dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008). For example, at the early stages of CME, only a small numbers of proteins are present before they polymerize and produce a brighter stable signal. Our observations indicate that the spots detected in our experiments can be tracked over the length of the movie (Figure  3B ). Once the tracking is complete, the fluorescence intensity and lifetime of every particle are examined (Figure  3C ). To do this, we extracted the raw tracking output of the cmeAnalysis program and used custom made Matlab scripts to screen the trajectories. Tracks that started too close to the start, or to the end, of the movie were excluded to ensure complete recording. Furthermore and as previously described, tracks less than 5 s were excluded to prevent a bias of short tracks caused during the gap linking stage  (Aguet et al.
As a test case for dual TIRF imaging-based departure assay, we monitored the recruitment of AP-2 compared to CLC. Plants expressing AP2A1-GFP were crossed with CLC-tagRFPexpressing plants and the F1 progeny was subjected to this departure assay. Time series movies were captured in both the red and green channels sequentially, with a frequency of 1 s between frames (Figure  6B ). Both AP2A1 and CLC formed discrete foci on the cell surface, as expected (Figure  6C ). Many more CLC spots were observed at the cell surface compared to AP2A1, consistent with the existence of another adaptor complex in plant that may serve different pools of CME  (Gadeyne et al. , 2014). Particles in both channels were detected and tracked (Figure  6C ), as described above. The raw tracking data was extracted from the cmeAnalysis package, and we used homemade Matlab scripts to examine the overlap between CLC and AP2A1 trajectories.
FUNDING This work benefited from the microscopes and expertise of the  Imagerie-Gif Cell Biology facility , which is supported by the  'Infrastructures en Biologie Santé et Agronomie' (IBiSA) , the  French National Research Agency  under Investments for the Future programmes ' France-BioImaging infrastructure'  ( ANR-10-INSB-04-01 ),  Saclay Plant Sciences initiative  ( ANR-10-LABX-0040-SPS ) and the ' Conseil Général de l'Essonne'. This work was supported by grants from  ATIP program  from  CNRS ,  Marie Curie Action  ( PCIG-GA-2012-334021 , to GV) and  Agence Nationale de la Recherche  ( ANR-13-JSV2-0004-01 , to GV).
(E) Average read enrichment (RPKM) of PRDM9, in B6 and B6 Spo11 KO mice centered on class 1, 2A, and 2B sites of each strain. (n. d. ) Not determined (only one class 2A peak was identified in B6 Spo11 KO ). (F ) Classification of the PRDM9-positive peaks detected in B6 Spo11 KO mice in subclasses with (+) or without (-) enrichment for DMC1 (measured in B6 mice) and/or H3K4me3 (specifically present in B6 Spo11 KO mice or also present in RJ2). Classes were defined as in Figure 1D. H3K4me3 ChIP-seq data for B6 Spo11 KO (PRDM9 Dom2 ) are from Brick et al. (2012) and downloaded from the GEO database (GSE35498).
Acknowledgments We thank all laboratory members for discussion and specifically  Frédéric Baudat  for comments, suggestions, and insight during the whole project and  Christine Brun  for cytological analysis. We thank  Claire Guillet  for performing the initial analysis of H3K4me3 ChIP-seq data and  Boyan Bonev  and  Jia-Ming Chan  for discussions. We thank  Jacques van Helden  for suggestions for motif analysis and  F. Pratto  and  K. Brick  for information and help in setting up the DMC1 ChIP-seq experiments and analysis. Sequencing and microscopy analyses were performed at the  MGX (Montpellier GenomiX ) and  MRI (Montpellier Ressources Imagerie ) facilities, respectively. We thank  RAM  ( Réseau des animaleries de Montpellier ) and specially  Frederic Gallardo  from the mouse facility.
