dataesr/scientific-paragraphs-categorization huggingface dataset:
4e ). TF motifs enriched at YAP1-binding loci in ST-EPN-YAP1s. Endogenous YAP1 wild-type mRNA and protein expression levels are comparable among molecular groups of human ST-EPNs (cf. Supplementary Fig. 1  and Fig. 1g, h ). In addition, overexpression of wild-type YAP1 did not result in tumors within the developing cerebral cortex (cf. Fig. 3k ). This data prompted us to hypothesize that activation of specific YAP1 fusion-mediated gene networks causes ST-EPN-YAP1 tumor formation. To gain further insight into the transcriptional regulation caused by the YAP1 fusion, we assessed genome-wide YAP1 occupancy in fresh-frozen specimens of primary human ST-EPN-YAP1 (YAP1 fusion-positive, n = 3; Supplementary Data 1) and ST-EPN-RELA (YAP1 fusion-negative, n = 3; Supplementary Data 1) tumors using chromatin immunoprecipitation and subsequent DNA sequencing (ChIP-seq).
YAP1 ChIP-seq was performed using an antibody against the N-terminus of the YAP1 protein to ensure recognition of the fusion protein. Data analysis revealed some loci of common YAP1 occupancy within ST-EPN-YAP1 and ST-EPN-RELA samples, but also significant differences between both groups (Supplementary Fig. 5a, b ). To minimize a potential influence of the YAP1 wild-type protein, only ST-EPN-YAP1-specific high-confidence YAP1-binding sites, relating to 9% of all YAP1-binding sites within this group (n = 1246/13,582), were selected for further analyses (Supplementary Fig. 5b-d ). YAP1 acts as a transcriptional co-activator, which is recruited to DNA via a second DNA-binding transcription factor (TF)  33. Therefore, we next assessed TF-binding motifs within annotated EPN subgroup-specific YAP1 target loci, as well as within common YAP1 peaks shared by both ST-EPN-RELA and ST-EPN-YAP1 subgroups.
Mycoplasma contamination was assessed periodically by GATC/Eurofins. LN229 cells were cultured in DMEM (Corning, 10-013-CV) supplemented with 10% FBS (Gibco, 10437028) and 1% antibiotic-antimycotic solution (Corning, 30-004-CI) at 37 °C with 5% CO 2. NIH/3T3 cells were cultured in Dulbecco's modified Eagle's medium (DMEM) (Corning, 10-013-CV) supplemented with 10% bovine serum (Gibco, 16170078) and 1% antibiotic-antimycotic solution (Corning, 30-004-CI) at 37 °C with 5% CO 2. None of these cell lines were listed in the database of misidentified cell lines maintained by ICLAC and NCBI Biosample. These cell lines were not authenticated in this study. All cell lines were examined to be mycoplasma negative before experiments. Unless otherwise indicated, experiments were performed with cells grown to 50% confluency.
8 ), Ki67 (ab15580, Abcam, 1:500 for IHC), HA (#3724, CST, 1:500 for ICC and IHC), HA (#MMS-101P, BioLegend, 1:2000 for WB), Myc (#9E10, Developmental Studies Hybridoma Bank, 1:500 for WB), Lats1 (#3447, CST, 1:1000 for WB), Actin (A5316, Sigma-Aldrich, 1:5000 and ab49900, abcam, 1:25000 for WB), CTGF (sc-14939, SCBT, 1:1000 for WB), Cyr61 (sc-13100, SCBT, 1:1000 for WB), 14-3-3 (sc-629, SCBT, 1:1000 for WB), FLAG (M2, Sigma, 1:100 for IP, 1:1000 for WB), Pan-Tead (#13295, CST, 1:1000 for WB), TBR2/Eomes (ab23345, Abcam, 1:500 for IHC), NG2 (ab129051, Abcam, 1:400 for IHC), PAX6 (PPRB-278P, Covance, 1:500 for IHC; Cat#sc-81649, SCBT, 1:200 for IHC), Olig-2 (AB9610, Millipore, 1:500 for IHC), Nfia (HPA008884, Sigma, 1:250 for IHC and 1:500 for WB), Nfib (HPA003956, Sigma, 1:250 for IHC and 1:500 for WB), Cleaved Caspase 3 (#9664, CST, 1:100 for IHC) and GFP (#ab13970, Abcam, 1:1000 for IHC).
