dataesr/scientific-paragraphs-categorization huggingface dataset:
Whole Exome Sequencing and Variant Filtering Genomic DNA was isolated from blood samples or saliva using the Oragen DNA Extraction Kit (DNA Genotek Inc. , Ottawa, ON, Canada). Genetic data were obtained from various sequencing centers, in particular Novogene and Integragen. Coding regions and intron/exon boundaries were sequenced after enrichment using SureSelect Human All Exon V6 from Agilent. An alignment-ready GRCh38 reference genome (including alternative sequences (ALT), decoy, and human leukocyte antigens (HLA)) was produced using "run-gen-ref hs38DH" from Heng bwakit package  [31]. The exomes were analyzed using a bioinformatics pipeline developed in-house. The pipeline consists of two modules, both distributed under the GNU General Public License v3. 0 and available on github. The first module (https://github.
com/ntm/grexome-TIMC-Primary (accessed on 15 January 2021)) takes FASTQ files as input and produces a single merged GVCF file, as follows. Adaptors are trimmed and low-quality reads are filtered with fastp 0. 20. 0  [32] , reads are aligned with BWA-MEM 0. 7. 17  [33] , duplicates are marked using samblaster 0. 1. 24 [34] , and BAM files are sorted and indexed with samtools 1. 9  [35]. SNVs (single nucleotide variants) and short indels are called from each BAM file using strelka 2. 9. 10 to produce individual GVCF files  [36]. These are finally merged with mergeGVCFs. pl to obtain a single multi-sample GVCF, which combines all exomes available in our laboratory. The second module (https://github. com/ntm/grexome-TIMC-Secondary (accessed on 15 January 2021)) takes this merged GVCF as input and produces annotated analysis ready TSV files.
Monoclonal mouse anti-acetylated α-tubulin antibody was purchased from Sigma-Aldrich (Irvine, UK) and diluted at 1:2000. The slides were then washed and incubated with secondary antibody diluted at 1:800 (Dylight-549-conjugated goat anti-mouse immunoglobulin G (IgG) and Dylight-488-conjugated goat anti-rabbit IgG, Jackson ImmunoResearch (Cambridgeshire, United Kingdom) and Hoechst 33342 (Sigma-Aldrich) for 1 h at room temperature, rinsed, and mounted with Dako Fluorescence Mounting Medium (Thermo Fisher Scientific). Images were acquired on a Nikon Eclipse 8 microscope and processed with ISIS software.
, Warrington, PA, USA). After staining with toluidine blue-Azur II, semi-thin sections were examined on a Zeiss Axioscope photomicroscope (Carl Zeiss, GmbH, Jena, Germany). The use of a Reichert OmU2 ultramicrotome (Reichert-Jung AG, Wien, Austria) allowed us to obtain ultra-thin sections (90 nm), which were then stained with uranyl acetate and lead citrate. These sections were examined with a JEOL JEM 100CX II electron microscope (Jeol Ltd. , Tokyo, Japan). All images were acquired with Digital Micrograph software coupled to a Gatan Erlangshen CCD (charge-coupled device) camera.
Sperm Fluorescent In Situ Hybridization (FISH) FISH was performed on ejaculated spermatozoa from patients P0166, P0386, and PS1, in addition to fertile controls (n = 5). Semen samples were prepared for a triple-color FISH using centromeric probes for chromosomes 21, X, and Y (XA TriScore Aneusomy Probe-MetaSystems ® Probes, Heidelberg, Germany). Sperm samples were washed in phosphate buffered saline solution and fixed in methanol/acetic acid solution (3:1). Sperm head decondensation was performed using a solution of NaOH (1 M) and then washed twice in saline sodium citrate (SSC) solution. Denaturation was performed on sperm nuclei and probes for 2 min at 75 • C. After overnight incubation at 37 • C, the slides were washed in 0. 4× SSC/0. 3% NP40, then twice in SSC/0. 1% NP40. The slides were counterstained with 4,6-diamino-2-phenylindole (DAPI) and analyzed with a Leica DM 5000B epifluorescence microscope equipped with a Metafer Slide Scanning System and MetaCyte software (Metasystems ® , Altlussheim, Germany).
