dataesr/scientific-paragraphs-categorization huggingface dataset:
1f ; patients with MET overexpression are highlighted with a red dot; Supplementary Fig. 3C-F ). For the HCC patient cohort from GSE56588, expression data (array) are only available for some patients and without information about mutations. Therefore, correlative studies were not possible with this HCC cohort. Together, these findings show that liver cancer modelled by the Alb-R26 Met genetic system is characterised by methylation changes of specific CGIs, with a predominant hypermethylation profile. A high proportion of these alterations are also found in HCC patient subgroups. Furthermore, there is a striking correlation between differentially methylated CGIs and MET overexpression in the HCC patient subgroup modelled by the Alb-R26 Met genetic setting. Enrichment in CGI hypermethylation is necessary for Alb-R26 Met tumorigenesis.
CGI hypermethylation correlates with gene upregulation in Alb-R26 Met HCC. Alterations in DNA methylation are known to impact gene expression. We analysed the expression levels of the 431 genes with differentially methylated CGIs in Alb-R26 Met tumours using high-coverage RNA-seq data (4 Alb-R26 Met tumours and 4 control livers). Studies highlighted 93 genes differentially expressed (log 2 FC > 1, FDR < 0. 05; Supplementary Data 3). According to the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database, several cancer-related pathways were significantly enriched, such as MAPK signalling, viral carcinogenesis, pathways in cancer, TGF-β signalling, cell cycle, renal cell carcinoma (Fig. 3a , Supplementary Fig. 5 ), strengthening the significance of genes differentially methylated and expressed in the Alb-R26 Met cancer model.
Interestingly, all 7 patients belonging to the HCC subgroup-3 (in Fig. 1f ) are characterised by more than 37% of genes both hypermethylated and overexpressed, and 5/7 patients belong to the H + E + subset (these 7 patients are highlighted with a red square and red % in Fig. 5a ). Next, we asked whether the H + E + patient subset could be also identified according to global gene expression or methylation features. Unsupervised cluster analysis of either global methylome or transcriptome data did not lead to the same patient clustering (Supplementary Fig. 7 ), thus strengthening the usefulness of combining methylation-expression features to identify specific HCC patient subsets. The remarkable correlation between data obtained in the Alb-R26 Met HCC model and analysed patient samples prompted us to perform integrative studies using another HCC model, for which methylation and expression data are available: the hepatitis-B virus-X mice (HBx tg ; GSE48052  27  ).
001; Supplementary Fig. 10 ). Curiously, in the Alb-R26 Met cancer model Cdkn2a, rather considered as a tumour suppressor, is overexpressed and hypermethylated in its gene body CGI, whereas no methylation changes were observed in its promoter CGI (Supplementary Data 4). We examined whether this phenomenon would also occur in HCC patients by analysing CDKN2A methylation and expression in HCC patients from TCGA and GSE56588 cohorts (for which methylation and expression data are available: 205/224 patients). as MAPK signalling, viral carcinogenesis, pathways in cancer, cell cycle (Fig. 6f , Supplementary Fig. 12 ). Consistently, some of these genes are well-known oncogenes, such as GRB10, MAP3K6, JUN (which belong to MAPK pathway), NFKB2, RELB (which belong to NFkB and MAPK pathways), MET, PTK7 (Supplementary Data 10).
These results conceptually illustrate that, although each oncogene contributes to the tumorigenic properties of cancer cells, they operate in a cooperative manner as an "oncogenic module" for ensuring robustness of the tumorigenic program. Our integrative studies using human HCC databases demonstrate that enrichment in genes both overexpressed and with hypermethylated CGIs also characterises 56% of the HCC patients, which we named as the "H + E + patient subset". For several genes, upregulation in expression levels is coherent with them being bona fide oncogenes. For example, it is the case of WT1, DLK1, TP73, EEF1A2, IGF1R, DKK1, SPOCK1, ITPKA, HOXA3, NOX4, FZD10, VASH2, GATA2, SOX8. Thus, our genetic studies together with a revisited analysis of human cancer databases reveal that raising dosages of oncogene sets characterised by hypermethylated CGIs is a robust mechanism operating in cancer.
