dataesr/scientific-paragraphs-categorization huggingface dataset:
These 2 plasmids were digested with EcoRV (restriction site in LEU2) and yeast cells were transformed with 0. 5 mg of digested DNA, to generate strains in which MITO-GFP or MITO-MCHERRY was integrated into the LEU2 locus. Yeast strains with GFP-HDEL (ER marker) integrated into the TRP1 locus were obtained by transformation with 0. 5 mg EcoRV-linearized Yiplac204/TKC-GFP-HDEL. 65  The plasmids Yep96-8HIS-UB, Yep96-8HIS-UB-K0, Yep96-8HIS-UB-K63only and Yep96-8HIS-UB-K48only were constructed as follows: the plasmid Yep96-Ub 66 was used as a template for lysine-to-arginine mutagenesis with the Quik-Change Lightning Site-Directed Mutagenesis Kit (Agilent Technologies, 210518). Mutagenic primers were designed with the web-based QuikChange Primer Design Program available online at www. agilent. com/genomics/qcpd.
Western blot analysis Total protein extracts were prepared by the NaOH and trichloroacetic acid (TCA) lysis technique. 68 Treatment with glycosidase EndoH f (New England BioLabs Inc. , P0703S) was performed as previously described. 69 Proteins were separated by SDS-PAGE and transferred onto nitrocellulose membranes (GE Healthcare, RPN2020D). The primary antibodies used for immunoblotting were monoclonal anti-MYC (clone 9E10), anti-Pgk1 (Abcam, Ab113687), anti-Vma2 (Abcam, Ab113684) and Anti-VDAC1/Porin (Abcam, Ab18988) antibodies. The primary antibodies were detected by incubation with horseradish peroxidase (HRP)-conjugated anti-mouse secondary antibodies (Sigma-Aldrich, A9044), followed by ECL ). Unless specified, immunoblotting images were acquired with a Gel Doc~XRC (Bio-Rad) and processed with Image Lab Software.
Free GFP was quantified with Image Lab 3. 0 build 11 software. For ubiquitin detection, total protein extracts were prepared as described above and separated by SDS-PAGE in 15% Tris-glycine gels. The proteins were transferred to a PVDF (polyvinylidene fluoride) membrane (Merck Millipore, IPVH00010) previously activated by incubation in 100% ethanol for 5 min at room temperature and washed twice in transfer buffer. After transfer, the membrane was boiled in water for 15 min, then saturated with 5% BSA (Sigma-Aldrich, A7906) and incubated with monoclonal anti-ubiquitin antibodies directly coupled to HRP (Enzo Life Sciences, BML-PW0935-0025). We analyzed protein turnover by adding 100 mg/mL cycloheximide (CHX; Sigma-Aldrich, 7698-1G) to the yeast cultures grown at 24 C, which were then shifted to 37 C.
Fluorescence microscopy Fluorescence microscopy was carried out with a Zeiss Axio Observer Z1 microscope (Carl Zeiss Microscopy GmbH, Germany) with a 63x oil immersion objective. Images were acquired with an SCMOS ORCA FLASH 4. 0 charge-coupled device camera (Hamamatsu Photonics, Japan). Images were acquired with Zen 2012 Package Acquisition/Analyze software and processed with Zen 2012, ImageJ, Photoshop CS5 and Illustrator CS5.
Acknowledgments We thank  Rodney J. Devenish ,  Rosine Haguenauer-Tsapis ,  Daniel J. Klionsky ,  S ebastien L eon ,  Hitoshi Nakatogawa ,  Yoshinori Ohsumi ,  Fulvio Reggiori , and  Benedikt Westermann  for sharing plasmids, strains and antibodies,  Agn es Delahodde ,  Svetlana Dokudovskaia ,  Ahmed El Marjou ,  Zo€ ı Erpapazoglou  and  Mar ıa Esther P erez-P erez , for stimulating discussions,  Geoffrey Adam ,  Am elie Buhiyan ,  Tianle Hou ,  Ang elina Hok ,  Ana€ ıs Roman  and  Nicolas Villeriot  for technical assistance. We thank  Alex Edelman & Associates  for editorial assistance.
Funding This work was supported by the  CNRS-INSERM ATIP-Avenir program , the " fondation pour la recherche m edicale" (INE 20100518343 ), a  Marie Curie IRG grant  (No. 276912 ), the  LabEx DYNAMO  ( ANR-11-LABX-0011-DYNAMO ) and the  HeliDEAD grant  ( ANR-13-BSV8-0009-01 ) from the  Agence Nationale de la Recherche.
