dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021). In addition, in a few pathways, some interactions are labelled as "indirect" in KEGG database. They involve part of signalling cascades belonging to other biological pathways, and they are not detailed in the considered pathway. For example, part of the PI3K-AKT pathway belongs to the Toll-like receptor signalling pathway but is not detailed in this pathway (See KEGG map for Toll-like receptor signalling pathway). In such cases, in order to build the 21 network based on complete cascades involving only direct interactions, we added the missing nodes and interactions. All the pathways modifications and the corresponding codes used to perform these modifications are available in the following github repository: sysbio-curie/CFnetwork.
Network building and pruning In the KEGG database, most of the 15 common DEPs display the same overall topology: some cell-surface receptor proteins activate one or more intra-cellular signalling cascades that in turn activate downstream transcription factors, thus triggering corresponding phenotypes. For example, the NF-κB pathway leads to the "inflammation" or "cell survival" phenotypes. However, 2 of the common DEPs, Cytokine-cytokine receptor interaction and Viral protein interaction with cytokine and cytokine receptor, are pathways that do not consist in such functional cascades. The Cytokine-cytokine receptor interaction pathway consists in a list of interactions between extra-cellular signal molecules and cell-surface receptors (see KEGG database to visualise this pathway's topology).
These interactions are also part of larger biological pathways that comprise their corresponding downstream cascades. This means that KEGG pathways are partially redundant (i. e. small pathways are part of larger pathways), which is also found in all commonly used pathway databases. In the case of the Cytokine-cytokine receptor interaction pathway, this DEP is dysregulated in the meta-analysis because some of the interactions between extracellular molecules and cell surface receptors are dysregulated, but not necessarily all of them. For example, interactions between TNF-α and its receptors, or IL17 and its receptors are dysregulated, but this information is also present in the DEPs containing the complete corresponding cascades, i. e. the TNF-α signalling pathway and the IL-17 signalling pathway.
It reveals unintuitive effects of targeting candidate proteins because of the complex interactions of the biological pathways in the network. Overall, the CF network can be seen as a tool to formulate hypotheses and interpret experimental observations. Although several transcriptomic datasets were gathered, the total number of samples globally included remains modest (57 CF and 46 control samples). Additional data may refine the list of dysregulated pathways, and help to improve the proposed CF network. To cope with the low number of samples per study, we opted for a meta-analysis combining various CF transcriptomic datasets, which highlighted that distinct differentiation media used for the primary cultures may favor different cell types, leading to 24 inconsistent transcriptomic profiles and potential erroneous interpretations.
This may explain why previous transcriptomic comparative studies reported incoherent signs of gene dysregulation (up-versus down-) between different datasets for many genes  (Clarke et al. , 2013). We observed the same phenomenon at the pathway level for datasets belonging to subgroup 1 or 2 (see Section 2. 3). Clustering studies based on the heatmap of common DEPs appears to be a good tool to select consistent data in future meta-analysis. Other types of dysregulations such as aberrant phosphorylations are not detectable in transcriptomic data. Including information from other types of omic data such as proteomic, phosphoproteomic, metabolomic,or volatilomic may help to refine the CF network. In particular, in the past three years, CF airway epithelial single-cell RNAseq (sc-RNAseq) datasets have been reported  (Carraro et al.
, 2021; Thurman et al. , 2022). Such data allow the study of dysregulations at the cell type level, and could facilitate building of the CF networks for specific epithelial cell types. Furthermore, CFTR is expressed in cell types beyond airway epithelial cells. Thus, refining this network within the context of these cell types could enhance our understanding of the role of CFTR in these specific cells such as macrophages, where CFTR seems to have non-channel functions  (Duan et al. , 2021). Prior knowledge gathered in the KEGG pathway database was used to identify and connect DEPs, but the proposed methodology can be followed using other pathways databases. Pathway names and definitions vary between databases, and therefore, the resulting network may slightly depend on the reference database that was used.
