dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, we observed that the basal density of repressive marks (H3K27me3) at TSS regions of genes upregulated by pro-inflammatory cytokines was also higher than the H3K27me3 at unaffected genes (Figure  1(c) ). Consistently, the density of permissive marks (H3K7ac and DNase sensitivity sites) was lower in the TSS regions of upregulated genes (Figure  1(c) ). As expected from DNA methylation data, these differences were not observed for the genes upregulated by IGF, in MCF7 cells (Figure  1(d) ), indicating that the hypermethylation of genes responding to pro-inflammatory cytokines was associated with the presence of repressive epigenetic marks. TSS regions of genes upregulated upon IL6, IL1b, and TNFa treatment in MCF7 cells are hypermethylated: Infinium HumanMethylation450K BeadChip data analysis In order to compare our findings in MCF7 cells with tumour tissues from TCGA databank, we also investigated the methylation levels of these regions using DNA methylation data for MCF7 cells obtained through bisulphite modification and analysis on Infinium HumanMethylation450K BeadChip (Supplementary Table  1 ).
Genes upregulated by IL6, IL1b, and TNFa are hypermethylated at shore regions, in human breast tumour samples Due to the discriminatory capacity of DNA methylation at pro-inflammatory target shores regions shown above, and owing to the close association between shore DNA methylation and gene expression level  [26, 27] , we focused on the shore regions identified through MCF7 cell treatments in the analysis of human breast cancers samples (TCGA databank). A large proportion of the genes identified from differential expression analyses of MCF7 cells treated with IL6, IL1b, and TNFa were retained, since more than 60% of the genes harboured a shore at their TSS regions (63% and 70% of the upregulated genes and unaffected genes, respectively). For each gene, we determined the mean methylation level of the CpG spanning the shore regions in order to avoid an overrepresentation of some genes, since the number of CpG probes per shore region can differ between genes, on the Illumina HumanMethylation450K BeadChip.
Discussion Thus, we reveal that the expression of genes identified following the treatment of MCF7 cells with pro-inflammatory cytokines IL6, IL1b, and TNFa is associated with the expression of these cytokines a. Tumours (TCGA databank, N = 775) were classified in quintiles according to the mean DNA methylation level for each gene of the FC2 group; Pearson correlation coefficients (rho) between the normalized expression of each gene and the stromal-score plotted against the adjusted p-value; blue points, the most methylated tumours (fifth quintile); yellow points, the least methylated tumours (first quintile); vertical dashed line, rho = 0. 2; horizontal dashed line, q-value = 0. 05. b. Slope of the correlation shown in a; for each gene, a linear model was used; red bars, fifth quintile; blue bars, first quintile.
The involvement of the pro-inflammatory cytokines IL1, Il6b, and TNFa in tumour progression, drug resistance, and metastasis has been described in many cancer types  [32]. These molecules modulate gene expression of cancer cells and are produced by both the tumour cells and stromal cells, in many cancers. The description of the genes responding to the cytokines is, therefore, of interest, since their expression level seems to be directly related to the pathophysiology of human cancers and may help in the understanding of the mechanisms of these cytokines in the tumorigenic process  [33]. Breast tumours are heavily infiltrated by nontumour cells  [28, 34] , and the breast cancer cell line, MCF7, has been extensively studied for their response to pro-inflammatory cytokines  [32]. Our study took advantage of the extensive publicly available data both on MCF7 cells and human databanks (TCGA) to identify genes responding to these cytokines, and analyse their expression and epigenetic modifications.
WGBS and Illumina's BeadArray assays indicated that genes upregulated by the pro-inflammatory cytokines harboured a high level of DNA methylation at their 5ʹend when compared with the nonresponding genes or the bulk of genes, in MCF7 cells. This hypermethylation was particularly observed at the shore sequences (sequences flanking the CGi), suggesting an epigenetic regulation of the responding-genes. Aberrant hypermethylation of CGi associated with gene silencing has been frequently observed in many cancers  [35] [36] [37] ; however, DNA methylation alterations (hyper/hypomethylation) of shore sequences are also frequently observed in cancers and closely associated with the level of gene expression  [26, 27]. Therefore, we focused on shore sequences for the analysis of human breast tumour samples (TCGA databank), this strategy offering the advantage of comparing genes from a common feature basis.
