dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, 99% of them reported that symptoms, PCa, PPi, and/or UCa were used as indicators by their physician. Eighty ePatients (89%) reported that the PCa value was an indicator and 42 of them (53%) reported that the low normal range of PCa was the target (Figure  4 ). Symptoms, PPi value, calcium-phosphate product, and UCa were reported as indicators by 81 (90%), 38 (42%), 12 (13%), and 67 (74%) of these 90 ePatients (Figure  5B ). In addition to PCa, no data were specifically recorded regarding the target values. From the Épi-Hypo 2019 cohort, we found that 101 (94%) patients had a PCa measured during this period; out of them, 33 (33%) had a value between 2. 0 and 2. 2 mM. This proportion highly differed (p<0. 001) from the objectives of ePatients and Physicians, among whom 81% and 53% reported targeting this specific range (Figure  4 ).
Finally, better communication between patients and physicians could minimize the perceptions gap: a longer time during visits has to be organized and could include dedicated Patient-Reported Outcome collection. Taken together, our data show i) endocrinologists are part of a broad network of physicians taking care of patients with chronic hypoparathyroidism, ii) medications vary a lot among them, especially off-label (teriparatide) and over-the-counter (magnesium salts) ones, and iii) compliance with international guidelines regarding follow-up seems low and should be increased. (Bordeaux), Eric Thervet (Paris), Isabelle Tostivint (Paris), Marc Ulrich (Valenciennes), Paul Elie Valensi (Bondy), Marie-Christine Vantyghem (Lille), Bruno Vergès (Dijon), Delphine Vezzosi (Toulouse). physicians were enrolled in the Épi-Hypo study.
Among them, 118 (69%) answered the questionnaire about their habits in terms of follow-up of patients with chronic hypoparathyroidism: their data are presented as 'Physicians' data. B. In November 2019, we conducted an online questionnaire dedicated to patients living with chronic hypoparathyroidism. We collected 155 answers. Among them, 110 (71%) were analyzed; we present that data as the 'ePatients' data. C. From September 2016 to September 2019, 939 patients were enrolled in the Épi-Hypo study. Among them, 916 (98%) met the inclusion criteria. Finally, 107 (12%) had a follow-up visit in 2019: we present that data as the 'Épi-Hypo2019' data. Trends in follow-up habits during chronic hypoparathyroidism. A. Data from the 'Physicians' cohort (blue, n=118) show that they are more akin to checking plasma calcium (PCa) twice a year, while the ePatients (green) report a broader distribution (more frequent checks), and data from the Épi-Hypo 2019 (orange, n=107) cohort report less frequent measurements of PCa (p<0.
001). In the ePatients (green) who reported screening for PCa (n=93), the distribution was broader. B. The most frequently checked organs during chronic hypoparathyroidism are the kidneys, eyes, brain (by bone mineral density, BMD), and brain. Some ePatients and Physicians also reported other organs (such as the heart) that could be among the 110 patients of the cohort, 93 (85%) were followed with kidney imagery, asked for eye checks, and/or checked for PCa (at least twice a year). Data are presented as percentages (%) of the total answers. The data were extracted from the Épi-Hypo study to determine the characteristics of patients who had at least one follow-up visit in 2019 (Épi-Hypo 2019). We compared these data to those obtained from an online survey conducted in patients with chronic hypoparathyroidism (ePatients).
Acknowledgments The authors would like to warmly thank the patients living with chronic hypoparathyroidism who participated in the present study and the physicians who took precious time to answer the questionnaire as well as to build real-life evidence of chronic hypoparathyroidism. We also thank  Ms. Aurélie Vilfaillot , who provided us with statistical analysis, and the  OSCAR Filière Maladies Rares  and  Assistance Accepted Manuscript  published as  EC-21-0350. R2. Accepted for publication: 23-Dec-2021 Copyright © 2021 the authors Downloaded from Bioscientifica. com at  01/04/2022 03:02 : 10PM  via free access.
, 2021). Taking advantage of the well-documented genomic databases of SCLC cell lines  (Gay et al. , 2021; George et al. , 2015; Rudin et al. , 2019; Tlemsani et al. , 2020) , we asked whether DNA methylation analyses based on the clinically used MethylationEPIC array could provide novel insights in the genetic regulatory pathways of SCLCs and be used as a classifier and therapeutic guide for SCLCs. DNA methylation is used clinically to correlate survival  (Gevaert et al. , 2015; Hao et al. , 2017) , as well as drug response  (Bacolod and Barany, 2021; Butler et al. , 2020; Krushkal et al. , 2020; Ortiz-Barahona et al. , 2020). To that effect, O 6 -methylguanine (O 6 -MeG)-DNA methyltransferase (MGMT) promoter methylation is an established prognostic biomarker in glioblastomas treated with temozolomide  (Butler et al.
