the eBURST software (http://eburst.
In silico identification of transcriptional promoters and terminators To identify  70 transcriptional promoters upstream of murB, the sequences from the start of folK to the start of murB of strain SA87, from the start of folK of strain S10-201 to the left direct repeat (DR) of IS1548, from the left to the right DR of IS1548, and from the stop of the transposase of IS1548 to the start of murB of strain S10-201 were analysed with the BProm software from the SoftBerry suite (http://www. softberry. com/berry. phtml?topic=bprom&group=programs&subgroup=gfindb). To identify rho-independent transcriptional terminators upstream of murB, the sequences of the intergenic regions between folK and murB of strain SA87, between folK and the transposase gene of IS1548 of strain S10-201, and between the transposase gene (tnp) of IS1548 and murB of strain S10-201 were analysed with the Arnold program (http://rssf.
IS1548 targets the folK-murB intergenic region exclusively in CC19 strains We analyzed the prevalence of IS1548 in the folK-murB intergenic region in a very large collection of S. agalactiae strains using BlastN searches (Suppl. Table  S1 ). This collection comprised the S. agalactiae strains whose genomes were available as whole genome contigs or as complete genome sequences at the NCBI database on the 19 th of January 2018. Our analysis indicated that one hundred and twenty-one of the nine hundred and eleven analyzed strains (13,3 % of the strains) possess IS1548 in their genomes. However, only seventy-three of them (8,0 % of the strains) have integrated IS1548 in the folK-murB intergenic region (Suppl. Table  S1 ). To better perceive their correlation with sequence types and clonal complexes, we further analyzed all the strains possessing IS1548 by Multilocus Sequence Typing.
In vivo tracking of all macrophages within a 60 minute timeframe demonstrated that no specific directionality was maintained during their migration (Fig. 2G , n=23) as opposed to macrophages attracted to a wound site as an example of typical oriented migration (Fig. 2H, n=27 ). The speed of migration remained the same in both cases (data not shown). Subsequently, we quantified the evolution of macrophage shapes over time. Every single macrophage in the AGM reveals an ability to change shape within a very short time span (measured every 5 minutes) and to pass repeatedly through distinct shape subgroups over a 30 minutes course (Fig. 2I, n=10 ). The round shape appeared less frequently than others and live imaging showed that cells often adopted a round shape under two specific conditions: during cell division or once the macrophage entered the bloodstream.
Live Imaging Zebrafish embryos (lateral views, rostral to the left) were embedded in 0. 7% low melting agarose and imaged using a Zeiss LSM510 confocal microscope through a 40x water immersion objective with a 1024x256 pixel resolution at 28°C. All live imaging experiments were performed at 46-48 hpf and all time-lapse imaging occurred at an acquisition rate of one minute at a 1µm z-interval. The acquisitions were performed using ZEN2009. Image processing such as maximum intensity projections, 3D view, and overall image contrast adjustment were performed using Fiji software.
Cell tracking and velocity measurement Maximum intensity projections of 60 minute time-lapses acquired every minute were analysed using a manual tracking plugin in Fiji. Measured data were transferred into a Chemotaxis and Migration tool programme (Ibidi) to design tracking and rose plots (Figure  2G -H for rose plots, 3G-H and 4G-H for tracking plots). A rose diagram maps single counts of the position of every macrophage in a selected area (black and grey sectors of angle π/18) every minute over 60 minutes with an (x,y 0,0) starting point. The tracking plot diagram represents the migration path and distance of macrophages in the AGM with an x,y 0,0 starting point, being measured every minute over 60 minutes. The average of single macrophage velocities (µm min -1 ) during 15-60 minutes were used for analysis.
Acknowledgements We would like to thank  E. Lelièvre ,  M. Rossel  and  G. Lutfalla  for their critical review of this manuscript;  V. Diakou  and the  MRI facility  for their assistance,  A. Sahuquet  for his help in the particle analysis. This work was supported by grants from the  ARC, Chercheur d'Avenir -Région Languedoc-Roussillon, FRM  and  ATIP-Avenir. J. T. was supported by a fellowship from the  MESR  and  FRM  ( FDT20150532507 ), M. N. -C. by a fellowship from the  Université de Montpellier.
