dataesr/scientific-paragraphs-categorization huggingface dataset:
Competition binding assays in the presence of radiolabeled T 4 revealed that RES was not able to displace the bound T 4. The stabilising effect against urea denaturation and the inhibition activity against TTR fibrillogenesis assessed by turbidimetric assay of RES and its metabolite resveratrol-3-O-sulphate, were found to be significantly similar to polyphenols  52. The first TTR-RES crystal complex was deposited at the PDB data bank in 2000 (1DVS code, Table  1 )  58  , while in 2015 a second structure in which TTR was crystallised with both RES and T 4 was solved (5CR1)  52. The presence of T 4 molecule is relatively easy to detect because, recording the anomalous signal given by iodine atoms, the high density picks unambiguously correspond to the iodine position. Using this strategy, the presence of the two ligands was investigated and the RES molecule was present essentially in the dimer B/B' while T 4 was located mostly in pocket A/A'.
This result suggested that RES is characterised by negative cooperativity binding mode as already observed for several TTR ligands. The density map shows that the 3,5-dihydroxyphenyl portion is placed in the inner binding pocket establishing an HB with Ser117, so the orientation of RES is opposite to that described in the first deposited structure (1DVS) (Figure  13(A) ). A plausible explanation was attributed to the different crystallisation protocols applied between the two laboratories: the structure 1DVS was obtained soaking the ligand into the TTR empty crystal while the other, PDB code 5CR1, was prepared by co-crystallisation  52. In order to investigate the binding mode of the major RES metabolites, the crystal structures of TTR in complex with RES 3 and 4 0 O-glucuronide and 3-O-sulphate were solved  52.
Spontaneous emotional reactions task ('EMOrea task') A funny video clip (from Famelart & Guidetti  [37] ) that was likely to induce the emotion of joy in children was used. The clip was shown on a laptop computer and the children's facial reactions were recorded via the webcam. Facial reactions were analyzed with FaceReader  [38] , a software for the automatic analysis of emotional facial expressions. It is based on the FACS system  [39] , which breaks down facial expressions into individual components of movement, called Action Units (AUs). The expression of each emotion corresponds to a specific combination of AUs (i. e. an AU pattern). For example, the combination of AUs in the Joy pattern is AUs 1   [12]  +  [6]  +  [25] : activation of the Zygomatic involving raising of the corners of the lips (open or closed mouth) and creasing (or not) of the corners of the eyes.
The adaptation applied in this study consisted in slightly specifying the MAX/AFFEX system by including more detailed descriptors from the FACS system (for more information on these two systems, see Sullivan and Lewis  [42] ). Bodily expressions were coded with the BEEOS system  [43]  which is based on the same coding method as FACS and MAX / AFFEX. The Elan software  [44] , an assistance application for the annotation and transcription of video, was used to annotate all the AUs (facial and bodily AUs) expressed by the child for each of the emotion conditions. Combinations of AUs produced in each emotion condition were then matched with a theoretical pattern by emotion, expressed as a proportion between 0 and 1. All the video material was analyzed by a principal coder who was naïve to the experiment.
Consequently, to acquisition, all spectra were pre-processed, calibrated, and ultimately "sliced" into "buckets" with pre-defined limits so as to maximize recovery of peak entities. The buckets were then scaled to the total summed integrals for each spectrum, and the integrals of peak entities were calculated to obtain continuous NMR variables that were used in further statistical analyses. Finally, NMR signals were assigned using ad hoc literature  (37). CHENOMX software (CHENOMX, Edmonton, Alberta, Canada) and Human Metabolome Database  (38)  were used to confirm the assignments using the medium spectrum (Supplementary Figure  3 )  (37). After the statistical analyses, the assignments of the buckets that associated with the gut microbiota were newly carefully looked and confirmed to be unambiguously assigned by gathering information from S-TOCSY, additional two-dimension NMR experiments (recorded on random samples) such as 1H-1H Total Correlated Spectroscopy (TOCSY) and J-resolved experiments.
These associations between covariates and gut microbial and metabolomic features were tested using PERMANOVAs and Principal Component Partial R-square (PC-PR2) analysis, respectively, enabling to measure the percentage of variation in microbial composition or metabolomic data explained by the factors tested. Additionally, α-and βdiversity models (partial Spearman correlations and PERMANOVAs) were adjusted for sequencing depth. P-values were adjusted for multiple testing using a Benjamini-Hochberg correction with a 10%-False Discovery Rate (FDR). To better visualize the interindividual variation in metabolomics signatures in our study sample, Principal Component Analysis (PCA) of the metabolomic datasets were plotted and color-coded based on age, sex, and BMI. Spearman correlations were performed using SAS 9.