Total RNA extraction: Total RNA from frozen tissues and cultured cells was isolated using the RNeasy Mini Kit (Qiagen), according to the manufacturer's instructions. DNase (Qiagen) treatment was included to avoid possible genomic DNA contamination. Regarding frozen samples, 20 mg of tissue were first homogenised in the specific lysis buffer by 6300 r. p. m. 2 × 30 s using Precellys 24 (Bertin technologies), then the RNeasy Mini Kit (Qiagen) was used. cDNA and quantitative RT-PCR analysis: cDNA was synthesised using a Reverse Transcription Kit (Bio-Rad). PCR reactions were performed using 2X SYBR Green qPCR SuperMix-UDG with Rox (ThermoFisher Scientific) and specific primers (1 µM; qPCR primer sequences are listed in Supplementary Data S11). Expression levels were quantified using the comparative Ct method (2 -ΔΔCT method) with the house-keeping gene Hprt as a control for internal normalisation, and results are expressed as RQ = 2 -ΔΔCT.
Fragments were ligated to pre-annealed adapters containing 5′-methylcytosine instead of cytosine according to Illumina's specified guidelines (www. illumina. com). Adaptor-ligated fragments of 150-250 bp and 250-350 bp in size were recovered from a 2. 5% NuSieve 1:1 agarose gel (Zymoclean™ Gel DNA Recovery Kit, Zymo Research Cat#: D4001). The fragments were then bisulfitetreated using the EZ DNA Methylation-Lightning™ Kit (Zymo Research, Cat#: D5020). Preparative-scale PCR was performed and the resulting products were purified (DNA Clean and Concentrator™-Zymo Research, Cat#D4005) for sequencing on an Illumina HiSeq. Alignments and data analysis. Sequence reads from bisulfite-treated EpiQuest libraries were identified using standard Illumina base-calling software and then analysed using a Zymo Research proprietary analysis pipeline, which is written in Python and used Bismark (http://www.
bioinformatics. babraham. ac. uk/projects/ bismark/) to perform the alignment. Index files were constructed using the Bismark-genome-preparation command and the entire reference genome. The non-directional parameter was applied while running Bismark. All other parameters were set to default. Filled-in nucleotides were trimmed off when doing methylation calling. The methylation level of each sampled cytosine was estimated as the number of reads reporting a C, divided by the total number of reads reporting a C or T (β-value). Overall sequencing results (for 13 samples) are: (a) mean total read: 30 million read pairs, (b) mean mapping efficiency: 40%, (c) mean unique CpGs: 4. 1 millions, (d) mean average CpG coverage: 16×, (e) mean bisulfite conversion rate: 98%. Data accessibility: Methylome datasets generated from this study are deposited with the Gene Expression Omnibus (accession GSE90093).
Assay Design, Sample Preparation and Multiplex Targeted Amplification. After assessment of DNA concentration and quality, DNA samples were bisulfite converted using the EZ DNA Methylation-LightningTM Kit (ref Cat#D5030) according to the manufacturer's instructions. Primers were designed with Rosefinch, Zymo Research's proprietary sodium bisulfite converted DNA-specific primer design tool (primer sequences are listed in Supplementary Data 5). Multiplex amplification of all samples using the specific primer pairs and the Fluidigm Access ArrayTM System was performed according to the manufacturer's instructions. The resulting amplicons were pooled for harvesting and subsequent barcoding according to the Fluidigm instrument's guidelines. After barcoding, samples were purified (ZR-96 DNA Clean and Concentrator™ -ZR, Cat#D4023), then prepared for parallel sequencing using a MiSeq V2 300 bp Reagent Kit and paired-end sequencing protocol, according to the manufacturer's guidelines.
Targeted Sequence Alignments and Data Analysis. Sequence reads were identified using standard Illumina base-calling software and then analysed using a Zymo Research proprietary analysis pipeline, which is written in Python. Sequence reads were aligned back to the reference genome using Bismark (http://www. bioinformatics. babraham. ac. uk/projects/bismark/), an aligner optimised for bisulfite sequence data and methylation calling  44. Paired-end alignment was used as default thus requiring both read 1 and read 2 be aligned within a certain distance, otherwise both read 1 and read 2 were discarded. Index files were constructed using the bismark_genome_preparation command and the entire reference genome. The non-directional parameter was applied while running Bismark. All other parameters were set to default.
bioinformatics. babraham. ac. uk/projects/fastqc/). Reads were mapped to the reference genome mm9 with STAR aligner 45 using default parameters; differential expression was calculated using the Cufflinks package  46. Cell culture-related experiments. Cell lines: Alb-R26 Met HCC cell lines (HCC3, HCC13 and HCC14) were established, characterised and cultured as previously described  24  ; cells were regularly tested by PCR-based assay to confirm their maintenance in free Mycoplasma culture condition. shRNA-mediated downregulation of candidate genes: The functional relevance of candidate oncogenes was determined using shRNA targeting sequences (Sigma; shRNA sequences are reported in Supplementary Data 12). In particular, plasmids carrying the shRNA sequence were transfected in cells using Lipofectamine 2000 reagent, according to the manufacturer' instructions (ThermoFisher Scientific).
