dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The UMOD gene is of particular interest because it encodes the most abundant protein excreted in the normal urine, is associated with an autosomal dominant kidney disease (ADTKD-UMOD), and is the top GWAS signal for eGFR and CKD in the general population  (4). By intersecting missense variants associated with ADTKD-UMOD or present in gnomAD, we detected a unique UMOD variant p. Thr62Pro with intermediate-sized biological effects associated with kidney failure and enriched in CKD families with a milder clinical course compared with ADTKD-UMOD. The identification of an intermediate-sized effect variant completes the spectrum of UMOD-associated kidney diseases and provides insights into the genetic architecture of CKD. Using large-scale reference datasets, we confirm that UMOD variants associated with ADTKD are generally ultrarare and absent from gnomAD, as expected for bona fide high-effect variants causing a dominant Mendelian disease  (11).
The eight UMOD variants detected both in ADTKD patients and gno-mAD showed a prevalence of ∼1/160 in gnomAD, similar to the prevalence (∼1/165) of purported pathogenic variants in PKD1 reported in the Exome Aggregation Consortium (ExAC) database  (30). These observations could be due to either annotation errors or altered penetrance and expressivity of purported pathogenic variants in control datasets. The MAF of p. Thr62Pro in the general population (between 0. 035 and 0. 05%) and the fact that it is among the most frequent UMOD missense variants listed in gnomAD are not compatible with a high-effect size variant for ADTKD-UMOD, whose prevalence is estimated between 1/50,000 and 1/100,000  (10). However, p. Thr62Pro is enriched in kidney disease populations, with an MAF of ∼0. 4% among UK patients with kidney failure and up to 4% among ADTKD family index patients.
These findings corroborate the recent evidence that, in ADTKD-UMOD, the severity of the mutant UMOD trafficking defect correlates with disease severity  (20). Further in vitro and in silico evidence substantiate the specific effect of the proline substitution on UMOD folding and ER exit, possibly by impacting the neighboring disulphide bond, in line with mutagenesis datasets showing missense proline insertions being associated with the highest predicted damage  (34). Whether and how p. Thr62Pro could have additional deleterious effects on the function of UMOD remain to be investigated. Our findings implicate ER proteostasis and different levels of UPR in a spectrum of UMOD-associated kidney disease risk. Strategies to rescue protein misfolding and UPR using, for instance, chemical chaperones might, therefore, be viable therapeutic options that should be tested in CKD patients with intermediate, potentially more rescuable, or highimpact UMOD variants.
Thr62Pro-expressing cells and on patient biopsies, or genes that determine the inflammatory response  (10). Arguably, the co-occurrence of functional nephrogene variants with UMOD p. Thr62Pro would not invalidate the hypothesis of p. Thr62Pro being an intermediate-effect variant under an oligogenic disease model. Our data indicate that CKD progression is significantly delayed in p. Thr62Pro carriers compared to carriers of classical ADTKD mutations and that disease might be subclinical in some younger adults. In line, we saw that p. Thr62Pro carriers that were labeled with CKD in the 100,000 Genomes Dataset were notably older than carriers in which no CKD was described (SI Appendix, Table  S7 ). In addition to its intrinsic milder effect on trafficking and ER stress, the intriguing observation that the p.
Thr62Pro, with very low population frequencies (MAF ∼ 10 À5 ). Taking into account a possible oligogenic disease model, we would encourage continuation of diagnostic workup even in individuals carrying UMOD p. Thr62Pro. The fact that numerous ultrarare and predicted pathogenic UMOD variants are present in gnomAD is noteworthy (SI Appendix, Table  S2 ). If the 45 individuals harboring such ultrarare variants with an REVEL score of >0. 75 [corresponding to a specificity of ∼0. 95 for pathogenic variants  (19)  and excluding the probable benign variant p. Gly131Asp] would develop mild or late-onset CKD, then the prevalence of UMOD-associated disease would rise close to 1 in 3,000 (45 carriers in ∼140,000 gnomAD subjects). This study is a systematic investigation of intermediate-sized effects for UMOD variants based on analysis of large databases; detailed characterization of the effect in silico, in vitro, and in patient samples; and validation in large patient and populationbased cohorts.
