dataesr/scientific-paragraphs-categorization huggingface dataset:
) associated with a charge-coupled device (CCD) camera. Images were obtained with a ϫ63 oil immersion objective and processed with MetaMorph software (Universal Imaging). Cytosolic bacteria were considered those stained with the Alexa 546 and Alexa 488 antibody but lacking Alexa 647 antibody labeling. Approximately 100 cells were counted in 3 representative fields to estimate the number of cytosolic L. monocytogenes cells. Evaluation of llsA promoter expression with a GFP and a luciferase reporter system. A transcriptional fusion of the llsA promoter was created by cloning 500 bp upstream of the ATG of the respective gene with the gene encoding GFP-mut2 (30) (generating PllsA-GFP). This construct was cloned into SalI/SmaI-digested pAD vector (generating pAD-PllsA-GFP). Gene synthesis to construct PllsA-GFP was produced by Genecust (Luxembourg).
) associated with a charge-coupled device (CCD) camera. Images were obtained with a ϫ63 oil immersion objective and processed with MetaMorph software (Universal Imaging). For in vivo bioluminescence experiments, 8-week-old female BALB/c mice were infected by intravenous inoculation with 10 4 L. monocytogenes F2365 llsA::lux (BUG3763) cells grown in BHI broth to an optical density (OD) of 1. 0 at 37°C. Bioluminescence imaging was accomplished using an IVIS Spectrum in vivo imaging system (Perkin Elmer) with a 5-min exposure time. Mice were anesthetized with isoflurane. For CFU determinations, whole luminal contents from stomach, small intestine, cecum, and colon, as well as tissues from stomach, small intestine, cecum and colon, liver, and spleen, were obtained, homogenized, and serially diluted and plated on Oxford agar plates (Oxoid).
ACKNOWLEDGMENTS We thank  Esteban Chaves-Olarte  for logistical support. P. C. is an  International Senior Research Scholar  of the  Howard Hughes Medical Institute. The authors declare no conflict of interest. This work was supported by the  Institut Pasteur , the  Institut National de la Santé et de la Recherche Médicale  ( INSERM Unité 604 ), the  Institut National de la Recherche Agronomique  ( INRA Unité Sous Contrat 2020 ),  Université Paris Diderot , grants from  Région Île-de-France , the  Institut Pasteur "Programmes Transversaux de Recherche"  ( PTR521  to J. P. C. ),  Agence Nationale de la Recherché  ( ANR-15-CE15-0017 StopBugEntry  to J. P. C. ),  Fondation Le Roch Les Mousquetaires ,  European Research Council   Advanced grant  ( 670823 BacCellEpi  to P. C. ), and  Région Île-de-France (DIM-MALINF  to J.
For the spring constant calibration, the cantilever was retracted $500 mm away from the substrate and thermal oscillations were measured in air for a few seconds. The resulting frequency curve was then tted using the JPK soware to generate the correct spring constant value. A typical resonance frequency in air was 10 kHz. AFM data were acquired using a high-speed force spectroscopy mode (Quantitative Imaging mode, JPK). In this mode, a complete force curve (approach and retract) was acquired at each pixel of the (128 Â 128 pixels 2 ) images, regardless of the lateral scan size. This minimizes the lateral interactions  83, 84  between tip and NR particles. For all the results presented here, the approach and retraction speeds at every pixel were constant (100 mm s À1 ). The height extension of the AFM cantilever was xed at 1 mm.
This will induce a higher error bar in case of the smallest NR particles with diameter less than 200 nm, since this zone carries more weight than for a bigger NR. In the following, the AFM images presented show the raw data with no ltering, attening or smoothing operation during acquisition unless specied. Mathematical treatment of the AFM data to extract physical parameters (such as adhesion, stiffness, etc. ) was performed by custom Matlab (MathWorks, Natick, USA) programs or by using OriginPro soware (Origin-Lab Corporation, Northampton, USA).
Acknowledgements The authors thank  ANRT  for CIFRE funding ( 2011/1324 ). PS acknowledges the  French Institute of the French Embassy in India for the Researcher Fellowship of the Charpak internship program. This work was partly supported by the program " Investissements d'Avenir du LabEx PALM  ( ANR-10-LABX-0039-PALM ). We express our gratitude to  Dr Andrew Mayne  ( ISMO, Orsay ) for his English revision of the manuscript.