, 2009; Spainhour et al. , 2019; Su et al. , 2018 ). Yet, gene body methylation has been shown to reflect gene activity  (Bacolod and Barany, 2021; Yang et al. , 2014) , suppress alternative transcript isoforms by blocking promoters, and to regulate splicing  (Huang et al. , 2021; Lev Maor et al. , 2015; Li et al. , 2018; Neri et al. , 2017; Teissandier and Bourc'his, 2017; Yang et al. , 2014). Studies have begun to explore the epigenetics of SCLCs by mapping the binding sites of the LTFs  (Borromeo et al. , 2016; Huang et al. , 2018) , the enhancers based on histone H3 lysine 27 acetylation (H3K27ac)  (Pozo et al. , 2021) , and promoter DNA methylation  (Krushkal et al. , 2020; Tlemsani et al. , 2020). To expand our understanding of how gene expression is regulated in SCLC and determine the potential translational value of the MethylationEPIC 850k array, we took advantage of the SCLC cell line databases (https:// discover.
RESULTS General overview of the small cell lung cancer cell line methylome data and classification of small cell lung cancer cell lines based on their methylome High-resolution DNA methylation (EPIC 850K array) was obtained for 68 different SCLC patient-derived cell lines  (Krushkal et al. , 2020; Tlemsani et al. , 2020) , H3K27ac data for 18 SCLC lines  (Huang et al. , 2018; Pozo et al. , 2021)  and ASCL1, NEUROD1, and POU2F3 ChIP-seq for 6 SCLC lines  (Borromeo et al. , 2016; Huang et al. , 2018). We analyzed each dataset and studied their predictive value as classifiers of SCLCs and predictors of gene expression. The data included in the present study are summarized in Figure  1A  and Table  S1 ; they are publicly accessible in the updated version (v. 1. 1) of SCLC_Cellminer (https://discover.
Notably, the gene body of highly expressed genes was consistently hypermethylated (top tracings), while gene bodies were significantly less methylated and more variable in genes with low or no expression (bottom tracings in Figure  2A ). These methylation profiles were similar in all cell lines in the SCLC database, and the methylation types were reproducible using reduced bisulfite sequencing data obtained from the CCLE  (Ghandi et al. , 2019)  (Figures  S2A-S2C ). The bisulfite data are also available in CellMinerCDB (https:// discover. nci. nih. gov/cellminercdb) (Luna et al. , 2021). As gene body methylation was consistently associated with expression for many genes, we created a genelevel summarization algorithm of gene body methylation taking into account all probes instead of using a single-probe approach  (Bacolod and Barany, 2021; Krushkal et al.
, 2020) , and which can be used to assess independently promoter and gene body methylation. We included gene body probes outside of promoters, excluding CpG island overlapping probes, which tend to be hypomethylated (see Figure  1 ). In cases where a gene had multiple transcripts, we selected the transcript with the most positive correlation between gene body methylation level and gene expression. Only a very small number of very short genes such as ASCL1 could not be analyzed for gene body methylation because of insufficient probe coverage. Based on this approach, we were able to provide scores for both gene body and promoter methylation and integrated these features in SCLC-CellMinerCDB (https://discover. nci. nih. gov/sclccellminercdb). Using the genome-wide methylome data from all 66 SCLC cell lines (see Figure  1A ), we compared promoter and gene body methylation as a function of gene expression.
Additional examples where gene body methylation predicts gene expression better than promoter methylation are provided in Figure  2F  for MGMT, SLFN11 and the cell surface glycoprotein and cancer biomarker CEACAM5, which is selectively expressed in neuroendocrine SCLC cell lines  (Tlemsani et al. , 2020). Thus, scoring gene body methylation in addition to promoter methylation can be particularly helpful to predict gene expression for genes with a wide range of expression, and this feature has been implemented in our new SCLC-CellMinerCDB release (v. 1. 1) along with other features that are listed in Table  S4  (https:// discover. nci. nih. gov/SclcCellMinerCDB/). Enhancer and small cell lung cancer-lineage-defining transcription factors distributions and their relationship with DNA methylation Because histone post-translational modifications are key epigenetic regulators, we integrated into our methylome analysis the recently available H3K27ac ChIP-seq data  (Pozo et al.