We took advantage of RNA-seq datasets (EGAS00001001296  24  ) for assessing whether TAX-related alternative splicing could pertain to asymptomatic carriers (AC) and ATLL patients. Overall, 542 (48%) TAXinduced splicing modifications were detected at least once across 55 clinical samples (Table  S1 ). Hierarchical clustering of these exons based on their inclusion rate (PSI) identified TAX-regulated exons that discriminate AC and ATLL samples from uninfected CD4+ T-cells (Figure  1D ). We furthermore confirmed that TAX promotes splicing events previously detected in HTLV-1 infected individuals, including AASS, CASK, RFX2 and CD44  4, 25. We firmly established that the expression of the splicing variant CD44v10 previously identified in HAM/TSP patients  25  fully relies on TAX expression (Figure  1C  and Figures  S1A-C ).
Importantly, TAX expression induced v10 inclusion at a similar level in both CD44DkB and parental cells (Figure  4C ). These results suggested that TAX-mediated effect on exon v10 splicing could depend on RELA binding in the vicinity of the alternative v10 exon. Supporting this hypothesis, the analysis of publicly available RELA ChIP-seq datasets revealed that intragenic RELA peaks are significantly closer to alternative exons than to constitutive exons (Figure  S3B ). In this setting, we observed that RELA binding sites are often found in the vicinity of TAX regulated exons (Figure  4D ). Using the MEME-ChiP suite as motif discovery algorithm  37  , we uncovered that RELA-binding sites located within the closest range (<1kbp) of TAXregulated exons coincided with the typical NF-kB consensus motif (Figure  4E ).
CRISPR design and construct The sequence-specific sgRNA for site-specific interference of genomic targets were designed using CRISPRseek R package  1  , and sequences were selected to minimize offtarget effect  64. Two complementary oligonucleotides were annealed and cloned into BbsI site of pSpCas9(BB)-2A-Puro (PX459) V2. 0 (Addgene plasmid #62988) for co-expression with Cas9 using 5U of T4 DNA ligase, T4 DNA ligase buffer (1X) (Roche). 293T-LTR-GFP cells were transfected with the mix of equimolar ratio of PX459-sgRNA1 and PX459-sgRNA2 (Table  S4 ). At 24h post-transfection, the medium was changed and 1μg/ml puromycin was added for selection and cells were cloned by serial dilution method.
ACCESSION NUMBERS The RNA-Seq data have been deposited on NCBI GEO under the accession number GSE123752. --------------------------------------  Oligonucleotides were previously described (36). The first round of amplification consisted in 15 cycles of PCR with the primers C13 and C12A, the second round consisted in 35 cycles with primers pv10 and C2A. Final PCR products were resolved on Agarose gel (1%).
(D) Bootstrapped distribution of median distance between intragenic RELA peaks and either TAX-regulated exons (red line, 1079 bp) or randomly chosen exons (10000 repetitions) (blue). pval : sample t-test (E) Consensus de novo motif for RELA binding sites <1kbp of TAX regulated exons. (F) Strength and minimum free energy (MFE) of 3' and 5' splice sites, and GC content (G) of TAX-regulated exons localized at <1kbp of RELA binding site. The exons of FasterDB database were used as control. (H) Relative RELA and DDX17 occupancies at TAX-regulated exons in control and TAX expressing cells. For each gene, the binding of RELA or DDX17 to the regulated exon is represented as a comparison to the binding to a neighbouring constitutive exon (*p-val≤0. 05). (I) Relative RELA occupancy at TAX-regulated exons in cells treated with DDX5/17-specific siRNA and expressing or not TAX.
Acknowledgments This study makes use of data ( EGAS00001001296 ) generated by  Department of Pathology and Tumor Biology, Kyoto University   24. We gratefully acknowledge support from the  PSMN (Pôle Scientifique de Modélisation Numérique)  of the  ENS de Lyon  for the computing resources. This work was supported by the  Ligue Contre le Cancer  ( Comité de la Savoie, de la Drome et du Rhône ), the  Fondation ARC  ( ARC PJA20151203399 ), and the  Agence Nationale pour la Recherche  (program EPIVIR and CHROTOPAS). L. B. A was supported by  Ligue Contre le Cancer. G. G was support by  ANR CHROTOPAS. M. T. was supported by a bursary from the  French Ministry of Higher Education and Science. F. M. , C. F. B, and D. A. were supported by  INSERM. E. W. was supported by  Hospices Civils de Lyon and Lyon I University (France).
