dataesr/scientific-paragraphs-categorization huggingface dataset:
Cross-species chaperone communication reveals additional specialization of eukaryotic J-proteins for Hsp70-based protein disaggregation Our data show that class-specific specialization among eukaryotic J-proteins restrict the aggregate sizes that can be solubilized by the Hsp70 system. We reasoned that the less specialized class A or class B bacterial J-proteins maybe able to override this selectivity when combined with the human Hsp70-based disaggregase, leading to increase of disaggregation activity. We first assessed the cross-species communication between bacterial J-proteins and human Hsp70 system. Both bacterial DnaJ and CbpA cooperated with human HSPA8 and HSPH2 to refold heat-denatured, but predominantly monomeric luciferase  (Nillegoda et al. , 2015)  (Figure  4A  scheme, Figure  4 -figure  supplement 2A ).
Sequence extraction and pre-processing Multiple sequence alignments (MSA) for class A and B J-proteins were built separately as follows: for each class, we collected a seed with curated and manually annotated sequences and aligned them using MAFFT  (Katoh et al. , 2002). Sequences in the class B J-protein seed comprised the J-domain, the GF region and both CTDs. Sequences in the class A J-protein seed additionally contained the characteristic zinc-finger domain. We then used HMMER  (Finn et al. , 2011)  to build a hidden Markov model and scan the union of the Uniprot and Swissprot databases to extract homologues for both sub-families. The retrieved sequences were then filtered by removing all hits containing more than 20% gaps. To ensure that no class A sequences were present in the class B alignment (and viceversa), we further filtered the two datasets as follows: all complete and unaligned sequences from the two MSAs were retrieved; from the class B MSA, we discarded all instances whose complete sequences contained the characteristic zinc-finger (ZF) CxxCxGxG motif.
Generation of phylogenetic trees Phylogenetic trees were built using the RaxML software suite  (Stamatakis, 2014). To decrease their size, MSAs for class A and class B proteins were first pruned, retaining only sequences with maximum 90% identity. Phylogenetic trees were then computed with a standard protocol (20 maximum likelihood searches, 100 bootstraps) and the best tree was returned. We observed that support values  (Yang and Rannala, 2012)  were generally low for the interior branches, but the overall phylogenetic separation was robust, reproducing a coherent phylogeny at large scale.
Protein structure preparation The same structures and models for human (DNAJA1, DNAJA2, DNAJB1, DNAJB4) and C. elegans (DNJ-12, DNJ-13) proteins as in  Nillegoda et al. (2015)  were used. For the other proteins studied, three dimensional structures were available and used for the following: J-domains of Sis1 (PDB ID: 2o37), E. coli class A (PDB ID: 1xbl), E. coli class B (PDB ID: 3ucs), human DNAJB8 (PDB ID: 2dmx), human DNAJB2 (PDB ID: 2lgw) and CTD of Sis1 (PDB ID: 1c3g) and Ydj1 (PDB ID: 1nlt, 1xao). The structure of the CTD dimer of Ydj1 was built using the structure of the CTD monomer (PDB ID: 1nlt), which was superimposed twice on a crystal structure containing the dimerization site (PDB ID: 1xao) by using PyMOL (http://www. pymol. org). For the remainder of the proteins studied, no crystal or NMR structure was available.
Afterwards, the three residues in the model structure were back-mutated using the mutagenesis tool in the PyMOL software. This model was then used as a template structure in the Swiss-Model webserver to slightly adapt the side chains in the mutated region. Only in the case of the Sphingomonas sp (strain SKA58) DnaJ (UniProt accession: Q1NCH5) was the model of Acetobacter aceti 1023 DnaJ (UniProt accession: A0A063 Â 4A7), which was built using the Ydj1 model, used as a template because the less conserved loop around the Zn 2+ binding region was modeled better for Zn 2+ ion binding than when the Ydj1 model was used. In the case of the Bordetella pertussis (UniProt accession: Q7VVY3) class A CTD, the sequence alignment was manually adapted to enable the modeling of the C-terminal region. For this purpose, a multiple sequence alignment of the four gamma and the beta bacterial sequences and the yeast sequence (template structure) was considered using the software DeepView  (Guex et al.