3 µM in the corresponding wells. After 48 h, cell viability was assessed in a Cell Titer Glo Luminescent Assay (Promega) and luminescent signals were measured with a luminometer microplate reader (Berthold). Data are expressed as means ± SEM of three independent experiments performed in triplicate. Anchorage-dependent growth assay (focus formation assay): To measure anchorage-dependent growth, 300 cells were seeded in 10 ml complete media in a 10 cm dish. After 7 days, foci were stained with a 0. 2% crystal violet solution (2% methanol). The total number of foci and individual foci size were quantified using ImageJ program. Data are expressed as means ± SEM of three independent experiments performed in triplicate. Anchorage-independent growth assay (soft agar assay): Assays were performed as previously described  [47] [48] [49].
Briefly, cells were cultured in 12-well plates containing two layers of agar. Cells (6 × 10 3 ) were resuspended in 0. 5% agar diluted in complete medium and poured onto a 1% layer of agar (diluted in medium). Fresh medium was added to the top layer every 3 days. After 2 weeks, colonies were stained with MTT, pictures were taken using a dissecting microscope, and colonies were counted using ImageJ software. Numbers are expressed as means ± SEM of three independent experiments performed in triplicate. Tumour sphere forming assay: Cells were cultured at a density of 2 × 10 4 / 35 mm dishes in a stem cell-permissive media. In particular, cells were cultured for one week in DMEM/F12 medium supplemented with 1% N-2 Supplement, 2% B27, 50 mg/ml of Penicillin-Streptomycin, glutamine (Gibco), 0. 01% Bovine Serum Albumin (BSA), 5 mg/ml of insulin (Sigma) and growth factors including 10 ng/ml of basic fibroblast growth factor (bFGF), 20 ng/ml of epidermal growth factor (EGF) and 10 ng/ml of hepatocyte growth factor (HGF; Peprotech).
After one week, pictures of the whole dish were taken using a dissecting microscope, and spheres were counted using ImageJ software. Numbers are expressed as means ± SEM of three independent experiments performed in triplicate. In vivo tumorigenesis assays (xenografts in nude mice): For in vivo demethylation studies, xenografts were performed using Alb-R26 Met HCC cells either untreated or pre-treated for 48 h with Decitabine (0. 3 µM). Cells (5 × 10 6 ) were then resuspended in a 1:1 Matrigel:PBS solution (Corning BV) and inoculated subcutaneously into the flank-leg region of nude mice (S/SOPF SWISS NU/NU; Charles River). After 5 days of cell inoculation, mice were treated with intraperitoneal injections of vehicle or Decitabine (2. 5 mg/kg) twice per week for 3 weeks. Mice were then sacrificed and tumour volume was measured as length × width × height.
Computational analyses. Unsupervised hierarchical clustering analysis: Clustering statistics was determined by using the methylation values of all CGIs for each sample. We applied the Principal Component Analysis and the Agglomerative Distance Tree using the "linkage" function with unweighted average euclidean distance for calculating the similarity matrix of samples and the "dendrogram", as well as "phylotree" function to plot the hierarchical and distant trees (both are from Matlab Statistical Toolbox). For studies reported in Supplementary Fig. 7 , clustering analysis of both methylome and expression data was performed using the function "hclust" on an Euclidean distance matrix of samples, which was computed with the function "dist". "hclust" then returned a tree-like structured object that could be plotted as dendrogram by "plot" (R, version 3.