The identified variant causes the extension of this repetitive sequence and is predicted to impact both splicing and translation, leading to the production of a short and non-functional SUN5 protein. The purple box represents a transmembrane domain (106-122 aa). The pink box represents a coiled-coil domain (155-183 aa), and the green box represents the Sad1/UNC-like C-terminal domain (205-364 aa) according to the Ensembl database (protein domains for ENSP00000348496. 3). The red box represents the predicted protein in silico.
Funding: This work supported by the  Inserm-Bettencourt Foundation , the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Centre National de la Recherche Scientifique (CNRS) , the  Université Paris Descartes , the  University Grenoble Alpes , and the  French National Research Agency  (grant FLAGELOME  ANR-19-CE17-0014  to PR, AT and NT-M; grant DIVERCIL  ANR-17-CE13-0023  to AT).
Bioinformatical analysis V. dahliae PGs and PMEs were identified using publicly available genome data (ftp. broadinstitute. org/). Sequences were checked for signal peptide using SignalP-5. 0 Server (http://www. cbs. dtu. dk/services/SignalP/). Glycosylation sites were predicted using NetNGlyc 1. 0 Server (http://www. cbs. dtu. dk/services/NetNGlyc/) and NetOGlyc 4. 0 Server (http://www. cbs. dtu. dk/services/NetOGlyc/). The sequence alignments were performed using MEGA multiple sequence alignment program. UCSF Chimera (http://www. cgl. ucsf. edu/chimera/) was used for creation of graphics.
Modelling, calculations and comparison of protein electrostatic VdPME1 and VdPG2 models were created using I-TASSER structure prediction software (https://zhanglab. ccmb. med. umich. edu/I-TASSER/). The linearized version of the Poisson-Boltzmann equation was used to solve the electrostatic potentials of the PMEs from Verticillium dahliae, Dickeya chrysanthemi, Citrus sinensis and Daucus carota after that radii and partial charges were assigned to each atom of the structures according to the parameters from the AMBER99 force field  [20] by using PDB2PQR (version 2. 1. 1, [21] ). The PMEs structures were superimposed to fairly compare the electrostatic potential differences across the calculated grids, and the electrostatic potentials were calculated using the APBS software version 3  [22].
The potentials were computed at pH 7, which is the pH used for the activity tests of VdPME1, with protonation states of single residues assigned using the PROPKA software, version 3. 3  [23]. Poisson-Boltzmann equation was solved by discretizing the molecule on a 19. 3 nm 3 grid (grid spacing equal to 6x10 -2 nm) centred on the Cα atom of one of the PMEs catalytic aspartic acid residues, conserved across PMEs. The computation of the electrostatic potential was carried out considering a dielectric term of 78. 5 for the solvent, in order to account for an aqueous environment, with solute dielectric set to 4. 0 and temperature set at 298. 15 K. Electrostatic potentials were numerically compared by calculating electrostatic similarity indices  [24, 25]. We calculated the cross-product between two electrostatic potentials calculated at each grid point as follows: , = 2 , , , , , , + , , Where , , and , , are the electrostatic potentials calculated at the grid points i,j,k for proteins a and b  [24, 25].
Determination of Km, Vmax, and specific activity The VdPG2 kinetic parameters were calculated using GraFit7 software (Michaelis-Menten/Hill; Erithacus Software, Horley, Surrey, UK) using PGA as a substrate. The reactions were performed using 1 to 20 mg. mL -1 PGA concentrations at 50 mM sodium acetate (pH 5) during 10 min at 50 °C. The same procedure was used for VdPME1 with 1 to 20 mg. mL -1 pectin DM 55-70 % concentrations in 50 mM sodium phosphate buffer (pH 7) during 20 min at 30 °C.