While this study is limited to UMOD, an important gene for individual and population burden of kidney disease, the approach and concept here are potentially applicable to other genes, organs, and traits. Limitations of the study include using previously reported ADTKD-UMOD variants rather than resequencing patients, thereby limiting the discovery stage. Most of the patient and control dataset is of European ancestry, thus neglecting potential variants operating in other backgrounds. For instance, the p. Leu180Val UMOD variant, ultrarare in UK databases, is principally detected in Africans. Approximately 1 in 1,000 people of European ancestry carry p. Thr62Pro; based on our findings, these individuals have a fourfold increase in risk for kidney failure, comparable with the risk we detected for Black individuals with two APOL1 risk alleles for all-cause kidney failure in the Genomics England database.
(50) variants detected in gnomAD and ADTKDOverlapping UMOD variants with frequencies in the general population, the number of ADTKD families, and in silico pathogenicity scores are shown. The 3 most frequent UMOD missense variants further studied are indicated in bold. GnomAD(18)allele counts (ACs) are reported as ACs/total allele number/number of homozygotes (with AF in parentheses). The theoretical maximum tolerated AC for high-effect UMOD variants (pathogenic [P]) in gnomAD is estimated to be one(46)(SI Appendix has more details). Annotation and in silico analyses were performed using the VarSome platform(51)and Ensembl VEP considering ENST00000302509. 8 as the reference transcript. For REVEL(19), a score >0. 5 corresponds to a sensitivity of 0. 75 and a specificity of 0. 89 for P variants in the training dataset.
ClinVar was last accessed April 2021. GRCh38, Genome Reference Consortium Human Build 38; rs, Reference SNP; HGVS, Human Genome Variation Society; AA, amino acid; ClinVar, Clinical Variations database; VEP, Variant Effect Predictor; neu, neutral; nonpol, non polar; pol, polar; neg, negative; pos, positive; ALFA, National Center for Biotechnology Information (NCBI) Allele Frequency Aggregator (https://www. ncbi. nlm. nih. gov/snp/docs/gsr/alfa/);B,benign; LB, likely benign; LP, likely pathogenic; TOPMed, Trans-Omics for Precision Medicine Program(50).
ACKNOWLEDGMENTS. We thank all participating patients and families. The  Cohorte Lausannoise  is acknowledged for providing reference urine samples and eGFR information. We thank the  Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard ; the  First Faculty of Medicine, Charles University  ( LM2018132 ); and the  Friedrich-Alexander Universit€ at Erlangen-N€ urnberg in Germany  for providing MUC1 testing. We also thank  Emily Cornec-Le Gall  and  Marie-Pierre Audr ezet  ( Centre Hospitalier R egional et Universitaire [CHRU] Brest, France ) for providing gene panel testing in CKD families,  Anne Kipp  for her help in generating preliminary results,  Valeria Berno  from the  San Raffaele Advanced Light and Electron Microscopy BioImaging Center  for helpful discussions, and  Nadine N€  agele for technical assistance.
E. O. is supported by  Postdoc Mobility-Stipendien of the Swiss National Science Foundation  Grants  P2ZHP3_195181  and  P500PB_206851 ,  Kidney Research UK  Grant  Paed_RP_001_20180925 ,  Fonds National de la Recherche Luxembourg  Grant  6903109 , and the  University Research Priority Program  " Integrative Human Physiology, Zurich Center for Integrative Human Physiology (ZIHP)" of the University of Zurich. K. K. , S. K. , and A. J. B. were supported by  Ministry of Health of the Czech Republic  Grant  NU21-07-00033. E. A. E. E. is funded by the  Royal College of Surgeons in Ireland Blackrock Clinic Strategic Academic Recruitment (StAR) Doctor of Medicine (MD). A. D. K. was supported by  University of Zurich  Grant  Forschungskredit FK-14-035. L. R. is supported by  Italian Ministry of Health  Grants  RF-2010-2319394  and  RF-2016-02362623.
