dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , PF07504, PF03413, PF012868 and PF01447), we identified LIC13322 and four additional predicted thermolysin orthologs only in pathogenic Leptospira spp. : LIC10715, LIC13320, LIC13321, and LEP1GSC059_0182 (S9 Table ), primarily among L. interrogans, L. kirschneri and L. noguchii. LEP1GSC059_0182 was found only in one species, L. noguchii. No thermolysin ortholog was found in intermediate or saprophytic species. Collagenase has been suggested to be a virulence factor in Leptospira based on observed in vivo expression, detection of specific anti-collagenase antibodies induced by infection, and the effects of ColA mutagenesis and complementation on traversal of cell monolayers and outcome of experimental animal infection  [140, 141]. Comparative genomic analysis identified two collagenase genes, restricted to pathogenic Leptospira spp.
ligB and ligC encode molecules which are comprised of a lipobox sequence, 12 tandem Big2 type domains and a C-terminal non-Big domain (S10B Table ), whereas ligA encodes a protein with 13 tandem Big domains which lacks a C-terminal non-Big domain. Of note, further prospection of the genome sequence and ortholog families identified four additional genes that encode Big2 and Big3_4 domain-type containing proteins (S10B Table ). These genes are different from the conventional lig gene family in that they encode proteins with a small (1-2) number of Big domains. Interestingly, a gene (LIC13050) encoding a protein with two Big3_4 domains was found in all Leptospira species, including the saprophytes. PF07598 paralogous gene family. Previous work identified a group I-specific family of proteins corresponding to Pfam model PF07598  [149]  that was expressed in vivo in a hamster model of acute leptospirosis  [149] , and expanded in strains, e.
g. , Copenhageni and Lai, that commonly cause severe disease, suggesting that these proteins contribute to Leptospira virulence. These prior studies focused on finding PF07598 orthologs to the L. interogans Lai attenuated strain in the 20 representative Leptospira spp. , but did not look for strain-specific homologs that match PF07598 HMM. To identify novel PF07598 family members, we identified clusters of protein orthologs from our pan-genome run that matched PF07598 above trusted cut-offs; this analysis also included matches within the genomes of previously sequenced strains L. borgpetersenii Hardjo L550 and JB197 (and previously annotated as conserved hypothetical proteins). At least 26 distinct orthologs ranging in length from 47 (LEP1GSC049_1303 unique to L. kirschneri) amino acids to 651 (LEP1GSC193_2756; L.
noguchii CZ214T and L. kirschneri 3522 CT not present in L. interrogans 56601 consistent with the hypothesis that serovar Lai has lost this ortholog. To better understand the evolution of this paralogous gene family, a phylogenetic tree of all PF07598 members detected in infectious Leptospira was constructed using homologs longer than 200 amino acids shown (Fig 7A ). A complex web of lineage specific gene duplications and loss was revealed. For example, as highlighted in (Fig  7A ), successive gene duplications and  kirschneri ( Ã ). The second, an L. interrogans-specific event ( ÃÃ ), led to the divergence of two orthologous groups comprised of LA_0589, LIC_12986 and LEP1GSC049_3370, and LA_3388, LIC_10778 and LEP1GSC049_0186, respectively. L. interrogans Lai 56601 has seemingly lost an ortholog belonging to a group containing LIC10639 and LEP1GSC049_1381 Motility and chemotaxis.
PF07598 paralogous gene family A novel gene family-first identified in a pathogenomic screen of L. interrogans serovar Lai  [219] -was found to have orthologs present in pathogenic Leptospira but not in intermediate or saprophytic Leptospira. In L. interrogars serovar Lai, these genes were reported to be upregulated in vivo  [219]  and here we report that the numbers of paralogs varies among the pathogens, with the L. interrogans, L. kirschneri and L. noguchi having the most. These observations suggest that the PF07598 genes contribute to leptospiral virulence but the mechanism(s) by which they do so remain to be elucidated; no functional annotations for the PF07598 gene family is yet possible. Experimental studies of this gene family will likely provide insight into leptospirosis pathogenesis.
