dataesr/scientific-paragraphs-categorization huggingface dataset:
Modeling of the field study at the Saint Germain les Belles Reservoir Water column copper concentration data from the Saint Germain les Belles Reservoir  (van Hullebusch et al. 2003a (van Hullebusch et al. , 2003b) )  were used to test the ability of the TICKET-UWM to describe copper fate in lakes. Metal sorption to SPM was described in the model by either 1) specifying a distribution coefficient (K D ) based on measurements in the water column and sediment (empirical K D values) or 2) having the model calculate the K D value at each time step with a WHAM VII speciation calculation (calculated K D values). The water column empirical K D was based on measurements in the Saint Germain les Belles Reservoir. The sediment empirical K D was based the copper partition coefficient review of  Heijerick and Van Sprang (2005).
e. , 50 for half-time and 30 for 70% removal). Plots of removal time t x versus H/S-the analog of the plots of  Santschi (1984) -are useful in interpreting the data in Table  1. Plots of the 70% removal time (t 30 ) versus H/S are shown in Figure  7  for systems that were dosed with copper and for which the information was available to calculate t 30 and H/S. Particle flux (S) data were not available for Lake Mathews, the catfish ponds, or Cazenovia Lake. These waterbodies, therefore, do not appear in Figure  7. For most lakes in Figure  7 , the t 30 value was calculated using data on the decrease in copper concentration with time. For the MELIMEX dataset, a theoretical t 30 value was calculated from the firstorder loss rate constant of 0. 0091 d -1. Also plotted is a theoretical line that was generated from Equation 4 using average m and K D values from the 7 systems (Supplemental Data).
The TICKET-UWM, used as a screening tool, was parameterized to assess the transport and fate of copper in a standardized lake environment. Overall, the outcomes of this modeling were consistent with the available field data. The selection of standardized model parameters was a critical element. However, a sensitivity analysis showed that varying several critical parameters did not affect the main conclusions. Both field and modeling data were successfully combined in a framework that relates metal removal processes in lentic systems to lake depth and particle settling fluxes. The available literature data indicate that oxidation of anoxic sediments or resuspension events generally do not lead to a significant remobilization of copper, due to the strong binding of copper to various particulate phases.
Acknowledgment-The present study was sponsored by the  European Copper Institute. The authors gratefully acknowledge the helpful comments on the manuscript from  A. Burton  and 2 anonymous reviewers. Disclaimer-S. Baken  and  K. Delbeke  performed their research as employees of the European Copper Institute. Data Accessibility-Data, associated metadata, and calculation tools are available from the corresponding author (krader@mutchassociates. com).
Results The native MORF complex acetylates histone H3K23. Mammalian MORF complexes have been identified through affinity purification using associated proteins as bait, transient cotransfections, or through baculovirus-mediated expression  10, 24, 29, 30. To characterize endogenous native forms of the complexes, we engineered K562 cell lines  31  that express epitope tagged (3xFLAG-2xStrep) MORF and paralogous MOZ from the AAVS1 safe harbor. Tandem affinity purification of both HAT proteins confirmed the tetrameric nature of the MORF and MOZ complexes but also identified BRPF1 as the sole scaffold subunit of these complexes (Fig. 1c  and Supplementary Data 1). Formation of the native MORF-BRPF1-ING4/5-MEAF6 complex was confirmed by Western blot analysis (Fig. 1d ). The acetyltransferase activity of the MORF complex was then assessed on unmodified and acetylated histone H3 peptides (residues 1-29 of H3).