This research was made possible through access to the data and findings generated by the 100,000  Genomes  Project. This research has been conducted using data from  UK Biobank  (Project  ID   43879 ), a major biomedical database with open access to researchers (https://www. ukbiobank. ac. uk/). Author affiliations: a  Institute of Physiology, University of Zurich, CH-8057 Zurich, Switzerland; b Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne   NE1 3BZ ,  United Kingdom ; c  Molecular Genetics of Renal Disorders, Division of Genetics and Cell Biology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale San Raffaele , Milan, 20132 Italy; d Section on Nephrology,  Wake Forest School of Medicine ,  Winston-Salem ,  NC 27101 ; e  Department of Pediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University, 128 08 Prague, Czech Republic ; f  Division of Nephrology, Beaumont General Hospital, 1297 Dublin, Ireland ; g  Department of Medicine, Royal College of Surgeons in Ireland, 1297 Dublin, Ireland; h Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, D-79106 Freiburg, Germany; i Faculty of Biology, University of Freiburg, D-79106 Freiburg, Germany; j Division of Nephrology, Cliniques Universitaires Saint-Luc, 1200 Brussels, Belgium; k Renal Services, Newcastle Upon Tyne Hospitals National Health Service Trust, Newcastle upon Tyne   NE7 7DN ,  United Kingdom ; l  Department of Nephrology and Hypertension, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland ; m  Department of Medicine, Cantonal Hospital Frauenfeld, 8501 Frauenfeld, Switzerland; n Biosciences Institute, Newcastle University, Newcastle upon Tyne   NE1 3BZ ,  United Kingdom ; o  Genetics Department, Laboratoire Eurofins Biomnis, Lyon, 69007 France ; p  Centre de N ephrologie et Transplantation R enale ,  Centre Hospitalier Universitaire (CHU) la Conception ,  Assistance Publique -Hôpitaux de Marseille  ( AP-HM ), Marseille, 13005 France; q  Marseille Medical Genetics, Bioinformatics & Genetics, Unit e Mixte de Recherche (UMR)_S910, Aix-Marseille Universit e, Marseille ,  13005  France; r  Department of Nephrology and Medical Intensive Care, Charit e-Universit€ atsmedizin Berlin, Freie Universit€ at Berlin and Humboldt-Universit€ at zu Berlin, 10117 Berlin, Germany ; s  Department of Nephrology and Hypertension, University Hospital Erlangen, Friedrich-Alexander Universit€ at Erlangen-N€ urnberg, 91054 Erlangen, Germany; t Centre for Integrative Biological Signalling Studies, University of Freiburg , D-79106 Freiburg, Germany; and u  National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre , Newcastle upon Tyne  NE4 5PL ,  United Kingdom Author contributions: E.
Data Availability. All study data are included in the article and/or SI Appendix. Previously published data were also used for this work. Data presented here were derived from the following resources available in the public domain: the gnomAD version 2. 1. 1 (https://gnomad. broadinstitute. org/) and Ensembl (release 100; https://useast. ensembl. org/index. html). The International ADTKD Cohort and the UMOD in vitro scoring have also been published  (11, 20).
