dataesr/scientific-paragraphs-categorization huggingface dataset:
Crystallisation and X-ray structure resolution of Bs TrmK Crystallisation of Bs TrmK was performed at 4 X-ray diffraction data were processed using XDS and scaled with SCALA. The structure was solved by molecular replacement with PHASER using an average model of the structures of sp1610 (Streptococcus pneumoniae ortholog of Bs TrmK, PDB code 3KR9), LMOf2365 1472 (Listeria Monocytogens, PDB code 3GNL) and of SAG1203 (Streptococcus agalactiae, PDB code: 3LEC). In the resulting model, non-conserved residues were mutated into alanine and model building was first performed with ARP/WARP using the warpNtrace automated procedure. Restrained refinements of the structure were performed with the program phenix. refine in the Phenix suite  (28). Model and map visualizations for manual reconstructions were performed with the program Coot  (29).
Isothermal titration microcalorimetry (ITC) ITC experiments were carried out at 18 • C in a MicroCal ® ITC 200 microcalorimeter with Bs TrmK extensively dialyzed against 50 mM Tris-Cl pH 8. 0, 100 mM NaCl, 10 mM MgCl 2 , 2% glycerol. Twenty four injections of 1. 6 l of Sadenosyl-L-homocysteine (SAH) at 800 M, dissolved in the buffer of Bs TrmK dialysis, were made into the cell containing Bs TrmK (58 M). Injections were spaced by 180 s. The ITC data were analysed with the software ORIGIN ® using a single set of sites model.
Molecular docking of Bs TrmK/SAM/tRNA Ser Bs tRNA Ser model was built using the T. thermophilus tRNA Ser crystal structure (PDB: 1SER) as template except for the variable region that was not defined in this structure. To model the variable region of Bs tRNA Ser , the Human tRNA Sec crystal structure (PDB: 3A3A) was used. It was manually mutated to the Bs tRNA ser sequence using Coot  (29). Model geometry was then idealised with 300 iterations of rna idealise from the Rosetta package, and subsequently minimised using the Rosetta force field through rna minimise  (35). Docking of B. subtilis tRNA Ser on Bs TrmK/SAM structure was performed using HAD-DOCK 2. 2  (36, 37). HADDOCK program is particularly well-suited to deal with data from NMR chemical shift mapping through the possibility to introduce ambiguous restraints (i.
A 1. 8 to 3. 2 Å distance constraint was applied between the N1-atom of A 22 and the SAM-methyl group. The HADDOCK modeling program allows any of the atoms from each residue designated as an ambiguous interaction restraint to interact with the nucleotides 12-23 of the tRNA Ser D-arm with the constraint that N1 of A 22 is placed near the methyl-group of SAM. Ten thousand structures of the complex were generated during the rigid-body energy minimization, and the 400 best solutions based on the intermolecular energy were used for the semiflexible refinement and were subsequently refined in the explicit solvent iteration. Semi-flexible zones (automatically determined by HADDOCK) and full-flexible ones defined on Bs TrmK loops (residues 3-10, 49-56, 93-99, 121-127, 146-153) were used. We set the Edesol weight to 0 for all stages of the docking as it is recommended for proteinnucleic acids docking.
Transcripts were generated in the presence of ␣ 32 P-ATP and used in an in vitro MTase assay with different amounts of purified recombinant Bs TrmK (Figure  1 ). tRNA Leu showed a two-fold decreased m 1 A 22 formation (Figure  1G ) compared to the other substrate tRNAs, and tRNA Gly was not methylated at all by Bs TrmK (Figure  1H ). Therefore, all B. subtilis tRNAs with an A 22 , except tRNA Gly , could be methylated, with no discrimination between the length of the variable region, indicating that these tRNAs could also be N1-methylated on A 22 in vivo. This observation would mean that the occurrence of m 1 A 22 is underestimated in tRNA modification databases  (1, 38) , due to a lack of systematic mapping of tRNA modifications in B. subtilis and in Bacteria in general.
Compared to the variants bearing a single mutation, the double-mutant (U 13 G/C 26 A) proved to be an even better substrate for Bs TrmK. The purine at position 13, important for m 1 A 22 formation catalysed by Bs TrmK, forms a non-Watson-Crick base pair, namely a Hoogsteen-Sugar base-pair, with the target nucleotide A 22 (as seen for instance in the structure of T. thermophilus tRNA Ser (39), PDB: 1SER). To further investigate the importance of the nucleotide at position 13 for m 1 A 22 formation, three variants of tRNA Ser (G at position 13, long variable region) and tRNA His (A at position 13, short variable region) were produced with varying nucleotides at position 13, i. e. A, U or C for tRNA Ser , and G, U or C for tRNA His , and tested as Bs TrmK substrates (Figure  2A  and B ). In agreement with the result obtained with tRNA Gly , a pyrimidine at position 13 (U or C) reduced the substrate potential of tRNA Ser and tRNA His for Bs TrmK.