The work leading to the H3K4me3 results received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 262055. This work was supported by the  Agence Nationale de la Recherche  ( ANR-15-CE12-0010-01/DaSiRe ). B. d. M. was funded by grants from the  Centre National pour la Recherche Scientifique (CNRS)  and the  European Research Council Executive Agency  under the European Community's  Seventh Framework Programme  ( FP7/2007(FP7/ -2013  grant agreement no. [322788] grant agreement no. [322788]).
Evaluation of the Median Lethal Dose (LD 50 ) The evaluation of the lethal potency of venoms was conducted according to the LD 50 value determination method, as recommended by the World Health Organization  [16, 28, 29]. Venom doses were first adjusted in NaCl solution (150 mM) then administered in final volumes of 500 μl via intravenous (IV) and intraperitoneal (IP) routes, to populations of six mice per dose of venom. Mortality rates were recorded 24 h following the injections. The software package GraphPad Prism 5, Inc. was used to determine the median lethal dose according to the supplied algorithm. The software generated a non-linear curve applying the four-parameter logistical equation with constraints set on minimum (0% mortality) and maximum values (100% mortality). GraphPad Prism 5 was also utilized to determine the median doses (ANOVA) and the plots were traced by the software Kaleida Graph 4.
Biochemical analysis of the serum samples The levels of electrolytes, i. e. sodium (Na + ), potassium (K + ), magnesium (Mg 2+ ), calcium (Ca 2+ ) and chloride (Cl -), in the various serum samples were determined using a Biochemical Analyzer. The data were analyzed by analysis of variance (ANOVA) followed by Dunnett's multiple comparison test using the SPSS statistical package. P values < 0. 05 were considered as statistically significant.
Bacterial Isolates WGS data from an internationally sampled collection of 229 ST1 or ST1-derived isolates were used in this study (Supplementary Table  1 ). Complete or draft genomes for 138 isolates have been previously published, whereas 91 isolates are newly sequenced. The collection includes 99 environmental isolates from the water systems of 17 hospitals spanning 5 countries (United Kingdom, France, Spain, Denmark, and Australia). Multiple environmental isolates were obtained from 5 of these hospitals (hospital A, n = 38; hospital B, n = 39; hospital C, n = 5; hospital D, n = 3; hospital E, n = 2), while a single environmental isolate was obtained from 12 hospitals. Forty-two clinical isolates from patients with confirmed or suspected links to 20 different hospitals, including 10 hospitals from which we also obtained 1 or more environmental isolates, were included.
Whole-Genome Sequencing Isolates were sequenced by the core sequencing facilities at Public Health England using the Illumina HiSeq platform with 100-bp paired-end reads, or at the Wellcome Trust Sanger Institute using the Illumina MiSeq platform with 150-bp pairedend reads. Library construction was performed as described previously  [22, 23]. Raw reads for all newly sequenced isolates were deposited in the European Nucleotide Archive (study accession numbers ERP003631 and ERP015468).
Mapping of Sequence Reads and Phylogenetic Analyses Sequence reads were mapped to the Paris genome  [24]  using SMALT software version 0. 7. 4 (http://www. sanger. ac. uk/science/tools/smalt-0). Bases were called using an in-house pipeline comprising SAMtools  [25] , mpileup, and BCFtools, as described previously  [26]. Recombined regions were identified and removed from the alignment using Gubbins software  [27]. A maximum likelihood tree was generated using variable sites that remained after recombination removal using RAxML version 7. 0. 3 [28]. Among-site rate variation was accounted for using a γ correction, and 100 random bootstrap replicates were performed to analyze support for nodes.
Hospital Populations Comprise Distinct Lineages of L. pneumophila ST1 We investigated the phylogenetic context of 99 environmental isolates sampled from the water systems of 17 hospitals, together with 42 clinical isolates from LD patients with confirmed or suspected hospital-acquired infections, within an internationally sampled collection of 229 L. pneumophila ST1 or ST1-derived genomes (Supplementary Table  1 ). To construct a phylogenetic tree, sequence reads were mapped to the complete genome of the Paris strain (an ST1)  [24] , and a total of 62 395 single-nucleotide polymorphisms (SNPs) were identified among all isolates. Because recombination accounts for a large proportion of diversity within the ST1 lineage  [29] , Gubbins software was used to identify and remove recombined regions from the alignment.