For each FC2 gene, the correlation between methylation levels and stromal cell content on gene expression was investigated by assigning samples either to the highest or lowest '20% DNA methylation level'. Thus, we observed that the transcriptional response of the FC2 genes to the stromal cell content of the tumours is associated with their methylation levels at shore sequences, suggesting that the repressive effect of the DNA methylation is counteracted, at least partially, by the presence of stromal cells. In line with this hypothesis, the significant negative correlation between gene expression and DNA methylation level was observed to a lesser extent in tumours with a high stromal-score (27 genes) than those with a lower stromal-score  (49 genes). A large amount of data about the potential relationship between DNA methylation and gene expression has been accumulated over the past 30 years.
Public data Transcriptomic data for MCF7 and MCF10A cell lines were collected from GEO DataSets (Supplementary Table  S1 ), MCF7 cells were treated for 4 days with IL6, 24 h with IGF, 3 h with TNFa, and 45 min with IL1b. MCF10A cells were treated for 3 days with TGFb. HepG2 cells were treated for 3 days with TGF. Histone marks and DNase hypersensitive sites for MCF7 were where downloaded from ENCODE at UCSC (human d a t a , h t t p : / / h g d o w n l o a d. c s e. u c s c. e d u / goldenPath/hg19/encodeDCC/). For the MCF7 cell line DNA methylation data, Infinium HumanMethylation450K β-values were collected from GEO DataSets (https://www. ncbi. nlm. nih. gov/gds/?term=GSE78875[Accession]). Wholegenome bisulphite sequencing data were from GEO Datasets, GSE54693 and GSM2308631 for MCF7 and HepG2 cell lines, respectively.
Transcriptomic and DNA methylation data for human breast cancer were collected from TCGA (https://cancergenome. nih. gov/) using the TCGA2STAT package (http://www. liuzlab. org/TCGA2STAT/) as rpkm for RNAseq data and β-values for DNA methylation data. Indexes for breast cancer tumour purity, stromal-score, immune-score, and estimate-score were collected from the ESTIMATE website (http:// bioinformatics. mdanderson. org/estimate/). The list of human TSS, annotated from hg19 version of human genome, was collected from UCSC (http://genome. ucsc. edu/cgi-bin/hgTables).
Data analysis Differential expression was determined from RNAseq using Cuffdiff  [24]  and GEO2R (http://www-ncbinlm-nih-gov. gate2. inist. fr/geo/geo2r/) for array data GSE54329, GSE67295, GSE67295, GSE7561, and GSE28569. Data from GSE127828 were analysed using R/Bioconductor package lumi as previously described  [56]. Using the RNAseq data obtained from MCF7 cell line (GSE67295), only genes with at least 0. 5 rpkm were kept for subsequent analysis. For upregulated genes, fold change (FC) ≥ 2, an FDR < 0. 05 was used as a threshold, unaffected genes were defined as genes exhibiting an FC > 0. 9 and < 1. 1. Heatmaps were constructed using either EnrichedHeatmap (R/Bioconductor package) for DNA methylation or Deep Tools package (https:// deeptools. readthedocs. io) for histone modifications and DNase hypersensitivity sites.
K-means clustering was performed with Cluster 3 (http://bonsai. hgc. jp/~mdehoon/software/clus ter/software. htm) and Java Tree view (http://jtree view. sourceforge. net) packages using the Euclidean distance to the mean option. For correlative analyses, RNA expression in rpkm was normalized to log2(1 + rpkm), and DNA methylation in β values (methylated CpG/(methylated CpG + unmethylated CpG) to M values: log2(β/1-β). Motif identification surrounding TSS regions of FC2 genes. Extended TSS regions (TSS ± 2kb) were scanned using the RSATmatrix scan module (https://rsat01. biologie. ens. fr/rsat/) for potential transcription factor binding motifs using the DNA sequences surrounding the TSS (TSS ±1 kb) of FC2 genes and DNA sequences surrounding the TSS (TSS ±1 kb) of FC1 genes as background. Statistical analyses were performed using Corrplot, ggplot2, dplyr R packages and GraphPad Prism software.
Data Acquisition Data from N = 30 healthy subjects (14 males) with age between 22 and 35 were used in this study. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The acquisitions were conducted on the Connectome Skyra, which is a customized Skyra platform with 100 mT/m gradients for diffusion encoding and 42 mT/m gradients for imaging. High resolution T1 anatomical images and functional images were used in this study. High-resolution anatomical MRI was acquired using a T1weighted 3D MPRAGE sequence with the following parameters: TR= 2,400 ms; TE = 2.