Nevertheless, it would comprise globally the same interactions and proteins. Similarly, CFTR interactors present in the network were identified according to PPI information in the CyFi-MAP. If new CFTR interactors are identified, this information may help improve the content of the network, highlighting new source nodes or routes for the propagation of dysregulations. In particular, missing interactions, because they are 25 not present in pathway databases, or have not been discovered yet, may explain the presence of unconnected components. If they exist, their discovery in the future may allow to link the two unconnected small components to the main component of the network. However, the proposed notion of targeting proteins as upstream as possible in the network, or among key hubs of the network, are still an interesting concept in order to prioritize candidate therapeutic targets.
An important issue of the present paper was to explore the link between absence of the CFTR protein, and more global pathway dysregulations that lead to CF cellular phenotypes. However, the precise definition of a diseased cellular phenotype is not clearly defined yet, and we used key words provided in the KEGG database or in the Gene Cards database  (Stelzer et al. , 2016). The present work proposes an answer this question in the context of systems biology studies. Associating phenotypes to the activity of outputs of the signalling cascades, referred to here as sink nodes, could be a first step towards the definition of the disease read-outs. This is of particular interest for in vitro evaluation of drug candidates, because we expect that drugs active in CF would reduce the activity of these sink nodes.
Availability of data and materials The codes and datasets supporting the conclusions of this article are available in the folowing github repository: sysbio-curie/CFnetwork. The Cytoscape session of the CF network, the TSV files of the nodes and the edges of the CF network and the XML file of the custom style of the Cytoscape session required to reproduce the Cytoscape session are available in the following github repository: sysbio-curie/CFnetwork cystoscape.
Removal of the PBM from SARS-CoV-1 E protein resulted in reduced expression of inflammatory cytokines and less pathogenicity in mice  (7). SARS CoV-1 and CoV-2 E proteins differ only in three amino acids and one residue deletion. Since their C-terminal region is ordered (PDB IDs: 2mm4 and 5x29)  (8, 9)  and disordered  (10)  in the presence and absence of lipids, respectively, it can be considered to contain a MemMoRF (Membrane Molecular Recognition Feature)  (11). Most of the E protein is distributed in the endoplasmic reticulum (ER) and the ER-Golgi intermediate compartment (ERGIC)  (12). The E protein can form homopentamers acting as a viroporin that is permeable for cations including calcium, and was demonstrated to alkalinize ERGIC and the lysosome lumen  (13). The structure of E protein and its monomeric and pentameric forms closely resemble to the human regulins, which regulate the activity of the sarco/endoplasmic reticulum Ca 2+ ATPase (SERCA)  (14, 15).
Structural models Human SERCA/PLN homology model was built based on the rabbit complex (PDBID: 4kyt) and the human SERCA (PDBID: 7e7s) structures using Modeller (42) (Figure  S3b  and http://www. hegelab. org/resources. html). E protein was docked to the human SERCA model using PIPER/ClusPro  (28). AlphaFold2 was run locally with full database mode as described in  (43, 44).
Molecular dynamics simulations The simulation systems were prepared using CHARMM-GUI  (45, 46). First, the structural models of SERCA/PLN and SERCA/E protein were oriented according to the Orientations of Proteins in Membranes database  (47). Then N-and C-termini were patched with ACE (acetyl) and CT3 (N-Methylamide) groups, respectively. The membrane bilayer was symmetric the CHARMM36m force field was used to run molecular dynamics simulations  (48, 49). Each system was energy minimized using the steepest descent integrator, which stopped when the largest force in the system becomes less than 500 kJ/mol/nm. In order to increase sampling, several simulations (n=3 for each system) were forked using the energy minimized system, with different velocities. Equilibration was performed in six steps and production runs were run for 500 ns.
Methods Study design, patient selection and data collection have been published previously  [8]. In summary, >100 national and affiliated member cardiac societies of the European Society of Cardiology (ESC) were invited to contribute to a global registry on PPCM, under the auspices of the ESC EORP Programme. This study complies with the Declaration of Helsinki. The investigators participated on a voluntary basis, with no remuneration. Participating centres managed the approvals of national or regional ethics committees or Institutional Review Boards, according to local regulations. This was an observational study with no specific protocols or recommendations for diagnosis or management. The baseline visit was the first visit to the specialist making the diagnosis of PPCM. Data collected included baseline clinical characteristics (demographics, comorbidities, obstetric history, symptoms and signs), blood tests and findings from electrocardiography, chest radiography and echocardiography.
