dataesr/scientific-paragraphs-categorization huggingface dataset:
However, whether it can mutate genomic DNA in cancer cells has remained unknown until our study. To dissect the mutagenic impact of a single member of the human APOBEC3 family in an experimentally controlled bladder cancer mouse model, we leveraged the differences between mouse and human APOBEC3 loci, which are separated by 76 million years of evolution  (62). In our examination of the TCGA pan-cancer dataset, we observed significant levels of APOBEC3G expression across several cancer types. However, determining a definitive causal relationship between the mRNA expression of individual APOBEC3 genes and their respective signatures in human cancer genomes is challenging for several reasons. First, mutational signatures can be imprinted on the nuclear genome cumulatively over long-time intervals or in punctuated episodes.
SBS. A3G had low cosine similarity to motifs attributed to other proteins in the APOBEC3 family. The differences in motif preferences and mutational patterns between APOBEC3G and APOBEC3A and APOBEC3B potentially explain the differences between our experimentally induced SBS. A3G and the COSMIC SBS2 and SBS13 signatures, which are classically associated with APOBEC3A and APOBEC3B(1,2,5,71). In addition, we found that bladder patients in the group dominated by APOBEC3G-induced mutational signatures had shorter overall survival than those enriched in signatures attributed to APOBEC3A and APOBEC3B, consistent with a previous report  (93). These data suggest that individual APOBEC3 proteins impact clinical outcomes differently. It is important to note that in non-muscle invasive bladder cancers, non-small cell lung, and breast cancers, APOBEC3 mutations are collectively associated with poor clinical outcomes  (94) (95) (96).
RNA extraction and qPCR analysis The bladder, lung, and spleen tissues were harvested from three hA3G(+) mA3(-/-) and mA3(-/-) mice, respectively. Tissues were snap-frozen using liquid nitrogen. Tissues were homogenized using BioMasher II (TaKaRa) with RLT Plus buffer (1% b-mercaptoethanol). Total RNA was extracted using the RNeasy Plus Mini Kit protocol (Qiagen). RNA concentration was determined by NanoDrop (ThermoFisher Scientific). cDNA was synthesized using the SuperScript III First-Strand Synthesis System (Invitrogen). Realtime PCR was performed by LightCycler 480 (Roche) using Power SYBR Green Master Mix (Applied Biosystems). All reactions were run in triplicate and analyzed using the LightCycler 480 software. APOBEC3G expression data were normalized to GAPDH. The qPCR primers were listed in Supplementary Table  S4.
After that, the slide was sealed with ProLong Gold Antifade Mountant with DAPI (Invitrogen, p36931). High-resolution imaging was performed using a DeltaVision Elite system, with a 60X/1. 42 oil objective (Olympus) and EDGE sCMOs 5. 5 camera. 12 different fields of view for each group were randomly picked for imaging. Different channels were acquired for DAPI, FITC, and TRITC filters. Z-stacks of 0. 2um increments for a total of 20 stacks (4um) were acquired per field of view. The captured images for the nuclei were adjusted and quantified using ImageJ Fiji software as follows. The Z stacks were summed to create a sum projection, and background subtraction was performed. Then the image was split into three channels, DAPI (blue), GFP (green), and a-Tubulin (red). The DAPI image was processed by Gaussian blur, auto-threshold using the Otsu method, and then converted into a mask to create a list of regions of interest based on size and circularity.
Confocal laser scanning fluorescent microscopy and 3D image reconstruction A single isolated nucleus fluorescent image was captured using Zeiss LSM880 inverted confocal microscope with a 63X/1. 4 oil Dix M27 objective. The DAPI and FITC channels were scanned using an auto-optimized setup by microscopy software, ZEN Black 2. 1 SP3 LSM. The image data was imported into the Imaris software (Oxford instruments) for 3D reconstruction. The nuclear surface was reconstructed based on the DAPI staining. Then the GFP signal within the nuclear surface was isolated and reconstructed by default. The green color represents the GFP signal, and the violet represents the reconstructed nuclear surface.