Homology modelling and structure analysis To fully understand the structural features of VdPG2 and VdPME1, their structures were modelled using the I-TASSER server for protein structure prediction. VdPME1 model was created using D. carota PME (P83218, PDB:1GQ8,  [36] ) as the template, with 31. 42 % amino acid (AA) identity and 46. 6 % similarity. Although VdPME1 doesn't share a high degree of identity with its template, the modelling remarkably provided a structure with a root mean square deviation (RMSD) of only 4. 1±2. 8Å. The model consisted of 314 AA, without the 16 AA of the signal peptide. Modelling shows that the enzyme is a right handed β-helix (Fig. 2A ) structure that shares four highly conserved regions, as well as key active site residues characterized in other PMEs. These four regions comprise one N terminal region (Gly47, Ser48, Tyr49, Ala50 and Glu51), two internal regions (Tyr166-Phe166 (mutation in VdPME1) Gly167, Asp168, Thr169 and Asp165, Phe166, Ile167, Phe168 and Gly169) and one C-terminal region (Leu227, Gly228, Arg229, Pro300 and Trp301)  [37].
The AA of the active site are Gln143, Asp144, Asp165 and Arg229 (Fig. 2B , Fig. S1 ) where the two Asp act as a general acid/base in the catalytic mechanism  [18, 38]  The VdPG2 model was created using A. acuelatus PGAI (O74213, PDB:1IA5,  [39]  as a best template with an estimated RMSD of 5. 6±3. 5Å. VdPG2 exhibits lower sequence identity with AaPGAI (55. 74 %) compared to ClPG1 (68. 14 %) but higher structural homology. The final model consists of 352 AA without the 18 AA of the signal peptide. The structure is a right handed β-helix with 10 complete turns (Fig. 2D ). Members of GH28 family, including endo and exo PGs have conserved AA motifs, including Asn167, Thr168, Asp169 (NTD) Asp189, Asp190 (DD), Gly211, His212, Gly213 (GHG), Arg244, Ile245, Lys246 (RIK, Fig. 2E , Fig. S2 ,  [40, 41] ) in VdPG2.
Data Analysis Test statistics were two tailed and interpreted at a. 05 significance level. Partial eta squared (η 2 p ) was interpreted with small =. 01, medium =. 06, and large =. 14 thresholds  [23]  and Cronbach's α with acceptable =. 7, good =. 8, and excellent =. 9 thresholds. For the path analysis of the structural equation models, criteria for acceptable global fit were p >. 05 for the model (cannot reject the exact fit hypothesis), RMSEA ≤. 05 (a cutoff for good fit  [72] ), RMSEA εL = 0 (confidence interval includes zero), and the combination rule: CFI ≥. 95 and SRMR ≤. 08  [51]. All betas (β) were standardized. ANOVAs were performed in SPSS. Mediation analyses used the PROCESS plugin. Why Real Citizens Would Turn to Artificial Leaders • 26:5 Path analyses were performed in AMOS  [58, 67, 114]  by maximum likelihood (ML) estimation using the variance-covariance matrix  [77].
Thus, in MM, GEP is useful for predicting prognosis and screening for drug resistance biomarkers with potential benefits for clinical management. However, most previous studies have been conducted using microarray technology, which entails the analysis of a limited number of genes and potential artefacts due to incorrect probe design, genetic variation, and inefficient hybridization. Microarrays are being phased out and replaced by RNA sequencing (RNAseq), which overcomes many of the limitations associated with microarrays. It is expected that RNAseq will enable the discovery of new prognostic markers, associations with drug response, and the mechanisms underlying MM pathophysiology. To that aim, we used RNAseq and clinical data for 674 newly diagnosed patients treated with standard therapy from the Multiple Myeloma Research Foundation CoMMpass study to build a 267-gene risk score.
Gene Expression Profiling We used the publicly available gene expression profiling RNA-seq data of newly diagnosed MM patients from the Multiple Myeloma Research Foundation's (MMRF) CoMMpass study (https://research. themmrf. org/ (28 September 2021), release IA12). Concerning the validation cohort, bone marrow samples were collected after obtaining written informed consent from the patients in accordance with the Declaration of Helsinki and after receiving institutional research board approval from Montpellier University Hospital. Bone marrow samples were collected from 76 newly diagnosed patients treated with high-dose melphalan (HDM) and autologous stem cell transplantation (ASCT), and this cohort was termed the Montpellier cohort. The bone marrow of patients presenting with previously untreated MM at the University Hospital of Montpellier was obtained after obtaining written informed consent from the patients in accordance with the Declaration of Helsinki and according to the IRB agreement and based on the approval of the Montpellier University Hospital Centre for Biological Resources (DC-2008-417).
