dataesr/scientific-paragraphs-categorization huggingface dataset:
This finding further suggests that saturation over such short timescales is unlikely to have a significant impact on TMRCA estimates of sobemoviruses. We next constructed the PoW-transformed time tree of the 68 sobemovirus sequences (Figure  3b ) and found that the root of the tree is 4. 3 (95% HPD: 2. 0 -6. 6) million years BP, largely similar to the estimates obtained with the 26 sobemovirus dataset. Interestingly, we see that many of the sequences of metagenomic origin share common ancestors with closely related known sobemovirus species (i. e. , they belong to the same species but may represent new subspecies or strains) and diverged less than 10,000 years BP. This suggests a major radiation event during the Neolithic period. To investigate this, we constructed a lineagesthrough-time plot, which shows a sharp increase in the number of diversification events around 5,000 years ago (see Figure  3b ).
Short-term substitution rate estimation The sequences of the coat protein gene of 51 isolates of cocksfoot yellow mottle virus (CfMV) collected between 1978 and 2022 (44 years) throughout the world were downloaded from sequence databanks or sequenced in this study (see Supplementary Table  1 ). As the 5'end of the sequences of some of the early isolates was not available, a truncated alignment was made of 640 nt long sequences (instead of 765 nt). We evaluated the strength of the temporal signal in the CfMV dataset using a tip cluster-randomisation test in root-to-tip regression, in addition to conducting a Bayesian assessment of temporal signal within the data  [38, 39]. Similarly, for rice yellow mottle virus (RYMV), we used a previously published dataset of 261 isolates of the coat protein gene (720 nt) collected in West Africa between 1975 and 2018 (43 years).
We found evidence of sufficient temporal signal for rate inference in both CfMV and RYMV datasets under an HKY85+Gamma substitution model and a constant population size coalescent prior in BEAST (v 1. 10. 4)  [40]. The substitution rate of the polymerase of RYMV was estimated from an alignment of 69 dated ORF2b sequences (1515 nt) by calibrating the root with a normally distributed prior with a mean of 160 years and a standard deviation of 20 years, derived from the analysis of the coat protein gene sequences  [30].
Phylogeny and taxonomy of the sobemovirus species The sobemovirus dataset consists of 26 species, including 21 species recognized by ICTV and five candidate species (see Table  1 ). The sequences were codon-aligned using MUSCLE  [41]  and the absence of recombination events between species was confirmed with the Phi test  [42, 43]. We used the ORF2b alignment (encoding the polymerase) as this is the most conserved part of the genome at the genus level to construct the sobemovirus phylogeny under a HKY substitution model with an optimised rate variation using PhyML implemented in SeaView (v 5. 05)  [44]. We truncated the 3' end of the ORF2b which had poor coverage across many samples to reach a final alignment length of 1140 nt. For 12 (out of  26)  sobemovirus species with at least two isolates available, we further assessed the interspecific range of identity within the ORF2b region by taking the two most divergent isolates of each species using the Sequence Demarcation Tool (SDT2)  [45].
Time tree reconstruction with PoW model We used the Prisoner of War (PoW) model of virus evolution to account for the power-law decay in the substitution rate estimates of RYMV over time in our estimation of the evolutionary time scale of the Sobemovirus genus  [10]. To achieve this, we first estimated the short-term substitution rate of RYMV and CfMV using time-stamped datasets (see above). We then constructed the ultrametric distance trees of the 26 sobemovirus species samples as well as the merged dataset of 68 sequences that contained also the sequences of metagenomic origin using the HKY substitution model under a strict clock in BEAST 1. The tree was then transformed into a time tree using the PoW model. Finally, we made a lineages-through-time plot for the full dataset (68 sequences) using the phytools library in R  [49].
Acknowledgments The authors would like to thank their team members for their critical reading of this manuscript. We also thank  Williams Brett  who helped us design the microfluidic control system. This work was supported by the  European Research Council grant SmartCells  ( 724813 ) and received support from grants  ANR-11-LABX-0038 ,  ANR-10-IDEX-0001-02  and  ANR-16-CE12-0025-01.
Data Treatment MNase sequencing reads were taken from  [18]  and  [12]  (GEO accession numbers GSE69400 and GSE73428, respectively) and treated as in our previous study  [50]. To be precise, reads from Fastq files were trimmed with trim galore (v0. 6. 5)  [51]  and cutadapt (v3. 1)  [52]. Subsequently, they were mapped on the Saccharomyces cerevisiae genome (University of California at Santa Cruz [UCSC] version sacCer3) using bowtie2 (v2. 3. 4. 3)  [53]. Files were converted with samtools (v1. 9)  [54]  and deeptools (v3. 5. 0)  [55]. Read counts were normalised in Reads Per Million (RPM) of mapped reads. We used the option --MNase of bamCoverage so that only the mononucleosome fragments were kept. This means that fragments shorter than 130 bp and longer than 200 bp were removed from analysis.
