dataesr/scientific-paragraphs-categorization huggingface dataset:
Irreversible genes are also not or lowly expressed, but they contain less Polycomb in their chromatin. Removal of PRC1 subunits induces chromatin opening and derepression of both classes of genes. Upon restoration of Polycomb components however, a difference emerges. High levels of Polycomb binding might restore a silent chromatin state, whereas lower levels of Polycomb binding might be unable to restore repression at irreversible genes. Extended Data Fig. 9  Extended Data Fig. Extended Data Fig. 10:  A tumor suppressive role of PRC1 core subunits in Multiple Myeloma a-PHC1, PHC2, PHC3 and CBX2 gene expression is significantly downregulated in malignant plasma cells (PCs) from patients with Multiple Myeloma (MM) compared to normal bone marrow PCs. Affymetrix U133 P gene expression profiles of purified bone marrow PC from 22 healthy donors and purified myeloma PCs from 345 previously untreated patients were compared using publicly available data (Gene Expression Omnibus, accession number GSE2658) from the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR).
Statistical difference was assayed using a Student t test. b-Prognostic value of the PRCA core components in MM. The prognostic value of PHC1, PHC2, PHC3, CBX2, CBX7 et BMI1 gene expression was analyzed in 6 independent cohorts of patients with MM using the Maxstat R function and Kaplan Meier survival curves as previously described. High expression of PHC1, PHC2, PHC3, CBX2, CBX7 and BMI1 was associated with significantly shorter overall survival in at least three independent cohorts of MM patients out of the six investigated (green color). The six cohorts included gene expression data of purified MM cells from the TT2, TT3 (accession number E-TABM-1138) and Hovon (accession number GSE19784) cohorts (345, 158 and 282 newly-diagnosed MM patients treated by high-dose melphalan and autologous hematopoietic stem cell transplantation); the Mulligan cohort (188 patients at relapse treated by proteasome inhibitor in monotherapy); the Mtp cohort non eligible to HDT (63 newlydiagnosed MM patients non eligible to high-dose melphalan and autologous hematopoietic stem cell transplantation) and the Mtp Dara cohort (51 patients at relapse treated by anti-CD38 monoclonal antibody (Daratumumab).
Acknowledgements We thank  Montpellier Resources Imagerie facility  as well as the Drosophila facilty ( both affiliated to  BioCampus University of Montpellier ,  CNRS, INSERM, Montpellier, France ). V. P. was supported by the  EpiGenMed cluster of Excellence funding  (PIA of the  French Ministry of Higher Education and Research)  and by  la Ligue Nationale Contre le Cancer. V. L. was supported by the  EpiGenMed cluster of Excellence funding  (PIA of the  French Ministry of Higher Education and Research). A-M. M was supported by a grant of the  Fondation ARC  (contract  N. 216574 , acronym "Epicancer). B. S. was supported by  INSERM. G. C. was supported by  CNRS. Research in the G. C. laboratory was supported by grants from the  European Research Council  ( Advanced Grant 3DEpi ), the  European CHROMDESIGN ITN  project ( Marie Skłodowska-Curie  grant agreement No  813327 ), the  European E-RARE NEURO DISEASES  grant " IMPACT ", by the  Agence Nationale de la Recherche (PLASMADIFF3D , grant  N.
1 Extended Data Fig. 1 :Extended Data Fig. 2 Extended Data Fig. 2 : 1122 Affymetrix gene expression data (HGU133plus2) of purified MM cells from the TT2 (Gene Expression Omnibus, accession number GSE2658), TT3 (accession number E-TABM-1138 accession number GSE4583) and Hovon (accession number GSE19784) cohorts (345, 158 and 282 newly-diagnosed MM patients treated by high-dose melphalan and autologous hematopoietic stem cell transplantation); the Mulligan cohort (188 patients at relapse treated by proteasome inhibitor in monotherapy); the Mtp cohort non eligible to HDT 11 (63 newly-diagnosed MM patients non eligible to high-dose melphalan and autologous hematopoietic stem cell transplantation) and the Mtp Dara cohort 11 (51 patients at relapse treated by anti-CD38 monoclonal antibody (Daratumumab)).
