dataesr/scientific-paragraphs-categorization huggingface dataset:
S3  J, upper panel). The protein complexes that assembled around CD6 OST bait proteins were identified by AP-MS and analyzed as described for the LAT OST bait. 18 interacting proteins showed a >10-fold enrichment, with a P value below 0. 005 in at least one of the three conditions of stimulation (Fig. 3  A and Data S2). Among the 18 high-confidence CD6 interactors, 7 showed interaction stoichiometry that increased or decreased at least twofold with a P value below 0. 05 following CD3 plus CD4 crosslinkage, compared with the nonstimulated condition (Fig. 3 B ). They include the ubiquitin-associated and SH3 domain-containing protein UBASH3A (also known as STS-2 or TULA;  Tsygankov, 2020) , the G protein-coupled receptor kinase (GRK) 6, SLP-76, GRB2, GRAP2, the ZAP70 PTK, and SHIP1. Six of the seven selected preys showed a transient pattern of binding that peaked 30 s after stimulation, whereas GRK6 showed maximal association to CD6 before TCR stimulation and decreased association thereafter (Fig.
The protein complexes assembling around CD5 OST bait proteins were identified by AP-MS and analyzed as described for LAT OST and CD6 OST baits. 173 interacting proteins showed a >10-fold enrichment, with a P value below 0. 005 in at least one of the four conditions of stimulation (Fig. 4  A and Data S2). Among them, four interactors showed interaction stoichiometry that increased at least twofold, with a P value below 0. 05 following CD3 plus CD4 cross-linkage, compared with the nonstimulated condition (Fig. 4 B ). They comprised the ankyrin repeat domaincontaining protein 13A (ANKRD13A), UBASH3A, and CBLB (Fig. 4, C  and D ). After the fold enrichment value was slightly relaxed, CD5 was also found to interact with CBL (Data S2), a finding consistent with the reciprocal presence of CD5 among high-confidence CBL interactors  (Voisinne et al.
In A and B, numbers shown in parentheses on the PCA1 and PCA2 axes indicate the percentage of overall variability in the dataset along each PC axis. Lat -/-CD4 + T cells were obtained using Lat fl-dtr maT-Cre mice  (Roncagalli et al. , 2014). signalosome of primary T cells in addition to that of CBLB further explains its negative regulatory function.
Flow cytometry Stained cells were analyzed using an LSRII system (BD Biosciences). Data were analyzed with FlowJo V10 (TreeStar). Cell viability was evaluated using SYTOX Blue or DAPI (49,6-diamidino-2-phenylindole, dihydrochloride; Life Technologies). The following antibodies were used: anti-CD90. 1 (HIS51), anti-CD4 (RM4-5), anti-CD8α (53-6. 7), anti-CD6 (REA311), anti-TCRβ (H57-597), anti-γδ TCR (GL-3), anti-CD3e (145-2C11), anti-CD44 (IM7), anti-CD25 (PC61), anti-CD45R (RA3-6B2), anti-CD5 (53-7. 3), anti-CD62L (MEL-14), and anti-human heparinbinding epidermal growth factor (HB-EGF; BAF259) from BD Biosciences, Miltenyi, R&D Systems, and eBioscience. Note that the human heparin-binding epidermal growth factor constitutes the receptor for diphtheria toxin. Mouse CD4 + T cell proliferation T cells were purified by immunomagnetic negative selection using the EasySep Mouse T Cell Isolation Kit and then stimulated with plate-bound anti-CD3 (145-2C11; Exbio) and soluble anti-CD28 (37-51; Exbio) antibodies or with phorbol 12-myristate 13-acetate (PMA) and ionomycin.