Mediator and Sth1 ChIP-seq were taken from  [50]  (ArrayExpress accession number E-MTAB-12198). We used Pol II ChIP-seq from our previous study  [56]. Following  [12, 18] , we retrieved positioning profiles along the coding regions 200 bp before and 1000 bp after the +1 nucleosome. Genes on the Crick strand were inverted. Consequently, all data is calibrated such that the +1 position is at 200 bp. The profile of genes for which the +1 position is known were considered as in  [18]. Clustering was performed by determining the inter and intra Pearson correlation of MNase-seq distributions per gene (Fig  7(A) ). To define the optimal number of k-mean clusters, we used the silhouette criterion measurement  [57, 58]. For all analysed strains, the highest silhouette value occurs at 2 groups, suggesting that the optimal number of clusters is 2 ( Cluster significance was validated by comparing the JS distance of the two determined groups with 500 random groups.
Acknowledgments This work was supported by  Fondation ARC  [ PGA1 RF20170205342 ];  Comité Ile-de-France -La Ligue Nationale Contre le Cancer. K. A. was supported by a PhD training contact from the  French Ministry of Higher Education and Research. L. Z. was supported by a PhD training contract from the  CEA NUMERICS program , which has received funding from  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  800945.
ACKNOWLEDGMENTS We thank  Dr. C. Delevoye ,  Dr. P. Stahl  and  Dr. Ph. Vernier  for insightful discussions and critical reading of the manuscript; This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Foundation for Medical Research  ( FRM EQU201903007827 ), the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  (Grant agreement No  847718 )", and the  Cell and Tissue Imaging core facility (PICT IBiSA, Institut Curie) , member of the  French National Research Infrastructure France-BioImaging  ( ANR10-INBS-04 ). C. G. was recipient of a post-doctoral fellowship from the  Foundation for Medical Research  ( FRM SPF201809006932 ). M. G. D. was supported by PhD fellowship from  Institut Curie EuReCa   PhD Programme  (Grant agreement No  847718 ).
Acknowledgements Funding:  European Research Council  ( Consolidator  grant # 683154 ) (NR) European Union's  Horizon 2020 research and innovation program  ( Marie Sklodowska-Curie Innovative Training Networks , grant # 722053 ,  EU-GliaPhD ) (NR)  Ligue Francaise contre l'Epilepsie (ED) Aviesan (ED) Grant Agency of the Czech Republic  ( GACR :  19-02046S ) (LV, HP)  French government 's scholarship program (HP)  Fondation pour la Recherche Medicale (OC) French Research Ministry  ( ED386 ,  Ecole Doctorale de Sciences Mathématiques Paris centre)  (LZ)  Fondation pour la Recherche Medicale  ( FRM FDT202012010690 ) (LZ).
The protein extract was freeze-dried and hydrated prior to use. Solid-State Magnetic Resonance. All 800 MHz (18. 8 Tesla) experiments were conducted on a Bruker Avance III HD NMR spectrometer using a 3. 2 mm triple resonance HCN probe at 10°C, and at 13. 5 kHz MAS to prevent rotational resonance and spinning sideband interferences. The typical radiofrequency eld strength was 83 kHz for 1 H decoupling, using the two-pulse phase-modulated (TPPM) scheme, with 5. 7 µs for each pulse. The radiofrequency eld strengths were also 83 kHz for 1 H hard pulses and 50 kHz for 1 H ramped spin-lock, 63 kHz for  13  C hard pulses and spin-lock, 42 kHz for  15  N hard pulses, 34 kHz for  15  N spin-lock. The key acquisition parameters of all 1D and 2D experiments are summarized in Supplementary Table  18. All assigned peaks will be deposited to the Complex Carbohydrate Magnetic Resonance Database (CCMRD)  68  , and the Bruker Topspin dataset is freely available upon request.