Recent findings indicate that genetic variations associated with pro-inflammatory responses are more frequently found in populations with long-term tropical ancestry, as opposed to those with prolonged residence in temperate areas  [15]. These genetic adaptations may offer valuable insights into the heightened prevalence of PPCM among women hailing from regions within the tropical belt, which extends beyond Africa. These countries are historically characterized by lower socioeconomic status, as evidenced by lower HDI and HE. The question remains whether the findings from this study are specific to PPCM on a global scale or if they are observable in other forms of cardiac disease during pregnancy as well. In a propensity-matched study conducted by Zahid and colleagues  [16] , which involved a database comprising over 2 million patients who experienced pre-eclampsia/ eclampsia during hospital deliveries, it was observed that there was a higher incidence of PPCM, heart failure, and pulmonary oedema among Caucasian women with low income in comparison to their counterparts with higher income levels.
Acknowledgements EORP Oversight Committee, The Registry Executive  Committee of the EURObservational Research Programme (EORP). Data collection was conducted by the  EORP  department from the ESC by  Rachid Mir Hassaine  and  Souad Mekhaldi  as Clinical Project Managers,  Emanuela Fiorucci  as Project Officer,  Marina Andarala  as Data Manager. Statistical analyses were performed by  Cécile Laroche ,  Charle Viljoen  and  Alice Jackson. Overall activities were coordinated and supervised by  Doctor Aldo P. Maggioni  ( EORP Scientific Coordinator ). We also acknowledge all investigators who entered patients into the registry (Appendix 1). We also acknowledge the support of  Ms. Olivia Briton , who spent days and weeks retrieving data from sites. All authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.
Quality control of the alignment was performed via MultiQC v1. 4. Finally, counts were normalized as counts per million. Differential gene expression analysis was performed with the DESeq2 R package  37. Principal Component Analysis (PCA) was performed with factorMineR package  38  and factoextra package  39. The differentially expressed genes with adjusted p values false discovery rate (FDR)≤0. 05 were subjected to GSEA with GO database using the ClusterProfiler R package  40. The density plots were produced using the ggplot2 R package  41. Weighted correlation network analysis (WGCNA) was applied on normalized data recovered from DESeq2 analysis. Pathways associated with each WGCNA modules were determined using METASCAPE web application  42. WGCNA module genes were also submitted to protein-protein interactions using STRING web software (https://string-db.
Acknowledgments We acknowledge  Alexandre Simon ,  Coline Rogue ,  Gaëlle Revet ,  Dorota Jeziorowska  from  CARTHER  team for their help in the daily cell culture;  Audrey Geeverding  and  Michaël Trichet  from the  IBPS electron microscopy core facility  and all personnel from the IBPS photon microscopy core facility for helpful advice and technical assistance during microscopy preparation, image acquisition and analysis. The authors thank  Brigitte Onteniente  and  Phenocell SAS  ( Grasse, France ) to generate hiPSCs from MFM1 patient. The authors would also like to express their gratitude to  Susanne Bolte  from the  IBPS photon microscopy core facility , who has sadly died recently.
Sources of Funding This work was supported by funds from  Sorbonne Université , the  CNRS , the  INSERM , the  Agence Nationale de la Recherche  ( ANR-21-CE19-0027-MoHeDis ) and the  AFM-Téléthon  (contract number:  22142 ). Y. H. and V. B. were supported by a Ph. D. fellowship from the  AFM-Téléthon  (contract number:  20479 ) and  ANRT  (contract number:  2020/0074 ), respectively.
Statistical analysis. All experimental data are presented as mean ± standard error of the mean. Normality was verified using the test of Shapiro-Wilk and, when necessary, non-parametric tests were used. Statistical significance between two groups was determined using the Mann Whitney test. When multiple comparison was necessary, One-Way ANOVA combined with Dunnett's multiple comparison tests or Kruskal-Wallis test combined with Dunn's multiple comparison tests were used. A p-value of less than 0. 05 was considered statistically significant. Data were analyzed and presented using GraphPad Prism 8. RNA-sequencing data that support the findings of this study have been deposited in Gene Expression Omnibus (GEO) repository with the accession codes XX.
