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      Kidney Organoids Generated Using an Allelic Series of NPHS2 Point Variants Reveal Distinct Intracellular Podocin Mistrafficking

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          Abstract

          Abstract

          Significance Statement

          Missense variants of NPHS2 that cause mistrafficking of the encoded protein, PODOCIN, have been associated with steroid-resistant nephrotic syndrome. However, most studies have overexpressed such variants in 2D nonpodocyte cells. This study describes the generation and characterization of human kidney organoids representing an allelic series of homozygous NPHS2 missense variants. The strategy revealed a previously unappreciated reduction in variant PODOCIN protein, variant-specific subcellular localization, and specific effects on NEPHRIN association. All variants showed apoptosis in the absence of endoplasmic reticulum stress. Engineering endogenous NPHS2 variants to model in 3D human organoids provides a more accurate view of the pathobiology and a toolkit to screen compounds for reduction of variant protein degradation and mistrafficking.

          Background

          NPHS2 variants are the most common cause of steroid-resistant nephrotic syndrome in children >1 month old. Missense NPHS2 variants were reported to cause mistrafficking of the encoded protein, PODOCIN, but this conclusion was on the basis of overexpression in some nonpodocyte cell lines.

          Methods

          We generated a series of human induced pluripotent stem cell (iPSC) lines bearing pathogenic missense variants of NPHS2, encoding the protein changes p.G92C, p.P118L, p.R138Q, p.R168H, and p.R291W, and control lines. iPSC lines were also generated from a patient with steroid-resistant nephrotic syndrome (p.R168H homozygote) and a healthy heterozygous parent. All lines were differentiated into kidney organoids. Immunofluorescence assessed PODOCIN expression and subcellular localization. Podocytes were transcriptionally profiled and PODOCIN-NEPHRIN interaction interrogated.

          Results

          All variant lines revealed reduced levels of PODOCIN protein in the absence of reduced transcription. Although wild-type PODOCIN localized to the membrane, distinct variant proteins displayed unique patterns of subcellular protein trafficking, some unreported. P118L and R138Q were preferentially retained in the endoplasmic reticulum (ER); R168H and R291W accumulated in the Golgi. Podocyte profiling demonstrated minimal disease-associated transcriptional change. All variants displayed podocyte-specific apoptosis, which was not linked to ER stress. NEPHRIN-PODOCIN colocalization elucidated the variant-specific effect on NEPHRIN association and hence NEPHRIN trafficking.

          Conclusions

          Specific variants of endogenous NPHS2 result in distinct subcellular PODOCIN localization within organoid podocytes. Understanding the effect of each variant on protein levels and localization and the effect on NEPHRIN provides additional insight into the pathobiology of NPHS2 variants.

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          Most cited references52

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Journal
                J Am Soc Nephrol
                J Am Soc Nephrol
                JASN
                JASN
                Journal of the American Society of Nephrology : JASN
                American Society of Nephrology
                1046-6673
                1533-3450
                January 2023
                27 September 2022
                : 34
                : 1
                : 88-109
                Affiliations
                [1 ]Murdoch Children’s Research Institute, Melbourne, Australia
                [2 ]Department of Paediatrics, University of Melbourne, Melbourne, Australia
                [3 ]Novo Nordisk Foundation Centre for Stem Cell Medicine, University of Copenhagen, Copenhagen, Denmark
                [4 ]Royal Children’s Hospital, Melbourne, Australia
                [5 ]Monash Bioinformatics Platform, Monash University, Clayton, Australia
                [6 ]Department of Paediatric Nephrology, Bristol Royal Hospital for Children, Bristol, United Kingdom
                [7 ]Australian Regenerative Medicine Institute, Clayton, Australia
                Author notes
                Correspondence: Dr. Melissa H. Little, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Flemington Road, Parkville, Victoria 3052, Australia; or Aude Dorison, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Flemington Road, Parkville, Victoria 3052, Australia. Email: melissa.little@ 123456mcri.edu.au or aude.dorison@ 123456mcri.edu.au
                Author information
                https://orcid.org/0000-0002-8329-8767
                https://orcid.org/0000-0002-7447-5370
                https://orcid.org/0000-0001-6635-8855
                https://orcid.org/0000-0002-6752-9633
                https://orcid.org/0000-0002-9808-4518
                https://orcid.org/0000-0001-6315-4777
                https://orcid.org/0000-0003-0380-2263
                Article
                JASN-2022-001011 00013
                10.1681/ASN.2022060707
                10101587
                36167728
                4aa045c1-097f-4945-982a-48c4ca4c7845
                Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Society of Nephrology.

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

                History
                : 23 June 2022
                : 6 September 2022
                Page count
                supplementary-material: 11, Figures: 8, Tables: 6, Equations: 1, References: 50, Pages: 22
                Funding
                Funded by: Medical Research Future Fund
                Award ID: 2007286
                Funded by: National Health and Medical Research Council, doi 10.13039/501100000925;
                Award ID: GNT1136085
                Funded by: Australian Research Council, doi 10.13039/501100000923;
                Award ID: SRI110001002
                Funded by: Novo Nordisk Foundation, doi 10.13039/501100009708;
                Award ID: NNF21CC0073729
                Categories
                Basic Research
                Genetic Disease of the Kidney
                Custom metadata
                T

                Nephrology
                kidney disease,genetic kidney disease,glomerular disease,nephrotic syndrome,podocyte,stem cell,malfolding proteins,organoids

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