004 ± 0. 008) were the 5 most abundant phyla. Supplementary Table  1  presents the 22 genera constituting the "core microbiota"defined as the genera shared by at least 95% of samples  (41)  in our study population. A complete description of genera and species composing the gut microbiota of our sample is available in Supplementary Table  2  and NOESY1D metabolomic datasets are shown in Figure  1  and Supplementary Figure  2 , respectively. Although no clear clustering appears, grouping patterns along gradient of sex, age, and BMI can be observed. Results of the PC-PR2 analysis measuring the percentage of variation in the metabolomic dataset that is explained by the covariates (age, sex, BMI, smoking status, and physical activity) are shown in Supplementary Table  6.
cambridge. org/core. INRAE -Saint Pee sur Nivelle, on 29 Dec 2020 at 14:49:04, subject to the Cambridge Core terms of use, available at https://www. cambridge. org/core/terms. https://doi. org/10. 1017/S0007114520004870 between gut bacterial and metabolomic datasets, beyond the sole assessment of global connections obtained through multivariate correlation-based approaches (e. g. co-inertia or Procrustes analyses). In addition, we were able to take into account a range of factors reported to influence the gut microbiota composition or the systemic metabolism of the host in our models, thus mitigating potential confounding. Nonetheless, residual confounding cannot be ruled out. For instance, intestinal transit time was shown to be a major confounding factor of gut microbiota composition  (63)  , and we were not able to collect this information.
Questionnaires and biological samples were administered and collected by trained medical investigators supported by a full clinical team. The study is sponsored by Institut Pasteur (Pasteur ID-RCB Number: 2012-A00238-35). It was conducted as a single center study and without any investigational product, and was approved by Comité de Protection des Personnes -Ouest 6 on 06/13/2012 (CPP Ouest 6-728/MS2) and by Agence Nationale de Sécurité du Médicament on 06/22/2012 (ID-RCB Number: 2012-A00238-35, ref. ANSM: B120239-70). The protocol, which is Downloaded from https://www. cambridge. org/core. INRAE -Saint Pee sur Nivelle, on 29 Dec 2020 at 14:49:04, subject to the Cambridge Core terms of use, available at https://www. cambridge. org/core/terms. https://doi. org/10. 1017/S0007114520004870 registered under ClinicalTrials.
1% Tween, 1% BSA. Anti-H3K27cr antibody (PTM-526 from PTM BIO) was diluted (at 1/100 for immunofluorescence, 1/300 for immunohistochemistry) in blocking buffer and incubated overnight at 4 • C. For immunofluorescence experiments, slides were incubated with Alexa Fluor 488-labeled goat anti-(mouse IgG) (1/500; Life technologies) and Alexa Fluor 594conjugated peanut agglutinin lectin (1/500; Life technologies) diluted in 1× PBS for 1 h at room temperature. Lectin was used to stain the developing acrosome and determine the stage of testis tubules as described in  (40). DAPI (in VECTASHIELD Mounting Medium) was used to stain nuclei. Immunofluorescence pictures were taken with an Olympus BX63 microscope and montage was performed using ImageJ 1. 48v (http://imagej. nih. gov/ij/). Immunohistochemistry pictures were taken with Perkin Elmer Lamina slide scanner and analyzed using CaseViewer software.
PTM identification and quantification Identification of modified peptides was obtained using the manually curated database MS histoneDB developed in our group  (41). MS/MS data interpretation was performed with the program Mascot (http://www. matrixscience. com/), using the following search parameters. The precursor and fragment mass tolerances were 5 ppm and 25 mmu, respectively; enzyme specificity was trypsin; the maximum number of trypsin missed cleavages was set to 5, carbamidomethyl (Cys) was specified as a fixed modification. We were interested in acetylation and crotonylation of Lys residues, methylation and dimethylation of Lys/Arg and trimethylation of Lys, which were indicated as variable PTMs in Mascot, in addition to N-terminal acetylation of proteins. All MS/MS spectra were visually scrutinized to validate peptide identifications and PTM site assignment.
