dataesr/scientific-paragraphs-categorization huggingface dataset:
e. 10-20%. The expression levels of previously-identified TOR-(A) and FoxO-responsive (B) genes were examined in the dataset from wing discs cultured in insulin or 20E. Shown are the changes in expression from that in uncultured discs (log2(x/uncultured)). In (A), genes are grouped based on whether they respond to only insulin (i) or to either insulin or 20E (ii). In (B), most of the FoxO target genes were found to respond to both insulin (at 4hr) and 20E (at 9hr) (i). The sesn gene is normally not expressed in wing discs, but becomes upregulated in certain culture conditions. For this case, we report its change in expression relative to our minimum cutoff for expression (5 fpkm). We also show in (ii) values for the insulin receptor (dInR) and myc, which are thought to be FoxO targets. Normalized expression of selected genes with potential growth regulating functions.
Acknowledgements RNA sequencing was performed by  Annekathrin Kränkel  and  Andreas Dahl  in the Deep Sequencing Group  SFB655  at the  Biotechnology Center of the Technische Universitaet Dresden. The processing of the RNAseq data was performed with considerable help from  Holger Brandl  of the  Bioinformatics  facility of the  MPI-CBG. We thank  Marko Brankatschk ,  Christian Dahmann , and  Savraj Grewal  for critical review of the manuscript prior to submission.
Cloning and Sequence Analysis The partial cDNA sequence that codes for PMCA1 was identified through the use of transcriptome data and alignment with nucleotide sequences from the genome datasets of Tetranychus urticae  [24]. The remaining 5 and 3 ends were amplified through a RACE (rapid amplification of cDNA ends)/PCR (Polymerase Chain Reaction)-based strategy. The full-length cDNA sequence, which was designated as TcPMCA1, was deposited in the GenBank database and with the accession number of KP455490. The full-length cDNA of TcPMCA1 is 4369 bp in length and contains a 3750-bp open reading frame (ORF), a 456-bp 5 -untranslated region (UTR), and a 163-bp 3 -UTR with a putative polyadenylation signal upstream of the poly(A) (Figure  1 ). The ORF encodes 1249 amino acid residues with a predicted molecular mass of 137.
Sequence accession numbers are given in Electronic Supplementary Material, Table  S1. Phylogenetic analysis of TcPMCA1 obtained from the carmine spider mite (Tetranychus cinnabarinus (Boisduval)). The phylogenetic tree was constructed using Molecular Evolutionary Genetics Analysis (MEGA) 5. 04 using the neighbor-joining method based on amino acid sequences. TcPMCA1 was indicated by " ". Bootstrap support values derived from 1000 replicates are shown on the branches. Sequence accession numbers are given in Electronic Supplementary Material, Table  S1.
Sequencing and Phylogenetic Analysis To obtain the full-length DNA sequences of TcPMCA genes, specific primers were designed using Primer 5. 0 (Available online: http://www. premierbiosoft. com/) based on the transcript unigene sequences obtained from the transcriptome (Table  S2 ). A set of gene-specific primers and nested primers were designed to amplify the fragments. The rapid amplification of cDNA ends (RACE) methodwas amplified using the SMARTer™ RACE cDNA Amplification Kit (Clontech, Palo Alto, CA, USA). The total PCR volume was 25 µL and contained 2. 5 µL of 10× PCR buffer (Mg 2+ free), 2. 0 µL of dNTPs (2. 5 mM), 2. 0 µL of Mg 2+ (2. 5 mM), 1 µL of cDNA templates, 1 µL of each primer (10 mM), 0. 25 µL of rTaq™ polymerase (TaKaRa), and 15. 5 µL of ddH 2 O. The PCR program was performed as follows: initial denaturation for 3 min at 94 • C, followed by 34 cycles of 94 • C for 30 s, 55 to 60 • C (depending on gene specific primers) for 30 s, and 72 • C extension for 2 min, and final extension for 10 min at 72 • C.
The PCR products were separated by agarose gel electrophoresis andpurified using Gel Extraction Mini Kit (Watson Biotechnologies, Shanghai, China). The purified PCR products were ligated into the pGEM-T vector (Promega, Fitchburg, MA, USA) and then sequenced (Invitrogen Life Technologies, Shanghai, China). BLAST searching was performed using the NCBI BLAST website (Available online: http: //www. ncbi. nlm. nih. gov/Blast. cgi). The molecular weight and isoelectric points of the deduced protein sequences were calculated by ExPASy Proteomics Server (Available online: http://cn. expasy. org/tools/pi_tool. html) [59]. The transmembrane domain positions and protein domain were estimated using Phobius (Available online: http://phobius. sbc. su. se/), Calmodulin Target Database (Available online: http://calcium.
