dataesr/scientific-paragraphs-categorization huggingface dataset:
Importance Ehrlichiae are obligate intracellular bacteria with a unique developmental cycle that includes attaching to and entering eukaryotic host cells, a process mediated by proteins in their outer membrane (OM). Thus far, few experimental data on ehrlichial OM proteins are available. To gain insight into the protein composition of the ehrlichial OM, we performed proteome analysis on OM fractions from Ehrlichia ruminantium elementary bodies, the infectious form of this bacterium. We compared our experimental results with an in silico analysis of the E. ruminantium proteome. We identified 18 proteins, whose OM localization was supported by both studies, and were, therefore, very likely to be located in the E. ruminantium OM. Among these proteins, 6 are completely new discovered OMPs and are therefore of importance as potential vaccine antigens.
MS/MS analyses were performed using collision-induced dissociation (CID) assisted with air, using collision energy of 1 kV and a gas pressure of 10 6 Torr. Two thousand laser shots were collected for each MS/MS spectrum using a fixed laser intensity of 4,500 V. Raw data were generated using 4000 Series Explorer Software v3. 0 RC1 (Applied Biosystems, Foster City, CA, USA), and all contaminant m/z peaks originating from human keratin, trypsin autodigestion, or matrix were placed on the exclusion list used to generate the peptide mass list used in the database search  [17]. 4. Database query. To identify proteins, Mascot generic format files combining MS and MS/MS spectra were used to interrogate a non-redundant protein database using a local Mascot v2. 2 license from Matrix Science and the Global Protein Server (GPS) v3.
6 (Applied Biosystems). Search parameters for the MS/MS spectra were as follows: i) the Uniprot (2013) sequence database (E. ruminantium with isoforms) was used; ii) taxonomy was set to "all entries" (302,409); iii) variable modifications were considered [i. e. , carbamidomethylation (Cys), deamidation (Asn and Gln), and oxidation (Met, Pro, Lys, Arg)]; iv) two missed cleavage sites were allowed; v) precursor tolerance was set to 50 ppm and MS/MS fragment tolerance to 0. 5 Da; vi) peptide charge was 1+; and vii) the algorithm used trypsin as the enzyme. A protein candidate provided by this MS/MS search was considered valid if the global Mascot score was >40 at a significance level of p<0. 05, if at least one peptide was identified with 95% confidence, and if it was found in at least two of the three biological replicates.
In silico genome analysis The publicly available proteome of the E. ruminantium strain Gardel, which was extracted from the Uniprot database  [31]  in FASTA format, was used for bioinformatics studies. The subcellular localization of the 948 E. ruminantium protein-coding genes was predicted using three global programs: PSORTb 3. 0  [32] , CELLO 2. 5  [33] , and MetaLocGramN  [34]. The predicted utilization locations of each protein were filtered from raw software output using inhouse scripts written in the R programming language and exported to Excel. In some cases, CELLO 2. 5 predicted multiple localization sites for the same protein. The proteins involved were grouped under the heading "unknown localization. " As a result of the varying predictions for a given protein, the consensus prediction was calculated using a majority vote procedure.
The six putative uncharacterized proteins had neither functional annotations in UniProt, nor hits in the Pfam database. Two of these (ERGA_CDS_04580, ERGA_CDS_05150) were predicted by SignalP to contain signal peptides. The first had no homology with known proteins and seemed to be unique in the E. ruminantium genome, whereas the second had similarity to ECH_0525, an ortholog of Esp73, an OMP in Anaplasma phagocytophilum. In summary, our study increased the number of OMPs experimentally identified accounting for 34% of total predicted OMPs in E. ruminantium (18/52), whereas the total number OMPs account only for 5. 5% of E. ruminantium proteome (52/948). Thus, the OM purification process described enriched OMPs.
These in silico predictions allowed us to estimate the quality of the enrichment of OMPs in the OM fraction obtained using our purification protocol. PSORTb 3. 0 is one of the most precise subcellular localization predictor for many Gram-negative bacteria  [32]. It uses a combination of factors based on motif and profile analyses, e. g. the presence of signal peptides, OM motifs, transmembrane helices, and similarity to proteins with known localization  [32]. However, in this study, it returned a high number of proteins with unknown localization (236 or 24. 8% of total proteins). This problem may be due to the absence of significant sequence similarity between some E. ruminantium proteins and proteins in the PSORTb 3. 0 database. Similar results have been observed in numerous other bacteria  [34].
