dataesr/scientific-paragraphs-categorization huggingface dataset:
UpSet Diagrams Standard Venn and Euler diagrams are an inadequate solution for quantitative visualization of multiple (n > 4) gene set intersections. Thus, we used the UpSetR package v1. 4. 0 in RIBAP, a scalable alternative method for visualizing intersecting sets and their properties  [45]. As input for UpSetR, we selected all 33 genomes as well as type strains of the 12 species included in this study and their homologous gene groups identified with RIBAP. We restricted the visualization to the largest 40 intersecting sets.
Multiple Blast to Identify Homologs of Pmp, Inc and SinC Genes All sequences of pmp/Pmp and inc/Inc family members of C. psittaci, C. avium, C. gallinacea and C. abortus type strains annotated in the NCBI (nucleotides) and/or UniProt (proteins) databases were compiled in multi-FASTA files (one query file per species) and blasted against the genome sequences of all 33 strains of this study. The resulting hits were sorted according to target strains and filtered at 50% sequence identity. Likewise, the amino acid sequence of SinC of C. psittaci (EGF85279. 1) was blasted against all 33 genomes.
Availability of Data and Materials The datasets generated and/or analyzed during the current study are available in an OSF repository at https://osf. io/j9zas/. All code is available at https://github. com/hoelzer-lab/ribap. Novel sequencing data were uploaded to ENA acc. no. PRJEB40883 and assemblies generated for eight strains in the course of this study are available at https://osf. io/j9zas/.
In agreement with the observations on cultured cells, the number of cvpF::Tn mutant GE in the spleen of infected animals was significantly lower than those found for the WT bacterium and complementation of the cvpF::Tn mutant partially restored this replication defect (Figure  1G ). The splenomegaly induced by the cvpF::Tn mutant was also much lower than the one provoked by WT bacterium (Figure  1H ), strongly suggesting that the virulence of the cvpF::Tn ). CCV area for each strain was determined using the Cell Profiler software. (C) U2OS cells were challenged as in B, and Genome Equivalents (GE) were determined by quantitative PCR (D) PMA-treated THP1 cells were challenged for 6 d either with Coxiella WT GFP (WT), cvpF::Tn mutant, or the complemented cvpF::Tn strain (cvpF::Tn Comp. ) The colony area for each strain was determined using the Cell Profiler software.
Real-time PCR Lightcycler 480 SYBR Green I master mix (Thermo Fisher Scientific, 4887352001) was used for real-time PCR and readouts was acquired on a Lightcycler480 real-time system (Roche) according to the manufacturer's instructions. The relative levels of transcripts were calculated by the ΔΔCT threshold cycle (CT) method using dotA as the internal control. The relative levels of mRNA from the WT samples were adjusted to 1 and served as the basal control value. Each experiment was done in biological triplicate. For the calibration curve construction, aliquots of the plasmid pCR2. 1::dotA (Provided by Dr. Robert Heinzen, 8 × 10 10 copies/μl) in dilutions ranging from 8 × 10 2 to 8 × 10 9 copy numbers were applied. Three replicative runs of a dotA real-time PCR for all samples were performed.
Microscopy and image analysis Samples were imaged with a Zeiss Axio Imager Z1 epifluorescence microscope (Carl Zeiss, Germany) connected to a CoolSNAP HQ 2 CCD camera (Teledyne Photometrics, Tucson, AZ). Images were acquired with 40x oil immersion objectives and processed with Metamorph (Molecular Devices, San Jose, CA). High throughput image acquisition was performed on an epifluorescence automated microscope (Cellomics, ThermoFisher Scientific, Pittsburgh, PA) equipped with a 20X objective. Image analysis was performed using the CellProfiler 3. 0 software, as previously described  (Martinez et al. , 2015). Briefly, Hoechst 33258 (Sigma-Aldrich, 94403) staining, cytoplasmic mCherry fluorescence produced by U2OS mCherry cells and LAMP1 labeling were used to identify nucleus, cytoplasm, and lysosomal/late endosomal compartments, respectively.
