dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and materials availability All data needed to evaluate the conclusions in the paper are present in the paper, in the Supplementary Materials and in public databases. Raw mass spectrometry data are available in the PRIDE database with identifiers PXD040169. (Cho et al. , 2022). Note that the known SH3-domain mediated interaction partners, that were studied by low-throughput methods, were only detected on a few occasions (DNM2, MYC, RIN3, marked in orange), or no detected at all (TAU/MAPT, CAVIN4). We also performed nHU-MS experiments with a set of closely or a distantly related SH3 domains and compared their affinity profiles with BIN1. This way, we could quantify that related SH3-domains, for example the one found in AMPH, show similarities in their affinity interactomes, displaying statistically significant correlation between the measured affinity constants.
Mammalian cell extract preparation Jurkat E6. 1 cells (ECACC #88042803, RRID: CVCL_0367) were grown in RPMI (Gibco) medium completed with 10% FBS (Gibco BRL) and 40 μM estim g/ml gentamicin (Gibco/Life Technology). The C25 myoblast cell line obtained from Institut de Myologie (Paris, France) were grown below 60% confluency in DMEM/199 medium (Sigma-Aldrich) supplemented with 20% FBS, 25 μM estim g/ml fetuin (Sigma-Aldrich), 0. 5 ng/ml basic fibroblast growth factor (Gibco BRL), 5 ng/ml epidermal growth factor (Gibco BRL), 0. 2 μM estim g/ml dexamethasone (Sigma-Aldrich), 5 μM estim g/ml insulin (Eli Lilly Co. , Indianapolis, USA), 50 U/ml penicillin (Gibco/ Life Technology), and 100 μM estim g/ml gentamicin. All cells were kept at 37°C and 5% CO2. To prepare total cell extracts, Jurkat cells were seeded onto T-175 flasks and grew until 3x10 6 cells/ml confluency, C25 myoblasts were seeded on T-75 flasks and grew until they reach ½ confluency, where we detected the highest expression for DNM2 in these cells before.
, 2022). Briefly, nHU samples were precipitated with TCA, and the urea-solubilized, reduced and alkylated proteins were digested with trypsin and Lys-C at 2M final urea concentration. Peptide mixtures were then desalted on C18 spin-column and dried on Speed-Vacuum. 100 ng peptide mixtures were analyzed using an Ultimate 3000 nano-RSLC coupled in line, via a nano-electrospray ionization source, with a LTQ-Orbitrap ELITE mass spectrometer (Thermo Fisher Scientific, San Jose, California) or with the Orbitrap Exploris 480 mass-spectrometer (Thermo Fisher Scientific, Bremen, Germany) equipped with a FAIMS (high Field Asymmetric Ion Mobility Spectrometry) module. Data was collected in DDA (data dependent acquisition) mode, proteins were identified by database searching using SequestHT (Thermo Fisher Scientific) with Proteome Discoverer software (Thermo Fisher Scientific).
Peptides and proteins were filtered with a false discovery rate (FDR) at 1%. Label-free quantification was based on the extracted ion chromatography intensity of the peptides. All samples were measured in technical triplicates. The measured extracted ion chromatogram (XIC) intensities were normalized based on median intensities of the entire dataset to correct minor loading differences. For statistical tests and enrichment calculations, not detectable intensity values were treated with an imputation method, where the missing values were replaced by random values similar to the 10% of the lowest intensity values present in the entire dataset. Unpaired two tailed T-test, assuming equal variance, were performed on obtained log2 XIC intensities. All raw LC-MS/MS data have been deposited to the ProteomeXchange via the PRIDE database with identifier PXD040169.
Obtained fold-change values were converted to apparent affinities using the hyperbolic binding equation and binding thresholds were determined as described before  (Zambo et al. , 2022). Proteins containing PRMs were identified with the help of SliMSearch using the class 1 and class 2 PxxP consensus motif definitions found in the ELM database (LIG_SH3_1 and LIG_SH3_2)  (Krystkowiak and Davey, 2017)   (Kumar et al. , 2019).
g. false negatives or positives). Affinity-weighted specificity logos were calculated as described before  (Gogl et al. , 2022). The obtained affinities (both fragmentomic and of FL proteins obtained from nHU experiments) were uploaded to the ProfAff affinity database and accessible at https://profaff. igbmc. science  (Gogl et al. , 2022). Membrane tubulation assay pTL1 myc-His plasmids containing the human DNM2 and pEGFP-BIN1 plasmid (human BIN1 isoform 8) have been previously used  (Nicot et al. , 2007). Mutant versions of BIN1 were created by standard QuickChange mutagenesis protocol. Cos-1 cells (ATCC #CRL-1650, RRID: CVCL_0223) were grown in DMEM (1g/L glucose) containing 10% FCS and 40 µg/mL gentamicin, kept at 37°C and 5% CO2 and were split twice a week for maintaining. The day before transfection, 0.
