dataesr/scientific-paragraphs-categorization huggingface dataset:
A new OrF from meloe RNA is efficiently translated in melanoma cells In the course of precisely defining the +1 transcription start of the meloe RNA, we have previously shown that the transcript is in fact 259 bp longer at the 5′ end than the public sequence reported in the NCBI data bank [NR-026664]  [5]. Within this added sequence, three putative ORFs are present and we focused our attention on ORF 132-296 (Supplementary Figure  S1 ) because it contained the best initiation sequence (AUGG) and would code for a 54 aa long polypeptide, coined MELOE-3. To check whether this ORF could be translated from meloe RNA in melanoma cells, we transfected the melanoma cell line M113 with a construct composed of the full length meloe RNA in which this ORF was replaced by a sequence coding for eGFP-MELOE-3 (Supplementary Figure  S2 ) and compared it to M113 transfected with the previously described eGFP-MELOE-1 construct or with the native meloe cDNA as a negative control  [10].
As shown on Figure  1A  in a typical experiment, the percentage of fluorescent melanoma cells detected with an HCS array scan reader was much higher following transfection with the eGFP-MELOE-3 construct than with the eGFP-MELOE-1 construct (29. 5% for MELOE-3 vs 4. 8% for MELOE-1). Similar percentages were obtained in two other experiments that were also confirmed by flow cytometry (data not shown). Moreover, the higher intensity of fluorescence of positive cells with eGFP-MELOE-3 suggested a more efficient translation than that of the eGFP-MELOE-1 construct. To confirm that these observed differences in fluorescence reflected differences in amounts of translated protein, we performed a Western blot analysis of lysates of M113 cells transfected with the two constructs using an anti-eGFP monoclonal antibody.
MELOE-3 is expressed in both melanoma cells and normal melanocytes A few years ago, after we had identified MELOE-1 as a source of T cell epitopes in melanoma cells, we had a monoclonal antibody made against MELOE-1, whose specificity and affinity was confirmed by ELISA on synthetic MELOE-1. However, despite our efforts, we could not detect the whole MELOE-1 polypeptide in melanoma cell lines by Western blot or by flow cytometry with this antibody (data not shown). We reasoned that this may be due to too low levels of MELOE-1 translation and/or the short half-life of the polypeptide that may be quickly degraded by the proteasome to generate T cell epitopes. In contrast, MELOE-3 seemed to be translated in much greater amounts according to the transfection experiments mentioned above and we figured that it may be possible to detect it in untransfected melanoma cells with a specific antibody.
Transient transfections Melanoma cell lines grown at 50-70% confluency were transfected with 10 µg eGFP reporter plasmid/106 cells and LTX lipofectamine (Invitrogen) according to the manufacturer's instructions. eGFP fluorescence was analysed 48 h post-transfection using the Image Stream ® Mark II Imaging flow cytometer (Merck Millipore, Darmstadt, Allemagne) or an automated fluorescence High Content Screening (HCS) microscopic device (Array Scan VTI, thermo Scientific, Courtaboeuf, France). Acquisition of Hoescht stained nuclei and eGFP expressing cells was made using a 386/420 nm and 485/515 nm excitation/ emission filters with a 10X objective. Forty-nine fields per well were analysed with Cellomics ® View Software (Thermo Fisher Scientific). For luciferase reporter assays, cells were lysed 48 h post-transfection and luminescence was measured on a VICTOR X3 apparatus (Perkin Elmer, Courtaboeuf, France).