ChIP-seq analyses Around 1 million spermatocytes and round spermatids were used for ChIP-seq analyses. The chromatin was prepared by Diagenode using the iDeal ChIP-seq kit for histones (Diagenode Cat# C01010059) and then sheared using the Bioruptor Pico sonication device (Diagenode Cat# B01060001) combined with the Bioruptor Water cooler. ChIP was performed using the IP-Star Compact Automated System (Diagenode Cat# B03000002). The antibody H3K27cr was used at 1 and 2 g per IP and each cell type was analyzed in two biological replicates. After checking that the two antibody amounts led to very similar ChIPseq peak profiles, we kept the data obtained with 1 g to be shown in the results. Libraries were prepared from input and immuno-purified DNA with 1ng as starting material using MicroPlex Library Preparation Kit v2 (Diagenode Cat# C05010013), then purified using Agencout ® AMPPure ® XP (Beckman Coulter) and quantified using Qubit™ dsDNA HS Assay Kit (Thermo Fisher Scientific, Q32854).
Bioinformatic analyses of ChIP-seq data and of 5hmC data Several published ChIP-seq datasets performed on mouse spermatocytes and round spermatid cells were downloaded from the Sequence Read Archive (SRA) database and re-analyzed using the same bioinformatic procedure (see below). The following ChIP-seq datasets were processed: H3K4me1 and H3K4me3 from GSE49621  (44) , pan crotonylation from GSE32663 and GSE69946 (5) (45), H3K27ac from GSE107398  (46) , BORIS and CTCF from GSE70764 (47), BRD4 from GSE56526  (48) , SLY from PR-JNA275694 (39), SOX30 from GSE107644 (49) and H3. 1/2 and H3. 3 from GSE42629 (50) (Supplementary Table  S1 ). A ChIP-seq dataset on BRDT  (51)  had been acquired on a SOLiD sequencing platform whereas Illumina sequencers were used for the above listed datasets; we obtained the BED files of already mapped BRDT peaks and then extracted signals around TSS and over putative enhancers as for the other ChIP-seq data.
We obtained the data acquired on the DNA methylation mark 5-hydroxymethylcytosine over SC and RS stages among the several steps of mouse spermatogenesis analyzed in  (52). The sequencing data was quality controlled, pre-processed and mapped to the mouse reference genome using the same bioinformatic procedure described for the ChIP-seq data. The 5hmC signal belonging to each exon was quantified in read counts using Bedtools intersect and normalized in BPM. The estimate of the 5hmC signal by gene was then calculated by summing the 5hmC signals associated with each of its exons. Quality control. The read quality of ChIP-seq datasets was assessed using Fastqc (Andrews S. , 2010, http://www. bioinformatics. babraham. ac. uk/projects/fastqc). Pre-processing. ChIP-seq reads were sampled using Seqtk v20101003 (https://github.
github. io/picard/). Read alignment metrics were calculated using Picard multipleMetrics. Peak calling. ChIP-seq peaks were called using Macs2 v2. 1. 1 (parameters: broad, broad-cutoff 0. 01)  (56). A control ChIP sample was used for peak calling depending on availability and sufficient depth of coverage (Supplementary Table  S1 ). TSS region quantification. Regions of ±500 bp around each gene transcription start site (TSS) were quantified in read counts using Bedtools intersect  (53). The read count signal assigned to each TSS region was then normalized in Bins Per Million reads (BPM) (i. e. similar to the Transcripts Per Million reads (TPM) normalization applied on RNA-seq reads). The read counts of each TSS region was divided by its length in kilobases to produce reads per kilobase (RPK).
The RPK values per sample were then summed up and divided by 1,000,000 to produce a 'per million' scaling factor. The RPK value assigned to each TSS region was then divided by the 'per million' scaling factor to produce the BPM value. Enhancer region quantification. Enhancer regions were first identified and delimited using the broad peaks called from the analysis of the H3K4me1 ChIP-seq data, independently on mouse spermatocytes and round spermatid cells, and then merged into a single list. These enhancer regions were then quantified for each ChIP mark using the reads mapped in these regions. The read count signal assigned to each enhancer region was then normalized in BPM, as described in the previous paragraph. The execution of the ChIP-seq data analyses was supervised using the Bpipe workflow manager  (57).
Bioinformatic analyses of RNA-seq data The RNA-seq dataset used to evaluate the effect of H3K27cr mark on transcription was downloaded from  (52). Quality control. The read quality of RNA-seq datasets was assessed using Fastqc. Pre-processing. Reads were trimmed for low quality bases and Illumina adaptors using Trimmomatic v0. 32 (parameters: ILLUMINACLIP:2:30:10, LEADING:30, TRAIL-ING:30, SLIDINGWINDOW:4:30, MINLEN:30). Mapping and gene quantification. Reads were mapped using Star v2. 5. 2 (ENCODE standard parameters) (58) on the Mus musculus genome assembly GRCm38 (mm10) using Illumina iGenomes reference sequences and annotations (http://emea. support. illumina. com). Isoform and gene abundances normalized in TPM values were calculated using Rsem v1. 3. 1 (parameters: estimate-rspd). Read alignments were cleaned and sequence duplicates were removed using the Picard tool suite (addReadGroup, reorder, sort, clean, markDuplicates).
