dataesr/scientific-paragraphs-categorization huggingface dataset:
OmniPath is a comprehensive collection of literature-curated human signalling pathways, which includes several databases such as Signor  (Perfetto et al. , 2016)  or Reactome  (Fabregat et al. , 2016)  and that can be queried using pypath, a Python module for molecular networks and pathways analyses. Fusion genes are frequently found in human prostate cancer and have been identified as a specific subtype marker  (The Cancer Genome Atlas Research Network, 2015). The most frequent is TMPRSS2:ERG, as it involves the transcription factor ERG, which leads to cellcycle progression. ERG fuses with the AR-regulated TMPRSS2 gene promoter to form an oncogenic fusion gene that is especially common in hormone-refractory prostate cancer, conferring androgen responsiveness to ERG. A literature search reveals that ERG directly regulates EZH2, oncogene c-Myc and many other targets in prostate cancer  (Kunderfranco et al.
Boolean model construction We converted the network to a Boolean model by defining a regulatory graph, where each node is associated with discrete levels of activity (0 or 1). Each edge represents a regulatory interaction between the source and target nodes and is labelled with a threshold and a sign (positive or negative). The model is completed by logical rules (or functions), which assign a target value to each node for each regulator level combination  (Abou-Jaoudé et al. , 2016; Chaouiya et al. , 2012). The regulatory graph was constructed using GINsim software  (Chaouiya et al. , 2012)  and then exported in a format readable by MaBoSS software (see below) in order to perform stochastic simulations on the Boolean model. The final model has a total of 133 nodes and 449 edges (Supplementary File 1) and includes pathways such as androgen receptor and growth factor signalling, several signalling pathways (Wnt, NFkB, PI3K/AKT, MAPK, mTOR, SHH), cell cycle, epithelial-mesenchymal transition (EMT), Apoptosis, DNA damage, etc.
This model has 9 inputs (EGF, FGF, TGF beta, Nutrients, Hypoxia, Acidosis, Androgen, TNF alpha and Carcinogen presence) and 6 outputs (Proliferation, Apoptosis, Invasion, Migration, (bone) Metastasis and DNA repair). Note that a node in the network can represent complexes or families of proteins (e. g. , AMPK represents the genes PRKAA1, PRKAA2, PRKAB1, PRKAB2, PRKAG1, PRKAG2, PRKAG3). The correspondence can be found in "Montagud2021_interactions_sources. xlsx" and "Montagud2021_nodes_in_pathways. xlsx" in Supplementary File 1. This model was deposited in the GINsim Database with identifier 252 (http://ginsim. org/model/signalling-prostate-cancer) and in BioModels  (Malik-Sheriff et al. , 2019)  with identifier MODEL2106070001 (https://www. ebi. ac. uk/biomodels/MODEL2106070001). Supplementary File 1 is provided as a zipped folder with the model in several formats: MaBoSS, GINsim, SBML, as well as images of the networks and their annotations.
Stochastic Boolean model simulation MaBoSS  (Stoll et al. , 2017 (Stoll et al. , , 2012 ) is a C++ software for stochastically simulating continuous/discrete-time Markov processes defined on the state transition graph (STG) describing the dynamics of a Boolean model (for more details, see  (Abou-Jaoudé et al. , 2016; Chaouiya et al. , 2012) ). MaBoSS associates transition rates to each node's activation and inhibition, enabling it to account for different time scales of the processes described by the model. Probabilities to reach a phenotype (to have value ON) are thus computed by simulating random walks on the probabilistic STG. Since a state in the STG can combine the activation of several phenotypic variables, not all phenotype probabilities are mutually exclusive (like the ones in Appendix 1, Section 6.
Data tailoring the Boolean model Logical models were tailored to a dataset using PROFILE to obtain personalised models that capture the particularities of a set of patients  (Béal et al. , 2019)  and cell lines  (Béal et al. , 2021). Proteomics, transcriptomics, mutations and CNA data can be used to modify different variables of the MaBoSS framework, such as node activity status, transition rates and initial conditions. The resulting ensemble of models is a set of personalised variants of the original model that can show great phenotypic differences. Different recipes (use of a given data type to modify a given MaBoSS variable) can be tested to find the combination that better correlates to a given clinical or otherwise descriptive data. In the present case, TCGA-patient-specific models were built using mutations, CNA and/or RNA expression data.
More on PROFILE's methodology can be found in its own work  (Béal et al. , 2019)  and at its dedicated GitHub repository: https://github. com/sysbio-curie/PROFILE. A description of the methods we have used for the personalisation of the models can be found in the Appendix 1, Section 3. The analysis of the TCGA personalisations and their patient-specific drug treatments can be found in Appendix 1, Section 4. The analysis of the prostate cell lines personalisations can be found in Appendix 1, Section 5, with a special focus on the LNCaP cell line model analysis in Section 6.