Western blots Cells were lysed 48 h post-transfection with HEPES pH3. 7 30 mM, KCl 160 mM, MgCl2 2. 5 mM, DTT 1 mM, NP-40 0. 1%, Triton X100 0. 5%, glycerol 10% with protease inhibitors (Roche, Boulogne-Billancourt, France). Protein content was quantified by a BC Assay (Interchim, Montluçon, France). Whole protein samples were run on a 12% SDS-PAGE gel and blotted onto Immobilon ® -P PVDF membranes (Millipore, Molsheim, France). Membranes were stained overnight with an eGFP Ab (1 µg/mL) (Clontech, Mountain View, USA). After hybridization with a HRP-conjugated secondary antibody ECL detection (BioRad, Marnes-la-Coquette, France) was performed. Staining was analysed with ChemiDoc TM MP Imaging system (BioRad). Actin was used as a loading control (MAB1501, Millipore). Images were adjusted for contrast and brightness using the Image J software.
Acknowledgments E. Martínez , Director of  Marismas del Odiel Natural Park , provided permission to work in the salt ponds. F. M. Miranda Castro  provided technical assistance for analysis of oxidative stress in the  Ecophysiology Laboratory (LEF) of the EBD-CSIC. We also thank  Raquel López Luque ,  Isabel Carribero  and  Rocío Requerey  for their assistance during experiments at the  Aquatic Ecology Laboratory (LEA) in the EBD-CSIC.
This work was funded by the  Spanish Ministry of Economy and Competitiveness through the Severo Ochoa Program for Centres of Excellence in R+D+I  ( SEV-2012-0262 ) and the  I+D+i National Plan  (Project  CGL2013-47674-P ). MIS was supported by a Ramón y Cajal postdoctoral contract from the  Spanish Ministry of Science and Innovation (MICINN). MMH was supported by a  Marie Curie Intra-European Fellowship for Career Development  ( PIEF-GA-2011-299747 ) within the  7th Framework Programme  ( FP7 2007-2013 ) of the  European Commission. Currently, MMH benefits from a postdoctoral contract funded by the  Junta de Comunidades de Castilla-La Mancha  ( POST 2014/ 7780 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
To address whether the molecular events induced by ZIKV infection display significant similarities with genetic microcephaly, we set out to compare the expression profiles of ZIKV-infected hNPCs  12  with the profiles of developing neural tissues obtained from three different mouse models of severe genetic microcephaly, that is, CitK -/mice,  24  Magoh+/mice  25  and conditional Elp3knockout mice. 26 CitK is a ubiquitous protein implicated in midbody maturation,  [27] [28] [29]  whose inactivation leads to cytokinesis failure and massive apoptosis in the mammalian developing brain. 24, 30 We obtained RNA-seq data from control and CitK -/developing cerebellum (Bianchi et al. , Gene Expression Omnibus data set no. GSE83465), which is the neural tissue more severely affected by CitK loss. 24 agoh protein is one of the core components of the exon junction complex, implicated in nonsense-mediated decay pathway.
5 brains obtained from Elp3 conditional knockout mice were publicly available in the Gene Expression Omnibus (with accession numbers GSE19168 and GSE74683, respectively). In all these data sets, the number of significantly modulated genes is much lower compared with that in the ZIKV data set (Supplementary Table  S1 ). Specifically, modulated genes were 370 in the CitK -/study (199 upregulated and 171 downregulated), 119 in the Magoh+/study (50 upregulated and 69 downregulated) and 413 in the Elp3 study (256 upregulated and 157 downregulated). Comparison of the genes in these lists (Figure  1 ) revealed a strong similarity between CitK and Magoh data sets, with 14 common upregulated genes (P = 3. 9E -17) and 9 common downregulated genes (P = 6. 9E -09). In addition, both data sets displayed a lower but statistically significant intersection with the Elp3 data set (Figure  1 , P-value = 3.
1E -03 and 3. 9E -04, respectively). Considering also that CitK and Magoh data sets have been obtained on two different developing neural tissues (P4 cerebellum versus E10. 5 cortex, respectively) and with two different gene expression analysis technologies (RNAseq and DNA microarrays, respectively), these results are consistent with the higher phenotypical similarity between these two models of microcephaly, if compared with microcephaly resulting from conditional Elp3 deletion. Thus, the 14 upregulated genes and the 9 downregulated genes common to the CitK and to the Magoh data sets (Supplementary Table  S2 ) are likely to have crucial roles in apoptotic microcephaly. Interestingly, 11 of the 14 common upregulated genes are P53 targets, and most of them have been involved in P53-dependent apoptosis and/or cell-cycle arrest (Supplementary Table  S2 ).