Homology Modeling The homology modeling was conducted on the I-TASSER server (Available online: http:// zhanglab. ccmb. med. umich. edu/I-TASSER/) [65] , and the 3D structure of TcPMCA1 protein was obtained. The details of I-TASSER protocol have been described previously  [66] [67] [68] [69] [70]. Briefly, it consists of three steps: template identification, full-length structure assembly and structure-based function annotation. Firstly, starting from the query sequence, I-TASSER identifies homologous structure templates from the PDB library  [71]  using LOMETS  [69, 72] , a meta-threading program that consists of multiple threading algorithms. Then, the topology of the full-length models is constructed by reassembling the continuously aligned fragment structures excised from the templates, where the structures of the unaligned regions are built from scratch by ab initio folding based on replica-exchange Monte Carlo simulations  [73].
The low free-energy states are further identified by SPICKER  [74]. To refine the structural models, a second round of structure reassembly is conducted starting from the SPICKER clusters. The low free-energy conformations refined by full-atomic simulations using FG-MD  [75]  and ModRefiner  [76]. Finally, the biological functions of the target proteins were derived by matching the I-TASSER models with proteins in the BioLiP library  [77] [78] [79]. Based on identity with the primary sequence of the target TcPMCA1, the crystal structure of the phosphoenzyme intermediate of the rabbit SERCA Ca 2+ -ATPase (PDB ID code: 3BA6) was retrieved from the Protein Data Bank (PDB, Available online: http://www. rcsb. org/pdb/home/home. do) and used as the template for homology modeling (the amino acid sequences of the trmplate was shown in Figure  S1 ).
Dataset and Molecular Modeling The acaricidal activities of the 30 collected compounds (Table  S3 ) were obtained from a previous study  [82]. These 30 compounds are natural or synthetic compounds that are readily available to coumarin, which were purchased from Chengdu Aikeda Chemical Reagent Co. , Ltd. and Shanghai yuanye Bio-Technology Co. , Ltd. The purity of these compounds was more than 98%. The structures and half-maximal inhibitory concentration (LC 50 ) of the compounds are shown in Table  2. These values were transformed into the corresponding pLC 50 [-log(LC 50 )] as the expression of inhibitor potency. The 30 compounds were placed in a training set of 24 compounds (80%) and a test set of 6 compounds (20%). The 3D structures of these ligand compounds were constructed in Sybyl 6.
9 (Tripos Software, St. Louis, MO, USA). Structures were energy minimized by using the Gasteiger-Hückel charge  [83] , Tripos force field  [84] , and Powell methods  [85]  with a convergence criterion of 0. 005 kcal/(mol Å). The iterations maximum number was set to 10,000, and multiple conformation search was used. Coumarin structure was used as the common scaffold for molecular alignment, and compound 2 with the highest acaricidal activity was used as the template molecule. All other compounds were aligned with the coumarin core using the "align database" command in Sybyl.
Molecular Docking The protein model was prepared using Sybyl prior to docking simulations. All bound water molecules and ligands were removed from the protein, and hydrogen atoms and AM1-BCC charges  [86]  were added to the amino acid residues. The generated homology model of TcPMCA1 was used for molecular docking, and the binding pocket was defined using Discovery Studio 2. 5 (Accelrys Software Inc. , San Diego, CA, USA). The 3D structure of the compound was prepared as the ligand, and all of the hydrogen atoms and AM1-BCC charges were added  [86]. Molecular docking was performed with AutoDock 4. 0  [87]. The grid spacing was changed from 0. 375 nm, and the cubic grid map was 40 × 40 × 40 Å toward the TcPMCA binding site. The docking parameters were set as follows: the number of GA Runs was set as 10, population size was set as 150, the maximum number of evaluations was set as 25,000,000, and 250 runs were performed.
All other parameters were set as the default. The docking process was performed as follows: first, molecular docking was performed to evaluate the docking poses. Then, defined docking was conducted on the binding pocket. Three to six independent docking calculations were conducted. The corresponding lowest binding energies and predicted inhibition constants (pK i ) were obtained from the docking log files (dlg). The mean ± SD of binding energies was calculated from the dockings. AutoDock Tools and Visual Molecular Dynamics (VMD, Theoretical and Computational Biophysics group at the Beckman Institute, University of Illinois at Urbana-Champaign)  [88, 89]  was used to visualize the docking result. Surface representation images that show the binding pocket of TcPMCA1 were generated using VMD software.
