dataesr/scientific-paragraphs-categorization huggingface dataset:
▸ Figure  2. Loss of REV3L disrupts the replication timing in specific genomic loci. A Cells were pulse-labeled with BrdU for 1. 5 h and sorted by flow cytometry in two fractions, S1 and S2, corresponding to early and late S-phase fractions, respectively. Neo-synthesized DNA was immunoprecipitated with BrdU antibodies. Early and late neo-synthesized DNAs were labeled with Cy3 and Cy5, respectively, and hybridized on microarrays. After processing analysis with the START-R software, replication-timing profiles were obtained from two replicates (see Fig  EV2A ). Shown are the zoomed microarray profiles of the timing of replication on chromosome 1 (136. 6-143. 5 Mb), chromosome 5 (127. 3-134. 7 Mb), and chromosome 9 (46. 1-51. 3 Mb) from Rev3l +/+ and Rev3l À/À MEFs overlaid. Blue lines represent replication timing from Rev3l +/+ MEFs, and red lines represent replication timing from Rev3l À/À MEFs.
Flow cytometry In order to study the S-phase progression in Rev3L À/À and Rev3L +/+ fibroblasts, cells were incubated with 50 µM bromodeoxyuridine (BrdU) for 15 min prior to be harvested at different time points. Cells were collected, washed in PBS, and fixed in 80% ice-cold ethanol overnight at À20°C. Total DNA was stained in 2. 5 µg propidium iodide (IP) and 50 µg/µl RNAse (Sigma) in PBS for 30 min at room temperature. Samples were analyzed on a C6 flow cytometer using the C6 Flow software (BD Accuri).
For each condition, 20-50 images were acquired under non-saturating conditions. Identical settings were applied to all samples within one experiment. Images were analyzed with the Axio Vision software (Zeiss). Specific module was generated for nuclei segmentation based on DAPI signal according to intensity threshold, generating a mask that identified each individual nucleus as an individual object. Focus segmentation for 51BP1 and RAD51 was performed using an integrated spot-detection module. After segmentation and pixel quantification, the quantified values for each cell/foci (mean and total intensities, area, number of foci) were extracted and exported to a home-made software as well as to CellProfiler software.
MetaMorph software (Roper Scientific) was used for image acquisition and analysis. Only unbroken replication signals, as evidenced by embedment in total DNA staining or SatIII FISH signal, were taken into consideration. Fork speed (in kb/min) was calculated by divided the length of the IdU or CldU track (in kb) by the labeling time (in min). Fork ratio was calculated for each unbroken bicolor replication signal as the ratio between max (IdU length, CldU length) and min (IdU length, CldU length). Statistical analysis was performed with the nonparametric Mann-Whitney rank-sum test using GraphPad Prism 6 software.
Proximity Ligation Assay (PLA) Mouse embryonic fibroblast (MEF) Flag-REV3L were seed on coverslips for 24 h and washed with PBS; then, protein pre-extraction was carried out with CSK50 (50 mM NaCl for PLA Flag-Histones) or CSK100 (100 mM NaCl for PLA DSB markers) for 5 min on ice, fixed with 4% paraformaldehyde, and blocked in BSA-PBS 3% for 30 min. Coverslips were incubated with primary antibodies for 1 h at RT. Proximity ligation was performed using the Duolink â In Situ Red Starter Kit Mouse/Rabbit (Sigma-Aldrich) according to the manufacturer's protocol. The oligonucleotides and antibody-nucleic acid conjugates used were those provided in the Sigma-Aldrich PLA kit (DUO92101). Images were quantified by counting the number of foci per nucleus using ImageJ software. Statistical analysis was performed with the nonparametric Mann-Whitney rank-sum test using GraphPad Prism 5 software.
Replication-timing experiments and microarrays Cells were incubated with 50 µM of BrdU for 1. 5 h and collected, washed three times with DPBS (Dulbecco's phosphate-buffered saline, Gibco Life Technologies), and then fixed in ethanol 75%. Cells were resuspended in DPBS with RNAse (0. 5 mg/ml) and then with propidium iodide (50 µg/ml) followed by incubation in the dark at room temperature for 30 min with low agitation. The log 2 -ratio timing profiles were smoothed using the Triangular Moving Average option of the Agilent Genomic Workbench 5. 0 software with the linear algorithm and 500 kb windows.