MORF complex colocalizes with H3K23ac and H3K14ac in vivo. To confirm that the MORF complex occupies genomic regions enriched in H3K23ac and H3K14ac, we analyzed ChIPsequencing (ChIP-seq) datasets generated in human RKO and HMEL cell lines  29, 36. The MORF complex core subunits BRPF1 and ING5 colocalized with H3K23ac and H3K14ac near promoters of target genes, suggesting that the MORF complex binds to these loci (Fig. 6d ). Co-occupancy of the MORF subunits, H3K23ac, and H3K14ac at the same genomic sites supported our model in which binding of MORF DPF to H3K14acyl promotes H3K23 acetylation by the MORF complex to activate transcription. The extent of colocalization of H3K23ac and H3K14ac in vivo was assessed through analyzing ChIP-seq datasets generated by the ENCODE consortium in human IMR90 and hESC cell lines and the dataset generated in human HMEL cell line  36.
The beads were then washed three times and analyzed using SDS-PAGE and Western blotting. NMR spectroscopy. 1 H,  15  N HSQC spectra were collected at 298 K on Varian INOVA 600 MHz and 900 MHz spectrometers. The binding was monitored by titrating H3 peptides (residues 1-19) into NMR samples containing 0. 1-0. 25 mM uniformly  15  N-labeled WT or mutated MORF DPF in 1×PBS pH 6. 5 buffer supplemented with 5 mM DTT and 8-10% D 2 O. NMR data were processed and analyzed as previously described  44. The K d values were determined by a nonlinear least-squares analysis in Kaleidagraph using the following equation: Δδ ¼ Δδ max L ½ þ P ½ þ K d ð Þ À ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi L ½ þ P ½ þ K d ð Þ 2 À4 P ½ L ½ q =2 P ½ ; ð1Þ where [L] is concentration of the peptide, [P] is concentration of the protein, Δδ is the observed chemical shift change, and Δδ max is the normalized chemical shift change at saturation.
Normalized chemical shift changes were calculated using the equation Δδ ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ΔδH ð Þ 2 þðΔδN=5Þ 2 q ; ð2Þ where Δδ is the change in chemical shift in parts per million (ppm). Fluorescence spectroscopy. Spectra were recorded at 25 °C on a Fluoromax-3 spectrofluorometer (HORIBA). The samples containing 0. 8-2 µM MORF DPF in PBS buffer pH 6. 5 supplemented with 5 mM DTT and progressively increasing concentrations of H3 peptides (residues 1-19) were excited at 295 nm. Emission spectra were recorded over a range of wavelengths between 310 and 405 nm with a 0. 5 nm step size and a 0. 6 s integration time. K d values were determined using a nonlinear least-squares analysis and the equation: X-ray diffraction data were collected from a single crystal on the UC Denver X-ray Crystallography core facility Rigaku Micromax 007 high-frequency microfocus X-ray generator equipped with a Pilatus 200 K 2D area detector.
Indexing and scaling were completed using HKL3000. The phase solution was found via molecular replacement using MORF DPF -H3K14bu structure (PDB ID 5U2J) as a model. Model building was carried out with Coot  45  and refinement was performed with Phenix  46. The final structure was validated by MOLProbity  47. ΔI ¼ ΔI max L ½ þ P ½ þ K d ð Þ À ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi L ½ þ P ½ þ K d ð Þ 2 À4 P ½ L ½ Á q 2 P ½ ; ð3Þ MD simulation of the MORF DPF -H3 complexes. The crystal structures of the MORF DPF -H3K14cr and MORF DPF -H3K14bu complexes were used for modeling. AMBER ff14SB force field was used to describe the interaction energy potentials  48. The atomic charges of the crotonyllysine and butyryllysine were determined by the RESP method  49.
Quantitative real-time PCR corrected for primer efficiencies in the linear range were performed using SYBR Green I (Roche) on a LightCycler 480 (Roche). ChIP-seq analysis. Raw ChIP-seq data of H3K14ac, H3K23ac in HMEL cell line and BRPF1/2 and ING5 in RKO cell line were downloaded from the GEO database (GSE47190 and GSE58953). The ChIP-seq accession numbers used in the UCSC profiles were GSM1146448, GSM1146449, GSM1146452, GSM1422905, GSM1422906 and GSM1422908. Processed RNA-seq data of H3K14ac, H3K23ac in IMR90 cell line were downloaded from the ENCODE project (accession number: ENCFF694APR). Raw ChIP-seq data of H3K14ac, H3K23ac in hESC and IMR90 cell line were downloaded from the ENCODE project (accession numbers for H3K14ac: ENCSR283STF and ENCSR057BTG; accession numbers for H3K23ac: ENCSR942NME and ENCSR815LBP).
