dataesr/scientific-paragraphs-categorization huggingface dataset:
Three-way χ 2 tests were used to test for significant differences in group proportions of subpopulations for the weighted data. Residual analysis was utilized to identify the degree of contribution to the test statistic from each categorical combination. Data preprocessing and statistical analyses were performed using R software version 3. 2. 5 and all plots were generated using the ggplot2 package. weighted N = 163,540,241) is shown in Table  1. Age and gender distributions were found to be significantly different between these two groups. 50. 2% of the RA population belonged to the age group of more than 60 years and 40% belonged to the age group of 41-60 years, while for the non-RA population these numbers were 18% and 35%, respectively. Consistent with the literature, females were more commonly affected by RA with 61% of RA subjects being female compared with 49% female population in the non-RA group.
47  Since NHANES provides a large nationwide dataset with a very well-structured sampling design, we believe that even with these limitations, it provides suitable data to conduct preliminary analysis. Finally, CRP is a good marker for the initial screening of the inflammatory state but has a very limited value in probing the potential crosstalk between inflammatory signaling pathways. Therefore, to understand the molecular underpinnings of the synergistic interactions observed here, measurement of a broader panel of inflammatory markers (e. g. , TNF-α, IL-1, IL-6) should be incorporated in future studies.
025 mM SDS, xylene cyanol and bromophenol blue) and heating the samples at 95 • C for 5 min. Products were then separated by denaturing gel electrophoresis using 25% polyacrylamide 7 M urea gels and 0. 5-fold Tris-borate-EDTA running buffer. Signals were visualized by phosphor screen autoradiography using a Typhoon FLA-7000 phosphorimager (Fujifilm) operated with the FLA-7000 software.
At 24 h post-transfection, either total cellular RNA was extracted for Northern blot analysis using an RNeasy Mini kit (Qiagen) or cells were lysed in 100 l of passive lysis buffer (Promega) per well, and firefly luciferase and Ren-Luc activity quantified using the dual-luciferase reporter assay system (Promega). Ren-Luc levels were corrected with the firefly luciferase levels (resulting in standardized relative light units [sRLU]) to compensate for differences in transfection efficiency or cell density. Data were evaluated in Prism and are always presented as the mean of a given amount (n) of biological replicates as well as the respective standard deviation (SD). To confirm general expressibility of the L protein mutants in BSR-T7 cells, the cells were transfected with 500 ng L gene PCR product expressing C-terminally 3xFLAGtagged L protein mutants per well.
Northern blot analysis For the northern blot analysis, 600 ng of total cellular RNA was separated in a 1. 5% agarose-formaldehyde gel and transferred onto a Roti-Nylon plus membrane (pore size 0. 45 M, Carl Roth). After UV cross-linking and methylene blue staining to visualize 28 S rRNA, the blots were hybridized with a 32P-labelled riboprobe targeting the Ren-Luc gene. Transcripts of the Ren-Luc gene and complementary replication intermediate RNA of the minigenome were visualized by autoradiography using a Typhoon FLA-7000 phosphorimager (Fujifilm) operated with the FLA-7000 software. Quantification of signals for antigenomic RNA and mRNA was done in Fiji.
Cryo-EM single-particle analysis Data collection. Grids were loaded into a 300-kV Titan Krios transmission electron microscope (Thermo Scientific) equipped with a K3 direct electron detector and a postcolumn GIF BioQuantum energy filter (Gatan). Micrograph movies were typically collected using the EPU software (Thermo Scientific) at a nominal magnification of 105 000× with a pixel size of 0. 85 Å using a defocus range of -0. 8 to -2. 6 m, unless stated otherwise. The sample was exposed with a dose of 15 electrons/physical pixel per second (estimated over vacuum) for 2. 5 s and thus, a total dose of 50 electrons/ Å2. Recorded movie frames were fractionated into 50 frames. A summary of the key data collection statistics for each cryo-EM structure is provided in Supplementary Table  S2.
Image processing All movie frames were imported in RELION 3. 1  (37)  or RELION 4. 0  (38)  and motion corrected using its own implementation of MotionCor2  (39). Particles were picked automatically using the pre-trained BoxNet in Warp  (40)  and the calculation of contrast transfer function (CTF) estimation and correction was done in RELION with CTFFIND4  (41). Picked particles were extracted and processed in RE-LION 3. 1/4. 0 with visual inspection done with UCSF Chimera  (42). First, particles were subjected to 2D classification at 4× binned pixel size typically as an initial check of the quality of the dataset. Then, all particles were used for 3D classification in an iterative manner using a 50 Å low pass filtered map from apo SFTSV L protein  (17). Incomplete, low resolution or 3D classes containing damaged particles were excluded from further data analyses.