These proteins are known to be differentially transcribed in vitro in endothelial and tick cell cultures  [54, 55]  and are well conserved, since omp-1, msp2, p44, p30, and map-1 belong to a superfamily harboring the PF01617 Pfam domain  [1]. Map1 family proteins are considered priority targets for candidate vaccines  [56] , as they are potentially involved in E. ruminantium adaptation to the mammalian host and its vector, the tick  [18]. However, few data are currently available on the expression and characterization of Map1 family proteins throughout the bacterial life cycle  [17]. Proteins of the β-barrel Assembly Machinery (BAM) complex are involved in diverse cellular functions, including solute transport, protein secretion, and assembly of protein and lipid components of the OM  [57].
Therefore, we cannot deny the possibility that these proteins with well-known functions in the cytoplasmic, periplasmic, or inner membrane are present in the OM of E. ruminantium and play unexpected roles in E. ruminantium -host interaction. Surprisingly, we also detected ribosomal proteins with a predicted cytoplasmic localization. These proteins may represent a contamination with cytoplasmic proteins. Such proteins have also been identified in OM fractions of Pseudomonas and Yersinia strains, however  [88, 89]. Moreover, it should be noted that among these ribosomal proteins, we obtained a majority of 50S ribosomal subunits, as has been shown in Legionella  [12]. Interestingly, one ribosomal protein we found in the OM fraction (ERGA_CDS_01640) has been predicted by S4TE software as a putative type IV effector  [27].
Study Oversight This study was conducted in accordance with the ethical standards of the Helsinki Declaration of the World Medical Association, and was approved by the institutional review boards of the following institutions: Massachusetts General Hospital, Mbarara University of Science and Technology, the Ugandan National Council for Science and Technology, the Comité de Protection des Personnes France, and Médecins Sans Frontières International. The US Food and Drug Administration was notified, but an Investigational New Drug approval was not required. The Ugandan National Drug Authority approved the study. A data and safety monitoring board was convened biannually, for a total of 3 meetings, to review the unblinded safety data based on serious adverse events and mortality. The trial was registered under the ClinicalTrials.
Statistical Analysis We compared proportions with a χ² or Fisher's exact test with 2-sided P values. We assessed data within groups using a 2tailed paired t test or Wilcoxon matched-pairs signed-rank test, and we assessed data between groups using a Mann-Whitney U test. Data between the N 2 and NO treatment cohorts, and within treatment cohorts at hours 0 and 48, were compared from patients who received gas for at least 48 hours and those for whom we had paired data. We compared survival and time to coma recovery curves using Kaplan-Meier curves and log-rank test (or Wilcoxon Breslow when appropriate). Statistics and graphs were generated with Stata software (Release 13, StataCorp. ) and GraphPad Prism 6 (GraphPad Software, Inc. ). The level of statistical significance was set at 0. 05. All results are displayed as mean ± standard deviation (SD).
Acknowledgments We sincerely thank the children and parents involved in the study. We also thank the following individuals:  Sheilla Mbabazi ,  Jackline Tumuhairwe ,  Inhaled Nitric Oxide  as an  Adjunctive Treatment  for Cerebral Malaria in Children • OFID • 7  James Kinyata ,  Amerias Arimpa ,  Gertrude Ngabirano ,  Allen Atukunda , and  Addy Ahairwe  for providing nursing care;  Karin Hergarden  and  Susan Logoose  for internal monitoring;  Victoria Katawera ,  Davis Kaganzi ,  Arnold Ayebare ,  Harriet Adrama , and  Daniel Omoding  for the laboratory work; and  Rinah Arinaite ,  Peace Magezi , and  Ronie Mutabazi  for data entry and management. We acknowledge  Brian Bergmark  and  Regan Bergmark  for contributing to the writing protocols while they were  Harvard Medical School  students working in Mbarara.