, 2016) , the ASCL1, NEUROD1, and POU2F3 binding sites were found mainly localized outside of promoters (Figure  3A ). Alignment of the genomic tracings for three representative cell lines corresponding to each subtype (NCI-H889 for ASCL1, H82 for NEUROD1 and H1048 for POU2F3) shows that the ASCL1, NEUROD1, and POU2F3 binding sites do not overlap with each other (Figure  3E ). Furthermore, alignment with the H3K27ac data shows that the differential distribution of the LTF sites is concordant with enhancer signals in each of these regions (Figure  3E ). For instance, regions bound by ASCL1 have matching active enhancers in NCI-H889 cells, but not in the other cell lines. Similar distribution could be seen in the case of the NEUROD1 sites in NCI-H82 cells and of the POU2F3 sites in NCI-H1048 cells (Figure  3E ).
, 2010; Greenberg and Bourc'his, 2019; Hervouet et al. , 2018; Roadmap Epigenomics et al. , 2015). Gene body methylation has been proposed to enhance transcription by suppressing the firing The Multivariate Analyses tool of SclcCellMinerCDB (https://discover. nci. nih. gov/sclccellminercdb) was used to generate the analyses and panels (snapshots from the website). Queried genes are listed above each panel. The bottom panel shows the SLFN11 gene body and promoter methylation as predictors for topotecan response in the 66 SCLC cell lines from the NCI-DTP database. Insets below each plot show technical details and significance for gene body methylation (bmt) and promoter methylation (mth). Asterisks indicate statistical significance. of alternate transcripts and by facilitating splicing  (Bacolod and Barany, 2021; Huang et al.
Limitations of the study Our study has some limitations: 1) DNA methylation was measured with 850K arrays and RRBS (reduced representation bisulfite sequencing), which cover more positions than the 450k array, are still limited to predetermined areas; 2) as gene body methylation is usually deposited through transcription, in cases where a gene is expressed in all cell lines, the gene body methylation level may be less predictive of expression levels (which is similarly seen in case of promoter methylation); 3) Our data are based on cancer cell line models. Therefore, follow-up clinical studies including both RNA-seq and EPICmethylation 850K arrays are warranted to validate the predictive value of the high-resolution DNA methylation arrays in patient samples.
Figure5. Predictive value of gene body and promoter DNA methylation to classify SCLC and assess the expression of therapeutically relevant genes The Multivariate Analyses tool of SclcCellMinerCDB (https://discover. nci. nih. gov/sclccellminercdb) was used to generate the analyses and panels (snapshots from the website). Queried genes are listed above each panel. The bottom panel shows the SLFN11 gene body and promoter methylation as predictors for topotecan response in the 66 SCLC cell lines from the NCI-DTP database. Insets below each plot show technical details and significance for gene body methylation (bmt) and promoter methylation (mth). Asterisks indicate statistical significance.
RESOURCE SOURCE IDENTIFIER Deposited data NCI-SCLC cell line methylation (850K array)  (Krushkal et al. , 2020; Tlemsani et al. , 2020 ) GSE145156 H3K27ac data  (Huang et al. , 2018; Pozo et al. , 2021)  GSE151002, GSE115124 ASCL1, NEUROD1, POU2F3 data (Borromeo et al. , 2016; Huang et al. , 2018) GSE69398, GSE115124 RRBS data (Ghandi et al. , 2019) PRJNA523380 CellMinerCDB cell line data (Rajapakse et al. , 2018) https://discover. nci. nih. gov/cellminercdb/ SCLC-CellMinerCDB cell line data (Tlemsani et al. , 2020) https://discover. nci. nih. gov/SclcCellMinerCDB/ Software and algorithms APE (Paradis et al. , 2004) https://cran. r-project. org/web/packages/ape/index. html rtacklayer (Lawrence et al. , 2009) https://bioconductor. org/packages/release/bioc/html/ rtracklayer. html clusterProfiler (Yu et al.
, 2012) https://bioconductor. org/packages/release/bioc/html/ clusterProfiler. html ReactomePA (Yu and He, 2016) https://bioconductor. org/packages/release/bioc/html/ ReactomePA. html umap (McInnes et al. , 2018) https://cran. r-project. org/web/packages/umap/ index. html minfi (Aryee et al. , 2014) https://bioconductor. org/packages/release/bioc/html/ minfi. html ChIPseeker (Yu et al. , 2015) https://bioconductor. org/packages/release/bioc/html/ ChIPseeker. html ComplexHeatmap (Gu et al. , 2016) https://www. bioconductor. org/packages/release/bioc/ html/ComplexHeatmap. html BAMscale (Pongor et al. , 2020) https://github. com/ncbi/BAMscale sva (Leek et al. , 2012) https://bioconductor. org/packages/release/bioc/html/ sva. html edgeR (Robinson et al. , 2010) https://bioconductor. org/packages/release/bioc/html/ edgeR.