All four genes are located on sex chromosomes (GRC6a chicken assembly): tropomodulin 1 (ENS-GALG00000002125, chromosome Z), heterogeneous nuclear ribonucleoprotein K-like (ENSGALG00000040086, chromosome W), E3 ubiquitin-protein ligase KCMF1-like (LOC431003, chromosome W) and a lncRNA (ENSGALG00000048050, chromosome Z). In the present article, we performed a new statistical analysis of the raw data  [22]  (supplementary data S1, Complete) to specifically explore the differentially expressed genes between the CAM from male and female embryos (sex-DEG), and how they are regulated between 11 and 15 days of incubation. The expression of ten selected sex-DEG was further studied from 6 days of incubation to 15 days of incubation by RT-qPCR, to investigate the onset of their sex-dependent expression.
Ethics statement, handling of fertilised eggs, preparation of CAM samples, RNA extraction and sequencing The procedure used to incubate fertilised eggs, collect CAM tissue and perform RNA sequencing is detailed in  [22]. As previously mentioned, the protocol was in compliance with the European legislation on the "Protection of Animals Used for Experimental and Other Scientific Purposes" (2010/63/UE). It followed the guidelines approved by the institutional animal care and use committee (IACUC). All experiments were conducted in accordance with the ARRIVE essential and Recommended Set of ARRIVE guidelines  [23]. The datasets supporting the results and the discussion of the article are available at the NCBI Gene Expression Omnibus (GEO) repository (http://www. ncbi. nlm. nih. gov/geo) using GSE199780 as the accession number.
The final cDNA library was ready after a round of purification, terminal repair, A-tailing, ligation of sequencing adapters, size selection, and PCR enrichment. Library concentration was first quantified using a Qubit 2. 0 fluorometer (Life Technologies, CA, USA). The insert size was checked (Agilent bioanalyzer, Santa Clara, CA, USA) and quantified using quantitative PCR. Libraries were analysed using Illumina technology (San Diego, CA, USA). The original raw data were transformed to Sequenced Reads by base calling. Raw data were recorded in a FASTQ file, which contains sequence information (reads) and corresponding sequencing quality information. Raw reads were then filtered to remove reads with adapter contamination or reads with low quality. Gene expression level was assessed by transcript abundance.
The gene expression levels were estimated by counting the reads that mapped to genes or exons. Alignments to the reference Gallus gallus genome (GRCg6a (GCA_000002315. 5),  [24] ) were performed with HISAT2  [25]  that uses a graph-based alignment. HTSeq software  [26]  was used to quantify the gene expression levels using the union mode. Resulting data are shown in Supplementary data S1 (S1_complete).
ncbi. nlm. nih. gov/) and synthesised (Eurogentec, Seraing, Belgium). The sequences and characteristics of each pair of primers used to explore gene expression in male and female samples are given in Supplementary data S2. The gene expression was analysed by RT-qPCR using LightCycler® 480 SYBR Green I Master and LightCycler® 480 instrument II (Roche, Basel, Switzerland). Four to eight biological replicates were used, and three technical replicates were performed for each sample. GenNorm® software was used for validation of housekeeping gene stabilities  [27]. The normalised expression of candidate genes were calculated using the following formula: gene efficiency^(ctcalibratorctsample) / geometric average quantity of housekeeping genes.
Statistical analyses For RNA-seq analysis, all statistical analyses were performed on raw data  [22]  using the R software  [28] , version v. 4. 2. 1, and the edgeR package v. 3. 38. 4 [29]  in Bioconductor project v. 3. 17  [30]. Briefly, after the filtering and the normalisation steps, the negative binomial generalized linear model (GLM)  [29]  with the factors "day of incubation" (EID11, EID15) and "sex", and the "day of incubation" x "sex" interaction effect, was fitted for each gene. P-values were adjusted by controlling the false discovery rate (<0. 05) using Benjamini-Hochberg correction. Genes differentially expressed between male and female CAM were obtained by testing three comparisons: differentially expressed genes between male and female CAMs at EID11 (Supplementary data S1, Sex_DEG_EID11), differentially expressed genes between male and female CAMs at EID15 (Supplementary data S1, Sex_DEG_EID15), and differentially expressed genes between male and female CAMs (Supplementary data S1, Sex_DEG (EID11_EID15)).