Acknowledgments This research was funded by the  Natural Sciences and Engineering Research Council (NSERC) of Canada  (grant  RGPIN-2018-06200  to I. M. ), the  Centre National de la Recherche Scienti que  ( UMR 7203  to D. E. W. ) and the  U. S. Department of Energy  (grant  DE-SC0021210  to T. W. ). A. P. would like to thank the  Fonds de recherche du Québec -Nature et technologies (FRQNT) , and the  Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO)  for the award of scholarships. The glycosyl composition and linkage analyses were supported in part by the  Chemical Sciences, Geosciences and Biosciences Division, O ce of Basic Energy Sciences, U. S. Department of Energy  grant ( DE-SC0015662 ) to P. A. at the  Complex Carbohydrate Research Center. The  National High Magnetic Field Laboratory  is supported by  National Science Foundation  through  NSF / DMR-1644779  and the  State of Florida , the MAS-DNP instrument is supported by the  NIH   P41 GM122698  and  NIH   S10 OD018519.
Acknowledgements We thank  Lindsay Peyriga  and  Cécilia Bergès  ( MetaToul-MetaboHUB, Toulouse, France ) for testing the software, and  Sophie Colombié  and  Sylvain Prigent  ( INRAE, Bordeaux, France ) for insightful discussions. Funding MetaToul-MetaboHUB (Metabolomics and Fluxomics facilities, Toulouse, France, http://www. metatoul. fr) is part of MetaboHUB (http://www. metabohub. fr) funded by  ANR  grant  MetaboHUB-ANR-11-INBS-0010.
Availability and Implementation: PhysioFit is implemented in Python 3 and was tested on Windows, Unix and MacOS platforms. PhysioFit is also freely available online at https://workflow4metabolomics. org. The source code, the data and the documentation are freely distributed under GPL3 license at https://github. com/MetaSys-LISBP/PhysioFit/ and https://physiofit. readthedocs. io/.
steady-state and dynamic models are provided by default with PhysioFit, and additional models can be created by advanced users. A model template denoting the required structure is provided with PhysioFit, and extensive details on model construction can be found in the documentation. As detailed in Supplementary information, we have extensively validated PhysioFit by calculating fluxes from different datasets taken from the literature. Fluxes estimated by PhysioFit are in good agreement with the published flux values (maximal differences of 1%), thereby validating the software and the models implemented. PhysioFit can be used via a Graphical User Interface, a Command-Line Interface or directly as a Python module to maximize its interoperability with other computational tools. We have implemented PhysioFit on Workflow4Metabolomics (Giacomoni et al.
biologically relevant GxG associations between humans and HCV We now apply our ABC framework combining a dataset of human diploid host sequences and their infecting HCV (hepatitis C virus) strains  [5]  (the infected sample) and additional sequences from the 1,000 genomes project  [2, 52]  (the non-infected sample). In order to limit confounding effects of population structure in the data, we restrict our analysis to the subset of 451 individuals of European ancestry (PCA in Fig. S6 ,  [5] ). As previously described  [5] , we convert the viral nucleotide sequence data into Single Amino Acid Polymorphisms (bi-allelic SAAPs) data. Our non-infected sample consists of 503 diploid individuals of European ancestry from the 1,000 genomes project (filtering SNPs corresponding to those from  [5] , PCA in Fig.
Acknowledgments The authors thank  Joy Bergelson ,  Dieter Ebert  and  Lars Raberg  for comments on an earlier version of the manuscript. AT acknowledges support from the  Deutsche Forschungsgemeinschaft  project numbers  274542535  ( TE809/3 ) and  221691301  ( TE809/6 ) within the  Priority Program   SPP1819  " Rapid adaptation ". The data aquisition was funded by a grant from the  Medical Research Council (MRC)  ( MR/K01532X/1 ; to the  STOP-HCV Consortium ). E. B. is funded by the  NIHR Oxford BRC. M. A. A. is supported by a  Sir Henry Dale Fellowship  jointly funded by the  Royal Society  and the  Wellcome Trust  ( 220171/Z/20/Z ).
With the increasing number of 3D DBD structures being solved or reliably predicted, it now becomes possible to propose a hierarchical classification for plant TFs based on the 3D structure and the DNA binding mode of their DBD. In human and mammals, TFClass is the reference structural classification  [8, 9]. This hierarchical classification starts with nine structurally defined superclasses, further decomposed into classes, families and subfamilies. It provides a framework for meta-analyses of TF properties and evolution  [10] , it facilitates comparisons between organisms and it is the reference classification used by JASPAR, the open access database for TFBS models  [11]. Here we built upon the TFClass framework to classify all plant TFs types described to date. The new framework is referred to as "Plant-TFClass" (Figure  1 , Table  S1 ).