Statistical Analysis All results were expressed as the mean ± standard error. The differences among the four developmental stages and time-dependent responses to scopoletin exposure were analyzed using one-way analysis of variance (ANOVA). The level of significance of the means was then separated by Fisher's LSD multiple comparison test (p < 0. 05). The fold change in TcPMCA gene expression was analyzed using SPSS (v. 16. 0, SPSS Inc. , Chicago, IL, USA), and significance was determined by independent sample t-test (p < 0. 05).
Acknowledgments: We are grateful to  Yuwei Wang  in  School of Pharmacy, Lanzhou University  for Molecular Docking and 3D-QSAR analysis. This research was partially supported by a combination of funding from the  National Science Foundation of China  ( 31272058 ,  31572041  and  31601674 ) and  Chongqing social undertakings and people's livelihood guarantee scientific and technological innovation  ( cstc2015shms-ztzx0129 ).
Gray boxes: genes non associated with either histone mark (none). The dotted line represents the median level of all genes analyzed (white box   [76]  with input control libraries from the corresponding cell types. Peaks were then annotated using a window of ± 20 kb with respect to the coordinates of the beginning and end of RefSeq transcripts. More than 150 million short reads were obtained for all samples. These short reads were uniquely aligned to the human genome, resulting in a 77 and 76% of the genome covered in TG1 and TG1-miR, respectively. The data have been deposited in NCBI's Gene Expression Omnibus  [20]  and are accessible through GEO Series accession number GSE98330 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE98330). ARNT2 ChIP was performed as described above using anti-ARNT2 antibodies (Santa Cruz, Cliniscience sc-5581X, France) and 100-1000 bp 5706** chromatin fragments.
Expression profiling Statistical and graphical analyses of ChIP-seq and microarray data were performed using the R software version 3. 2. 3 (http://cran. r-project. org/). Gene ontology (GO) analysis was performed with DAVID software (version 6. 8, http://david. abcc. ncifcrf. gov/). GO analysis of all genes changing histone marks in TG1-miR compared to TG1 was achieved using all human genes as background (Homo Sapiens from DAVID). GO analyses of genes exchanging an active for a repressive histone mark and vice versa between TG1 and TG1-miR were achieved using as background all the genes with differing histone marks in TG1-miR and TG1. Genes encoding transcription factors were retrieved using the KEGG (http:// www. genome. jp/kegg/), and Genomatix databases (Genomatix, Germany). Interactions between the retrieved set of 202 transcription factors were analyzed with the STRING database (version 10.
0, http://string-db. org/). Heat maps and z scores were downloaded from the IVY dataset (http:// glioblastoma. alleninstitute. org), and analyzed with XLSTAT version 1. 2. z-score graphs were generated with Prism 6. 0 software (GraphPad). ARNT2 mRNA expression was analyzed using publicly available data using the R2 Genomics Analysis and Visualization Platform (http://r2. amc. nl) (Lee, mixed glioblastoma dataset, GEO ID: GSE4536) and the HGGC website (http://130. 238. 55. 17/hgcc/). TCGA transcriptome dataset of 481 surgical tissue samples of untreated primary glioblastoma (tcga 540 glioblastoma) was analyzed using the R2 Genomics Analysis and Visualization Platform. Single glioblastoma cell transcriptomes were obtained at https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE57872, and analyzed with XLSTAT version 1.
Cryostat sections of 30-µm-thickness were cut in the frontal plane. Thirteen sections, from the olfactory bubs to the posterior end of cerebellum were selected for the analysis. Sections were incubated with DAPI (Sigma, France) for 10 min at room temperature. Sections staining was analyzed with a fluorescent microscope (Axioplan 2, Zeiss). Images were acquired on digital camera (DXM 1200, Nikon, USA) using Zen 2 software (Zeiss) and prepared using Adobe Photoshop software (Adobe Systems, San José, USA).
Statistical analysis R version 3. 2. 3, XLSTAT version 1. 2 or Prism 6. 0 software (GraphPad) were used for statistical analyses. The level of significance was set at p < 0. 05. The type of statistical test used is provided in the figure legends. All experiments were performed using independent biological samples with the exception of the ChIP-seq. All experiments were repeated at least three times in an independent manner with the exception of the microarray experiment. PCA analysis was performed on XLSTAT version 1. 2, based on a Pearson correlation matrix. First and second component (F1 and F2 axis) were used to generate a correlation circle where the variables (genes) were plotted as vectors according to their correlation with F1 and F2 axis. The figures were prepared using Adobe Illustrator (Adobe Systems).