Statistics SAS ® (V9. 1. 3; SAS Institute Inc. , Cary, North Carolina, USA) was used to perform the statistical analyses. The immunogenicity analyses were performed on the per protocol set (PPS) that included all randomised participants who received the study vaccine and had at least one valid (either pre-or post-vaccination) immunogenicity evaluation for VZV antibody (PPS) or VZV ELISPOT count (ELISPOT PPS). The gpELISA GMT ratio (IM group/SC group) and the corresponding 95% CI were calculated using a longitudinal data analysis model including pre-and post-vaccination log-transformed titres as response variables and age at vaccination, group and visit (preand post-vaccination) as covariates  [50]. This model handles missing data and provides the same results as an ANCOVA model when there are no subjects with missing data.
Acknowledgements The authors would like to thank the subjects who participated in the trial, as well as the following investigators for their contribution to the study:  Rolf Dominicus  ( Dülmen, Germany );  Christine Grigat ,  Martina Lilie  ( Hamburg, Germany );  Dorothee Kieninger-Baum  ( Mainz, Germany );  Wiefried Steinborn  ( Straubing, Germany );  Peter Kindermann  ( Riesa, Germany );  Enrique Guinot ,  Vincenta Pineda  (Valencia, Spain). They also would like to thank  Xavier Cornen ,  Sylvie Dard ,  Nicolas Goulleret  and  Elodie Turpaud  ( Sanofi Pasteur MSD ),  Martina König  ( THEOREM Clinical Research GmbH & Co. KG ) for their contribution to the conduct of the study;  Janie Parino  and  Paula Annunziato  ( Merck & Co. , Inc. ) for their input during the study design and for their critical review of the study results and  Laura Malette  and  Barbara E.
Methodology/Principal Findings The objective of the present study was to evaluate the use of MALDI-TOF MS to identify tick species, and to determine the presence of rickettsia pathogens in the infected Ticks. Rhipicephalus sanguineus and Dermacentor marginatus Ticks infected or not by R. conorii conorii or R. slovaca, respectively, were used as experimental models. The MS profiles generated from protein extracts prepared from tick legs exhibited mass peaks that distinguished the infected and uninfected Ticks, and successfully discriminated the Rickettsia spp. A blind test was performed using Ticks that were laboratory-reared, collected in the field or removed from patients and infected or not by Rickettsia spp. A query against our inlab arthropod MS reference database revealed that the species and infection status of all Ticks were correctly identified at the species and infection status levels.
Both pieces of information, if obtained quickly may be clinically helpful, particularly with regard to decisions about the use of antibiotic prophylactic treatment to prevent tick-borne diseases. The routine method of identifying Ticks has traditionally been morphological identification using taxonomic keys, entomological expertise and specific documentation  [1]. In the past decade, molecular tools have been developed to identify Ticks but these techniques also have their limitations including the selection of ideal primers, the requirement for technically timeconsuming and expensive of PCR assays, and the availability of gene sequences in GenBank  [1, 3]. More recently, we implemented the use of Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) in our laboratory as an effective tool to rapidly identify arthropods including Ticks  [4] [5] [6] [7].
Furthermore, with the creation of a database of reference spectra MALDI-TOF MS profiling of tick leg protein extracts will allow the rapid, cost-effective and accurate identification of Ticks. For the detection and identification of Rickettsia species in infected Ticks, the most widely available tools remain molecular methods  [1] , and several Rickettsia DNA sequences can be detected and precisely identified in Ticks by different PCR methods  [1]. However, to date, no system allows for the rapid and accurate identification of both the tick species and the Rickettsia spp that the Ticks harbor. Although the MALDI-TOF MS approach has emerged as a routine method for the identification and classification of bacteria for clinical diagnostics  [8] , no reference spectrum is available for the identification of intra-cellular Rickettsia in the commercial reference spectra database.
