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      Large-scale epidemiological study on feline autosomal dominant polycystic kidney disease and identification of novel PKD1 gene variants

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          Abstract

          Objectives

          Autosomal dominant polycystic kidney disease (ADPKD) is a common inherited disease in cats. In most cases, the responsible abnormality is a nonsense single nucleotide polymorphism in exon 29 of the PKD1 gene (chrE3:g.42858112C>A, the conventional PKD1 variant). The aim of this study was to conduct a large-scale epidemiological study of ADPKD caused by the conventional PKD1 variant in Japan and to search for novel polymorphisms by targeted resequencing of the PKD1 using a next-generation sequencer.

          Methods

          A total of 1281 cats visiting the Veterinary Medical Center of the University of Tokyo were included in this study. DNA was extracted from the blood of each cat. We established a novel TaqMan real-time PCR genotyping assay for the conventional PKD1 variant, and all cases were examined for the presence of this variant. Targeted resequencing of all exons of the PKD1 was performed on the DNA of 23 cats with the conventional PKD1 variant, six cats diagnosed with cystic kidneys but without this variant, and 61 wild-type normal cats.

          Results

          Among the 1281 cats examined in this study, 23 (1.8%) harboured the conventional PKD1 variant. The odds of having the conventional PKD1 variant were significantly higher in Persian cats, Scottish Folds and Exotic Shorthairs than in the other breeds, although the number of cases in each breed was small. Furthermore, we identified four variants unique to cats with cystic kidneys that were not found in wild-type normal cats, all of which were in exon 15. In particular, two (chrE:g.42848725delC, pGly1641fs and chrE:g.42850283C>T, pArg2162Trp) were candidate variants.

          Conclusions and relevance

          This study revealed that the conventional PKD1 variant was prevalent in Scottish Fold, Persian and Exotic Shorthair breeds in Japan, and variants in exon 15 of PKD1, in addition to the conventional variant in exon 29, would be key factors in the pathogenesis of ADPKD in cats.

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          Most cited references29

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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              Fast and accurate short read alignment with Burrows–Wheeler transform

              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Journal
                J Feline Med Surg
                J Feline Med Surg
                JFM
                spjfm
                Journal of Feline Medicine and Surgery
                SAGE Publications (Sage UK: London, England )
                1098-612X
                1532-2750
                25 July 2023
                July 2023
                : 25
                : 7
                : 1098612X231185393
                Affiliations
                [1 ]Department of Veterinary Clinical Pathobiology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
                [2 ]Veterinary Medical Center, The University of Tokyo, Tokyo, Japan
                [3 ]Anicom Specialty Medical Institute, Kanagawa, Japan
                [4 ]Department of Informatics, National Institute of Genetics, Research Organization of Information and Systems, Shizuoka, Japan
                Author notes
                [*]Shingo Maeda DVM, PhD, Department of Veterinary Pathobiology, Graduate School of Agricultural and Life Science, University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan Email: amaeda@ 123456g.ecc.u-tokyo.ac.jp
                [*]

                Fumitaka Shitamori, Ayaka Nonogaki and Tomoki Motegi contributed equally to this work

                Author information
                https://orcid.org/0000-0002-6811-0973
                https://orcid.org/0000-0003-4636-6959
                https://orcid.org/0000-0003-3457-9295
                https://orcid.org/0000-0002-6211-8704
                https://orcid.org/0000-0003-1413-1258
                Article
                10.1177_1098612X231185393
                10.1177/1098612X231185393
                10812055
                37489504
                bdeb2e71-638e-4543-866b-5166eb6bbe7a
                © The Author(s) 2023

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                Funding
                Funded by: Japan Society for the Promotion of Science, FundRef https://doi.org/10.13039/501100001691;
                Award ID: KAKENHI (Grant Number:19H00968)
                Funded by: Japan Society for the Promotion of Science, FundRef https://doi.org/10.13039/501100001691;
                Award ID: KAKENHI (Grant Number:23H00357)
                Funded by: Anicom Capital, ;
                Award ID: Research Grant (EVOLVE)
                Categories
                Original Article
                Custom metadata
                July 2023
                ts1

                Surgery
                autosomal dominant polycystic kidney disease,epidemiology,genotyping,target resequencing

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