dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This comparison would not have been possible without the financial support of the European program  FP-7 ICE2SEA , grant no. 226375 , and the financial and logistical support of the  French Polar Institute IPEV  (program CALVA-1013). Additional support by  INSU  through the  LEFE/CLAPA  project and OSUG through the CENACLAM/GLACIOCLIM observatory is also acknowledged. The MAR simulations were run on CNRS/IDRIS and  Université Joseph Fourier CIMENT computers. We would like to thank all the on-site personnel in  Dumont d'Urville and Cap Prud'homme  for providing precious help in the field, and the two anonymous reviewers for their constructive remarks that helped to improve the manuscript considerably. Edited by:  P. Marsh.
BS-seq Procedures Isolated DNA was truncated into 100-300 bp fragments by sonication followed by DNA-end repair, 3 ′ -dA overhang addition and ligation of methylated sequencing adapters. Samples underwent bisulfite treatment with the ZYMO EZ DNA Methylation-Gold kit. Desalted, size-selected, PCR amplified fragments were size-selected again. Quality controlled libraries were selected for Illumina sequencing by the Beijing Genomics Institute (BGI). Data filtering included removing adaptor sequences, contamination and low-quality reads from raw reads. These reads were filtered by custom BGI scripts, as described in  Li et al. (2010). There were two types of low-quality reads removed from the data: (1) when the ratio of N in whole read was over 10%; (2) when the ratio of base whose quality was <20 was over 10%.
Data Annotation In order to investigate qualitative differences between samples, data were subset for regions corresponding to genomic features of interest. Annotations from Ensembl (version 72) were retrieved using the interface biomaRt, which accesses ensembl via BioMart, a federated system of databases. Annotation was performed using custom scripts and functions built in the IRanges, GenomicFeatures and ChIPpeakAnno. The UCSC CpG island track was imported into the R environment using the package rtracklayer. CpG island shores were defined as those regions up to 2000 bp upstream and downstream of CpG islands. The annotation for the human genome repeat regions was downloaded from the repeat masker website hosted at the Institute for Systems Biology (http://www. repeatmasker. org/cgibin/AnnotationRequest).
Gene Expression Microarrays RNA quality was monitored using Agilent RNA Pico LabChips (Agilent Technologies, Palo Alto, CA). One hundred nanogram of RNA from HEK-CT or HEK-BAHD1 cells, was used as templates for the synthesis of hybridization probes for Affymetrix GeneChip Microarrays (Genechip HuGene 1. 0 ST). Hybridization was carried out with three biological replicates according to the expression analysis technical manual with wash and stain kit (Affymetrix). Gene-level expression values were derived from the CEL file probe-level hybridization intensities using the model-based Robust Multichip Average algorithm (RMA;  Bolstad et al. , 2003). RMA performs normalization, background correction and data summarization. An analysis was performed using the LPE test  (Jain et al. , 2003)  and a p-value threshold of p < 0.
BAHD1 Overexpression Differentially Changes the DNA Methylation Landscape of Autosomes and chrX In order to obtain high-resolution DNA methylation profiling, we performed whole-genome BS-seq (MethylC-seq/BS-seq  Lister et al. , 2009)  of two HEK-BAHD1 replicates and of the HEK-CT control DNA, generating complete reference methylomes of these cell lines at single base resolution (see Materials and Methods and Supplementary Figure  S1A  for detailed analytical procedures). We verified that our BS-seq results were in agreement with partial methylomes previously released for HEK293 cells in ENCODE RRBS and HEK293 chromosome 21 datasets  (Zhang et al. , 2009; ENCODE Project Consortium, 2012 ; Supplementary Figure  S2 ). Comparison of these methylomes highlighted a significant gain of methylation (on average ∼2 ± 0.
Relationship Between BAHD1-associated Differential DNA Methylation and Gene Expression With the aim of investigating the potential association between BAHD1-induced DNA methylation changes and gene expression, we generated transcriptome datasets of HEK-CT and HEK-BAHD1 lines, using Affymetrix DNA arrays. Analysis of differential gene expression between the two lines yielded 1304 up-regulated and 1137 down-regulated transcripts upon BAHD1 overexpression (LPE, BH-adjusted p < 0. 05). Given the role of BAHD1 in transcriptional repression, our analysis principally focused on genes that were down-regulated in HEK-BAHD1 cells. We found BAHD1-specific DMRs in 701 (61%) repressed genes, within a window encompassing 10 kb upstream to 10 kb downstream of each gene. In most autosomes, there was a significantly higher proportion of hyper-DMRs than hypo-DMRs associated with repressed genes (Figure  3 ), suggesting that BAHD1-associated de novo DNA methylation on autosomes is mainly associated with gene repression.