Background: Tobacco users on average have less adequate diet as compared to non-users. While the majority of the studies are from developed countries, less is known about the influence of tobacco on dietary composition in developing countries where malnutrition is a major public health challenge. Additionally, the effect of smokeless-tobacco use on dietary composition are unknown. Methods: We used the nationally representative Household Income Expenditure Survey (HIES-2010) from Bangladesh. Detailed dietary data including both ethnic and regional specific foods were collected for 14 days and comprised of 7 visits with two days' recalls. Findings: Overall, 71% of the households reported expenditure on tobacco (smoking and/or smokeless), and were considered tobacco users. Out of 12240 households, 2061 used smoking tobacco only (16.
Molecular and clinical heterogeneity critically hinders better treatment outcome for glioblastomas (GBMs); integrative analysis of genomic and epigenomic data may provide useful information for improving personalized medicine. By applying trainingvalidation approach, we identified a novel hypomethylation signature comprising of three CpGs at non-CpG island (CGI) open sea regions for GBMs. The hypomethylation signature consistently predicted poor prognosis of GBMs in a series of discovery and validation datasets. It was demonstrated as an independent prognostic indicator, and showed interrelationships with known molecular marks such as MGMT promoter methylation status, and glioma CpG island methylator phenotype (G-CIMP) or IDH1 mutations. Bioinformatic analysis found that the hypomethylation signature was closely associated with the transcriptional status of an EGFR/VEGFA/ANXA1-centered gene network.
Discovery and validation of a DNA hypomethylation signature for poor prognosis of GBMs The included datasets and study workflow are schematically shown in Figure  1A -1B, and patient characteristics are summarized in Supplementary Table  1. By employing a multi-step selection criterion, we identify a novel hypomethylation signature for GBM prognostication, which comprises of three CpGs interrogated on both Infinium 27 k and 450 k platforms: cg23855093 (GPR128), cg13997435 (S100A2) and cg10106284 (FAM49A) (Figure  1C ). Interestingly, the CpGs are all located at non-CGI open sea regions (≥ 4000 bp far from relevant CGIs), and are all hypomethylated in GBMs. Losses of DNA methylation at those CpGs are all correlated with poorer prognosis as their Cox coefficients are all negative (Figure  1C ).
Regarding CpGs-specific gene expression, S100A2 and FAM49A are differentially expressed in GBMs, and the expression levels of S100A2 and GPR128 are moderately correlated with open sea DNA methylation (Pearson r coefficients range from -0. 3 to -0. 2; P < 0. 0001; Figure  1D ). Based on the three CpGs, a risk-score formula that is the sum of DNA methylation levels of each CpGs weighted by its Cox regression coefficients is constructed as follows: risk score = (-4. 235 × β value of cg23855093 + (-4. 765 × β value of cg10106284) + (-3. 542 × β value of cg13997435). Using the cutoff calculated by maxstat R package (-5. 047), the hypomethylation signature could assign each patient to a low-risk (with lower scores) or a high-risk (with higher scores) group from both training sets, TCGA  [7]  and GSE22891  [15] , where low-risk patients are associated with significantly longer OS than high-risk ones (Figure  2A ).
The hypomethylation signature is an independent prognostic factor and has interrelationships with known molecular biomarkers Univariate Cox regression analysis with all Rennes patients (GSE22891  [15]  and Rennes cohort collectively) shows that age, glioma-CpGs island methylator phenotype (G-CIMP) status, TCGA gene expression subtypes, MGMT methylation status, and the hypomethylation signature are all significantly correlated with OS (Table  1 ). Multivariate Cox model further demonstrates the hypomethylation signature as an independent prognosticator (Table  1 ). Cox regression analysis yields similar results by using all available patients, which also indicates the treatmentindependent prognostic nature of the hypomethylation signature (Supplementary Table  2 ). The hypomethylation signature also shows correlations with known molecular marks; the assigned low-risk groups had a higher frequency of MGMT methylated tumors (Fisher's exact test, P = 0.