Only pathogenic Leptospira contain CRISPR/Cas systems, suggesting not only a potential mechanism for this clade's refractoriness to gene targeting but also possible novel means to be able to genetically modify pathogenic Leptospira. Whether restriction modification systems might contribute to gene targeting has yet to be analyzed in detail, but the publicly available whole genome data sets provided in support of the present work will be contribute to carrying out such analyses. A novel virulence-related genes/gene family epitomized by the PF07598 group of paralogs suggests adaptation and diversification of this protein family within the pathogenic clade. Identifying large scale changes in infectious (pathogenic and intermediate pathogenic) as compared to non-infectious Leptospira has yield large-scale, novel insights into the evolution of a bacterial pathogen, provides the basis for new directions in leptospirosis pathogenesis research.
22 NR-35361 4. 44 NR-20171 3. 89 ATCC 4. 63 BAA-1198 NR-22255 4. 41 NR-22254 4. 42 NR-22283 4. 71 Not 3. 99 Deposited NR-20183 4. 26 NR-22253 4. 40 NR-22252 4. 29 NR-22251 4. 46 ATCC 4. 21 BAA-1110 NR-22250 4. 40 ATCC 3. 95 23582 NR-29052 4. 19 ATCC 4. 09 700639 ATCC 4. 23 700522 NR-35357 4. 08 ATCC 4. 06 700523 Bioproject WGS Master or GenBank Accession PRJNA74069 AHMT00000000. 2 PRJNA177154 AOHD00000000. 2 PRJNA74113 AHNP00000000. 2 PRJNA10687 AE016823/4 PRJNA74057 AHMN00000000. 2 PRJNA74061 AHMP00000000. 2 PRJNA167231 AKWY00000000. 2 PRJNA178172 AOHB00000000. 2 PRJNA74087 AHNC00000000. 2 PRJNA74059 AHMO00000000. 2 PRJNA167230 AKWZ00000000. 2 PRJNA74055 AHMM00000000. 2 PRJNA74167 AHOO00000000. 2 PRJNA167232 AKWX00000000. 2 PRJNA58993 CP000786/7 PRJNA167225 AKXE00000000 PRJNA178717 AOGW00000000.
2 PRJNA177160 AOGY00000000. 2 PRJNA177156 AOGZ00000000. 2 PRJNA178716 AOGX00000000. 2 Species Serovar Strain Lifestyle Source Country of Contributing Origin Lab alexanderi Manhao 3 L 60 T pathogenic Homo sapiens China Hartskeerl alstoni Pingchang 80-412 pathogenic Frog China Galloway borgpetersenii Javanica UI 09931 pathogenic Homo sapiens Thailand Peacock interrogans* Copenhageni Fiocruz pathogenic Homo sapiens Brazil - L1-130 kirschneri Cynopteri 3522 C T pathogenic Chiroptera (bat) Indonesia Hartskeerl kmetyi Malaysia Bejo-pathogenic environmental Malaysia Hartskeerl Iso9 T noguchii Panama CZ 214 T pathogenic Didelphis Panama Hartskeerl marsupialis (opossum) santarosai Shermani 1342K T pathogenic Homo sapiens Panama Hartskeerl weilii undetermined LNT pathogenic Homo sapiens Thailand Peacock 1234 broomii Hurstbridge 5399 T intermediate Homo sapiens Denmark Hartskeerl fainei Hurstbridge BUT 6 T intermediate Sus scrofa (wild Australia Hartskeerl boar) inadai Lyme 10 T intermediate Homo sapiens USA Hartskeerl licerasiae Varillal VAR intermediate Homo sapiens Peru Vinetz 010 T wolffii Khorat Khorat-intermediate Homo sapiens Thailand Hartskeerl H2 T biflexa  † Patoc Patoc I saprophytic stream water Italy - (Paris) meyeri Hardjo Went 5 saprophytic unknown Canada Galloway terpstrae Hualin LT 11-saprophytic unknown China Hartskeerl 33 T vanthielii Holland WaZ saprophytic water Netherlands Galloway Holland wolbachii Codice CDC saprophytic unknown USA Galloway yanagawae Saopaulo Sao saprophytic water Brazil Hartskeerl Paulo T § Improved High-Quality Draft (IHQD); High-Quality Draft (HQD) *J.
