dataesr/scientific-paragraphs-categorization huggingface dataset:
When cloned and expressed, antibodies from clonotypes 120, 160, and 198 specifically bound all four genotypes of BKPyV, a property that they shared with the 41F17 antibody, and that was found in only one of nine other antibodies from the dataset that were tested (antibody 206; Fig  6A ). Clonotypes 120, 160, and 198 had identical heavy-and light-chain V-gene usage (IGHV4-39/IGKV3-11), which differed from that found in 41F17 (IGHV4-31/IGKV3-11). Interestingly, clonotypes 120, 160, and 198 were characterized by a proportion of >85% IgG within the clonotype, so despite the dominance of IgM in the overall BKPyV-specific repertoire, this cluster of broadly neutralizing antibodies was predominantly IgG. Binding avidity to VLPs was in the sub-nanomolar range, with antibodies 120 and 160 binding to all four BKPyV genotypes with an avidity similar to that observed for the 41F17 antibody (Fig  5C ).
In terms of neutralization, like 41F17, antibodies 120, 160, and 198 neutralized all four BKPyV genotypes when assayed in 293TT cells (Fig 5D, upper panels), whereas only antibodies 120, 198, and 41F17 were broadly neutralizing in immortalized renal tubular epithelial cells (Fig 5D, lower panels). The same approach was used to identify antibodies with similar properties to the 27O24 monoclonal; however, although the "nearest-neighbor" IgG antibody in our dataset (clonotype 256) specifically bound to more than one BKPyV genotype, it was not broadly neutralizing (data not shown). Epitope mapping against a panel of alanine-scanning mutants showed that five of seven genotype I-specific monoclonals bound to the BC-loop of the VP1 protein (Fig  6B ). In contrast, binding of antibodies 120, 160, and 198 was not affected by BC-loop mutations, whereas mutation of the E326 residue, which contributes to the 41F17 epitope  (26) , reduced binding of clonotypes 120 and 160  (Fig 6).
In addition, alanine scanning showed that antibody 304 did not bind to the variable BC-loop, but rather to a site that included the VP1 residue 175. This amino acid is not conserved between BKPyV genotypes, showing a D/E/Q polymorphism, and indeed, antibody 304 was not broadly neutralizing (data not shown). However, because antibody 304 was a single clone selected from a group of two closely related clonotypes (304. 1 and 304. 2) comprising 33 distinct antibodies, it is possible that another clone from this group may show broader neutralization and would therefore be a more effective partner for the antibody 120. In addition to the identification of broadly neutralizing antibodies, the availability of a large number of sequences in the SpecB dataset made it possible to explore the general properties of the BKPyVspecific BCR repertoire in KTx recipients.
Firstly, BKPyV-specific repertoires in all four patients were dominated by lambda light-chain antibodies, with a clear inversion of the kappa/lambda light-chain ratio between TotB and SpecB repertoires. Secondly, although significantly less diverse than the total B-cell repertoires observed in the same patients, BKPyV-specific repertoires were highly varied in all four patients, with many different VH-and VL-genes contributing to the repertoire. Estimates of the overall clonotype diversity obtained in patients 3. 1 and 3. 2 indicated that an individual's BKPyV-specific BCR repertoire consists of 200-700 distinct clonotypes, with a more precise estimate of 400-500 clonotypes in patient 3. 1, no doubt because of the better sampling achieved in this patient. These results imply that accurate measurement of an individual's antibody repertoire against a given antigen requires data from at least two and preferably three independent samples, with BCR sequences obtained for ~500 antigenspecific B cells per sample.
The third reproducible feature of the BKPyV-specific BCR repertoire was the high frequency of IgM antibodies, which confirms previous observations in healthy donors  (26). Surprisingly, the BCR repertoires mobilized by BKPyV-specific IgM and IgG were not identical, with significant differences in V-gene usage between IgM and IgG antibodies from the same individual (Fig 3D  and E ). An important caveat is that our estimates of repertoire diversity rely on the assumption that all the antibody sequences in the SpecB dataset were in fact specific for BKPyV. Unlike in preliminary experiments, where only four of 10 antibodies expressed from sorted B cells were BKPyV-specific (Fig  S2 ), all 10 of the antibodies with no sequence homology to known BKPyV-specific monoclonals that we expressed from the SpecB dataset bound specifically to BKPyV VLP, even when the antibody clonotype was represented by only two or three cells (Fig  6A  and C ).