The raw reads were mapped to human reference genome NCBI 36 (hg19) by Bowtie 1. 1, allowing up to two mismatches. The ChIP-seq profiles were generated using model-based analysis of ChIP-seq (MACS) (v. 2. 1) with parameters --nomodel and --bdg. The profile signals were subtracted by H3 or Input control data. Clonal reads were automatically removed by MACS. The ChIP-seq profiles were drawn using the UCSC Custom tracks utility. For the Venn diagrams, peaks are overlapping if any peaks are overlapping or near within 1000 bp. The ChIP-seq profiles for H3K14ac and H3K23ac are centered around TSSs of RefSeq genes with ±4000 bp extensions. The ChIP-seq data used in the plots were normalized to 30,000,000 total tag numbers. HBO1 knockdown and knockout cells. HBO1 knockdown was performed in U2OS cells by using siRNA HBO1 (Dharmacon)  10  and siLuciferase (Doyya) was used as a control.
Transfection was performed with lipofectamine RNAiMAX transfection reagent (Life Technologies) following the manufacturer's instructions, and cells were collected after 66 h. Hbo1 +/+ and Hbo1 -/-mouse embryonic fibroblasts were described in ref. 37  , immortalized using a shRNA against Trp53 and fully controlled. MS analysis of histone H3 PTMs. Raw data were obtained from Schwammle et al. 40  , in which histones were extracted from mouse embryonic stem cells wild type, Suz12 -/-, Ring1A/B -/-and Dnmt triple knockout (strain E14). Histones were digested with GluC at a ratio of 1:50 enzyme:sample in 50 mM ammonium bicarbonate overnight at room temperature. Obtained intact histone tails (aa 1-50) were analyzed as previously described  54. Briefly, histones were loaded and separated using a two column system consisting on a 5 cm pre-column (100 µm ID) packed with C 18 bulk material (ReproSil, Pur C18AQ 5 µm; Dr.
Maisch) and a 22 cm analytical column with pulled needle (75 µm ID) packed with Polycat A resin (PolyLC, Columbia, MD, 3 µm particles, 1500 Å). Loading buffer was 0. 1% formic acid. Buffer A was 75% acetonitrile, 20 mM propionic acid (Fluka), adjusted to pH 6. 0 using ammonium hydroxide (Sigma-Aldrich), and solvent B was 25% acetonitrile adjusted to pH 2. 5 with formic acid. Histones were run at least in four replicates at a flowrate of 250 nl/min, with a gradient of 5 min 100% solvent A, followed by 55-85% solvent B in 150 min. Data were acquired in an LTQ-Orbitrap Velos with ETD source (Thermo Scientific) was coupled online with the nanoLC used for separation (Dionex Ultimate 3000). The acquisition was performed in the Orbitrap for both precursors and products, with a resolution of 60,000 (full-width at half-height) for MS and 30,000 for MS/MS.
Precursor charge state 1+, 2+ and 3 + were excluded. Isolation width was set at 2 m/z. The six most intense ions were isolated for fragmentation using ETD with an activation Q value of 0. 25, activation time of 20 ms, and supplementary activation. m/z window was set at 450-750 to include only charge states 8-10. The acquisition method was set for not using dynamic exclusion. Spectra were then deconvoluted with Xtract (Thermo Scientific) and searched using Mascot (v2. 5, Matrix Science, London, UK) with the following search parameters: MS mass tolerance 2. 2 Da; MS/MS tolerance 0. 01 Da; enzyme GluC with 0 missed cleavages allowed, database containing only histone H3 sequences (updated October 2012). Variable modifications were: mono-and dimethylation (KR), trimethylation (K) and acetylation (K).