The GFP signal was used to identify C. burnetii colonies, and CCVs were segmented based on the LAMP1 antibody labeling and GFP fluorescence, respectively. After sorting non-infected and infected cells, a morphometrical analysis was carried out over a population of at least 200 objects (CCVs and/or C. burnetii colonies) per condition. Confocal microscopy was performed using a TCS SP8 HyVolution microscope (Leica, Germany) connected to a DFC9000 sCMOS camera or an SP5-SMD microscope (Leica, Germany). Images were acquired with 63x oil immersion objectives and processed with LAS-AF (Leica, Germany). Fluorescence was measured using Fiji analysis software (version 2. 0. 0) and the Multicolor line profile plot macro (University of Leicester). LC3B and RAB26 recruitment at CCVs were scored as follow: The mean ± confidence interval (CI) Pearson's correlation coefficient between LAMP1 and the cellular marker of interest (LC3B or RAB26) was calculated using Icy analysis software from triplicate experiments representing a total of at least 80 CCVs per condition (WT, cvpF::Tn, cvpF::Tn Comp.
) The colony area for each strain was determined using the Cell Profiler software. (E) THP1 cells were challenged as in D and Genome Equivalents (GE) were determined by quantitative PCR. (F) Coxiella WT GFP (WT), dotA::Tn mutant, cvpF::Tn mutant and the complemented cvpF::Tn strain (cvpF::Tn Comp. ) were grown for 8 d in ACCM-2 and Genome Equivalents (GE)/ml were determined by Pico Green assay. (G) Genome Equivalents (GE) calculated using TaqMan real-time PCR with DNA purified from infected spleens of 5 SCID mice per group on day 14 after challenge with 1 × 10 6 GE equivalents of the strains shown. (H) Splenomegaly calculated as spleen weight as a percentage of total body weight at the time of necropsy on day 14 after infection with 1 × 10 6 GE equivalents of the strains listed in the figure legend.
Acknowledgments This work was supported by the  French National Research Agency  ( ANR ;  ANR-14-CE14-0012-01 , project  AttaQ ) and by the  ERA-NET   Infect-ERA  ( ANR-13-IFEC-0003 , project  EUGENPATH ). FAS is the recipient of a fellowship from the  Agence Nationale Bourses Gabon (ANBG). We acknowledge the imaging facility MRI, member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  «Investments  for the future»). We thank  Dr. Martine Biard-Piechaczyk ,  Dr. Lucile Espert , and  Dr. Fabien Blanchet  ( IRIM CNRS UMR9004 Montpellier, France ) for scientific advice and sharing material.
Global acetylome profiling (GAP) assay To analyze the substrate specificity of the AtNAA60 20-200 protein, AtNAA60 20-200 was fused with a SUMO Tag (see Cloning) and expressed for 5 h in the Rosetta II (DE3) Escherichia coli cells (Novagen) at 37°C. Extraction of E. coli proteins and analysis of the N-terminal acetylome were performed as described by  Dinh et al. (2015). The resulting MS data have been deposited in the PRIDE archive (https://www. ebi. ac. uk/pride, Project: PXD016533).
Protein N-terminal characterization and N-terminal acetylation quantification Soluble leaf proteins from 6-wk-old soil-grown wild type and the NAA60-depleted mutants were extracted for protein N-terminal characterization and quantification of N-terminal acetylation. The extracted proteins were processed and enriched by an SCX approach for quantification of N-terminal peptides using MS as described by  Bienvenut et al. (2017). The resulting MS data have been deposited in the PRIDE archive (https://www. ebi. ac. uk/ pride, Project: PXD016494). Localization of AtNAA60 in A. thaliana protoplasts or in N. benthamiana leaves The isolation and transfection of mesophyll protoplasts from Arabidopsis were performed as described by  Sparkes et al. (2006). Agrofiltration of Nicotiana benthamiana was done as described by  Yoo et al.