Furthermore, two different approaches to assess potential contamination of the SpecB dataset with BCR sequences from non-specific B cells (re-analysis of the sorted cells by flow cytometry, and comparison between the SpecB and TotB repertoires within the same patient) indicated that 90% or more of SpecB sequences were specific. It is likely, however, that a proportion of the singletons (i. e. , antibody clonotypes only observed once in the SpecB dataset) are not BKPyV-specific antibodies. This will have a significant impact on estimations of the repertoire size because singletons are crucial for both the Chao1 and Jackknife1 diversity estimators. As the number of non-specific singletons increases, the number of "real" BKPyV-specific singletons decreases, as does the associated repertoire size, so our estimates for the size of the BKPyV-specific repertoire should be considered as an upper limit.
In the extreme case, in which all the singleton antibodies are non-specific, the repertoire size in patient 3. 1 would be equal to the 192 clonotypes represented by at least two B cells, so we can infer that the lower limit for an individual's BKPyVspecific antibody repertoire is ~200 clonotypes. Our estimate for the size of an individual's BKPyV-specific BCR repertoire (200-700 clonotypes) is therefore robust with respect to the presence of nonspecific singleton sequences in the dataset. Furthermore, our observations concerning the VH-gene usage and the size of the BKPyV-specific antibody repertoire must be interpreted with caution, because they are based on data from only two patients, who shared a rather specific clinical profile: kidney transplant recipients over 60 yr old who were receiving immunosuppressive therapy.
Patients and clinical samples Patients included in this study were transplanted in 2011 and 2012 and had previously been included in a prospective observational study. All patients gave informed consent authorizing the use of archived urine and blood samples for research purposes. Anonymized clinical and biological data for these patients were extracted from the hospital databases. The six patients in this study were diagnosed with BKPyV reactivation based on the detection of viruria >10 7 copies/ml correlated with an increase in serum neutralizing titer >1 log 10. A total of 17 PBMC samples (two to four per patient, with summary data available in the BioProject https://www. ncbi. nlm. nih. gov/bioproject/PRJNA633999) were collected at different timepoints (1-12 mo) after peak viral load and were cryopreserved in liquid nitrogen.
The effective concentration 50% (EC50) was calculated using GraphPad Prism software. Competition ELISA experiments were conducted to determine binding epitopes of different generated BKPyV-specific antibodies. Antibodies to test were biotinylated following the manufacturer's instructions (Thermo Fisher Scientific). In brief, 10 ng of BKPyV gI VLPs was coated into ELISA 96-well plates overnight. After washing, the plates were blocked with 5% powdered milk in PBS for 2 h. Nonlabeled antibodies were added into wells at 500 ng in 50 μl blocking buffer. After 30-min incubation at 37°C, biotinylated antibodies were added at the EC70 concentration for 1 h. Plates were washed, and specific binding was detected using peroxidase streptavidin (Jackson ImmunoResearch) diluted 1:2,500 in blocking buffer.
Monoclonal antibody binding avidity measurement The binding strength and kinetics of produced BKPyV-specific antibodies to each BKPyV VLP genotype were assessed using surface plasmon resonance. Briefly, anti-IgG antibody diluted to 20 μg/ml in sodium acetate buffer, pH 5. 0, was immobilized to a CM5 sensor chip (GE Healthcare) until the surface plasmon resonance signal reached 10,000 response units (RUs) using a Biacore T200 (GE Healthcare). BKPyV-specific monoclonal antibodies were then captured at 50 nM concentration. Different BKPyV VLPs including genotypes Ia, II, III, and IVc2 were injected with a series of threefold dilutions from 6 nM to 500 nM. Kinetic parameters and avidity were determined by non-linear regression analysis using Biacore Evaluation software.
5 mM MgCl 2 before fixing and staining in PBS/0. 5 mM CaCl 2 , 0. 5 mM MgCl 2 , 1% paraformaldehyde (EMS), and 10 μg/ml Hoechst 33342 (Thermo Fisher Scientific). The number and percentage of GFP + cells were quantified using a Cellomics ArrayScan VTI HCS Reader (Thermo Fisher Scientific). Neutralization curves were constructed using GraphPad Prism software.