2b ). Particles greatly vary in the extent of this shift that therefore probably represent a continuous movement of the DNA (compare Fig. 2b1 with 2b2 ). The Rossmann fold moves with and is attached to the DNA whereas the zinc finger domain remains fixed at the acidic patch of the nucleosome. In the full extent of the DNA movement two turns from SHL 6 and 7 are detached from the histone octamer. This alters the path of the terminus DNA by 37 o and breaks DNA interactions with histones H2A, H2B, and H3 (Supplemental Fig. 4b ). Such a large change in DNA trajectory is reminiscent of the chromatin-remodeler ATPases or pioneer transcription factors binding to nucleosome but was not observed in other histone modifiers. Importantly, DNA terminus without bound SIRT6 or free nucleosomes in our dataset did not show any deviation from the canonical conformations (Supplemental Fig.
cryo-EM data acquisition Two different datasets were collected. For the first dataset images were acquired on a Cs-corrected Titan Krios G1 (Thermo Fisher) microscope operating at 300 kV in nanoprobe mode using serialEM for automated data collection. Movie frames were recorded on a Gatan K3 direct electron detector after a Quantum LS 967 energy filter using a 20 e -V slit width in zero-loss mode. Images were acquired hardware-binned at a nominal magnification of 130,000, which yielded a pixel size of 0. 862 Å. Forty movie frames were recorded at a dose of 1. 12 and 1. 30 e -per Å 2 per frame. The second image dataset was acquired on a Titan Krios G4 microscope operating at 300 kV in nanoprobe mode using EPU for automated data collection. Movie frames were recorded on a Flacon 4i direct electron detector after a Selectris X energy filter using a 10 e -V slit width in zeroloss mode.
Image analysis For the first dataset movie frames were aligned, dose-weighted in Warp  (Tegunov and Cramer, 2019)  together with CTF estimation and particles picking. For the second dataset frame alignment, dose-weighting and CTF estimation was done in cryoSPARC  (Punjani et al. , 2017) , picking was done with crYOLO  (Wagner et al. , 2019). These datasets were analyzed in cryoSPARC and RELION 3. 1  (Zivanov et al. , 2018)  according to standard protocols. Briefly, images were subjected to reference-free 2D classification in cryoSPARC to remove images with contaminations. Ab-initio reconstruction was performed on the selected images followed by heterogenous and homogenous refinements in cryoSPARC. Structural variability and flexibility were explored with Relion 3. 1 using 3D classification without alignment with different T values and cryoDRGN  (Zhong et al.
Model building and refinement Crystal structure of Widom 601 sequence containing nucleosome (PDB: 3LZ0) and human SIRT6 (PDB: 5X16) were rigid-body fitted into the cryo-EM structures in Chimera  (Pettersen et al. , 2004). Flexible-fittings were performed with Isolde  (Croll, 2018)  in ChimeraX  (Goddard et al. , 2018). The models were refined in real-space with PHENIX  (Liebschner et al. , 2019)  and Isolde.
Data availability The cryo-EM maps have been deposited in the Electron Microscopy Data Bank (EMDB) under accession codes EMD-16842 (High-resolution on the nucleosome), EMD-16843 (SIRT6nucleosome refined for SIRT6) and EMD-16845 (composite map). The model coordinates for SIRT6-nucleosome were deposited in the Protein Data Bank (PDB) under the accession code 8OF4. All MD input files are publicly available on Github: https://github. com/emmanuellebignon/NCP-SIRT6. e, H2A c-terminal tail (yellow) interacts with SIRT6(magenta). f, SIRT6 binding to H2A or H2AZ containing nucleosomes. Bars show the fraction of residual nucleosomes that did not shift with bound SIRT6 in an electron-mobility shift assay (Supplemental Fig. 7 ). Bars represent mean ± SD of three biological replicates (shown as dots).
Acknowledgement We acknowledge support from the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Centre National pour la Recherche Scientifique (CNRS) , the  Ligue contre le Cancer. We acknowledge the use of resources of the  French Infrastructure for Integrated Structural Biology FRISBI   ANR-10-INBS-05  and of Instruct-ERIC. E. B. thanks GENCI (HPC resources of IDRIS, allocation  A0120713412 ) for computational resources.
Acknowledgements We thank  Dr Arnaud Besson  and  Yvan Canitrot  ( CBI-MCD ) for their technical support in mice sampling and CRISPR-Cas9 experiments, respectively. We also thank  Paul Duchesne  and all the DT's team for their help or helpful discussions. We acknowledge the  CBI animal facility  for housing the mice, the  CBI-LITC facility  for microscopy experiments analysis, and the  CBI-Big -A bioinformatics platform members ( Marion Aguirrebengoa  and  Vincent Rocher ) for their biostatistical support. This work was supported by a grant from the  Ligue Nationale Contre le Cancer  as a " Equipe labellisée " to DT. JR was recipient of a studentship from the  French Ministry of Research.