Approximately 100-300 cells were counted for each sample. The data is presented as percentage of cells for each category (HA localization category/total cells nuclei × 100). For proliferation quantification in developed tumors, a 6 mm 2 area was selected in the tumor center. Two sections per tumor were quantified in three independent tumor samples. The sections were stained with EGFP and Ki67 and the EGFP(+) Ki67(-), EGFP(+)Ki67(+) and EGFP(-)Ki67(+) cells were counted manually using ImageJ (Maryland, USA). Only clearly labeled cells with a minimum five foci were included. Around 300-400 cells were counted in total for each tumor. The data is shown as percentage of positive cells = (positive nuclei cells/total cells nuclei × 100). For proliferation quantification of Nfia-related constructs, two sections per P0 electroporation sample were used from three independent electroporation samples.
For quantification of EGFP-positive cells inside YAP1-MAMLD1-driven tumors, one section per adult mouse electroporated at E13. 5 that developed neurological signs was counted in six of arbitrarily selected fields from two independent mice. Stained sections with anti-EGFP and DAPI were submitted to imaging acquisition at a confocal microscopy and tile images were obtained. An area of 0. 05 mm 2 was selected in the center of the tumor and total of EGFP + cells were counted. All cells counterstained with DAPI within the selected area were manually counted using ImageJ. Quantification data is presented as percentage of EGFP(+) cells. For in vitro cultured cell staining, LN229 and NIH/3T3 cells on coverslips were fixed with 4% PFA/PBS for 20 min and incubated in permeabilization buffer (PDT: 0.
The comparison of mouse tumor samples to patient tumor samples was performed by applying semi-supervised clustering on Affymetrix microarray gene expression data from ependymoma tumors (YAP1: 11 samples, RELA: 49 samples). For cross-species comparison in Fig. 4t  and u , differentially expressed genes between the groups were detected with Limma package  64  and sorted according to their adjusted p-value (<0. 05). The top 1000 differentially expressed Affy probes were selected and combined based on the selection of the highest expressed probe per gene leading to 671 genes. Next, using ENCODE databases 513 human-mouse orthologous genes were selected from the 671 genes. For the selected orthologous gene, final verification was performed with unsupervised hierarchical clustering and principal component analysis of ST-EPN-RELA/ST-EPN-YAP1 human tumor data combined with five YAP1 tumor model samples.
For Supplementary Fig. 4e , differentially expressed Affy gene probes between the YAP1-MAMLD1-driven and C11orf95-RELA-driven mouse models were selected with high confidence (n = 4581, p < 0. 05). The orthologues among the selected genes were compared to the differentially expressed Affy gene probes between YAP1 and RELA human tumors (top 5000 most confident probes), resulting in 539 probes representing common 308 orthologous genes (Supplementary Data 6). Additional verification of the YAP1 mouse model was performed by inclusion of Affymetrix data from human pGBM tumors (n = 9) and the RELA mouse models (n = 5) in the principal component analysis with the selected 308 genes across species. YAP1 and NFIA TF ChIP sequencing. Enrichment of YAP1-bound and NFIAbound DNA fragments by ChIP and the subsequent library preparation of the ChIP-derived DNAs by Active Motif (Carlsbad, CA)  5.