In a similar manner, we tested the substrate potential of an RNA transcript covering the D-arm sequence or the D-arm sequence plus the anticodon stem-loop of Bs tRNA Ser. None of these transcripts were substrates for Bs TrmK (data not shown). Next, the importance of the anticodon stem-loop for tRNA recognition by Bs TrmK was evaluated. In order to determine whether Bs TrmK interacts with tRNA anticodon loops or anticodon stems, five mutated tRNA His constructs by altering either the loop or the stem were generated. In the loop, variants with a point mutation (U 33 G), an insertion of a G near position 36 (+G 36 a), and deletion of G 37 ( G 37 ) were produced. In the stem, a variant with three additional GC base pairs (C-G, G-C and C-G between A 29 -U 41 and G 30 -C 40 ) (ac stem +3 bp), and a variant of tRNA His in which the entire anticodon stem-loop was removed ( (ac stem-loop), Supplementary Figure  1 ) were generated.
The four molecules in the ASU show little conformational difference between them (RMSD of 0. 25 Å over all C␣ atoms). The superimposition of the Bs TrmK structure with its orthologs (PDB: 3KU1, 3GNL, 3KR9) gave an RMSD of 1. 5 Å over around 220 C␣ atoms, consistent with the high sequence identity of these proteins.
from COG2384 proteins, a ribosomal protein L11 MTase (PDB: 3GRZ, Z-score 19, RMSD 2. 1 Å over 147 C␣), the m 2 G 6 tRNA MTase Trm14 (PDB: 3TM4, Z score 17. 4, RMSD 2. 4 Å over 152 C␣), the m 1 A 58 tRNA MTase TrmI (PDB: 2PWY, Z-score 15. 8, RMSD 2. 4 Å over 151 C␣) and the m 1 G 37 tRNA MTase Trm5 (PDB: 2ZZM, Z-score 14. 5, RMSD 2. 9 Å over 147 C␣). These proteins are all Class-I type RFMs, confirming that Bs TrmK belongs to this class of proteins, but no obvious information for catalysis or substrate recognition could be drawn from these similarities. The C-terminal domain of Bs TrmK contains four ␣-helices, the two largest of which form a coiled-coil motif. The domain displays low sequence homology across COG2384 proteins (Figure  3B  and Supplementary Figure  S6 ), and a DALI search did not reveal any ideal protein-fold matches.
However, two proteins with coiled-coil domain were identified with low Z-scores: the seryl-tRNA synthetase (PDB: 1SRY, Z-score 6. 0, RMSD 1. 1 Å over 50 C␣) and the ribosomal protein L19 (PDB: 1S72, Z-score 6. 1, RMSD 2. 0 Å over 53 C␣). In these proteins, the encompassed coiledcoil motif binds an RNA, suggesting that in Bs TrmK the C-terminal domain could also play such a role. The N-and C-terminal domain of Bs TrmK are held together by numerous contacts, and a normal mode analysis of the Bs TrmK structure confirms a lack of free movement between the two domains by identifying only the very end of the coiled-coil motif as flexible (Figure  3D ). The interaction between Bs TrmK and the cofactor product SAH was studied by ITC. The interaction is enthalpydriven ( H ≈ -17 kcal/mol) with a single binding site and a dissociation constant (K D ) of 1.
7 M (Supplementary Figure  S7 ). Although SAM or SAH were present in crystallisation trials, crystals were of apo Bs TrmK, i. e. no ligand bound. The electron density is well-defined across the Bs TrmK structure and particularly for residues constituting the SAM-binding site, even in the absence of the cofactor. Only two residues, K151 and Y153, near the active site, show poor electron density suggesting mobility (Supplementary Figure  S5B ). SAM was modelled into the active site of Bs TrmK, guided by the crystal structure of SAMbound TrmK from S. pneumoniae (PDB: 3KU1). In the Bs TrmK/SAM model, the SAM cofactor is bound at the centre of the catalytic domain in a pocket with residues harbouring negative electrostatic potentials con- served amongst COG2384 proteins (Figure  3B ). The methyl group is pointing towards a region of positively charged and well conserved residues, likely important for tRNA binding (Figure  3C ).