Data availability The authors declare that all data supporting the findings of this study are available within the paper and its supplementary information files or from the corresponding author [FE] upon request. All data from genome-wide experiments were deposited to Gene Expression Omnibus (GEO) public functional genomics data repository with the following reference: GSE232114 (https://www. ncbi. nlm. nih. gov/projects/geo/query/acc. cgi?acc=GSE232114 ).
Other studies highlighted links between CFTR and signalling pathways involved in the disease (see  (Pankonien et al. , 2022)  for a review), but they did not provide a global view of how CFTR is linked to dysregulated molecular mechanisms and to CF phenotypes. Recently, transcriptomic data have been produced to identify differentially expressed genes in CF. These genes were connected within a PPI network, based on information available in PPI databases  (Trivedi et al. , 2023). Although this network comprises genes that are consistent with current knowledge in CF, it does not contain CFTR, which prevents understanding the functional link between CFTR and the differentially expressed genes, or with CF cellular phenotypes. To overcome the limitation of previous studies, in the systems biology approach proposed here, we build a comprehensive signalling network, called the CF network in the following, that recapitulates CF pathway dysregulations, using transcriptomic data available for CF and control patients and information available in biological pathway databases.
Our study relies on a meta-analysis of public transcriptomic datasets for CF respiratory epithelial cells and their Non-Cystic Fibrosis (NCF) control counterparts, allowing the identification of the signalling pathways dysregulated in CF. Based on information available in pathway databases, these dysregulated pathways share many common proteins, which allowed to connect them into a network. As detailed below, CFTR was absent from this network, because it did not belong to any of the differentially expressed signalling pathways. However, we observed that several proteins of the network were also present in the CFTR PPI interactome, either as direct interactors of CFTR, or as indirect interactors of CFTR via a single intermediate protein. This important result was consistent with the assumption that CFTR direct interactors may be perturbed in CF, and initiate the propagation of dysregulations within the CF network.
Selection of publicly available transcriptomics data Many transcriptomic studies have been performed in CF over the last 15 years  (Ideozu et al. , 2019). However, these data suffer from a few limitations that are obstacles to improve our understanding of CF. First, they consider a wide range of cell types, including native nasal or bronchial cells, primary cultures of these cells, whole blood, peripheral mononuclear cells, leukocytes, or immortalized cell lines. Therefore, comparison between studies to identify common key molecular determinants can lead to inconsistent results. Then, compared to studies on more common diseases such as cancer, most of CF transcriptomic studies have very few samples per condition (disease and control), decreasing the statistical power of these datasets when analyzed alone.
Finally, these studies rely on various experimental biological models and transcriptomic technologies which rarely lead to consistent results between studies  (Clarke et al. , 2013) , particularly when the analyses are performed at the gene level. To try and overcome these limitations, we focused on studies considering only samples from human Airway Epithelial Cells (hAEC hereafter), i. e. , bronchial, tracheal, or nasal cells. Indeed, functional modifications in these cells are expected to reflect some of the most severe symptoms in the lung. We included studies of cell lines or primary cultures, in order to gather a statistically significant number of samples, because as shown in Table  1 , each dataset comprises a very limited number of samples. We also focused on studies on the F508del mutation, for which most data are available.
We discarded two studies  ((Virella-Lowell et al. , 2004)  and  (Rehman et al. , 2021) ) that provide transcriptomic data for other mutations, because the corresponding cells could display disparities with respect to F508del cells. We retrieved from the literature all the CF transcriptomic studies with publicly available data that matched these criteria (see Methods section), which led to 10 CF transcriptomic datasets shown in Table  1.
Meta-analysis of transcriptomic studies at the level of biological pathways The most straightforward way to analyse transcriptomic data is to identify Differentially Expressed Genes (DEGs), and to search for biological pathways enriched in these DEGs. This approach failed in the present meta-analysis, because the number of DEGs common to at least 3 out of 7 studies was too small to be enriched in any pathway, even though many reference pathway databases were considered (the Hallmark gene sets from the the MSigDB Database  (Liberzon et al. , 2015) , the Pathway Interaction Database (PID)  (Schaefer et al. , 2009) , the KEGG database  (Kanehisa et al. , 2021) ). In fact, it has become clear that, in complex diseases, identification of pathway dysregulations based on DEGs is not optimal and does not provide robust results  (Wang et al.