GC content For the GC content, two steps were required: (i) Loading of the DNA sequence from interval files was performed with the Extract Genomic DNA tool with the mm9 genome annotation and (ii) calculation of the GC content for each interval by the GeeCee EMBOSS tool installed on the GALAXY Web site. Intersection and coverage with H3K27ac, H3K4me3, and H3K27me3 marks Data are loaded from the UCSC Web site. Peak coordinates of H3K27me3 come from the Broad Institute in the mm8 genome annotation (http://genome-euro. ucsc. edu/cgi-bin/hgTrackUi?hgsid= 199709818_uvGUfB28yATavzb9eAlxuHR5KGbQ&c=chr12&g=broad ChromatinChIPSeq). The conversion of coordinates to mm9 genome annotation was performed with the Lift-over tool available in the GALAXY Web site (https://usegalaxy. org/). The peak coordinates of H3K4me3 and H3K27ac were obtained from the Ludwig Institute for Cancer Research via the UCSC Web site (http://genome-euro.
Structural variant analysis Single cells from Rev3L À/À and Rev3L +/+ fibroblasts were isolated, amplified, and subjected to whole genome sequencing. The genomes were sequenced according to the manufacturer protocols (BGI Tech solutions, Hong Kong, Co. , Ltd) with a mean coverage of 30× using 150 bp paired-end reads with BGISEQ-500 sequencer. Reads were then aligned to the mm9 mouse genome using BWA mem software  (Li & Durbin, 2009)  and sorted with SAMtools  (Li et al, 2009). Then, we removed duplicates from the BAM file according to the GATK Best Practices pipeline (Van der Auwera et al, 2013). To identify structural variants (SV) from genomic data, we used GRIDSS2  (Cameron et al, 2017)  and Manta  (Chen et al, 2016)  software and compared replicates 1 and 2 between each other for Rev3l +/+ and Rev3l À/À samples.
Gene expression profiling data analysis RNA was extracted using Qiagen column (RNeasy kit) according to the manufacturer's protocols. The quantity and purity of the total RNA were evaluated using a NanoDrop spectrophotometer. Quality of RNA samples was assessed using Lab-on-a-chip Bioanalyser 2000 technology (Agilent Technologies), based on the 28S/18S ribosomal RNAs ratio. All samples included in this study displayed a ratio of ribosomal RNAs between 1. 5 and 2. Labeling of RNA samples was done according to Agilent oligo Cy5 or Cy3 probes labeling protocol using the Agilent Low Input QuickAmp labeling kit (ref 5190-2306) adapted for small amount of total RNA (100 ng total RNA per reaction). Hybridization was performed using the Agilent Hybridization Protocol (Gene expression Hyb kit Large -ref 5188-5280).
Scanning was performed with an Agilent G2505C DNA Microarray scanner using default parameters: 20 bits mode, 3 µm resolution, at 20 C in low ozone concentration environment. Microarray images were analyzed by using Feature extraction software version 10. 7. 3. 1 from Agilent technologies and Agilent normalization protocol GE2_107_Sep09 with 028005_D_F_20130207 as design. Default settings are used. A quantile array normalization was performed on raw MedianSignal values with in-house scripts using Bioconductor LIMMA package. Controls and flags spots were filtered. Missing values were completed with KNN imputation method. Then, the median of all probes for a given transcript was computed to summarize the data. Normalized data (intensities in log2) were imported in BrB Arrays Tools (https://brb.
Acknowledgements We thank  T. Jenuwein  ( Max Planck Institute, Germany ) for providing Suv39h dn cells. We are also grateful to  Niels de Wind  ( Leiden University, Netherland ) and  Julien Duxin  ( Copenhagen University, Denmark ) for sharing unpublished results on HP1 and SCAI, respectively. P. L. K. and E. D. express their gratitude to  Michelle Debatisse  and  Stephane Koundrioukoff  for their help and valuable advice on the application of the DNA combing-FISH technique to study replication dynamics in pericentromeric heterochromatin. We thank  Nikola Djordjevic ,  Charl ene Garandeau ,  Lo elia Babin , and  Kei-ichi Takata  for experimental help and all members of the  Kannouche lab  for helpful discussions, especially  Said Aoufouchi. This work was performed thanks to  Gustave Roussy core facilities (Cell imaging, Genomics and Bio-informatics).