0004). Meta-analysis at individual-patient and dataset levels both shows that, among patients treated with radiation (RT) and temozolomide (TMZ), the hypomethylation signature has significant prognostic ability within subgroups of each MGMT status (Figure  2D ). Moreover, we find that G-CIMP GBMs, a distinct epigenetic subgroup with favorable prognosis  [6] , are nearly exclusive in the lowrisk groups, but the hypomethylation signature still has robust prognostic value in the subset of G-CIMP-negative tumors (Figure  2D ). Finally, somatic mutations in IDH1 (Fisher's exact test, P < 0. 0001) and ATRX (Fisher's exact test, P = 0. 0002) are significantly enriched in the lowrisk tumors, while the unfavorable mesenchymal subtype appears to be evenly distributed among the risk tumors www. impactjournals.
The hypomethylation signature is closely associated with the transcriptional status of an EGFR/VEGFA/ANXA1-centered gene network Based on TCGA gene expression data, gene set enrichment analysis (GSEA) shows that highrisk tumors are enriched with many cancer-promoting gene sets relating to activation of NF-kB signaling, immune response, and pro-angiogenic signaling (Figure  3A  and Supplementary Table  3 ). To explore the core network of transcriptional changes that may underlie the hypomethylation signature, we calculate the differentially expressed genes (DEGs) between the TCGA risk subgroups, and subject the top ten percent DEGs (699 genes, ranked by their log2 fold change values) to STRING database. The bioinformatic tool identifies an EGFR/VEGFA/ANXA1-centered gene interaction network, which comprises of an up-regulated gene set (55 genes) and a down-regulated gene set (33 genes) (Figure  3B  and Supplementary Table  4 ).
The clusters and relevant features are also observed in non-TCGA samples (Supplementary Figure  2  and Supplementary Table  6 ). The overlap of some gene classifiers and the correlation to specific neural cell types may result in the similarity of the gene network clusters to the gene expression subtypes by Verhaak et al. [4] : the proneural and neural subtypes and the classical and mesenchymal subtypes were respectively enriched in each cluster (Figure  4A ). However the gene network signature could identify tumors with different states of immature neural cell signatures within each Verhaak subtype (Supplementary Figure  3 ). We therefore name the clusters in accordance with their neuroglial genesis activity respectively: the IOlike and the IA-like subtypes. To characterize the two clusters, a comprehensive integrative analysis of clinical and molecular data from TCGA is performed:.
Epigenetic level Correlation with DNA methylation clusters by Noushmehr et al. [6]  shows that the IO-like and IA- (C) The three-CpGs signature was further validated in three independent validation cohorts, by yielding apparent OS difference in GSE36278 and Rennes cohort, and a trend for significance in GSE60274. (D) The three-CpGs signature was also able to identify patients with different prognoses within MGMT methylated tumors (left), unmethylated tumors (middle), and G-CIMP-negative tumors (right) among all available patients; the prognostic abilities were also confirmed by dataset-level meta-analysis, which was shown aside in a manner of forest plot. like clusters are respectively enriched with G-CIMP and cluster#2 GBMs whilst cluster#3 tumors are evenly distributed across the clusters (Figure  4A ).
The clinical features are also seen in non-TCGA datasets (Supplementary Figure  4 ). Of note, the integrative analysis reveals apparent and concordant activation of pro-angiogenic signaling at multiple molecular levels in the IA-like cluster. Therefore it is reasonable that the clusters may have differential responses to anti-angiogentic therapy. Using the TCGA drug data, we find that, among patients with RT/TMZ, the addition of bevacizumab (either first-line or at recurrence; a humanized monoclonal antibody against VEGFA  [32, 33] ) do confer a clear OS benefit to patients within the IA-like cluster, but is associated with similar OS within the IO-like cluster (Figure  4F ). Of note, the results are not conclusive, and should be validated within prospective trials.
Most of the network genes have been shown to have roles in key biological processes of glioma and neural cell development (Supplementary Table  4 ). Clustering based on the gene network expression data could stratify GBMs into two distinct clusters of tumors with similar gene network expression patterns. Integrative analysis demonstrates the distinct molecular and clinical features of the defined clusters at multi-platform levels. The reproducibility of the clusters and relevant features across each dataset suggests that it is unlikely that the new clusters are a spurious finding due to technical artifact or sample bias. All the data support that the gene network may have important roles in GBM biology and prognosis. Therefore, it is reasonable to believe that the core gene network may be key molecular effectors that could biologically explain the survival impact of the hypomethylation signature in GBMs.