B-cell single-cell RNAseq data analysis TotB and SpecB raw sequencing reads in FASTQ files obtained from Illumina sequencing were aligned with the human reference transcriptome (GRCh38) by the 10× Genomics cellranger count pipeline. Output matrices of filtered features of each B population were loaded separately into R version 3. 5. 1 using the Read10X function. Barcode matrices containing antibody-based hashtag oligonucleotide(HTO) count tables were added into each dataset, followed by a combination of parameters from the TotB and SpecB dataset to create a merged object. Log 10 HTO counts were graphed to set thresholds for positive and negative labeling by each antibody (Fig  S4 ). SpecB and TotB datasets were analyzed separately, as they were obtained in different sequencing runs, resulting in different HTO count thresholds.
In a standard setting, doublets are removed; however, our experiments were designed to obtain double HTO-labeled cells or doublets. Therefore, singlets and cells with wrong HTO combinations were eliminated from the dataset for transcriptome analysis, whereas for BCR repertoire analysis, cells with an unambiguous patient identifier were retained, even if the precise timepoint of the sample could not be determined. Another quality control step was applied to filter out low-quality cells, which had <500 and >3,000 genes detected, and <1,000 and >15,000 RNA counts. Cells with the >10% mitochondrial genes were also excluded. The remaining cells were then joined with the single-cell VDJ data to obtain a merged dataset with productive pairs of heavyand light-chain sequences in addition to RNAseq read counts.
The merged data were used for further gene expression analysis. Next, immunoglobulin variable genes were deleted from the dataset. To remove batch effects, the combined dataset was processed into the standard workflow of the Seurat Integration and Label Transfer pipeline  (69). Raw RNA counts were normalized in a log scale with a factor of 10,000 by default including the percentages of mitochondrial expression and the number of RNA counts as regression factors. Highly variable genes were identified by running FindVariableFeatures functions with the vst selection method, and the results were used as the input for principal component analysis. The RunPCA function returned 30 principal components (PCs) that were used to generate two-dimensional representations via the RunUMAP function. Using the top 15 PCs together with a resolution of 0.
B-cell single-cell VDJ data analysis Sequencing FASTQ files were submitted to the 10× Genomics cellranger vdj pipeline, the human VDJ reference (GRCh38), for sequence assembly and paired clonotype calling. For further analysis, data were extracted from the all_contig_annotations. json and filtered_contig_annotations. xls output files. Data were rearranged in R and OpenOffice Calc for repertoire analysis using the BRepertoire webtools (70), IMGT-V-Quest (71), and Immunarch (www. immunarch. com). Firstly, clonotypes were identified in BRepertoire based on heavy-chain V-and J-segment usage, then CDR3 homology initially using a Levenshtein distance of 0. 20, a slightly more relaxed threshold than the BRepertoire default value of 0. 18. Somatic hypermutations (SHM) were identified in heavy-chain V-regions in IMGT-V-Quest, using the cell barcode as the sequence ID, and then, the number of non-synonymous heavy-chain mutations was integrated into the dataframe so that the proportion of IgM clones and mean number of SHM could be analyzed by clonotype using the dplyr package in R.
Heavy-and light-chain V-gene usage, repertoire diversity, and repertoire overlap were calculated and visualized using Immunarch. The Chao1  (32)  and first-order Jackknife (33) estimators for repertoire diversity were independently calculated using the vegan package in R. In order to identify antibodies in our dataset with similar heavyand light-chain sequences to the previously described 41F17 and 27O24 monoclonals  (26) , antibodies with fewer than five heavychain SHM were excluded, and then, the heavy-and light-chain CDR3 amino acid sequences were concatenated and aligned in UGene (72) using a two-step procedure, first with Clustal Ω, then with Muscle to align conserved C-terminal sequences. Aligned sequences were clustered as a phylogenetic tree by neighbor joining. Antibodies with κ and λ light chains were analyzed separately, and trees were visualized using iTOL 5.
Acknowledgements This research was funded by grants from the  Agence de la Biomedecine  ( AO Recherche et Greffe 2017 ), the  Fondation Centaure  ( PAC10 ,  2017 ), the  Agence Nationale de la Recherche  (Project  ANR-17-CE17-0003 ), and the  R égion Pays de la Loire , project  DENISOVIRUS  (AO Paris Scientifiques, 2019). Patient recruitment was made possible by funding from the  CHU Nantes  ( AO Interne CHU Nantes 2011 ). The authors would like to thank  Chris Buck  for advice in setting up VLP and PSV purification protocols, and  Jacques LePendu  and  Antoine Touz  é for discussions at several stages of this work. We are most grateful to the  Genomics Core Facility GenoA , a member of Biogenouest and  France Genomique , and to the  Bioinformatics Core Facility BiRD , a member of  Biogenouest and Institut Français de Bioinformatique (IFB)  ( ANR-11-INBS-0013 ), for the use of their resources and their technical support.