On chrX, analysis of the transcriptome datasets identified 119 genes that were differentially expressed in HEK-BAHD1 when compared to HEK-CT cells, of which 72 (60%) were down-regulated. In contrast to autosomes, the majority of chrX genes that were repressed in HEK-BAHD1 cells were associated with hypomethylated DMRs in both BS-seq replicates (i. e. , 58/72; Supplementary Table  S2 ; Figure  3 ). Since BAHD1 targets the Xi (Figure  1 ), we searched for evidence linking BAHD1associated repression to known methylation changes on the Xi. As mentioned above, there was no BAHD1-specific hyper-DMR on CGIs of chrX (Table  3B ), ruling out an effect of BAHD1 in CGI methylation on the Xi. We investigated whether chrX genes that were down-regulated in HEK-BAHD1 were known to be always inactivated on Xi, or could escape X inactivation, by using published data on the status of Xi genes  (Zhang et al.
ACKNOWLEDGMENTS We thank  Vinko Besic  for his valuable help in the final editing of the manuscript. We are also grateful to  Guillaume Soubigou  and  Caroline Proux  for technical help,  Morwenna Le Guillou  and  Jean Feunteun  for help in XIST FISH,  Bernd Jagla  for informatics assistance and  Emmanuel Quevillon  and the informatics center of the  Pasteur Institute  for data handling and storage.
FUNDING This work was supported by the  French National Research Agency  ( ANR 11 BSV3 003 01 , EPILIS), the  French Ligue Nationale Contre le Cancer (comité régional d'Ile-de-France ,  LNCC RS10/75-76  and  LNCC 131/12 ), the  European Research Council  ( ERC advanced grant  to  P. Cossart: BacCellEpi  grant  670823 ), and  Institut National de la Recherche Agronomique  (AO blanc 2011, MICA). The Beck lab was supported by the  Wellcome Trust  ( WT99148 ),  Royal Society   Wolfson Research Merit Award  ( WM100023 ) and  EU-FP7  projects  EPIGENESYS  ( 257082 ),  EpiTrain  ( 316768 ), and BLUEPRINT (282510).
However, the 472 co-IPed transcripts that do not bind in vitro raise the possibility that PfAlba1 may associate with a few RNAs via other proteins or protein complexes. Subsequently, we performed GO analysis to identify the cellular components, molecular functions and biological processes enriched in the in vitro-bound transcript dataset (Additional file 5). We found that, in addition to the classes found in the co-IPed dataset, GST-PfAlba1 directly bound to transcripts encoding components of the food vacuole, ubiquitin-dependent proteasomal catabolic process, entry into host cell, and carbohydrate biosynthetic process, to name a few (see Figure  S4a  in Additional file 1 for details). Normalized sequencing coverage plots of select transcripts that both co-IPed with the PfAlba1-Ty1 complex and bound in vitro to GST-PfAlba1 are shown in Figure  S4b  in Additional file 1.
In contrast, 926 transcripts were twofold or more differentially regulated (635 upregulated and 291 downregulated) in 3D7+PfAlba1-Ty1 trophozoites (Table  S5b  in Additional file 6); these constitute 16 % of the total parasite transcriptome. To validate the transcriptional changes, we performed quantitative reverse transcriptase-PCR (qRT-PCR) on a random selection of 14 genes (Additional file 5; genes highlighted in blue) and, as shown in Fig. 5b , confirmed the changes determined by RNA-seq. More generally, these results indicate that PfAlba1 participates in maintaining the mRNA homeostasis of trophozoite stages. We next used a maximum likelihood-based statistical analysis method  [29]  to estimate the developmental age of our PfAlba1-Ty1 transcriptomic datasets. Given the periodicity of gene expression in P.
falciparum  [6, 16] , such an analysis separates true differential expression from cell-cycle-dependent, temporal changes in expression patterns and is an important measure of the quality of our RNA-seq replicates for technical and biological variability  [29]. Notably, the 3D7+PfAlba1-Ty1 trophozoite transcriptome mapped to a later time point of the parasite lifecycle -38-40 h or 44-46 h for the two replicateswhile the timing of the ring transcriptome, at 8-10 h, was nearly identical to wild-type and empty vector-containing parasites (Fig. 5c ). A multidimensional scaling plot also supported the shift in developmental age of the 3D7+PfAlba1-Ty1 trophozoite datasets (Figure  S5a  in Additional file 1). Taken together, these observations suggest that PfAlba1 overexpression results in the early onset of a schizont-like transcriptomic profile due to perturbation of mRNA homeostasis, and provides one explanation for the reduction of 3D7+PfAlba1-Ty1 growth.