Structural characterization of AtNAA60 To characterize AtNAA60 at the atomic level, AtNAA60 20-200 was crystallized in complex with AcCoA (AtNAA60/AcCoA) and in complex with the bisubstrate analog CoA-Ac-MVNAL (Fig. S5a ) (AtNAA60/CoA-MV). The design of the bisubstrate was based on the most effectively acetylated substrate MVNALE (Fig. 2a ). For both complexes, well-diffracting crystals in space group P 2 1 2 1 2 1 with two NAA60 molecules per asymmetric unit but with different cell dimensions could be obtained. Initial phases for the AtNAA60/AcCoA complex were obtained by molecular replacement with the HsNAA60/CoA-MK structure  (Stove et al. , 2016). The AtNAA60/CoA-MV data set was phased using AtNAA60/AcCoA as the molecular replacement search model. Subsequently, X-ray structures could be built at 1.
(2015b). MQPT is the Nterminal sequence of DUF662 (At5g03660). Data represent mean AE SE. Different letters indicate individual groups identified by pairwise comparisons with a Holm-Sidak, one-way ANOVA (P < 0. 05, n = 4). in humans, we determined Golgi-stack formation in the Arabidopsis wild type and the naa60-1 mutant by electron microscopy. In contrast to the function of NAA60 in human cells, absence of AtNAA60 in plant cells had no impact on the formation of the Golgi apparatus (Fig. 5e, f ), which agrees with the exclusive localization of the full-length AtNAA60 at the plasma membrane (Fig. 1b-g ). Next, we profiled the N-terminal acetylome in leaf cells of naa60-1 to identify AtNAA60 substrates in vivo. Characterization of more than 2059 N-terminal peptides from 1623 distinct nonredundant proteins by MS/MS analysis suggested that loss of AtNAA60 function had no significant impact on the NTA at the global scale (Fig.
Acknowledgements Data collection was performed at ESRF Grenoble beamlines ID23 and ID30B. We acknowledge the data storage service SDS@hd supported by the  Ministry of Science, Research and the Arts Baden-W€ urttemberg (MWK)  and the  German Research Foundation (DFG)  through grant  INST 35/1314-1 FUGG  and  INST 35/1503-1 FUGG. We thank  C. Siegmann  from the  BZH/Cluster of Excellence:CellNetworks crystallization platform for protein crystallization , and  G. Stier  for initial cloning. We are grateful to M. K€ ohn,  M. Fontanillo Dolz  and  N. H€ offlin  ( Freiburg University ) for support in the bisubstrate synthesis. We thank  M. Mayer  for access to the circular dichroism spectrophotometer and T. S€ ollner ( Heidelberg University ) for providing lipids. Electron microscopy was performed at the  EM Core Facility of Heidelberg University  and we are thankful to  Steffi Gold  for helping with sample preparation and  Stefan Hillmer  for microscopic analysis.
The CG team benefitted from the support of the  Labex Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ) and used the facilities and expertise of the I2BC proteomic platform SICaPS, supported by  IBiSA ,  Ile de France Region ,  Plan Cancer ,  CNRS  and  Paris-Sud University. We thank  G. Bernal  for the preparation of samples for GAP analysis. Research at Heidelberg was funded by the  German Research Council (DFG)  via Project-ID 201348542 -SFB 1036 ( TP22  to IS and  TP13  to MW) and the  Leibniz programme  ( SI 586/6-1 , to IS), and the  ERA-CAPS   Research Programme 'KatNat'  ( ANR-17-CAPS-0001-01  to IF, CG, MW). Research in CG's lab was supported by the  French Agence Nationale de la Recherche agency  ( ANR-13-BSV6-0004 ). RH and IS are investigators of the Cluster of Excellence: CellNetworks.