3. 1). Identification of human CGIs corresponding to the mouse CGIs of interest: To compare methylome outcomes identified in the Alb-R26 Met genetic system with those available for human studies, genomic coordinates were converted from mm9 to GRCh37/hg19 by using the "Lift-Over" tool available from UCSC (https:// genome-euro. ucsc. edu/cgi-bin/hgLiftOver). This allowed us to successfully map 501 out of 513 CGIs from mouse to human regions (Supplementary Data 2). Among them, we only kept 501 unique human regions by discarding duplicate liftovers. We also discarded 14 human regions not overlapping with any human CGI. We then check into TCGA patient datasets the presence of methylation data for those CGIs. We focused the analysis on the patient having both tumour and control samples (adjacent liver) methylation data, and we discarded the CGIs having no entry into any of the TCGA patient dataset.
Finally, the total CGIs used for comparative analyses between mouse and human is 416. Analysis of public available DNA methylome data: The human methylome data is available through firebrowse (www. firebrowse. org) by the BROAD Institute and is based upon data generated by the TCGA Research Network: http:// cancergenome. nih. gov/. The publicly available methylome data (Level 3 data) of HCC patients from TCGA is generated with the platform Illumina Infinium Human DNA Methylation 450 and contains beta values for 485778 CpGs. Patients with both tumour and control samples were extracted and calculation of methylation difference per CpG was applied (β-values of tumour-β-values of control). Student's T-test was used to compare between tumour and normal samples, and the P-values were corrected with Benjamini-Hochberg False Discovery Rate (FDR).
As our methylome screen focused on CGIs, we revisited the human data (from TCGA and from GSE56588) to generate a list of all CpGs within CGIs with the corresponding methylation β-values. By applying the same methylation difference and FDR thresholds used for Alb-R26 Met methylome data, we extracted a list of differentially methylated CpGs from the human HCC dataset. Methylome overlap between Alb-R26 Met outcomes and human data: A methylation overlap between Alb-R26 Met and human HCC was considered only when a given CGI was differentially methylated in both species. To define the methylation status of a given CGI, the CpG with an absolute maximum methylation difference among all patient samples was chosen as a representative probe (with P-value threshold and fold change cut-off defined above).
This CpG was analysed in all HCC patients. An overlap score (in percentage) was determined by calculating the number of human CGIs differentially methylated versus the total number of lifted-over CGIs subset. Analysis of public available RNA-seq data: The human RNA-seq data from TCGA was available through firebrowse. The data is generated with the platform Illumina HiSeq 2000 Sequencing System and uses MapSplice 50 to do the alignment and RSEM  51  to perform the quantitation. The scaled estimate from RSEM output was used as this value could be multiplied by 10 6 to obtain a measure in terms of transcripts per million (TPM), which is preferred over RPKM  52  or FPKM  53  as it is independent of the mean transcript length and therefore more comparable across samples  51. The TPM is calculated for each gene and the calculation of Log 2 Fold Change (Log 2 (tumour sample)-Log 2 (control sample)) was applied to each patient with available data from both tumour and control samples.
Calculation of the relative position to the ATG: For calculating the position of CpGs, we used the longest transcript for each gene. The gene length was reported with values ranging from -100% and +100% (transcription end site: TES), with the ATG at position 0. The relative position for each CpG was then reported relative to its distance to the ATG. A positive relative position corresponds to a genomic region located downstream the ATG, whereas a negative relative position stands for a genomic region located upstream the ATG. Analysis of public available data from a mouse HCC model carrying the viral hepatitis B virus X expression: Using available methylome and expression data based on a HCC model induced by the viral hepatitis B virus X (HBx tg ; GSE48052  27  ), we performed the same analysis done for the Alb-R26 Met model (Fig.
3b ). For each CpG, the methylation difference between HBx tg tumour and control sample was calculated as the difference of the RPKM. For those CpGs found differentially methylated, the expression of the corresponding gene was then calculated as the difference of the RPKM sum within the TSS and TSE. Pathway enrichment analysis: For these analyses (shown in Supplementary Figs. 5, 9 ), identified genes were used as an input for KEGG pathway enrichment analysis with the REST API tool (http://rest. kegg. jp). Pathways were further ranked by -log 10 P-value after applying the hypergeometric probability density function (Matlab function "hygepdf" from Statistical Toolbox). Statistical analysis: All data were analysed using GraphPad Prism software (version 7. 01) and Matlab Statistical Toolbox (version R2015b).
