dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-seq and bioinformatics analysis Sequencing was carried out using 2x100 cycles (paired-end reads 100 nucleotides) for all samples on Illumina HiSeq2500 or NovaSeq6000 instruments. Reads were aligned with STAR 2. 5. 3 (Supplementary Reference 47) to the human genome (GRCh37/hg19 version). We used the count matrix generated by STAR with the human gene annotation v19 of GENCODE as reference. DESEQ2 (Supplementary Reference 48) was used to normalize data and performed differential analysis with the Wald test. The p-value was adjusted using the Benjamini-Hochberg method. We considered a gene expressed if the normalized expression was higher than 10. STAR-Fusion v1. 4. 0 (Supplementary Reference 49) was applied to predict fusion transcripts. No statistical methods were used to predetermine sample size.
ChIP-Seq Chromatin Immunoprecipitation (ChIP) experiments were performed following manufacturer instructions using iDeal ChIP-seq kit for transcription factors and for histones (Diagenode) with respectively rabbit polyclonal anti-FLI1 antibody (ab15289, Abcam), rabbit polyclonal anti-H3K4me3 (C15410003, Diagenode) and rabbit polyclonal anti-H3K27ac (ab4729, Abcam). For ChIP sequencing, libraries were generated using TruSeq ChIP library preparation kit (Illumina) and sequenced on Illumina HiSeq 2500 (single end, 100 bp). Reads were aligned to human reference genome (GRCh37/hg19) with bowtie2 2. 2. 9 (Supplementary Renference 50). Peaks were called with MACS2 2. 1. 1 (Supplementary Reference 51) with the option narrow for FLI1 ChIP-seq and broad for H3K27ac histone mark. ChIP-seq data were normalized according to their respective input DNA sample.
N= 22. See also Supplementary Table1. D-Schematic circos plot of the main rearrangements seen in EWIma1 cells (dot lines: rearrangements not found in all cells). E-Integrative GenomicsViewer representation for FLI1 and H3K27ac ChIP-seq profiles at DKK1 locus showing the disappearance of super-enhancer in EWIma1, and A673 cells compared to MSC Pat. Super-enhancers are framed in red. F-Integrative Genomics Viewer representation for FLI1 and H3K27ac ChIP-seq profiles at PRKCB, CCND1 and NKX2-2 loci showing the appearance of super-enhancers in EWIma1, and A673 cells compared to MSC Pat. Super-enhancers are framed in red. G-Top two motifs predicted by ChIPMunk corresponding to known motifs in Jaspar database identified in EWIma1 and MSC Pat FLI1 ChIP-seq data. H-Left: Heatmap representation of FLI1 ChIP-seq peaks ranked by intensity at GGAA microsatellite (GGAAm) or ETS sites in EWIma1 only or MSC Pat common sites.
Acknowledgements We thank the iPS ( Nathalie Lefort ), Imagine cytometry ( Olivier Pellé ), and microscopy ( Meriem Garfa-Traore ) platforms for help with SNP karyotyping, cell analysis and image analysis,  Lina El Kassar  ( CECS/I-STEM ),  Dr. Chloé Lescale  and  Dr. Ludovic Deriano  ( Institut Pasteur ) for help with the multicolor FISH analysis and  Dr. Marion Piganeau  for technical support and helpful discussions. We thank  Rosalie Borry  and each member of the  'Genome Dynamics in the Immune System' lab for scientific discussions  and technical support. We thank  E. Lapouble ,  G. Pierron ,  C. Thirant ,  R. Leclerc ,  V. Raynal ,  S. Baulande ,  P. Legoix ,  C. Kamoun ,  M-M. Aynaud  and  E. Barillot  and all members of the  Genetics and Biology of Pediatric Cancers laboratory  for helpful discussions and/or technical or.
This work was supported by the  Institut National du Cancer  ( PLBIO16-291 ), by  La Ligue Nationale Contre le Cancer  (P. R/E. B. and O. D/D. S teams: Équipes Labellisées), the  Fondation ARC  (L. B. ); the AIDA association (M. H. ), by grants from the  Institut Curie ; the INSERM; the  Canceropôle Ile-de-France ; the  projet de Recherche 'Enfants, Adolescents et Cancer '; the  Agence Nationale de la Recherche  ( ANR-10-EQPX-03 ,  Institut Curie Génomique d'Excellence (ICGex)  and the société française de lutte contre les cancers de l'enfant et de l'adolescent. This project also received support from European funding:  ERA-NET TRANSCAN JTC 2014  ( TRAN201501238 ) and  TRANSCAN JTC 2017 (TRANS201801292 ). D. S. is supported by the  Institut Curie-SIRIC  ( Site de Recherche Intégrée en Cancérologie) program.