tRNA is bound via interactions with both domains of Bs TrmK A model of the Bs TrmK/SAM/ Bs tRNA Ser complex was generated with the high-ambiguity-driven biomolecular docking program HADDOCK (36) using the data obtained from NMR as ambiguous interaction restraints (see Materials and Methods). Input data for HADDOCK calculations used Bs TrmK residues with significant chemical shift perturbations as residue-level ambiguous interaction restraints, and further included the nucleotides 12 to 23 in the D-arm of tRNA Ser , assuming that nucleotides around A 22 could interact with Bs TrmK. A distance restraint between the N1atom of A 22 and the methyl of SAM was also added. Six clusters of models were extracted from HADDOCK calculations (Supplementary Figure  S13 ), each exhibiting a very different relative orientation of the protein on the tRNA (Supplementary Figure  S14 ).
Future investigations are needed to establish the real occurrence of m 1 A 22 in tRNAs The presence of m 1 A 22 is not frequently reported in tRNA database and has to date only been identified in some bacteria. In our opinion, this may largely be due to the lack of systematic modification mapping in tRNAs. In vitro, Bs TrmK modifies any provided tRNA bearing a purine at position 13, indicating that many tRNAs carrying an A at position 22 could be modified in B. subtilis in vivo. The seemingly low occurrence of this modification therefore reflects either the low availability of B. subtilis tRNA sequences for which the complete identification of modifications has not been performed or that Bs TrmK specificity is different in vivo like previously observed for the yeast m 1 G 9 forming enzyme Trm10  (51).
ACKNOWLEDGEMENTS We thank the TGIR-RMN-THC FR3050 at  Gif-sur-Yvette  for NMR measurement time and  Nelly Morellet  for helpful discussion and technical advice. The X-ray data were collected on the ID14-eh1 beamline at the  European Synchrotron Radiation Facility (ESRF, Grenoble, France). We are grateful to the Local Contact at the  ESRF  for assisting during data collection. Abdalla Al Refaii was supported by a  Khaled Al-As'ad fellowship  delivered by the  Université libre de Bruxelles. The Fonds D. et  A. Van Buuren  and the  Fonds J. Brachet (ULB)  are gratefully acknowledged for financial support. We also thank  Kevin Englebert ,  Jessica Nucleic Acids  Research, 2019, Vol. 47, No. 9 4749 Berlier and  Marie Morel  for taking part to the early stages of this work.
Acknowledgments: We The authors would like to thank the interviewing practitioners who collected data:  N. Guillon ,  I. Rousseau ,  A. Pétillon ,  B. Turck ,  P. Fosse ,  S. Auzou ,  M. Leconte ,  C. Le Gallo , and  D. Rime , and the  European Charity DigestScience Foundation  and all adult and pediatric gastroenterologists who participated in this study.
Domain organization of drought stress responsive RBPome To further characterize the composition of the drRBPome, we looked at the RBD enrichment and noted that 50 proteins contain the RNA recognition motifs (RRM), representing the most prominent RBD in this dataset (Additional file 3: Figure  S2 ). Six of the RRM containing proteins also contain the NTF2 domain. Other predominant "classical" RBDs include the zinc finger (ZF)-CCCH, K-homology, DEAD helicases, like-Smith (commonly know as LSm), poly(A) binding protein and cold shock domain (Additional file 3: Figure  S2 ). Additionally, 300 proteins contain unknown or unconfirmed RBDs (Additional file 4: Table  S2 ). The RDBs gave rise to a three-way classification. Category I comprises proteins linked to RNA biology based on their RBDs and/or role in RNA processing (42% of the RBPome), category II contains ribosomal proteins (5%) and category III contains proteins with currently unknown RNA-interactions (53%) (Fig.
Biophysical characteristics and sequence topology of drRBPome Biophysical and amino acid (aa) sequence characteristics were also analyzed to determine the physical properties that enable RBPs to interact with RNA. The drRBPome, much like the input reference and the RBP repertoire data  [41] , span the full spectrum of protein sizes, with the majority of proteins being < 1000 aa long (Fig. 3a ). However, compared to the reference data, we notice that drRBPs linked to RNA biology behave the same as the RBP repertoire linked to RNA biology compared to the drRBP with unknown RNA biology and RBP repertoire with unknown RNA biology. Proteins linked to RNA biology show a high density for proteins with amino acid sequence length of between 1000 and 2000 compared to proteins whose RNA biology is unknown.