ClinVar was last accessed April 2021. GRCh38, Genome Reference Consortium Human Build 38; rs, Reference SNP; HGVS, Human Genome Variation Society; AA, amino acid; ClinVar, Clinical Variations database; VEP, Variant Effect Predictor; neu, neutral; nonpol, non polar; pol, polar; neg, negative; pos, positive; ALFA, National Center for Biotechnology Information (NCBI) Allele Frequency Aggregator (https://www. ncbi. nlm. nih. gov/snp/docs/gsr/alfa/);B,benign; LB, likely benign; LP, likely pathogenic; TOPMed, Trans-Omics for Precision Medicine Program(50). Position rs HGVS HGVS gnomAD TOPMed ALFA ADTKD (GRCh38) identification coding protein AC (AF) AC (AF) AC (AF) families AA change REVEL ClinVar 16:20349117:T:G rs143248111 c. 184A > C p. (Thr62Pro) 99/282,046/0 51/125,568 47/94,922 2 neu, pol > 0.
Image analysis Images were deconvolved using the Huygens software (Scientific Volume Imaging) Classic Maximum Likelihood Estimation (CMLE) deconvolution algorithm. Masks of cells were made using ImageJ  (Schindelin et al, 2012) , and data analysis was performed using MATLAB (MathWorks). To quantify the fraction of protein localization over time, MATLAB was used as described in Figs S1 and S2 and previously  (Kelley et al, 2015; Shellhammer et al, 2019). The fluorescent intensity of each fluorescent protein was extracted over time using a line width of 5 pixels. Peak Bem1 was used as a reference to normalize the spatial distribution of proteins of interest in relation to the polar cap. This was done by setting peak Bem1 as the midpoint and shifting the protein of interest in the same manner.
For profiles reporting fraction of protein at each position, fluorescence was normalized by subtracting the minimum value from each line-scan, followed by normalization of the subtracted data to sum to one. The normalized fluorescence intensity was plotted at each point along the cell periphery with shaded regions showing 95% confidence intervals derived by bootstrapping with 10,000 resamplings. Statistical analysis was performed between profiles using a sliding one-way ANOVA and Tukey's honestly significant difference test followed by false discovery rate adjustment with the MATLAB mafdr() function with P-values < 0. 05 denoted as significant. Where indicated, a pairwise Kolmogorov-Smirnov test was performed using the MATLAB kstest2() function. When excluding nuclear fluorescence from Fus3-GFP images, we modified the "granulinator" script  (Hunn et al, 2022)  to select nuclei for removal.
0 water-immersion objective (Zeiss) and coupled to a Ti:Sapphire laser (Chameleon Vision II, Coherent, > 3 W, 140 fs pulses, 80MHz repetition rate). The average power of the laser emitted was set at $40-50 mW on sample. Fluorescence was detected with a GaAsP detector. Scanning and image acquisitions were controlled with Imspector software (LaVision BioTec, Germany) (15. 3 frames per second for 1024 x 1024 pixels, between 50 to 150 mm underneath the brain slice surface, with no digital zoom). Typical field of view for calcium imaging was 392 x 392 mm. Cortical stimulation protocols Electrical stimulations were applied with a bipolar electrode (MicroProbes, USA). For DMS recordings, stimulating electrode was placed in the layer 5 of cingulate cortex from para-sagittal slices, and for DLS recordings, it was placed in the layer 5 of somatosensory cortex from horizontal slices, as previously described  (Fino et al.
Signals were amplified using EPC10-2 amplifiers (HEKA Elektronik, Lambrecht, Germany). Current-clamp recordings were filtered at 2. 5 kHz and sampled at 5 kHz and voltage-clamp recordings are filtered at 5 kHz and sampled at 10 kHz using the program Patchmaster v2x32 (HEKA Elektronik). Recordings were performed at 32-35 C to maintain physiological temperature conditions.
QUANTIFICATION AND STATISTICAL ANALYSIS Behavior The time to fall (latency) from the accelerating rotarod was recorded to measure the performance of the animals in the motor skill learning. In accordance with previous studies  (Buitrago et al. , 2004; Yin et al. , 2009 ) trial 1 and 2 data are pooled as first trials and trial 9 and 10 data pooled as last trials for each day. We computed a learning index (LI)  (Buitrago et al. , 2004; Kupferschmidt et al. , 2017).