Pindel was implemented with a blacklist reference made available through the ENCODE Blacklist resource. Post-hoc filtering of the variants identified by each caller was done identically to the MC3 pipeline except as follows. For MuSE, variant calls were retained at least Tier 4-level significance, which corresponds to the calls with an added false positive and false negative probability of less than 1%. For Strelka2, the variant calls were filtered to those that passed the tool's internal significance testing. For Mutect2, the variant calls were filtered in two steps, with the first entailing filtering based on the significance scoring of each call and the estimation of sample contamination. These methods were implemented via the CalculateContamination and FilterMutectCalls method in GATK4.
The second step consisted of quantifying nucleotide substitution errors caused by mismatched base pairings during various sample/library preparation stages. Such errors include artifacts introduced before the addition of adapters and those introduced after target selection. These sequencing artifacts were then filtered from the original Mutect2 calls using the CollectSequencingArtifactMetrics and FilterByOrientationBias methods of GATK4. All filtered variant calls from each tool were sorted using vcf-sort from the vcftools package and standardized as in the MC3 pipeline. After standardization, the calls from multiple tools were merged into a single file. The merged variant calls were further filtered for sequence artifacts described above and calls from regions in the capture region designed by SureSelect.
The significantly increased counts in each trinucleotide context were averaged to construct the APOBEC3G signature de novo as previously described  (12). To validate the signature we extracted, we use two additional methods for signature extraction using the SigneR  (44)  tool, which provides a full Bayesian treatment to the non-negative matrix factorization (NMF) model, and the HDP package  (45) , which uses the hierarchical Dirichlet process to de novo extract mutational signatures. For each approach, the trinucleotide mutational matrices of mA3(-/-) and hA3G(+) mA3(-/-) tumors were separately imported to de novo extract the signatures. After extraction, the cosine similarity was calculated between the signatures from mA3(-/-) and hA3G(+) mA3(-/-) tumors by MutationalPatterns. Then, the unique signature from hA3G(+) mA3(-/-) tumors, which had low cosine similarity to the signatures extracted from the mA3(-/-) tumors, was considered the signature induced by APOBEC3G (SBS.
Mutational signature induced by transgenic APOBEC3A expression The raw sequencing data from 5 tumors from transgenic APC min C57BL/6J mice expressing human APOBEC3A and 4 tumors from transgenic APC min C57BL/6J mice were downloaded from the SRA database (BioProject ID: PRJNA655491)  (11). We reanalyzed the data and performed mutational calling using our MC3 pipeline with the post-filters as below: 1) At least two callers should call the variants. 2) Only the single nucleotide variants (SNVs) were included for further analysis. 3) The total tumor read counts ³ 20. 4) The normal VAF £ 0. 01. 5) The tumor VAF > 0. 02. The trinucleotide mutational context for each sample was generated by MutationalPatterns.
Statistical tests One-tailed Mann-Whitney U test for continuous variables, two-tailed 2X3 table Chi-Square test or Fisher's exact test for categorical variables, Kruskal-Wallis nonparametric test for multiple groups, nonparametric Spearman correlation, and log-rank test for survival analysis was performed using GraphPad Prism version 8. 4. 3 statistical analysis software. P<0. 05 was considered statistically significant.
Acknowledgments B. M. F was supported by the  Starr Cancer Consortium  grant ( I12-0030 ). We thank  Dr. Jenny Xiang  ( Genomics Resources Core Facility ) for whole-exome sequencing and  Dr. Tuo Zhang  for his input on bioinformatic analysis. We thank  Dr. Edwin Sandanaraj  for his input on bioinformatic analysis. We thank  Bing He  from the  Translational Pathology Core lab at Weill Cornell Medicine. We thank  Dr. John Maciejowski  and  Alexandra Dananberg  for the kind gift of the doxycycline-inducible GFP-tagged APOBEC3G vector and PiggyBac transposase vector and their input and advice on the immunofluorescence microscopy (DeltaVision Elite system). We thank  Dr. Zhengming Chen  for his input on statistical analyses. We thank  Dr. Sushmita Mukherjee  for her input and advice on confocal laser scanning microscopy, 3D reconstruction by Imaris, and the quantification of immunofluorescent images.