Acknowledgment We thank  Pascal Eberling  for the synthesis of the DNM2 PRR peptide,  Camille Kostmann  for help in cloning,  Nadege Diedhiou  for her help in the tubulation assay, as well as the cell culture and the photonic microscopy platform of the  IGBMC  for their help in cell culturing and imaging. GG was supported by the collaborative post-doc grant of  IGBMC. The project was supported by the  Ligue contre le cancer  ( équipe labellisée 2015  to GT), the  Agence Nationale de la Recherche  (grant  ANR-18-CE92-0017 ,  ANR-22-CE44-0018 , and  ANR-22-CE11-0026  to GT) and  AFM-Téléthon  ( 23933 ). As a member of the  IGBMC institute , we benefited from the  French Infrastructure for Integrated Structural Biology  ( FRISBI )  ANR-10-INSB-05-01 , from  Instruct-ERIC , from  IdEx Unistra  ( ANR-10-IDEX-0002 ), from  SFRI-STRAT'US  project ( ANR 20-SFRI-0012 ), and from  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program  as a member of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm.
Enrichment analyses We performed enrichment analyses of different gene sets: genes within 1 Mb, 100 kb or 10 kb of the identified genetic loci, genes within 10 kb of the identified genetic loci with intragenic genetic variants, and genes within 10 kb of the identified genetic loci with intragenic genetic lead variants. As a reference, we used the rest of the protein-coding genome. First, the Online Mendelian Inheritance in Man (OMIM) database 45 was used to retrieve information on genes related to heritable phenotypes affecting head size. Second, the Catalogue of Somatic Mutations in Cancer (COSMIC) database 25 was used to extract Tier 1 cancer genes. Taking the rest of the genome as our reference gene set, we calculated the enrichment of these macrocephaly, microcephaly and cancer genes in the abovementioned gene sets.
Acknowledgements Acknowledgements are provided for the studies who contributed new samples in addition to samples in previous efforts 4,5. The  Rotterdam Study  is funded by  Erasmus Medical Center  and  Erasmus University, Rotterdam ,  Netherlands Organization for the Health Research and Development (ZonMw) , the  Research Institute for Diseases in the Elderly (RIDE) , the  Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports , the  European Commission (DG XII) , and the  Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the  Rotterdam Study  and the participating general practitioners and pharmacists. The generation and management of  GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III ) were executed by the  Human Genotyping Facility of the Genetic Laboratory  of the  Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands.
The GWAS datasets are supported by the  Netherlands Organisation of Scientific Research NWO Investments  (nr. 175. 010. 2005. 011 , 911-03-012 ), the  Genetic Laboratory of the Department of Internal Medicine, Erasmus MC , the  Research Institute for Diseases in the Elderly  ( 014-93-015 ;  RIDE2 ), the  Netherlands Genomics Initiative (NGI) / Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA) , project nr. 050-060-810. We thank  Pascal Arp ,  Mila Jhamai ,  Marijn Verkerk ,  Lizbeth Herrera  and  Marjolein Peters , and  Carolina Medina-Gomez , for their help in creating the GWAS database, and  Karol Estrada ,  Yurii Aulchenko , and  Carolina Medina-Gomez , for the creation and analysis of imputed data. HHHA is supported by  ZonMW  grant number  916.
Differential expression analysis was performed with limma v3. 4. 2 R package, using TMM normalization. The voom function  (18)  was used to estimate mean-variance relationship and to compute observation-level weights. The linear model was fitted with the voomtransformed counts and contrasts were extracted. Genes were considered differentially expressed (DEG) with a p-value adjusted < 0. 05 and subsets of DEG were represented in heatmaps with the pheatmap R package, using voom-transformed counts scaled by row. A functional enrichment analysis was performed on the DEG with gprofiler2 v0. 1. 8  (19)  using ENSEMBL databases as reference. Additionally, a gene set enrichment analysis (GSEA) was performed with the list of pre-ranked genes by a t-statistic with the R package fgsea v1. 12. 0,  (20)  against mouse Reactome database and M5 gene set collection from MSigDB.