Immunohistochemistry Images were acquired using a confocal microscope (LSM710, Zeiss, Germany). Z-stacks (20-30 mm) with 2 mm step size were acquired in DMS or DLS with a 20x/0. 8 objective. Concerning the cFos immunostainings, we quantified the density of cFos expressing neurons in 3 mice per condition. For each mouse, we acquired Z-stacks on 2-3 different coronal slices on the anteroposterior axis per territory (DMS and DLS) in one or two hemispheres. Each Z-stack was first filtered using a White Top Hat Morphological filter (MorphoLibJ plugin, FIJI software). Then, 3D object counter plugin was applied on the Z-stack obtained, with a Threshold set to 25 and a size filter set to 200 to remove low signal and small objects. Finally, one plane from the Z-stack was taken and 2 or 3 fields (400 x 400 mm) were used to determine the density of cells with high cFos expression.
The quantification of mCherry+ and hM4Di-mCherry+ cells was done in fields of 400 x 400 mm to be compared with the number of HA cells we observed in DMS and DLS (Figures  1  and S6 ). We quantified the number of cells with automatized detection using FIJI software in 2-5 mice per condition (no tamoxifen, mCherry+ and hM4Di-mCherry+ for cFos-cre virus and mCherry+ and hM4Di-mCherry+ for hSyn-cre virus), in 2-3 different coronal slices on the anteroposterior axis and within 2-3 fields of 400 x 400 mm per slice.
CTB quantification For the quantification of CTB-555+ cells in cingulate cortex (Cg -> DMS projections) and CTB-488+ cells in somatosensory cortex (Somatosensory -> DLS projections), 3 slices per region for each animal were imaged with a microscope slide scanner (Axio Scan. Z1, Zeiss, Germany) with a 20x /0. 8 objective. The analysis was performed blindly to the training conditions. Analysis of the CTB-555+ cells in cingulate cortex was performed for slices spanning 1. 2 mm to 0. 2 mm AP from Bregma according to Paxinos Atlas, and for CTB-488+ cells in somatosensory cortex from -0. 1 mm to -1. 1 mm. Quantification of striatal projecting neurons was performed over all the cingulate and somatosensory cortex layers for each slice using the 'Cell Counter' plugin by Kurt De Vos (University of Sheffield, UK) (FIJI software).
For cell counting, we only considered cells in which the soma was distinguishable for showing its nucleus labelled with DAPI. For each slice, cortical area was delimitated based on Paxinos Atlas, then we used DAPI staining to identify the layering in each cortical region. Density values per layer for each slice were obtained dividing the total number of neurons by the area of the cortical layer in which it was present. These density values per slice for each animal were normalized to the corresponding total area of the injection site (Figure  S4 ). To obtain the total area of the injection site for each animal, all slices containing fluorescence from the injections were imaged with an inverted microscope (AxioObserver7 microscope, Zeiss, Germany) with a 5x /0. 15 objective. Then for each slice a region of interest delineating the injection site was drawn and its area was measured using FIJI software.
Calcium imaging analysis GCaMP6f fluorescence signals were analysed with custom-built procedures using R3. 5. 2 in RStudio environment. The analysis was performed blindly to the training condition and by different experimenters. Several semi-automatic algorithms have been developed for Region of interests (ROIs) identification but we found that they were not robust enough to identify all the neurons in the striatum. We thus manually selected ROIs based on morphology using FIJI software. Then we extracted mean grey values and (x, y) coordinates for each ROI/slice. Calcium recordings were 700-1000 frames long (around 1 min) and included 7-9 stimulations of cortical afferents (Figure  S1 ). Let x(t) be the averaged intensity values of pixels in the ROI at time t for one cell. DF/F is obtained using y(t) = (x(t) -x0) / x0, where x0 is the mean value of the 50 % lowest values in the last 10 s.