Despite the limited overlap, six genes, corresponding to well-known targets of P53, were common to the three lists (expected 0. 08 Po1E -10, χ 2 test). Similarly, six genes were common to the corresponding lists of downregulated genes (expected 0. 07 Po1E -10, χ 2 test). ZIKV-upregulated genes overlapped with Elp3-upregulated genes even more than with CitK-and Magoh-upregulated sequences (107 common genes P = 9. 5E -21; Supplementary Table  S4 ), whereas the overlap between downregulated genes was not statistically significant. These common genes were enriched of aminoacyl-tRNA synthases (7E -8) by GO analysis. 37, 38 Moreover, Gene Set Enrichment analysis (GSEA)  39, 40  in the Molecular Signature Database (http:// software. broadinstitute. org/gsea) revealed a strong overlap with genes upregulated in unfolded protein response (UPR) (set M5922, P = 2.
,  41  GEO accession number GSE19345). Strikingly, in this case only two sets showed significant overlap, and with relatively high P-values (Table  1 ). In addition, the overlap between genes upregulated by ZIKV and CMV was not significant. In contrast, we found a significant overlap between genes upregulated by   S1. (b) Graphical representation of intersections between genes downregulated in the same conditions. Venn diagrams were obtained using the web tool http://bioinformatics. psb. ugent. be/webtools/Venn/ CMV and downregulated by ZIKV (64 genes, P = 6. 10E -04; Supplementary Table  S5 ). This group included many genes involved in cell-cycle control (Supplementary Table  S5 ; P = 4. 1E -5), such as E2F1 and many of its targets. In conclusion, the above analysis of gene expression profiles revealed that, similarly to three microcephalic mutations, but differently from CMV, ZIKV activates in hNPCs an antiproliferative and proapoptotic P53-related response.
Conversely, the expression of CDKN1C was significantly downregulated in the infected cells (Figure  2e ), further validating our bioinformatic analysis. The table reports the significant (Po0. 01) corrected P-values for the overlaps between the indicated GSEA data sets and the genes upregulated in ZIKV-infected hNPCs (ZIKV), CMV-infected hNPCs, CitK -/ -P4 cerebellum, Magoh+/ -E10. 5 neocortex and Elp3 -/ -E13. 5 brain. The corresponding gene lists are reported in Supplementary Table  S1  Apoptosis and genotoxic stress in ZIKV-infected cells. In agreement with previous reports, ZIKV-infected hNSC cultures displayed increased rate of apoptotic cells, as revealed by analysis of pyknotic nuclei (not shown) and by immunostaining with antibodies directed to activated caspase-3 (Figures  3a  and b ).
Materials and Methods Comparison of expression profiles. RNA-seq data of ZIKV-infected hNPCs and controls were obtained from GEO RNA-seq data set GSE78711. 12 The lists of differentially expressed genes (Supplementary Table  S1 ) were defined as in the original paper. 12 NA-seq-based gene expression data of CitK -/mice and controls were obtained from P4 developing cerebellum (Bianchi et al. , unpublished, data set is being submitted to GEO). Differentially expressed genes were defined using Cuffdiff v. 2. 0. 2, 70 with a 0. 05 false discovery rate cutoff. Microarray-based gene expression data of E10. 5 Magoh-deficient mouse cortices  25  were obtained from GEO data set GSE19168. The lists of differentially expressed genes (Supplementary Table  S1 ) were defined as in the original paper. 25 NA-seq-based gene expression data of E13.