ACKNOWLEDGMENTS SR would like to acknowledge Ikerbasque (The  Basque Foundation for Science)  and moreover, this research is supported by the  Basque Government  through the  BERC 2018-2021 program  and by the  Spanish State Research Agency  through  BCAM Severo Ochoa excellence accreditation   SEV-2017-0718  and through project  RTI2018-093860-B-C21  funded by ( AEI/FEDER, UE)  and acronym MathNEURO. JC acknowledges financial support from  Ikerbasque ,  Ministerio Economia ,  Industria y Competitividad (Spain)  and  FEDER  (grant  DPI2016-79874-R ) and the  Department of Economical Development and Infrastructure of the Basque Country  ( Elkartek Program ,  KK-2018/00032 ). Finally, PG acknowledges BCAM's hospitality during a visiting fellowship in fall 2017.
Today most image-analysis software (such as ImageJ  [4, 5] , MetaMorph and CellProfiler  [6] ) or toolboxes (like the EBImage package in R  [7]  or the scikit-image from SciPy library  [8, 9]  in Python) can "automatically" extract features on a cell-bycell basis. However, one important exception from this general approach is the quantification of colocalization, i. e. the presence of two fluorescent signals in the same subcellular compartment. Most software packages still assess colocalization image by image in a way that is not compatible with the analysis of cell-based highthroughput assays. Standard colocalization methods have been criticized for their lack of reliability, mainly because single cells in the same population can exhibit opposing phenotypes  [10]  and even single-cell analysis can become challenging.
This R-based application mainly uses the EBImage package for image processing, and has been endowed with a user-friendly Graphical User Interface (GUI) based on the shiny package  [13]. ColocalizR is suitable for high-throughput analyses, has been optimized for CPU multithreading usage, and operates on multiple platforms; it can be easily installed and operated by any user, even without any specialized skills in image-analysis or programming. ColocalizR was initially developed to assess the ability of certain lipophilic molecules, such as oleic acid, to induce the redistribution of red fluorescent protein (RFP) fused to MAP1LC3B (LC3) to the green fluorescent protein (GFP)-GALT1 marked Golgi apparatus and subsequent morphological changes  [14, 15]. We successfully applied ColocalizR to data obtained from a pharmacological drug screen and identified agents that induce a phenotype similar to the one caused by oleate.
Image storage and compression Images acquired on an ImageXpress MicroXL automated bioimager were stored as uncompressed MetaMorph 7. 5 TIFF files with a pixel depth of 16 bits and a resolution of 5 MegaPixels (2160x2160 pixel matrix). Pixel size was 350 nm along both x and y axes. Images were also compressed using the lossless Lempel-Ziv-Welch (LZW) algorithm and tested without any impact on the results. Images were annotated following the nomenclature described in the ColocalizR user manual (available at https://github. com/kroemerlab/ColocalizR). Images acquired on a TCS SPE microscope in the LIF file format were converted into 16-bits TIFF files using the Bio-Format java library (https://www. openmicroscopy. org/bio-formats/). Of note, for the annotation of images not acquired by means of the Molecular Devices MetaMorph® software, applications such as Bulk Rename Utility (www.
Data processing and analysis ColocalizR allows to export results in different file formats, namely, commaseparated values (csv), flow cytometry standard (fcs) or R data (rda). The first two options (csv and fcs) allowed for transferring data to other analytic software platforms. FlowJo® was used as a proof-of-concept tool for plotting bi-parametric graphs from exported fcs files. Routinely, we chose the third option (rda) to process data using R. Both ColocalizR analysis and further data processing were performed on a high-end computer equipped with an Intel Xeon E5-2620 v2 (16 threads) and 48 GB DDR3 RAM, running under Ubuntu 16. 04.
Combining pixel-based and object-based approaches improves assessment of colocalization. To develop an approach combining pixel-based and object-based colocalization methods for high-throughput analysis, we first focused on PCC, the gold-standard for colocalization assessment. We obtained microphotographs from untreated cells For all methods but the cell-based one, we evaluated the average PCC in 4 viewfields; in the latter case, we calculated the mean of the modes from each of the 4 PCC distributions. ColocalizR yielded a better separation of positive and negative controls, especially when using the cell-based method, as compared to traditional analysis methods (Figure  S1 ). Using the same positive and negative controls, we then compared the speed and quality (characterized using relative increase calculation) of ColocalizR 's optimized cell-based and image-based methods with traditional software packages (CellProfiler, Coloc2  [34]  & JACoP  [30]  ImageJ plugins).