, 2009). A DeepView project with the adapted alignment was uploaded to the Swiss-Model webserver. For the class B CTDs, the Swiss-Model webserver was used to find a template structure and, if multiple templates were found, the one with the highest sequence identity to the target structure was chosen and then, in the case of more than one structure for this sequence, the corresponding structure with the highest QMEAN4 score. For the following class B CTDs (UniProt accession numbers), the template structure 3lz8. B was used: P36659, P63262, W9BQH2, J7RE62, F4JY55. The PDB ID 3lz8. A was used as a template structure for the following class B CTDs (UniProt accession numbers): A0A0D7FE35, Q1NEX3, M1ZLZ3, O75953. For the Type B CTD of A0A063XA16, the structure with the PDB ID 4j80. A was used as a template.
The dimer structure of Sis1 was built by superimposing the crystal structure of the monomer (PDB ID: 1c3g) twice on the 19 C-terminal residues of the crystal structure of the JB1 dimer (PDB ID: 3agz). For the class A J-domain of Acetobacter aceti 1023 DnaJ (UniProt accession: A0A063 Â 4A7), the structure with the PDB id 4j80 was chosen, and for the Sphingomonas sp (strain SKA58) DnaJ (Uni-Prot accession: Q1NCH5) and the ATJ3 (UniProt accession: Q94AW8), the structure with PDB ID 4rwu was chosen as the template. In the case of DNAJA4 (UniProt accession: Q8WW22), the structure with PDB ID 2lo1 was taken as the template. For all other class A J-domains, the structure with the PDB ID 1xbl from E. coli was used as the template. For the class B J-domains, the following templates were used (UniProt accession: PDB ID of template structure): A0A063XA16:4j7z, Q1NEX3:2dmx, M1ZLZ3:2yua, F4JY55(At5g25530):2m6y, O75953(DNAJB5):4wb7, O75190 (DNAJB6):4j7z.
For all other class B J-domains, the E. coli structure with the PDB id 3ucs was used. The structures were prepared by adding polar hydrogen atoms to the protein structures with WHATIF5  (Vriend, 1990). The electrostatic potential of each protein was calculated by numerically solving the linearized Poisson-Boltzmann equation with UHBD  (Madura et al. , 1995). Electrostatic potential grids with 250 3 grid points with 1 A ˚spacing were used for all proteins. The relative dielectric constants of the solvent and the protein were set to 78. 0 and 4. 0, respectively, and the dielectric boundary was defined by the protein's van der Waals surface. The ionic strength was set to 50 mM at a temperature of 300 K, with an ion exclusion radius (Stern layer) of 1. 5 A ˚. The protein atoms were assigned OPLS atomic partial charges and radii  (Jorgensen et al.
, 1996). All class A J-domain structures were superimposed on the DNAJA2 J-domain. The class A CTDs were superimposed on the lower CTD-II domain of DNAJA2. All class B J-domain structures were superimposed on the DNAJB1 J-domain. The class B CTDs were superimposed on the upper CTD-I domain of DNAJB1. All structures were superimposed with the alignment tool of the PyMOL software. The similarity of the calculated electrostatic potentials of the superimposed structures was computed using the PIPSA (Protein Interaction Property Similarity Analysis) software  (Wade et al. , 2001). The resulting distance matrix was used for a Ward's clustering. Only for Type A CTDs was an average-clustering used, but this yielded similar results to the Ward's clustering. For the local PIPSA analysis, a center and a radius were defined as follows.