The histogram shows mean ± sd of three independent experiments. (D) Bootstrapped distribution of median distance between intragenic RELA peaks and either TAX-regulated exons (red line, 1079 bp) or randomly chosen exons (10000 repetitions) (blue). pval : sample t-test (E) Consensus de novo motif for RELA binding sites <1kbp of TAX regulated exons. (F) Strength and minimum free energy (MFE) of 3' and 5' splice sites, and GC content (G) of TAX-regulated exons localized at <1kbp of RELA binding site. The exons of FasterDB database were used as control. (H) Relative RELA and DDX17 occupancies at TAX-regulated exons in control and TAX expressing cells. For each gene, the binding of RELA or DDX17 to the regulated exon is represented as a comparison to the binding to a neighbouring constitutive exon (*p-val≤0.
Time-Kill Curves Six individual TK assays were performed for each pathogen. Initial inoculum count was in the range 5 × 10 6 to 7 × 10 7 CFU/mL. Duration of incubation was 24 h with sampling at times of 0, 1, 2, 4, 8, and 24 h. Drug concentrations were expressed in the initial publication  (Sidhu et al. , 2014)  as multiples of MIC (0 = growth control, 0. 25, 0. 5, 1, 2, and 4 times measured MIC). For data fitting, MICs were back calculated to mg/L. The lowest detectable count was 33 CFU/mL; lower counts were set as below the quantification limit (BQL). All TKC data sets analyzed for this study are included in the manuscript and the supplementary File S2.
Data Analysis Pharmacodynamic data analyses were conducted using Phoenix R WinNonlin R 8. 0 (Pharsight Corporation, St Louis, MO, United States). For each pathogen, the 6 TKC data sets were analyzed simultaneously using a non-linear mixed effect model (NLME). A semi-mechanistic structural model of bacterial growth, incorporating a compartment for growing drug-sensitive bacteria (S) (CFU/mL) and a compartment named persisters (P) (CFU/mL), corresponding to a pool of non-growing and insensitive-drug bacteria (phenotypic resistance) was adopted (Figure  1 ;  Nielsen and Friberg, 2013). Visual Inspection of TKC indicates an initial phase of slow growth. To capture the delay required to achieve a maximal steady-state growing rate, a mitigating function for K GROWTH of the form was introduced (Equation  1 ): K GROWTH = K GROWTHMAX × 1 -EXP -Alpha × Time (1) where Alpha (per h) = rate constant to describe a progressive increase of K GROWTH over time; at time 0, K GROWTH = 0 then, K GROWTH increases progressively to reach K GROWTHMAX with a mean time equal to 1/Alpha.
Data were then log10 transformed for regression. When the bacterial count had decreased to 30 CFU/mL, it was considered that regrowth would not occur and curves were truncated for this cut-off value. PK/PD indices are conventionally determined using plasma protein unbound (free) concentration. The latest study at the time of writing reported that florfenicol protein binding was only 5% at the high concentration and was negligible at the low concentrations, representing a fu of essentially 1. 0  (Foster et al. , 2016). We therefore hypothesized that the binding of florfenicol to plasma protein could be ignored and that we simulated free plasma concentrations in the dose fractionation (vide infra). The area under the plasma concentration-time curve (f AUC PK(0-96 h) ) and percentage time plasma concentration exceeded MIC within 96 h (f T > MIC%) were computed using the statistical tool of Phoenix.
The PK data are generated by solving a model readily obtained through either classical or population investigations. The PD model predicts a microbiological response for a given drug exposure at two inoculum levels of 10 5 and 10 7 CFU/mL (corresponding to metaphylactic and treatment circumstances, respectively). Currently, rodent models are widely used but they raise questions of cost and ethical use of animals in research. As an alternative to animal studies, the hollow fiber model has been developed as a dynamic infection model  (Michael et al. , 2014)  but its use in veterinary medicine has not yet been reported. Hollow fiber technology is costly and resource demanding; few alternatives like chemostats can be explored but have their own limitations. The present adaptation of TKC results offers the advantages of using historical data and its availability for many veterinary pathogens.