Code availability statement The open source code used in this paper were listed : BWA MEM (https://github. com/lh3/bwa), GATK (https://github. com/broadinstitute/gatk), Picard (https://github. com/broadinstitute/picard), Strelka2 (https://github. com/Illumina/strelka), MuSE (https://github. com/danielfan/MuSE), MuTect2 (https://github. com/broadinstitute/gatk), Pindel (https://github. com/genome/pindel), EXPANDS (https://github. com/noemiandor/expands), TitanCNA (https://github. com/gavinha/TitanCNA), ggtree (https://github. com/YuLab-SMU/ggtree), Gardner-Altman plots (https://github. com/ACCLAB/dabestr), MutationalPatterns (https://github. com/UMCUGenetics/MutationalPatterns), mutationProfiles (https://github. com/nriddiford/mutationProfiles), SigneR (https://github. com/rvalieris/signeR), HDP (https://github.
com/nicolaroberts/hdp), deconstructSigs (https://github. com/raerose01/deconstructSigs), sigLASSO (https://github. com/gersteinlab/siglasso), factoextra (https://github. com/kassambara/factoextra), vegan (https://github. com/vegandevs/vegan). Corrplot (https://github. com/taiyun/corrplot). The adapted code for the APOBEC3G signature is available at https://github. com/APOBEC3G.
By adding more data or tighter constraints, the predictions can be made more precise. Model variants differing in network structure, flux distributions, thermodynamic forces, regulation, or rate laws can be realised by different model ensembles and compared by significance tests. The thermodynamic forces have specific effects on flux control, on the synergisms between enzymes, and on the emergence and propagation of metabolite fluctuations. Large kinetic models could help to simulate global metabolic dynamics and to predict the effects of enzyme inhibition, differential expression, genetic modifications, and their combinations on metabolic fluxes. MATLAB code for elasticity sampling is freely available.
Just like ORACLE and SKM, the procedure consists of three phases: In the steady-state phase, we choose thermodynamically feasible fluxes, metabolite levels, and equilibrium constants (or possibly, the Gibbs free energies of formation), which determine the chemical potentials and driving forces. The state variables can be sampled or chosen by any method for thermodynamic flux analysis. In the elasticity phase, saturation values are sampled independently in the range ]0, 1[, and the elasticity matrix is computed by Eq. (  1 ). In the MCA phase, we compute variables like unscaled elasticities Ēv l ci , rate constants, the Jacobian matrix, response and control coefficients, or properties like the stability of the steady state. The algorithm is described in appendix D and MATLAB code for elasticity sampling is freely available at https://github.
The upper and lower bounds, signs, inserted values, and distributions used in sampling reflect model assumptions and available data; in choosing them, we can adjust the model to specific metabolic states and to kinetic or metabolic data. Fluxes and driving forces may be sampled uniformly with linear constraints and with predefined sign patterns; metabolite levels, enzyme levels, and dissociation constants may be sampled from log-normal distributions, and saturation values may be sampled from uniform or beta distributions. Free matlab code, using the sparse tensor toolbox  [61, 62]  is available at https://github. com/wolframliebermeister/elasticity sampling. Reconstruction of kinetic rate laws from state variables and elasticities The scaled elasticities in elasticity sampling are not directly given by saturation values as in SKM, but are computed from them and from the known driving forces.