ACKNOWLEDGMENTS We are grateful to  Nathalie Leonhardt ,  Thierry Simonneau  and  Alain Vavasseur  for fruitful discussions, to  Sonia Mohamed  and  Manuel Nieves-Cordones  for help in genotyping, and to  Thanh Danh Nguyen ,  Thi Xuan Vo  and  Binh Anh Thu Nguyen  for help in analysis of raw transpiration rate data.
In addition, we introduce a novel genotype-independent test to detect methylation imbalance between chromosomes. Of the ~2. 2 million CpGs tested for ASM, mQTL, and genotype-independent effects, we identify ~32% as being genetically regulated (ASM or mQTL) and ~14% as being putatively epigenetically regulated. We also show that epigenetically driven effects are strongly enriched in repressed regions and near transcription start sites, whereas the genetically regulated CpGs are enriched in enhancers. Known imprinted regions are enriched among epigenetically regulated loci, but we also observe several novel genomic regions (e. g. , HOX genes) as being epigenetically regulated. Finally, we use our ASM datasets for functional interpretation of disease-associated loci and show the advantage of utilizing naïve T cells for understanding autoimmune diseases.
GIT allows us to detect differences in methylation between the two alleles regardless of the genetics of the underlying chromosome, permitting us for the first time to interrogate phased methylation for putative epigenetically driven effects. We focused on the ~2. 2 M CpGs that were tested using ASM and mQTL analysis and GIT in any of our datasets and identified a total of 1,043,828 CpGs that were genetically or epigenetically regulated (Fig. 2 ). Of these, 69. 7% showed either significant (q < 0. 1) ASM (2. 2%) or mQTL (60. 1%) or both (7. 5%), while the remainder (30. 3%) showed potentially significant (q < 0. 01) allelic imbalanced methylation (GIT) without genetic basis, i. e. , that was epigenetically driven (Fig. 2a ; Additional file 2). We noted 543,863 of the regulated CpGs were significant in the GIT, which identified proximal and distal genetic effects (ASM and mQTL) in addition to putative epigenetic effects.
Allelic and non-allelic patterns of non-CpG methylation Recent studies have shown evidence of non-CpG (or CpH) methylation in multiple human tissues  [15]. However, we recently showed that CpH methylation might partly be driven by potentially "erroneous/nonspecific" methylation from the methylation machinery at neighboring CpGs  [33]. To study this further as well as to characterize allelic and non-allelic effects of CpH methylation events at the genome-wide level we extended our analysis to include CpH sites. We restricted this to sites interrogated in at least 50 individuals from our deepest covered dataset, adipose tissue. First, we confirmed our earlier results that most CpH contexts show complete unmethylation in adipose tissue  [33] ; therefore, we restricted the data set to CpH sites where at least 25 individuals have methylation greater than zero (N = 189,891 CpHs).
While the total number of nominally significant (p < 0. 05) correlations detected was slightly lower in AS analyses  (27,324 versus 38,585  in MCC-Seq, 2927 versus 3274 in WGBS), AS analyses detected a higher rate of strong correlations when compared with NAS correlations for both MCC-Seq (Fig. 4a ) and WGBS (Fig. 4b ) across our datasets (Kolmogorov-Smirnov p value < 2. 2 × 10 -16 for both WGBS and MCC-Seq). We observed significant differences in the magnitude of the detected correlations from AS analysis compared to NAS analysis, where the median NAS significant correlation was R = 0. 23 and R = 0. 54 for WGBS and MCC-Seq, respectively, compared to the median AS significant correlations of R = 0. 58 and R = 0. 83, respectively (chi-squared p value <2. 2 × 10 -16 at |R| = 0. 5 in both cases).
Discussion We report a comprehensive integrative analysis from multiple large-scale human epigenomic datasets, across methylation, transcription, and chromatin modifications. For the first time we used tests that integrate allelic events across populations linked either to the reference allele or purely to recurrent imbalanced methylation states of chromosomes observable only in population NGS data. We have not only generated the largest catalogue of methylation changes in haploid human genomes, but by combining these three levels of genomic data with allelic profiling, we reveal novel relationships between genetic and non-genetic allelic variation. Nongenetic effects are seen in imprinted regions of the human genome but are also strikingly enriched in classes of developmental transcription factors.
The non-genetic variation appears to have less involvement in active allelic chromatin states (28% of functional AS CpG-gene correlations). Our CpG allelic variation catalogue can be utilized to intersect with other population variation datasets to interrogate potential epigenetically variable (e. g. , partial imprinting) regions in the However, our allelic methylation observations are based on terminally differentiated cells and tissues, whereas these effects may be rooted in epigenetic memory of allelic exclusion events not active in the studied cells. The ~350,000 allelic events not driven by detectable genetic or known imprinted events call for further studies in developmental cell lineages to further clarify their potential functional roles. We show that allelic CpG methylation and gene expression analysis allows for more sensitive detection of functional epigenetic effects and has the ability to reveal correlations not seen in non-allelic methylation to gene expression analyses.
