dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank the  National Reference Center for Respiratory Viruses (Institut Pasteur, Paris)  for the gift of the SARS-CoV -2 isolate. We thank  B. Delache ,  E. Burban ,  C. M. Fovet ,  N. Dhooge ,  S. Langlois ,  P. Le Calvez ,  M. Potier ,  F. Relouzat ,  J. M. Robert  and  C. Dodan  for help with animal studies (IDMIT);  J. Morin , for help with the cytokine measurements and preparation of reagents;  C. Aubenque ,  M. Barendji ,  L. Bossevot ,  N. Dimant ,  J. Dinh ,  M. Leonec , and  K. Storck  for help with sample processing;  F. Ducancel  and  Y. Gorin  for help with the logistics and safety management.
Funding This work was supported in part by the  Institut National de la Santé et de la Recherche Médicale (Inserm) , the  Centre National de la Recherche Scientifique (CNRS) , the  University of Lille , the  Pasteur Institute of Lille  and l; Agence Nationale de la Recherche  (AAP générique  2017 ,  ANR-17-CE15-0020-01 , ACROBAT). This work was also supported by the  Fondation pour la Recherche Médicale (FRM, France)  under reference AM-CoV-Path and the  REACTing task force (Inserm, France). The  Infectious Disease Models and Innovative Therapies (IDMIT)  research infrastructure is supported by the ; Programme Investissements d'Avenir , managed by the  ANR  under reference  ANR-11-INBS-0008. The  Fondation Bettencourt Schueller  and the  Region Ile-de-France  contributed to the implementation of IDMIT's facilities and imaging technologies.
Gap density quantification Cells were fixed at 8 h post-seeding using 4% PFA in PBS and stained for β-catenin to label the cell-cell junctions. Samples were imaged using a Nikon A1R confocal microscope with 60X water-immersion objective lens and the images were stitched using the NIS-Elements software to obtain a high-resolution and large field-of-view image. The images were then analyzed using ImageJ to quantify the gap area in each sample.
Image Segmentation and Particle Image Velocimetry Analysis Image segmentation and Particle Image Velocimetry (PIV) analysis of phase contrast data were performed using in-house software developed in MATLAB. To segment the cell monolayer region, a 25x25 pixels range filter was applied to the raw image. The rangefiltered image is a map with high values at large intensity variations region (cell monolayer region) and low values at homogeneous intensity region (wound region). This map was then thresholded and refined by morphological operations to generate a binary mask. The segmented cell monolayer region was then divided into 2 regions of interest based on the distance from the wound edge (i. e. , 0-50m, 50-100m). To measure instantaneous speed and directional persistence in these regions of interest, Particle Image Velocimetry (PIV) analysis was performed using MatPIV 1.
6. 1  [73]. Single-pass PIV with a window size of 128x128 pixels and 50% overlap was used. The directional persistence is measured by the cosine of the angle between each velocity vector and the monolayer's overall moving direction (i. e. , from left to right, along the x-axis of the image). The average cosine value ranges from -1 to 1, where a higher value indicates a higher directional similarity between the local cell movement and the overall cell monolayer movement. Note that for data with cells on gratings, pre-processing was performed to remove the gratings on the image before the analysis by subtracting a static background image from the data. The average of images from all time points was calculated to generate the static background image.
Cell proliferation assay Cells were seeded with a density of 10x10 3 cells on both patterned and planar control fibronectin-coated PDMS pieces. At 8 h post seeding cells were treated with PTP1B inhibitor for 1 h followed by EdU staining. Cells were fixed for 10 min with 2% PFA and permeabilized with 0. 01% triton X100 for 15 min. Proliferating cells were visualized by EdU staining using Click -iT™ EdU Alexa Fluor® 555 Imaging Kit (Invitrogen, C10338). The fixed samples were imaged using Nikon A1R confocal microscope. The images were then analyzed using Imaris software to count the number of EdU positive nuclei per image area.
Image analysis of the recoil speed was performed in ImageJ using the MTrackJ plugin  [44]. This plug-in allows the tracking of the two edges of the cut in subsequent frames and extracts the coordinates of the two points as a function of time. The recoil speed (µm/s) was defined as the rate of change of the two edges  [43]. The initial recoil speeds were measured using the data points from the first 2 s after ablation.