However, molecular replacement with models generated by AlphaFold2 (ref. 11  ) allowed us to solve the structure, which was subsequently used to phase two additional crystal forms diffracting to ~1. 4 Å resolution (Extended Data Figs. 3  and 4  and Supplementary Table  1 ). The electron density maps reveal that the GP2 branch is a protein module (henceforth referred to as 'decoy module') that consists of a β-hairpin stabilized by a disulfide bond (C x 48-C y 59), packed against a globular 'D10C' domain with a new fold including two 3 10 helices, nine β-strands (βA-βI) and five intermolecular disulfides (C 1 63-C 8 157, C 2 85-C 9 172, C 3 107-C 6 145, C 4 113-C 10 177, C 5 138-C 7 146) (Fig. 1a  and Extended Data Fig. 1 ). Notably, the extent of the latter and its C 1 -C 8 , C 2 -C 9 disulfides are not compatible.
01 M spermidine, 0. 01 M 1,4-diaminobutane, 0. 01 M dl-ornithine (MORPHEUS II condition H4). Before data collection at synchrotron, crystals were fished directly from the crystallization drops and flash frozen in liquid nitrogen. X-ray data collection and reduction. Datasets for the P1, P2 1 2 1 2 1 and C2 crystal forms were collected from single specimens at 100 K at European Synchrotron Radiation Facility beamlines ID23-1 (ref. 25 ) (λ = 1. 0052 Å), ID30B  26  (λ = 0. 9763 Å) and ID30A-3 (λ = 0. 9677 Å), respectively, using MXCuBE3 (ref. 27 ). All data was processed with XDS  28  (Supplementary Table  1 ), with high-resolution data cutoffs chosen on the basis of statistical indicators CC 1/2 and CC*  29, 30. Although the P1 crystals diffracted reproducibly to better than 3. 0 Å resolution, a single specimen yielded data extending well beyond a Bragg spacing of 2.
NATUrE STrUcTUrAl & MOlEcUlAr BiOlOGy software, on a Titan Krios electron microscope (ThermoFisher Scientific) operated at 300 kV, using a K3 camera equipped with a BioQuantum energy filter (Gatan-Ametek). Videos were taken at ×105,000 nominal magnification in counting mode with a dose rate of 15 e px -1 s -1 and a total dose of 40 e/Å 2 distributed over 40 subframes, gain-corrected and then compressed using video compression in RELION  39. Motion correction with dose weighting was also performed in RELION  40  within the Scipion software suite  41. Cryo-EM data processing. Processing of the cryo-EM data of the UMOD-FimH L complex followed the general workflow used for reconstructing the full-length UMOD filament 6. First, contrast transfer function determination was carried out using CTFFIND in RELION.
An in-house script designed specifically for filament picking (Cryo-EM-filament-picker)  42  was then used to select end-to-end filament coordinates. After two-dimensional classification in cryoSPARC  43  , selected particle coordinates were transferred back to RELION for three-dimensional (3D) classification, 3D helical refinement, particle subtraction and final non-helical refinement and polishing. Specifically, starting from a total of 13,616 raw micrographs, 3,767,790 particles (helical segments with 70 Å step size) were auto-picked and extracted on the basis of motion correction and contrast transfer function estimation; based on two-dimensional classification quality evaluated with cryoSPARC, a subset of 1,139,808 particles was then selected for further processing. Because FimH L occupancy varied among filaments, segments with higher FimH L occupancy were selected during iterative RELION 3D classification runs.
The resulting map fit correlations of the AlphaFold2 model and the homology models were 0. 884 and 0. 892/0. 896, respectively. Fourth, the placed AlphaFold2 model was locally rebuilt, taking into account-if availablealternative possibilities suggested by the superimposed homology models. At this stage, we also connected the C terminus of D10C to the N terminus of the atomic model of the UMOD filament core (PDB ID 6TQK) 6 , consisting of the EGF IV domain and the ZP module (Extended Data Fig. 1a ); rebuilt the C-terminal end of the ZP-C domain interacting with D10C 6 ; and built the glycan chains attached to N232 and N275. The resulting coordinates were then subjected to global real-space and group ADP refinement in PHENIX  48  , essentially as described 6 (CC mask 0. 74; CC box 0. 79; CC peaks 0.