High-throughput mutant analysis of Boolean models MaBoSS allows the study of knock-out or loss-of-function (node forced to 0) and gain-offunction (node forced to 1) mutants as genetic perturbations and of initial conditions as environmental perturbations. Phenotypes' stabilities against perturbations can be studied and allow to determine driver mutations that promote phenotypic transitions  (Montagud et al. , 2017). Genetic interactions were thoroughly studied using our pipeline of computational methods for Boolean modelling of biological networks (available at https://github. com/sysbiocurie/Logical_modelling_pipeline). LNCaP-specific Boolean model was used to perform single and double knock-out (node forced to 0) and gain-of-function (node forced to 1) mutants for each one of the 133 nodes, resulting in a total of 32258 models.
Identification of drugs associated with proposed targets To identify drugs that could act as potential inhibitors of the genes identified with our models (Table  1 ), we explored the drug-target associations in DrugBank  (Wishart et al. , 2018). For those genes with multiple drug-target links, only those drugs that are selective and known to have relevance in various forms of cancer are considered here. In addition to DrugBank searches, we also conducted exhaustive searches in ChEMBL  (Gaulton et al. , 2017)  (http://doi. org/10. 6019/CHEMBL. database. 23) to suggest potential candidates for genes whose information is not well documented in Drug Bank. From the large number of bioactivities extracted from ChEMBL, we filtered human data and considered only those compounds whose bioactivities fall within a specific threshold (IC50/Kd/ Ki<100 nM).
We performed a target set enrichment analysis using the fgsea method  (Korotkevich et al. , 2016)  from the piano R package  (Väremo et al. , 2013). We targeted pathway information from the GDSC1 and GDSC2 studies  (Iorio et al. , 2016)  as target sets and performed the enrichment analysis on the normalised drug sensitivity profile of the LNCaP cell line. We normalised drug sensitivity across cell lines in the following way: cells were ranked from most sensitive to least sensitive (using ln(IC50) as drug sensitivity metrics), and the rank was divided by the number of cell lines tested with the given drug. Thus, the most sensitive cell line has 0, while the most resistant cell line has 1 normalised sensitivity. This rank-based metric made it possible to analyse all drug sensitivities for a given cell line without drugspecific confounding factors, like mean IC50 of a given drug, etc.
Tables and their legends Table 1: List of selected nodes, their corresponding genes and drugs that were included in the drug analysis of the models tailored for TCGA patients and LNCaP cell line. We gathered data from Fumiã and Martins (2013) Boolean model, Omnipath  (Türei et al. , 2021)  and pathways identified with ROMA  (Martignetti et al. , 2016)  on the TCGA data to build a prostate-specific prior knowledge network. This network was manually converted into a prostate Boolean model that could be stochastically simulated using MaBoSS  (Stoll et al. , 2017)  and tailored to different TCGA and GDSC datasets using our PROFILE tool to have personalised Boolean models. Then, we studied all the possible single and double mutants on these tailored models using our logical pipeline of tools  (Montagud et al.
Figure1: Workflow to build patient-specific Boolean models and to uncover personalised drug treatments from present work. We gathered data from Fumiã and Martins (2013) Boolean model, Omnipath (Türei et al. , 2021)  and pathways identified with ROMA (Martignetti et al. , 2016)  on the TCGA data to build a prostate-specific prior knowledge network. This network was manually converted into a prostate Boolean model that could be stochastically simulated using MaBoSS (Stoll et al. , 2017)  and tailored to different TCGA and GDSC datasets using our PROFILE tool to have personalised Boolean models. Then, we studied all the possible single and double mutants on these tailored models using our logical pipeline of tools (Montagud et al. , 2017). Using these personalised models and our PROFILE_v2 tool presented in this work, we obtained tailored drug simulations and drug treatments for 488 TCGA patients and eight prostate cell lines.
Acknowledgements The authors acknowledge the help provided by  Jelena Čuklina at ETH Zurich, Vincent Noël at Institut Curie, Annika Meert at Barcelona Supercomputing Center and Aurélien Naldi at INRIA Saclay. XXX thank reviewers The authors acknowledge the technical expertise and assistance provided by the  Spanish Supercomputing Network (Red Española de Supercomputación) , as well as the computer resources used: the  LaPalma Supercomputer , located at the  Instituto de Astrofísica de Canarias  and  MareNostrum4 , located at the  Barcelona Supercomputing Center. This work has been partially supported by the  European Commission  under the  PrECISE  project ( H2020-PHC-668858 ), the  INFORE  project ( H2020-ICT-825070 ) and the  PerMedCoE  ( H2020-ICT-951773 ).