For the PIPSA analysis of the metazoan JDs, average-clustering was applied. All metazoan JD structures were superimposed on the DNAJB1 JD and a sphere with a radius of 25 A ẘas set to cover the region around a-helix I and IV and the RRR mutation site of DNAJB1 RRR. For the Brownian Dynamics simulations, the SDA software  (Martinez et al. , 2015)  was used with the same conditions and the same clustering procedure for the docked J-domain as described in our previous study  (Nillegoda et al. , 2015). The docked cluster representatives were used to calculate the average Euclidean distance between their center of geometry and the center of geometry of the previously docked JD DNAJB1 (cluster one and two) to the CTD DNAJA2  (Nillegoda et al. , 2015). The CTD of E. coli used for the docking simulations was superimposed on the CTD DNAJA2 before carrying out the docking simulations.
S. cerevisiae strains were grown in yeast extract-peptone-dextrose (YPD) media at indicated temperatures using standard methods. Log phase cultures in YPD media obtained at 30 ˚C. Sis1 depletion strain tet07-sis1 (MATa; his3-1; leu2-0; met15-0; pSIS1::kanR-tet07-TATA URA3::CMV-tTA) was obtained as kind gifts from Dr. D. Cyr (University of North Carolina, USA). For Sis1 depletion, tet07-sis1 cells (control: tet-off cells) were grown overnight in YPD media, diluted back to OD600 = 0. 05 in YPD containing 10 mg/ml doxycycline and grown for 20 hr. Log phase cells for experiments were obtained by diluting cells back to OD600 = 0. 05 and allowing three cells divisions in fresh doxycycline containing media. ydj1 deletion strain  (VCY010, MATa; his3D1; leu2D0; lys2D0; ura3D0; ydj1D::kanMX4)  was derived from BY4742.
HeLa cells were obtained from American Type Culture Collection (ATCC-CCL2; RRID:CVCL_0030) were grown in DMEM (Gibco, Thermo Fisher Scientific, UK) supplemented with 10% (v/v) FBS at 37 ˚C in 5% CO 2. Mycoplasma contamination of the HeLa cell culture was tested negative with Look- Out Mycoplasm Detection kit (Sigma-Aldrich; MP0035, MO, USA). Plasmids pcDNA5/FRT/TO V5-DNAJA2, V5-DNAJB1, V5-DNAJB2a and V5-DNAJB8 were kind gifts from Harm Kampinga (University of Groningen). Plasmid transfections were carried according to standard protocols using Lipofectamine 2000 (Thermo Fisher Scientific, UK). siRNA transfections were performed according to standard protocols using DharmaFECT one transfection reagent (Dharmacon, CO, USA). HeLa cells were transfected with 50 mM siRNA smartpools (Dharmacon onTarget Plus) against DNAJA2 (Dharmacon, L-012104-01), DNAJB1 (Dharmacon, L-012735-01) or scrambled non-targeting siRNA (Dharmacon, D-001810-10) for 72 hr.
Acknowledgements We thank  Holger Lorenz  and  Aliakbar Jafar Pour  ( ZMBH Imaging Facility, Heidelberg University ) for their support with confocal microscopy and image processing. We are grateful to  Matthias Mayer  for critically reading the manuscript and  Vidyadhar Nandana  for technical and experimental support. This work was funded by the  Deutsche Forschungsgemeinschaft  ( SFB1036 ,  BU 617/19-3  to BB),  Alexander von Humboldt Foundation Postdoctoral Fellowship (NBN) ,  German Federal Ministry of Education and Research (BMBF) Virtual Liver Network  ( 0315749  to RCW),  EU   FEP Flagship Programme Human Brain Project  ( 604102  to RCW),  Klaus Tschira Foundation  (AS and RCW),  French Agence Nationale de la Recherche  ( ANR-14-ACHN-0016  to AB) and the  Swiss National Science Foundation  (http://www.