75; CC box 0. 81; CC peaks 0. 48; CC vol 0. 73; mean CC carbohydrates 0. 77). The final model consists of 1,127 protein residues, corresponding to a complete chain (chain A, D25-F587) and two half chains (chain B, S444-F587; chain C, D25-S444) that together recapitulate all the proteinprotein interactions in the UMOD filament, as well as 84 N-glycan residues. The model of the UMOD branch + EGF IV/FimH L complex was generated by manually docking the crystallographic structure of FimH L bound to trimannose (chains A and F of PDB ID 6GTW)  51  into the difference density between the cryo-EM maps of the FimH-bound and free UMOD branch + EGF IV (calculated using TEMPy:DiffMap  52  and masked around the decoy module region), so that the lectin made an equivalent interaction with the α1,3 branch of the high-mannose glycan attached to UMOD N275.
NATUrE STrUcTUrAl & MOlEcUlAr BiOlOGy Extended Data Fig. 1  | Structure of the gP2 N-terminal branch and its relation with the corresponding regions of uMoD and additional mammalian proteins. a, Domain architecture of mature human GP2 and UMOD. Domains are indicated by their acronyms, except for UMOD epidermal growth factor (EGF) domains that are labeled according to their roman number, the single EGF domain of GP2 (corresponding to UMOD EGF IV) that is labeled as 'E' and the β-hairpin of the decoy module ('β'). The UMOD D10C epitope recognized by Bence-Jones proteins (BJP)  14  is shown as a green stripe. Black and magenta inverted tripods indicate the N-glycosylation sites of GP2 and UMOD, respectively, with the high-mannose chains attached to GP2 N65 (this study) and UMOD N275 8,12 colored cyan.
Software and code Policy information about availability of computer code Data collection EPU 2. 11. 1, MXCuBE3 3. 0 Data analysis AlphaFold Monomer 2. 0; AS2TS server 09/2019; ByonicTM 3. 4. 0; ConSurf 2016; Coot 0. 8. 9. 2-0. 9. 6; Cryo-EM-filament-picker 1. 0; cryoSPARC 3. 2; CTFFIND 4. 1. 14; Dali 5; ESPript 3. 0; ISOLDE 1. 2; MAFFT 7; MODELLER 10. 1; MolProbity 4. 5. 1; MView 1. 68; Namdinator 2. 0; Phaser 2. 8. 3; PHENIX (phenix. autobuild, phenix. refine, phenix. table_one) 1. 19. 2_4158, dev_4282; Privateer MKIII-MKIV; PyMOL 2. 4. 2; RELION 3. 0. 8; Scipion 3. 0. 9; STRIDE 1. 0; TEMPy:DiffMap 2; UCSF Chimera 1. 11-1. 15; UCSF ChimeraX 1. 1-1. 2; XDS Feb 5, 2021 BUILT=20210322; YASARA Structure 21. 4. 22 For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy The UniProt (https://www. uniprot. org/) IDs for hGP2 and hUMOD are P55259 and P07911, respectively; the IDs of other sequences reported in the alignment of Extended Data Fig. 1b  are Q9D733 (mGP2), Q91X17 (mUMOD), Q8WWZ8 (hLZP), Q8R4V5 (mLZP), Q8N2E2 (hVWDE) and Q6DFV8 (mVWDE). The Electron Microscopy Data Bank (EMDB; https://www. ebi. ac. uk/emdb/) ID of the UMOD filament map used for assembling the composite map shown in this work is.