A similar trend is noted on the isoelectric point (pI) distribution (Fig. 3b ). The pIs of proteins enriched in RNA interaction show similar patterns distinct from the reference data. In addition, proteins with unknown RNA biology from both drRBP and RBP repertoire sets have the same configuration and the same for drRBP and RBP repertoire proteins linked to RNA biology. The pI distribution of the latter significantly shifts towards higher pI (≥8) as compared to the reference proteome. A slight hydrophobicity bias is noted on the proteins with unknown RNA biology compared to the proteins linked to RNA biology, however, the enhanced density peak for drRBP with unknown RNA biology could be attributed to the much smaller number of proteins in this data set (Fig. 3c ). Overall, the consistent trend observed on the pI, number of amino acids and hydrophobicity distribution on proteins with unknown RNA biology compared to the proteins whose RNA biology are known, may suggest additional properties with implications in RNA interactions of the novel proteins in this set.
Mass spectrometry data analysis Raw files were processed using the Proteome Discoverer v2. 1 (Thermo-Fisher Scientific) interlinked with the local MASCOT server (Matrix Science, London, UK). MASCOT searches were carried out against Arabidopsis thaliana database (built using the Arabidopsis information resource (TAIR; release 10)) using a precursor mass tolerance of 20 ppm, a fragment ion mass tolerance of ±0. 5 Da and trypsin specificity allowing up to two missed cleavages, peptide charges of + 2, + 3 and + 4. Carbamidomethyl modification on cysteine residues was used as a fixed modification, oxidation on methionine residues as variable modifications and the decoy database was selected. Further stringency was applied on the peptide spectrum matches (PSMs) by allowing "forward" and "decoy" searches by MASCOT to be re-scored using the Percolator algorithm in Proteome Discoverer v2.
cau. edu. cn/agriGO/) and pathway analysis was done with the KEGG mapper (http://www. kegg. jp/kegg/tool/anno-tate_sequence. html; February 2017), which annotates sequences by BlastKOALA. BlastKOALA is an internal annotation tool in KEGG that assigns KEGG Orthology numbers by BLAST searches against a non-redundant set of KEGG GENES using SSEARCH computation  [53]. Co-expression for functional and data correlation analysis of selected up-and down-regulated proteins was performed using ATTED database (http://atted. jp).
Biophysical characteristics and sequence topographies analyses Analyses of biophysical properties including length of proteins (number of amino acids), isoelectric points (pI) and hydrophobicity were performed using R (version 3. 3. 1). Amino acid composition enrichment between the drought stress responsive RBPome and input total proteome as reference as the background set was determined using the web-based composition profiler program (http://www. cprofiler. org/) using default setting and ordering amino acids by hydrophobicity (Kyte-Doolittle) and the significance level was further assessed using Bonferroni correction  [54]. Length and sequences of amino acid were retrieved from TAIR (https://www. arabidopsis. org/tools/bulk/sequences/index. jsp), pI were obtained from TAIR (https://www.
Evolutionary conservation of drRBPs To understand the conservation and potentially, the role of drRBPs, InParanoid version 8 (http://inparanoid. sbc. su. se/cgi-bin/index. cgi, [55] ) was used to identify their predicted orthologs among Arabidopsis, selected dicots (Glycine max, Solanum lycopersicum, Vitis vinifera), monocots (Brachypodium distachyon, Hordeum vulgare, Oryza sativa, Sorghum bicolor), Saccharomyces cerevisae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus and Homo sapiens. Here, a two-way prediction was possible and data was compiled in Excel. The InParanoid program generates ortholog groups including all inparalogs with scoring below 0. 05, which is achieved by using clustering rules based on genome-wide pairwise sequence similarity matches between two species  [55].
Acknowledgements This work was nancially supported by the  National Natural Science Foundation of China  (Grant No. 21822107 ,  21571115 ,  21671172 ), the  Natural Science Foundation of Shandong Province  (No. JQ201803  and  ZR2017MB061 ), the  Taishan Scholar Project of Shandong Province of China , the  Qilu Youth Scholar Funding of Shandong University and the Fundamental Research Funds of Shandong University  ( 104. 205. 2. 5 ). Dr Zhuang  was nancially supported by the  Zhejiang Provincial Natural Science Foundation  (No. LR19B010001 ).