Analysis of MS profiles Protein mass profiles were acquired using a Microflex LT spectrometer (Bruker Daltonics) with Flex Control software (Bruker Daltonics). The spectra were recorded in a linear, positive ion mode with an acceleration voltage of 20 kV, within a mass range of 2,000-20,000 Da. Each spectrum corresponds to an accumulation of 240 laser shots from the same spot in six different positions. To control the loading on the steel target, the matrix quality and the MALDI-TOF apparatus performance, the matrix solution was loaded in duplicate onto each  MALDI  Comparisons of the mass spectra of tick specimens infected or not by Rickettsia spp MALDI-TOF MS spectra from the leg protein extracts of 9 D. marginatus infected or not by R. slovaca, and 10 Rh. sanguineus infected or not by R.
c. conorii were imported into ClinPro-Tools v. 2. 2 (Bruker Daltonics, Germany) to identify the specific peaks related to the infection status of the tick. The parameters for ClinProTools software analysis were similar to those previously described  [4]. An average spectrum was generated for each condition (i. e. , tick species infected or not by Rickettsia spp), using the algorithm "average peak list calculation" tool within the range of 2-20 kDa. The detection of discriminating peak masses was performed by comparison of the average spectrum generated between two classes. The Genetic Algorithm (GA) model of the ClinProTools software was then used to automatically display a list of discriminating peak masses. Based on the selected peak masses, the values of Recognition Capability (RC) and Cross Validation (CV) were determined  [16, 17].
Blind tests The accuracy of MALDI-TOF MS for the detection both of the Ticks and pathogens was assessed in a validation step involving a blind test using other tick specimens that were infected or not by Rickettsia spp, including Ticks collected in the field or removed from patients. MALDI-TOF MS spectra from the leg protein extracts of 3 uninfected D. marginatus, 3 D. marginatus infected by R. slovaca, 2 uninfected Rh. sanguineus and 4 Rh. sanguineus infected with R. c. conorii, were used for a blind test (Blind test 1) with 1 to 4 new specimens per species against our laboratory's database of reference spectra for (Database 1). This database includes the leg protein spectra of 6 rickettsia free tick species (Amblyomma variegatum infected by R. africae, Rh. sanguineus, Hyalomma marginatum rufipes, Ixodes ricinus, D.
canis, Archaeopsylla erinacei, Xenopsylla cheopis and Stenoponia tripectinata)  [4] [5] [6] [7]. Then, MALDI-TOF MS spectra from uninfected D. marginatus (n = 4), D. marginatus infected by R. slovaca (n = 4), uninfected Rh. sanguineus (n = 4) and Rh. sanguineus infected with R. c. conorii (n = 5) were added to our database; this upgraded database is referred to as Database 2. The same specimens of D. marginatus, D. marginatus infected by R. slovaca, uninfected Rh. sanguineus and Rh. sanguineus infected with R. c. conorii, were tested in a blind test against Database 2 (Blind test 2). Additionally, the spectra from the leg protein extracts of 3 Ticks removed from 3 patients were also tested against Database 2. The presence of Rickettsia spp was previously confirmed by qPCR including 1 specimen of Rh.
sanguineus infected by R. c. conorii, confirmed the reproducibility of the spectra and also revealed changes in the MS pattern according to the infectious status. Comparable results were obtained from MS spectra of D. marginatus specimens infected or not by R. slovaca. Although several protein peaks were conserved in the spectra from specimens belonging to the same species, modifications of the MS patterns were detectable in Rickettsia-infected specimens compared to uninfected specimens (Fig. 2 ). Technical and biological replicates yielded reproducible spectra (Fig. 1 ). The spectra of at least 4 specimens of each species (infected and uninfected) were added to our arthropod database (Database 1) in MALDI-Biotyper 3. 0, which was designated as Database 2. In parallel, MALDI-TOF MS spectra of each Rickettsia strains were compared to that of the L929 cell line.
Singularity of the MS patterns according to tick species and infectious status To determine whether the mass spectra data were suitable for the identification of discriminating peaks (m/z-values) according to the Rickettsia-infectious status, 16 to 20 MS spectra per group were selected for further analysis and loaded into the ClinProTools software. Among the Rh. sanguineus and D. marginatus Ticks that were infected or not, by R. c. conorii or R. slovaca, respectively, 76 spectra from 19 specimens that were selected for the MALDI-Biotyper database were imported into the ClinProTools software. The Genetic Algorithm model displayed the peak masses that discriminate between the Ticks that were infected or not by Rickettsia spp with RC and CV values of 100% for both comparisons. After verification of the peak report in the averaged spectrum of the Rh.