1B ). The data indicate that the NEDD8 pathway is part of the arsenite stress response. Under these conditions, we found by imaging on GFP-G3BP1 cells (Fig. 1, C  and D ) and on endogenous TIA1 (fig. S1C ) that NEDP1 KO accelerates both the formation and elimination of SGs. Similar results were obtained using heat shock as another inducer of SG formation (fig. S1D ). We found that the effect of NEDP1 KO on SG dynamics is not due to changes in the levels of SG proteins (fig. S1E ) but due to the increase in protein NEDDylation, as inhibition of the NEDD8 pathway with the inhibitor of NAE (NAEi; MLN4924) reversed the NEDP1 phenotype in SG disassembly (Fig. 1E ). In addition, we assessed the effect of transient inhibition of NEDP1 on SG dynamics. For this, we transiently expressed an anti-NEDP1 Nb (Nb9) that displays nanomolar binding affinity and specifically inhibits NEDP1 activity  (53).
The coordinated action of conjugating and deconjugating enzymes such as NEDP1 ensures NEDD8 recycling, preserving the so-called free pool of NEDD8. The observed decrease in NEDDylation upon NEDP1 KO is most likely an indirect consequence of blocking the release and recycling of NEDD8 into the free pool. The decrease in free NEDD8 available for conjugation would subsequently reduce the NEDDylation of substrates that are regulated by other deconjugating enzymes than NEDP1. Thus, the observed decrease probably indicates NEDDylation events that are not directly controlled by NEDP1  (32). In contrast, increase in NEDDylation indicates proteins that are direct NEDP1 substrates. By comparing our dataset with previous proteomic analysis that defined the SG proteome (37), we found as potential NEDP1-dependent NEDD8 substrates several SG proteins, such as the core protein G3BP1; translation initiation factors EIF4A1, EIF3D, EIF3E, EIF3I, and EIF3L; and ribonucleoproteins HNRNPA1, HNRNPD, HNRNPH1, and HNRNPK (fig.
After mounting with ProLong Gold Antifade reagent (Molecular Probes), images were acquired with a Zeiss Axio Imager Z2 microscope with a 40× Plan Neofluar 1. 3 NA (oil) or a 63× Plan Apochromat 1. 4 NA oil objective and a scMOS ZYLA 5. 5 camera controlled by the Meta-Morph software (Universal Imaging, Roper Scientific). Images were treated with ImageJ or Fiji.
Live-cell microscopy For live-cell microscopy, images were acquired using a 40× LUCPLFLN 0. 6NA RC2 lens on an Inverted Olympus IX83 microscope controlled by MetaMorph software and equipped with a fullenclosure environmental chamber heated to 37°C with 5% CO 2 and a 1 ZYLA 4. 2 MP scMOS camera. Frames were recorded every 3 min over 1 hour for SG assembly and every 30 min for 16 hours for SG recovery. Parental and NEDP1 KO U2OS cells were followed during the same time lapse. Images were imported as a sequence and analyzed using the Fiji software. Fiji was also used for brightness adjustment, cropping, and creating scale bars and insets.
Microtubule dynamics For the time-lapse acquisition of EB1-mcherry signal, parental and NEDP1 KO U2OS cells stably expressing EB1-mcherry were imaged with a Nikon inverted microscope coupled to the Andor Dragonfly spinning disk. Images were acquired using an EMCCD iXon888 Life Andor camera (pixel size: 0. 11 μm) with a 100× oil-immersion objective (Plan Apo lambda 1. 45 NA 0. 13 mm WD). Images were acquired every 0. 3 s during 45 s at 37°C in a thermo-regulated atmosphere. Signal was then tracked in each individual cell using Matlab and the PlusTipTracker  (75)  open source software package with the following parameters: σ1 = 1, σ2 = 4, K = 3, search radius range = 1 to 5 pixels, minimum subtrack length = three frames, maximum gap length = 30 frames, maximum shrinkage factor = 1. 5, maximum angle forward = 30°, maximum angle backward = 10°, and fluctuation radius = 2 pixels.