Acknowledgements We thank  X. J. Yang  for kindly providing the cDNA of wild-type FLAG-MORF N and  R. A. Dickins  for kindly providing the Trp53 shRNA knockdown construct. This work was supported by  NIH  grants  R01 GM100907 ,  GM106416  and  GM125195  to T. G. K. ,  CA204020  to X. S. ,  GM126900  to  B. D. S. ,  R01GM121584  to W. R. L. , and  GM110174  and  CA196539  to B. A. G. , by  JSPS   KAKENHI JP18H05534  and  Platform Project for Supporting Drug Discovery  and  Life Science Research (BINDS)  from  AMED JP18am0101106j0002 ,  JP19am0101106j0003  to H. K. , by  Canadian Institutes of Health Research  ( CIHR FDN-143314 ) to J. C. , Welch Grant  A-1715  to W. R. L. , and  Leukemia & Lymphoma   Society Career Development Award  ( 1339-17 ) to X. S. and by the  Australian Government's National Health and Medical Research Council  through Project Grants  1084352  (to A.
Data availability The atomic coordinates and structure factors of MORF DPF -H3K14cr have been deposited in the Protein Data Bank under the accession code 6OIE. Raw mass spec data for MORF and MOZ have been deposited to the MassIVE database, ID number MSV000084304. All other relevant data supporting the key findings of this study are available within the Article and its Supplementary Information files or from the corresponding authors upon reasonable request. The source data underlying Figs. 1d, 2b, 2c, 2e, 7e and 7f are provided as a Source Data file. A Reporting Summary for this Article is available as a Supplementary Information file.
Illumina Truseq indexed adapters were ligated on the resulting fragments, using the Quick Ligation module (New England BioLabs, E6056), according to the manufacturer's recommendations. Following heat denaturation, BrdU-labelled DNA was isolated by immunoprecipitation using an anti-BrdU monoclonal antibody (BD Biosciences, 347580). Immunoprecipitated fragments were amplified for ten cycles using the KAPA Hifi DNA polymerase (KAPABiosystems, KK2502) and the resulting libraries were purified with AMPure XP beads (Beckman Coulter, A63881). Illumina libraries were pooled and sequenced on a NextSeq 500 instrument on Pairedend 2 × 43 or 2 × 75 bases, using a NextSeq 500/550 High Output Kit v2. Repli-Seq data processing. The Repli-Seq data were demultiplexed using the distribution of CASAVA software (CASAVA-1.
8. 2 bcl2fastq2 v2. 18. 12). Illumina adapters were removed using Cutadapt-1. 15, keeping only reads with a minimal length of ten nucleotides. The reads were mapped on the human genome (Hg19), the chicken genome (galGal4, BrdU-labelled DNA as positive control) and the salmon genome (GCF_000233375. 1_ICSASG_v2, unlabelled DNA as negative control) using bwa-0. 6. 2-r126. The mapped data were then processed as previous described  53  , with the following modifications. The PCR duplicates were removed with the Picard tools (http://broadinstitute. github. io/picard) and the paired-end reads that were mapped properly to a unique position of the genome were kept for downstream analysis. The sequence reads located within the regions likely resulting from the sequencing hotspots (defined as the 0. 5% windows with the highest amount of reads within 200 bp windows) were also removed.
The S50 values of biological replicates were strongly correlated to each other (R > 0. 95, P < 10 -15 ) (Fig. 1c  and Supplementary Fig. 1 ). The mean S50 values of the biological replicates were therefore used in the downstream analyses. The raw sequencing data and the processing data are available in Gene Expression Omnibus (GEO) with accession number GSE134709. The raw Repli-Seq data of other lymphoblastoid cells (GM06990 and GM12878) were downloaded from the Encode project (EncodeUwRepliSeq [http:// genome. ucsc. edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeUwRepliSeq]) and the S50 were computed. SDR identification. To identify the genomic loci, the replication of which is specifically delayed upon Aph treatment, we computed the difference between the amount of newly replicated DNA measured by Repli-Seq in Aph-treated cells (Aph) and in non-treated cells (NT).