Statistical Analyses All of the statistical analyses were performed with the R statistics software (version 3. 2. 3; available from https://www. r-project. org) (28 September 2021) and with R packages developed by the BioConductor project (available from https://www. bioconductor. org/) (28 September 2021)  [24]. The expression level of each gene was summarized and normalized using the DESeq2 R/Bioconductor package  [25]. Differential expression analysis was performed using the DESeq2 pipeline  [25]. p-values were adjusted to control the global FDR across all comparisons with the default option of the DESeq2 package. Genes were considered differentially expressed with an adjusted p-value < 0. 05 and a fold change >1. 5. For the Montpellier cohort, Affymetrix U133P chips were also used, as previously described  [22, 26] , to analyze GEP and to calculate previously published risk scores, including the RS score  [8] , UAMS HRS score  [5] , and IFM score  [7].
Multiple Myeloma Cell Lines XGs human myeloma cell lines (HMCLs) were obtained as previously described  [27, 28]. AMO-1, LP1, L363, OPM2, MOLP2, MOLP8, Lopra, and SKMM2 were purchased from DSMZ (Braunsweig, Germany), and RPMI8226 was purchased from ATCC (American Tissue Culture Collection, Rockville, MD, USA). JJN3 was kindly provided by Dr. Van Riet (Bruxelles, Belgium), and was provided MM1S by Dr. S. Rosen (Chicago, USA). HMCLs were authenticated according to their short tandem repeat profiling and their gene expression profiling using Affymetrix U133 plus 2. 0 microarrays deposited in the ArrayExpress public database under accession numbers E-TABM-937 and E-TABM-1088.
RNA-Seq-Based Gene Risk Score in Multiple Myeloma We used the gene expression profiling (GEP) data of 674 newly diagnosed MM patients from the Multiple Myeloma Research Foundation's (MMRF) CoMMpass study. We also performed the RNA sequencing of purified MMC from 76 newly diagnosed MM patients treated by high dose therapy (HDT) and autologous stem cell transplantation. Using the Maxstat R function and the Benjamini-Hochberg multiple testing correction, 267 genes were found to have a prognostic value for overall survival (OS) (adjusted p value < 0. 05) in the CoMMpass cohort of patients with previously untreated MM. These 267 prognostic genes comprised 142 genes associated with a poor outcome in MM and 97 genes associated with high expression related to a good prognostic value. The 267 genes were used to build an RNA-seq-based risk score.
The RNA-seq-based risk score is defined by the sum of the beta coefficient derived from the Cox model for each prognostic gene weighted by -1 or +1 according to the MMC gene expression above or below the Maxstat defined cutpoint  [29]. Patients from the CoMMpass cohort were ranked according to increased RNA-seq-based risk scores, and the Maxstat algorithm was used to find the cutoff associated with the maximum difference in the OS (Figure  1A ). The RNA-seq-based risk score split patients into a high-risk group (22. 8%) and a low-risk group (77. 2%) in the CoMMpass cohort (p-value = 1. 7 × 10 -46 ) (Figure  1B ). The prognostic value of the RNA-seq-based risk score was validated in the Montpellier cohort (p-value = 2. 8 × 10 -11 ) (Figure  1C ). When applied to a non-disease dataset, i. e.
RNA-Seq Risk Score Revealed New Genes Significantly Associated with MM Pathophysiology We used public datasets of RNAi  [33, 34]  and CRISPR-Cas9  [35]  viability assay screening (Dependency Map data, Broad Institute, www. depmap. org) (28 September 2021) to identify the essential genes in myeloma cell lines compared to other cancer cell lines. Among the 142 genes associated with poor survival composing the RNA-seq-based risk score, we found seven genes (ATP8B1, FGFR4, FOXD4, MX1, NPTXR, TMEM171, and TNFRSF10B) with a significantly lower DEMETER2 score in the myeloma cell lines (n = 16) compared to other cancer cell lines (n = 695) (Figure  4A  and Table  4 ). The CERES score of four genes (ISG20, NDC1, SF3B3, UMPS) was significantly lower in the myeloma cell lines (n = 20) compared to in the other cancer cell lines (n = 769) (Figure  4B  and Table  4 ).