Proteomic analysis of the secretome. Proteomic analysis of the conditioned media obtained in the in vitro experiments explained above was performed in the Proteomics Unit of the Complutense University of Madrid. A labelfree experiment was conducted. Briefly, secretome samples were concentrated with speed vac and resuspended in urea 8M. Proteins were digested using an iST kit (Preomics, Planegg, Germany). The resulting peptides were analyzed using liquid nano-chromatography (Vanquish Neo, Thermo Fisher Scientific), coupled to high-resolution mass spectrometer Q-Exactive HF (Thermo Fisher Scientific,). Proteins were identified using Proteome Discover 3. 0 software (Thermo Scientific) and the search engine Mascot 2. 6 (matrixscience. com). The database used was Uniprot (UP-000000589). For quantitative proteomics, using the aforementioned software, chromatograms and retention times of all samples were aligned.
Data analysis of publicly available human gene expression Fujiwara et al. cohort of liver biopsies from HCC-naïve NAFLD patients was accessed through Gene Expression Omnibus (GEO) accession number GSE193066. Relative log-expression normalized data was directly downloaded from GEO. Two EGFR signaling gene signatures were obtained from MSigDB v2023. 1:(  22 ) "EGF/EGFR signaling" from WikiPathways (C2 CP) and "EGFR target genes" from TFT (C3) and gene set variation analysis (GSVA)  (23)  was used to assess the relative activation of the signature in the samples. Correlation in fibrotic samples between the EGFR signature and genes of interest was assessed using Pearson correlation. Kendall's τ was used to assess the association between either EGFR signatures or gene expression with fibrosis stage.
The EGFR pathway is activated during the progression of fibrosis in humans, correlating with expression of specific fibrotic and inflammatory genes. To analyze the translational relevance of the results presented above, data from gene expression in a cohort of liver biopsies from HCC-naïve NAFLD patients was accessed through GEO accession number GSE193066 (see Material and Methods section). We could observe that the relative enrichment of EGF/EGFR signaling (WikiPathways) was significantly increased during progression of liver fibrosis (Fig. 7A ). The relative enrichment of EGFR target genes (Gene Transcription Regulation Database) also presented a tendency, almost significant, to increase at advanced stages of fibrosis. Interestingly, the expression of fibrosis-related genes, such as collagen genes (COL1A1, COL3A1, COL4A1, COL5A1), LOXL1, MMP2, MMP9 or TIMP1 presented a significant positive correlation with the EGF/EGFR signaling (Fig.
Financial support and sponsorship. This work was supported by i)  Fundacion Ramon Areces 20th National Competition for Scientific and Technical Research in Life and Matter Science 2020 , grant # CIVP20A6593 , to IF; ii)  Agencia Estatal de Investigación (AEI) ,  Ministry of Science and Innovation (MICIN) ,  Spain , cofounded by  FEDER  funds /Development Fund- a way to build Europe, grant numbers  RTI2018-094079-B-100 ,  RTC2019-007125-1  and  PID2021-122551OB-100  to IF;  RTI2018-099098-B-100  to AS /BH ;  RTI2018-094052-B-100  to AMV. JV was funded by  AEI ,  MICIN , through the  Retos Investigación  grant number  PID2019-108651RJ-I00/DOI 10. 13039/501100011033  and " Ramon y Cajal" program   RYC2021-034121-I. The  CIBEREHD ,  National Biomedical Research Institute on Liver and Gastrointestinal Diseases , is funded by the  Instituto de Salud Carlos III, Spain.
The ACSM measurers the non-refractory (NR) sub-micrometer particulate matter mass (i. e. material evaporating at 600º) with an aerodynamic diameter less than 1 µm (PM1). A thorough description of the ACSM measurements are available in  Heikkinen 195 et al. , 2021. Finally, the modeled particle size distribution was compared against Differential Mobility Particle Sizer (DMPS) measurements. Additionally, measurements of total particulate matter with an aerodynamic diameter less than 2. 5 µm (i. e. , PM2. 5) were taken from the Air Quality e-Reporting (AQ e-Reporting) database (Table  S1 ). Daily measurements were extracted for rural stations below 200 meters above sea level (avoiding urban hot spots and complex terrain) located in the most southern regions of the 200 domain, which are likely to be exposed to higher oxidant levels.
Acknowledgment This work was supported by the  European Research Council  via the project  CHAPAs  (No. 850614 ) and by the  Academy of Finland  (Nos. 311932 ,  307537 ,  334792 ,  337549 ). Model simulations were performed "online" with the meteorological model 445 on the mahti supercomputer of the  Finnish IT center  for science (CSC) using 1 computing node for the European grid, and 4 computing nodes for the high-resolution domain. We would like to thank  Juha Lento  for his continuous support at the  Finnish IT center for science (CSC). We are also thankful to  Liine Heikkinen  and  Mikael Ehn  for their inputs on the manuscript.