Data availability ChIP-seq and RNA-seq data have been deposited in NCBI's Gene Expression Omnibus under accession number: GSE150783. Raw data are available at Mendeley. Reserved DOI: doi:10. 17632/fx29by5k43. 1 https://data. mendeley. com/datasets/fx29by5k43/draft?a=63bd5c8f-f13b-4100-b9bf-988ddb0c9131.
A Core Set of Chromatin Sites Bound by CDX2 During Mouse Development Next, publicly available ChIPseq data  (Amin et al. , 2016; Rhee et al. , 2017; Banerjee et al. , 2018)  were used to compare the location of the CDX2 protein on chromatin at the three developmental stages analyzed above by RNAseq. It gave a core set of 1,047 chromosomal regions sharing overlapping peaks in the three conditions (Figure  2A ; Supplementary Table  S2. 1). 265 and 466 of these peaks respectively fell into protein coding genes and their promoters (defined as the 2-kb segment upstream of the transcription start site), 52 into non-protein coding genes and their 2-kb promoters, and 264 into intergenic regions. Among the 1,047 regions, 835 (77. 75%) exhibited at least one conserved motif analogous to the mouse CDX2 binding site reported in the JASPAR database (#PH0013.
Mouse and Human RNAseq and ChIPseq Data Mouse RNAseq and ChIPseq data were retrieved from the GEO database (https://www. ncbi. nlm. nih. gov/geo/): GSE62149  (Cambuli et al. , 2014)  and GSE90752  (Rhee et al. , 2017)  for ES cells, GSE84899  (Amin et al. , 2016)  for E8 growing embryos, GSE115541  (Banerjee et al. , 2018)  for E16 intestinal endoderm, GSE123559  (Grall et al. , 2019)  for the head dysgenesis model, GSE89992  (Balbinot et al. , 2018)  for gastric-type intestinal hetero-and metaplasia, GSE133679  (Vu et al. , 2020)  for myelodysplasia, and GSE120487  (Galland et al. , 2021)  for monoblastic leukemia. The identifiers of samples used for this study are given in the Supplementary Table  S1. 1. Human RNAseq data from colon adenocarcinoma (COAD), stomach adenocarcinoma (STOAD) and acute myeloid leukemia (AML) were obtained from the database The Cancer Genome Atlas (The TCGA research network: https://www.
Differential Expression Analysis For mouse ES cells data, as no replicate was available, differential expression was assessed by computing the delta of the gene expression values between control and experimental condition in each of the two datasets  (Rhee et al. , 2017 and Cambuli et al. , 2014). Then, common differentially expressed genes between both datasets were selected with a threshold of 2 on delta. For the other mouse embryos data, namely the growing embryo  (Amin et al. , 2016)  and the intestinal endoderm  (Banerjee et al. , 2018) , DESeq2  (Love et al. , 2014)  was used. Gene expression was normalized with a regression model and differential expression was tested with the Wald test corrected by Bonferonni. False positives were identified with the Cook distance and flagged. Samples segregation was assessed by Principal Component Analysis (PCA, Supplementary Figure  S1 ).
Genes with significant variations in transcript levels were selected applying a threshold of 2 on |log2 (fold-change)| and a threshold of 0. 05 on adjusted p-value. These genes were then compared between the datasets of the three developmental stages, i. e. , ES cells, growing embryo and gut endoderm, to create the core set of common differentially expressed genes (DEGs). The enrichment in genes of the core was tested with the exact Fisher test. Orthology between the mouse DEGs and the human genome was evaluated with Ensembl Compara information based on the annotation of the mouse reference genome GRCm38, with a confidence score of 1 (high) or a minimal sequence homology of 30%, using a custom-made R-script as previously published  (Mayrhofer et al. , 2017). Enriched biological functions (Gene Ontology Resource, http://geneontology.
ChIPseq peaks were then selected applying a threshold of 0. 05 on p-values and visually controlled in the genome browser IGV  (Robinson et al. , 2011). A core was created with ChIPseq peaks of the compared datasets overlapping with at least 10 bp in the three conditions: trophectoderm formation, antero-posterior patterning and gut specification. ChIPseq peaks were annotated with an in-house developed R script based on genes present in the annotation of the reference genome GRCm38. Both upstream and downstream genes were annotated. Intergenic ChIPseq peaks were further compared to Super-Enhancers from the database dbSUPER  (Khan and Zhang, 2016).