The P. L. K lab was supported by  La Ligue Nationale contre le Cancer  (Equipe labellis ee). This work was supported by grants from  Institut National du Cancer (INCa)   PLBIO16-011  (P. L. K. and J-C. C),  INCa-DGOS-Inserm   12551 ,  ARC-ARCPGA12019120001055_1578 , and  ANR-14-CE10-0008-01  (P. L. K. and J-C. C). S. A. S received support from the  Minist ere de l'Enseignement Sup erieur et de la Recherche  and the  Foundation ARC  and B. B received support from the  Minist ere de l'Enseignement Sup erieur et de la Recherche  and the  Foundation pour la Recherche M edicale. The  J-C. C lab  is supported by the  IdEX Universit e de Paris   ANR-18-IDEX-0001. R. D. W. and J. T were funded by  National Institutes of Health  grant  CA193124 ,  Department of Defense  grant  W81XWH-17-10239 , and the J.
Data availability • Microarray data: EMBL-EBI E-MTAB-8338 (https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-8338/). • Replication-timing data: GSE178927 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE178927). Expanded View for this article is available online.
Methods This analysis uses the framework of the Dynamic Interactive Vulnerability Assessment (DIVA) model which has been applied to problems such as coastal erosion  30  , coastal flooding  31  and coastal wetland change  32  , among others. The underlying structure is a dataset of coastal areas and floodplains based on 12,148 coastal segments which divide the world's coasts (excluding Antarctica) into lengths of similar coastal characteristics  33. The segments are variable in length with an average of 70 km. All data, such as SLR rates, socioeconomic development, extreme water levels, subsidence rates and so on, are associated with the appropriate segment. To analyse the global effects of subsidence on relative SLR we combine data on four components of relative sea-level change: (1) Satellite observations of sea-level change from 1993 to 2015, (2) GIA, derived from the model of Peltier et al.
11  , (3) Delta subsidence, which includes natural and anthropogenic subsidence for 117 deltas worldwide, building on the earlier work of Ericson et al. 13  , (4) City subsidence, which captures the additional subsidence beyond delta subsidence that coastal cities in deltaic and alluvial plains experience. We thereby consider susceptible coastal cities with populations exceeding one million people in 2005 that are prone to subsidence (following Nicholls et al. 34  ). These four components are independent and hence can simply be summed for each segment. We exclude uplift and subsidence due to other processes such as tectonics  6  as there are no consistent global datasets available. Most analyses of these processes are local or regional (for example, ref. 35  ) and obtaining the data required to create relative SLR scenarios at the global scale is problematic  27.
Data. The following datasets are used in this analysis. Sea-level change. For the sea-level change observations, we use the satellite altimetry product from the European Space Agency (ESA) Climate Change Initiative (CCI) sea level project. We use this product because the data are computed with consistent geophysical corrections throughout the whole record and are thus specifically tailored for trend estimates  21, 22. The product is a two-dimensional gridded sea surface height of delayed time anomalies described and validated in Legeais et al. 22. We analysed the data over the period from 1993 to 2015. The dataset is provided at monthly intervals on an ¼° regular grid as anomalies computed with reference to the 1993-2012 period. Sea-level anomalies for the study sites were extracted from the global dataset.
GIA. Local sea-level change data due to GIA caused by ice loading and unloading are taken from the ICE-6G_C (VM5a) model  11. Local land movement in response to deglaciation is combined with global topography and bathymetry to compute local sea-level change on a 0. 2° × 0. 2° grid. This gridded dataset is projected to the DIVA coastal segments by assigning the average sea-level-change value over all intersected grid cells to each segment. Delta subsidence. Delta subsidence is estimated for 117 deltas comprising the world's major deltas. For each delta, a single indicative average value is assumed (except where stated otherwise), covering a similar time period to the sea-level measurements. For 40 of the more populated deltas, data are based on those developed by Ericson et al. 13  , with a few corrections.
As the population projections do not differ much until the middle of the twenty-first century, we only report the one based on the middle-of-the-road scenario from the SSP2 in the main paper. Results for other population projections are shown in Extended Data Fig. 3. We note that other population datasets would lead to different quantitative outputs as demonstrated in the analysis of Hinkel et al. 31. However, the main results in the article based on population weighting are unlikely to be sensitive to these differences as the relative distribution of population along the coast is common to all global population datasets. In terms of population statistics, we consider the population living below the 10 m elevation contour in 2015 for the population weighting of SLR rate, comprising 768 million people.