However, it should be noted that our study only demonstrates the close correlation between the two-level molecular signatures, the causality, or their exact molecular interaction, remains further investigation. The characterization of the gene network clusters provides novel insights into the understanding of molecular heterogeneity of GBMs. Rather than specifically correlating to neuroglial lineages at mature stage, which has been shown in the Verhaak subtypes  [4] , the expression patterns of the gene network could identify subsets of GBMs reminiscent of distinct immature neural cell types. The clusters may be more consilient with the hypothesis that GBMs are likely to be derived from neural progenitor cells. Moreover, instead of a purely data-driven approach, the gene network is constructed by a bioinformaticsbased approach, which takes advantages of validated molecular data from STRING database  [39].
The gene network is actually a collection of functionally relevant genes that may collaboratively have roles in determining tumor phenotypes. The expression patterns of the whole gene network can serve as a more reliable indicator than individual gene expressions to predict such phenotype. In our study, we find that the two clusters exhibit stable but distinct functional features across different datasets: the IA-like tumors appear to be more aggressive and less differentiated as they are featured by activation of NF-kB signaling, immune response, and pro-angiogenic signaling, and down-regulation of neural developmental signatures. It is believed that those cluster-specific features may serve as potential therapeutic targets for differential treatment towards GBMs, among which the abnormal activation of pro-angiogenic pathways represents the most promising one.
Gene expression profiling was done with the Agilent Whole HumanGenome 8 × 60 K Microarray Kit (Agilent Technologies). Microarray data were log 2 transformed, and normalized (scale 50th percentile and baseline transformation) within GeneSpring GX software (Agilent Technologies). The genomic region spanning wild-type R132 of IDH1was analyzed by direct sequencing as previously described  [15]. A published cohort of fifty newly diagnosed GBMs (Stupp regimen) from the Neurosurgery Departments of Rennes and Angers University Hospitals was also included with available genome-wide DNA methylation (GSE22891; Infinium27k platform, Illumina Inc. ) and gene expression data (GSE22891; Agilent 4 × 44 K Microarray Kit, Agilent Technologies)  [15]. www. impactjournals. com/oncotarget.
Public datasets The cancer genome atlas (TCGA) Multi-platform molecular and clinical data from 570 GBM samples were downloaded from TCGA data portal  [7]. Multi-level data sets included 1) level 3 customized AgilentG4502A_07 Microarray data for gene expression; 2) level 3 Infinium 27 k or 450 k Array data for DNA methylation; 3) level 2 Whole Exome Sequencing data for somatic mutation; 4) level 3 Affymetrix Genome-wide Human SNP6. 0 Array data for copy number; 5) level 3 Agilent 8x15K Human miRNA Microarray data for microRNA, and 6) level 3 Reverse Phase Protein Array data for protein targets. In addition, gene expression data from ten non-tumor brain samples were obtained as controls  [7].
Gene expression omnibus (GEO) Four datasets of GBMs were also obtained from GEO, including a Switzerland cohort (n = 79; GSE60274 for DNA methylation and GSE7696 for gene expression; recurrent samples were excluded; ref. [18, 19] ), a Germany cohort (n = 57; GSE36278 for DNA methylation and GSE36245 for gene expression; tumors harboring mutations in H3F3A and those from TCGA were excluded; ref. [17] ), a American cohort (n = 52) with only DNA methylation data (GSE50923; ref. [16] ) and a European cohort (n = 144) with only gene expression data (GSE16011; ref. [3] ). Of note, among the heterogeneous datasets of gliomas of all grades and ages, only patients with age over 18 years old, and a histological diagnosis of GBMs, were included in this study. Patients with a follow-up data ≥ one month were included for survival analysis, to reduce the bias caused by non-cancer death.
Across-dataset (or platform) microarray data processing For DNA methylation data, batch effects caused by different datasets and different platforms were adjusted by a non-parametric empirical Bayes approach (ber package)  [43]. For gene expression data, expression values represented by multiple probes (or probe sets) were collapsed by taking the mean value of the set of probes (or probe sets). Gene expression datasets were standardized independently by z-score transformation, in which expression values of each gene were transformed to have a mean of zero and a standard deviation (SD) of one  [17]. Missing values were imputed by impute package within R software.
Construction of a risk-score signature from reported differentially methylated CpGs in GBMs The list of 1548 differentially methylated CpGs between GBMs and non-tumor brain tissues from both TCGA and GSE50923 (1548 CpGs) reported by Lai et al  [16]  was downloaded as GBM-specific CpGs candidates. After removal of those targeting the sex chromosomes, those containing a single-nucleotide polymorphism within five base pairs or the targeted loci, and those not interrogated on both the 27 k and 450 k platforms, 1176 CpGs were kept for subsequent analysis. The training-validation approach was employed to construct a prognostic signature. First, univariate Cox regression analysis with permutation correction was performed using methylation levels of the GBM-specific loci and overall survival (OS) from the first training set -GSE22891.