However, this does not manifest as a change in parasite DNA replication and morphology as assessed by flow cytometry (Fig. 2d ) and Giemsa staining of the relevant stages (Fig. 5d ), respectively. Finally, we identified enriched GO terms in the differentially regulated dataset and they mapped to the following groups (Figure  S5b  in Additional file 1; Additional file 7): host cell plasma membrane, host cell cytoplasm part, microneme, rhoptry neck, inner membrane complex, heparin binding, host cell surface binding, actin binding, single organismal cell-cell adhesion, antigenic variation, and entry into host cell. A closer analysis grouped these terms into two major categories: components required for host interaction to achieve cytoadherence and components essential for erythrocyte invasion.
Interestingly, only one significant GO term overlapped with the co-IPed and/ or in vitro-bound RNA datasets: entry into host cell. In fact, we found that transcripts encoding erythrocyte invasion components including the inner membrane complex, microneme, and rhoptry proteins were all upregulated in 3D7+PfAlba1-Ty1 trophozoites. For example, 20 of ~25 rhoptry components were over threefold upregulated at this stage (Table  S5b  in Additional file 6; gene names are in red letters). Because these transcripts reach transcription peaks only at 38-42 h (Figure  S6  in Additional file 1), we believe that the schizont-like transcriptomic shift is partly due to premature upregulation of such transcripts in PfAlba1-Ty1 trophozoites, i. e. , ~8-14 h earlier than in wild-type cells. a Schematic representation of the transcriptomic profiling experiment.
3D7, 3D7+empty vector, or 3D7+PfAlba1-Ty1 parasites were grown in white blood cell (WBC)-free blood to ring (8-10 h post-infection (h p. i. )) or trophozoite (28-30 h p. i. ) stages, and RNA harvested and mRNA enriched and analyzed by strand-specific RNA-seq. Differential expression in 3D7+PfAlba1-Ty1 relative to 3D7 and empty vector transfectants was quantified by edgeR  [71]. b qRT-PCR analysis of the indicated genes was performed using total RNA isolated from empty vector or PfAlba1-Ty1 transfectants. The left, middle and right panels include genes that were upregulated, unchanged, or downregulated, respectively, according to RNA-seq. The y-axis denotes -ΔΔC t , calculated as in "Materials and methods". Having identified PfAlba1's in vivo and in vitro RNA interactome and RNAs that are differentially regulated when parasites produce excess PfAlba1, we posited that RNAs detected in all of these datasets would likely be the first set of transcripts targeted by PfAlba1 for posttranscriptional regulation.
6a ; Additional file 8), expanding the primary list of mRNAs targeted by PfAlba1 complexes for regulation during the IDC to 262. To visualize the cellular and functional distribution of annotated genes in the Venn intersections of Fig. 6a , we designed a schematic of a P. falciparum asexual blood stage cell and mapped each gene and its intersection category onto the schematic using MapMan  [30]  (Figure  S7  in Additional file 1). It became apparent that mRNAs encoding proteins that contribute to erythrocyte invasion, including components of apical organelles and the actin-and myosin-related motor proteins, constitute a significant portion of PfAlba1 targets. The PfAlba1 RNA interactome contains transcripts that are described to be under translational control In order to assign a post-transcriptional function to PfAlba1, we next compared the PfAlba1 RNA interactome with post-transcriptionally regulated gene sets identified in previous studies of asexual blood stages  [16, 19].
The first dataset we evaluated was generated by Bozdech and colleagues  [16]  using correlative transcriptomic and proteomic analysis, and contains 127 asexual stage mRNAs that are subject to translational regulation (Table  S8a  in Additional file 9). We found that 104 (or 82 %) of the 127 mRNAs are bound by PfAlba1 in vivo and/or in vitro (Table  S8a  in Additional file 9), suggesting that PfAlba1 may modulate mRNA translation during asexual development. Thirteen of these bound RNAs are misregulated in PfAlba1-Ty1 trophozoites and include invasion transcripts such as Rap1, Rap3, RhopH2, RhopH3, and RON4. The second dataset we analyzed was generated by DeRisi and colleagues  [19]  by ribosomal profiling and contains 297 asexual stage mRNAs that are regulated at the level of translational efficiency (Table  S8b  in Additional file 9).