05% trifluoroacetic acid, at a flow rate of 20 µl/ min. After 5 min of desalting, the precolumn was switched online with the analytical C18 column (75-µm inner diameter × 50 cm, Acclaim PepMap C18, 2 µM; Thermo Fisher Scientific or inhouse packed with 3 µm Reprosil C18) equilibrated in 95% solvent A (5% acetonitrile and 0. 2% formic acid) and 5% solvent B (80% acetonitrile and 0. 2% formic acid). Peptides were eluted using a 10-45% gradient of solvent B over 60 min at a flow rate of 350 nl/min. The MS was operated in data-dependent acquisition mode with Xcalibur software. On the Q Exactive HFX or Q Exactive Plus MS, MS survey scans were acquired with a resolution of 60,000 or 70,000, respectively, and an AGC target of 3e6. The 12 or 10 most intense ions, respectively, were selected for fragmentation by high-energy collision-induced dissociation, and the resulting fragments were analyzed at a resolution of 15,000 or 17,500, respectively, using an AGC target of 1e5 and a maximum fill time of 22 ms or 50 ms, respectively.
Protein identification and quantification for interaction proteomics Raw MS files were processed with MaxQuant software (version 1. 5. 2. 8) for database search with the Andromeda search engine and quantitative analysis. Data were searched against Mus musculus entries of the UniProt KB protein database (release UniProtKB/Swiss-Prot+TrEMBL 2017_01, 89,297 entries including isoforms) plus the One-Strep-tag peptide sequence, and the set of common contaminants was provided by MaxQuant. Carbamidomethylation of cysteines was set as a fixed modification, whereas oxidation of methionine, protein N-terminal acetylation, and phosphorylation of serine, threonine, and tyrosine were set as variable modifications. Specificity of trypsin digestion was set for cleavage after K or R, and two missed trypsin cleavage sites were allowed.
The precursor mass tolerance was set to 20 ppm for the first search and 4. 5 ppm for the main Andromeda database search. The mass tolerance in tandem MS mode was set to 0. 5 daltons. Minimum peptide length was set to seven amino acids, and the minimum number of unique or razor peptides was set to one for validation. The I = L option of MaxQuant was enabled to avoid erroneous assignation of undistinguishable peptides belonging to very homologous proteins. Andromeda results were validated by the target decoy approach using a reverse database, with a false discovery rate set at 1% at both peptide sequence match and protein level. For label-free relative quantification of the samples, the match between runs option of MaxQuant was enabled with a match time window of 1 min to allow cross-assignment of MS features detected in the different runs after alignment of the runs with a time window of 20 min.
, 2019). Tryptic peptides were resuspended in 125 µl of 2% acetonitrile and 0. 05% trifluoroacetic acid. 5 µl of each sample was analyzed with nanoLC-MS on the Q Exactive HFX as described above, but with a longer separation gradient (10-45% of solvent B over 120 min). Raw MS files were processed with MaxQuant as described above, except that phosphorylation was not included as a variable modification for the database search. Calculation of copy numbers per T cell and of fraction of cell mass occupied by a protein Analysis of the proteome of long-term-expanded CD4 + T cells identified 6,261 protein groups. Protein entries from the Max-Quant proteinGroups. TXT output were first filtered to eliminate entries from reverse and contaminant databases. Cellular protein abundances were determined from raw intensities using the protein ruler methodology  (Wiśniewski et al.
22. 2 after removing genes with less than one total read across all samples. Normalization factors were computed on the filtered data matrix using the concept of variance stabilizing transformations. Pairwise comparisons were performed between treatment groups to obtain log 2 fold change, and adjusted P values were corrected for multiple testing using the Benjamini-Hochberg (BH) method and used to select genes with significant expression differences (q < 0. 05). Deletion of the CD5 and CD6 genes in primary CD4 + T cells CD4 + T cells were purified by immunomagnetic negative selection from mice constitutively expressing Cas9 and edited as described above. The CD5 or CD6 genes were ablated using the sgRNA specified in Table  S2. The efficiency of gene deletion was checked by flow cytometry on day 3 after transfection.