Potentially mediate Leptospira adhesion to host cells (in case of infectious Leptospira); may be involved movement of Leptospira in and through tissue interstitium; may generally promoting invasion/colonization processes Cobalamin biosynthesis Production of vitamin B12 (Table 2) Infectious Leptospira predicted to be autotrophic for B12 biosynthesis, predicted to be important during in vivo mammalian infection PF07598 family Unknown (Table 7) Upregulated in vivo; found only in pathogenic Leptospira doi:10. 1371/journal. pntd. 0004403. t009.
Acknowledgments: This work was supported by the  French Institut National de la Santé Et de la Recherche Médicale (INSERM) , by the  Assistance Publique -Hôpitaux de Paris  ( AP-HP ), by the laboratoire d'excellence INFLAMEX, by the  DHU FIRE , by the  Centre National de la Recherche Scientifique (CNRS)  and by grants from the  Mairie de Paris  (Emergences 2010), the  Agence Nationale de la Recherche (ANR)   JCJC SVSE1 2011 BASILE ,  JCJC CE17 2015 BATTLE  and the  French Kidney Foundation  ( Fondation du Rein)  to NC.
Real-time quantitative PCR (qPCR) RNA was reverse transcribed using random hexamers and the Multiscribe reverse transcriptase (Life Technologies). qPCR were performed as previously described  [2]. Briefly, reactions were carried out with 300 nM primers in a final reaction volume of 25 μl of 1 X SYBR Green PCR Master Mix (Applied Biosystems). PCR cycling conditions were 95˚C for 10 min, linked to 40 cycles of 95˚C for 15 s and 60˚C for 1 min. Real-time qPCR data were collected by the Mastercycler 1 e0p realplex-Eppendorf system and 2-ΔCt calculations for the relative expression of the different genes (arbitrary units) were performed with the Realplex software using ovRPS24 (ovine Ribosomal Protein S24) as reference gene. This gene has been carefully chosen after a transcriptomic screen (data not shown).
Here, we first assessed the number of each DC/Macrophages populations in the parenchyma and BAL of 4 pigs. In order to compare the absolute number of cells in the alveoli and tissue, we worked on the right cardiac lobe which is small enough to collect the whole tissue. It appeared that cDC2 and cDC1 were 2 and 3 times less numerous in the alveoli than in the tissue (Fig  1C ), with 213. 10 3 cDC2 and 89. 10 3 cDC1 in the BAL to be compared with 489. 10 3 cDC2 and 295. 10 3 cDC1 in the parenchyma. In the BAL, Sirpα interm /CD163 interm cells, first identified as moMacro, presented a gene expression pattern identical to AM (data not shown), so they were pooled with the Sirpα high /CD163 high AM population. Thus, moMacro could not be identified in BAL, probably due to their absence or their very low proportion compared with AM.
Indeed, in the parenchyma, not more than 14. 10 3 moMacro cells/lobe were counted. The moDC were equally present in BAL and parenchyma (respectively 262. 10 3 and 343. 10 3 per lobe). However, the main MHCII high populations in the lung were AM (33. 10 6 cells per lobe) and AM-like cells (3. 10 6 cells per lobe), the latter being 10 times less represented than AM (Fig  1D ). AM-like cells still represented the main population in the interstitium and were at least 5 times more numerous than the other 4 populations we observed there. The BAL cells were then sorted by flow cytometry and the transcriptomic expressions of 10 genes previously identified as differentially expressed in these populations by us  [2, 14]  and others  [4, 15, 16]  were measured by. In order to easily compare those results with those we previously obtained, we depicted previously published data from parenchymal cells  [2]  as closed symbols in Fig 2A and as the "PAR" columns in the heat map (Fig 2B ) we designed to summarize the RT-qPCR data.