WGS Can Be Used to Support or Refute Links Between LD Cases and Hospitals We next investigated whether the WGS data support confirmed or suspected links between hospital water systems and Nosocomial Legionnaires' Disease • CID 2017:64 (1 May) • 1253 Figure  1. A maximum likelihood tree of 229 sequence type (ST) 1 and ST1-derived isolates constructed using 1741 single-nucleotide polymorphisms (SNPs) identified after the removal of recombined regions. Environmental isolates from and clinical isolates linked to 27 different hospitals are included. Isolates from or potentially linked to the water systems of 10 of these hospitals (from which at least 1 environmental isolate and 1 clinical isolate was obtained) are colored within the tree itself. Clinical isolates from 28 suspected cases linked to these 10 hospitals are indicated by small circles (colored according to the hospital) and numbered within the tree.
Acknowledgments. We thank the library-generation, sequencing, and informatics teams at the  Wellcome Trust Sanger Institute and Public Health England  for their assistance. We also thank our Public Health and Microbiology colleagues for epidemiological data and submission of specimens and isolates, as well as the staff within the  Respiratory and Vaccine Preventable Bacteria Reference Unit at Public Health England  for isolation, identification, and discrimination of Legionella. Disclaimer. The funder had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Financial support. This work was supported by the  Wellcome Trust  (grant number  098051 ). Potential conflicts of interest. J. P. has received funding for conference travel and accommodation from  Illumina Inc.
All data presented in this paper were generated with the same cantilever (with a nominal spring constant of about 40 N. m -1 ) and at a scan rate between 0. 1 and 0. 5 Hz. AFM study was made under ambient conditions (T = 20-21 °C), in air, and in tapping mode to preserve sample integrity  [46]. For each experiment, three independent samples and different zones were scanned. The surface roughness was obtained with the NanoScope setup by the average of 5 to 10 measurements for each scan. Scratching experiments were performed using contact mode (typically on a surface of 1μm × 1μm), the force applied onto the sample being higher than typical force used for imaging (around 80 nN). Height sections performed between intact and scratched areas gave the thickness of the film, assuming that the lower surface is mica.
ACKNOWLEDGMENT The authors thank  Gérard Couturier ,  Denise Mondieig  and  Philippe Négrier  ( Laboratoire Ondes et Matière d'Aquitaine, LOMA ,  Université de Bordeaux ,  UMR CNRS 5798 ,  Talence, France)  and the  NSI platform of LOMA  ( CPER COLA2 ) for helpful discussions and technical support.
085% formic acid and 20% H 2 O). The mass spectrometer acquired data throughout the elution process and operated in a data dependent scheme with full MS scans acquired in the orbitrap analyzer, followed by up to  15   S1  (60 sequences; 13,004 amino acids). Peaklists extracted from MS/MS spectra were compared with the above databases using Mascot search engine 2. 5. 1 (Matrix Science). The following settings were applied: mass tolerances were 4 ppm for precursors and 10 mmu for fragments, a significance threshold score corresponding to p Ͻ 0. 05 was applied to filter identifications but a minimum individual peptide Mascot score value of 25 excluded poorly annotated spectra. Based on these criteria, a nontargeted (bottom up) analysis of the samples was performed. In order to avoid possible ambiguities, the following restrictive conditions were applied for mascot queries: trypsin proteolysis specificity without missed cleavage, methionine oxidation as unique modification allowed (as partial) and mass-to-charge (m/z) states with zϾ2 or more.