5 Elp3 -/brains and controls 26 were obtained from GEO data set GSE74683. The lists of differentially expressed genes (Supplementary Table  S1 ) were defined as in the original paper. 26 icroarray-based gene expression data of CMV-infected hNPCs  41  were obtained from GEO data set GSE19345. Differentially expressed genes (Supplementary Table  S1 ) were defined as those showing statistically significant deviation from control in the three time points of the time-course experiment, as defined the original paper. 41 -values of the intersections between the different lists were calculated using the Fisher's exact test. For GSEA analysis,  40  P53-related data sets were downloaded from Molecular Signature Database (http://software. broadinstitute. org/gsea) and their overlap with genes upregulated in the above-described data sets was determined.
Total RNA (120 ng) was subjected to two independent reverse transcriptions using the iScript cDNA Synthesis Kit (Bio-Rad). qPCR was performed in duplicate on the 2 RT for each sample using SYBR Green Supermix (Bio-Rad) for 40 cycles with a two-step program (5 s of denaturation at 95 °C and 10 s of annealing at 60 °C) on a CFX384 (Bio-Rad, Marnes-la-Coquette, France). Amplification specificity was assessed with a melting-curve analysis. Primers were designed using the Primer3 plus Software (http://primer3plus. com). Specific mRNA levels were calculated after normalization of the results for each sample with those for GAPDH mRNA (reference gene). The data are presented as relative mRNA units with respect to control group (GOI/GAPDH expressed as fold over control value). Analyses were performed with the Bio-Rad CFX Manager 3.
0 Software (http://primer3plus. com). Statistical analyses were performed using the GraphPadPrism 5. 0 software (GraphPad Software, Inc. , La Jolla, CA, USA) and results were compared using the Mann-Whitney U-test. A two-tailed Po0. 05 was considered significant. Primers used (5'-3') were as follows: hCDKN1C-FAGCTGCACTCGGGGATTT and hCDKN1C-RCTTCTCAGGCGCTGATCTCT; hEDA2R-FTGGATTGCCAAGAAA ATGAG and hEDA2R-RGCATCTCCACCCTCTCCATA; hCCNG1-FTCCAAGCACA GAAGTGTGTAGAG and hCCNG1-RTGGTTTGGAACACTTATTTGATG; hAEN-FCA TCACTCGGCAGCACAT and hAEN-RCCTGGAAGTCGTTGTGCAG; hCDKN1A-FGGAGACTCTCAGGGTCGAAA andhCDKN1A-RTAGGGCTTCCTCTTGGAGAA; hPHLDA3-FCAGGCCATCCAGACAGTG and hPHLDA3-RCCCCCACAAGCCAGA GG; hGAPDHAAAGGGTCATCATCTCTGCC and hGAPDHAGGGGTGCTAAGCA GTTGGT.
Acknowledgements. We are grateful to  Dr Hongjun Song ,  Johns Hopkins University , for discussing unpublished data. BCM and MV were supported by the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  (Grant No. ANR-10-LABX-62-IBEID ). PG is supported by:  ERA-NET  grant from the  E-Rare call 2013 , no. ANR-13-RARE-0007-01 ; ' CEFIPRA ', Grant no. 4903-2 ; INSERM;  Université Paris Diderot ;  ERA-NET Neuron Microkin , no. ANR-15-NEUR-0003-01  and  PRC MicroGol , no  ANR-16-CE16-00025-01. FDC is supported by:  Telethon Foundation  Grant Nos. GGP12095  and  GGP13081  (to FDC);  Associazione Italiana per la Ricerca sul Cancro (AIRC) , Grant No. IG17527 ; EPIGEN consortium.
RNA were quantified using a Nanodrop (ThermoFisher Scientific, Inc. ) and RT was performed using the High Capacity Reverse Transcription kit (Life Technologies, ThermoFisher Scientific, Inc. ). qPCR was performed in duplex reactions, mixing TaqMan FAM-labelled probes for human egfl7 (Hs00211952_m1) or for ICAM-1 (Hs00164932. m1) with the normalizing β2-microglobulin (B2M) VIC-labelled probe in the same tube and processed in a StepOne machine. Data were expressed as 2 -ΔΔCq where ΔCq=Cq of gene-Cq of B2M, and ΔΔCq=ΔCq sample-ΔCq control. Ethics. Sample storage, handling and analysis were performed according to the European regulations and the Helsinki Declaration. Patient consent and legal authorizations were obtained for all the analyses performed and for the processing of patients personal data.