Pellets were resuspended and the resulting DNA was quantified on a NanoDrop spectrophotometer. Extrapolation to the original chromatin volume allowed quantitation of the total chromatin yield. An aliquot of chromatin (15 μg) was precleared with protein A agarose beads (Invitrogen). Genomic DNA regions of interest were isolated using 4 μg of antibody against NFIA (Atlas Antibodies, catalog number HPA008884) or 8 μg of antibody against YAP1 (Santa Cruz, catalog number sc-15407×). Complexes were washed, eluted from the beads with SDS buffer, and subjected to RNase and proteinase K treatment. Crosslinks were reversed by incubation overnight at 65 °C, and ChIP DNA was purified by phenol-chloroform extraction and ethanol precipitation. Quantitative PCR (QPCR) reactions were carried out in triplicate on specific genomic regions using SYBR Green Supermix (Bio-Rad).
The resulting signals were normalized for primer efficiency by carrying out QPCR for each primer pair using Input DNA. The ChIP-sequencing reads were aligned using BWA v0. 5. 10 tool  65  to the GRCh37 1000G reference genome. Data quality controls were performed by employing Qualimap v2. 2  66  (Supplementary Data 1). DNA interaction sites of YAP1 and NFIA were identified by applying MACS v1. 4  67  with a p-value threshold of 1e-9 and low coverage whole genome sequencing data of matched samples as control data. Unsupervised clustering of top 5000 variable normalized YAP1 peak signals with subtracted control read counts was applied by performing principal component analysis. To identify high-confidence YAP1 DNA interaction sites common in all three tested YAP1 or RELA EPNs, respectively, we have combined YAP1 interaction sites per group using the merge function available in Bedtools v2.
24  68  by requesting all three samples per group to support the presence of YAP1 DNA interaction sites. The two resulting sets of YAP1 DNA interaction sites were compared to each other to discriminate between interaction sites common and specific for YAP1 or RELA EPNs, respectively. Overlapping peak sets among the samples were visualized using UpSetR v1. 3. 3  69  based on the number of samples covering each peak. High-confidence NFIA peaks obtained from the subset of ST-EPN-YAPs were also merged and compared to YAP1 sites in terms of the overlaps of genomic regions. The TF motif enrichment analysis was performed using HOMER v4. 8. 3  70. Here, we have added the TEAD3 motif (ID: MA0808. 1) taken from the JASPAR CORE database  71  to HOMER's default database of 388 motifs (including TEAD1, TEAD2, and TEAD4 motifs).
The set of YAP1 peaks common between both groups was applied as additional control for the motif-enrichment analysis. Group-specific enhancer and super enhancer regions and the expression of their correlated target genes were obtained from the previous study  5. RNA-sequencing. Sequencing reads were aligned to GRCh37 1000G reference genome using STAR 2. 3. 0 72 by reporting only reads with one best alignment. Gene counting of uniquely aligned reads was performed using the package Subread v1. 4. 6  73  based on Gencode v19 annotations. InFusion toolkit v. 0. 6. 3 was applied for fusion gene discovery  74  with default parameter settings. The weight of YAP1 fusion in comparison to WT was calculated as a proportion of split reads covering only breakpoint between exons of genes forming the fusion compared to the total number of split reads covering the exon border including those that support WT YAP1 exon connections (see Supplementary Data 1).
Acknowledgements This work was supported by the  CERN Research Fellowship  (to K. W. P. ), the  ICGC PedBrain Tumour Project  funded by the  German Cancer Aid  ( 109252 ) and the  German Federal Ministry of Education and Research  (to P. L. and S. M. P. ), the KIKA grant (# 90 ) (to M. K. ). the  U. S. National Institutes of Health  Grant  K22 5K22CA190440  (to W. L. ), the  Four Diamonds Fund for Pediatric Cancer Research  (to W. L. ), and the  Deutsche Forschungsgemeinschaft ,  KA 4472/1-1  (to D. K. ). We thank  Y. Takahashi  ( Kyoto University ) and  K. Kawakami  ( National Institute of Genetics, Japan ) for Tol2 transposon related vectors;  Lena Kutscher  ( DKFZ ) for a proof-reading and editing;  J. LoTurco  ( University of Conneticut ) for pPB-eGFP vector;  R. Gronostajski  ( State University of New York at Buffalo ) for Nfia-En cDNA;  D.