This work was facilitated by the implementation of a decision tree reading of the IGHG nucleotide sequences, represented in supplemental Table  S4. Proteogenomic Analysis-The IGHG nucleotide sequences resulting from the sequencing of amplified DNA fragments of all experimental samples were artificially spliced between the CH2 and CH3-CHS exons and translated using the converter software (http:// didac. free. fr/seq/dna2pro. htm). This allowed to create for each sample an experimental "sample database" to be imported in Mascot software for a new peptide query aiming at detecting single amino acid variants (SAAV) unknown to date  (26, 27)  and therefore not listed in the concatenated IMGT® IGHG allele and SwissProt database used in the first MS analysis. The peptide interrogation was performed according to the same conditions as previously used.
Otherwise, all samples presented the G1m17,1 allele, known to be encoded by IGHG1*01, IGHG1*02, or IGHG1*05 (Gene table Homo sapiens IGHC, IMGT Repertoire) (http://www. imgt. org) (4). NP49 and PA31 samples were Gm28-positive with a plausible attribution of this allotype to the gamma 1 chain (IGHG1*05p, IGHG1*06p) (4) (Allotypes, IMGT Repertoire, http://www. imgt. org). a G3m alleles are characterized by specific combinations of G3m allotypes and are written by increasing numbers of markers (separated by commas)  (2, 4). As Gm allotypes are encoded on chromosome 14 (14q 32. 3), two G3m alleles are expressed and mentioned using a slash mark. b the simplified G3m form contains the number of one representative allotype followed by *. IGHG AA Alleles Deduced from Mass Spectrometry Analysis Using Usual Peptide Databases-As we routinely use the SwissProt Homo sapiens FASTA protein database we quickly realized it was not pertinent regarding the IGHG diversity.
Thus we added the IMGT® database of all IGHG AA alleles. Using two FASTA sequence databases with overlapping sequences can be confusing. Indeed if unique peptides were attributable to specific IGHG genes and alleles to each IG heavy chain, as listed in Table  II , many peptides were shared between all alleles of a given IGHG gene. Because Mascot sorts resulting identifications according to the hits with the best matching set of peptides, only peptides comprised in the same sets of the Mascot analysis were considered and by default not those included in the subsets. This allowed exclusion of possible ambiguities corresponding to alleles with lesser occurrence probability. Following these rules, two heterozygous IGHG allele pairings could be easily deduced. It was for example the case of IGHG3*03/IGHG3*13 for AS50 and PA16 where the unambiguous attribution of IGHG3*03 (based on the presence of the discriminatory R.
IGHG AA Alleles Deduced from Mass Spectrometry Analysis Using Sample-specific Databases-Molecular analysis allowed the identification of novel IGHG2 (6) (Table  III  and supplemental Table  S5 ) and IGHG4 (4) (Table  V  and supplemental Table  S6 ) nucleotide sequences, and among them, 1 IGHG2 and 2 IGHG4 sequences were associated with a total of four AA changes. As the previous Mascot searches could only match known sequences, we compiled a sample-specific database with the aim to perform the Mascot query again for checking the presence of putatively four new trypsin-cut peptides in the purified IgG samples. The same conditions as already described earlier were used, except that query results considered sets, samesets but also subsets of peptides. The use of the SwissProt Homo sapiens FASTA database from Expasy was kept to match all nonrelevant proteins that were co-purified, thus preventing an increase of false positive matching.
It was not the case of the transcribed R. VVSVLTIL-HQDWLNGK. E peptide (CH2 85. 1-101, V91ϾI), never referenced among IGHG sequences, that Mascot observed and assigned to an IGHG4 set of peptides for purified IgG from both AS50 and PA16 samples, and not in PA07 where it was also expected. In order to check the accuracy of these last results, a final analysis consisted for each sample in a Mascot query among pooled IMGT®, SwissProt and sample-specific databases. Nomenclature adapted from Human Genome Variation Society (www. hgvs. org) and (  29 ); amino acid (AA) EU numbering according to IMGT® www. imgt. org (23). b Nomenclature adapted from Human Genome Variation Society (www. hgvs. org) and (  29 ); amino acid (AA) EU numbering according to IMGT® www. imgt. org (23). S2 ).