4. This trend is replicated when we look in the WGBS dataset, where there are distinctly far fewer significantly correlated sites and the correlations are higher when passing significance due to the smaller numbers of samples involved. However, allelic analysis comes at the price of a reduced read depth for the individual alleles, thereby increasing the error rates in the allelic methylation and expression rate estimation. We observe that the allelic analysis of the lower coverage WGBS data shows fewer significant highly correlated sites, highlighting the impact of lower read depth coverage when profiling all CpGs genome-wide. Conversely, when examining the MCC-Seq data-where the read coverage is focused on a much smaller subset of the genome and we have a larger number of samples-we are able to identify a far greater number of significant, highly correlated sites.
Developing deeper and larger WGBS datasets will be useful to further investigate some of our observations, such as primary concentration of epigenetic variation outside canonical regulatory elements. Further meta-analyses, incorporating additional eQTL/ASE, mQTL/ASM and hQTL/AS-ChIP sequencing datasets, would allow us to independently confirm the same relationships in similar cells, determine whether the observed relationships are maintained in more distantly related cell types, as well as further differentiate cell type-specific patterns. Corresponding sequencing datasets in other species would allow us to investigate the evolutionary conservation of these multi-level effects. The large number of relationships detected uniquely by allelic or non-allelic analysis alone indicates that these analyses can identify fundamentally different relationships between the epigenome and the transcriptome.
At present, we are using non-allelically resolved histone mark datasets to train the ChromHMM model, and we show different behavior of the epigenome and transcriptome in different states. We could more tightly integrate allelic data into the genomic state analysis by adapting the allelic information and incorporate allelespecific epigenomic information in the ChromHMM inputs so that the states themselves take into account allelic information, while integrating across multiple epigenome and transcriptome layers, and see how this affects the model of the genome that is generated. As well, our case study demonstrates how the allelic relationships between methylation and histone marks are altered in the different forms of the disease, further emphasizing the future utility of allelic resolution in disentangling the functional outcome.
Finally, more sequencing in distinct tissues and cell types would allow us to confirm the preliminary cell-specific methylation results we observe as well as allow us to investigate the interaction of cellspecific effects across the epigenetic layers. In summary, we have generated the largest combined human allele-specific methylation, chromatin modification, and transcription dataset to date (Additional files 2, 3, 4, 6, and 7). We show allelic methylation enables discovery of novel links to transcription, whereas total methylation shows weaker correlations in layers of the epigenome. We further demonstrate that harnessing the power provided by allelic resolution across methylation, transcription, and chromatin modification is key to interpreting population variation in our epigenomes and its alterations in disease.
[21]  and below. We obtained 599 whole blood samples from 358 individuals in the framework of the EGEA (https://egeanet. vjf. inserm. fr/), a French longitudinal cohort study based on an initial group of asthma cases and their first-degree relatives and controls (first survey EGEA1). The protocol and descriptive characteristics have been described previously  [38] [39] [40] [41] [42]. Blood samples from the Uppsala Bioresource, Uppsala, Sweden were drawn from 28 normal healthy Swedish individuals and purified to extract T cells (CD14-CD4+) and monocytes (CD14+). Nine skeletal muscle human samples were also obtained. Sequencing data are available through the McGill Epigenomics Mapping Portal (http://epigenomesportal. ca). These samples and the sequencing datasets derived from them are described globally in Fig.
WGBS and MCC-Seq data processing In-house generated methylome libraries were aligned using BWA 0. 6. 1  [43]  after converting all the reads in bisulfite mode to the human hg19/GRCh37 genome reference. Both reads in a pair were trimmed of any low-quality sequence at their 3′ ends (with Phred scale score ≥30). Post-process read mappings were made as previously described  [44] , including clipping 3′ ends of overlapping read pairs in both forward and reverse strand mappings, filtering duplicate, low-mapping quality reads, read pairs not mapped at the expected distance based on the library insert size, as well as reads with more than 2% mismatches. Methylation calls of individual CpGs were extracted using Samtools in mpileup mode. CpGs overlapping SNPs from dbSNPs (137) and CpGs located within ENCODE DAC blacklisted regions or Duke excluded regions  [35]  were discarded.