Oligopaint libraries Oligopaint libraries were constructed from the Oligopaint public database (http://genetics. med. harvard. edu/oligopaints). All libraries consisted of 42mer sequences discovered by OligoArray2. 1 run with the following settings: -n 30 -l 42 -L 42 -D 1000 -t 80 -T99 -s 70 -x 70 -p 35 -P 80 -m 'GGGG;CCCC;TTTTT;AAAAA' -g 44. Oligonucleotide for libraries 1-18 and BX-C were ordered from CustomArray (Bothell, WA). The procedure used to synthesize Oligopaint probes is described below. Chr3R-69 borders oligonucleotides were purchased from MYcroarray (Ann Arbour, MI). Oligopaint probes for this library were synthesized using the same procedure as for the other libraries except for the initial emulsion PCR step. Secondary, fluorescently-labeled oligonucleotides were synthesized by Integrated DNA Technologies (IDT; Coralville, IA for Alexa488) and by Eurogentec (Angers, France for Cy3b).
Image acquisition and post-processing of 3D-SIM data Samples were prepared as described above and mounted on an OMX V3 microscope (Applied Precision Inc. ) equipped with a 100X/1. 4 oil PlanSApo objective (Olympus) and three emCCD cameras. 405 nm, 488 nm and 561 nm excitation lasers lines were used to excite DAPI, Alexa488 and Cy3b, respectively. Each channel was acquired sequentially. A transmission image was also acquired to control for cell morphology. For each channel, a total of 1455 images made of 97 different Z-planes separated by 125 nm were acquired, in order to acquire a stack of 12 μm. Three different angles (60°, 0° and +60°) as well as five phase steps were used to reconstruct 3D-SIM images using softWoRx v5. 0 (Applied Precision Inc. ). certified by peer review) is the author/funder.
3D nuclei segmentation from 3D-SIM data 3D-SIM images were analyzed employing homemade software written in Matlab. In order to identify nuclear shells, nuclei are first segmented by manually selecting rectangular regions of interest (ROIs) of the DAPI signal in the XY-plane and keeping all the Z-planes and then a low-pass filter is applied to the DAPI intensities so that only the large-scale information (i. e. nuclear shape) is kept. For each plane of the 3D ROIs, an intensity threshold is computed as described by Snell et al. 51  in order to distinguish voxels inside or outside the nucleus. The average intensity threshold calculated from the threshold of the single planes is used to identify the complete nuclear shell. After nuclei segmentation, foci were identified by calculating, for each channel separately, the maximum entropy threshold of the fluorescence intensities in the 3D ROIs.
Image acquisition of two-color dSTORM data Super-resolution experiments were carried out in a custom-made inverted microscope employing an oil-immersion objective (Plan-Apocromat, 100x, 1. 4NA oil DIC, Zeiss) mounted on a z-axis piezoelectric stage (P-721. CDQ, PICOF, PI). For 2D imaging, a 1. 5x telescope was used to obtain a final imaging magnification of 150 fold corresponding to a pixel size of 105 nm. Three lasers were used for excitation/photoactivation: 405 nm (OBIS, LX 405-50, Coherent Inc. ), 488 nm (OBIS, LX 488-50, Coherent Inc. ), 561 nm (OBIS, LX 561-50, Coherent Inc. ), and 640 nm (OBIS, LX 640-100, Coherent Inc. ). Laser lines were expanded, and coupled into a single beam using dichroic mirrors (427, 552 and 613 nm LaserMUXTM, Semrock). An acoustooptic tunable filter (AOTFnc-400.
) was expanded twice and directed certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. towards the objective lens by a dichroic mirror (z1064rdc-sp, Chroma). The reflected IR beam was redirected following the same path than the incident beam and guided to a CCD detector (Pixelfly, Cooke) by a polarized beam splitter cube (PBS). Camera, lasers and filter wheel were controlled with software written in Labview  52. For image acquisition, on average of 30,000 frames (per detection channel in twocolor acquisitions) were recorded at a rate of 50 ms/frame. Continuous excitation and activation was employed for all fluorophores employed in this work with powers as follows: 1 kW cm -2 at 641 nm (for AF647), 0. 8-1. 2 kW cm -2 at 561 nm (for mEos2), and 0-0.