Acknowledgements We thank  D. Briggs  ( The Francis Crick Institute, London ) for advice on transient transfection of Expi293F cells;  A. Vegvari  ( Karolinska Institutet Proteomics Biomedicum core facility ) for the MS analysis of the FimH L bands; the  Plateforme d' Analyses des Glycoconjugués (PAGés)  and the  Plateforme d' Analyse Protéomique et de Protéines Modifiés  ( P3M ) for GP2 N65 glycan LC-MS/MS; the staff of the  European Synchrotron Radiation Facility (ESRF; Grenoble)  and the  Swedish National Cryo-EM Facility (Stockholm)  for help with X-ray and cryo-electron microscopy data collection and preprocessing;  A. Zemla  ( Lawrence Livermore National Laboratory, Livermore ) for help with GDT_TS calculations; and  T. Terwilliger  ( New Mexico Consortium, Los Alamos ) for discussion.
Data availability The UniProt (https://www. uniprot. org/) IDs for hGP2 and hUMOD are P55259 and P07911, respectively; the IDs of other sequences reported in the alignment of Extended Data Fig. 1b  are Q9D733 (mGP2), Q91X17 (mUMOD), Q8WWZ8 (hLZP), Q8R4V5 (mLZP), Q8N2E2 (hVWDE) and Q6DFV8 (mVWDE). The Electron Microscopy Data Bank (EMDB; https://www. ebi. ac. uk/emdb/) ID of the UMOD filament map used for assembling the composite map shown in this work is EMD-10553; the UMOD filament core and FimH L /trimannose coordinates used as starting models can be retrieved from the Protein Data Bank (PDB; https:// www. rcsb. org/) with IDs 6TQK and 6GTW, respectively. Structure factors and atomic models for the P1, P2 1 2 1 2 1 and C2 crystal forms of the GP2 decoy domain have been deposited in the PDB with accession codes 7P6R, 7P6S and 7P6T, respectively.
Background: The absence of efficient inhibitors for diabetic kidney disease (DKD) progression reflects the gaps in our understanding of DKD molecular pathogenesis. Methods: A comprehensive proteomic analysis was performed on the glomeruli and kidney cortex of diabetic mice with the subsequent validation of findings in human biopsies and omics datasets, aiming to better understand the underlying molecular biology of early DKD development and progression. Results: LC-MS/MS was employed to analyze the kidney proteome of 2 DKD models: Ins2Akita (early and late DKD) and db/db mice (late DKD). The abundance of detected proteins was defined. Pathway analysis of differentially expressed proteins in the early and late DKD versus the respective controls predicted dysregulation in DKD hallmarks (peroxisomal lipid metabolism and β-oxidation), supporting the functional relevance of the findings.
The full list of identified proteins using a 55% frequency threshold is shown in Table  S4 ; Sheet 1 (Ins2Akita glomerular dataset). These protein lists largely overlapped with similar published proteomics datasets  (Waanders et al. 2009 [37] ). In brief, most of the identified proteins in our study were also detected in these published data (60% for wild-type mice and 73% for the Ins2Akita)  [37]. Correlation analysis of the protein abundance detected in our study with  Waanders et al. 2009 [37]  resulted in correlation factor R = 0. 7 for both the wild-type and the Ins2Akita mice (Figure  S2 ), demonstrating consistency between the different studies.
WT2 (Table  S6 ) were subtracted from the above glomerular proteins associated with DKD; this gave rise to a final list of 39 proteins (21 up-and 18 downregulated) with consistent expression trends (increased/decreased abundance) in early and late DKD versus controls (Figure  1 ; Tables  3  and 4 , respectively). According to the literature, and the databases Uniprot and Human Protein Atlas, among the 21 upregulated proteins, 7 are already related to diabetes (Table  3  and in detail in Table  S7 ) and 15 are attributed to kidney expression and/or function (Table  3  and in detail in Table  S7 ). Among the 18 downregulated proteins, 7 are related to diabetes (Table  4  and in detail in Table  S7 ) and 14 have documented kidney expression and/or function (Table  4  and in detail in Table  S7 ).