Data availability The all sequencing data in this study are available in the following public databases: EGAS00001002696 for RNAseq and ChIP seq on ST-EPNs, GSE65362 for the 450Kmethylation arrays for ST-EPNs, GSE64415 for the Affymetrix expression datasets of ST-EPNs, GSE134404, for the Affymetrix expression datasets of glioblastomas, and GSE110625 for the Affymetrix expression datasets of murine YAP1-MAMLD1-driven and C11orf95-RELA-driven tumors.
5 and 0. 1 M magnesium acetate. Crystals were frozen with the same solution containing 25% ethylene glycol. Enterobactin structure complexes were obtained by soaking apo crystals for few hours with mother liquor containing 5 mM Fe 3+ -enterobactin before cryoprotection. For the others complexes, crystals were cross-linked by diffusion of 25% glutaraldehyde prior the soaking  44. Co-crystallisation attempts have been performed by incubating the protein for 1 h at room temperature with the ligand before been loaded on a S200 16/60 column and concentrated for setting up the crystallisation plates. No crystals have been obtained by co-crystallisation. Crystallisation conditions of the mutants were the same as for the wild-type. The data were collected at 100 K temperature at the beamlines IO3 (0.
Due to high mobility of the molecule, only two acyl chains have been modelled in the enterobactin structure. Final refinement statistics are given in the Table  1. Atomic coordinates and structure factors have been deposited in the Protein Data Bank (5M9B, 6Q5E, 5MZS, 5NC4, 5NR2, 5OUT, 6I2J and 6R1F). The quality of all structures was checked with MOLPROBITY  57. There are no Ramachandran outliers in either of the structures and at least 96% of the residues are in the most favoured region. Figures were drawn using PYMOL  58. Isothermal microcalorimetry titration. Affinities of PfeA wild-type and mutants for Fe 3+ -enterobactin were measured by isothermal titration calorimetry using a VP-ITC instrument (GE Healthcare) at 25 °C. Titrations were performed using 57 × 5 μl injections of Fe 3+ -enterobactin (70-55 μM) into 10 μM protein.
The heats of dilution measured from injection of the ligands into the buffer were subtracted, and titration curves were fitted using AFFINImeter software with a two-site binding model. Concentrations of Fe 3+ -enterobactin solutions were verified with UV/vis spectrophotometry using a published molar extinction coefficient of ε 495 = 5600 M -1 cm -1. Determination of thermodynamic parameters is not fully accurate because errors in Fe 3+ complex, active PfeA concentrations and unusual profile giving estimates of K1 = 23-100 nM and K2 = 185-128 μM for the wild-type. Reverse titration has been also performed (PfeA into Fe 3+ -enterobactin), but data suffer from high heats of dilution in the beginning of the blank titration of PfeA into the buffer giving estimates of K1 = 95 nM and K2 = 4. 5 μM for the wild-type.
0, 1% (w/v) N-lauroylsarkosine buffer and incubated during 1 h at room temperature to solubilise specifically all inner-membrane proteins. Afterwards, outer membranes were pelleted by ultracentrifugation (40 min at 100,000×g). Computational methods. The complete system under investigation, the PfeA receptor, was inserted in a phospholipid membrane and fully solvated by employing the CHARRM-GUI web server  61. In particular, the lipid bilayer is composed of 233 POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) molecules with xy planar dimensions of 100Å × 100Å. The system was then immersed in an explicit water solution adding KCl ions to a concentration of 0. 15 M. Molecular dynamics (MD) trajectories were produced by using the ACEMD software  62. We chose the AMBER14 and the LIPID14 force fields  63, 64  , respectively, for the protein and the lipids, whereas we used the TIP3P model  65  for waters.