Model building, refinement and validation The published corrected apo structure of the SFTSV L protein  (30)  was used as the starting point for the RNA-bound structures published in this study which then underwent iterative rounds of model building in Coot  (47)  and real-space refinement in Phenix  (48). The comprehensive validation tool in Phenix was used to assess model quality  (49, 50). Coordinates and EM maps are deposited in the EMDB and PDB databases as indicated in Table  1. Figures have been generated using CCP4mg  (51) , PyMOL (Schr ödinger), and UCSF ChimeraX  (52). A summary of the key refinement and validation statistics for each cryo-EM structure is provided in Supplementary Table  S2.
The L protein during pre-initiation of viral genome replication The 5 HOOK structure demonstrates that the SFTSV L protein undergoes several conformational changes as a result of binding to the terminal 20 nts of the 5 cRNA in comparison to the apo structure (PDB: 6L42, 6Y6K and 7ALP)  (30) (31) (32) (33). The most apparent changes being an opening up of the vRNA binding lobe (vRBL), the neighbouring fingers domain, and the PA-C like core lobe, allowing the 5 RNA to bind in a positively charged cleft (Figure  1A ). This cleft is covered by a short 3-turn ␣-helix (M437-H447) and supported on the periphery by a short loop (H1038-D1046) (Figure  1A ). Of note, both of these structural motifs were missing in the published apo structures, suggesting here that the 5 RNA has a role in stabilizing this region of the L protein.
Initiation and priming of genome replication by the SFTSV L protein To test how the SFTSV L protein could initiate replication, we incubated the L protein with 5 cRNA, 3 cRNA and a short 2 nt primer (AC) in the absence of any NTPs. Given that the AC primer could only base-pair with either one of the two UG repeats at the 3 end of the 3 cRNA, we had hoped this might allow us to visualize a priming loop (as seen in influenza virus)  (58)  or prime-and-realign loop (as reported for LACV)  (13). Processing of this dataset gave one major class, which we have named a RESTING state structure. Here, the 5 cRNA hook is bound in the normal manner nestled betwixt the vRBL, PA-C like domain and fingers, while the 3 cRNA is bound to the 3 secondary binding site. Whereas in the early-elongation structures in which the 3 RNA clearly feeds into the L protein core, in the RESTING structure, the distal duplex is absent.
The RESTING structure is reminiscent therefore of the LASV 3END-CORE (PDB: 7OEA) (28) structure in which excess 3 vRNA binds to the 3 secondary binding site instead of entering the L protein core. Although we clearly see 3 cRNA binding to the 3 secondary binding site, we also found that we could fit 4 nts of the template 3 cRNA in the L protein core, however, only 2 of these (U3 and G4) could be fit with confidence. The region of the map where these 3 RNA nts are found is rather noisy but we think that similar to our early-elongation structures, the nts in the L protein core are likely the start of the 3 cRNA. Although we could not visualize initiation, given that the 3 cRNA repeatedly appears to reach at least 4 nts into the L protein core, and that we detect an RNA product corresponding exactly to the terminus of the 3 template without additional or missing nts in our biochemical assays  (17) , we think that for vRNA synthesis SFTSV primes internally and then re-aligns this primer to the 3 template extremity before proceeding with replication.
ACKNOWLEDGEMENTS We want to thank  Stephan G ünther  and  Tomas Kouba  for support and helpful discussions throughout the project. We thank  Imre Berger  for providing the DH10EMBaY E. coli and the team of the  Eukaryotic Expression Facility (EEF)  at  European Molecular Biology Laboratory (EMBL) Grenoble  for support and advice. We also acknowledge support by  Carolin Seuring ,  Cornelia Cazey  and  Ulrike Laugks  for access to the Cryo-EM multi-user facility at CSSB and providing time for sample preparation, screening, and data collection;  Wolfgang Lugmayr  for help and support in using the CSSB partition on the DESY computer cluster for cryo-EM data processing. We also want to thank  Benoît Arragain  for feedback on the manuscript.
