dataesr/scientific-paragraphs-categorization huggingface dataset:
The sup35-M0 Allele Can Induce and Propagate the [PSI + ] Prion but With Low Efficiency Next, we analyzed the effect of sup35-M0 allele on the prion induction in vivo. The presence of another prion [PIN + ] is required for [PSI + ] de novo formation in yeast cells  (Derkatch et al. , 1997). For this experiment [psi -][PIN + ] strains (derivatives of 7A-D832 or 12-D1682 bearing SUP35 or sup35-M0) were transformed with plasmids for Sup35NM-GFP (positive control), Sup35NM-M0-GFP or GFP (negative control) overproduction. The presence of sup35-M0 in cells significantly decreased the frequency of [PSI + ] formation in different yeast strains. Furthermore, the overproduction of Sup35NM-M0-GFP induced [PSI + ] with very low efficiency (Figures  4A, B ). This result allowed us to conclude that investigated substitutions significantly decrease the aggregation propensity of the protein, as was predicted by the ArchCandy program (Figure  1 ).
ACKNOWLEDGMENTS We are grateful to  Yury O. Chernoff  and  Ivana Malcova  for plasmids and strains. We thank  Ksenia V. Sukhanova ,  Anton B. Matiiv ,  Yury A. Barbitoff  and  Ekaterina Davydova  for critical reading of the manuscript and suggestions. The work was supported by the  RRC MCT SPbSU  and we thank  Maxim Vorobyov ,  Nikolai Kostin  and  Alexei Masharskiy  from this facility for help with experiments.
FUNDING The authors acknowledge  St. Petersburg State University  for the research grant (id:  28229898 , SB, the ArchCandy analysis). This work was also supported by  RFBR  grants ( 19-04-00173 , GZ, the analysis of sup35-M0 effects on [PSI + ] propagation;  17-54-150002 , GZ, and  PRC CNRS  grant  PRC1524 , AK, the in vitro experiments;  18-34-00536 , AM, the analysis of colocalization of Sup35NM-M0 with the wild-type protein in vivo) and RSF grant ( 18-14-00050 , GZ, the analysis of sup35-M0 effect on the translation termination).
However, the viability percentage was also lowered in untreated oocysts, while no effect on viability was measured by RT-qPCR analyses (data not shown). This suggests that untreated oocysts were likely more permeable to PMA at 45 °C. Alternatively, the PMA pre-incubation step might impact on the efficacy of DNA extraction and/or DNA integrity, rather than on the oocysts' viability. Taken together, none of the preincubation conditions we tested allow an optimal discrimination of viable and heat-killed oocysts. The length of the nucleic acid target has been shown to play a crucial role in the suppression of PMA-PCR signal  (Contreras et al. 2011). qPCR signal was abolished for heated G. duodenalis cysts with a 605-bp but not 77-bp amplicon of the tpi gene  (Alonso et al. 2014) ). In the same way, Agullo-Barcello and collaborators (2014) also described a larger signal reduction using PMA-qPCR assays targeting a 834-bp versus 108-bp sequence of the 18S gene of C.
Preliminary results for T. gondii oocysts exposed to detergents showed a larger PMA-qPCR signal reduction than following heating treatment while still being infective (data not shown). Hence, the efficacy of PMA-qPCR assays for T. gondii oocysts submitted to other inactivation treatments have to be evaluated. In conclusion, in the conditions tested in this study, PMA-qPCR is able to assess the reduction of T. gondii oocyst viability following heating. However, PCR signals can still be detected in heat-killed oocysts leading to an underestimation of the inactivation efficacy of heating. In this regard, further works are required to evaluate the potential of this technique following other inactivation treatments than heating and the relevance of PMA-qPCR assays to characterize inactivation efficacy of industrial processes.
Acknowledgements This work was supported by the  UMT ACTIA PROTORISK  and the  Universities of Reims Champagne-Ardenne  and  Rouen Normandie , in the frame of a PhD with a CIFRE agreement. A. D. is supported by the  Institut Hospitalo-Universitaire (IHU) , the  National Research Agency  (grant  10-IAHU-03 ), the  Région Provence Alpes Côte d'Azur, and European funding FEDER PRIMI. The authors thank MOBICYTE,  URCACyt , and PICT platforms,  University Reims Champagne-Ardenne. We thank  Mrs. Sabourin-Gibbs ,  Rouen University Hospital , for checking spelling and grammar. Compliance with ethical standards.