The two cDC populations were highly similar between BAL and tissue, expressing both high levels of Flt3, whereas cDC1 specifically expressed XCR1 and cDC2 specifically expressed FCεR1α. BAL moDC clearly expressed the monocytic genes CSF1R, MerTK and MAFB, proving their belonging to the moDC subset. The main differences, although not significant, with their interstitial counterpart resided in their higher expressions of CCR2 and CX3CR1. Finally, and as we had previously shown, AMlike cells were highly similar to AM. They had a strong expression of the Macrophage-associated gene MerTK, and low expressions of genes which are specific of hematopoietic cells such  (A) The different subsets from BAL were sorted by flow cytometry. mRNA expression levels of 10 genes were assessed by RT-qPCR. For each gene, data were normalized to the reference gene RPS24 (Ribosomal Protein S24) expression and presented as relative expression (arbitrary units (AU)): for each animal, the population with the highest expression was considered as 100 and the other populations were normalized to it.
Acknowledgments We would like to thank  Christophe Staub , the  UEPAO  staff and especially  Jean-Philippe Dubois  for their help in samples collection. We thank  Mickael Bourge  from  I2BC  ( CNRS, Gif-sur-Yvette, France ), for the help in cell sorting. We thank  Isabelle Schwartz-Cornil  for critical reading of the manuscript.
Two technicians performed all of the analyses according to the manufacturer's instructions. Prewarmed (37°C) citrated whole blood samples (300 lL each) were added to clotting reagents g with the supplied electronic pipette. Two profiles were conducted as follows: extrinsic TEM (exTEM) h to evaluate the extrinsic pathway (with tissue factor activation) and fibrinogen function TEM (fibTEM) i to assess fibrinogen function (with tissue factor activation and platelet inactivation with cytochalasin D). These 2 profiles were run a total of 60 minutes. The data obtained from the manufacturer's modeling software included clotting time (CT), clot formation time (CFT), amplitude 10 minutes after CT (A10), maximum clot firmness (MCF), a-angle (a), lysis index 45 minutes after CT (Li45), and maximum lysis (ML).
Statistical Analysis All data are presented as the median (range). Normality was assessed by the d'Agostino and Pearson omnibus test. Comparisons between survivors and nonsurvivors, between dogs with and without DIC, and between dogs with and without hemorrhagic diatheses, were performed by Student's t-test for normally distributed data and the Mann-Whitney U-test for non-normally distributed data. Comparisons among dogs with hypocoagulable, normal, and hypercoagulable profiles were performed by one-way analysis of variance (ANOVA). Qualitative data were compared by Fisher's exact test and by calculating the odds ratios (ORs) associated with the 95% confidence interval (95% CI). No correction for multiple comparisons was made in this pilot exploratory study. Survival was assessed by Kaplan-Meier product limit estimates, and differences among groups were assessed by the log-rank test.
Acknowledgments The authors thank  Dr Ana€ ıs Boyeaux ,  Dr Maxime Cambournac ,  Dr Robin Perrin ,  Catherine Boisvineau ,  Aur elie Pin ,  Faustine Rioux , and  Jean-Yves Ayoub  for their assistance in caring for the dogs used in this study and  Dr Benoit Rannou  for his help. Source of Funding: This study did not receive any grant or financial support.