html bismark (Krueger and Andrews, 2011) https://www. bioinformatics. babraham. ac. uk/projects/ bismark/ trimmomatic (Bolger et al. , 2014) http://www. usadellab. org/cms/?page=trimmomatic bwa mem (Li, 2013) http://bio-bwa. sourceforge. net/bwa. shtml samtools (Li et al. , 2009) http://www. htslib. org/ MACS (Zhang et al. , 2008) https://github. com/macs3-project/MACS bedtools (Quinlan and Hall, 2010) https://bedtools. readthedocs. io/en/latest/ intervene (Khan and Mathelier, 2017) https://intervene. readthedocs. io/en/latest/ introduction. html R (4. 1. 1) https://www. r-project. org/ Analysis scripts This paper https://github. com/pongorlorinc/SCLC_methylation.
Data and code availability d Analysis scripts used in the study have been deposited to the github code repository and is publicly available as of the date of publication. DOIs are listed in the key resources table. d DNA methylation and H3K27ac ChIP-seq data have been deposited at GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
Analysis of RRBS based methylation data The RRBS sequencing data for the CCLE SCLC cell lines were obtained from the SRA (https://www. ncbi. nlm. nih. gov/sra, accession number PRJNA523380). Reads were processed using the bismark (v0. 22. 3) pipeline  (Krueger and Andrews, 2011) , first aligning the reads with bismark to the hg19 genome version, followed by extracting the methylation values using the bismark_methylation_extractor with the ''-comprehensive -cytosine_report -CX -bedGraph'' flags to generate a comprehensive report.
Alignment and analysis of ChIP-seq data We obtained ChIP-seq data from GEO for H3K27ac (GSE151002 and GSE115124) and ASCL1 (GSE69398), NEUROD1 (GSE69398) and POU2F3 (GSE115124). Raw reads were trimmed using trimmomatic (v0. 36)  (Bolger et al. , 2014) , followed by read alignment using the bwa mem aligner (v 0. 7. 17)  (Li, 2013). Reads were sorted using samtools (v1. 8)  (Li et al. , 2009) , followed by duplicate marking using picard-tools (v2. 9. 2). H3K27ac peaks were called using the MACS peak caller  (Zhang et al. , 2008)  setting the ''-nomodel'' flag using the broad peak calling setting. ASCL1, NEUROD1 and POU2F3 peaks were called using the default settings of the MACS peak caller. Normalized sequencing depth coverages were prepared using the BAMscale (v0. 06) tool  (Pongor et al. , 2020).
Peaks were annotated using the ChIPseeker package  (Yu et al. , 2015). In case of the ASCL1, NEUROD1 and POU2F3 ChIP-seq experiments, we compared binding site BED files using the intervene venn program  (Khan and Mathelier, 2017) , which is also able to separate peaks based on the colocalization. The resulting BED files from the intervene package and bigwig files from BAMscale were used to create the colocalization heatmaps with the heatmapper script available in the BAMscale github repository (https://github. com/ncbi/ BAMscale).
Genetic Analysis and Bioinformatics Genomic DNAs were extracted from whole blood using Qiagen mini kit (QIAamp DNA Mini). Ampli-Seq technology on the Ion Proton platform (Thermo Fisher Scientific, Inc. ) was performed to sequence the F-HTG gene panel: LPL, APOC2, APOA5, GPIHBP1, and LMF1. The exon flanking introns and promotor regions were included in the panel. For individuals with negative exome results, DNA samples were sent to a service provider for whole-exome sequencing, Macrogen © South Korea. DNA was barcoded and enriched for the coding exons of targeted genes using hybrid capture technology (Agilent SureSelect Human All-exons-V6). Prepared DNA libraries were then sequenced using Next-Generation Sequencing (NGS) technology (NovaSeq 6000, 150) bp paired-end, at 200X coverage). Sequencing data were processed by the Torrent Suite and reads were aligned to the hg19 reference genome.
Variant call files were then generated using Torrent Variant Caller plugins. Variant annotation was performed using ANNOVAR and variants were linked to ExAC, and Greater Middle East-Arabian Peninsula (GME-AP) databases for allele frequencies. Effect of amino acid changes was predicted by LRT, CADD, and MutationTaster. Pathogenic variants were identified from allele frequency of <1% or novel, coverage depth >30, the damaging effect from at least two of the three prediction algorithms, and segregation with the disease in the family. For CNV analysis, we used the CNVkit tool for patients with no pathogenic variant identified in the LPL gene  (Talevich et al. , 2016).