FUNDING  Centre National de la Recherche Scientifique ;  Aix-Marseille Université ;  Agence Nationale de la Recherche  [ ANR-17-CE11-0039 ];  Fondation pour la Recherche Médicale  [FRM, DEQ20180339165];  Fondation Bettencourt Schueller  (to E. C. ); D. J. was supported by  FRM  post-doctoral fellowship [SPF201809007142]; V. H. acknowledges financial support by the  European Regional Development Fund  through the  Centre of Excellence for Molecular Cell  Technology as well as by the  Swedish Research council  [ 2017-03783  to V. H. ]; M. R. was supported by  MIMS Excellence by Choice Postdoctoral Fellowship Programme grant   2018. Funding for open access charge:  ANR  [ ANR-17-CE11-0039 ].
CTCF binding is increased in the total absence of SMCHD1. We have shown that mutations in SMCHD1 ATPase domains affect distribution of H3K27 trimethylation but are not accompanied by an increase in CTCF binding as in KO mice  (Jansz et al. , 2018). To further investigate the difference between SMCHD1 deficiency and a total absence of the protein on CTCF distribution in human cells, we analyzed publicly available ChIP-Seq data (GEO, GSM1130654) where SMCHD1 binding was determined by comparing HCT116 cells and HCT116 in which SMCHD1 has been invalidated using a Zn finger nuclease (HCT116 KO). After filtering for repetitive DNA sequences, 526 SMCHD1 binding sites were found. Most of them are located at a distance of 50-500 kb from TSS consistent with an indirect long-distance effect on gene regulation.
Acknowledgements and Fundings We are indebted and thank all patients for participating in this study. We acknowledge the  GBiM genomics and bioinformatics core facility  for RNA seq and the  Marseille Stem Cells Core facility (MaSC)  for stem cells. This study was funded by " Association Française contre les Myopathies " (AFM ; TRIM-RD) and  Fondation Maladies Rares. CD and CL were the recipient of a fellowship from the  French Ministry of Education and FSH Society. MD is the recipient of a fellowship from the  French Ministry of Education. The project leading to this publication has received funding from the  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "investissement d'avenir programme "  AMX-19-IET-007  through the  Marseille Maladies Rares (MarMaRa) Institute.
First, we assessed the impact of XBP1s on autophagic flux. Xbp1s cDNA transfection led to an increased LC3-II:LC3-I ratio (Figure  2A, B ) and reduced SQSTM1 (Figure  2A, C ) expression. Importantly, bafilomycin A 1 (baf), which is classically used to trigger autolysosome acidification and to disrupt autophagosome-lysosome fusion  [33] , increased XBP1smediated modulations of LC3-II:LC3-I and SQSTM1 (Figure  2A , C) thereby confirming that XBP1s increased autophagic/ mitophagic flux. Next, we showed that Xbp1s transient multiple comparisons test) were analyzed as described in Materials and Methods. Data are expressed as percent of control DMSO-treated cells (taken as 100%) and are the means ± SEM of 3-4 independent experiments performed in triplicates. GAPDH expression (A) is provided as a control of protein load.
(C-E) SH-SY5Y cells were transiently transfected with an empty pcDNA3 vector (Ev) or wild-type Xbp1s cDNA. Twenty-four hours after transfection, PINK1 protein expression (C, N = 12, analyzed by Student's t test), promoter transactivation (D, N = 15, Student's t test) and mRNA levels (E, N = 12, Student's t test) were analyzed as described in Materials and Methods. SH-SY5Y cells treated with CCCP (CP, 10 µM for 6 h) were included as migration controls for PINK1. Data are expressed as percent of control Ev-transfected cells (taken as 100%) and are the means ± SEM of 4-5 independent experiments performed in triplicates. ACTB and XBP1s expressions are provided in (C) as a control of protein load and Xbp1s transfection efficiency. (F-H) SH-SY5Y stably expressing scrambled (SC) or shRNA-targeting XBP1 (XBP1 KD) were assessed for PINK1 protein expression (F, N = 9, analyzed by Student's t test), PINK1 promoter transactivation (G, N = 15, Student's t test) and PINK1 mRNA levels (H, N = 18, Student's t test) as described in Methods.
Data are expressed as percent of control SC cells (taken as 100%) and are the means ± SEM of 3-6 independent experiments performed in triplicates. ACTB expression is provided in (F) as a control of protein load. (I) The scheme represents the full-length (FL, P2. 0) mouse Pink1 promoter region and 5ʹ end deletion constructs (P1. 3, P0. 8 and P0. 4) in frame with luciferase. Black boxes on P2. 0 construct correspond to two putative Xbp1s responsive elements. (J) Promoter constructs were then co-transfected in SH-SY5Y cells with the GLB1 (galactosidase beta 1) reporter gene (in order to normalize transfection efficiencies) and either empty vector (Ev, black bars) or Xbp1s (gray bars) cDNAs. Twenty-four hours after transfection, luciferase activity was measured (N = 12, analyzed by One-way ANOVA, Tukey's multiple comparisons test) then expressions of XBP1s and ACTB were analyzed as described in Methods.