DNA Binding Sites Analysis All known binding motifs of vertebrate transcription factors present in the core of ChIPseq peaks and in the gene promoters of the core of DEGs (defined as the 2-kb segment upstream of the canonical transcription start site(s) of each gene) were retrieved from the database JASPAR  (Khan et al. , 2018) , classified with TFclass relying on "class" and "family" subdivisions  (Wingender et al. , 2018) , and their position weight matrixes were reformatted. Enrichment for transcription factor binding motifs was tested with HOMER  (Heinz et al. , 2010)  in the direct vicinity (+/-50 bp) of mapped CDX-type homeobox motifs identified in the promoters of the DEGs and in the overlapping ChIPseq peaks. To test transcription factor binding motif enrichment, background sets of DNA sequences were created.
Analysis of Mouse Validation Samples and Human Pathological Samples For samples obtained from mouse models of embryonic head dysgenesis  (Grall et al. , 2019) , gastric-type heteroplasia and metaplasia (GastHet and GastMeta,  Balbinot et al. , 2018) , myelodysplasia  (MyeloDys, Vu et al. , 2020)  and monoblastic leukemia  (MnLK, Galland et al. , 2021) , the log2 (fold-change) and p-value were retrieved from the literature. For human pathological samples, raw levels of transcripts were computed with HTSeq  (Anders et al. , 2015)  for colon adenocarcinoma (COAD), stomach adenocarcinoma (STOAD) and acute myeloid leukemia (AML). Each human gene symbol was associated to the corresponding Ensembl gene identifier and the transcript levels were normalized by computing reads per kilobase per million in order to identify groups with high and low levels of CDX2 transcripts.
These groups were defined as upper and lower quartiles or deciles with a purpose of comparable size. For pair-wise comparison of groups, raw levels of transcripts were processed with DESeq2  (Love et al. , 2014)  as described above. Genes with significant variation in transcript levels were selected applying a threshold of 0. 05 on adjusted p-value (Bonferroni multiple testing method). The significance of the difference in expression level of CDX2 among samples with high versus low expression of CDX2 in COAD, STOAD and AML is shown in the boxplot of the Supplementary Figure  S2  and confirmed with a Wilcoxon test. For Barrett's syndrome and esophagus adenocarcinoma, log2 (fold-change) and p-value were retrieved from the literature  (Maag et al. , 2017). For pathologies and validation datasets, the enrichment in gene of the core was tested with the one-tailed exact Fisher test with gene sets defined by significantly differentially expressed genes and a stringent gene Universe defined as genes confidently associated with a Gene Ontology.
Materials and methods In this study a particular Al powder produced by Toyal Europe (Accous, France), the 1070-B5 (composition >99. 7% Al, 0. 09% Fe and 0. 04% Si), was chosen for its almost-perfectly spherical shape, making it an ideal candidate for the comparison of experimental results with numerical simulations. Size distribution had a d 50 of 53 µm. The quasi-static test consisted in applying compression loadings on single particles. To this aim, a SEM equipped with in-situ nano-indentation stage, namely the ASA (Alemnis Standard Assembly) by Alemnis (Thun, Switzerland), was used. Force-displacement data were acquired by a static sensor at 10 Hz calibrated up to 0. 5 N in force and with sensitivity in the nm scale. A diamond punch with a flat head of 116 µm diameter applied the load to the particle.
Instead, a FEM 2D axisymmetric model was developed on Abaqus/Standard® commercial software. The different size of each particle made difficult a direct comparison between experiments. For each particle, therefore, experimental forcedisplacement data could be compared to modelling results and be used to fit the three static JC parameters A, B and n (Eq. 1). Taking advantage of the symmetry of the problem, a particle quarter was sufficient as simulation domain. The diamond flat punch was modelled as a rigid-body and simulations were displacement-driven. Normal contact with the "hard" option was set between the particle and the flat punch and a Coulomb friction model with penalty formulation was chosen for the tangential part. The friction coefficient was set to 1. 4 for all simulations. The following material parameters were used: Young modulus 70 GPa, Poisson ratio 0.
Moreover, methylated molecules or macromolecules by SAM play an important role in cellular processes such as epigenetics, signal transduction, protein function, and genetics  [25]. The importance of these biological processes makes the SAMsynthesizing enzyme, namely MAT, an important and essential enzyme for cell growth and survival  [26]. The structure of the MAT enzyme is unique and classified by the SCOP database in a/b class  [27]. Each monomer of MAT is composed of a babbab secondary structure  [28]. No sequence conservation is observed among the repeats. Like many other enzymes in nature, MAT is active as an oligomer (either as a homo-oligomer or as a hetero-oligomer; Fig. 1 )  [29]. The oligomeric state provides a clear advantage over the monomeric state in terms of fitness, stability, and functions  [30, 31].