The switch function is applied to non-bonded interactions with the switch-distance set to 7. 5 Å and cut-off 9. 0 Å. In order to accelerate the simulations, mass of hydrogen atoms were scaled to 4 uma, which allowed a time step of 4 fs. Cavity analysis was conducted by using the VOIDOO software  67, 68. We used a probe radius of 1. 5 Å (size of a water molecule) rolling over a grid with maximum bin size set to 1. 0 Å. After the initial cavity identification, we refined the search by reducing the grid size in an iterative manner until a convergence of the detected cavity is not reached. We adopted a convergence criterion of 0. 1% between successive volume evaluations. The entire procedure was then repeated for all conformers, which were saved at the frequency of 90 ps (~10,000 conformers in total or 900 ns on total time).
After collecting cavity data for all of our simulations, we performed a cluster analysis imposing an r. m. s. d. of 3. 0 Å. The Cα correlation was calculated by using the bio3d package within R  69  and a statistical mechanical approach independent of relative atomic motions known as LMI (linear mutual information)  70. The main advantage of the LMI over the standard methods for detecting correlation in protein motions is the invariance to a relative orientation of atomic fluctuations. Due to this property, LMI captures a more detailed picture of how protein motions are correlated. Moreover, LMI omits unwanted non-linear correlations and makes a perfect candidate for investigation of the protein dynamics. To test convergence, we performed a block analysis where the total trajectory is divided into 50-ns windows, and a correlation matrix was calculated for each window.
The hydrogen-bonding correlation was calculated using a specifically designed protocol. In short, first we calculate possible hydrogen-bonding interactions for every 100 ps. After a list of all possible hydrogen bonds at a given frequency is collected, we record a minimum distance profiles for each hydrogen-bonding pair. These profiles are then used to calculate a correlation between the specific hydrogen bonds. All figures and plots were produced using the VMD  71  and R 72 softwares. Docking. All molecular docking calculations have been performed using AutoDock Vina  73. We adopted a docking volume represented by a rectangular box 60 × 60 × 60Å in size, centred at (0. 0, 0. 0, 20. 0 Å), which completely encloses the binding domain of the protein. The exhaustiveness of the search was set to 4096 (default 8).
Acknowledgements The research leading to these results was conducted as part of the  Translocation consortium  (www. translocation. eu)and benefitted from support from  ND4BB ENABLE Consortium  has received support from the  Innovative Medicines Initiatives Joint Undertaking  under Grant Agreement nos. 115525  and  115583 , resources which are composed of financial contribution from the  European Union 's seventh framework programme (FP7/ 2007-2013) and EFPIA companies in kind contribution. This is work is supported by a  Wellcome Trust Investigator  ( 100209/Z/12/Z ) award. M. C. and S. M. thank the additional financial support of  MIUR  with the  PRIN  project  2015795S5W_005. I. S. , G. L. A. M. , V. G. and E. B. thank also  Vaincre la Mucoviscidose  and  Association Gregory Lemarchal , French associations against cystic fibrosis for additional financial support.
Study Design Plasma samples were collected at the following times: pre-dose and at 10 min, 20 min, 1 h, 2 h, 4 h, and 6 h after oral HU administration at the patient's usual dose. Whole blood samples were transported and/or stored at 2-8 • C for a maximum of four h before centrifugation, and then aliquoted plasma was rapidly frozen at -20 • C. Demographic information and standard laboratory parameters were collected: Neutrophil, reticulocyte, and platelet counts were measured in turn to identify the individual tolerability of the treatment, hemoglobin, MCV, and the percentage of HbF to monitor the clinical efficacy of HU. We evaluated the adherence of our patients by asking them about the frequency of missed doses over the past six months. Genotypic data were collected, looking for associated alpha thalassemia and haplotype.
Modelling and Statistical Analysis The plasma concentration-time data was analyzed by a non-compartmental method to obtain a concentration-time curve. The pharmacokinetics' parameters were defined for each patient: the maximum plasma concentration (C max ), the time to reach the C max (T max ), and the area under the curve (AUC). The C max were identified by a graphical analysis. The mean, standard deviation, and median were calculated using Microsoft Excel. The simulated AUC 0-6h were calculated by the linear log trapezoidal rule (version 6, GraphPad Prism, San Diego, CA, USA).