Data are expressed as percent of control Ev/GLB1-transfected cells (taken as 100%) and are the means ± SEM of 4 independent experiments performed in triplicates. (K) The scheme (left panel) represents the PINK1 promoter construct (P2. 0∆) lacking the -1024/-1021 Xbp1s-responsive element. P2. 0 and P2. 0∆ promoter constructs were co-transfected with the GLB1 reporter gene and either empty vector (Ev, black bars) or Xbp1s (gray bars) cDNAs in SH-SY5Y cells. Twenty-four hours after transfection, luciferase activity was measured (N = 9, analyzed by One-way ANOVA, Tukey's multiple comparisons test) then expression of XBP1s and ACTB were analyzed as described in Methods (right panel). Data are expressed as percent of control Ev/GLB1-transfected cells (taken as 100%) and are the means ± SEM of 3 independent experiments performed in triplicates.
Electrophoretic mobility gel shift assay (EMSA) We performed EMSA by means of a commercial gel shift chemiluminescent EMSA assay kit (Promega, E3050). In brief, purified wild-type XBP1s recombinant protein (300 ng) or control nuclear extracts were pre-incubated in 1X Gel Shift Binding buffer (Promega, E3050) at 20°C for 10 min. When indicated, an excess (4 pmol) of unlabeled competitor oligonucleotides was added. Positive control of the experiment corresponds to a non-related labeled control DNA (20 fmol) added to the nuclear extract (data not shown). After this pre-incubation step, we added when indicated 20 fmol of double-stranded 5ʹ biotin end-labeled PINK1-derived oligonucleotides (forward: 5ʹ-GGATTTCTGAGTTCGAGGCCAGCCTGGTCT-3ʹ; reverse: 5ʹ-AGACCAGGCTGGCCTCGAACTCAGAAATCC-3ʹ) containing the delineated -1025-1021 XBP1s responsive element and incubated all the reactions at 20°C for 20 min.
Immunological complexes were revealed with adequate anti-rabbit or anti-mouse IgG-coupled peroxidase antibodies (Jackson ImmunoResearch, 111-036-045) by the electrochemiluminescence detection method (Roche Diagnostics S. A. S). Chemiluminescence was recorded using a luminescence image analyzer LAS-4000 (Raytest, Fuji) and quantifications of non-saturated images were performed with the FUJI Film Multi Gauge image analyzer software. Two µg of total RNA were reverse transcribed (GoScript Reverse Transcriptase; Promega, A5002) using oligo-dT priming then samples were subjected to real-time PCR by means of a Rotor-Gene 6000 apparatus (Qiagen), using the SYBR Green detection protocol (Roche Life Science). Specific primers (Eurogentec) for human, mouse and rat PINK1/Pink1, XBP1s/Xbp1s and housekeeping genes were designed with the Universal Probe Library Assay Design Center software (Roche Applied Science) and are listed in Table  S1.
TMRM intensity was quantified on Z-stack maximal projection images after thresholding, using ImageJ software (NIH)  [91]. The TMRM signal was also analyzed using the Novocyte TM flow cytometer (ACEA Biosciences Inc. ). TMRM was excited with the 543 nm laser line, and emission was performed at 600 ± 10 nm. SH-SY5Y neuroblastoma cells and primary cultured neurons were loaded for 30 min at 37°C with TMRM (5 nM). TMRM fluorescence from 10,000 cells was acquired and the median value was obtained using the NovoExpress TM software (ACEA Biosciences Inc. ). Cellular gating was set the same way in all measurements. Unstained-and FCCP-treated cells were used as controls.
Acknowledgments We wish to thank  Drs. T. Kawarai  and  P. St-Georges Hyslop  for providing PINK1 constructs. We sincerely thank  Dr. B. de Strooper  for providing us with PINK1-depleted cells and  Dr. J. Shen  for providing us control and PINK1 knockout mice brains. We thank the  Neuro-CEB (Pitié-Salpêtrière Hospital)  for providing us with human brain samples.