Acknowledgments: GK is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR) -Projets blancs ;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC) ;  Cancéropôle Ile-de-France ;  Chancelerie des universités de Paris  ( Legs Poix ),  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior; the  European Commission (ArtForce) ;  European Research Area Network on Cardiovascular Diseases  ( ERA-CVD, MINOTAUR ); the  European Research Council (ERC) ;  Fondation Carrefour; Institut National du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ;  LeDucq Foundation ; the  LabEx Immuno-Oncology ; the  RHU Torino Lumière ; the  Seerave Foundation ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and the  SIRIC Cancer Research and Personalized Medicine (CARPEM) ;.
8) giving FASTQ formatted reads. Using the Galaxy platform, high quality one-end reads for the three replicates (siRNA SETDB1, MPP8 and control) were mapped onto mouse genome (mm10) using Bowtie2 v2. 3. 4. 1  [22]  with default alignment parameters (-I 0 -X 500). Mus musculus GRCm38. 9 GTF file was downloaded from Ensembl (https://www. ensembl. org) and the exon annotation lines were extracted from the file to use as GTF annotation file. FeatureCounts v1. 6. 0. 2  [23]  was used with this GTF file to treat the BAM alignment files and calculate the gene expression values in transcripts per million (TPM). Differentially expressed genes were identified using he DESeq2 Galaxy wrapper v2. 11. 40. 6  [24]. mm10 repeat elements were downloaded from the RepeatMasker site (www. repeatmasker. org) and repeats annotated as "Low complexity" or "Simple repeat" were filtered out.
RepEnrich  [25]  was used to annotate and count the reads aligned to repeats. The ARTbio Galaxy wrapper (edgeR-repenrich v1. 5. 3) of EdgeR  [26]  was used to identify differentially expressed repeats and repeat classes. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6. 8 (https://david. ncifcrf. gov/) [27, 28]  was employed for Gene Ontology (GO) analysis.
Additional Statistical Analyses The collected data was analyzed using Statistical Package for Social Sciences (SPSS Version 25, Chicago IL). Normality of the data was assessed using the Shapiro-Wilk test. Results were presented as mean and standard deviation (SD) or median and interquartile range (IQR). Differences between groups were compared using t-test or Mann Whitney's unpaired test based on normality test. Statistical significance was assessed at the α < 0. 05 levels for RT-qPCR.
MPP8 and SETDB1 Co-Bind Repetitive Elements To search for MPP8/SETDB1 common regulated genomic regions, we performed endogenous MPP8 ChIP-seq in mESCs (from the 129S1 mouse strain) and analyzed an already published SETDB1 ChIP-seq in mESCs (GSE18371;  [4] ). Our data showed that the distribution of MPP8 binding sites on the genome is almost 60% on intergenic regions (including repeated elements), 37% on introns and 4% on exons (Figure  2A ). Since MPP8 co-purified with the H3K9 KMT SETDB1, we next studied the extent of MPP8 co-localization with SETDB1. We found MPP8 co-localization on 6. 5% binding sites with SETDB1 (Figure  2B ). We found that 1916 genes are bound by MPP8. Interestingly, we observed co-localization of MPP8 and SETDB1 genome-wide on 1329 genes, which are mainly involved in nucleosome assembly (Supplementary Figure  S2  and Supplementary Table  S2 ).
Concerning transposable elements analyses, MPP8 peaks were proportionally located in LTR and LINE and SINE repeats, which is similar to the binding sites reported for SETDB1 (Figure  2C ). A total of 9499 transposable elements enriched in the binding sites of MPP8 were found, among which around 20% (1843/9499) are also bound by SETDB1 (Figure  2D  and Supplementary Table  S3 ). Interestingly, MPP8 and SETDB1 co-bind more LINE and ERVK elements but also (peri-)centromeric satellite DNA repeats including GSAT_MM, SYNREP (Figure  2D ). Altogether, the ChIP-seq data suggest that MPP8 cooperate with SETDB1 in transposable elements and satellite DNA repeats silencing in mESCs.