Recently, Jang JS et al. reported a single cell RNA-seq of 597 cells derived from 15 MM patients. They developed a gene expression signature associated with MM progression that demonstrated significant prognostic value using the microarray GEP data of the APEX trial dataset  [47]. We investigated the overlap between the two reported gene signatures and six common genes that were identified, including BST2, FAM214A, HBP1, ISG20, KLF6, and RAB30. The low degree of overlap could be explained by the fact that the signatures were defined using bulk RNA-seq or single-cell RNA-seq. The number of expressed genes detected from single cell RNA-seq is typically lower compared to bulk RNA-seq. The high-risk patients identified with our score were characterized by the genes involved in several major pathways implicated in MM pathophysiology, including interferon response, cell proliferation, hypoxia, IL-6 signaling pathway, stem cell genes, MYC, and epigenetic deregulation (Figure  2A ).
Aurora A kinase inhibitor in combination with bortezomib is currently in clinical development to treat relapsed or refractory MM patients (NCT01034553)  [59]. MELK and PBK were identified as being of therapeutic interest in MM  [22, 60]. The RNA-seq score may be of interest for patient stratification in clinical trials. The validation of these results using the primary MM cells of patients will be of interest. Interestingly, RNA-seq-based risk score values were significantly higher in the MM cells of patients characterized by ASXL1, ATM, BRAF, DIS3, EP300, FGFR3, KMT2B, LRP1B, MAP3K1, MAX, NOTCH2, NUP214, PRDM1, PTPRD, RB1, ROS1, SETD2, TP53, TRRAP, and ZFHX3 mutations compared to patients with unmutated MM cells. Among these mutated genes, TP53, DIS3, BRAF, PRDM1, are RB1 are part of the most frequently mutated genes in MM patients  [61, 62].
Contributions: E. A. and V. V. designed and performed the research, analyzed the data, and wrote the paper; A. B. , C. B. and A. K. performed the research and analyzed the data; N. R. , L. V. , C. H. , G. R. and G. C. analyzed the data; O. E. analyzed the data and wrote the paper; J. M. designed and supervised the research and wrote the paper. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by grants from INCa (Institut National du Cancer) PLBIO18-362 PIT-MM and PLBIO19-098 INCA_13832 FATidique, ANR (the French National Research Agency) under the "Investissements d'avenir" program with the reference ANR-16-IDEX-0006), ANR (TIE-Skip; 2017-CE15-002401), ANR-18-CE15-0010-01 PLASMADIFF-3D, SIRIC Montpellier Cancer Grant INCa-DGOS-Inserm_12553, PCSI ITMO (SMOOTH-MM), FFRMG, Labex EpiGenMed, and l'Institut Universitaire de France.
Data Availability Statement: HMCLs were authenticated according to their short tandem repeat profiling and their gene expression profiling using Affymetrix U133 plus 2. 0 microarrays deposited in the ArrayExpress public database under accession numbers E-TABM-937 and E-TABM-1088. The RNA-seq data of normal B to PC differentiation are available in Gene Expression Omnibus under the accession number GSE148924.
Author Contributions: E. A. and V. V. designed and performed the research, analyzed the data, and wrote the paper; A. B. , C. B. and A. K. performed the research and analyzed the data; N. R. , L. V. , C. H. , G. R. and G. C. analyzed the data; O. E. analyzed the data and wrote the paper; J. M. designed and supervised the research and wrote the paper. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by grants from INCa (Institut National du Cancer) PLBIO18-362 PIT-MM and PLBIO19-098 INCA_13832 FATidique, ANR (the French National Research Agency) under the "Investissements d'avenir" program with the reference ANR-16-IDEX-0006), ANR (TIE-Skip; 2017-CE15-002401), ANR-18-CE15-0010-01 PLASMADIFF-3D, SIRIC Montpellier Cancer Grant INCa-DGOS-Inserm_12553, PCSI ITMO (SMOOTH-MM), FFRMG, Labex EpiGenMed, and l'Institut Universitaire de France.