We included FIKK7. 1 and FIKK9. 6 as upregulated, but unbound, controls and analyzed three unchanged and unbound mRNAs as controls for immunoprecipitation: aldolase, actin I and seryl tRNA synthetase. For each gene, we also determined the fold change in transcript levels from trophozoite to schizont stages relative to actin I mRNA (Figure  S8  in Additional file 1) and used this value to calculate the fold change in association from trophozoites to schizonts. As illustrated in Fig. 7c  (left panel), the mRNAs of RhopH3, CDPK1, and Rap1 significantly associated with PfAlba1-Ty1 in trophozoite stages, while association was lost in schizont stages. Consequently, we detected their protein products, PfRhopH3, PfCDPK1, and PfRap1, in the schizont stage alone (Fig. 7c , right panel). AMA1, which was not detected in our co-IPed/in vitro -bound transcript dataset (Additional files 2 and 4), significantly associated with the PfAlba1 complex in trophozoite stages alone (Fig.
S3a, S4a, and S5a in Additional file 1) need to be further explored to assign RNA regulatory functions to PfAlba1. Moreover, because components of eukaryotic metabolic processes appear to be regulated in a coordinated manner posttranscriptionally  [45] , we propose that this property can be exploited to assign functionality to some of the uncharacterized mRNAs that are bound by PfAlba1. For example, some of the 43 uncharacterized mRNAs in the intersection of our various datasets in Fig. 6a  may encode novel components of the invasion machinery. Another indication of PfAlba1's master regulatory role during the IDC is the early onset of a schizont-like transcriptomic profile upon PfAlba1 overexpression. One explanation for this may be the direct binding of PfAlba1 to the transcripts of putative DNA and/or RNA-binding proteins (Additional file 2, rows highlighted in green) and 19 ApiAP2 transcription factors (Additional file 2, rows highlighted in yellow), three of which are also upregulated in PfAlba1-Ty1 trophozoites (Additional file 8; Fig.
Flow cytometry To measure the growth rate of 3D7+empty vector or 3D7+PfAlba1-Ty1 parasites, a synchronous culture of ring stages was diluted to 0. 2 % parasitemia in 200 μl RPMI complete medium at 4 % hematocrit. At 0 h, 24 h, 48 h, 72 h, 96 h, and 120 h, 5 μl of the culture was stained in 95 μl of D-PBS (Gibco) supplemented with 2× Sybr Green I (Ozyme; stock = 10,000×) for 30 min at room temperature, diluted 20-fold in D-PBS (final volume = 200 μl), and the Sybr Green fluorescence measured in a Guava easyCyte Flow Cytometer (EMD Millipore). We counted 10000 events in duplicate to establish an accurate parasitemia value for each culture. Data were analyzed using the InCyte software (EMD Millipore). To analyze the 48 h replication cycle of 3D7+empty vector or 3D7+PfAlba1-Ty1 parasites, synchronous ring stages growing in RPMI complete supplemented with 5 μg/ml BS were diluted to a parasitemia of 0.
5 % at 4 % hematocrit: this was considered to be the "0 h" time point. The parasitemia of the culture was subsequently analyzed every 3 h for 70 h by flow cytometry as described above. To accurately demarcate ring, trophozoite, and schizont stages, data were analyzed using FlowJo v. 9 software (FlowJo, LLC). Immunoprecipitation of PfAlba1-Ty1-associated RNA An equivalent of 1 × 10 9 3D7+empty vector or 3D7 +PfAlba1-Ty1 parasites were grown to 28-30 h trophozoite stages in RPMI complete medium supplemented with 5 μg/ml BS, infected red blood cells harvested, and free parasites collected by saponin lysis. The resulting parasite pellet was lysed under non-denaturing conditions in the presence of RNAsin (Promega) and subjected to immunoprecipitation analysis with rabbit anti-Ty1 antibodies or rabbit IgG antibodies (Sigma).
Analysis of RNA-seq data Quality control of fastq files was performed using the FastQC software  [67]. Sequencing reads were mapped to the P. falciparum 3D7 genome (v. 3, GeneDB) using the BWA MEM algorithm under default settings  [68]. Properties of the datasets generated in this study and related statistics are provided in Additional file 10. Samples obtained from the immunoprecipitation and in vitro binding experiments were treated differently from the transcriptomic data (see below). Here, the goal was to identify genes of interest (GOIs) that were enriched in PfAlba1-bound samples, relative to the control. Each sample was submitted to the following pipeline (Figure  S10  in Additional file 1): enriched peaks were identified using MACS2 software  [69]  after controlling for a false discovery rate of 5 % (default setting of MACS2) using Benjamini and Hochberg's correction method  [70] ; peak data were normalized to reads per million; a peak-to-GOI network was constructed, with edges being created only if a peak overlapped a GOI; the log 2 (fold change) was calculated for each peak for each contrasting sample pair (co-IPed or in vitro-bound versus corresponding control); GOIs were given an aggregate fold-change score by taking the mean of associated peaks; GOIs with twofold or more associated peaks in the bound sample were further analyzed.