The top significant prognostic loci (5 CpGs; P < 0. 0001) were then subjected to Cox regression model within the second training set -TCGA. Three of the five loci remained significant in TCGA (P < 0. 05), and were then combined using a risk-score model. Cox regression coefficients of each CpGs were calculated from the clinically homogeneous set -GSE22891. The optimal cutoff to stratify different risk tumors was determined by maxstat package within the both training sets  [44]. Bioinformatic analysis GSEA was run to evaluate functional profiles between defined subgroups on reported gene sets from Molecular Signature Database (MSigDB)  [45] , with nominal P value ≤ 0. 05 for significance. DEGs were computed by two-sample t test with a parametric P value ≤ 0. 05 being significant. STRING database  [39]  was run to construct gene interaction network from the top DEGs, with the input options "experiment", "co-expression", "database", "textmining" and "high confidence (0.
700)". The correlation of the hypomethylated signature and gene network was assessed by Pearson correlation analysis, with the risk scores of the hypomethylated signature and the expression scores of the gene network as variables  [4]. The expression scores were defined as the z-score transformed ssGSEA projections of the up-regulated gene set minus the z-score transformed ssGSEA projections of the down-regulated gene set in each dataset  [4]. DAVID database was run to provide functional annotations for input gene list  [46]. www. impactjournals. com/oncotarget.
Integrative analysis of multi-dimensional molecular data Unsupervised clustering was performed on the EGFR/ VEGFA/ANXA-centered gene network. Increasing values of k (2 to 6) were used to identify optimal segregation. Cluster significance was evaluated using sigclust package  [47]. Multi-level data sets were analyzed by the following statistical models: 1) copy number data were analyzed by GISTIC2. 0  [48] , with amplitude threshold being ± 0. 2, and others being default; 2) somatic mutations were analyzed by MutSigCV  [49]  with false discovery rate (FDR) q-value ≤ 0. 05 for significance; and 3) differentially expressed microRNAs and proteins (DEPs) were computed by twosample t test. Both GSEA and ssGSEA were performed to assess the enrichment of signatures characteristic of different neuroglial lineages: classifiers of neurons, (immature) astrocytes and (immature) oligodendrocytes were generated from a mouse central nervous system developmental dataset (GSE9566; ref.
Statistical analysis Pearson correlation analysis was performed to evaluate the correlation between DNA methylation and gene expression. Hierarchical clustering analysis was performed within GenePattern (http://software. broadinstitute. org/). The distributions of known molecular features with respect to each subgroup were tested by Fisher's exact or Chi-square test. OS were estimated by the Kaplan-Meier Method, and compared by log-rank test. Multivariate Cox regression analysis was used to evaluate the independence of potential prognosticators, only incorporating variables that are significant in univariate Cox model. Pooled analysis of hazard ratios (HR) at dataset level was done by the inverse-variance method. The application of either fixed-or random-effect models was based on the intra-dataset heterogeneity which was calculated by I-square statistic with I-square > 50% being significant.
(C) Pearson correlation analysis showed that the risk scores of the hypomethylation signature were consistently and strongly in positive correlation with the expression scores of the gene network not only in the deriving TCGA, but also in two independent databases -Rennes (GSE22891 and Rennes cohort collectively) and GEO (GSE36278 and GSE60274 collectively); only samples with corresponding DNA methylation and gene expression data were analyzed.
ACKNOWLEDGMENTS We gratefully acknowledge all the patients who agreed to participate in this study and to those who have provided their medical care. We gratefully acknowledge  Pr Menei ,  Dr Le Reste ,  Dr. Vauleon ,  Dr. Quillien , and the  Tumor Banks from Angers and Rennes  for their constant support in the collection, processing and histological analysis of tumor samples. We also gratefully thank those who are willing to share their valuable scientific data ( Dr. Rose K Lai  and  Dr. Siyuan Zheng ). I (A. Y. ) thanks my fiancée for her great support and would you marry me? The results published here are in part based upon data generated by The  Cancer Genome Atlas ,  German Cancer Research Center , the  Repository of Molecular Brain Neoplasia Data , and the research teams mentioned in this study.