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      Pathogenic Mutation of TDP-43 Impairs RNA Processing in a Cell Type-Specific Manner: Implications for the Pathogenesis of ALS/FTLD

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          Abstract

          Transactivating response element DNA-binding protein of 43 kDa (TDP-43), which is encoded by the TARDBP gene, is an RNA-binding protein with fundamental RNA processing activities, and its loss-of-function (LOF) has a central role in the pathogenesis of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). TARDBP mutations are postulated to inactivate TDP-43 functions, leading to impaired RNA processing. However, it has not been fully examined how mutant TDP-43 affects global RNA regulation, especially in human cell models. Here, we examined global RNA processing in forebrain cortical neurons derived from human induced pluripotent stem cells (iPSCs) with a pathogenic TARDBP mutation encoding the TDP-43 K263E protein. In neurons expressing mutant TDP-43, we detected disrupted RNA regulation, including global changes in gene expression, missplicing, and aberrant polyadenylation, all of which were highly similar to those induced by TDP-43 knock-down. This mutation-induced TDP-43 LOF was not because of the cytoplasmic mislocalization of TDP-43. Intriguingly, in nonneuronal cells, including iPSCs and neural progenitor cells (NPCs), we did not observe impairments in RNA processing, thus indicating that the K263E mutation results in neuron-specific LOF of TDP-43. This study characterizes global RNA processing impairments induced by mutant TDP-43 and reveals the unprecedented cell type specificity of TDP-43 LOF in ALS/FTLD pathogenesis.

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          Most cited references41

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              fastp: an ultra-fast all-in-one FASTQ preprocessor

              Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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                Author and article information

                Journal
                eNeuro
                eNeuro
                eneuro
                eNeuro
                eNeuro
                Society for Neuroscience
                2373-2822
                31 May 2022
                7 June 2022
                May-Jun 2022
                : 9
                : 3
                : ENEURO.0061-22.2022
                Affiliations
                [1]Department of Physiology, Keio University School of Medicine , Shinjuku, Tokyo 160-8582, Japan
                Author notes

                H.O. serves as a paid scientific advisor to SanBio Co. Ltd. and K Pharma Inc. All other authors declare no competing financial interests.

                Author contributions: K.I., S.M., and H.O. designed research; K.I., H.I., and T.S. performed research; K.I. analyzed data; K.I. and H.O. wrote the paper.

                This work was supported by funding from Takeda Pharmaceutical Company, Ltd, Japan; Japan Agency for Medical Research and Development (AMED) Grants 19bm0804003, 20bm0804003, and 21bm0804003; and Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research 20H00485 (to H.O.) and Grant-in-Aid for Scientific Research on Innovative Areas “Singularity Biology (No. 8007)” Grant 21H00438 (to K.I.).

                Correspondence should be addressed to Hideyuki Okano at hidokano@ 123456a2.keio.jp .
                Author information
                https://orcid.org/0000-0002-3565-5248
                https://orcid.org/0000-0001-7482-5935
                Article
                eN-NWR-0061-22
                10.1523/ENEURO.0061-22.2022
                9186108
                35641224
                29562cb7-45a9-4743-8a7f-86215bc1ceaa
                Copyright © 2022 Imaizumi et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 7 February 2022
                : 19 April 2022
                : 22 April 2022
                Page count
                Figures: 5, Tables: 0, Equations: 1, References: 41, Pages: 12, Words: 00
                Funding
                Funded by: Takada Pharmaceutical Company
                Funded by: Japan Agency for Medical Research and Development (AMED), doi 10.13039/100009619;
                Award ID: 19bm0804003
                Award ID: 20bm0804003
                Award ID: 21bm0804003
                Funded by: MEXT | Japan Society for the Promotion of Science (JSPS), doi 10.13039/501100001691;
                Award ID: 20H00485
                Award ID: 21H00438
                Categories
                3
                Research Article: New Research
                Disorders of the Nervous System
                Custom metadata
                May/June 2022

                amyotrophic lateral sclerosis,frontotemporal lobar degeneration,induced pluripotent stem cell,tdp-43

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