Acknowledgements These results are in part based upon public data generated by  TCGA Research Network : http://cancergenome. nih. gov/. We are particularly grateful to  F. Helmbacher  for extremely valuable feedback on the study. We thank: all members of our labs for helpful discussions and comments;  F. Castets  for advises on biochemical analysis;  J. Zucman-Rossi  and  E. Letouzé  for valuable discussion and feedback on the bioinformatics;  V. Girod-David  and  L. Jullien  for excellent help with mouse husbandry;  C. Giaccherini ,  A. Dobric  and  E. Chrabaszcz  for their contributions to studies on molecular and functional characterisations of shRNA-targeted HCC cells. This work was funded by  INCa (Institut National du Cancer) ,  FdF  ( Fondation de France)  and  GEFLUC-Les Entreprises contre le Cancer  to F.
5 2Fig. 1 51 Fig. 1 Methylome studies identify an enrichment of CGI hypermethylation in Alb-R26 Met tumours, also present in a subset of HCC patients. a Schematic representation of the overall strategy employed. DNA methylation and gene expression levels were analysed in Alb-R26 Met tumours and control livers. Outcomes were compared with HCC human database. b Mean methylation levels in controls and Alb-R26 Met tumours, focusing on all CpGs, CpGs outside CGIs and CpGs in CGIs. c Unrooted distance tree using the overall DNA methylation content subdivides Alb-R26 Met tumours and controls in two distinct clusters. d Volcano plot reporting methylation differences with significance (expressed as -Log 10 FDR) for CpGs in CGIs in Alb-R26 Met tumours versus control (left). Significant differences (methylation difference > 0.
Acknowledgements The nancial assistance of the  Office Chérien des Phosphates (OCP S. A. )  in the Moroccan Kingdom toward this research is hereby acknowledged. This work was performed as part of collaboration between the  INRA (UMR-IATE, Montpellier, France)  and the  Materials Science and Nanoengineering Department (MSN)  of the Mohamed 6 Polytechnic University ( UM6P ) in Morocco. The authors would like to thank  Dr A. Barakat  and  B. M. Ruesgas Ramón  from  INRA-UMR-IATE  for their help to improve this work, and  Dr Mirna Leonor Suárez Quiroz  and  Dr Oscar González Ríos  from the  Instituto Tecnológico de Veracruz (Veracruz, Mexico)  for supplying the original CBS.
THE OBSERVATORY AND THE DATASET The Pierre Auger Observatory, located near Malargüe, Argentina (The Pierre Auger Collaboration 2015c), has an array of surface detectors (SD) that covers an area of 3000 km 2. The array contains 1660 water-Cherenkov detectors, 1600 of which are deployed on a triangular grid with 1500 m spacing with the remainder on a lattice of 750 m covering 23. 5 km 2. The array is overlooked by 27 fluorescence telescopes (FD) that are used to monitor the longitudinal development of the air showers during moonless and clear nights, with a duty cycle of about 13%. The SD has a duty cycle of about 100% so that it provides the vast majority of the events, and it is hence adopted for the present study. The energy of these events is calibrated using hybrid events measured simultaneously by both SD and FD.
The dataset analyzed in this work is the same one as that considered in The Pierre Auger Collaboration (2017a), including events from the SD array with 1500 m separation detected from 2004 January 1 up to 2016 August 31. We retain events with zenith angles up to 80 • and energies in excess of 4 EeV, for which the array is fully efficient over the full zenith angle range considered. 2  The events with zenith angles θ ≤ 60 • , referred to as vertical, have a different reconstruction and calibration from the ones having 60 • < θ ≤ 80 • , referred to as inclined events. The energies of the vertical sample are corrected for atmospheric effects (The Pierre Auger Collaboration 2017b), since, otherwise, systematic modulations of the rates as a function of the hour of the day or of the season, and hence also as a function of right ascension, could be induced.
The data show indications of a growth in the amplitude with increasing energy that is similar to the one obtained in the models. Note that this kind of scenario is also in line with the composition favored by Pierre Auger Observatory data (The Pierre Auger Collaboration 2017c). Regarding the direction of the dipolar modulation, it is important to take into account the effect of the Galactic magnetic field on the trajectories of extragalactic cosmic rays reaching the Earth. 5 The facts that the Galactic magnetic field model is not well known and that the CR composition is still uncertain make it difficult to infer the dipole direction associated to the flux outside the Galaxy from the measured one. As an example, we show in Fig. 7  the change in the direction of an originally dipolar distribution after traversing a particular Galactic magnetic field, modeled in this Comparison of the dipole amplitude as a function of energy with predictions from models  (Harari et al.