, 2010). Therefore, the meta-analysis was conducted at the pathway level. Many methods have been proposed to capture pathway dysregulations when they do not appear clearly based on enrichment from lists of DEGs  (Martignetti et al. , 2016; Landais and Vallot, 2023; Schubert et al. , 2018; Vaske et al. , 2010). In the present study, we used the Gene Set Enrichment Analysis (GSEA)  (Subramanian et al. , 2005)  approach. GSEA was performed separately on each dataset identified as over-activated or underactivated signalling pathways in hAEC CF cells, based on the complete expression matrix of CF and NCF samples, and taking into account the expression level of all 7 genes belonging to the same pathway. We used pathway definitions provided by the KEGG pathway database  (Kanehisa et al. , 2021) , because this database provides graphical pathway representations that also include phenotypes, which helped the analysis of the CF network, as detailed in Section 2.
5. We tested 131 KEGG biological pathways, and Differentially Expressed Pathways (DEPs, hereafter) were identified according to a adjusted p-value lower or equal to 0. 25, as detailed in Section 3. 4. The number of up-and down-regulated pathways for each dataset is provided in Table  2  The analysis of DEPs showed that 15 of the 134 biological pathways tested were differentially expressed in at least 3 studies. However, a closer analysis highlighted discrepancies between studies. As shown in the heatmap presenting the GSEA Normalised Enrichment Score (NES) (Figure  2 ), for these 15 common DEPs, the 10 datasets can be gathered into 2 subgroups: subgroup 1 comprising 7 datasets in which common DEPs tend to be up-regulated, while they tend to be down-regulated in subgroup 2 comprising the 3 other datasets.
This appeared somewhat puzzling. Our hypothesis is that datasets belonging to subgroups 1 or 2 arise from studies in which the differentiation media used for the primary cultures did not favor the same cell type, and therefore, should not be analyzed together. This was confirmed by the Saint-Criq (UNC) and Saint-Criq (SC) datasets (see Table  1 ), belonging respectively to subgroups 1 and 2, where it was shown that the UNC and SC differentiation media (two common differentiation media used on CF and non-CF epithelia) significantly impact cell lineage in primary cultures of CF hAEC, and consequently, the resulting transcriptomic profiles  (Saint-Criq et al. , 2020). In this study, it was shown that the UNC medium promoted differentiation into club and goblet cells, while the SC medium favored the growth of ionocytes and ciliated cells.
Consistent with this result, the Ling transcriptomic dataset, which belongs to subgroup 2, was also obtained from primary cultures of CF and NCF airway epithelia that were differentiated into ciliated pseudo-stratified airway cells  (Ling et al. , 2020). 8 Datasets from subgroup 2 appeared in contradiction with the main CF phenotypes. In particular, the TNF-α signalling pathway or NF-κB signalling pathway are downregulated in this subgroup, although the over-activation of these pathways is a wellknown feature of CF disease. Therefore, we only considered the 7 datasets belonging to subgroup 1 for further analysis. In this subgroup, the transcriptomic analysis appears to be highly consistent, since among the 15 DEPs common to at least 3 studies, 5 are up-regulated in CF vs NCF samples in 4 studies (NOD-like receptor signalling pathway, Cytosolic DNAsensing pathway, Cytokine-cytokine receptor signalling pathway, and Regulation of actin cytoskeleton), 2 are up-regulated in CF vs NCF samples in 5 studies (Osteoclast differentiation and Toll-like receptor signalling pathway), and the IL-17 signalling pathway is up-regulated in CF vs NCF in 6 studies.
Building the CF network The 15 individual DEPs of the KEGG database provide interesting information about what is dysregulated in CF, but a lot of these pathways are partially redundant and show a high overlap of genes and interactions, indicating that they are highly intertwined. A dysregulation in one of these pathways will have a consequence in another 9 pathway. To study the connection between them, we propose to merge them into a single network called the CF network. The DEPs were extracted with the OmniPathR package  (Türei et al. , 2016)  and curated, as described in Section 3. 5. The rules that were used to build and clean this network are detailed in Section 3. 6. The network, comprising 330 nodes and 529 interactions, is not fully connected: it contains one main component including 317 nodes connected by 515 interactions, and two small additional components that are non connected to the main component, and called unconnected components hereafter (See Figure  3 ).