Acknowledgments We thank  A. Carmo  for discussion,  F. Angelis  for the construction of mice deficient in CD5 and  CD6 , and  F. Lozano  for C57BL/ 6-backrossed Cd5 -/-mice. This work was supported by  Centre National de la Recherche Scientifique ,  Institut National de la Santé et de la Recherche Médicale , the  European Research Council  under the European Union's  Horizon 2020 Research and Innovation Program  (grant agreement no. 787300 [BASILIC] to  B. Malissen ), Plan Cancer 2015/ Project  C15091AS  (to B. Malissen), the  MSDAvenir Fund  (to B. Malissen), the  SRecognite Project  ( ANR-Infect-ERA-2015  to  B. Malissen ), the  SUPER-BASILIC   Project  ( ANR-AAP-2018 , to  B. Malissen ), the  Investissement d'Avenir program PHENOMIN  ( French National Infrastructure for Mouse Phenogenomics ;  ANR-10-INBS-07  to  B.
Malissen ), the  GenomEast Platform (France Génomique Consortium ;  ANR-10-INBS-0009 ), and  ProFi (French National Infrastructure for Proteomics ;  ANR-10-INBS-08  to  O. Burlet-Schiltz ), and by fellowships from  Astra Zeneca-MedImmune  ( J. Celis-Gutierrez ),  Sanofi-Aventis  (J. Argenty), and  MSDAvenir  ( D. Mori ).
Data availability The MS proteomics data have been deposited in the Proteo-meXchange Consortium via the PRIDE partner repository (http://www. ebi. ac. uk/pride) with the dataset identifiers: PXD018526 (LAT interactome of long-term-expanded CD4 + T cells), PXD018527 (CD6 interactome of long-term-expanded CD4 + T cells), PXD018552 (CD5 interactome of short-termexpanded CD4 + T cells), and PXD018766 (proteome of longterm-expanded CD4 + T cells). RNA-sequencing data have been deposited in the Gene Expression Omnibus public database under accession no. GSE148721. Data S3 lists differentially expressed genes in CD4 + T cells of the specified genotype activated for 20 h with anti-CD3 plus anti-CD28 antibodies versus their unstimulated counterparts.
Data The cerebral signature of Amyotrophic lateral sclerosis (ALS) is primarily associated with motor cortex  [2, 3] , corpus callosum  [4] , corticospinal tract  [5]  and brainstem pathology  [6]. Precentral gyrus degeneration is a hallmark feature of ALS  [7] , but extrapyramidal  [8]  and extra-motor  [9]  involvement are also increasingly recognised  [10]. The hippocampal profile of ALS is poorly characterised and is seldom contrasted to disease-controls with other neurodegenerative conditions  [11]. Clinical and imaging data were acquired from 50 amyotrophic lateral sclerosis patients, 18 patients with Alzheimer's disease and 40 healthy controls using a standardised magnetic resonance imaging protocol  [1]. Diffusion tensor imaging data were processed to provide tract-wise fractional anisotropy (FA), radial (Drad) and axial (Dax) diffusivity values for the fornix and perforant pathway for each participant.
High resolution, T1-weighted imaging with a voxel size of 1 mm 3 was used for the estimation of total intracranial volumes (TIV), hippocampal segmentation and subfield volumetry. Raw volumetric data are available online at 'Mendeley Data; https://doi. org/10. 17632/d4crz2cg2x. 1'. Patients with amyotrophic lateral sclerosis underwent memory testing using the Rey Auditory Verbal Learning Test; Babcock Story Recall Test and the Rey-Osterrieth Complex Figure Test. Relevant demographic details  [12]  and memory performance are available online at 'Mendeley Data'; https://doi. org/10. 17632/d4crz2cg2x. 1 Memory performance in ALS is also presented in Fig. 1  using z-scores based on Greek population-specific normative data. The volumetric profile of the left and right hippocampi are shown in Figs.