Repression of GBM stem-like cell properties is accompanied by discrete changes in epigenetic profiles Lentiviral expression of miR-302-367 in the TG1 human GBM stem-like cell line (referred to as TG1-miR) resulted in loss of their stem-like and tumorigenic properties  [22]. H3K4me3 and H3K27me3 profiling of TG1 and TG1-miR was performed by ChIP followed by deep sequencing (data accessible at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE98330). For each cell type analyzed, approximately 16,000 genes (~ 70% of the complete human exome) were found to be associated with the H3K4me3 and/or H3K27me3 mark (Online Resource 3). This analysis revealed a predominance of genes (~ 48%) associated with the active H3K4me3 mark in TG1 and in TG1-miR (Fig. 1b ). Only ~ 10% were associated with the repressive H3K27me3 mark.
To further decipher the molecular basis of this phenotypic change, we focused our analysis on transcription factors, which are likely to play a pivotal role in driving the changes in functional state. Genes encoding transcription factors were retrieved using the KEGG (http://www. genome. jp/kegg/), and Genomatix databases (Genomatix, Germany). Interactions between the set of 202 transcription factors retrieved from the 5151 genes presenting a change in epigenetic mark (Online Resource 6) were then analyzed using the STRING software (http:// string-db. org/ version 10. 0,  [69]. Only interactions with a high confidence level (0. 7) were selected. Addition of three supplementary transcription factors, which were associated with the active H3K4me3 mark in both TG1 and TG1-miR (hypoxia inducible factor 1α/HIF1A, hypoxia inducible 1 3 factor 2α/EPAS1 and beta-catenin/CTNNB1), allowed optimization of the modeling of a network including a maximal number of elements.
ARNT2 is functionally associated with a molecular signature linked to glioblastoma cell tumorigenicity within the patients' tumors We first analyzed ARNT2 expression in two published independent transcriptome datasets of glioblastoma cells either devoid of or endowed with tumor-initiating properties  [41, 73]. In agreement with our observations, we found that ARNT2 expression was downregulated in nontumorigenic cells compared to tumorigenic cells in both datasets (Fig. 4d, e ). Further, the analysis of the TCGA transcriptome dataset of 481 surgical tissue samples of untreated primary glioblastoma using the GlioVis Platform  [9]  showed lower ARNT2 mRNA levels in glioblastoma tissues than in non-tumoral brain tissues (Online Resource 8A). The finding of higher ARNT2 mRNA levels in normal brain tissues than in GBM tissues from which neurons are absent is coherent with the high ARNT2 expression in mature neurons  [18, 19, 32].
Analysis of the TCGA glioblastoma dataset and the French glioma dataset gse16011 Analysis of published transcriptome dataset of early passage (P3) glioblastoma cells isolated from four human tumors, either endowed with self-renewing and tumor-initiating properties or devoid of them following serum-treatment  [41]. ***p < 0. 001, unpaired t test with Welch's correction, mean ± SD, n = 4. e Analysis of the publicly available HGCC transcriptome dataset of glioblastoma cells isolated from distinct patients' tumors and characterized for their ability to initiate tumors  [73]. *p < 0. 05, unpaired t test with Welch's correction, mean ± SD, n = 15 (Tumorigenic), n = 9 (non-Tumorigenic)  [26] , showed no variation in ARNT2 expression according to MGMT status, IDH1 mutation or EGFR amplification (not shown).
In accordance with the narrow distribution of ARNT2 expression levels across glioblastoma samples (Online Resource 8A), no correlation could be disclosed using the R2 Genomics Analysis and Visualization Platform (http://r2. amc. nl) between variations in ARNT2 mRNA levels and the overall survival of patients (Online Resource 8B). Glioblastoma are characterized by the intermingling of differing tumor tissues including dense tumor areas where cancer cells predominate ("cellular tumor areas"), necrotic and perinecrotic areas with sparser tumor cells, areas more or less angiogenic, and infiltrated areas where tumor cells are distributed through the brain parenchyma. To refine the analysis of ARNT2 and its family kin in glioblastoma, we explored its expression using the IVY dataset, which provides gene mRNA levels in distinct glioblastoma zones (http://glioblastoma.
5b ). To further explore the relevance of ARNT2 expression in the context of the human tumors, we compared its expression in the tumor core areas of glioblastomas (IVY dataset) with the expression of genes associated with glioblastoma cells endowed with tumorigenic and stem-like properties. We used a 28 molecules' signature delineated by the Bernstein laboratory from the combined analysis of single cell transcriptome profiles of cultured GBM stem-like cells and of 254 cells sampled from five different glioblastomas  [55]. We retrieved expression data from 27 of the 28 components of the signature from the IVY dataset (Online Resource 10). Principal component analysis (PCA) showed that ARNT2 expression co-varied with the tumorigenic/stem signature along the first principal component axis (F1 axis) (Fig.