Family Pedigrees The lipid profiles of the affected family members are described in Figure  1. Table  1  summarizes the identified mutations in the patients. Family 1 (Figure  1A ) is made of double cousin marriage with three affected individuals AA1 (age 11-yrs), GG1 (12-yrs), and GG4 (19yrs) with a severe elevation of triglycerides. Clinically, three patients suffered from recurrent pancreatitis and did not have lipemia retinalis, hepatosteatosis, and a history of cardiovascular disease, obesity, alcoholism, renal disease, or diabetes mellitus. Sequencing of the 5 candidate genes showed a novel LPL gene variant (c. 983T > C, p. M328T) with all affected siblings being homozygous. The rest of the family members were found heterozygous carriers. The LPL p. M328T mutation is not reported in the LPL-Leiden Open Variation Database [14] and is located in exon 6 of the gene with the predicted damaging effects, Table  1.
686A > T, p. H229L) according to the predictions algorithm. However, the healthy Allele frequencies were reported using ExAC-all and GME-AP databases. Exonic variant effects were assessed using the Mutation Taster, LRT, and CADD prediction tool. mother was also heterozygous like the affected siblings. Therefore, we analyzed the family using a double heterozygous model. We identified a polymorphic variant in the APOA5 gene in affected patients but not in the mother. Therefore, this family had a compound heterozygous mutation in LPL and APOA5 genes. In family 6, the TG levels of an affected son (FF1) was 77. 3 mmol/L which was higher than the affected father (FF2) with a TG level of 16. 5 mmol/L. Both patients had recurrent attacks of acute pancreatitis and no sign of lipemia retinalis. The affected father had a history of diabetes mellitus with nephropathy.
Participants and protocol overview In this study, we included and analyzed the data of 20 bvFTD patients and 16 HC, matched to patients for age, gender and education level. bvFTD patients were recruited in two tertiary referral centers, at the Pitié-Salpêtriére Hospital and the Lariboisière Fernand-Widal Hospital, in Paris. They were diagnosed according to the International Consensus Diagnostic Criteria  [6]. Healthy controls (HC) were recruited by public announcement. The data gathered for this article are part of the ECOCAPTURE protocol (Clinicaltrials. gov:NCT02496312; see Batrancourt et al. [23]  and Supplementary Material 1A for further details on the global protocol) designed to investigate the behavioral signature and mechanisms of neuropsychiatric syndromes. For this study, we used data allowing to: 1) disentangle subgroups of bvFTD showing distinct profiles of apathy and 2) characterize the clinical phenotypes of identified subgroups.
Measures of apathy profiles including objective behavioral metrics To accurately characterize the apathy profiles of identified subgroups of bvFTD, we used complementary objective behavioral data collected within an ecological context (ECOCAPTURE). We selected: 1) two "composite" dimensions (mixing objective and subjective measures) from a previous study by Godefroy et al. [32]  and 2) a complementary objective behavioral metric. Figure  1  summarizes all these measures used to describe apathy profiles, how they Fig. 1. Summary of data flow to manage measures of apathy profile and hypotheses on their conceptual validity. SAS, Starkstein Apathy Scale; DAS, Dimensional Apathy Scale; F1 and F2 are the two factors extracted by factor analysis; Questio time ratio: ratio of time spent exclusively for goal-directed actions related to the completing of a questionnaire in the guided phase.
Neuroimaging data preprocessed for voxel-based morphometry analysis We used structural MRI data to elucidate the atrophy patterns of identified bvFTD subgroups. MRI data acquisitions were performed at CENIR (Human MRI Neuroimaging core facility, ICM, Salpêtrière hospital, Paris, France) using a 3T Siemens MRI scanner 64-channel TIM system. The brain MRI protocol includes a 3D T1 scan allowing the study of structural abnormalities. Structural data were analyzed with FSL-VBM  [33]  (http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FSLVBM), an optimized VBM protocol  [34]  carried out using FSL tools  [35]. First, structural images were brainextracted and grey matter (GM)-segmented before being registered to the MNI 152 standard space using non-linear registration  [36]. The resulting images were averaged and flipped along the x-axis to create a left-right symmetric, study-specific GM template.
Ethical statement This study is part of clinical trial C16-87 sponsored by INSERM, the French national institute for biomedical research. It was granted approval by the local Ethics Committee ("Comité de Protection des Personnes") on 05/17/2017 and registered in a public registry (clinicaltrials. gov: NCT03272230). All study participants gave their written informed consent to participate, according to the Declaration of Helsinki and in line with French ethical guidelines.