2017. The ChEMBL database in 2017. Nucleic Acids Res 45: D945-D954. doi: 10. 1093/nar/gkw1074Gillespie DT. 1976. A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys 22: 403-434. doi: 10. 1016/0021-9991(76 ) 90041-3Gleason DF. 1992. Histologic grading of prostate cancer: A perspective. Hum Pathol , The Pathobiology of Prostate Cancer-Part 1 23: 273-279. doi: 10. 1016/0046-8177(92 ) 90108-FGleason DF. 1977. The Veteran's Administration Cooperative Urologic Research Group: histologic grading and clinical staging of prostatic carcinomaUrologic Pathology: The Prostate. Philadelphia:  Lea & Febiger. pp. 171-198. Goldbeter A. 2002. Computational approaches to cellular rhythms. Nature 420: 238-245. doi:10. 1038/nature01259Gómez Tejeda Zañudo J, Scaltriti M, Albert R.
NFAT, NFkB, and AP-1 Promoter Activity Assay TPR cells transfected with shcontrol or shSNX27 were incubated with TCS at 2. 5:1 ratio for the indicated time points. Then, cocultures were stained with anti-mouse CD45-APC in PBS staining buffer (PBS, 1% FBS, 0. 5% BSA, 0. 01% sodium azide) (30 min, 4°C, darkness) to exclude TCS cells from analysis. Expression of NFAT-GFP, NFkB-CFP and AP-1-Cherry was determined by flow cytometry using a CytoFLEX S Flow cytometer (Beckman Cloulter). Live cells were gated using forward and side scatter parameters. All conditions were carried out in triplicates and data were analyzed using FlowJo 10 software (FlowJo, Ashland, OR) and Prism 5.
For membrane proteins, accumulation at the IS was compared with the other areas of the plasma membrane: in T cell/TCS conjugates, maximal intensity Z-projections of contiguous optical sections (0. 2 µm-wide) that included all the three-dimensional fluorescence information were stacked. Analysis was carried out using an in-house designed plugin for Fiji software developed by Carlos Oscar Sorzano and updated by Ana Cayuela  (32). This plugin measures the mean fluorescence intensity (MFI) in circular regions of interest at the background, the IS, and the plasma membrane of the T cell outside the IS. Then the IS/plasma membrane MFI ratio was calculated as: (MFI IS -MFI background)/(MFI plasma membrane -MFI background). To quantify synaptic recruitment of proteins present at internal compartments/ endosomes, fluorescence accumulated at the IS was compared with total cell fluorescence.
Microtubule network organization patterns at the IS were categorized by three researchers by observation of unlabeled images into two classes: pattern 1 (P1), radial microtubules anchoring at the periphery of the IS; pattern 2 (P2), non-radial microtubules unable to reach the periphery of the IS. Maximum intensity projections of 4 consecutive sections (0. 8 µm) at the T cell-coverslip contact were generated using the Fiji software  (42)  and Huygens Pro software (version 14. 10) was used to perform image deconvolution. To quantify formation of the filamentous actin (F-actin) ring and its fluorescence intensity at the IS, analysis was carried out on a 1-mm-thick section at the cell-coverslip interface. Cells from multiple microscopic fields were manually defined, and their Factin MFI was computed.
Statistical Analysis For co-localization analysis, Pearson's correlation and Mander's overlap coefficients were calculated using the JACoP plugin (Just Another Co-localization plugin) (43) of ImageJ software. Statistical analysis was performed with GraphPad Prism 5 software and samples were assumed to fit normality. Details about the data presentation, the experimental replication, and the adequate statistical tests used are included in the individual figure legends. Briefly, unpaired student's t-test was used to analyze differences between two conditions. Unless otherwise indicated, two-way ANOVA with Bonferroni post-test was applied for multiple comparisons. The level of statistical significance is represented by * p <0. 05; ** p <0. 01; *** p <0. 001; **** p <0. 0001. Data are shown as mean ± standard error of the mean (SEM) unless otherwise specified.