F_EID11, genes overexpressed in female CAMs after 11 days of incubation (EID11); F_EID15, genes overexpressed in female CAMs after 15 days of incubation (EID15); F_EID11_EID15, genes overexpressed in female CAMs; M_EID11, genes overexpressed in male CAMs after 11 days of incubation (EID11); M_EID15, genes overexpressed in male CAMs after 15 days of incubation (EID15); M_EID11_EID15, genes overexpressed in male CAMs. Venn diagrams were prepared with JVenn tool  [53]. (Fig. 2A ). As expected, W-linked DEG are associated with overexpression in female CAMs except for the coding W-linked gene ENS-GALG00000047434 that is overexpressed in male CAMs. However, this discrepancy may be due to a misplacement of this latter gene in the GRC6a chicken assembly (GCA_000002315. 5) as the accession number is no longer referenced in the last released database (GCA_016699485.
Expression of ten sex-DEG in CAM from a broiler strain (validation of RNA-seq data) and a layer strain, by RT-qPCR To validate the RNA-seq data, seven genes that exhibited an overexpression in female CAMs (LOC100859072, RPTC15L, LOC107049046, HINTW, LOC431003, NIPBLL, MAP1B) and three genes showed to be overexpressed in male CAMs (PPIC, FAM172A, and ALDH7A1) (Supplementary data S2) were further analysed by RT-qPCR using the same EID11 and EID15 CAM samples that were used for RNA sequencing (ROSS 308, broiler strain). Some of these genes (PPIC, NIPBLL, RPTC15L, and LOC431003) were chosen from the top-ten DEG lists (Table  1  and Table 2 ). All these genes are located on the W or Z chromosomes (Supplementary data S2). The expression of these genes was also investigated in CAM samples collected at EID6, EID8, EID11 and EID15 on a layer strain (ISA Brown).
Acknowledgements The authors acknowledge  Joël Delaveau  and  Christophe Rat  ( INRAE, PEAT, Centre Val de Loire , DOI: 10. 15454/1. 5572326250887292E12) for egg handling, and  Laura Caillaud  ( INRAE, Université de Tours, BOA ,  37380, Nouzilly, France ) for her contribution to sampling and analysis. The authors wish to thank the  Région Centre Val de Loire  and the  PHASE Department (INRAE)  for supporting  M. Halgrain 's PhD.
, 2013; DeLand et al. , 2012). The Merged GOME/SCIAMACHY/GOME2 (GSG) TCO dataset is a comprehensive compilation of measurements from three satellite instruments: Global Ozone Monitoring Experiment (GOME), Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and GOME-2  (Lerot et al. , 2014). By combining data from multiple instruments, GSG offers improved coverage and temporal continuity from 1995 to date  (Weber et a l. , 2018). This merged dataset provides a consistent and extended record of global TCO. The ozone retrievals are based on the University of Bremen weighing function DOAS (WFDOAS) v4 algorithm  (Coldewey-Egbers et al. , 2005). These data are in good agreement with the World Ozone and UV Data Centre (WOUDC) ozonesonde measurements, with an average bias of 2-3% for the zonal and global averaged values  (Fioletov et al.
The measurements in WOUDC were performed mainly with VIZ radiosondes during the period 1980-1991, followed by RS-80 radiosonde until 2009 and the iMet radiosondes to date. The VIZ radiosondes use a hypsometer for pressure measurements, and they have an accuracy of ±0. 2 hPa at altitudes above 20 hPa  (Conover and Stroud, 1958). The RS-80 radiosondes are paired with electronic boards, which are capable of transmitting data every 7 seconds. However, the advanced V2 versions have improved electronic components that transmit data every second. The i-Met radiosondes are equipped with a GPS receiver that measures the geometric altitude, in addition to atmospheric pressure  (Johnson et al. , 2018). Trajectory mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) is a global 3-dimensional heightresolved ozone dataset, derived from WOUDC ozone sounding records across the globe using trajectory mapping.