Acknowledgments The authors gratefully acknowledge all the patients, their caregivers and families for participating in this research study. Dr Foteini Christidi  is supported by the  State Scholarships Foundation  (I. K. Y. ; Postdoctoral Support;  EP ANADEDBM/ESPA 2014e2020 ). Prof Peter Bede  is supported by the  Health Research Board  (HRB e Ireland;  HRB EIA-2017-019 ), the  Andrew Lydon scholarship , the  Iris O'Brien Foundation , the  Research Motor Neuron (RMN-Ireland) Foundation  and the  Irish Motor Neurone Disease Association (IMNDA).
Complete Genome Annotation Each complete genome was annotated to determine the three open reading frames and the length of the domains in ORF1 (methyltransferase, Y domain, papain-like domain, PPR, X domain, helicase and RNA dependent RNA polymerase). All possible ORFs were determined with ORF Finder. 2 Each ORF was then submitted to BlastP versus the UniProtKB/SwissProt database 3  to find corresponding sequences and identify ORF1, ORF2, and ORF3. ORF1 was aligned with the best Uniprot BlastP result and the matching domains were collected to create a GFF file which annotated each complete genome.
Identification of Insertion or Duplication in the Hepatitis E Virus Genome The annotated files were used to determine the length of each region in ORF1, ORF2, and ORF3 so as to identify strains with insertions. Analysis of the PPR took into account the fact that the length could vary from 183 to 315 nt, depending on the HEV clade. All sequences in each clade with longer than normal PPRs were considered to have insertions. The inserted segments were identified by aligning each complete genome sequence with the closest HEV sequence identified by BLAST on NCBI. 4  The origin of the inserted segment (human or HEV genome) was then identified by a BLAST on NCBI. The duplicated regions were determined by aligning them on the complete genome with MUSCLE. The sequences of the recombinant variants have been deposited in the Genbank database under accession numbers MF444083, MF444086, MF444119, MF444145, MF444152, and MN646689-96 (Supplementary Table  S1 ).
Characterization of Insertions/ Duplications The sequences of all the PPRs were identified with reference to the 11,938 sequences of Orthohepevirus A (including 338 complete HEV genomes) available in the Virus Pathogen Resource (VIPR) database. 5 Selected sequences were systematically searched to identify insertions so that they could be used, together with those identified by PacBio sequencing, for further analysis. The compositions of HEV PPR insertions/duplications were determined and their posttranslational modifications predicted by analyzing a range of parameters. Potential ubiquitination sites were identified using the BDM-PUB server 6  with a threshold of >0. 3 average potential score. Potential phosphorylation sites were identified using the NetPhos 3. 1 server 7  with a threshold of >0.
5 average potential score. Potential acetylation sites were identified using the Prediction of Acetylation on Internal Lysines (PAIL) server 8  with a threshold of >0. 2 average potential score. Potential N-linked glycosylation sites were identified using the NetNGlyc 1. 0 server 9  with a threshold of >0. 5 average potential score. Potential methylation sites were identified using the BPB-PPMS server 10  with a threshold of >0. 5 average potential score. Nuclear export signal (NES) sites were identified using the Wregex server 11  with parameters NES/CRM1 and Relaxed. Nuclear localization signal (NLS) sites were identified using SeqNLS 12  with a 0. 86 cut-off. The amino acid composition (proportions of amino acids), physico-chemical composition, and net load were analyzed with R. Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in a data set.
Features of Polyproline Region With Insertion or Duplication A search in the VIPR database identified eight additional recombinant strains with genomic rearrangement in the PPR: HQ709170 (HEV-3a) with an RPS17 fragment  (Shukla et al. , 2011) , strain JN564006 (HEV-3a) with an RPS19 fragment  (Nguyen et al. , 2012) , strains KC166952, KJ917704, KJ917720 and KJ917717 (all HEV-3f) with an ITIH2 fragment, a PPR + RdRp duplication, a TAT fragment and a PPR duplication   (  Lhomme et al. , 2014) , strain KC618402 (HEV-3c) with a PPR duplication  (Johne et al. , 2014) , and strain KT591534 (HEV-3f) with a PPR duplication not reported as a recombinant virus. Thus, we analyzed 13 PPR sequences with human gene fragment insertions and seven PPR sequences with duplications of HEV genome fragments (Supplementary Table  S1 ).