DATA AVAILABILITY Coordinates and structure factors or maps included in this paper have been deposited in the Worldwide Protein Data Bank (wwPDB) and the Electron Microscopy Data Bank (EMDB) with the following accession codes: SFTSV L protein bound to 5 promoter RNA [5 HOOK] EMD-15607 PDB-8AS6; SFTSV L protein early elongation structure [EARLY-ELONGATION] EMD-15608 PDB-8AS7; SFTSV L protein early elongation raised endonuclease structure [EARLY-ELONGATION-ENDO] EMD-15610 PDB-8ASB; SFTSV L protein late elongation structure [LATE-ELONGATION] EMD-15614 PDB-8ASD; SFTSV L protein resting structure [RESTING] EMD-15615 PDB-8ASG.
We compiled the results into a comprehensive database containing the position of each L1 copy in the 12 cell lines and their DNA methylation levels at single locus-, nucleotide-, and moleculeresolutions (Table  S3  and Data S1). These data can be easily and interactively interrogated through a dedicated web portal (https://L1methdb. ircan. org). On average, we identified 312 full-length L1HS in each cell line, of which 42 are non-reference insertions and assumed to be full-length (Figure  1D ). In addition to these copies, we also detected an average of 81 L1HS reference elements with an amplifiable 5' end but annotated as 3' truncated (Table  S3 ). Among them, half contain Alu insertions (AluY or AluS) or other forms of internal rearrangements, or represent chimeras with older L1PA elements.
Local epivariation at L1 loci is associated with distinct transcription factor landscapes To further understand what differentiates methylated from unmethylated L1 loci, we examined whether these subsets could be associated with distinct transcription factor (TF) binding profiles. We performed differential TF binding site enrichment analysis, comparing the 5' junction of unmethylated versus methylated L1. The analyzed region spans the 300 bp of upstream sequence under L1 epigenetic influence, as well as the first 500 bp of the L1 5' UTR which appears to be variably methylated (Figure  S4B  and Figure  5A ). For this purpose, we screened the entire Unibind database  90  , a catalogue of highconfidence TF binding sites (TFBS) predictions based on ~3500 publicly available chromatin-immunoprecipitation and sequencing (ChIP-seq) experiments from various tissues and cell types, including those from ENCODE (Figure  5A ).
The number of unmethylated L1HS being relatively small in most cell types, we also included the L1PA2 family to increase the statistical power of this analysis, as it is the most recent primate-specific family after the L1HS family (Figure  S3A ) and shows a similar pattern of proximal upstream epivariation (Figure  S4A ). Note that for each cell line in our panel, we compared the methylated and unmethylated L1 subsets to all ChIP-seq data stored in Unibind, irrespective of the cell-type or conditions in which they were obtained. The rationale is that even if datasets from our cell line of interest are not present in Unibind, a similar cell type or condition may be represented. Indeed, the screen revealed several TFs specifically associated, in at least one biological condition, with the subset of methylated or unmethylated L1s found in one of the cell lines of the panel (Figure  5B ).
Closer examination of the motifs underneath the ChIP-seq peaks indicates that some of the binding sites are internal to the L1 promoter (such as for YY1), and some are found in both the upstream and internal sequences (such as for ESR1, FOXA1, or CTCF) (Figure  5C )  91, 92. Among the top hits, YY1 is strongly enriched at the unmethylated subset of L1s from 2102Ep cells (Figure  5B ). The YY1 binding site is internal to the L1 promoter, but close enough to the 5' end (position +10 ± 2 bp) that the ChIP-seq signal largely overlaps the flanking sequence. The strong YY1 enrichment at unmethylated L1s from 2102Ep cells was detected in Unibind datasets obtained in different cell types, including two embryo-related cell lines, the H1 embryonic stem cells and NTera2/D1 embryonal carcinoma cells. To confirm this association in matched cell types, we performed YY1 ChIP-seq in 2102Ep cells (Figure  5D ) and tested if it was also observed in other cell types of the panel for which YY1 ChIP-seq data were publicly available (Figure  5E ).
Limitations of the study In our study, we identified a set of TFs associated with distinct L1HS or L1PA2 methylation states. Given the high degree of identity between individual copies of these young L1 families and the resolution of ChIP-seq experiments, only TFs binding sufficiently close to the 5' end of the element can be unambiguously assigned to individual elements. Therefore, TFs that potentially bind L1s more internally might be missed, even if documented in Unibind. As approaches capable of mapping TF binding sites within repeated sequences, such as PAtChER 74 , DiMeLo-seq 139 , nanopore-DamID 140 , or nanopore-based NOMe-seq 141 , become more widely available, this problem will become less acute in the future. Additionally, we present data showing association between L1 methylation state and other genomic features.