Data availability. The input data used for the simulations are available at https://www. lmd. polytechnique. fr/chimere/2020_getcode. php. The CAM anthropogenic emission can be downloaded at https://eccad3. sedoo. fr/catalogue, dataset name: CAMS-GLOB-ANT. 440 Measurements data from the SMEAR-II station can be downloaded at https://smear. avaa. csc. fi/download. The ACSM NR-PM1 OA concentration data are available on the EBAS database under the EMEP ACTRIS framework (http://ebas. nilu. no/).
Pu-Seq The published protocol  52  was used with minor modifications: size selection was performed using a Blue Pippin (Sage Science). We used rnh201-RED instead of rnh201::kan  26. Sequence files were aligned with Bowtie2 and alignment data converted to counts with custom Perl script  52. Analysis of polymerase usage was performed with custom R script  52. Sequence data is available under GEO dataset GSE247371.
DATA AVAILABILITY The source data files have been deposited to Mendeley data and are available at "Schirmeisen, Naiman et al 2023", Mendeley Data, V1, doi: 10. 17632/2kgnb9d66r. 1. The source data underlying Figs  2a, 2c-d, 2b-d , 2g, 3a-b, 4b-c, 4e, 5a-c, and Supplementary Figs  1a, 2a , 2c, 3a-d, 4a-d are provided as a Source Data file. RAW data from Pu-seq experiments are available under GEO dataset GSE247371. All relevant data are available and further information and requests for reagents and resources should be directed to and will be fulfilled by Dr. Sarah A. E. Lambert (sarah. lambert@curie. fr). c. Expression of Ulp1-GFP in indicated strains and conditions. An untagged WT strain was included as a control for antibody specificity. Tubulin was used as a loading control.
Funding and Acknowledgements The authors thank the  Multimodal Imaging Center Imaging Facility of the Institut Curie -CNRS UMS2016 / Inserm US43 / Institut Curie / Université Paris -and the  Flow Cytometry Facility of the Orsay site of Institut Curie. This study was supported by grants from the  Institut Curie , the  CNRS , the  Fondation LIGUE contre le cancer  " Equipe Labellisée 2020  ( EL2020LNCC/Sal ), the  ANR  grant  NIRO  ( ANR-19-CE12-0023-01 ). KS has received a PhD fellowship from the  Fondation LIGUE contre le  cancer and a 4  th -year PhD grant  from  Fondation ARC. KK was supported by the program " Excellence Initiative -Research University"  for the  University of Wrocław of the Ministry of Education and Science from Poland , under grant number  IDN. CBNDR 0320/2020/20. AC acknowledges  Wellcome  grant  110047/Z/15/Z.
Proteogenomic characterization of the immunopeptidome We have previously demonstrated that a significant fraction of the AML immunopeptidome derives from non-exonic genomic regions such as introns, EREs, or intergenic regions  (28). Typically, MS search engines rely on reference protein databases (such as Uniprot) to match each acquired MS/MS spectrum to a peptide sequence. However, these databases do not contain nonexonic sequences, and building a personalized database containing all genomic sequences would generate an unmanageable database for search engines. Therefore, we used a proteogenomic approach to create MS databases containing only MAP sequences corresponding to the RNA transcripts expressed by our cell lines. These customized cell-line-specific MS databases had manageable sizes for the PEAKS search engine (Figure  2A  and Supplementary Fig.
S3 ). Precisely, the RNA-seq reads were first chopped into shorter sequences (k-mers, of 24-33 nucleotides) corresponding to the different possible MAP lengths (8-11 residues). These k-mers were then filtered based on inter-sample sharing and abundance since abundant transcripts have higher chances of generating MAPs  (28, 29). K-mers possibly deriving from sequencing artifacts or lowly abundant polymorphisms were removed. The resulting k-mers were then in silico translated into peptide sequences, and those present in the canonical proteome were discarded. Finally, peptides were evaluated for their predicted capacity to bind MHC-I allotypes of their respective cell line, and binders were concatenated with the canonical proteome to generate MS databases ranging between 250 and 350 Mb (sizes tolerated by MS search engines  (28) ).