sanguineus species, 30 biomarker masses were identified that could distinguish Rh. sanguineus specimens that were infected or not by R. c. conorii (Table  2 ). Among them, 22 peak masses were observed uniquely in the R. conorii-infected specimens and 8 peak masses were associated with the uninfected Rh. sanguineus specimens (Table  2 ). To confirm the specificity of several of these discriminant biomarker masses, a comparison of the MSP between Rh. sanguineus infected by R. c. conorii and the purified R. c. conorii strain was performed (Table  2 ). Twelve peak masses were common to both samples, and they were localized in the spectra of Rh. sanguineus infected by R. c. conorii using Flex analysis software (Fig. 3A ). Using a comparable strategy for D. marginatus specimens, 35 discriminating peak masses were identified, among which 21 peak masses were specific to spectra from D.
Blind tests A total 15 specimens, including uninfected and Rickettsia-infected Ticks, were queried successively against the MS reference Database 1 and Database 2 (i. e. , Database 2 = Database 1 plus the spectra from Rickettsia-infected Ticks). Using Database 1, the blind test yielded 100% correct identification at the species level for the specimens tested irrespective of their infectious status and their origin of collection (i. e. , Ticks that were laboratory-reared, collected in the field or removed from patients). The LSVs of the first top-ranking hits against Database 1 varied from 1. 756 to 2. 449 (Table  1 ). Interestingly, the tick specimens infected by Rickettsia spp had lower LSVs than the uninfected specimens. The same specimens were then tested against Database 2, and 100% of the specimens tested possessing a corresponding reference spectrum in Database 2 were correctly identified at the levels of tick species and infectious status (Table  1 ).
Moreover, with the exception of the Rh. sanguineus specimen infected by R. massiliae, only the LSVs from Rickettsia-infected Ticks were increased, and all of these specimens had an LSV larger than 1. 85. Interestingly, no association was observed between the cycle threshold value of qPCR and the LSVs. Although no reference spectrum was included in the database for the Rh. sanguineus specimen infected by R. massiliae, it was correctly identified at the level of the tick species as an uninfected Rh. sanguineus specimen, with an LSV greater than 2.
In total 30 and 35 biomarker mass sets distinguished the uninfected specimens of Rickettsia spp from the infected specimens of D. marginatus and Rh. sanguineus species, respectively. Interestingly, although the majority of the discriminating peaks appeared in the protein profiles of the infected Ticks, some were not maintained. This loss of some peak masses could be detrimental to the level of significant identification (i. e. , LSVs) of Ticks at the species level. Effectively, our blind test experiments indicated that the LSVs of the infected specimens were lower than those of the uninfected Ticks when compared with Database 1, which included only uninfected specimens. In the future, it will be necessary to test the infectious status of a specimen of new species prior to including the results in the reference database.
Moreover, the addition of MS spectra from specimens infected with pathogens will improve the identification of arthropod species and the pathogens that they carry. In addition, among the discriminating peak mass sets found in the infected Ticks, few of them were shared with their respective purified Rickettsia strains. These masses could correspond to Rickettsia-specific proteins. Moreover, some discriminating peaks detected uniquely in the Rickettsia-infected Ticks were not present in the spectra peaks of the bacteria strains. These differential peak masses could be attributed either to Rickettsia strains (i. e. , variations between laboratory and field strains)  [26]  or to a response of the Ticks to infection  [27]. Complementary experiments are needed to test these hypotheses. The validity of the databases was established by blind tests using infected and uninfected specimens.
A query against Database 2 demonstrated that all the specimens possessing reference spectra in the database were correctly identified at the level of the tick species and the Rickettsia-infectious status. Moreover, 86% (n = 12/14) of these spectra presented LSVs greater than 1. 9, which is considered to be reliable score for bacterial species identification  [28]. Thus, the spectral variations that are detected following Rickettsia infection are sufficient to avoid cross-recognition between uninfected and infected Ticks. Moreover, the presence of Rickettsia in the Ticks did not mask the protein profiles for unambiguous identification at the species level (e. g. , querying the MS spectra against Database 1). These results are in agreement with a previous study showing that these variations do not interfere with species determination  [24].