Genotyping and phased genome The T-cell, mono cell (in Temporal Change project), and muscle tissue samples were genotyped using the Illumina HumanOmni2. 5-8 (Omni2. 5 M) or HumanOmni5-4 (Omni5M) BeadChip according to protocols recommended by Illumina. Genotypes of BluePrint samples were obtained from whole genome sequencing  [34]. Genotypes of samples from other tissues which were not genotyped using BeadChip were inferred directly from the WGBS data using BisSNP  [45]. Rare variants and singletons were confirmed using targeted sequencing data of coding and non-coding regulatory regions. We used 1000 Genomes project data as a reference set (release 1000G Phase I v3, updated 26 Aug 2012) for the imputation of genotypes (either genotyped from Illumina BeadChip or inferred from BisSNP). Untyped/un-inferred markers were inferred using algorithms implemented in IMPUTE2  [46].
ChromHMM genomic states An eight-state ChromHMM  [25]  model trained using default parameters on a panel of 352 T-cell, monocyte, and muscle histone (H3K27ac, H3K27me3, H3K36me3, H3K4me1, H3K4me3, and H3K9me3) ChIP-Seq datasets was used to assign states to each CpG position. State identities were assigned based on the ChromHMM report (Additional file 9).
We applied the G-SCI test to all available histone mark ChIP-Seq data with SNPs detected from imputed genotypes of each corresponding individual. Peak heights of each region were normalized by quantile-quantile normalization and were finally log-transformed. We also filtered out SNPs if the total number of non-reference reads across all ChIP-Seq data was less than five or none of the ChIPseq datasets had three or more non-reference reads. After obtaining the p value of each G-SCI test, the Benjamini and Hochberg (BH) approach for FDR  [54]  was used to correct for multiple testing and the adjusted p value of 0. 01 was chosen as the significant cutoff.
Positional allelic methylation in enhancer regions Focusing on the deposition of the enhancer histone marks H3K27ac and H3K4me1, we linked to allelic gene expression, histone deposition, and methylation back to the genomic states and looked at the positional pattern of allelic methylation. We identified ChromHMM state regions where we observed a correlation (|R| > 0. 4, p < 0. 05) between gene expression and the histone mark deposition in nTC cells (100-bp windows from the center of H3K27ac peaks, 500-bp windows for H3K4me1 peaks) overlapping the state region, considering ChromHMM state regions up to 10 kb in size. For each of these putative enhancer regions, we then phased the allelic methylation for all CpGs in the region based on the high gene expression and the low gene expression allele-for genes with multiple transcripts, we selected the transcript isoform with the highest number of exonic reads, breaking ties with the largest overall gene reads.
Disease GWAS enrichment of ASM event We used GWAS variants from eight diseases (celiac disease [27], Crohn's disease  [28] , inflammatory bowel disease  [28] , ulcerative colitis  [28] , multiple sclerosis  [29] , rheumatoid arthritis  [30] , type 1 diabetes  [31] , and type 2 diabetes  [32] ) for which we retrieved publicly available genome-wide summary statistics  [34]. We tested genomewide enrichment for independent variants (LD r 2 < 0. 1) nominally associated with disease (p value ≤10 -5 ) among significant ASM-SNPs (q < 0. 1) or with a SNP in high LD (r 2 > 0. 8). The background was defined as all independent GWAS SNPs tested overlapping any SNPs' ASM that was tested. The significance of the enrichment was assessed using Fisher's exact test. LD information was calculated for each SNP ±250 kb using phased data from whole genome sequencing of the whole blood samples.
Acknowledgements We would like to acknowledge the  EGEA Cooperative group. Coordination:  V. Siroux  (epidemiology, PI since 2013);  F. Demenais  (genetics);  I. Pin  (clinical aspects);  R. Nadif  (biology);  F. Kauffmann  ( PI 1992-2012 ). Respiratory epidemiology: Inserm ex-U 700, Paris:  M. Korobaeff  ( Egea1 ),  F. Neukirch  ( Egea1 );  Inserm ex-U 707 , Paris:  I. Annesi-Maesano  ( Egea1-2 );  Inserm ex-U 1018 , Villejuif:  F. Kauffmann ,  M. P. Oryszczyn  ( Egea1-2 );  Inserm U 1168, Villejuif :  N. Le Moual ,  R. Nadif ,  R. Varraso ;  Inserm U 1209 Grenoble: V. Siroux. Genetics: Inserm ex-U 393 , Paris:  J. Feingold ;  Inserm U 946 , Paris:  E. Bouzigon ,  F. Demenais ,  M. H. Dizier ; CNG, Evry:  I. Gut  ( now CNAG, Barcelona, Spain ),  M. Lathrop  (now  McGill University, Montreal, Canada ).