Flow cytometry Spleen from mice were collected and immune cells were filtered through a sterile nylon membrane. Cell suspensions were stained at 4°C in FACS Buffer (PBS, 1% FBS, 2 mM EDTA) with the following fluorescent-labelled antibodies: anti B220-BV421 (clone RA3-6B2, 1/400), anti CD4-FITC (clone RM4-5, 1/2000), anti CD8a-APC (clone 53-6. 7, 1/400), anti CD62L-BV421 (clone MEL-14, 1/200), anti CD44-PE (clone IM7, 1/200), anti-PD-L1-PE (clone 10F. 9G2, 1/80), anti PD-1-PECy7 (clone 29F. 1A12, 1/50), anti CD80-APC (clone16-10A1, 1/2500) and anti CD86-FITC (clone GL-1, 1/600). Stained cells were analyzed using a BD-Fortessa SORP flow cytometer (BD Bioscience; US). Results were analyzed using Kaluza Flow Cytometry software 1. 2 (Beckman Coulter; France).
Foxp3 and CD25 expressions were unchanged (data not shown). The severe disease of Foxp3 Cre Rela lox mice in the absence of major Treg quantitative defect suggests that Tregs may be dysfunctional. In vitro assays showed that RelA-deficient Tregs, purified from 5 to 6 week-old mice, were able to suppress proliferation of conventional T cells almost as efficiently as control Tregs (Figure  3D ). To further analyze their function, we assessed their capacity to suppress colitis induced by effector T cells transferred into lymphopenic mice, measured by weight loss and histology. Surprisingly, not only RelA-deficient Tregs were unable to control colitis but the disease was even more severe compared to mice transferred with effector T cells        3E, F ). This exacerbated colitis was not associated with increased number of cells from Tconv origin (CD90.
Antibodies and Flow Cytometry Analysis The following mAbs from BD Biosciences were used: anti-CD45 (30-F11), anti-CD8 (53-6. 7), anti-CD4 (RM4-5), anti-CD62L (MEL-14), anti-CD90. 1 (OX-7), anti-CD45. 1 (A20), anti-CD45. 2 (104), anti-CD25 (PC61 or 7D4), anti-ICOS (7E. 17G9), anti-GITR (DTA-1), anti-CD103 (M290), anti-Helios (22F6), anti-CTLA-4 (UC10-4F10-11), anti-CD11b (M1/70), anti-CD11c (HL3), anti-CD19 (1D3), anti-IA/E (M5/114. 15. 2), anti-Ly6C (AL-21), anti-Ly6G (1A8). Anti-GFP antibody was purchased from Life Technologies. Anti-CD3 (145-2C11), anti-Foxp3 (FJK-16s), anti-CD44 (IM7), anti-Ki-67 (SOLA15), anti-Nrp1 (3DS304M), anti-NKp46 (29A1. 4) and anti-F4/80 (BM8) were purchased from eBioscience, and Foxp3 staining was performed using the eBioscience kit and protocol. Cells were acquired on a BD LSRII and a BD Fortessa X20 cytometers and analyzed using FlowJo software.
ACKNOWLEDGMENTS We are grateful to  Pr. Alexander Rudensky  and  Pr. Falk Weih  for providing us the Foxp3 Cre and Rela lox mice, respectively, and  Christelle Enond ,  Doriane Foret ,  Flora Issert ,  Olivier Bregerie ,  Bocar Kane  and  Maria Mihoc  for their expert care of the mouse colony. We also want to thank  Fatiha Bouhidel  for helping for the histological analysis. Part of the data presented in this manuscript have been described in the PhD thesis of ER that is freely available online (https://tel. archives-ouvertes. fr/tel-01884171). This work was supported by the  Agence Nationale de la Recherche  (grant  ANR-15-CE15-0015-03 ), the  Fondation pour la Recherche Médicale  (Equipes FRM 2015;  FDT20160435696 ), the  Fondation Bettencourt Schueller  and  NIH  grant  AI-116834.
FUNDING This study was supported by The  European Institute for Research in Cystic Fibrosis (IERFC)  non-profit foundation (to  Dr. L Maiuri ),  E-Rare (Rescue CFTR preclinic)  (to  Dr. L Maiuri  and  Dr. G Kroemer );  Agence National de la Recherche (ANR) -Projetsblancs ;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC) ;  Cancéropôle Ile-de-France ;  Institut National du Cancer (INCa) ;  Fondation Bettencourt-Schueller ;  Fondation de France ;  Fondation pour la Recherche Médicale (FRM) ; the  European Commission (ArtForce) ; the  European Research Council (ERC) ; the  LabEx Immuno-Oncology ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; the  SIRIC Cancer Research and Personalized Medicine (CARPEM) ; and the  Paris Alliance of Cancer Research Institutes (PACRI)  (all to  Dr.