1c ). This led to improvements in kidney tissue and function as assessed by histology (Supplementary Fig. 2b ) and plasmatic urea levels (Fig. 1d ), respectively. Since increased cell proliferation might contribute to cyst formation, cell cycle progression was scored by anti-Bromodeoxyuridine (BrdU) staining, after a 2-hpulse with BrdU, and anti-phospho Histone H3 staining. The former labels S-phase cells and the latter mitotic cells. As shown in Fig. 1e-h , Tsc1 deletion caused a more than 20-fold increase in tubular cell proliferation. Surprisingly, S6k1 inactivation did not affect the proliferation rate of Tsc1-mutant cells. Taken together, our data indicate that it is possible to separate cell proliferation and cyst formation driven by mTORC1. In the absence of S6K1, mTORC1 hyperactivation still drives cell proliferation, but cyst formation is strongly prevented.
An average of 100 spheres pooled from three independent experiments were quantified for each genotype. Horizontal lines represent the mean ± SEM. c Volume and d mass were analyzed by optical measurement at G1 (2. 5 h after mitotic division) for the different genotypes. An average of 100 cells pooled from three independent experiments were quantified for each genotype. Horizontal lines represent the mean ± SEM. Source data are provided as a Source Data file. spindle, depending on the presence of neighboring cells. In fact, in Matrigel embedded cultures or in vivo, cells coordinate their division according to tissue polarity, orienting the spindle by communication through cell-cell junctions  40. Phoshoproteomic screening identifies Afadin as a S6k1 substrate. The published literature on S6K substrates does not readily suggest putative targets involved in mitotic spindle positioning and intercellular adhesion cues.
11f, g ). Moreover, the ability to form spheres with polarized cilium was reduced in cells expressing S1795E-Afadin (Fig. 8c, d ). These data suggest that Afadin phosphorylation impairs the adhesion system leading to defects in oriented cell division. Therefore, Afadin localization was determined in mitotic cells during sphere formation. E-Cadherin-based adhesions are the main found in tubular cells. As Afadin is involved in their regulation  51  , the relative localization of the two proteins was assessed by immunofluorescence. γ-Tubulin was used to determine the spindle poles in mitotic cells. In wild-type cells, Afadin was mainly at adherens junctions (AJ) in the two apicolateral domains of both mitotic and interphase cells (Fig. 9a  and insets). The tight-junction protein ZO-1 was also localized at the apicolateral domains (Supplementary Fig.
d Representative pictures of pre-cystic post-natal day 20 mice, immunolabelled with Afadin antibody to show the localization of the protein in the indicated genotypes. Scale bar, 10 µm. e Representative pictures of pre-cystic post-natal day 20 mice, immunolabelled with α-catenin, ZO-1 and DAPI to show the localization of the proteins in the indicated genotypes. Source data are provided as a Source Data file. leads to a harmonious overgrowth of the kidney without cyst formation. Ultimately, the concept that concomitant OCD disruption and hyper-proliferation are both necessary to develop cysts is in agreement with previous studies claiming that simple OCD loss or simple hyper-proliferation are not sufficient  33, 34, 65. The great efficacy of rapamycin derivatives in several mouse models of PKD raised hope for a cure for these devastating diseases  2, 3, 7, 66.
The primers used for Tsc1 mRNA were the following: forward (5′-AGC TCC GGA CCC TCC GAG AC-3′) and reverse (5′-AGC CGC TGC TCG GAT CAC CT-3′). The primer used for Afadin mRNA were the following: forward (5′-ACT CCC TCT ATG AAG TGC ATG T-3′) and reverse (5′-TAA GGA CGA ATC GAC CCT CTC-3′). Immunoblotting. Cells and tissues were lysed in NP-40 lysis buffer (20 mM Tris pH 8. 8; 138 mM NaCl; 5 mM EDTA; 2. 7 mM KCl; 20 mM NaF; 5% Glycerol; 1% NP-40-IGEPAL®; #CA-630; Sigma-Aldrich) supplemented with complete protease inhibitor and phosSTOP phosphatase inhibitor cocktails (Roche). Protein extracts were resolved by SDS-PAGE, transferred to PVDF membranes, and incubated with the primary antibodies. Uncropped and unprocessed scans of the most important blots are provided in the Source Data file. Human biopsies were obtained from TSC and non-TSC patients undergoing partial or total nephrectomy.