Post-processing and Analysis of two-color dSTORM data Unless stated otherwise, all homemade software and routines were developed in Matlab. Before further processing, super-resolution image quality was quantitatively assessed by using NanoJ-SQUIRREL  54. Next, single-molecule localizations were obtained by using Multiple Target Tracing (MTT)  55. Localization coordinates were further processed using SMLM_2C, a custom software written in Matlab  32. Fluorescent beads were used to correct for drift and chromatic aberrations. Lateral drift was corrected with 5 ± 3 nm precision as previously described  52. Chromatic aberration correction was performed as described in  32, 56   Clustering of localizations was performed as described in Cattoni et al. 53. Colocalization of single-molecule detections was performed by using a custom implementation of the Coordinate-based colocalization (CBC) analysis  57  adapted for whole-cell automated analysis  32.
Analysis of one-color dSTORM data Single-molecule localizations are converted into a Voronoi diagram using a modified version of the Voronoi tesselation algorithm of Levet et al. 33. Compartment segmentation is directly calculated from the Voronoi diagram using three steps. First, densities of each polygon are calculated as the inverse of their area. Densities are then thresholded using the general criteria of Levet et al. 33. Using this criterion, in which the threshold is determined by the average localization density, a random distribution of localizations did not provide any segmented polygon. Finally, polygons that have a density higher than the threshold and that are touching each other are merged to define the compartment outline. Compartment sizes are obtained by interpolating each segmented compartment on a grid of 5 nm size and calculating their equivalent diameter using standard morphological operations.
Acknowledgements We thank  Brian Beliveau ,  Hien Hoang  and  Ting Wu  for help with oligopaints design. This research was supported by funding from the  European Research Council  under the 7 th  Framework Program  ( FP7/2010-2015 ,  ERC  grant agreement  260787  to M. N. and  FP7/2007-2013 ,  ERC  grant agreement  609989  to M. A. M-R. ). M. A. M-R and G. C. acknowledge support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  676556. This work has benefited also from support by the  Labex EpiGenMed , an  «Investments  for the future» program, reference  ANR-10-LABX-12-01 , the  Spanish Ministry of Economy and Competitiveness  ( BFU2013-47736-P  to M. A. M-R), and from  'Centro de Excelencia Severo Ochoa   2013-2017', SEV-2012-0208  to the CRG.
Data availability The Hi-C data reported in this study is available at the Gene Expression Omnibus (GEO) repository under accession code GSE104961. Computer code and other data that support the findings of this study are available from the corresponding author upon reasonable request. certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted October 24, 2017. ; https://doi. org/10. 1101/159814 doi: bioRxiv preprint.
Out of the 35 sequences in the recipient mice, all were found in the progeny IgE data sets, 32 were found in the progeny IgG1 dataset, and 33 in the precursor DP IgG1 MBC. To better illustrate these findings, the presence and frequency of the 20 more frequent high affinity CDR3 clones in DP donor cells and their progeny in one representative mouse is shown (Fig. 4a, b ). All 20 high affinity CDR3 clones (Fig. 4a ) were present in the donor DP IgG1 MBC and in their IgE and IgG1 progenies (Fig. 4b ). Furthermore, donor and progeny VDJ sequences sharing the same CDR3 amino acid sequence tended to share the same CDR2 and CDR1 sequences (Supplementary Fig. 8 ). Clonal relationships were analysed at the nucleotide level. All nucleotide-encoding sequences for the 35 high affinity CDR3 were retrieved and their frequency in the DP donor and DP-progeny data sets was determined (Supplementary Dataset 1a).