8C ). Targeted gene set enrichment analysis (GSEA) analyses further validated the enrichment of these three processes (Figure  6C ), supporting the rationale of inhibiting autophagy to improve AZA immune effects. Given that autophagy degrades EREs, we wondered if autophagy could prevent AML blasts from being recognized by CTLs. Therefore, we segregated the 437 Leucegene AML patients based on two parameters. The first parameter was the expression of CD8A and CD8B transcripts, a reliable marker of CTL abundance in RNA-seq datasets  (49). The second was the count of highly expressed AZA-induced EREs (HE-EREs), i. e. , the number of EREs whose expression is above their median RNA expression across all patients having a non-null expression of the given ERE (a metric aimed at reflecting the density and diversity of epitopes possibly presented by leukemic blasts (  28 )) (Figure  6D ).
A GSEA comparing CD8 high vs. CD8 low patients within ERE high and ERE low groups revealed that the presence of CTLs was associated with the same processes in ERE high and ERE low patients, except for two gene sets related to DNA repair/proliferation, three related to metabolism, and two related to unfolded protein response and mTORC1 signaling (Figure  6E ). As mTORC1 regulates autophagy, we performed additional GSEAs with four gene sets related to autophagy from the REACTOME database. We found that all four were inversely associated with the presence of CD8 T cells in ERE high patients (two significantly), while the opposite was found for ERE low patients (Figure  6F ). Altogether, these results further support that autophagy prevents the generation of ERE-derived MAPs, thereby precluding AML blasts from being recognized by CD8 + T cells.
). The translation and cultural adaptation into French was performed by the MAnagement de Projets Insu (MAPI) Research Institute (www. mapi-trust. org; French project management support network), in accordance with international guidelines (38). Our group has performed psychometric validation of the self and proxy French versions of the PedsQL™ (age range 8-12 years) (39)  and shown the sensitivity of this instrument in various controlled prospective HRQoL studies among healthy controls and children with various chronic diseases (34, 35). Clinical outcomes in the MFS population Personal history, as well as clinical data (including ocular, cardiovascular and skeletal features of MFS), were obtained by retrospective chart review from the French national rare disease patient database (Bamara).
4E ) (Fig. S6A-B ). When combined with AtEH1/Pan1, TOL6 and TOL9 partitioned into the condensates (Fig. 4E ). These data show that AtEH1/Pan1 is a scaffold which drives the recruitment of endocytic proteins into condensates. Next, we asked if we could identify sequence features of proteins that would specify their interaction with AtEH1/Pan1. When we assessed the individual datasets, we found that the AtEH1/Pan1-TurboID dataset was highly enriched in disordered and prion-like domain containing proteins when compared to TPLATE-TurboID (Fig. S6A-B, D ). These included many prion-like domain containing cytosolic and nuclear proteins which are implicated in phase separation  (Cho et al. , 2018; Fang et al. , 2019). We reasoned that these proteins may be recruited to cytosolic condensates caused by overexpression of AtEH1/Pan1, which we confirmed by partitioning in N.
TurboID PSB-D A. thaliana cell suspension cultures were transformed with the AtEH1/Pan1-linker-TurboID and experiments were performed as previously described  (Arora et al. , 2020). The TPLATE-linker-TurboID dataset is described before  (Arora et al. , 2020). To calculate enrichment scores, the datasets were compared to a large set of TurboID experiments using unrelated baits performed using the same conditions.
Multiple sequence alignment and phylogenetic analysis To identify AtEH1/Pan1 homologues, predicted proteins of selected genomes from the Phytozome v13 database (https://phytozome-next. jgi. doe. gov/blast-search) were searched using the BLASTP algorithm  (Altschul, S. F. et al. 1997)  with Arabidopsis AtEH1/Pan1 as an input sequence. Multiple sequence alignment was constructed with the MAFFT algorithm in the einsi mode  (Katoh et al. , 2017). Phylogenetic analysis was carried out utilizing PhyML v3. 0  (Guindon, S. et al. , 2010)  with the smart model selection  (Lefort et al. , 2017). The phylogenetic tree was visualized using iTOL v6 (Letunic and Bork 2021). A tree represents the maximum likelihood phylogeny of EH proteins. The phylogenetic tree was arbitrarily rooted to reflect phylogenetic relationships between chlorophytes lineages and streptophytes.
Acknowledgements We thank  Evelien Mylle  and  Matthieu Buridan  for technical support. Work in the D. V. D lab is supported by the  European Research Council Consolidator ERC  grant (T-REX; 682436). J. MD. is supported by a  Research Foundation-Flanders (FWO) postdoctoral fellowship  ( 12S7222N ), and an  EMBO   Scientific exchange grant  (# 9253 ). A. M. D is supported by an  FWO  PhD Fellowship grant. R. P. is supported by the  Czech Science Foundation  grant nr. 22-35680M. C. C. and A. B are supported by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  852136  to A. B. ).