, 2018; Béziat et al. , 2020; Bousfiha et al. , 2020; Engelhardt et al. , 2009; Felgentreff et al. , 2014; Fleisher, 2014; Frey-Jakobs et al. , 2018; Lyons et al. , 2017; Ma et al. , 2017; Sassi et al. , 2014; Schwerd et al. , 2017; Spencer et al. , 2019; Su, 2010; Tangye et al. , 2020; Zhang et al. , 2010b; Zhang et al. , 2009; Zhang et al. , 2014). These five new nonsense and frameshift STAT3 variants (Fig. 1 A ) were not found in the Human Gene Mutation Database of pathological variants, the 1,000 Genomes Project, or Genome Aggregation Database (gnomAD). The screening of our whole cohort (>8,000 individuals with severe or recurrent infections) led to the identification of another three individuals (P8, P9, and P10) from two kindreds with two previously unknown frameshift variants [P8 and P9: c.
Activity and mechanism of dominance in all STAT3 variants reported in AD-HIES patients In the Human Gene Mutation Database of pathological variants and PubMed, 143 variants are identified as causal variants of AD-HIES (Fig. S3  and Table  S3 ;  Abolhassani et al. , 2018; Al Khatib et al. , 2009; Alcántara-Montiel et al. , 2016; Anolik et al. , 2009; Avery et al. , 2010; Egawa et al. , 2019; Eken et al. , 2020; Felgentreff et al. , 2014; Freeman et al. , 2013; Giacomelli et al. , 2011; Hagl et al. , 2016; He et al. , 2012; Heimall et al. , 2011; Holland et al. , 2007; Jiao et al. , 2008; Khourieh et al. , 2019; Kim et al. , 2009; Kumánovics et al. , 2010; Larsen et al. , 2019; Ma et al. , 2012; Ma et al. , 2008; Merli et al. , 2014; Minegishi et al. , 2009; Minegishi et al. , 2007; Miyazaki et al.
Activity and mechanism of dominance of 14 STAT3 variants reported in gnomAD We investigated a subset of STAT3 variants found in the general population. The gnomAD database includes 1,095 STAT3 variants, of which 141 are missense, 1 is an in-frame deletion, 1 is a nonsense variant, and 167 are synonymous. We selected 14 gno-mAD variants, including 12 missense variants (Q125E, R246Q, A376V, G402S, D427G, V461L, I498V, R518Q, A702T, G743V, S763L, and A766T) with either a minor allele frequency above 1 × 10 -4 or a CADD score higher than 30, 1 variant (E239del) with an in-frame deletion, and the only variant pLOF (Q247*; minor allele frequency, 4 × 10 -6 ; CADD, 39; Fig. 7  A and Table  S6 ). We assessed their allele activity and the mechanism of dominance in the same luciferase assay system. Only Q247* was severely hypomorphic, three variants (Q125E, D427, and G743V) were mildly hypomorphic, and two variants (A376V and R518Q) were hypermorphic, whereas the remaining eight variants had normal activity (Fig.
All variants are classified according to the rules described in Fig. 2, B  and C. Each experiment (A and C) was independently performed three times. Error bars represent the means with SEM. remaining 149 variants were predicted to be deleterious (CADD >16. 4; Table  S3 ). By contrast, 99% (141/143) of the 143 heterozygous nonsynonymous coding variants of STAT3 in the gno-mAD database (missense, 141; in-frame deletion, 1; nonsense, 1) had CADD scores above the MSC, suggesting that they may be deleterious (Fig. 7 A ). We analyzed the correlation between CADD and STAT3 activity for all the variants tested. There was no correlation between STAT3 activity and CADD score (R 2 = 0. 013). Five variants (3. 3%) identified as isomorphic by our assay were predicted to be deleterious by CADD and may therefore be considered false positives (FPs).