For each IgE and IgG1 progeny data set, we determined the percentage of sequences present in the donor DP data set. We used a cutoff of 100 reads (cc) per sequence in the progeny data set (Supplementary Dataset 1b, c). The summary analysis showed that most high affinity IgE and IgG1 progeny sequences of spleen and bone marrow (BM) were present in the donor population (Fig. 4c ). As exemplified for one high affinity CDR3 sequence all but one mutation (missense and silent) in nucleotide-encoding sequences were found in progeny and donor cells (Supplementary Table  2 ). The results indicate that antigen-driven selection steers differentiation of rare DP high affinity memory clones into PC, as opposed to additional mutation and selection of memory clones. All IgG1 MBC subsets give rise to IgE PC in memory responses.
We identified 17,012 expressed genes in the data set. Inter-sample similarity analysis using multidimensional scaling (MDS) showed that all IgG1 MBC subsets were distantly related to IgG1 GC cells, the SP subset was intermediate between DP and DN subsets, and notably, the DN subset was the closest to naive B cells (Fig. 6a ). The functional experiments in vivo suggested that there may be pre-existing differences affecting activation among IgG1 MBC subsets. Thus, we compared their expression profiles to signatures of activated murine B cells. Differentially expressed gene (DEG) signatures of LPS, and LPS plus anti-CD40 activated B cells were generated from available data sets (GSE35998) and used in a connectivity map (CMap) analysis  31. In the CMap, IgG1 GC cells and naive B cells yielded positive and negative scores, respectively (Fig.
6b ). Strikingly, the analysis yielded positive scores for DP IgG1 MBC, and negative scores for DN and SP IgG1 MBC, indicating that DP cells were the MBC subset most related to activated cells. As MBC are non-dividing cells, these correlations may reflect their promptness to activation. The comparison of the three IgG1 MBC subsets identified 1114 DEG (Supplementary Dataset 2a-d), of which 512 had higher expression in DP MBC, 514 in DN MBC, and only 88 in SP MBC. Gene Ontology analysis (GO) identified transcription regulation, cell surface molecules (signalling receptors, transporters, cell adhesion molecules and endocytosis genes), and kinases as mainly represented among DEG of DP or DN subsets, with the SP subset presenting an intermediate and more heterogeneous profile (Fig. 6c -e and Supplementary Dataset 3a)  25.
Among DEG with higher expression in the DP subset were the transcription factors Arid3a (Bright) and Gtf2i, which regulate the IgH locus transcription  32  , E-box binding proteins Tcf4, Bhlhe41 and Zeb2, and, importantly, Zbtb20, a regulator of PC differentiation and longevity  33, 34. Importantly, DN IgG1 MBC expressed higher level of genes that promote the GC fate and inhibit the PC fate, such as Bcl6 35 , Foxo1 36, 37 , Foxp1 38, 39 , Bach2 40 and Ets1 38. Metacore analysis of pathway/processes indicate B cell receptor (BCR) pathway, cell adhesion, proliferation, cytokine signalling, among the top enriched entities (Supplementary Dataset 3b, c). In the BCR pathway it is worth pointing out the upregulation, in the DP subset, of the IP 3 receptor gene Itpr3, and of Pdk1 and Lrrk1, supporting promptness to BCR mediated calcium release and NF-κB activation.
The library was sequenced using the Illumina MiSeq to generate 250 bp paired-end reads at a sequencing ranging from 0. 1 to 0. 8 million reads per sample. The VDJ sequences were analysed with the MiXCR software  49. Clonotypes were clustered based either on CDR3 alone or CDR1, CDR2 and CDR3 together. Resulting clonotypes were then further grouped by amino acid identity. Selected clonotypes were further characterised by DNA identity. Sequence alignment, clustering and general data morphing was performed with custom scripts in pipeline pilot (www. accelrys. com). The VDJ H data sets have been deposited in NCBI Bioproject, under the accession code PRJNA319596. Quantitative PCR analysis of immunoglobulin transcripts. Total RNA from spleen cells was isolated using TRIzol (Thermo Fisher, catalogue # 15596026).