The elution consisted of an isocratic step of 1 min at 0% phase B, 0. 1 min at 5% B followed by a linear gradient from 5 to 25% of phase B in 5. 9 min and then up to 40% B in 1 min. These proportions were kept constant for 3 min before returning to 0% of phase B in 0. 1 min and letting the system equilibrate for 8. 9 min. HRMS data were acquired between m/z 700 and 1050 at 1. 0 Hz in the profile mode and with a resolution set at 40 000 (m/z 1222). Nitrogen was used both as a nebulizer and drying gas. The source conditions were set as follows: end plate offset 700 V, capillary 3800 V, nebulizer 1. 7 bar, dry gas 12. 0 L/min and dry temperature 240 • C. The mass spectrometer was calibrated internally with a sodium formate/acetate solution, which provides a mass measurement accuracy below 2 ppm on average.
Localization of H3K27cr on the genome Interest of keeping ChIP-seq reads mapped at multiple positions on the genome. ChIP-seq analyses were performed on two biological replicates of SC and RS cells using the antibody raised against H3K27cr. Chromatin from EC cells is rarely analyzed by ChIP-seq, because of its peculiarly compacted structure and its heterogeneous nature, which renders the nucleosome extraction for ChIP-seq analysis difficult  (5, 73, 69). Given that chromosomes X, Y, 5 and 14 contain multicopy genes expressed during spermiogenesis  (79) , in order not to under-estimate the ChIP-seq signals associated with these evolutionary conserved sequences, we kept reads mapped at both unique and multiple positions on the genome. This mapping strategy allowed us to gain a total of 1105 genes in the four pooled datasets when compared to the conservation of only uniquely mapped reads (Supplementary Figure  S6 ).
CTCF, together with cohesin, have long been shown to bridge enhancers and their target promoters by forming chromatin loop anchors  (94, 95, 90). A high CTCF signal was harbored by a minority of enhancers; their genomic coordinates were provided to the program GREAT  (96)  to study the ontology of neighbor genes. The subset of 23 enhancers solely bearing H3K27ac did not allow extracting any GO term. By contrast, the 318 enhancers bearing only H3K27cr led to highlighting 'positive regulation of MAP kinase activity' (most notably by MAP3K7, FGFR1, FGFR4, VEGFA, FL1, KDR and NTF3) (Supplementary Figure  S13 ). In addition, the cluster of 486 enhancers marked by all transcription regulators and bearing both H3K27ac and H3K27cr allowed extracting genes involved in the regulation of nuclear division and in Rac protein signal transduction (Supplementary Figure  S13 ).
Besides, it would also be interesting to estimate the open chromatin status of regions bearing H3K27ac and H3K27cr either alone or together. Our proteomic analyses indicated the preferential presence of H3K27cr, and to some extent of H3K27ac, in the variant H3. 3. Comparison with ChIP-seq data acquired for H3. 1/2 and H3. 3 in RS cells (50) confirmed the preferential association of H3K27ac and H3K27cr with H3. 3 observed by proteomics. The original publication describing Kcr  (5)  highlighted that increased crotonylation lev-els from SC to RS cells were associated with gene expression activation, and that a large fraction of these genes resided on the X chromosome; this group of genes thus overcoming the repressive chromatin environment following meiotic sex chromosome inactivation (MSCI)  (5).
ACKNOWLEDGEMENTS M. C. and D. P. wish to thank the staff of EDyP Service who contributed excellent technical support for proteomic analyses. We also want to thank staff of the animal house, cytometry (CYBIO) and histology (HistIM) core facilities from  Cochin Institute  ( INSERM U1016 ,  CNRS UMR8104 ,  Université Paris Descartes ). D. P. and M. C. greatly appreciated fruitful discussions with  Saadi Khochbin  and  Sophie Rousseaux , and are grateful to them for their critical reading of this manuscript. We thank  Nicolas Wiart  for his support on the use of the high-performance computing clusters at CNRGH. We wish to thank  Marie Arlotto  for preparing yeast histones and yeast cells for metabolomic analyses,  Sandrine Miesch-Fremy  for preparing mice testis,  Marie Courc  ¸on for technical advice in biochemistry,  Yves Vandenbrouck  for advice on the use of Pro-teoRE, and  Naganand Rayapuram  and  Myriam Ferro  for critical reading of this manuscript.