Data sources Gene-disease associations. We built the sets of genes associated to A, D and R by integrating data from four sources:  (1)  The Comparative Toxicogenomics Database  [38] , which provides highly reliable gene-disease associations characterized through various experimental procedures combined with a process of expert curation of the literature and other databases (e. g. OMIM  [39] ). (2)  The DisGeNet catalog, that contains curated gene-disease associations extracted from literature  [40]. (3) UniProt-derived gene-disease associations, extracted from the Involvement in disease section of the Uniprot Knowledgebase  [41]. (4)  The Phenotype-Genotype Integrator database, that integrates information various NCBI genomic databases with association data from the National Human Genome Research Institute GWAS Catalog  [42].
This is the only data source containing solely GWAS-derived gene associations  [43]. Genes associated to a disease d (any of A, D or R) will be hereinafter referred to as d-associated genes. The interactome. We built the functional interaction network (hereinafter called the interactome for brevity) by combining data from:  (1)  The Reactome Functional Interaction Network (v. 022717)  [44] , which includes not only protein-protein interactions but also gene expression interaction, metabolic interactions and signal transduction. (2)  The STRING interaction network (v. 10. 5) [45]. Cell-type-specific gene expression. Gene expression levels were obtained from the human gene expression atlas available at ArrayExpress under accession number E-MTAB-62  [46]. This is a cell-type-wide compendium of high-quality microarray-derived expression data that has been previously used in other network-based analysis of gene expression  [47] [48] [49]  and has been incorporated into a number of biomedical software packages  [50] [51] [52].
Cellular pathways Cellular pathways were downloaded from Reactome database in the UniProt2Reactome format files  [44]. Pathway-associated genes either without expression data or not present in the interactome were not considered. Disease-related cellular pathways (e. g. Constitutive Signaling by Aberrant PI3K in Cancer) were not considered. Reactome is a collection of pathways built in a hierarchical manner, where larger pathways are subdivided into smaller pathways with more specific functionalities. This implies a trade-off between the specificity in the representation of cellular functions and the average number of genes per pathway  [57]. To minimize the overlap between pathways in order to avoid redundancies that could negatively affect our analysis  [58] , we calculated the pairwise overlap between pathways at distinct levels of the Reactome hierarchy using the Sorensen-Dice method  [59] [60] [61].
If two pathways had an overlap of > 50% genes, the one with the lowest number of associated genes was removed from the set. We chose pathways of at depth 3 of the hierarchy because it provided a mean overlap < 1% while annotating 4,809 genes (this is 87,9% of the total genes annotated in the database, all levels considered). Genes associated to a pathway p (any of our pathways of interest) will be hereinafter referred to as p-associated genes. Pathway annotation in our previous study of A, D and R were extracted from BioCarta  [62]. There is not a perfect equivalence between cellular pathways from BioCarta and Reactome databases, so in order to compare our results to those from our previous whole-organism multimorbidity study  [21]  we performed an association test to identify which BioCarta pathways significantly overlapped with Reactome pathways (Fisher's Exact test, adjusted P <0.
Cell-type-specific networks In order to generate the specific network for any cell type c, we selected all edges from the interactome connecting c-specific genes  [6, 64]. Because of the interactome-based nature of our analysis, those genes not present in the interactome or not present in the expression dataset were removed from the analysis. The statistical significance of the number of d-associated genes present in each c-specific network was calculated by means of a Fisher's Exact test (adjusted P <0. 05).