Reactions were initiated with an initial denaturation for 5 min at 95 • C followed by 35 cycles at 95 • C for 30 s, 50 • C for 30 s and 72 • C for 1 min. PCR products were visualized on 0. 8% agarose gel containing 0. 5 ng/µl of BET and images were captured with a camera coupled with Biocapt software, Vilber Lourmat. L. johnsonii La1 stock cultures were kept frozen in de Man Rogosa Sharpe (MRS) Broth (Sigma-Aldrich) media with 15% glycerol. Bacteria were subcultured in MRS or modified TYI-S-33 medium (MTYI)  (Pérez et al. , 2001)  and incubated anaerobically for 12-18 h at 37 • C. Bacteria were subsequently grown in MTYI or KM supplemented with or without 10% heat-inactivated FCS for 12-16 h in the presence or the absence of 0. 6 g/L bovine bile. After centrifugation (3000 × g, 10 min) and 0.
The MS data were acquired in negative mode in the range m/z 250-1200. Each LC/ESI-MS experiment was conducted twice. Data-dependent LC/ESI-MS/MS experiments were also conducted on each sample, alternating 1-s full-scan MS followed by 2-s product ion collision induced dissociation of the major ions detected at the first step, using a -50 V collision voltage. The raw LC/ESI-MS data were converted into Network Commun Data Form (NetCDF) using the translation tool provided by Sciex and processed with the XCMS package  (Smith et al. , 2006) , a software implemented in the freely available R environment (www. r-project. org). This analysis allows automatic retention time alignment, matched filtration, peak detection, and peak matching.
Raw data were viewed in Xcalibur v. 2. 1 (Thermo Scientific) and data processing was performed using Proteome Discoverer v. 1. 3 (Thermo Scientific). The generated raw files were submitted to searching using Proteome Discoverer with an in house Mascot v. 2. 3 algorithm against a database of proteins predicted from the L. johnsonii isolates NCC533 and FI9785 and the L. johnsonii prophage Lj965 (http://www. ncbi. nlm. nih. gov/bioproject). Contaminant proteins (human keratins, BSA and porcine trypsin) were also added to the database and all contaminant proteins identified were manually removed from the result lists. The searches were performed with the following parameters: ms accuracy 10 ppm, MS/MS accuracy 0. 6 Da, trypsin digestion with one missed cleavage allowed, fixed carbamidomethyl modification of cysteine and variable modification of oxidized methionine and N-terminal protein acetylation.
(B) Supernatant from L. johnsonii La1 in MTYI medium was adjusted to pH 6. 2, 6. 7, 6. 9, or 7. 2 before Giardia growth inhibition assays. Growth inhibition (%) was normalized according to control, i. e. , lactic acid-adjusted MTYI subsequently raised to pH 6. 2, 6. 7, 6. 9, or 7. 2. Values are the mean ± SD of three independent experiments. (C) Molecular weight determination of inhibitory compounds from L. johnsonii La1 supernatant. Supernatant from a culture of L. johnsonii La1 in KM-FCS was filtrated through 10, 30, or 50 kDa MW cut-off membranes. Acidified KM-FCS alone was processed similarly. Fractions above and under respective thresholds were assayed for Giardia growth inhibition in the presence of bile (0. 5 g/L). Inhibition values (%) were normalized according to KM-FCS controls. Data are the mean ± SD of three independent experiments performed in triplicate.
2, 6. 7, 6. 9, or 7. 2 before Giardia growth inhibition assays. Growth inhibition (%) was normalized according to control, i. e. , lactic acid-adjusted MTYI subsequently raised to pH 6. 2, 6. 7, 6. 9, or 7. 2. Values are the mean ± SD of three independent experiments. (C) Molecular weight determination of inhibitory compounds from L. johnsonii La1 supernatant. Supernatant from a culture of L. johnsonii La1 in KM-FCS was filtrated through 10, 30, or 50 kDa MW cut-off membranes. Acidified KM-FCS alone was processed similarly. Fractions above and under respective thresholds were assayed for Giardia growth inhibition in the presence of bile (0. 5 g/L). Inhibition values (%) were normalized according to KM-FCS controls. Data are the mean ± SD of three independent experiments performed in triplicate.