DATA AVAILABILITY ChIP-seq data produced and used in this study were deposited on ArrayExpress under accession number E-MTAB-8328 and in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PR-JEB34135. Proteomics data were deposited on the PRIDE archive under the accession number PXD015380 (Project DOI: 10. 6019/PXD015380)  (109).
a Schematic representation of the experimental approach. PC3 cells were transfected with esiRNAs against either GFP or INO80. Three days later, cells were treated with α-amanitin (α-a) or cordycepin (crd) for 3 h or left untreated as control and then subjected to fibre labelling analysis. b Representative images of spread fibres from each condition. Similar results were obtained in five independent experiments (cordycepin treatment-2). c Distribution of fork speed rates in INO80-proficient (siGFP) and INO80-deficient (siINO80). Data is from five independent experiments (in cordycepin-treated cells-2), at least 250 fibres were measured per condition in each experiment. ****p-value < 0. 0001, (two-tailed unpaired Student's t-test). d Schematic representation of the experimental setup used. PC3 cells were co-transfected with esiRNAs against either GFP (siGFP) or INO80 (siINO80) along with either a control (CTRL) or RNase H1-overexpressing (RNAseH1) vector.
Two days later RNAse H1 expression was induced by doxycycline for 24 h. Cells were labelled with CldU for 5 min followed by IdU pulse for 20 min and subjected to DNA fibre labelling analysis. e Representative images of spread fibres from each condition. Similar results were obtained in four independent experiments. f Distribution of fork speed rates (kilobase/min) in siGFP and INO80-deficient cells transfected with control or RNAse H1 overexpression plasmids. Data are from 4 independent experiments, at least 250 fibres were measured per condition in each experiment. ****p-value < 0. 0001, (two-tailed unpaired Student's t-test). g Schematic representation of the experimental setup. Cells were co-transfected and induced as in d and prior to labelling were treated with 5 µM vorinostat for 6 h.
h Representative images of spread fibres from each condition. Similar results were obtained in three independent experiments. i Distribution of fork rates from (h, at least 250 fibres were measured per condition in each experiment; ns non-significant, ****p-value < 0. 0001, *p-value < 0. 05, (two-tailed unpaired Student's t-test). c, f, i Kruskal-Wallis test p-value was < 0. 0001. Data is presented as Tukey boxplot (box representing first quartile, median and third quartile, whiskers 1. 5 times interquartile range). found to be separate, distinct foci when resolved by STED (Supplementary Fig. 8 ). To distinguish between random and nonrandom co-localization events, we conducted a Van Steensel's cross-correlation function analysis (CCF)  34. Co-localization events between the STED imaged channel (INO80 or S9.
To distinguish between these possibilities, positive and negative FC-I(log2) values were clustered separately in LacI-eGFP and LacI-INO80E cells. The positive FC-I(log2) values in the LacI-INO80E cells were significantly smaller than in the LacI-eGFP (Fig. 7g ). If INO80 affects R-loop formation but not R-loop resolution, then the same 'amount' of resolution should be applied to both the LacI-eGFP and LacI-INO80E datasets. In that case, because LacI-INO80E cells have a lower mean positive FC-I(log2) value than LacI-eGFP, the negative FC-I(log2) values upon tethering INO80 should be greater. However, when the negative FC-I(log2) values were analysed, no significant difference was found between LacI-INO80E and LacI-eGFP (Fig. 7g ). This contests the hypothesis that resolution of R-loops is independent of INO80, and suggests that tethering INO80 does not impact R-loop synthesis.
Methods Cell culture, transfections and treatments. Human PC3 cells were grown in DMEM (Gibco) supplemented with L-glutamine, 10% fetal bovine serum, 1 mM sodium pyruvate and antibiotics in 5% CO 2 atmosphere at 37 °C. Cells were treated with 0. 5 mM hydroxyurea to induce replication stress. To inhibit transcription αamanitin (2 µg/ml) and cordycepin (50 µM) were used. Induction of the Tet-ON promoter was achieved with 1 µg/ml doxycycline for 24 h. 5-Ethynyl-2′-deoxyuridine (EdU)to label S-phase cells was used at 25 µM final concentration and 5ethynyl uridine (EU) was used at 1 mM to assess the overall nascent transcription. EsiRNAs targeting the coding regions of human INO80 (3440-3894, transcript NM_017553. 1) or EGFP (132-591) were synthesized following standard procedures  58, 59. Primers used to amplify the targeted regions were selected using Riddle database  60     The inducible expression of RNAse H1 has been achieved using pEBTet-BLAST-RNAse H1-myc/His and pEBTet-EGFP-BLAST was used as control.