Cellular pathways The number of pathways in Reactome database was 519 after filtering, with an average pairwise overlap of 0. 01%. Overall, 6,989 genes were associated to at least one pathway. On average, ALDH2 • aldehyde dehydrogenase 2 family (mitochondrial) CCL22 • C-C motif chemokine ligand 22 ALOX5 • arachidonate 5-lipoxygenase CCL24 • C-C motif chemokine ligand 24 AREG • amphiregulin CCR5 • C-C motif chemokine receptor 5 (gene/pseudogene) ARG1 • arginase 1 CD207 � CD207 molecule ARG2 • arginase 2 CSTA • cystatin A BACH2 � BTB domain and CNC homolog 2 CTLA4 • cytotoxic T-lymphocyte associated protein 4 BCL2 • BCL2, apoptosis regulator CXCL10 • C-X-C motif chemokine ligand 10 CAT • catalase CYP24A1 � cytochrome P450 family 24 subfamily A member 1 CCL2 • C-C motif chemokine ligand 2 EMSY • EMSY, BRCA2 interacting transcriptional repressor CDH17 � cadherin 17 FOXP3 • forkhead box P3 CDK2 � cyclin dependent kinase 2 GLB1 • galactosidase beta 1 CFTR • cystic fibrosis transmembrane conductance regulator IFNG • interferon gamma CHIT1 � chitinase 1 IL10 • interleukin 10 CPN1 • carboxypeptidase N subunit 1 IL15RA � interleukin 15 receptor subunit alpha CRB1 � crumbs 1, cell polarity complex component IL18RAP � interleukin 18 receptor accessory protein CRBN � cereblon IL2RA • interleukin 2 receptor subunit alpha CXCL14 • C-X-C motif chemokine ligand 14 IL6 • interleukin 6 (Continued ) ~37% of genes on cell-type-specific networks were associated to at least one pathway.
This is why we chose to focus our attention in other cell types, important yet no so well-studied in connection to ADR multimorbidity. Furthermore, our dataset reflects only expression levels in healthy individuals because no cell-type-wide expression compendium in subjects with ADR multimorbidity exists. Another limitation of our study is data completeness. The intersection of expression and interactome data sources yields a low coverage of the complete genome. Although this is a common limitation (and authors have argued that the current coverage of the human interactome does not limit its successful application to the investigation of disease mechanisms  [5, 16] ), some data loss is unavoidable: for instance, a protein such as filaggrin (FLG), commonly associated to multimorbidity between A and D  [78] , was not present in our expression dataset and could not be incorporated to the study.
Also, our expression dataset contains data primarily from adult subjects. Thus, it is unclear if our results can be generalized to other age groups like young children or elderly people. However, we believe that gain in knowledge largely compensates these limitations. As for disease-gene associations, we are including gene-disease associations partially derived from GWAS studies, whose reliability has been questioned  [79] [80] [81]. Additionally, the current human interactome is highly biased toward highly studied genes (a category that includes many disease-associated genes), representing only a very small densely connected fraction of the full interactome  [82] [83] [84] [85] [86]. This bias might be larger than expected and may have an impact on the biological conclusions extracted from the studies of the interactome  [87].
IL1 is a known inflammatory marker associated to D and bronchial A  [90] , amongst other diseases with inflammatory components. Interestingly, a role for IL1 as a mediator in multimorbidities has already been hinted, as IL1 blocking therapies have proven effective against conditions encountered as comorbidities in patients with rheumatic diseases  [91, 92]. We have to point out, however, that the definition of a pathway (as a functionally annotated gene set) should be taken into account when analyzing those results. For instance, the pathway Antigen Processing and Cross-Presentation is associated to AR multimorbidity in erythrocytes (Table  4 ). This contradicts evidence on MHC presence in human nonnucleated cells  [93]  because of the definition of the pathway in Reactome database, that includes genes also annotated in TLR-mediated pathways.
Methodologically, differences in the gene-disease data sources used in both studies have an impact in the characterization of disease-associated genes. Also, availability of expression data limited the number of genes present in the study. For instance, thymic stromal lymphopoietin (TSLP), found to be associated to A, D and R in our previous study, is absent in this study because it was not present in the expression compendium. Also, pathway annotation in our previous study was extracted from BioCarta database, which is no longer updated, which made us chose Reactome database instead. One of the main findings in our previous study was the significant role of eosinophilicmediated pathways in AD multimorbidity (BioCarta pathways CCR3 signaling in Eosinophils and The Role of Eosinophils in the Chemokine Network of Allergy were identified with a high score).
Because eosinophils were not included in the present study, the Reactome equivalents of those pathways (S1 Table ) are not present in our results, confirming that our observations can be linked to mechanisms mediated by other cell types. IL10 signaling pathway, a relevant mechanism in AD multimorbidity across many cell types, was also identified amongst the highest-scoring pathways in our previous study (under the BioCarta denomination Regulation of hematopoisesis by cytokines). Our previous study also linked IL4-mediated, GATA3-mediated mechanisms and 4-1BB-dependent immune responses to ADR multimorbidity. GATA3mediated mechanisms are represented in our dataset by interleukin pathways in the Cytokine signaling in immune system category, and, from a cell-type-specific point of view, these processes seem more relevant in AD multimorbidity (despite the fact that IL1, IL4 and IL13 signaling in particular also contribute to ADR multimorbidity in some cell types).