5c ). We verified whether this co-variation occurred also at the single cell level using the published transcriptome profiles of 254 glioblastoma cells  [55]. This dataset contains 25 of the 28 signature's components (Online Resource 10). Principal component analysis showed that ARNT2 expression co-varied also with the stem signature at the single cell level (Online Resource 11). To probe the functional relevance of the co-variations disclosed by analysis of glioblastoma tissues and single cells transcriptomes, we determined the effect of ARNT2 knockdown on the expression of SOX9, POU3F2 and OLIG2 in independent GBM stem-like cell cultures (6240** and 5706**) distinct from TG1 and TG1-miR. These three genes were selected with respect to their previously demonstrated role in glioblastoma cell tumorigenicity  [31, 44, 68].
This was confirmed at the protein level by immunohistochemistry of ARNT2 expression in sub-populations of cells within proliferative zones of patients' glioblastoma. Furthermore, we demonstrated that ARNT2 knockdown inhibits tumor-initiating properties in vivo, supporting a role of ARNT2 in the tumorigenicity of glioblastoma cells. Examination of tumor patients' transcriptome datasets further associated ARNT2 with a tumorigenic/stem signature of glioblastoma cells  [55]  at both the tissue and single cell levels. To ascertain the functional relevance of this association, we focused on three transcription factors members of this stem signature SOX9, POU3F2 and OLIG2, since the knockdown of these factors has previously been reported to inhibit glioblastoma cell tumorigenicity in vivo  [31, 44, 68].
Acknowledgements We are obligated to the members of the Cancer stem cell network of Ile-de-France headed by  Dr. Christine Chomienne , for their continuous support and helpful discussions. We are grateful to  A. Dias-Morais  for technical assistance, and  Dr. Tomohiro Yamaki  ( Chiba Ryogo Center, Japan ) for helpful discussions. We thank for their precious help  A. Borderie ,  S. Destree  and  C. Hagnere  ( Hôpital Pasteur, CHU Nice ).
To test if HCV-infected cells were prone to apoptosis, HCV-infected primary memory B cells isolated from patients were stimulated with CD40L þ IL2 þ IL-10 or vehicle control ex vivo (Supplementary Fig. 9A ). Treated memory B cells were stained with annexin V (to measure apoptosis) and anti-CD27. After incubation with vehicle control or CD40L þ IL2 þ IL-10 treatment, the percentage of annexin V þ cells was higher in the CD40L þ IL4-stimulated HCV-infected B-cell subset than that of UV-irradiated supernatant-treated cells (Supplementary Fig. 9A ). As CD40L stimulation transactivates pro-apoptotic molecule Bax (ref. 65), Bax mRNA levels were also examined. The treatment was found to induce BAX after cross-linking B7. 2 on HCV-infected memory B cells (Supplementary Fig. 9A, right ). These data suggested that, in the absence of survival signals, HCV-infected CD27 þ B cells were prone to apoptosis.
2. Full-length of B7. 2, B7. 2/CD8, full-length of ICAM or ICAM/CD8 were overexpressed in 293T cells. After 24 h, the expression level of B7. 2 was determined by western blots. For infection, the culture media was replaced with fresh media containing either SB HCVpp or JFH1 HCVpp and incubated an additional 24 h. Cell lysates were used for mmunoprecipitation with anti-B7. 2, followed by immunoblot for B7. 2 proteins in 293T cells. After incubation, the cells were harvested and lysed for immunoprecipitation. B7. 2 only interacted with HCV E2 after SB infection, but not JFH1 infection. These data indicate that SB E2 protein, but not JFH1 E2 protein, directly interacted with B7. 2. Electron microscopy. Raji cells were transfected with HCV RNA and cell-free supernatant was harvested 96 h post transfection and concentrated via ultracentrifugation.
5 ml min À 1 ). The column was washed sequentially with buffer A, buffer B (50 mM sodium phosphate [pH 6], 300 mM NaCl, 10% glycerol) and buffer C (50 mM sodium phosphate [pH 8], 1 M NaCl, 10% glycerol). The proteins were eluted in buffer A containing 200 mM imidazole and buffer exchanged into phosphate-buffered saline without Ca2 þ and Mg2 þ [PBS( À )] plus 10% glycerol. Statistical analysis. ANOVA analysis was used for multiple comparisons. For correlation analysis, Pearson's correlation test was performed in GraphPad Prism software. For all other statistical analyses, the nonparametric Mann-Whitney test or two-tailed t-test was employed. Values of Po0. 05 were considered to be statistically significant.