The protocol was approved by the 'Comité de Protection des Personnes Nord-Ouest IV' on January 12, 2010. Statistical analysis. All statistical analysis was performed using STATA 11. 2 statistical software (StataCorp, College Station, TX, USA). Analyses used Chi2 and Fisher's exact tests for categorical data and Kruskall-Wallis test for continuous variables and Cuzick's test for trend across ordered groups. P<0. 05 was considered to indicate a statistically significant difference.
Materials and methods An online survey (using the SurveyMonkey software) was sent to all full ESR members in Europe  (22, 000)  in September 2015. A total of 22 questions were posed, initially regarding the country and city of work, the work setting (private centre versus hospital), size of the establishment and number of yearly US procedures undertaken. Thereafter, known incidents involving transmission of infection were queried, with free text available for further clarification. The final part of the survey gathered detailed data on the US gel used, both in direct contact with the patient and inside probe covers. Decontamination procedures were surveyed over a wide range of US examinations: standard surface US, when scanning patients with known transmissible infections, endo-cavity (trans-vaginal and trans-rectal) US, and US used in an interventional setting (US-guided biopsy, drainage or US used in theatre).
Study population and design The structure of the multicentre and international, prospective, longitudinal European Scleroderma Trials and Research (EUSTAR) database has been described previously  [8, 16]. Ethics approval according to the Declaration of Helsinki has been obtained from all respective contributing centers' local ethics committees and ethics committee approval for the EUSTAR study was obtained from the Ethik Kommission Beider Basel (EKBB, now Ethikkommission Nordwest-und Zentralschweiz, EKNZ). Each participating centre obtained local ethics committee approval and written informed consent was required to be signed by each patient. Demographic and disease characteristics were collected between the time of the database implementation in 2004 and February, 2014. Data were considered for analysis on the condition that patients were older than 18 years at the time of the visit, and fulfilled the 1980 American College of Rheumatology (ACR) classification for SSc  [17].
Patient characteristics At the time of censoring, a total of 11,290 patients were followed in the EUSTAR database. Of these patients, 9,891 adult patients fulfilled the 1980 ACR classification criteria for SSc, and were therefore included in the subsequent analysis of the entire EUSTAR cohort  [17]. The study population, consisting of patients with a baseline visit within the first year after RP onset, was composed of 695 subjects (Fig 1 ) with a median observation time of 2. 1 years (IQR 0. 7-4. 6; mean 3. 1 years, SD 3. 0). The median age of the study population was 52. 7 years (IQR 42. 3-62. 5) at RP onset, 27% were men (Table  2 ). The other patients followed in the entire EUSTAR cohort were on average about 9 years older and about 13% of these were male (Table  2 ). Compared to the study population, a higher percentage of the other patients followed in the entire EUSTAR cohort was ACA positive and a lower percentage had anti-TOPO or anti-RNAP-III autoantibodies (Table  2 ).
The IIEF-5 questionnaire was not included in the EUSTAR database at its inauguration. As a consequence, data on erectile dysfunction was only available for 17% (n = 32) of men in the study population. Around 52% (95%CI 36-70) of these patients reported an erectile dysfunction (IIEF-5 score<22) during the first 3 years after RP onset progressing to 95% (95%CI 79-100) during the subsequent 6 years. In the first 3 years after onset of RP, about 30% (95%CI 18-52) of the men had developed a severe erectile dysfunction (IIEF-5 score<8). Owing to the small number of men with data on erectile dysfunction, no further stratification was feasible.