Whenever possible, normal tissue adjacent to tumoral tissue was resected and analyzed separately. This study was approved by the French national ethics committee (Comité de Protection des Personnes Number 19. 05. 27. 61541; national identification 2018-A03147-48). Uncropped western blots are provided in the Source Data file. Immunohistochemistry and immunofluorescence. Mouse tissues were fixed in 4% paraformaldehyde (PFA), embedded in paraffin and sectioned at 4 μm. Sections were deparaffinized and hydrated before being incubated in citrate buffer (pH6), then boiling for 3 min and then sub-boiling for 10 min. Sections were blocked in 3% normal goat serum, 1% BSA and 0. 1% Triton X-100 for 30 min-1h and then incubated with the primary antibodies (or lectins) overnight at 4 °C. Next, sections were incubated with biotinylated secondary antibodies and detected with the Vectastain Elite ABC kit (Vector) and DAB chromogen system (DAKO) For in vivo BrdU pulsing, mice were injected i.
Immunofluorescence slides were analyzed using an ApoTome2 microscope (Zeiss) or a Confocal TCS SP5 (Leica). ImageJ software (National Institutes of Health) or Imaris v 9. 2, ImarisXT, and Bitplane AG was used for quantification. Clearing and picture acquisition. Tissue clearing with CUBIC reagents was performed as described  72  , with some modifications. Briefly, mice were fixed by transcardial perfusion with 4% PFA, post-fixed at 4 °C for 6 h and included in agarose to be sectioned with a vibratome (Leica) sagittally at 1 mm. The sections were then incubated in CUBIC-reagent 1 at 37 °C for 1 week, washed thoroughly in PBS and then incubated at 4 °C for 30 days in primary antibody and DNA staining described in the text diluted in PBS containing 2% Triton X100 (PBS-2T), adding again the antibody after 2 weeks.
Images were deconvolved using Incucyte software and measurements of the angle of division were then performed relative to pattern orientation at anaphase onset using ImageJ software  38. Cystic index analysis. The cyst formation was quantified from sagittal sections of whole kidneys  75. Briefly, whole kidney sections were scanned using a Nanozoomer 2. 0 HT (Hamamatsu) after H&E staining; 4x images for each kidney section were selected. We used Image J to quantify the surface, expressed in pixels, and covered by cysts in each kidney. We used a cut-off of 2000 pixels as the minimum surface that a cyst should have to be included in the analysis. We next calculated the ratio between the obtained cystic area and the area of the entire kidney section and expressed it as the percentage of cysts for the given kidney.
Acquisitions were performed on a Ti inverted (Nikon) or an Axio Observer microscope (Carl Zeiss) at 37 °C, with 5% CO2 atmosphere, using a ×20 dry objective (NA 0. 5 phase), or a SFluor 20× objective (NA 0. 75 without phase ring) for the mass measurements. Images were acquired using MetaMorph (Molecular Devices). Images were acquired with a CoolSNAP HQ2 camera (Photometrics) for fluorescence or a PHA SICS camera (PHA SICS) for mass measurements. Images were analyzed with a custom-made MatLab program (MathWorks). Phosphoproteome enrichment and mass spectrometry. For phosphoproteomic analysis, cell lysates were prepared according to specifications from Cell Signaling Technology. Briefly, 2 × 10 8 cells of each genotype were starved for 2 h in Earl's Balanced Salt Solution (EBSS; #24010043; Gibco) and then rinsed with ice-cold PBS.