Identification of CFTR interactors in the CF network It is striking to note that CFTR does not belong to any of the 15 DEPs, and therefore, is not part of the network. In fact, CFTR is present in only 7 biological pathways of the KEGG database (ABC Transporters, cAMP signalling pathway, AMPK signalling pathway, tight junction, Gastric and acid secretion, pancreatic secretion and bile secretion), but these pathways did not belong to the DEPs. Therefore, we searched for the presence of CF network proteins in the network of proteins reported to be involved in protein-protein interactions (PPI) with wt-CFTR or F508del-CFTR  (Pereira et al. , 2021). Indeed, according to the CyFi-MAP, 4 direct interactors of wt-CFTR but not of F508del-CFTR (CSNK2A1, PRKACA, SYK and TRADD) belong to the CF network.
Datasets selection Based on the search engines of the National Center for Biotechnology Information (NCBI) and the European Nucleotide Archive (ENA), we selected 10 datasets from 8 studies published between 2007 and 2021. The selection criteria to include CF transcriptomic datasets were the following: (1) they should correspond to human Airway Epithelial Cells (hAEC); (2) the cells should be homozygous for the most common mutation F508del;  (3)  the transcriptomic data should be publicly available. Therefore, studies including samples heterozygous for the F508del mutation  ((Virella-Lowell et al. , 2004)  and  (Rehman et al. , 2021) ), studies with no data available  (Zabner et al. , 18 2005 ) and  (Wright et al. , 2006) ) were not included. In addition, studies with less than two samples were excluded (  (Bampi et al.
Biological pathways databases We initially considered a total of 380 gene sets corresponding to 380 biological pathways: 50 Hallmark gene sets from the the Molecular Signatures Database (MSigDB)  (Liberzon et al. , 2015) , 196 from the Pathway Interaction Database (PID)  (Schaefer et al. , 2009)  and 134 from the KEGG database, restricted to the Genetic Information Processing, Environmental Information Processing; Cellular Processes and Organismal systems subdivision. However, most of the analyses were performed using only KEGG database. Indeed, in the Hallmark and the PID databases, gene sets are defined as gene signatures rather than as biological pathways. Thus, the genes are not necessarily connected to each other through functional interactions. Conversely, gene sets retrieved from the KEGG database correspond to biological pathways defined as genes corresponding to proteins that participate in oriented molecular cascades.
Identification of Differentially Expressed Pathways (DEPs) For each of the 10 transcriptomic datasets, identification of DEPs was performed using the fgseaSimple function of the Bioconductor package fgsea  (Korotkevich et al. , 2021) , for fast preranked Gene Set Enrichment Analsyis (GSEA)  (Subramanian et al. , 2005). The fgseaSimple method takes two inputs: a gene-level signed statistics and a defined list of genes known as gene set. The method ranks the genes in descending order based on the chosen statistics, and then computes the Enrichment Score (ES) for the gene set. The ES reflects how often members of that gene set occur at the top (e. g. , upregulated) or the bottom (e. g. , downregulated) of the ranked gene list. To account for differences in gene sets size, a normalisation step is performed to obtain the Normalised Enrichment Score (NES).
Besides, random gene sets are generated and their NES computed. These NES are then used to create a null distribution from which the significance of the NES of the tested gene set is estimated. In our study, we used the t-statistics from the differential expression analysis comparing gene expression levels of CF sample to NCF samples as the control condition. In order to compare all the studies together, all the microarray and RNA-Seq datasets were processed using the same pipeline, involving the limma  (Ritchie et al. , 2015)  and edgeR  (Robinson et al. , 20 2010)  packages. After removing technical outlier samples and retrieving gene symbols using the biomaRt package  (Durinck et al. , 2009) , differential expression analysis at the gene level was performed by fitting a linear model using weighted least squares for each gene.
Up-dating Omnipath DEPs pathways The CF network was built from DEPs among pathways in the KEGG database, as extracted with the OmniPathR package. We observed a few inconsistencies between the corresponding list of genes and interactions downloaded with OmnipathR R package, and those in the 'up to date' pathways maps, as they are displayed on KEGG website. Therefore, we updated the OmnipathR version of the KEGG pathways by adding (or removing) a few nodes or interactions, in order to map the OmnipathR pathways with their corresponding pathways in KEGG. For each modification, bibliographic references were manually checked into other databases stored in Omnipath, in particular in the high confident databases SignorDB  (Lo Surdo et al. , 2022) , and the Human Reference Interactome  (Drew et al.