To estimate population exposure to coastal flooding, we also consider the coastal floodplain population living below the 100-year-flood elevation, which is dynamic in time due to relative sea-level change and population change. This population was 235 million people in 2015. Mean and extreme SLR scenarios to 2050. For SLR projections we use three global-mean SLR scenarios taken from Hinkel et al. 31  : the 50th percentile of RCP2. 6, RCP4. 5 and RCP8. 5 using the UK Met Office Hadley Centre Earth System Model (HadGEM-ES2)  56. Extreme water levels are assumed to uniformly increase with SLR, following twentieth-century observations  57. Extreme-water-level distributions are taken from the Global Tide and Surge Reanalysis (GTSR) database  58  and referenced to EGM96 geoid63 (ref. 59 ) to be compatible with SRTM data  60.
Acknowledgements We thank  J. Ericson  for sharing his dataset on delta subsidence. R. J. N. was supported under the  Deltas, Vulnerability and Climate Change: Migration and Adaptation  project ( International Development Research Centre (IDRC) ,  Canada ,  107642 ) under the  Collaborative Adaptation Research Initiative in Africa and Asia programme  with financial support from the  UK Government's Department for International Development , the  IDRC ,  Canada  and the  PROTECT  Project. The views expressed in this work are those of the creators and do not necessarily represent those of the  Department for International Development  and  IDRC  or their boards of governors. We thank the  ESA CCI sea level  project for providing the level 4 sea-level data and  Centre National d'Etudes Spatiales  ( CNES ) for providing the level 2 data from the Topex and Jason 1, 2 and 3 satellites.
Other portions of this research were made possible by support from the  European Union  through the projects  Responses to coastal climate change: Innovative Strategies for high End Scenarios -Adaptation and Mitigation (RISES-AM ) funded by the  European Commission's Seventh Framework Programme ,  2007-2013 , under the grant agreement number  603396 ), and Green growth and win-win strategies for sustainable climate action (GREEN-WIN) and  Articles Nature Climate ChaNge CO-designing the Assessment of Climate CHange costs (COACCH) , both of which are funded by  European Union 's  Horizon 2020 research and innovation programme  under grant agreement numbers  642018  and  776479 , respectively. Further funding was received through two projects, INSeaPTION and ISIpedia, which are part of  European Research Area for Climate Services  ( ERA4CS ), an  ERA-NET initiative by JPI Climate , and funded by the  Research Council for Sustainable Development (Formas), Sweden; Federal Ministry of Education and Research (BMBF) ,  Germany  (grant numbers  01LS1711C  and  01LS1703A );  Federal Ministry of Science, Research and Economy (BMWFW) ,  Austria; Innovation Fund Denmark (IFD) ;  Ministry of Economic Affairs and Digital Transformation (MINECO) , Spain; and  National Research Agency (ANR) ,  France , with co-funding by the  European Union  (grant  690462 ).
Data availability All datasets used in the production of this paper are available from: https://doi. org/10. 5281/zenodo. 4434773 (ref. 65 ). The sea-level data are referenced under the following: https://doi. org/10. 5270/esa-sea_level_cci-1993_2015-v_2. 0-201612. It is freely available from: http://www. esa-sealevel-cci. org/products. Source data are provided with this paper.
The effect of the missense variants on protein function was further investigated through structural modeling and molecular dynamics simulation studies. Our results establish DLG4-related synaptopathy as a new and rare brain disorder. these individuals have been updated, while further clinical information could not be obtained for seven individuals. The previously unreported individuals were identified through GeneMatcher (http://genematcher. org/ statistics/)  18  or the Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER, https://decipher. sanger. ac. uk). 19 The clinical information of each individual was reviewed including neurodevelopment, growth parameters, neurological manifestations, behavior, dysmorphology, and MH by the local clinicians (Tables  1  and S1 ).
The prevalence of each clinical manifestation related to the total number of cases for whom this information was available is shown in Table  S2. The presence of ID was evaluated in individuals over 5 years old and ASD in those over 3 years old. Regression was defined as a loss of previously acquired skills. Statistical calculations were made using Pearson chi-squared test with Yates continuity correction. A heat map for clinical features was built by means of gplots package in R and hierarchical clustering was made according to binary distances (Fig. 2S ).
Identification and evaluation of the variants DLG4 variants were identified in the probands using massively parallel sequencing (next-generation sequencing; NGS) based technologies (exome/ genome sequencing with or without employing virtual gene panels) in clinical diagnostic or research settings, and parental testing for the identified variant was performed when possible (n = 48). Pathogenicity of the identified DLG4 variants was established according to American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) criteria (Table  S3 ). 20, 21 The Genome Aggregation Database (gnomAD v. 2. 1. 1; https://gnomad. broadinstitute. org/) was employed to check the presence of the variants in control populations. NMDEscPredictor tool  22  (https:// nmdprediction.
shinyapps. io/nmdescpredictor/) was employed to predict whether the truncating variants escaped nonsense-mediated decay (NMD). SpliceAI  23  (https://github. com/Illumina/SpliceAI,) a deep learning-based splice variant prediction tool was used to annotate the variants for their predict effect on splicing (Table  S3 ). All the variants are described using the NM_001365. 4 (GRCh37/hg19) transcript of DLG4 (Fig. 2 , Tables  2  and S3 ).