Moreover, the sequencing of the IGHG among these samples originating from Beninese individuals led to the identification of 6 IGHG2 and 4 IGHG4 nucleotide sequences not yet described. When translated, two of these sequences led to 2 putative IGHG peptides not yet described and assigned to IGHG4 by Mas-cot, that were R. VVSVLTILHQDWLNGK. E (CH2 85. 1-101, V91ϾI) and K. VSHK. G (CH2 106 -108, N108ϾH). By means of proteogenomics, R. VVSVLTILHQDWLNGK. E was identified in the genomically modified set peptide group of the Mascot analysis for 2 (AS50 and PA16) out of 3 samples where this peptide was expected. Regarding this last peptide, a sequence alignment performed by a BLAST search on NCBI® database (National Center for Biotechnology Information, http://blast. ncbi. nlm. nih. gov/Blast. cgi) did not identify any referenced peptide allowing 100% sequence cover: whatever the aligned peptide, the amino acid at position 308 of CH2 85.
MS/MS higherenergy collisional dissociation (HCD) spectra on the most abundant ions detected in the MS scan. Mass spectrometer settings were: full MS (AGC: 3. 10e6, resolution: 70,000, m/z range 400 -2000, maximum ion injection time: 100 ms); MS/MS (Normalized Collision Energy: 30, resolution: 17,500, intensity threshold: 2. 10e4, isolation window: 4. 0 m/z, dynamic exclusion time setting: 12 s, AGC Target: 1. 10e5 and maximum injection time: 100 ms). The fragmentation was permitted for precursor with a charge state of 2 to 4 excluding isotopes. The software used to generate Mascot generic format (MGF) files was Proteome Discoverer 1. 3. Database Queries of Extracted Experimental Data-Three FASTAformatted protein databases were used in combination in order to assign a majority of fragmentation spectra: 1-the Homo sapiens entries from the UniProt/SwissProt database release 2016 -02 (20,273 sequences), 2-the 2014 IMGT® IGHG allele database (2, 3, 5) (IMGT Repertoire.
DATA AVAILABILITY The newly described IGHG2 nucleotide sequences have been deposited in the GenBank database under GEDI (GenBank/ENA/DDBJ/IMGT/LIGM-DB) Accession Numbers KX670549 to KX670554, sequences KX670550 and KX670551 differing in the intron. Similarly, the newly described IGHG4 exon nucleotide sequences have been deposited in the GenBank database under GEDI Accession Numbers KX670555 to KX670558. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (42) partner repository with the dataset identifier PXD005021.
Acknowledgments This work was funded by a grant from  NSERC  to RMT and a  FQRNT exchange studentship  to RMT and SF. MTM held a studentship from  PROTEO, Quebec. We would like to thank  Je  ´remie  Hamel  for help with SEC,  Halim Maaroufi  ( IBIS Quebec ) and  Stephen Weeks  ( KV Leuven ) for valuable discussion.
The gels were imaged using a Fusion FX7 spectra instrument (Vilber Lourmat TM ) equipped with "Fusion-Capture Advance" software. The rhodamine images were captured by adjusting the excitation and emission wavelengths to 540 and 595 nm, respectively. The TMRM signals were quantified by measuring the density of each band with Fusion-Capture Advance software (Vilber Lourmat TM ). The first-order rate constants for TMRM maleimide labeling were derived by plotting the TMRM-band signals versus time (for each gel), and the experimental points were fit using GraFit 7. 1 software (Erithacus Software, Ltd. ) according to the equation y ϭ limit ϫ (1 Ϫ e Ϫkt ).