Acknowledgments: We thank  Prof. Dan Kaplan  for sharing huCD207-cre mice and  Prof. Ulrich Kalinke  for sharing IFNAR flox mice. We thank the  Lahl, Agace, Bekiaris , and  Johansson-Lindbom  laboratories for many fruitful discussions,  Matthias Schiemann  and  Immanuel Andrä  for flow cytometry support,  Katrine Fog Thomsen  for technical support, and  Allan Mowat  for editing of the manuscript. KL was supported by a  Vetenskapsrådet Young Investigator Award  ( 2014-3595 ), the  Ragnar Söderberg Foundation  Fellowship in Medicine, a  Lundbeck Foundation Research Fellowship , the  Åke Wiberg Foundation , the  Carl Trygger Foundation , and the  Crafoord Foundation. JH was supported by a  MARIE CURIE Fellowship  as part of the  EU-funded  project  HC Ørsted Postdoc. AGL received travel support from the  Niels Bohr Foundation.
Bioinformatic analysis of NGS sequences The bioinformatic workflow includes common data preprocessing steps for quality control and filtering. Then, data complexity is reduced before the identification of the PV-related sequences. Groups of sequences are defined based on similarity between identified sequences and available PVs sequences in the NCBI database. De-novo assembly is then performed to reconstruct the full amplified region covered by several primers systems. Finally, the reconstructed sequences are taxonomically classified based on two independent methodologies: alignment-based, and homology-based, respectively, before generation of diverse output reports. Details of the bioinformatic pipeline named "PVAmpliconFinder" and parameters used can be found in (https:// github. com/IARCbioinfo/PVAmpliconFinder).
Each read was matched against the National Center for Biotechnology Information (NCBI) sequences database by means of BLAST algorithm and assigned to its closest PV types. The different PV sequences were analyzed following the official taxonomic HPV classification based on the similarity in L1 ORF  [5]. Data analysis obtained using RAxML-EPA, a method that offers an accurate classification of short PV fragments, reported that the 1,208,356 reads analyzed comprised 1,204,447 (99. 7%) reads from known PVs (≥90% of identity with L1 ORF of any known PV), while the remaining reads (n = 3909) corresponded to novel putative PVs (< 90% of identity with L1 ORF of any known PV). The majority of the reads (81. 1%, 976,693 reads) corresponded to β PVs, followed by γ (17. 3%, 208,932 reads) and α types (0.
The readout was the number of GFP+ cells measured by Guava Technology. The cut-off for each individual plate was determined by taken the average of "cell only" values +3× the standard deviation. "Cell-only" is the fluorescence of the AD293 cell-line alone (autofluorescence). Percentage neutralization was determined by virus + serum over virus-only values. The data depicted (Supplementary Fig. 2 ) are at 10× diluted serum.
To detect granulomas that were metabolically active, a 20 min static PET scan was acquired. Static PET data were also acquired under isoflurane anesthesia after 45 min uptake time of an intravenous injection of 100 MBq 18 F-FDG with 1:9 coincidence mode and a time-window of 5 ns. Post-CT reconstruction was performed using Butterworth filter, with a convolution scatter correction considering 16 cm diameter of the RM and having a slice thickness and voxel size of 320 µm. List mode PET emission data were iteratively reconstructed (OSEM3D, 8 iterations and 9 subsets) into a single frame PET image voxel size of 800 µm. PET data were corrected for radioactive decay, scatter and random coincidences. In addition, attenuation correction was performed via the formation of a material map for which the CT without contrast was used.
Statistical analysis Statistical analyses were conducted using GraphPad Prism 7 software (version 7. 0d; GraphPad Software, La Jolla, CA). For group comparison analysis, a non-parametric rank test (Kruskal-Wallis) was performed with post-hoc Dunn's correction for multiple comparison, to compare the vaccine treatment groups to the non-vaccinated group. Subsequently, a Kruskal-Wallis test with post-hoc Dunn's correction for multiple comparison was performed among the four experimental groups to establish whether one strategy was statistically better than others. A Mann-Whitney test was performed to compare TB infection and disease signals between complete parenteral immunization (group A) and mucosal immunization (group B) directly. All tests were two-tailed, and results with p < 0. 05 were considered to be statistically significant.