P. and M. L. The work was also supported in part by a grant to M. L. from  Génome Québec, le Ministère de l'Enseignement supérieur, de la Recherche, de la Science et de la Technologie Québec (MESRST) , and  McGill University  as well as by the  RESERt-AID grant  ( ANR-15-EPIG-0004-05 ) awarded to T. P and F. D. and by a grant from the  French national clinical research program (PHRC)  awarded to I. P. which also covered salary support to D. A. and X. S. E. G. is Tier 2  Canada Research Chair in Disease Genomics and Epigenomics , T. P. is Tier 2  Canada Research Chair in Human Genomics , and M. -C. V. is Tier 1 Canada Research Chair in Genomics Applied to Nutrition and Health. W. C. and A. M. are supported by a fellowship from the  Fonds de Recherche du Quebec  ( FRSQ-32203  and  FRSQ-27644 ).
Availability of data and materials The datasets generated and/or analyzed in this study are available as previously described  [34, [36] [37] [38] [39] [40] [41] [42]  (see also Sample collections). Raw data from the EMC studies are available through EGA under the study "McGill Epigenomics Mapping Centre" (EGA: EGAS00001000995; https://www. ebi. ac. uk/ega/studies/ EGAS00001000995). The complete data set from the three large-scale tests (ASM, mQTL, and GIT) can also be visualized in the UCSC genome browser using the Track Hub feature typing the URL http://hubs. hpc. mcgill. ca/~elin/FunctionEpigenome. Hub. txt. A UCSC browser track for the GIT, mQTL, and ASM results is available at: https://genome. ucsc. edu/cgi-bin/hgTracks?hgS_doOtherUser= submit&hgS_otherUserName=warrenac&hgS_otherUserSessionName= GIT%2DmQTL%2DASM.
Cell cycle analysis Neuro-2a cells were transfected as above with 1 g of pRK5-CD2 together with 4 g of pRK5-mZac1 or pRK5-CAT as a control and 5 g of carrier plasmid. We selected CAT as a control for the cell cycle experiments because, among the three genes that we tested, i. e. CAT, GFP and ␤-galactosidase, CAT was the most neutral with respect to cell proliferation compared to untransfected Neuro-2a cells (data not shown). Thirty hours later, cells were prepared and stained with 0. 5 g FITC-conjugated anti-CD2 antibody and 50 g/ml propidium iodide as previously described  (7). Propidium iodide and FITC fluorescence intensities were determined using a FACScan flow cytometer (Becton-Dickinson) and the cell cycle distribution of the 10% most CD2-positive cells was analyzed using Modfit software (Verity Software House).
The tags were ligated using Illumina's GEX Adapter 2, amplified by PCR (15 cycles), and purified on a polyacrylamide gel. Libraries were validated using a 2100 Bioanalyzer (Agilent), denatured using 0. 1 M NaOH, diluted to 8 pM, and sequenced (single reads 36 nt, Illumina Genome Analyzer) at MGX facility following the manufacturer's instructions. Image analysis and base calling were performed using the Illumina pipeline version 1. 3. A tag library was made from a UCSC refFlat table of transcripts (refFlat. txt. gz; 15 January 2012) using an inhouse Perl script available upon request. The 16-nucleotide sequences 3 of all DpnII sites in every transcript were extracted. Unique tags that matched only one transcriptomic location were kept and annotated as canonic when the corresponding DpnII site was the last 3 site on the cDNA, or transcriptomic when the corresponding DpnII site was not the last 3 site.
GeneIDs were retrieved from the NCBI (gene2refseq. gz; 19 January 2012). Reads passing Illumina purity filters were kept for further analysis. After trimming the adapter sequences, the 16ntand 17nt-trimmed reads were aligned to the DpnII-tag library without mismatch; counts for canonic and transcriptomic tags corresponding to the same GeneID were pooled using an in-house Perl script. Genes whose sum of tags in the four libraries was <10 were filtered out. Raw counts were normalized using the TMM (Trimmed Mean of M values) method  (19)  as implemented in the edgeR package. edgeR was used to identify differentially expressed genes with a generalized linear model likelihood ratio test using a paired design and a FDR set at 0. 05  (20). DGE data from this study have been submitted to the NCBI GEO database under accession number GSE75942.