Note that any given sample only contains a subset of reference L1HS due to insertional polymorphisms in the human population. See also Figure  S1  and Table  S1. (C) Sensitivity analysis. Computational down-sampling of high depth bs-ATLAS-seq sequencing data (MCF7) shows that L1 recovery reaches a plateau above 10 million of total read pairs. Thus, all samples were subsequently sequenced to a depth greater than 10 million of total reads. S1. (G) PCR validation of unknown non-reference insertions. PCR was done with genomic DNA of the indicated cell lines (+) or with water as non-template control (-). The insertion in chr10 is pericentromeric and embedded in other repeats. Therefore, only primers to amplify the 5' junction could be designed. Ct, unrelated locus used as PCR control. (H) DNA methylation level of selected L1 elements was profiled using an antibody-based enrichment of DNA methylation (MeDIP) and compared with bs-ATLAS-seq data.
The DNA methylation level of MeDIP data is expressed as log2 of the percentage of immunoprecipitation (log2 %IP). See also Table  S2. (I) Comparison of bs-ATLAS-seq with PCR-free targeted sequencing and methylation calling by Oxford Nanopore Technology sequencing (ONT-seq) for the L1 region common to both methods (1-207) or the full CpG island (1-500). See also Table  S4. For all correlation analysis, r and p represent Pearson correlation coefficient and p-value, respectively. See also Figure  S2  and Table  S3. (G) Genome browser view of a region on chromosome 5 encompassing three full-length L1 elements (green rectangles) with distinct promoter DNA methylation profiles, integrated with nascent transcription (GRO-seq), H3K36me3 histone modifications (ChIP-seq), and DNA methylation (WGBS) datasets in K-562 cells.
(A) Screening strategy to identify TFs differentially associated between unmethylated and methylated L1HS and L1PA2 copies using curated datasets publicly available in the UniBind database  90. Note that for each cell line in our panel, we compared each pair of methylated and unmethylated L1 subsets to all ChIP-seq data stored in Unibind (~3500 datasets), irrespective of the cell-type or conditions in which they were obtained. The rationale was that even if our specific cell line is not necessarily represented in Unibind datasets, a similar cell type may be represented. The main hits were then subsequently confirmed using matched datasets (see panels E and F). (E) DNA methylation level of L1HS bound (+) or unbound (-) by YY1 in embryonal cell lines (H1 and 2102Ep) and other cell lines for which matched YY1 ChIP-seq were also publicly available (K-562, HCT116, HepG2, HEK-293T).
Note the distinctive spliced RNA-seq reads, antisense relative to the L1 element, linking L1 antisense promoter with the adjacent BCAS3 exon. (I) SiRNA-mediated knock-down of ESR1 leads to reduced L1 chimeric transcripts. Top, schematic representation of chimeric transcripts initiated from L1 antisense promoter and leading to truncated or alternative isoforms of the surrounding gene. Upon siRNA-mediated knock down (siESR1), the number of L1 chimeric splice junctions is expected to decrease if ESR1 drives chimeric transcript synthesis, as compared to a scrambled siRNA control (siScr). Bottom left, chimeric transcripts at the BCAS3 locus quantified by the number of normalized spliced-RNA-seq reads (RPM) detected in MCF-7 cells treated by an siRNA against ESR1 (+) or a control scrambled siRNA (-) (data from GSE153250).
Outliers beyond the end of the whiskers are plotted individually. *p < 0. 05, **p < 0. 01, ***p < 0. 001, and ****p < 0. 0001, two-sided twosample Wilcoxon test. See also Figure  S5  and Table  S5. (E) Genome browser view of two example L1HS loci with distinct promoter DNA methylation profiles (bs-ATLAS-seq), integrated with RNA-seq and YY1 ChIP-seq data in H1 and 2102Ep. Top, locus in chromosome 18, the YY1 signal is close to the background level, the L1HS element is hypermethylated and non-expressed. Both cell lines have similar profiles. Bottom, locus in chromosome 7, a strong YY1 peak is detected in 2102Ep cells, where the L1HS is completely unmethylated and robustly expressed. In contrast, in H1 cells, the same locus does not appear bound by YY1, is hypermethylated and nonexpressed. (F) Differential expression of transposable element (TE) families between MCF-7 cells treated by an siRNA against ESR1 (+) or a control scrambled siRNA (-) measured by RNA-seq using TEtranscripts 142 (data from GSE153250).