FUNDING NG-M received funding from the  European Union   Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant  agreement  713673  and  "La Caixa" Foundation  ( ID 100010434 ), with the fellowship code being  LCF/BQ/DI17/ 11620027 , as well as an EMBO short-term fellowship. CR-R held a predoctoral fellow of the A ́lvaro Entrecanales and  Jeŕome Lejeune Foundations. Research in IM's lab is funded by grants from the  Spanish Association Against Cancer (AECC ,  CICPF18 ),  Aplastic Anemia  and  MDS International Foundation  (AAMDSIF,  OPE01644 ),  Spanish Ministry of Science and Innovation  ( PID2019-108357RB-100/AEI/10. 13039/ 501100011033 ), and the Madrid regional government (IMMUNOTHERCAM Consortium  S2010/BMD-2326 ) to IM. Research in AA's lab is funded by the  French Ligue Nationale Contre le Cancer , Equipe Labelliseé 2018).
For each lamb, 12 phases were observed (30 scans per phase) and data have been expressed in mean ± SD of number of times a behavior was observed (the maximum value being 30). The first observation (obs1) was done about two hours after the arrival of lambs in their respective park and human interventions (blood samples, weight…). The impact of music exposure was tested by ANOVA repeated measures (# observation) with independent factor (CONT vs MUS) followed by post-hoc analyses corrected by Holm.
Acknowledgements This project is supported by  Region Centre Val de Loire  and  INRAE Centre Val de Loire. Experimentation was supported by the  APR Neuro2Co  grant ( 2018-2022 ), the PhD ( Marine Siwiaszczyk ) salary is covered by the  APR Neuro2Co grant ,  Beauval Nature  and  INRAE  ( Phase department ), internship gratification (Guillaume Ubiema) was covered by the  APR Neuro2Co  grant from the  Region Centre Val de Loire. The authors thank the UEPAO unit (animal facilities) for taking care of the animals during the experimentation.
To determine r h of particles within aqueous Kleptose ® HPB solution in absence and presence of HB-IgG-A (η-data required-see Table  A2  in the Appendix A of the manuscript), D t was measured as a function of Kleptose ® HPB concentrations using dynamic light scattering device DynaPro ® NanoStar ® from Wyatt Technology Corporation (Santa Barbara, CA, USA). Due to two photodiodes (1st with a correlator for the analysis of DLS; 2nd arranged at a 90 • angle for the analysis of the static light scattering (SLS) of the sample), the hydrodynamic radius r h (r h > 0. 2 nm  [26] ) and the weight average molecular weight M W were determined at the same time. The software DYNAMICS ® Version 7. 9. 0. 5 from Wyatt Technology Corporation (Santa Barbara, CA, USA) was used to determine r h and M W from the scattering intensities of the online measurement.
The duplicate measurements of long-term samples (sample preparation as described in Section 2. 2. 4) were performed with a protein concentration of 1 g L -1. A 50 mM K 2 HPO 4 -NaH 2 PO 4 buffer solution at pH 7 was used as the mobile phase. The flow rate was chosen to 0. 75 mL min -1 combined with a sample injection volume of 100 µL over 35 min (method duration) at 25 • C. The ASTRA ® V. 7. 3. 2 software from Wyatt Technology Corporation (Santa Barbara, CA, USA) was used to evaluate the respective mass fraction (aggregate, monomer, and fragment) and weight average molecular weight within the sample. The assessment of HB-IgG-B aggregation and fragmentation was conducted on a Waters high resolution SEC column (XBridge Premier Protein SEC 250 Å, 2. 5 µm, 7. 8 × 300 mm). All the runs were carried out in 50 mM K 2 HPO 4 -NaH 2 PO 4 buffer solution with 150 mM NaCl at pH 7.
The unique environment of the actin module An actin hetero-dimer is found in several chromatin re-modelers, in which it binds a long HSA helix and forms a mobile module poorly resolved in EM maps. Conversely, in the head part of NuA4, the Eaf1 HSA -Act1-Arp4 module is strongly coupled to the neck and is well resolved. This stability stems from multiple additional interactions between this module and the SANT-like domain of Swc4, an Epl1 loop that intercalates between the two actin proteins, as well as a loop from Eaf1 (Extended Data Fig. 6 ). This complementary interaction network completely envelopes and shields the Eaf1 HSA helix, which is unlikely to play a role similar to its analogue in the INO80 complex, where it serves as a long-range sensor for extra-nucleosomal DNA  28. The nearly neutral charge of the Eaf1 HSA helix also supports this conclusion.
Uncovering the HAT module We next sought to resolve how the HAT module is connected to the core. It was previously shown that the N-terminal half of Epl1 is part of the HAT module whereas the C-terminal half, which we resolved in the core, is crucial for its inclusion into NuA4  9. Focussed classification within a sphere expanding from the first Epl1 residue detected in our map revealed a fuzzy density that was only apparent at low density threshold, suggesting that the HAT module is flexibly attached to NuA4. We posited that mobility is likely to be conferred by a stretch of amino acids predicted to be disordered (residues 359-418). This linker is conserved between yeasts and vertebrates (Extended Data Fig. 7 ) and connects the last Epl1 residue resolved in the crystal structure of the HAT module (K358) with the first residue discernible in our cryo-EM map (M419).