Thus, only genes containing significant peaks of protein binding with signal at least 100 % above background noise were considered for further analysis. After initial normalization and processing with edgeR  [71]  (Figure  S10  in Additional file 1), the transcriptomic data were explored using the plotMDS function from the R:limma modeling package  [72] , which showed consistent clustering of replicates and meaningful separation of clusters by time and genotype (Figure  S5a  in Additional file 1). The resulting multidimensional scaling plot represents each sample in two dimensions, with the distance between two samples reflecting log 2 (fold change) in expression. The edgeR pipeline was then followed to compare PfAlba1-Ty1 datasets (n = 2) to control samples (n = 3 to 4) and to select transcripts that were twofold or more differentially regulated genes in either ring or trophozoite stages at a significance level of ≥95 %, after correcting for a false discovery rate of 5 %.
Acknowledgements We are grateful to  Chetan Chitnis  and  Shailja Singh  for helpful scientific discussions and providing antibodies against PfAMA1, PfCDPK1, and  PfTRAMP , and  Odile Puijalon  for providing antibodies against PfRhopH3. We also thank  Julien Guizetti ,  T. Nicolai Siegel  and  Adela Nacer  for critical reading of the manuscript. We acknowledge  Hiroshi Sakamoto  for constructing the gene knockout plasmids. This work was supported by a  European Research Council   Advanced Grant  ( PlasmoEscape 250320 ) and the  French Parasitology consortium ParaFrap  ( ANR-11-LABX0024 ). S. S. V. was supported by the  European Molecular Biology Organization Long-Term Fellowship , the  Marie Sklodowska-Curie International Incoming Fellowship  ( FP7-MC-IIF-302451 ) and the  Pasteur Roux post-doctoral fellowship.
Social and health data are collected from the French national databases. Data collected for participants include social and demographic characteristics, socioeconomic status, life events, behaviors, and occupational factors. The health data cover a wide spectrum: self-reported health scales, reported prevalent and incident diseases, long-term chronic diseases and hospitalizations, sick-leaves, handicaps, limitations, disabilities and injuries, healthcare utilization and services provided, and causes of death. To take into account non-participation at inclusion and attrition throughout the longitudinal follow-up, a cohort of non-participants was set up and will be followed through the same national databases as participants. Inclusion begun at the end of 2012 and more than 82,000 were already included by September 2015.
We estimated the number of some major health outcomes expected in the CONSTANCES cohort over a moderately long term in a cohort with an age and sex structure identical to that of the French general population aged 18-69 years at the last available national census. Table  1  presents the number of expected events at the end of 5, 10, and 15 years for events for which we have reliable national reference data  [30] [31] [32] : deaths and incidence of cancer, ischemic heart disease, and Alzheimer disease. For these major outcomes, the number of these serious events is high and will make possible numerous studies with satisfactory power.
Procedures for longitudinal follow-up Participants are followed-up through ''active'' procedures (implying them directly). An annual self-administered questionnaire is completed by the subjects at home, using either a paper questionnaire or internet. They will also be French national reference data come from  [30] [31] [32]  The French CONSTANCES population-based cohort: design, inclusion and follow-up invited every 5 years for a new health examination in a HSC. Maximizing their personal participation rate is essential. Accordingly, regular contact with participants includes a CONSTANCES Cohort Journal, which will present results, nested projects, etc. , and is sent regularly to participants. A website was also created (www. constances. fr). The subjects included in CONSTANCES are also followed up ''passively'' (so-called because this follow-up does not require the subjects' participation) by annual linkage with three national social and health data databases.
The National Retirement Insurance Fund administered by CNAV ensures the retirement pension for every individual in France who had health insurance from CNAMTS at least once during his or her life. CNAV has therefore set up a system that allows it to collect social and occupational data from different organisms and schemes that manage various forms of insurance and other social protection. The CNAV regularly receives for its databases employers' annual reports (occupation, salary), and information about periods of employment and unemployment from social welfare organizations (e. g. , sick leave, maternity leave, unemployment, and diverse social benefits)  [33]. The National Health Insurance Fund administered by CNAMTS manages the SNIIRAM database which covers the entire French population  [34].