PolyA+ mRNAs were sequenced by Illumina Technology in HiSeq 3000 sequencers from the MGX-Montpellier GenomiX facility (https://www. mgx. cnrs. fr/ ; Montpellier, France). The four samples were sequenced in 2x150 bp reads with an average insert size of 350 bp and produced about 100 Millions of reads per sample. All data have been deposited on the SRA website (project ID PRJNA691737). The sequencing quality of each sample was evaluated using the FastQC software version 0. 11. 2  (32). Although the data were globally of very good quality, the reads were cleaned using the UrQt software  (33)  with parameter t=10 to remove low quality bases.
Differential expression analysis of genes To identify differentially expressed genes between cancer and normal tissues, we followed the protocol proposed by Pertea et al. (34). The reads were mapped using Hisat2  (35)  on the mouse reference genome version GRCm38. p6 downloaded on the NCBI website (https://www. ncbi. nlm. nih. gov/assembly/GCF_000001635. 26/). For all four samples, the overall alignment rate was about 94%. Transcripts were then assembled and quantify from the mapped reads using Stringtie  (36)  and the annotated gtf file from the mouse reference genome (Mus_musculus. GRCm38. 97. gtf). The differential expression analysis was performed on the read count produced by Stringtie using DESeq2  (37). It has to be noted that the analyses were performed using two replicates for each sample, which could prevent the identification of differentially expressed genes with very low fold changes and could also lead to some false positive detection32.
To prevent this issue we used a very stringent p-value threshold. In that respect, transcripts are classified as significantly differentially expressed if the p-value, after correction for multiple tests with the False Discovery Rate (FDR), is below 0. 01. To determine the number of genes differentially expressed between the two conditions, we retrieved the Ensembl ID of genes corresponding to the transcript ID using biomart  (38). Among the 16,548 differentially expressed transcripts identified, we were able to retrieve 8,386 genes identifiers.
Estimation of the transposable element (TE) environment of each mouse gene We downloaded the repeat-masker output file obtained on the latest version of the mouse genome assembly (http://hgdownload. cse. ucsc. edu/goldenpath/mm10/bigZips/). The file was parsed using the program onecode-to-find-them-all  (40)  with the --strict option to avoid false positive identification. In short, this tool allows to assemble each TE copy and provide their positions in the genome. For each annotated gene  (54, 067  genes), we determined all TE present inside the gene and in its 2-kb surrounding regions to cover the promoter region, and we computed the TE density and the TE coverage as proposed by Grégoire et al. (41). All genes were then clustered according to their level of TE density and TE coverage using the K-medoids algorithm as implemented in the pam() function of the R package  (42).
Expression analysis of transposable elements (TEs) To determine specifically the differentially expressed TE families between the two conditions, we used the TEtools pipeline  (43)  (https://github. com/l-modolo/TEtools). In this procedure, the reads were first aligned on all TE copies annotated in the mouse reference genome using Bowtie2  (44)  with the most sensitive option and keeping a single alignment for reads mapping to multiple positions (-very-sensitive option). The TE sequences were retrieved using the program one-code-to-find-them-all  (40). The read count was computed per TE family, adding all reads mapped on copies of the same family. The differential expression analysis of TE families between the two conditions was performed using DESeq2  (37)  on the raw read counts, using the Benjamini-Hochberg multiple test correction (FDR level of 0.
TE-initiated chimeric transcript detection We determined the presence of TE-initiated chimeric transcripts in our dataset by using the pipeline LIONS  (46). By providing the raw paired-end RNA-seq data for each condition, the genome sequence, the repeat masker output file and the gene annotation file (Mus_musculus. GRCm38. 97. gtf), this tool is able to identify transcripts originated in a TE sequence in each sample and gives information concerning their positions relative to annotated genes.
Few TE initiated chimeric transcripts in cancer condition but involving differentially expressed genes Since TEs are known to provide alternative promoters to genes, especially in cancer condition, we have investigated the presence of chimeric gene transcripts originated in TE sequences. Using the complete RNAseq dataset, we identified 189 TE-initiated transcripts. Among these 189 chimeric transcripts, 24 are found exclusively in the two tumor datasets, whereas 156 are found in either one of the tumor datasets but not in the two normal datasets. Among the TEs involved in chimeric transcripts, the majority corresponds to LTR retrotransposons and LINEs (18 elements over 24) (Table  3 ). We thus looked into the 24 chimeric transcripts expressed in the two tumor datasets in more details and we were able to determine that four of them are associated with no gene, four are associated with more than one gene and 16 are associated to a single gene.