In a subset of experiments combining different frequencies, the normalized amplitude corresponded to the ratio between the fourth EPSP of the train compared to the first one. We compared the effect for each EPSP of the train and the fourth one was chosen as a representative. Spiking probability was measured as the occurrence of a single action potential induced by a single cortical stimulation of the cerebral cortex. Spiking probability was measured at a stimulation intensity giving around 50 % spikes for the LA cells and the corresponding spiking probability for the same stimulation intensity for HA cells. We repeated 6 to 8 trials to calculate the averaged spiking probability. The latency to spike was the time between the electrical stimulation and the peak of the action potential. Data analysis was carried out in Fitmaster (HEKA Elektronik, Germany).
ACKNOWLEDGMENTS The authors thank  M. Albrieux ,  C. Kellendonk ,  M. Cazorla ,  A. S. Nicot ,  D. Robbe , and  I. Bureau  for careful reading of the manuscript and helpful comments. The authors would like to thank  K. Deisseroth  for making available the AAV-cFos-ERT2-Cre-ERT2-PEST-no WPRE virus,  O. Manzoni  and  I. Bureau  for sharing D1-tomato mice,  S. Rotariu  for help with open-field experiments, the  Photonic Imaging Center, PIC-GIN facility  ( GIS-IBiSA ISdV, GIN ),  Y. Saoudi  for help with image acquisitions, and the GIN animal facility for mouse care. This work was supported by grants from  Neuroglia  (E. F. ; supported G. Z. post-doctoral fellowship); the  University of Grenoble Alpes Data Institute  (N. T. and E. F. );  Agence Nationale de la Recherche   ANR-15-IDEX-02  NeuroCoG in the framework of the  Investissements d'Avenir program  (PhD fellowship for N.
B. ; E. F. and S. A. ),  ANR-19-CE37-0026-01  ProMeSS (E. F. ), and  ANR-18-CE16-0009-01  AXYON (F. S. ); the  Fondation pour la Recherche Medicale (FRM ;  DEI20151234418 , F. S. ); the  Fondation pour la Recherche sur le Cerveau  (FRC; F. S. ); the  Fondation Bettencourt Schueller  (F. S. ); and  AGEMED program  from  INSERM  (F. S. ). The Saudou laboratory is a member of the  Grenoble Center of Excellence in Neurodegeneration (GREEN).
Thermopetrological modeling of present-day subduction zones We model the thermal state of all present-day subduction zones (56 subduction transects  26  , see Methods). We use the thermodynamic database ref. 27 and code Perple_X  28  to build phase diagrams, assuming bulk compositions relevant for the crustal and mantle layers at the subduction trenches ("basalt", Fig  S5a, " gabbro" and "simplified peridotite model" Fig. 1a, b ). To enhance the accuracy of the modeled mineralogical evolution of hydrated peridotites up to mid-mantle depths, we combine thermodynamic modeling to experimentally-derived assemblages from naturally complex systems  24  and we build a hybrid-phase diagram ("complex peridotite model" Fig. 1c , see Methods). At shallow depths, H2O subduction in the crustal layers occurs via amphibole, and at subarc to postarc depths via lawsonite  30.
The phase relations are computed with the thermodynamic code Perple_X 28 using the thermodynamic database of ref 27. More details on the thermodynamic calculations are given in Supplementary Information. For the complex peridotite model, we combine the phase assemblages predicted by thermodynamic calculations to experimental data on phase relations at high PT obtained from natural serpentinites containing magnetite  24  (see below). Complex (natural) peridotite modeling. We first compile available experimental data on phase assemblages in natural  24  and modeled peridotite systems containing Fe 3+ , Al, Ca and Na  35, 36  at temperatures higher than 600 °C and pressures higher than 4 GPa (see Fig. S2b ). These reported phase relations serve as a basis for modifying the pseudosections modeled using Perple_X under these conditions (Fig.