Approach to the Data Analysis The responses to the questions were analyzed using qualitative methodology. To questions 1-4, we applied inductive content analysis  (Elo and Kyngäs 2008) , also named conventional content analysis  (Hsieh and Shannon 2005). In this method, codes, categories, and subcategories are extracted from the text, without references to existing theories or previous studies. In the preparation phase of the inductive content analysis, two competent judges working independently (the first author and a psychologist familiar with the method of content analysis) read carefully through the participants' responses to each of the four questions several times so as to find the essence of the responses  (Elo and Kyngäs 2008). The unit of analysis was a part of the response referring to one discrete theme  (Zhang and Wildemuth 2005).
This work resulted in the development of the final code scheme, comprising codes, categories and subcategories, their definitions, and examples. Further analysis of the responses to questions 1-4 was performed by two new competent judges, psychologists who had not yet taken part in the study at any stage. Their task was to encode the responses to each question according to the code scheme described above. The judges worked separately and independently. Agreement between judges was assessed using Cohen's kappa (κ) coefficient. As the obtained values ranged from. 71 to. 79, the interrater reliability was satisfactory  (Mayring 2000). Calculations were done using IBM SPSS version 24. 0 (IBM Corp 2016). For questions 5 and 6, which asked the respondents to provide examples of techniques and sources of knowledge and inspiration concerning spirituality in mental health care, the analysis was simplified compared to the analysis for questions 1-4.
5) Statistics The full description of the statistics is provided as part of the supplementary information; Appendix S1 includes the results of the complete statistical analyses, while appendix S4 gives the necessary instructions to reproduce those analyses. When dependent variables lasted until the occurrence of certain event, we conducted survival analyses using the R package coxme  (55). For the remaining dependent variables, we did linear analyses using the R packages lme4 (56) and lmerTest  (57). For the experiments done in homogeneous environment, we tested the four different treatments (control, nutritive and adverse) as fixed factors. For the spot experiments, we tested the effect of the nature of the nearest spot (neutral or NaCl 200mM) and that of the furthest spot (glucose 100mM or 200mM), as well as the interaction between the two.
We always considered the date of the experiment as a random factor as all treatments were conducted over multiple days, with several replicates per day for each treatment. For each statistical analysis, we performed a nested model comparison using the R package MuMIn (58) by ordering models according to their Akaike criterion. We represented the selected model by plotting estimators with the pairwise comparison (a posteriori Tuckey test) p-values using the R package emmeans  (58)  in linear models, and the hazard ratio associated p-values in cox models.
Acknowledgements This work was supported by the  U. S National Science Foundation , under grant CMMI# 1552368 : " CAREER: Multiphysics Damage and Healing of Rocks for Performance Enhancement of Geo-Storage Systems -A Bottom-Up Research and Education Approach. "A. D. and A. B. were supported by a grant from the  Agence Nationale de la Recherche  (reference number:  ANR-17-CE02-0019-01 -SMART-CELL ).
Comprehensive profiling of primate lymphoblastoid cell lines (LCLs) We have extensively characterized a panel of lymphoblastoid cell lines (LCLs) from human, chimpanzee, gorilla, orangutan, and macaque, including two independent biological replicates for each species. This characterization includes chromatin immunoprecipitation data (ChIP-seq) from five key histone modifications (H3K4me1, H3K4me3, H3K36me3, H3K27ac, and H3K27me3) and deep-transcriptome sequencing (RNA-seq) (Fig. 1 ). We integrate these datasets into gene regulatory architectures (Fig. 2a  and Supplementary Figs. 1  and 2 ) to (1) understand how primate gene expression levels are controlled and how expression changes between species occur and to (2) study patterns of evolutionary conservation of regulatory elements in primates.
Discussion The evolution of human and non-human primates is an area of major interest, but ethical, legal, and practical constraints often limit access to direct biological material. In this study, we have generated a unique, comprehensive, and unified dataset of epigenomic landscapes in LCLs for human and four non-human primate species. Despite the artificial nature of our cellular model  [46] [47] [48]  , previous studies have shown the value of LCLs as an experimentally convenient model of somatic cells that accurately resembles the phenotype of its cell type of origin  49  and which can be robustly used for comparative studies in humans and primates  12, [50] [51] [52]. Moreover, its clonality ensures a cell type-specific experimental system reducing the confounding factors associated with cell population diversity in bulk tissue samples.
These echoes that we detect as human-specific weak enhancer activities provide an unexpected window to the study of regulatory evolution in the human lineage. Further research will be needed to clarify the specific role of these elements in different tissues and cell types. Taken together, our results show that the evolution of gene regulation is deeply influenced by the coordination of epigenetic activities in gene regulatory architectures. Our insights call for incorporating better integrative datasets and refined definitions of regulatory architectures in comparative evolutionary studies to fully understand the interplay between epigenetic regulation and gene expression.