Method Experimentally-determined 3D structures were downloaded from the Protein Data Bank. AlphaFold2  [85]  computed structure models were downloaded from UniProt. For each model, position of the DBD was obtained directly from the UniProt "Family and Domains" annotation or identified using CD-search against the CDD database  [86]. Structure predictions at the DBD positions were extracted from the PDB files using the Gemmi library (https://gemmi. readthedocs. io/en/latest/). Protein 3D structure comparisons and representation were performed using DALI  [87] , FATCAT  [88]  and PyMOL. In the text and for the sake of clarity, we colored names of TF classes and families in blue and orange, respectively.
Acknowledgments We thanks  Edgar Wingender ,  Chloe Zubieta  and  Gabrielle Tichtinsky  for their advices on this work. This work was supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ) to RBM, by the  ANR-18-CE12-0014 ChromAuxi project to RD and by the  ANR-17-CE20-0014-01 Ubiflor  and  ANR-21-CE20-0024  Beflore projects to FP, and a PhD Fellowship from  CNRS Prime80  to LT.
ACKNOWLEDGEMENTS We are grateful for logistic and administrative support of  Xim Cerda  and his team at the  Spanish National Research Council (CSIC Seville)  and appreciate the help during field work preparation and data collection of  Cornelia Buehlmann ,  Florent Le Moël  and  Christelle Gassama. This study was funded by the  European Research Council   759817-EMERG-ANT ERC-2017-STG.
RNA extraction and real-time qPCR Hepoid-HepaRG cells were retrieved from the gels by incubation (30 min, 37 • C) with 10 μg/mL Liberase TL (Sigma-Aldrich). RNA was extracted from PBS-washed cell pellets using NucleoSpin RNA (Macherey-Nagel, Oensingen, Switzerland) and quantified by NanoDrop ND-1000 (ThermoFisher Scientific). Reverse transcription of RNA to cDNA was done using Applied Biosystems High-Capacity cDNA reverse transcription kit (ThermoFisher Scientific). Real-time qPCR analysis was carried out with Roche Light Cycler® 480 (Roche, Basel, Switzerland) using a SensiFAST SYBR No-ROX kit (Bioline, London, UK). The sequences of all the primers used and qPCR conditions are listed in Table  S1. Light Cycler 480 software and the second derivative maximum method were used to calculate the crossing point cycle (Cp) value for each gene.
Statistical analysis Data from chemical treatments were normalized to the solvent control and expressed as a percentage of the control, a fraction of the control (FOC) or a fold change. Time-dependent changes in OATP expression during Hepoid-HepaRG were presented as a fold change relative to the monolayer culture of differentiated HepaRG. EROD/ MROD activities were expressed as a percentage of 3-MC-induced controls. For the selected gene pairs, gene expression ratios were calculated by comparing their housekeeping gene-normalized Cp values. All the results represent a minimum of three independently repeated experiments (n ≥ 3) with data expressed as mean ± standard deviation (SD). Values of 20% or 50% effective concentrations (EC 20 and EC 50 ) were estimated using a four-parameter logistic regression model in GraphPad (GraphPad Software, Boston, MA).
Funding The study was funded by the  Czech Science Foundation (GA ČR) project no. GA19-19143S. Authors thank the  Research Infrastructure RECETOX RI  ( LM2023069 ) financed by the  Ministry of Education, Youth and Sports, and Operational Programme Research, Development, and Education-project CETOCOEN EXCELLENCE  ( CZ. 02. 1. 01/0. 0/ 0. 0/17_043/0009632 ), for supportive background. This work was supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 857560-CETOCOEN  Excellence. This publication reflects only the author's view and the  European Commission  is not responsible for any use that may be made of the information it containsT. Mobility of  R. R. Chowdhury  was supported by the  Barrande Fellowship   Programme  funded by  The French Institute in Prague  and the  Czech Ministry of Education, Youth and Sports.
The authors thank the platform H2P2 from the  SFR Biosit (UMS CNRS 3480/ US INSERM   018 ,  Biogenouest, University of Rennes ). This work was also supported by the  Institut National de la Santé et de la Recherche Médicale (Inserm) , the  University of Rennes  and the  European Digital UniverCity EDUC. EDUC-Share  project funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101017526  is also gratefully acknowledged for encouraging collaboration between  Rennes  and  Masaryk Universities.