To find out to which expression category these genes belong, we retrieved the ENSEMBL identifiers, which led to 23 genes. Among them, 11 are up-regulated in tumor condition, whereas three are down-regulated, the remaining nine do not show any expression divergence. This distribution is statistically different from what is observed when considering all genes (5,942 up-regulated genes, 2,444 down-regulated genes, and 45,681 genes with no differential expression on the total dataset; X-squared = 95. 727, df=2, p-value<2. 2e-16) meaning that there is an excess of differentially expressed genes among those presenting a transcription initiation inside a TE sequence. For example, the gene ENSMUSG00000031422 (mortality factor 4 like 2 (Morf4l2)), which is up-regulated in the tumor condition, contains a LINE element in its last intron which is responsible for the transcription of a chimeric sequence.
Gene functional classifications were determined using the PANTHER V14. 1 software and allowed us to determine that the majority of these genes are involved in binding (GO:0005488) and catalytic activity (GO:0003824), mainly located inside the cell (GO:0005623) and in processes corresponding to biological regulation (GO:0065007), metabolic process (GO:0008152), and cellular process (GO:0002376) (Supplementary Fig. S10 ).
Such genes have already been described as having a role in the development and progression of pancreatic cancer  (51, 52). Although we identified only one miRNA (miR155) known to be up-regulated in the human pancreatic cancer among our dataset, we also identified in the differentially expressed non-coding genes, three lncRNAs, Morrbid, Malat1 (both up-regulated) and Trp53cor1 (down-regulated), known to be implicated in several other cancers in human  (53) (54) (55). These results show that the mouse pancreatic model is indeed very representative of the human disease in term of gene expression. We then wanted to determine whether there may be an association between the expression divergence of genes between the two conditions and their neighborhood in TEs. For that, we classified each gene according to the proportion of TEs that can be identified in and around them.
Coupling constants (J values) are given in Hertz (Hz). HRMS spectra were recorded on a Bruker Maxis Impact QTOF Spectrometer. Single-crystal X-ray diffraction of 4o was selected with SuperNova, Dual, and AtlasS2 diffractometer, using CuKα radiation. The crystal was kept at 295 K during data collection. Using Olex2, the structure was solved with the ShelXT structure solution program using Intrinsic Phasing and refined with the ShelXL refinement package using Least Squares minimisation.
Acknowledgements This work has been financially supported by the  CNRS , the  Ministère de l'Enseignement Supérieur et de la Recherche  and the  Direction Générale de la Recherche Scientifique et du Développement Technologique (DG-RSDT)  at  MESRS (Ministry of Scientific Research, Algeria). We thank  Dr. M. Giorgi  for providing X-ray analysis of 4o.
It is a unified multi-tasking prediction tool that uses the 3D structure of proteins to predict ligandable binding for iPPIs and so-called interactability patches for epitope binding. We show that InDeep outperforms the state of the art of binding pockets detection methods and that our tool is especially efficient to detect iPPI and epitope binding sites. While remaining competitive on annotating the protein sequence with interactability, it also predict the spatial location of its putative partner. Our tool also enables tracking of these druggability and interactability scores for a given detected pocket along molecular dynamics trajectories. It is integrated in a PyMol  [38]  plugin (see Section D) for easy visualization of the predictions, making it a real toolbox for iPPI drug design. It is freely available at https://gitlab.
Data curation and splitting For training and assessing the 3D-FCN models, we have used the dataset that is presently accessible on the website of our database iPPI-DB (https://ippidb. pasteur. fr/targetcentric/). The dataset relies on two subsets: one contains Hetero-Dimeric complexes (HD interactions) and the other iPPI-bound protein complexes (PL interactions) where ligands bind one of the two partners at the interface within a HD complex (orthosteric inhibitors). These data were retrieved from the PDBe database  [39]  using a filtering procedure and different quality control checks. The procedure operates directly on a JSON file containing all available information within PDBe to select PDB structures accordingly. Ligands were selected as to be not more than 6 Å away from the interface and contain at least five heavy atoms.
Heavy atoms within compounds must be one the following (C, N, O, S, P, I, B, Br, Cl, F). For Xray structures and CryoEM structures, resolutions should be below 3. 5 Å. For Xray structures R Free -R Factor were below 0. 07. For CryoEM structures, FSC was below 0. 143. Once the selected PDB structures were downloaded, all PL structures were superimposed onto the corresponding HD complexes (sharing an identical UNIPROT ID) in order to identify orthosteric compounds competing for the same interface. Finally, we removed structures with alternative locations for residues at the interface within HD or PL complexes. Thus, we built a dataset of 2290 PL complexes and 6736 HD complexes for the training and the assessment of the 3D-FCN models. We have then split this data based on CATH  [40]  folds to avoid any structural overlap between our train, validation and test data sets.