1 ). Hence, the close windows (n ≥ 2) passing the filtering process and separated by a distance < 250 kb (maximum distance between adjacent SDWs located within fine-mapped CFSs) were then merged and defined as an SDR. Metadata analyses. The RNA-Seq data of GM12878 cells from the ENCOE project (GEO: GSM758559, GSM758559_hg19_wgEncodeCshlLongRna-SeqGm12878CellPapGeneGencV7. gtf [https://www. ncbi. nlm. nih. gov/geo/ download/?acc=GSM758559&format=file&file=GSM758559%5Fhg19% 5FwgEncodeCshlLongRnaSeqGm12878CellPapGeneGencV7%2Egtf%2Egz]) were used. Annotation of genes was retrieved according to gencode V7. The level of transcription was calculated in RPKM (reads per kilobase per mmillion mapped reads) for each protein coding gene. The genes with RPKM > 0. 0001 for both biological replicates were kept and the mean values of the two replicates were used.
ncbi. nlm. nih. gov/sra/?term = SRP065949]). The Repli-Seq, Ok-Seq, Bubble-Seq and GRO-Seq data were analysed using custom scripts written in Python (v2. 7. 9) and R (v3. 4. 4), and the data were visualized with the Integrative Genomics Viewer  54. Cytogenetic analysis. The populations were enriched in metaphasic cells by 2 h of treatment with 200 nM nocodazole (Merck, M1404) prior to cell recovery. Total breaks were counted on metaphase plates stained with Giemsa (Prolabo) without pre-treatments to obtain a homogeneous staining of the chromosomes. Preparations were then de-stained in 70% ethanol and treated to reveal R-bands as previously described  55. Preparations were re-stained with Giemsa and the previously detected breaks were localized relative to the bands. FISH on metaphases, Giemsa counter-staining and immunofluorescence revelation of BrdU-labelled DNA or FHIT-specific FISH probes were carried out as previously described  38, 56.
Acknowledgements The three teams contributing to the work (M. D. , C. -L. C. , C. T. ) have been supported by the  Fondation pour la Recherche Médicale (FRM)  (programme DBI20131228560). M. D. 's team is also supported by the  Institut National du Cancer (INCa)  (subvention  AAP INCa 2017 -PLBIO17-194 ). C. -L. C. 's team is also supported by the  ATIP-Avenir  programme and by the  Plan Cancer programme. S. E. -H. was supported by the  FRM  programme. D. A. was supported by a fellowship from the  Ligue Nationale Contre le Cancer. We acknowledge the Imaging and  Cytometry Platform  ( UMS 3655 CNRS/US 23.
Data availability All sequencing files and processed count matrices were deposited in Gene Expression Omnibus (GEO) under accession number GSE134709. Previously published data (accessions numbers) have been included in the Methods section where appropriate. The source data for Figs. 1b-e, 2a, e  and 4a, c, d , and Supplementary Figs. 1b-d , 2b, 3a and 4a, b are provided as a Source Data file. The Integrative Genomics Viewer session for Figs. 1f, 2c and 3a, b and Supplementary Figs. 1e, 2a and 3b is also included in the Source Data file. All data are available from the authors upon reasonable request.
Dickeya strain selection All D. fangzhongdai genomes publicly available in the NCBI database were included in this study. These genomes were compared to five D. solani, four D. dadantii, five D. dianthicola, five D. chrysanthemi, seven D. zeae, one D. aquatica, two D. paradisiaca and two unassigned Dickeya genomes extracted from the NCBI database. Information on the provenance and genomic data of the D. fangzhongdai strains used in this study are summarized in Table  1. The accession numbers and phylogenetic position of the other Dickeya strains used for the SiLix analyses are presented in Additional file 1: Figure  S1.