0; exclusion list size, 500; exclusion duration, 40. 0; exclusion mass width relative to mass; exclusion mass width, 10 parts per million (ppm)]. MS/MS spectra were evaluated using SEQUEST 3G and the SORCERER 2 platform from Sage-N Research (v4. 0, Milpitas, CA). Searches were performed against the most recent update of the National Center for Biotechnology Information (NCBI) human database with mass accuracy of ±50 ppm for precursor ions and 1 Da for product ions. Results were filtered with mass accuracy of ±5 ppm on precursor ions and the presence of the intended motif (K-ε-GG). LTQ-Orbitrap Velos was used (Thermo Fisher Scientific). A 5% default false-positive rate was used to filter the SORCERER results.
Imaging of C. elegans For imaging of SGs, GFP-GTBP-1-expressing young adults (L4) were placed on a fresh NGM-seeded plate, and for heat shock, plates were incubated at 30°C for 4 hours. For the arsenite treatment, unseeded NGM plates were soaked with sodium arsenite to achieve a final concentration of 10 mM. Arsenite-coated plates were then seeded with bacteria, and young adults were placed on these plates for 2 hours. After treatment, animals were mounted on 2% (w/v) agarose pads and immobilized using 25 mM levamisole in M9 buffer. Images of the anterior region of the worm consisting of pharynx were captured using a fluorescence microscope (Zeiss AxioImager Z2) with a Plan-Apochromat 100×/1. 40 oil objective using ZEN software (version 3. 4. 91, Blue edition). Representative images are presented following maxproject of Z-stacked images using maximum intensity projection type with Fiji ImageJ software (version 2.
Swimming behavior (motility) assay The protocol described in  (65)  was used. Young adults (L4) were treated overnight with 5 mM paraquat (oxidative stress) in NGMseeded plates. Three worms were placed in a drop of 5 μl of M9 buffer inside the ring drawn using a hydrophobic PAP pen on microscopic slide. Movies were recorded at 22 frames/s for 30 s and analyzed with the CeleST software in Matlab R2019b (version 9. 7. 0. 1216025; MathWorks Inc. , Natick, MA, USA). Worms were randomly selected for analysis, and the number of body waves initiated from either the head or tail per minute is presented as wave initiation rate. Swimming rates from three repeats are presented.
Statistical analysis Leica LAS (FRAP analysis), SEQUEST 3G and SORCERER 2 (mass spectrometry analysis), GraphPad Prism, PlusTipTracker, ImageJ, and Cytoscape were used. For SG assembly/disassembly process, about 100 cells randomly selected per condition were scored for the presence of SGs. Other Supplementary Material for this manuscript includes the following: Movies S1 to S11 Data S1 and S2.
1) Agreement between scoring obtained from different MR acquisition protocols The scores obtained from the inventory tool were compared when images were from a basic or an advanced MR imaging protocol. More specifically, the agreement between WMH scores obtained with 3D or 2D Fluid-attenuated inversion recovery (FLAIR) images, CMB scores obtained with T2* or SWI, or dPVS scores obtained with T1 or T2weighted images, was analyzed. For WMH, 40 randomly selected 3D FLAIR images from Cohort 1 were re-sampled to 2D FLAIR images, and then the original 3D images and the 2D images re-sampled from the 3D images were evaluated by rater 1 during two separate sessions at 4 weeks interval. Similarly, for CMB, data from 83 patients from Cohort 1 who had both SWI and T2* images were also analyzed by rater 1 in two separate 4-week sessions.
Images were then displayed using the ITK-SNAP 3. 0 software (Cognitica, Philadelphia, PA, USA) and assessed both by rater 1 and rater 3. Rater 1 also analyzed the same data a second time after a 4-week interval. Each rater was blinded to the other rater's ratings. Before these independent rating sessions, the two raters had together training sessions on 10 randomly selected patients, and cases of disagreement were solved by discussion. Appropriate guidance for using the inventory tool was prepared based on this experience. For additional validation, 40 patients were also randomly selected from Cohort 3 and assessed by rater 2 and rater 4 separately. Similarly, 40 patients were randomly selected from Cohort 2 and assessed twice by rater 1 for intrarater agreement. We also built a mixed database with 14 patients from Cohort 1, 13 patients from Cohort 2, and 13 patients from Cohort 3, who were randomly selected from the three cohorts.
To search for an independent association between the inventory tool scores and the different clinical features, multivariate regression analysis was performed with statistical significance set at a probability value of < 0. 05 (Model 2: all imaging items included simultaneously in the model without selection). Finally, the models obtained after selecting the significant items from the CADA-MRIT for predicting each clinical outcome were compared to the predictive models obtained using a global score as the total CSVD score. 31  All analyses were done by R version 3. 6. 2 or SPSS 22. 0.