The best correlation between AUC and HU concentrations was found at two hours. This result is consistent with the literature  [29, 35]  and would make PK analysis easier to apply routinely. Furthermore, a recent study (TREAT, ClinicalTrials. gov NCT02286154) demonstrated that the PK guided dose strategy, with a target set at 115 h. mg/L, was more efficient and without excess hematologic toxicities than classical dose escalation based on hematological parameters  [33]. This pharmacokinetic approach, offering no additional toxicities and optimizing the dose of HU more efficiently and quickly, is an important asset in SCA patient care. Reaching MTD will help compliance by helping the patients see the quick benefits of taking this medication. This is all the more important given the necessity to be efficient before the occurrence of irreversible organ damage.
FRET measurements To measure the fluorescence signal of these strand exchange reactions, the ssDNA-RecA filament, dsDNA, dNTPs, and polymerase mixture was placed in a quartz cuvette. Using Fluor-Essence spectroscopy software and the automated FluoroMax spectrofluorometer (Horiba, Edison, NJ), the emission of the fluorescein label was read by using a 2-nm slit and 493-nm excitation wavelength. The fluorescence emission in counts per second (cps) was detected at 518 nm with a 2-nm slit. The reaction was run for 30 min with emission measurements every 1 s and an integration time of 0. 5 s. Temperature was kept constant at 37 °C. To calibrate for the formation of free ssDNA outgoing strand, measurements of dilutions of the corresponding ssDNA containing fluorescein were performed. The fluorescence signal in cps was considered equivalent to 100% yield of free outgoing ssDNA formation.
The reads were mapped to the reference operational taxonomic units (OTUs) and species/strains using the 16S Greengenes and 23S SILVA databases. We found that the occurrence of diarrhea during the travel was associated with a higher relative abundance of Prevotella copri before departure and after return. The composition of microbiota, before travel as well as at return, was not correlated with the acquisition of MRE. However, the clearance of MRE one month after return was linked to a specific pattern of bacterial species that was also found before and after return. In conclusion, we found specific OTUs associated to a higher risk of diarrhea during a stay in tropical regions and to a faster clearance of MRE after their acquisition.
Population The travelers' cohort originates from the VOYAG-R study (ClinicalTrials. gov n°NCT01526187) funded by the French Ministry of Health. 6, 18 From February 2012 to April 2013, subjects attending six international vaccination centers in the Paris area, prior to traveling to tropical regions, were asked to provide fecal samples before and after their trip. Only volunteers who had no detectable MRE in the feces taken before their departure, were asked to send a further stool sample within a week after their return. Travelers who revealed positive for MRE after their return were asked to provide additional stool samples 1, 2, 3, 6 and 12 months after their return until MRE was no longer detected. Among the 574 included subjects, 292 (51%) acquired at least one MRE. 6 rom November 2012 to March 2013, travelers were provided a supplementary 50 mL RNALater® (Applied Biosystems, Villebon-sur-Yvette, France) containing vial before and after travel and one month after return when found positive for the acquisition of MRE.
Bioinformatics and statistical analyses Detailed bioinformatics and statistical methods are reported in Supplementary Materials. Briefly, paired reads were merged and quality-filtered using BBMerge 35. 43 (http://bbmap. sourceforge. net) and Mothur v1. 35. 1,  19  respectively. Operational taxonomic units (OTUs) and bacterial strains species were identified by mapping reads to 16S Greengenes  20  and 23S SILVA  21  databases by USEARCH. 22 The mean mapping rates of qualityfiltered merged reads against the Greengenes 16S and SILVA 23S databases were 31. 0% (median = 31. 0%) and 42. 8% (median = 43. 2%), respectively. The fraction of non-ribosomal mapping reads was on average 26% (median = 25%). Statistical analyses were performed in PRIMER v6 (PRIMER-E Ltd, Plymouth, UK) and in the R software v3.