, 2018; Naryshkin et al. , 2014). However, the mode of action and mechanism of these few compounds remain uncertain. As a matter of fact, the exact comprehension of whether small molecules can specifically and/or selectively bind RNA remains quite challenging  (Falese et al. , 2021; Manigrasso et al. , 2021b). RNA-binding molecules typically tend to be unspecific and promiscuous, by interacting in a sequence-independent manner with the phosphodiester backbone of RNA or by intercalating stacked nucleobases inducing structural misfolding  (Warner et al. , 2018). Moreover, describing the principles of RNA recognition by small organic compounds and elucidating their mode of action at the molecular level is severely impaired by the availability of only 746 high-resolution structures of RNA-ligand complexes in the Protein Data Bank (PDB) as of April 2 nd , 2023.
Principles of molecular recognition between the conserved group II intron active site and splicing modulators To visualize the exact binding mode and characterize the inhibitory mechanism of intronistat B at high resolution, we first solved the co-crystal structure of intronistat B bound to a previously-described construct of the O. iheyensis I1 intron  (Marcia and Pyle, 2012) , which encompasses its structural domains  [1] [2] [3] [4] [5] , in the exon-free state and in the presence of Mg 2+ and K + ions. Our structure, determined at a resolution of 3. 0 Å, shows that in the presence of the compound, the intron maintains an overall folded structure, similar to that of the compoundfree intron (PDB entry 4E8M), with a root-mean-square deviation (RMSD) = 0. 5 Å between the two states (Table  S2 ).
23 ± 0. 36 Å; RMSD(intronistat B) = 1. 54 ± 0. 12 Å, Figure  S5 ). In this position, the ligand mimics the splicing substrate (i. e. the scissile nucleotide) and forms a pseudo-Michaelis-Menten complex at the intron active site, stabilizing the M1-M2 internuclear distance at a value compatible with catalysis (dM1-M2 = 4. 01 ± 0. 46 Å; for reference in PDB entry 4E8M dM1-M2 = 3. 7 Å, Figures  3  and S5-6 ). Taken together, our OiD1-5 structures with intronistat B and its brominated derivative, and the corresponding MD simulations, demonstrate that intronistat B inhibits splicing because it binds to the active site of group II introns, where it forms direct, specific interactions with conserved catalytic nucleotides and metal ions.
(F) The catalytic K1 ion (purple sphere) engages the interaction with N7 atom of G288 (purple sticks) at the J2/3 junction (purple cartoon). If the ion is not in place (purple semitransparent sphere), the K1-N7 interaction is not established, ultimately inhibiting the catalytic activation of the intron for the first splicing step, and its structural rearrangement necessary for splicing progression  (Manigrasso et al. , 2020). The representation follows that of panel A. apo-form (RMSD = 0. 6 Å with respect to PDB id: 4FAQ, Figure  S7A ). We also determined a co-crystal structure at 3. 1 Å resolution of Oi5eD1-5 and intronistat B in the state immediately following the first step of splicing, which is obtained in the presence of Mg 2+ and K +. Analogously to the structure obtained in the presence of calcium, also the structure obtained in the presence of magnesium shows the compound-free intron, in which the scissile phosphate and the 5'-exon coordinate M1 and M2 in the same conformation reported for the corresponding apo-form (RMSD = 0.
4 Å with respect to PDB id: 4FAR, Figure  S7B ). These two structures of Oi5eD1-5 suggest that under the experimental crystallization conditions (100 mM Mg 2+ , 30°C) the splice junction outcompetes intronistat B from the active site. We had previously established, however, that immediately after 5'-exon hydrolysis these crystallization conditions trap the scissile phosphate in an unproductive conformation, which is unlikely to be of physiological relevance  (Manigrasso et al. , 2020). To mimic a more physiological conformation of the active site immediately after 5'-exon hydrolysis, we thus co-crystallized OiD1-5 (the free intron) with the 5'-exon-like oligonucleotide 5'-AUUUAU-3', and we solved the X-ray structure of this complex in the presence of intronistat B and of Mg 2+ and K + at 3.
3 Å resolution. This structure reveals a well-folded intron possessing an intact catalytic site that adopts the expected triple helix conformation (RMSD = 1. 0 Å with respect to the corresponding apo-form, PDB id: 4FAW, Figure  4 ). An omit map, generated before modelling the compound, displays a peak in the active site, compatible with the binding of intronistat B (highest contour value = 4. 0 σ, volume of the peak at 3. 0 σ = 301 Å 3 ). Unexpectedly, this volume is oriented differently from the volume occupied by intronistat B in the exon-free state (RSCC = 0. 90, Figure  2B ). The group II intron active site still recognizes the compound's pyrogallol motif through M1 (1. 9 Å from intronistat B O22 atom and 2. 1 Å from intronistat B O24 atom), M2 (1. 9 Å from intronistat B O24 atom and 2.