The proteins were deuterated for 15, 120, or 1200 s at 4 °C. Back-exchange quench and digestion were performed using 50 l of a porcine pepsin (Sigma) solution at 0. 23 mg/ml in 100 mM glycine-HCl, pH 2. 5, at 4 °C for 1200 s. The peptides were desalted for 6 min using an HPLC pump (Agilent Technologies) with 0. 03% TFA in water at a flow rate of 100 l/min. The peptides were then separated using an HPLC pump (Agilent Technologies) at 50 l/min for 6 min with a 15-50% gradient of Buffer B (Buffer A: TFA 0. 03% in water; Buffer B: acetonitrile 90%, TFA 0. 03% in water), followed by 9 min at 50% B and 1 min at 100% B. The peptide masses were measured using an electrospray-TOF mass spectrometer (Agilent 6210) in the 300 -1300 m/z range. Each deuteration experiment was replicated in triplicate. Mass Hunter (Agilent Technologies) software was used for data acquisition  (28, 46).
HD Examiner software (Sierra Analytics) was used for HDX-MS data processing  (28). Peptides were identified as described previously  (28). The identified peptides were plotted against the protein sequence using a webbased software package, MSTools, to visualize the sequence coverage  (47). 45 Ca 2؉ -uptake assay in E. coli-derived vesicles E. coli-derived cell-membrane vesicles, containing overexpressed WT-G42C, WT-G201C, 5L6 -8-G42C, and 5L6-G201C were obtained as described for WT-NCX_Mj and stored at Ϫ80 °C until use  (23, 27, 28). The initial rates (t ϭ 5-10 s) of the Na ϩ /Ca 2ϩ and Ca 2ϩ /Ca 2ϩ exchange reactions were assayed by measuring 45 Ca 2ϩ uptake in the E. coli-derived vesicles as established for the standard assay conditions  (23, 27, 28). Briefly, Na ϩ -or Ca 2ϩ -loaded vesicles were rapidly diluted (25-50-fold) in an assay medium containing 20 mM MOPS/ Tris, pH 6.
5, 100 mM KCl, and varying concentrations (5-2000 M) of 45 CaCl 2 (at 35 °C). The 45 Ca 2ϩ uptake was quenched by rapid addition of cold buffer containing 10 mM EGTA, and quenched samples were filtrated on GF/C filters, as outlined before  (23, 28, 48). The K m and V max values were measured for each mutant in at least three independent experiments, and the data were analyzed with GraFit 7. 1 software (Erithacus Software, Ltd. )  (23, 28, 27). The observed K m and V max values of WT-G42C, WT-G201C, 5L6 -8-G42C, and 5L6-G201C were ranging in the range of 80 -100% as compared with WT-NCX_Mj, thereby suggesting that the single-cysteine mutations have a negligible (if any) effect on the ion transport activities.
Immunomodulatory agents lenalidomide and pomalidomide treatment does not affect WM cell viability and apoptosis Cell viability was assessed using MTS assays 48 hours after lenalidomide and pomalidomide, IMid compounds, treatment, at increased concentrations. As previously published  [8] , MM. 1S cell line showed a decline of its viability in a dose-and time-dependent manner. BCWM. 1 and MWCL-1 however were not responsive to either lenalidomide or pomalidomide (Figure  1A ). The same results were obtained when treating for 72 hours and any other time point (data not shown). We next investigated the effects of lenalidomide and pomalidomide treatment on apoptosis using an annexin V and propidium iodide staining. Similarly, apoptosis was induced in MM. 1S cells after 48 hours of treatment whereas no significant difference was observed in WM cells (Figure  1B ).
, St. Louis, MO), Ikaros and Aiolos (Cell Signaling, Beverly, MA). Blots were then visualized using the Amersham ECL Prime Western Blotting System on a Las 4000 biomolecular imager (GE Healthcare Europe GmbH, Velizy-Villacoublay, France). Quantification was done using ImageQuant TL software (GE Healthcare, Chicago, IL).