Aside from differences in pathway annotation of genes, this could reflect a more systemic role for these pathways in ADR multimorbidity. However, 4-1BB-dependent immune response (represented by Toll-like receptor cascades in our dataset) is clearly associated to AR, DR and ADR multimorbidity in a number blood-derived cell types. In all, we believe that our results are complementary to those of our previous study since they focus on the cell-type-specific mechanisms of multimorbidity instead of global (or systemic) ones.
Conclusions We designed an in silico approach that integrated current public expression and network interaction databases and applied an interactome-based analysis to uncover the cell-type-specific pathophysiological mechanisms of multimorbidity between A, D and R. We observed that interleukin-mediated signaling is present in all multimorbidities involving asthma but not rhinitis, while rhinitis-associated multimorbidities have a strong TLR-mediated component. IL1 signaling is the only type-2 pathway candidate for AR multimorbidity, found in esophageal epithelium. We also generated a collection of genes potentially linked to cell-type-specific multimorbidity, some of which were not previously associated to any of the diseases. Our results provide a better understanding of the pathophysiological mechanisms triggering ADR multimorbidity, assisting in the design of new mechanistic and clinical studies.
This work was supported by  Mechanisms of the Development of ALLergy (MeDALL) , a collaborative project done within the  EU  under the  Health Cooperation Work Programme of the Seventh Framework programme  (grant agreement number  261357 ). EM is supported by grants from the  European Research Council  ( n7 57919 ) and the  Swedish Research Council. NL is a recipient of a postdoctoral fellowship from the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 6AM Data Mining provided support in the form of a salary for DA, but did not have any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Export of the mature peptide or its precursor can be ensured by different types of transporters reviewed in  [19, 24]. Two major types of ABC-exporters are involved in selfimmunity: transporters that exclusively export AMPs such as those of the NisT-or McjD-type, and bifunctional transporters that ensure both maturation of the AMP by cleavage of the leader region of the precursor and secretion of the mature peptide  [17] [18] [19]. These dual function proteins belong to the ABC-transporter maturation secretion (AMS) superfamily, or Peptidase-Containing ATP-binding Transporters (PCAT), also called the SunT-type transporter family  [18, 19]  after the peptidase characterized for the glycopeptide sublancin system in Bacillus subtilis strain 168  [25]  (Figure  1B  and C ). Database searches for NisT-type transporters in Firmicutes, the main group of Gram-positive bacteria among which lactic acid bacteria, show they are quite rare, whereas AMS/PCAT/SunT transporters are quite common  [24].
Examination of the hip showed a purulent swelling and an inflammatory scar but no fever. Multiple irrigations with pulsatile lavage of the implanted hip and retention of the arthroplasty were performed. Specimens were collected intraoperatively and submitted for aerobic, anaerobic and broth cultures. An empirical antimicrobial treatment with meropenem and daptomycin was initiated. Following negative conventional cultures, direct 16S rRNA gene amplification and sequence analysis were performed on frozen specimens. A portion of the 16S rRNA gene was amplified, using universal primers which correspond to positions 8e27 and 1384e1400 using Escherichia coli numbering, respectively. 2 The sequence was analyzed with the Bio Informatics Bacterial Identification (BIBI) database. All the 5 per-operative samples were positive for U.
urealyticum (Genbank accession no MF372933). Secondary cultures were performed using A7 agar (bioMerieux) and broth media adapted to ureaplasma (Fig. 1 ). 1  Two specimens were positive by specific cultures and the species identification of U. urealyticum was confirmed using a specific real-time PCR. 3  Minimum inhibitory concentrations (MICs) of levofloxacin, moxifloxacin, tetracycline, and doxycycline were then determined using a broth dilution method and showed susceptibility of the isolate with MICs of 1 mg/ml, 0. 25 mg/ml, 0. 5 mg/ml and 0. 12 mg/ml, respectively. 1 Unfortunately, the patient died of multiple comorbidities before the antimicrobial treatment could have been adapted. Ureaplasma spp. belongs to the class Mollicutes and are one of the smallest free-living organisms. Recently, they have been classified into two species, U.