(C) Differential expression of transposable element (TE) families between 5-aza-or DMSO-treated HCT-116 cells measured by poly(A) + RNA-seq using TEtranscripts 142. In the MA-plot, each data point represents an aggregated TE family. TE families found significantly up-or down-regulated upon 5-aza treatment are colored in purple and green, respectively, and data points corresponding to the L1HS to L1PA8 families are labelled (of which all but L1PA8 are upregulated). (D) Heatmaps showing the average difference of L1HS methylation (∆mCG, bs-ATLAS-seq, 2 replicates) between HCT-116 cells treated by DMSO and 5-aza (AZA), as well as the expression levels of each L1HS in RNA-seq replicates (L1 3' readthrough, see legend of Figure  6A ). Heatmaps were sorted by decreasing L1 expression (average of the 3 replicates).
sh (v 1. 1, available at https://github. com/retrogenomics/bs-ATLAS-seq), which steps are summarized below. In each read pair, read #1 is 90 bp long and corresponds to the 5' flanking sequence of L1, while read #2 is 210 bp-long and corresponds to L1 5' UTR internal sequence. Read trimming, mapping, and filtering. We demultiplexed FASTQ files according to their samplespecific barcode using cutadapt (v 3. 1, https://github. com/marcelm/cutadapt). We then verified the presence of bs-ATLAS-seq adapters in the reads and trimmed them with cutadapt. Once trimmed, reads #2 were mapped locally against the first 250 bp of L1HS consensus sequence (Repbase Rel. 10. 01) using Bismarck (v0. 22. 1) 147 allowing soft-clipping. Only pairs for which read #2 mapped to the L1HS consensus in the correct orientation were subsequently analyzed (Samtools v1.
Assessment of bs-ATLAS-seq false positive rate. To estimate the percentage of false positive L1 detected by bs-ATLAS-seq, we compared candidate L1 elements with databases of known nonreference insertions (KNR), such as euL1db  29  , the 1000 genome project (1KGP)  23  or previous mapping of L1HS in the same cell lines using 3' junction amplification and Ion Torrent-based single-end sequencing (3'-ATLAS-seq)  9. Only 3 candidate non-reference insertions appeared unknown (Table  S3 ). chr18:15193133-15193135:-:L1HS:NONREF was validated by nanopore sequencing (Table  S4 ). The two others, chr7:140709367-140709369:-:L1HS:NONREF and chr10:38190899-38190901:+:L1HS:NONREF, were validated by PCR of their junctions with the flanking sequence (Table  S1  and Figure  S1G ).
Bioinformatic analysis. To map reads obtained by the protocol described above, we prepared a custom genome including the two possible alleles (empty or filled) for each target locus. Both alleles contained 50 kb upstream and 1 kb downstream of L1, extracted from the human reference genome hg38. L1 insertion sites were deduced from bs-ATLAS-seq experiments. If the targeted L1 is present in the reference genome, the empty allele was made by removing the L1 sequence with bedtools maskfasta (v2. 3). If the targeted L1 was absent from the reference genome, the filled allele was built using reform (https://github. com/gencorefacility/reform) by introducing an L1 consensus sequence at the insertion point. Thus, the custom genome comprises 250 sequences concatenated in a multifasta file. After indexation, nanopore reads were mapped to the custom genome with minimap2 (v20.
Transcription factor enrichment at unmethylated L1 Differential transcription factor binding between unmethylated (mCG<25%) and methylated (mCG>75%) L1HS and L1PA2 subsets was analyzed for each cell line using the Unibind enrichment command line tool (UniBind_enrich. sh, available at https://bitbucket. org/CBGR/unibind_enrichment) and the entire Unibind database (Hg38_robust_UniBind_LOLA. RDS) using the twoSets option  90.
Enrichment of genomic features To allow a fair comparison of the associations of reactivated and non-reactivated L1HS upon 5-aza treatment with a wide range of genomic features, we used a statistical approach in which we generate a large number of controlled in silico randomizations of each dataset, and we express the magnitudes of each association as a z-score, which reflects the number of standard deviations by which the measured similarity of any pair of datasets differs from the similarity expected by chance, as previously performed  67.
ACKNOWLEDGMENTS This research was funded, in whole or in part, by the  Agence Nationale de la Recherche , Grant  ANR-21-CE12-0001. A  CC-BY 4. 0 public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions. We thank  C. Baudoin  and the  IRCAN Genomics Core Facility (GenoMed)  for sequencing,  O. Croce  and IRCAN bioinformatics service for computing resources,  B. Meyer  for deploying the web portal on IRCAN server, and members of the  Cristofari lab  for helpful discussions. We are grateful to the  ENCODE Consortium  for RNA-seq and ChIP-seq data, and to  J-L Garcia-Perez  ( Univ. of Edinburgh, UK ) for providing H1 genomic DNA.