Genome sequencing and assembly The D. fangzhongdai B16 and S1 draft genomes assembled from Ion Torrent sequencing data  [16]  exhibited poor quality. Therefore, both genome sequences were improved by Illumina sequencing performed by Viroscan 3D (Lyon University, Faculté de Médecine et de Pharmacie, 69,008 Lyon). Paired-end 2 × 150 bp sequencing was conducted on an Illumina NextSeq500 instrument, with a High Output 150-cycle kit. CLC Genomics Workbench (Version 9. 5. 2, Qiagen Bioinformatics) was used to assemble 9,529,152 (mean length 149 bp) and 9,834,144 reads (mean length 149 bp) for strains S1 and B16, respectively. Final sequencing coverages were 143x and 150x with 51 and 53 scaffolds for strains S1 and B16, respectively.
Annotation and comparison of the Dickeya genomes The functional annotation of predicted genes was achieved using Rapid Annotation Subsystem Technology (RAST) server (http://rast. nmpdr. org/rast. cgi, Aziz et al. [17] ) with the Glimmer 3 gene caller  [18]. Genes of interest (Tables 3, S1, S2 and S3) were manually annotated and assigned to functional classes with the help of the Psi-Blast server. Average nucleotide identity (ANI) was computed using the JSpecies package version 1. 2. 1 with the MUMer algorithm (http://jspecies. ribohost. com/jspeciesws/). In silico DNA-DNA Hybridization (DDH) was calculated according to  [19] , using a dedicated pipeline (http:// ggdc. dsmz. de/). The pan-genome (the entire set of gene families found in genomes of a species), core genome (set of gene families shared by all strains of a species), and species-specific genes (genes unique to one species) were determined using a homology constraint of 80% amino acid identity and 80% alignment coverage.
Two R scripts were used to calculate rarefaction (core genome) and accumulation (pan genome) curves, as described by Meric et al. [20]. As we were working with draft genomes, some split or truncated genes were noted in the final genome assembly. Additional analysis of the annotated draft genomes was conducted to eliminate sequencing errors for the genes of interest (virulence genes, secondary metabolite pathways, specific genes). Presence and position of those genes was manually inspected to detect split genes, truncated genes at the end of contigs and missing genes. Visualization and comparison of genomes was conducted on the CGView server (Circular Genome Viewer, https://server. gview. ca/) with the BlastAtlas tool  [21]. Two complete genomes (ND14b and JS5) were used as reference genomes.
Phylogenetic analysis In silico multilocus sequence analysis (MLSA) was performed on 1162 concatenated amino acid orthologous sequences. Clustering of orthologous sequences into homologous families was conducted using the SiLix software package  [22] , using an 80% identity threshold on full-length proteins. Orthologous sequences were aligned with the Muscle software  [23] , then concatenated, and alignments were curated using Gblocks  [24]. Phylogeny was performed using the PhyML algorithm  [25]  with the following settings: substitution model LG, 100 bootstraps, model-given amino acid equilibrium frequencies, invariable sites optimized, tree searching NNI, starting tree BioNJ. Pectobacterium atrosepticum was used as an outgroup. The iTOL tool was used to visualize the phylogenetic trees  [26].
Presence of virulence-associated genes The presence of Dickeya virulence-associated genes previously reported by Reverchon and Nasser  [4]  was assessed using BLASTp analysis. The D. fangzhongdai core genome was compared to D. dadantii 3937genome annotation from the ASAP database  [31]  to determine the presence of known virulence determinants and regulators. Furthermore, genomic regions containing secondary metabolite biosynthesis gene cluster were identified using AntiSMASH software (version 4. 1. 0, https:// doi. org/10. 1093/nar/gkv437) [32]. Functional classification of the specific genes D. fangzhongdai species-specific gene families i. e. families absent in or sharing below 80% homology with the gene families present in other Dickeya species, were extracted from the SiLix output. The COG categorization of species-specific genes was conducted by submitting predicted CDS to the EggNOG 4.