Khodadoust MD). NCT02593045). Findings: From November 2015 -November 2017, 44 patients were enrolled (median age: 69years). 35 patients (80%) had SS, 8 (18%) had mycosis fungoides and 1 (2%) had primary cutaneous T-cell lymphoma, not-otherwise specified. Median prior systemic therapies was 3. In the dose-escalation, no dose-limiting toxicity was identified and the trial's safety committee recommended a flat dose of 750mg for the cohort-expansion, corresponding to the maximum administered dose. Most common adverse events (AEs) were peripheral oedema (27%), and fatigue (20%). Lymphopenia was the most common grade 3-4 AE (7%). One patient developed fulminant hepatitis following IPH4102 discontinuation and subsequently died with evidence of HHV-6B infection. In the efficacy population (n=44, 100%), median follow-up was 14•1months (IQR : 11.
Statistical Analysis In the dose-escalation, an accelerated 3+3 design was adopted to determine the MTD  17. One patient was treated at each of the first three dose levels, and subsequently three patients were treated per dose level until the maximal administered dose (MAD) of 10 mg/kg. Cohort expansion was designed to confirm the safety of the recommended dose and investigate clinical activity without a priori assumption regarding sample size. Safety data were summarized for the safety population; patients who received at least one dose of IPH4102. Efficacy analysis was performed according to CTCL subtype (i. e. SS, MF) for the efficacy population; patients who received at least one dose of IPH4102, and had a baseline disease assessment and at least one subsequent scheduled disease assessment.
Response was assessed between the first administered dose until end of treatment. In order to have a best global response, patients had to have a confirmatory response assessment no sooner than 4 weeks after the initial documentation of response  13. DOR and PFS were estimated using the Kaplan-Meier method and were censored at the latest disease assessment. QOL analysis is detailed in the Appendix (page 5). The association between exploratory biomarkers, evaluated on weeks five and 14, and clinical activity was evaluated using descriptive statistics. All statistical analyses were performed using SAS 9. 3 and R 3. 5. The study is registered with clinicaltrials. gov number: NCT02593045.
He had normal liver function and lymphocytic count throughout the treatment course. Investigations revealed positive human herpes virus-6B (HHV-6B) serology confirmed by PCR in both serum and liver biopsy. The second patient experienced isolated grade 3 AST increase while on treatment, which recovered two weeks later. Liver function parameters of all the remaining patients were otherwise uneventful (Appendix, page 9). At the data cut-off date of October 15, 2018, median follow-up for the whole population was Compliance to QOL questionnaires was high; 97% and 95% of the VAS and Skindex questionnaires were completed, respectively. Treatment with IPH4102 was associated with a decrease in pruritus (Figure  3A ) and Skindex global (Figure  3B ) as well as symptoms, emotional and functional scores (Figure  3C-E ) overtime.
This patient developed ADA on In skin, no clear correlation was observed between the degree of KIR3DL2 expression at baseline and response to IPH4102 (data not shown). Pre-and post-treatment biopsies were obtained in 31 out of 35 patients (88. 5%). A post-hoc analysis showed that at least 50% reduction of KIR3DL2expressing cells in skin or MRD in blood at week five was associated with numerically higher global and longer PFS (Appendix, page 14). Similar trends were observed on week 14 for MRD and KIR3DL2 expression in both skin and blood compartments (Appendix, page 15).
Acknowledging the limited on-treatment liver function changes reported in this study and considering the profile of the included patients being elderly and heavily pre-treated, we believe that the observed hepatic safety profile of IPH4102 is rather reassuring. Six patients (14%) developed lymphopenia of which 50% were grade 3-4, yet none resulted in opportunistic infections. KIR3DL2 is expressed on around 30% of NK cells 20 ; however we did not find that IPH4102 reduces the count of KIR3DL2-expressing NK cells (data not shown). It is plausible that the observed lymphopenia is a result of IPH4102 reducing the CD4 neoplastic lymphocytosis thus unmasking an underlying lymphopenia caused by the disease, a phenomenon that is common in CTCL. In SS, IPH4102 showed a high global and blood response of 42•9% and 55•9%, respectively.