Structural modeling and molecular dynamics simulation of the missense variants The 3D structure of the human PSD-95 protein (UniProtKB id: P78352-2, The UniProt Consortium 2019) was modeled using as templates the homologous structures present in the Protein Data Bank: 1KJW  24  and 2XKX  25  (Fig. 3 ). Models for the wild type protein and the Gly220Val, Asp229Val, Gly241Ser, Asp375Gly, Arg629Gln and Thr654Ile variants were built using the SWISS-MODEL server (https://swissmodel. expasy. org), and their structural quality was within the range of that accepted for homology-based structure (Anolea/Gromos/QMEAN4). 26  Molecular dynamics simulation was carried out as described previously  27  and the details can be found in the Supplementary text.
The other synonymous variant, p. (Gly373=) (c. 1119C>T), identified in individual 22 was in the middle of exon 11 and SpliceAI predicted a donor gain with a probability of 44%. Further analyses with reverse transcriptase polymerase chain reaction (RT-PCR) revealed a deletion in the RNA transcript (r. 1118_1212del) predicted to result in a frameshift, p. (Glu374Glnfs*11). The missense variants were localized to the functional domains of the protein (three in the PDZ2 domain, one in the PDZ3 domain, and two in the Guanylate kinase-like domain). None of these variants were detected among the 125,748 exomes and 15,708 genomes from unrelated individuals in the gnomAD database, and they were all classified as pathogenic or likely pathogenic, except for two missense variants that were classified as variants of uncertain significance (VUS) according to   ACMG/AMP criteria.
3 ). The simulation studies suggest that all these substitutions alter the structure of the PDZ2 domain (Fig. 3b, c ). Furthermore, the p. (Asp229Val) substitution is predicted to modify the kinase/ phosphatase recognition motif, and the p. (Gly241Ser) substitution is predicted to modify the ubiquitylation recognition motif, both of which are likely to affect the protein function. Notably, modeling of the p. (Gly241Arg) variant, which is reported in a single individual in the gnomAD database and alters the same amino acid as the p. (Gly241Ser) variant (ID-15), did not suggest a structural effect on the protein structure supporting that this variant was likely benign (Fig. 3d ; Supplementary material). The Asp375 residue is located in a loop between two betasheets in the PDZ3 domain, which is enriched in negatively charged amino acids (Asp374, Asp375, and Asp377), and it is probably involved in the interaction with other adjacent structures such as potassium channels  25  (Fig.
16  In the present study, we could re-examine 38 individuals specifically for MH features, which were present only in 9, suggesting that MH is not a major clinical feature of DLG4-related synaptopathy. In a very recent study comprising 31,058 individuals, LoF variants of DLG4 were identified in 15 individuals,  40  suggesting that incidence of this synaptopathy is about 0. 05% among the individuals with ID. DLG4 is likely to be intolerant to both missense and LoF variants (Z = 4. 93 for missense and observed/expected (o/e) value = 0. 06-0. 24 for LoF variants, gnomAD database). All the variants described in this study meet criteria for classification as pathogenic or likely pathogenic, except for two of the missense variants, p. (Gly241Ser) and p. (Asp375Gly), which are classified as VUS. We included these variants as they were associated with ASD in a previous study,  15  and the modeling studies show an effect on the protein, acting in highly conserved regions of its functional domains.
LoF variants reported in this study are distributed throughout the protein, whereas the missense variants are localized to the functional protein domains (PDZ1, PDZ3, GKLD). Structure modeling suggests that the missense variants affect structural conformation and/or protein function and are therefore likely to act as LoF variants. These modifications in highly conserved positions of the protein presumably alter its function, thereby affecting its interaction with other proteins, which is fundamental for synaptogenesis, functional dynamics, and plasticity. Modeling studies were helpful to predict the functional consequences of the missense variants and thus supported the pathogenicity classification. Furthermore, modeling of the p. (Gly241Arg) variant, which is reported in a single presumably unaffected individual in the gnomAD database and affects the same amino acid as the p.