Accession number in the NCBI protein database. All accession numbers refer to sequences from Lactobacillus johnsonii NCC 533; b Probability-based Mowse score of MASCOT software that evaluates if the peptides subjected to search are the same as those found in the database originated by in silico digestion of a known protein; c Coverage (Cov. ) is the percentage of predicted protein sequence covered by matched peptides via MASCOT; d Peptide sequences identified via MASCOT following the experimental peptide masses after parental ion fragmentation; e Doubly-to quintuply-charged ions of selected peptides were detected; f Theoretical molecular masses of proteins calculated from amino acid sequences; g Theoretical isoelectric points of proteins calculated from amino acid sequences.
ACKNOWLEDGMENTS We kindly thank  Jan Tackezy  ( Charles University of Prague, Czech Republic ) for providing the G. duodenalis HP1 strain,  James P. Coleman  ( Brody School of Medicine, East Caroline University, USA ) for providing the recombinant Clostridium perfringens BSH which initiated the study and  Pascal Quené  ( INRA, Jouy en Josas , for the L. johnsonii La1 strain. We gratefully acknowledge  Lisy Raveendran  for her technical support and contribution to this study,  Amandine Labat  for L. johnsonii La1 genotyping, and  Peter Roepstorff  ( Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark ) for access to the mass spectrometry facility. The  LC/ESI-MS  bile acid profiling was performed at the Plateau technique de spectrométrie de masse bio-organique, UMR 7245  Molécules de Communication et Adaptation des Micro-organismes ,  Muséum National d'Histoire Naturelle, Paris, France.
a Accession number in the NCBI protein database. All accession numbers refer to sequences from Lactobacillus johnsonii NCC 533; b Probability-based Mowse score of MASCOT software that evaluates if the peptides subjected to search are the same as those found in the database originated by in silico digestion of a known protein; c Coverage (Cov. ) is the percentage of predicted protein sequence covered by matched peptides via MASCOT; d Peptide sequences identified via MASCOT following the experimental peptide masses after parental ion fragmentation; e Doubly-to quintuply-charged ions of selected peptides were detected; f Theoretical molecular masses of proteins calculated from amino acid sequences; g Theoretical isoelectric points of proteins calculated from amino acid sequences.
While these studies focus on a model-based description of the initial repertoire of the adaptive immune system arising mainly from the V(D)J genetic rearrangement, here we focus on the affinity maturation process. A number of statistical mechanics inspired methodologies have been recently successfully devised to analyze evolutionarily related proteins for inferring structural properties and, in particular, residue-residue contacts  [10]. In particular, homologous proteins can be characterized in terms of multiple sequence alignments (MSAs). In spite of the considerable sequence heterogeneity (up to only 40% sequence identity) in families of homologous proteins, their folded structures are often almost completely conserved  [11]. A MaxEnt modeling technique developed more than a decade ago, could detect signals of the evolutionary pressure beyond the sequence variability in MSAs of homologous proteins  [12].
In all S Files headers are constructed with the following rule: 'sequence_id':'multiplicity', where the multiplicity represent the number of time that the (amino acid translation of the productive) sequence is found in the experimental set. Sequences presenting the suffix "_SEQMA" in the sequence_id are those for which the neutralization titer has been assessed; The sequence presenting the suffix "_3SE9" in the sequence_id is that of VRC-PG04 as deposited in the Protein Data Bank under 3SE9 accession code; note that in MSAs of clusterPG04 and clusterVJ the multiplicity of SEQMA and 3SE9 sequences has been set to zero as the testing set is not included in the learning set of the affinity prediction procedure). An easy-to-use and highly performing software in the new open-source language called Julia for the inference method that we used in the present work has been made available from our group at the permanent link https://github.