However, as also demonstrated in this study, more than half of all patients in the entire EUSTAR cohort experienced their first non-RP feature of the disease within one year of the onset of RP. It must be also noted, that some SSc patients had documented organ manifestations already at their baseline visit, leading to an overestimation of the time to its onset. Lastly, our data may be biased by centre specific differences in the assessment of some organ manifestations. With regard to diastolic dysfunction for example, different diagnostic approaches are available, each differing in sensitivity and specificity and predictive value  [31]. Also, the in the EUSTAR database the DLCO collected is not corrected for haemoglobin. Despite these limitations, our data will likely improve the counselling and management of SSc patients early after RP onset.
Sera Sera tested in this study were from a phase I trial in which adults 18-50 y of age were vaccinated twice, 30 d apart, with an aluminium-adjuvanted PcpA-PhtD vaccine containing 10, 25, or 50 mg/antigen (ClinicalTrials. gov no. NCT01444339). 24 Sera were stored at ¡20 C and were heated to 56 C for 30 min to inactivate complement components. Bacteria S. pneumoniae serotype 3 strains A66. 1 and WU2 were from D. Briles, University of Alabama-Birmingham, USA. Bacteria were grown in Todd Hewitt Broth with 0. 5% yeast extract adjusted to pH 7 with MOPS. Because expression of PcpA is repressed by Mn 2C ,  23  the medium was depleted of metals using a Chelex-100 column (Bio-Rad, Hercules, CA, USA) and then adjusted to 1 mM FeSO 4 , 1 mM CaCl 2 , 1 mM MgCl 2 , 1 mM ZnCl 2 , and 0. 1 mM MnSO 4.
Statistical analysis Statistical analysis was performed using SAS version 9. 1 (SAS Institute, Cary, NC). Differences in survival rates with pre-and post-immune sera were compared using logistic regression with logic link and a random subject effect. Time to death was assessed by Kaplan-Meier analysis and compared between groups by a one-sided Fisher exact test. The antibody concentration (in EU/ml) producing 50% survival (ED50) was estimated by logistic regression analysis with probit link and a random subject effect. Pearson correlation coefficients were calculated between activities for pairs of different in vitro assays (anti-PhtD ELISA, anti-PcpA ELISA, and surface binding). P < 0. 05 was considered to be statistically significant.
234 unique genes were at least fourfold regulated in T. gondii-infected IFN-γ-stimulated cells in a TgIST-dependent manner when comparing 2fTGH with U3A cells (data are accessible through NCBI GEO accession no. GSE81613). Hierarchical clustering of the 70 top-ranked genes (more than fourfold, false discovery rate <1%) delineated clusters of coregulated IFN-γ-induced transcripts that are repressed by T. gondii infection in both a TgIST-and STAT1-dependent manner (Fig. 5 A ). Analysis of regulatory elements of these genes revealed enrichment of IRF1 and STAT1 (ISRE) TFBSs in their promoters (Fig. 5, B  and C ). GSEA hallmark analysis highlighted that gene products belonging to IFN-γand IFN-α-related pathways were highly enriched (Fig. 5 D ). These data suggested that beyond negatively regulating IRF1 (Fig.
The complete set of genes is listed in the GEO dataset (accession no. GSE81613). (B) Top-scoring pathways regulated by TgIST in a STAT1-dependent manner and upon IFN-γ stimulation in HFFs were determined by GSEA. (C and D) Human and mouse cells were infected for 24 h with Pruku80 (WT) and Pruku80 ΔTgIST (Δ) or left uninfected (ui) and stimulated for 6 h with IFN-γ or left unstimulated (US). Levels of CXCL9, CXCL10, CII TA, MX1, GBP2, IGTP, and SOCS1 mRNAs were determined by RT-qPCR. β2-microglobulin was used for normalization. Data are displayed as fold difference relative to the uninfected cells. The mean of two experiments is shown; error bars represent SEM. This experiment is representative of at least three experiments and was repeated with 76KGFP (WT and Δ) strains with similar results (not depicted).