This residue is closely followed by the first Epl1 helix in the core (Helix H1, S431). We introduced a TEV protease cleavage site into the linker to investigate its role in connecting the HAT module to the core. Using a SBP affinity-tag on Eaf1, holo-NuA4 was bound to streptavidin beads and then incubated with TEV protease. We observed that all HAT subunits were efficiently and specifically released from holo-NuA4 upon cleavage, demonstrating that the HAT module is tethered to the core mainly by the long unstructured Epl1 linker (Extended Data Fig. 8 ). This observation agrees with cross-linking coupled to mass spectrometry analyses, which identified only a few interactions between the HAT module and the core  23. To better visualize the HAT module connected to NuA4, deletion mutants were designed to gradually shorten the linker (Epl1-SL1, Epl1-SL3, Epl1-SL5) and bring the HAT module closer to the core (Fig.
Indeed, deletion of the scaffolding Eaf1 subunit or the C-terminal half of Epl1 is viable. Our result supports this model and suggest that Piccolo masks the putative effects of Epl1 linker mutations. In marked contrast, in the fission Schizosaccharomyces pombe, the Eaf1 homologue Vid21 is essential for viability, despite similar subunit composition  27  (Extended Data Fig. 1 ). We thus reasoned that, rather than Piccolo, holo-NuA4 is critical for proliferation in fission yeast. Accordingly, we observed that deleting the C-terminal end of S. pombe Epl1 causes lethality (Fig. 4a-b ), whereas an analogous truncation mutation detaches the HAT module from holo-NuA4 and is viable in S. cerevisiae  9. Thus, unlike S. cerevisiae, incorporation of the HAT module into NuA4 is essential in S. pombe. This finding prompted us to test the importance of the distance between the HAT module and the core-NuA4 in this yeast.
For each transformation, 2-4 individual clones were purified and analyzed. For tetrad analyses in both S. cerevisiae (Extended Data Fig. 9 ) and S. pombe (Fig. 4 ), mutations were introduced in diploid strains to generate heterozygotes, which were then sporulated to analyze growth phenotypes. At least 20 distinct meiotic events were analyzed for growth and allele segregation by replica plating and PCR. The four progenies from each tetrad are labelled A-D (Fig. 4b ).
Cryo-EM sample preparation and data acquisition Three µl of sample was applied onto a holey carbon grid (Quantifoil R2/2 300 mesh) rendered hydrophilic by a 90 s treatment in a Fischione 1070 plasma cleaner operating at 30% power with a gas mixture of 80% argon: 20% oxygen. The grid was blotted for 1 sec at blot force 8 and flash-frozen in liquid ethane using Vitrobot Mark IV (FEI) at 4˚C and 95-100% humidity. Images were acquired on a Cs-corrected Titan Krios (Thermo Fisher) microscope operating at 300 kV in nanoprobe mode using the serialEM software for automated data collection  35. Movie frames were recorded either on a Gatan K2 Summit or on a Gatan K3 direct electron detector after a Quantum Ls 967 energy filter using a 20 eV slit width in zero-loss mode. Images were collected in super-resolution mode at a nominal magnification of 105,000x (K2 summit) or 81,000x (K3) which yielded a pixel size of 0.
Acknowledgements: We are grateful to  Simonetta Piatti  and  Lorraine Pillus  for kindly sharing S. cerevisiae strains. We thank the mass spectrometry service of the  IGBMC  for helpful advises and the  Nanotumor Consortium , with financial support from  ITMO Cancer of AVIESAN (National Alliance for Life Sciences & Health)  within the framework of the  Cancer Plan , for helpful discussions. 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 , the  University of Strasbourg Institute for Advanced Study (USIAS)  for a fellowship to P. S. within the  French national program  "investment for the future" ( IdEx-Unistra ), the  Agence Nationale de la Recherche (ANR)  grants  ANR-17-CE12-0022  to P.
The slides were scanned on a Tecan scanner MS200 and analyzed by Feature Extraction V. 11. 5. 1. 1. Signals of each probe set were filtered according to the coefficient of variations with a cut-off value of 50%. Significant changed expression was acquired by moderated t-test with a value of p of <0. 05 and a fold-change cut-off value of 2. 0. Benjamini-Hochberg correction was performed  (Hochberg and Benjamini, 1990). Further details on transcriptomic analyses is reported in an accompanying paper to be submitted to Data in Brief.