Regulation of Gene Expression by MPP8 and SETDB1 in mESCs In light of the co-immunoprecipitation and ChIP-seq results described above, showing a physical interaction between MPP8 and SETDB1 and genomic co-binding in mESCs, we further investigated the possible functional interplay between MPP8 and SETDB1. We asked whether MPP8 and SETDB1 co-regulate gene expression in mESCs. To further confirm the functional overlap between MPP8 and SETDB1, we performed RNA-seq analyses in HM1 ESCs after MPP8 KD or SETDB1 knockdown (KD). RNA-seq data were further analyzed by differential expression pattern of genes and transposable elements using the scrambled siRNA condition as a control on the whole dataset. siRNA-mediated acute KD of MPP8 or SETDB1 in HM1 ESCs showed a 70% decrease in mRNA level (not shown) of the specific targets and about the same decrease at the protein level (Figure  3A ).
SETDB1 and MPP8 Cooperates to Silence Satellite DNA Repeats Concerning transposable elements, the differential expression compared to scrambled siRNA-transfected HM1 cells showed that the most de-repressed ones upon MPP8 KD are GSAT and SYNREP satellites, L1, X21 and Lx3 LINE repeats (Figure  4A ). Thus, our data showed for the first time that MPP8 is required in the silencing of satellite repeats in mESCs. Interestingly, the majority of these transposable elements were also upregulated upon SETDB1 KD (Figure  4A  and Supplementary Table  S5 ). Of note, in accordance to previous reports  [16, 30] , our results also show many families of ERV and LINE sequences upregulated after SETDB1 KD (Supplementary Figure  S4 ). Next, we checked the overlap with the SETDB1 KD results. Interestingly, we found that the satellite repeats GSAT and SYNREP from the (peri)-centromeres are also de-repressed upon SETDB1 KD (Figure  4B ).
Of note, the consensus sequence of the primers we used in RT-qPCR could amplify many families of satellite and LINE repeats, not only the specific ones that are upregulated in the RNA-seq. Interestingly, upregulation of major and minor satellites as well as LINE elements is significantly higher upon concomitant MPP8 and SETDB1 loss-of-function (Figure  4C ), suggesting their synergistic roles. Finally, the analysis of the MPP8 and SETDB1 ChIP-seq-associated reads confirmed a good recruitment on the GSAT and SYNREP repeats (Figure  4D ). Altogether, these data suggest that SETDB1 and MPP8 cooperate in satellite DNA repeats silencing in mESCs.
In general, H3K9 methylation and its effectors play key roles in the non-coding genome and transposable elements silencing in pluripotent ESCs compared to more committed cells in which an additional layer of DNA methylation is established  [10, 31, 32]. Our data showed that in addition to co-regulating coding genes, the association of the H3K9 KMT SETDB1 with the H3K9 methylation reader MPP8 extend to more genomic elements, including major and minor satellite DNA as well as some LINE elements. The MPP8 gene is present only in vertebrates, reminiscent of some other proteins that are linked to the H3K9 methylation epigenetic mark such as TRIM28/KAP1 and ATF7IP (the SETDB1 cofactor), while some others are not that conserved, such as Heterochromatin Proteins HP1. MPP8 has been proposed to be involved in "position-effect variegation" (PEV)-like in the human and mouse genomes in the HUSH complex  [33].
Acknowledgments: We warmly thank  Dr. Claire Francastel ,  Dr. Guillaume Velasco  and  Louis Chauvière  for technical and critical help. We thank  Pr. Yoishi Shinkai  for generous sharing of biological material. We thank  Christophe Antoniewski  and  Naira Naouar  from  ARTbio Bioinformatics Analysis Facility (Sorbonne Université ,  CNRS FR3631 ,  Institut de Biologie Paris Seine)  for technical help with the bio-informatic analyses.
Experiment 1, TUS modulation of SMA connectivity Transcranial focused ultrasound stimulation of SMA induced spatially specific changes to the connectivity profile of SMA. At rest, in the control state, SMA's activity is coupled with activity throughout sensorimotor regions in frontal and parietal cortex, inferior parietal, prefrontal, and parts of cingulate cortex (Figure  3a ). Many of these regions are anatomically connected with SMA (Dum and In experiment 1 the 40 s TUS protocol was delivered over the supplementary motor area (SMA), while in experiments 2 and 3, the protocol was targeted at frontal polar cortex (FPC). In all sessions, three consecutive runs of resting fMRI (26 min per run) were acquired starting ~40 min after the TUS or control protocol. Bottom left panel: Data from the resting fMRI runs were pre-processed following a standardized pipeline to address artefacts, improve image quality and signal-to-noise ratio, and prepare for connectivity analyses.