ACKNOWLEDGEMENTS The authors thank  Marie-Ange Demoité  ( GSK ) for her support to the program and critically reading the manuscript,  Lynda Stuart  for discussions on study design, and  Megan Fitzpatrick  for virus generation and nebulizing testing. The current study was sponsored by  Aeras  and co-funded by  BPRC. The  PET-CT infrastructure at BPRC  and the  TB  project management at  BPRC  (KGH) were supported by a grant from the  Bill and Melinda Gates Foundation  (Grant No. OPP1130668 ). The PET-CT Reading Core at the  University of Pittsburgh  is supported by  BMGF  award  OPP1130668 , granted to  Dr. J. Flynn.
Major Venom Peptide and Protein Families Retrieved From L. tarantula Venom Gland Transcriptome To gain an unprecedented insight into the venom repertoire of L. tarantula, high throughput sequencing technology was employed to decipher the venom gland transcriptome. From the 28,793,065 raw Illumina reads obtained, a total of 389,316 contigs were assembled using Trinity version 2. 1. 1. All Contigs were annotated using an improved version of the previously published Ekenda Hidden Markov Models (HMM) library and the hmmcompete program  [21]. Identified hits were classified into toxins family using Ekenda Hidden Markov Models (HMM) and the hmmcompete program. This classification is supported by an exhaustive set of 219 new profile.
Major Venom Peptide and Protein Families Retrieved from L. tarantula Venom Gland Transcriptome To gain an unprecedented insight into the venom repertoire of L. tarantula, high throughput sequencing technology was employed to decipher the venom gland transcriptome. From the 28,793,065 raw Illumina reads obtained, a total of 389,316 contigs were assembled using Trinity version 2. 1. 1. All Contigs were annotated using an improved version of the previously published Ekenda Hidden Markov Models (HMM) library and the hmmcompete program  [21]. Identified hits were classified into toxins family using Ekenda Hidden Markov Models (HMM) and the hmmcompete program. This classification is supported by an exhaustive set of 219 new profile hidden Markov models (HMMs) able to attribute a given peptide to its precise peptide type, family, and group  [21].
Sequences were then annotated based on two consecutive BLASTs, one using a spider specific database and the second using the whole UniprotKB database (version of 2019_03). Signal Peptide and propeptide were predicted using respectively SignalP version 5. 0  [22]  and SpiderP tool available on the Arachnoserver webserver  [23]. This automatic procedure takes advantage of the ability of HMMs to detect distantly related sequences and allowed to retrieve and to annotate a total of 18 putative sequences from 10 structural families including both venom proteins and neurotoxin-like typical peptides (including cysteine-rich, neurotoxin-like and linear peptides; Figure  2 ). specific database and the second using the whole UniprotKB database (version of 2019_03). Signal Peptide and propeptide were predicted using respectively SignalP version 5.
0  [22]  and SpiderP tool available on the Arachnoserver webserver  [23]. This automatic procedure takes advantage of the ability of HMMs to detect distantly related sequences and allowed to retrieve and to annotate a total of 18 putative sequences from 10 structural families including both venom proteins and neurotoxinlike typical peptides (including cysteine-rich, neurotoxin-like and linear peptides; Figure  2 ). The sequences were deposited to GenBank (accession numbers: MT725462-MT725492). Among the 10 distinct structural families of venom related sequences, we found four families of spider neurotoxins (SN) and six families of venom proteins (VP). Classification as SN or VP were directly provided by the HMM-based annotation system used indicating sequence homology with spider neurotoxins and venom protein respectively  [21]  (Table  1 ).