OT-1 cells and bacterial burden were analyzed 4 days after transfer (day 50, see Figures  S1, 2G,  and 2H ). (G) Day 46, from left to right: the percentage in CD8 + T cells of OT-1 cells, the percentage of OT-1 cells expressing granzyme B, the corrected geometric MFI of granzyme B in OT-1 cells (expressed as [MFI OT-1 -MFI non-OT-1 ]), and the corrected geometric MFI of IL-21R in OT-1 cells (expressed as [MFI OT-1 -MFI non-OT-1 ]). (H) The bacterial burden measured in the spleen 4 days after infection (day 50). The data are from five independent experiments. Mann-Whitney test (E-G) and Kruskal-Wallis test (H) were used for statistical analysis. p. i. , post-infection. *p < 0. 05; **p < 0. 01; ***p < 0. 001. may be related to the higher expression of CCR7 by CXCR5 + OT-1 cells (Figure  S2C ).
ACKNOWLEDGMENTS I. V. is supported by the  Associazione Italiana per la Ricerca sul Cancro  (AIRC, IG 2017, grant number  20417 ),  Ministero Italiano della Salute  (grant number  RF_GR-2011-02351355 ), and a startup grant from the  Italian Institute for Genomic Medicine (Turin, Italy ). L. M. C. is supported by  National Institutes of Health/National Cancer Institute , the  Breast Cancer Research Foundation , and the  Brenden-Colson Center for Pancreatic Health. G. K. is supported by the  Ligue contre le Cancer  (equipe labelisee);  Agence National de la Recherche (ANR)-Projets blancs ;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases ;  Association pour la Recherche sur le Cancer (ARC) ;  Canceropole Ile-de-France ;  Chancelerie des Universites de Paris  ( Legs Poix );  Fondation pour la Recherche Medicale (FRM) ; a donation by Elior;  European Research Area Network on Cardiovascular Diseases  ( ERA-CVD, MINOTAUR ); the  European Union   Horizon 2020 Project Oncobiome ;  Fondation Carrefour ;  Institut National Du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ;  LeDucq Foundation ; the  LabEx Immuno-Oncology ; the  RHU Torino Lumie  `re; the  Seerave Foundation ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and the  SIRIC Cancer Research and Personalized Medicine (CARPEM).
After centrifugation 10 min at 1200 rpm at 4°C, cells were resuspended in Sorensen-Sorbitol (15 mM KH2PO4, 2 mM Na2HPO4, 120 mM sorbitol) and the fluorescence was measured by FACS. FACS measurement was performed on a FACScalibur (Beckton Dickinson) and the data analysed with FlowJo (TreeStar, USA).
Infection assay Infections were performed as previously described  (Hagedorn and Soldati 2007 , Arafah, Kicka et al. 2013 , Sattler, Monroy et al. 2013). D. discoideum cells were infected with 5. 10 8 mycobacteria or 500 µL of prepared BBCs inoculum. Infections with GFP-expressing mycobacteria were monitored by FACS  (Sattler, Monroy et al. 2013). At times of interest, infection aliquots were mixed V:V with Sorensen-azide (15 mM KH2PO4, 2 mM Na2HPO4, 5 mM azide) and pelleted for 4 min at 12 000 rpm, RT. The pellet was resuspended in Sorensen-Sorbitol (15 mM KH2PO4, 2 mM Na2HPO4, 120 mM sorbitol). Prior to FACS measurement, 4. 5 µm fluorescent beads (100 beads/µL, YG-beads, Polyscience) were added to the sample as an internal particle concentration standard. FACS measurement was performed on a FACScalibur (Beckton Dickinson) and the data analysed with FlowJo (TreeStar, USA).
Protein identification Protein identification was performed with the help of the Easyprot platform. The Easyprot platform proceeds as follows: peak lists are generated from raw data using (ReadW). After peaklist generation, the CID and HCD spectra are merged for simultaneous identification and quantification  (Dayon, Pasquarello et al. 2010  and http://www. expasy. org/tools/HCD_CID_merger. html). The peaklist files were searched against the uniprot_sprot database (2011_02 of  08-Feb-2011). Dictyostelium discoideum, Mycobacterium marinum and Mycobacterium smegmatis taxonomies were specified for database searching. The parent ion tolerance was set to 10 ppm. TMT-sixplex amino terminus, TMT-sixplex lysine (229. 1629 Da) and carbamidomethylation of cysteines were set as fixed modifications. Oxidized methionine was set as variable amino acid modification.