Probing the MAT sequence space In the present study, we conducted extensive searches to systematically probe the MAT sequence space across the three domains. The sequences were collected by combining the outcomes of different databases (nr db, eggnog, orthodb, etc. ). We built sequence similarity networks (SSNs) upon compiling the first input dataset (˜800 sequences), which allowed us to check the MAT sequence space distribution across the three domains (Fig. 2A ). During this analysis, we considered that the archaeal domain has not been sequenced as extensively as the other two domains of life  [40]. Because phylogenetic trees may not be an optimum tool to highlight the overall sequence distribution pattern, we utilized two approaches, EFI and CLANS  [41] , to visualize the sequence distribution pattern (Fig.
S1 ). Despite the variation in the percentage sequence identity clustering ranging from 100% to 40% ID, the overall topology remained consistent with two major clusters representing: (a) 'bacteria + eukarya' and (b) archaea. The SSN outcomes reflected the sequence identities as observed in nature, with archaea found to be nearly equidistant from eukarya and bacteria, at 20% sequence identity. The SSN method provided an overall view of the topology for the dataset of interest and underlined the uniqueness of the archaeal domain.
Inferring the ancestral MAT sequences We reconstructed the phylogenetic relationships by utilizing MAT sequences from the three domains of life (Fig. 2B ). In the case of archaea, two major phyla [Crenarchaea and Euryarchaea (EuryAnc)] were tested for the overall tree topology based on the MAT sequences (gene tree), as well as corresponding 16s rRNA sequences (species tree) from the SILVA database (not shown here). Herein, the two topologies clearly indicated that the tree bifurcates into two distinct clades for Crenarchaea and EuryAnc; that is, the  (A) SSN of MATs from the three domains-the sequences from eukarya (green) and bacteria (red) form a distinct cluster, while the sequences from archaea (cyan) form a separate cluster. This SSN was created with the EFI enzyme similarity server and visualized using the Cytoscape program.
Clustering pattern within the network at different levels of sequence similarity (similarity %) can be observed in the supplementary information (Fig. S1 ). (B) Unrooted ML tree for MATs from the three domains of life. This unrooted tree was built by applying the ML method with IQ-TREE, followed by visualization using FigTree viewer program and iTOL server. The archaeal MATs branch off, thereby highlighting their divergence, an observation that was further substantiated through SSN. The archaeal MATs are represented in cyan, eukarya in green, and bacteria in red. major phyla are well classified into their respective monophyletic groups. Subsequently, we prepared another set of trees by utilizing bacterial MAT sequences as an outgroup to extract the putative common archaeal ancestor ArchAnc (Fig.
Active site residues In the case of archaea, we observed that the MAT active site residues were highly conserved among the two major branches, that is, Crenarchaea and EuryAnc (residue numbers correspond to Pyrococcus furiosus (pfMAT) structure: Protein Data Bank (PDB): 6S83,  [33] ). These include the following residues involved in the interaction with SAM from chain A: H59, N61, D141, L142, and D280 and chain B: H30, D168, and Y268. Furthermore, these active site residues were completely conserved in the putative archaeal ancestor. A similar trend was observed in the cases of both eukarya and bacteria. This provides us with a key insight that despite observing differences in the active site residues, MAT enzymes from the three domains can still essentially retain the same catalytic reaction (Fig.
Large interface: charge and hydrophobicity distribution In case of MAT enzymes, the constituent monomers pair up in an inverted configuration by exposing the a-helices toward the surface, while the b-strands interact to form a hydrophobic large interface that harbors the active site residues, thereby making this homodimer the obligate functional unit  [32]. Herein, we noticed that divergence is not only limited to active residues, or the residues located close to the substrate, but most of the interdimer interface (large interface) residues also diverge across the three domains of life. For instance, in cases of bacteria (eMAT, PDB: 1RG9  [42] ) and eukarya (hMAT1A, PDB: 6SW5  [43] ) 38 out of the 51 aligned residues were identical. In contrast, archaea (pfMAT, PDB: 6S83) shares only 12 identical residues with bacteria (eMAT) and eukarya (hMAT1A; Figs  S3  and S4 ).