Although classified into venom proteins, VP-11 and VP-12 families contain short and disulfide rich peptides. Therefore, we included these two families in the next section on the description of disulfide rich peptide toxins. The sequences were deposited to GenBank (accession numbers: MT725462-MT725492). Among the 10 distinct structural families of venom related sequences, we found four families of spider neurotoxins (SN) and six families of venom proteins (VP). Classification as SN or VP were directly provided by the HMM-based annotation system used indicating sequence homology with spider neurotoxins and venom protein respectively  [21]  (Table  1 ). Although classified into venom proteins, VP-11 and VP-12 families contain short and disulfide rich peptides. Therefore, we included these two families in the next section on the description of disulfide rich peptide toxins.
Proteomic Analysis of the Electrically Stimulated Venom With the aim of identifying a maximum of the peptides and proteins present in the venom of L. tarantula, shotgun proteomics on a high-resolution mass spectrometer was performed on the more complex electrically stimulated venom. After reduction, alkylation, and trypsin digestion of the venom sample, the resulting peptides were fragmented, leading to the acquisition of 15,224 MS and 89,834 MS/MS scans, and further analyzed using PEAKS software (Bioinformatics solutions, Waterloo, ON, Canada). The search database was composed of our translated transcriptome, and a false discovery rate of 1% was applied. The results were filtrated in PEAKS Studio using stringent parameters, including peptide -10lgP ≥ 24. 6, protein -10lgP ≥ 20, proteins unique peptides ≥2, and de novo average local confidence (ALC) score ≥80%.
The final cDNA libraries were validated with a Fragment Analyzer (Agilent Santa Clara, CA, USA) and quantified with a KAPA qPCR kit (Kapa Biosystems, Wilmington, MA, USA). The transcriptome of L. tarantula was sequenced as part of a larger project comprising 15 other venom gland transcriptomes. On three sequencing lanes of V2 flowcells, the 16 libraries were pooled in equal proportions, denatured with NaOH and diluted to 18 pM before clustering. Cluster formation, primer hybridization and single-end read, 125 cycles sequencing were performed on cBot and HiSeq2500 (Illumina, San Diego, CA, USA) respectively. Image analysis and base calling were performed using the HiSeq Control Software v. 2. 2. 68 (Illumina, San Diego, CA, USA) and Real-Time Analysis component v. 1. 18. 66. 3 (Illumina, San Diego, CA, USA).
Demultiplexing was performed using Illumina's conversion software (bcl2fastq 2. 18). The quality of the data was assessed using FastQC from the Babraham Institute v. 0. 11. 5 and the Technologies Inc. , Santa Clara, CA, USA) according to the manufacturer's specifications. Peptides were dehydrated in a vacuum centrifuge. Samples were then subjected to nano-flow liquid chromatography coupled to tandem mass spectrometry (NanoLC-MS/MS). Samples were resuspended in 20 µL formic acid (0. 1%, buffer A) and 1 µL was loaded onto an analytical 25 cm reversed-phase column (75 mm inner diameter, Acclaim Pepmap 100 ® C18, Thermo Fisher Scientific) and separated with an Ultimate 3000 RSLC system (Thermo Fisher Scientific, Waltham, MA, USA) coupled to a Q Exactive HF-X (Thermo Fisher Scientific, Waltham, MA, USA) via a nano-electrospray source, using a 123 min gradient of 6% to 40% of buffer B (80% ACN, 0.
Bioinformatic Integration of Proteomic and Transcriptomic Data PEAKS Studio 8. 5 software (Bioinformatics solutions, Waterloo, ON, Canada) was used to match MS/MS spectra obtained from proteomic analysis of L. tarantula venom. MS spectra were elucidated based on a personalized database resulting from assembled contigs translated into their six reading frames. Carbamidomethylation was set as fixed modification, while oxidation (M) was set as variable modifications, with maximum missed cleavages at 3 for trypsin digestion. Parent mass and fragment mass error tolerance were set at 5 ppm and 0. 015 Da respectively. False discovery rate (FDR) of 1% and unique peptide ≥2 were used for filtering out inaccurate proteins. A -10lgP > 120 was used to estimate whether the detected proteins was identified by enough reliable peptides MS/MS spectra.