This further prompted us to perform a systematic analysis of the large interface region across the three domains of life, and thus, we assessed the charge and hydrophobicity distribution across the large interface and compared it with the ancestral sequences to gain more insights into the evolutionary patterns involved. We studied 17 experimentally solved MAT structures from PDB to probe the physiochemical properties, by creating two datasets: (a) for identification of 51 structurally aligned positions along the large interface (which includes up to five noninterface residues as well) and (b) for identification of 24 structurally aligned 'interface-only' residue positions.
Here, *we highlight the activity of MjMAT as representative of domain archaea, with the data as reported previously  [48]. Ancestral sequences were inferred for the ancestor of Crenarchaea MAT (CrenArc in red star), EuryAnc MAT (EuryArc in blue star), and the common ancestor for the archaeal MAT (ArcheaArc in violet star). Bayesian posterior probability values > 90% are shown. One time point assay for ancestral sequence activities was performed with corresponding enzyme (20 µM) using NTPs (5 mM) and methionine (10 mM) in HEPES (100 mM), KCl (50 mM), and MgCl 2 (10 mM), pH 8, at 37 °C for hMAT1A and 55 °C for archaea, 1 h. For kinetics analysis, ArcheaArc MAT (0. 5 µM), ATP, and GTP (0. 1-2 mM) and methionine (10 mM) using the same above-mentioned buffer and conditions were used. The experiments were performed in duplicates.
The error bars represent SD. Tables show the extrapolated K M for ArcheaArc. The production of SAM and SGM was analyzed by UPLC, and data were fitted to the Michaelis-Menten equation using GRAPHPAD software 7. 02. (B) Mutations found by the ancestral reconstruction analysis are highlighted on the available structure of pfMAT (PDB: 6S83). The structures are represented as gray surface and show the large interface. CrenAnc MAT interface mutations are highlighted in red, Eur-yAnc in blue, and ArchaeaAnc in violet. A 2 ). The hierarchical clustering plots (based on the pvclust package in R; Fig. 6  [44] ) show that the sequence identities further dictate the physiochemical properties as well. For instance, a look at both the P-values provided by pvclust for comparison of the clusters, that is, approximately unbiased (AU) P-value and bp (bootstrap probability) value, reveals that the hydrophobicity and charge distribution are clustered together for bacterial and eukarya MAT structures, with the archaeal MAT structures clustering off separately.
Structural comparison supports sequence divergence Structural comparisons of MAT full domains from archaea, bacteria, and eukarya were performed to identify their common and unique structural features. We noticed that at a structural level, bacteria and eukarya show a higher degree of structural similarity compared with archaea (Fig. S7 ). In addition, an Nterminal extension in archaea segment-swaps into the C-terminal subdomain in contrast to bacteria and Fig. 5. Comparison of the active site residues in the different domains. (A) Active site residues involved in the interaction with SAM. SAM is highlighted in black, while the active site residues from eMAT (PDB: 1RG9) have been highlighted in metal gray. The residues derived from chain A (E55, Q98, I102) have been demarcated with a pinkish border, and residues from chain B (H14, D163, R229, F230, D238) have been demarcated with a grayish border.
(B) Equivalent active site residues from pfMAT  [33]  (PDB: 6S83) that interact with SAM have also been highlighted from chain A (H59, N61, D141, L142, D280) and chain B (H30, D198, Y268) using the same color codes. (C) The active site residues identified based on structural alignment, from all three domains of life, have been highlighted as WebLogos (https://weblogo. berkeley. edu/logo. cgi). Residues derived from the second monomeric subunit have been marked using '*'. WebLogo was generated using alignment of 526 sequences from bacteria (Appendix S1), 273 sequences from eukarya (Appendix S2), and 49 sequences from archaea (Appendix S3). Details of sequence search are in the Methods section. Fig. 6. Clustering analysis of hydropathy (A) and charge (B) of representative structures from the three domains of life.
Clustering analysis of the physiochemical properties (hydropathy and charge) of 51 structurally aligned large interface residues from chain A of 17 MAT structures (PDB list in the supplementary information). Based on the 51 aligned positions, we calculated the per-site hydropathy using the Kyte-Doolittle scale from the ProtScale server. In addition, we calculated the per-site charges for the 17 MAT structures using the EMBOSS charge server. Subsequently, we conducted a clustering analysis using the pvclust package in R. This further provides a statistical score in terms of AU (AU scores depicted in red), P-value, and BP (bootstrap probability scores depicted in green) value, for comparison of the clusters, which reveals that the hydropathy (A) and charge distribution (B) clustered together for bacterial and eukarya MAT structures, with archaeal MAT structures clustered separately.