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Functional characterization of variants of unknown significance in a spinocerebellar ataxia patient using an unsupervised machine learning pipeline
CAG-expanded ATXN7 has been previously defined in the pathogenesis of spinocerebellar ataxia type 7 (SCA7), a polyglutamine expansion autosomal dominant cerebellar ataxia. Pathology in SCA7 occurs as a result of a CAG triplet repeat expansion in excess of 37 in the first exon of ATXN7, which encodes...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010413/ https://www.ncbi.nlm.nih.gov/pubmed/35422034 http://dx.doi.org/10.1038/s41439-022-00188-8 |
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author | Nath, Siddharth Caron, Nicholas S. May, Linda Gluscencova, Oxana B. Kolesar, Jill Brady, Lauren Kaufman, Brett A. Boulianne, Gabrielle L. Rodriguez, Amadeo R. Tarnopolsky, Mark A. Truant, Ray |
author_facet | Nath, Siddharth Caron, Nicholas S. May, Linda Gluscencova, Oxana B. Kolesar, Jill Brady, Lauren Kaufman, Brett A. Boulianne, Gabrielle L. Rodriguez, Amadeo R. Tarnopolsky, Mark A. Truant, Ray |
author_sort | Nath, Siddharth |
collection | PubMed |
description | CAG-expanded ATXN7 has been previously defined in the pathogenesis of spinocerebellar ataxia type 7 (SCA7), a polyglutamine expansion autosomal dominant cerebellar ataxia. Pathology in SCA7 occurs as a result of a CAG triplet repeat expansion in excess of 37 in the first exon of ATXN7, which encodes ataxin-7. SCA7 presents clinically with spinocerebellar ataxia and cone-rod dystrophy. Here, we present a novel spinocerebellar ataxia variant occurring in a patient with mutations in both ATXN7 and TOP1MT, which encodes mitochondrial topoisomerase I (top1mt). Using machine-guided, unbiased microscopy image analysis, we demonstrate alterations in ataxin-7 subcellular localization, and through high-fidelity measurements of cellular respiration, bioenergetic defects in association with top1mt mutations. We identify ataxin-7 Q35P and top1mt R111W as deleterious mutations, potentially contributing to disease states. We recapitulate our mutations through Drosophila genetic models. Our work provides important insight into the cellular biology of ataxin-7 and top1mt and offers insight into the pathogenesis of spinocerebellar ataxia applicable to multiple subtypes of the illness. Moreover, our study demonstrates an effective pipeline for the characterization of previously unreported genetic variants at the level of cell biology. |
format | Online Article Text |
id | pubmed-9010413 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90104132022-04-28 Functional characterization of variants of unknown significance in a spinocerebellar ataxia patient using an unsupervised machine learning pipeline Nath, Siddharth Caron, Nicholas S. May, Linda Gluscencova, Oxana B. Kolesar, Jill Brady, Lauren Kaufman, Brett A. Boulianne, Gabrielle L. Rodriguez, Amadeo R. Tarnopolsky, Mark A. Truant, Ray Hum Genome Var Article CAG-expanded ATXN7 has been previously defined in the pathogenesis of spinocerebellar ataxia type 7 (SCA7), a polyglutamine expansion autosomal dominant cerebellar ataxia. Pathology in SCA7 occurs as a result of a CAG triplet repeat expansion in excess of 37 in the first exon of ATXN7, which encodes ataxin-7. SCA7 presents clinically with spinocerebellar ataxia and cone-rod dystrophy. Here, we present a novel spinocerebellar ataxia variant occurring in a patient with mutations in both ATXN7 and TOP1MT, which encodes mitochondrial topoisomerase I (top1mt). Using machine-guided, unbiased microscopy image analysis, we demonstrate alterations in ataxin-7 subcellular localization, and through high-fidelity measurements of cellular respiration, bioenergetic defects in association with top1mt mutations. We identify ataxin-7 Q35P and top1mt R111W as deleterious mutations, potentially contributing to disease states. We recapitulate our mutations through Drosophila genetic models. Our work provides important insight into the cellular biology of ataxin-7 and top1mt and offers insight into the pathogenesis of spinocerebellar ataxia applicable to multiple subtypes of the illness. Moreover, our study demonstrates an effective pipeline for the characterization of previously unreported genetic variants at the level of cell biology. Nature Publishing Group UK 2022-04-14 /pmc/articles/PMC9010413/ /pubmed/35422034 http://dx.doi.org/10.1038/s41439-022-00188-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Nath, Siddharth Caron, Nicholas S. May, Linda Gluscencova, Oxana B. Kolesar, Jill Brady, Lauren Kaufman, Brett A. Boulianne, Gabrielle L. Rodriguez, Amadeo R. Tarnopolsky, Mark A. Truant, Ray Functional characterization of variants of unknown significance in a spinocerebellar ataxia patient using an unsupervised machine learning pipeline |
title | Functional characterization of variants of unknown significance in a spinocerebellar ataxia patient using an unsupervised machine learning pipeline |
title_full | Functional characterization of variants of unknown significance in a spinocerebellar ataxia patient using an unsupervised machine learning pipeline |
title_fullStr | Functional characterization of variants of unknown significance in a spinocerebellar ataxia patient using an unsupervised machine learning pipeline |
title_full_unstemmed | Functional characterization of variants of unknown significance in a spinocerebellar ataxia patient using an unsupervised machine learning pipeline |
title_short | Functional characterization of variants of unknown significance in a spinocerebellar ataxia patient using an unsupervised machine learning pipeline |
title_sort | functional characterization of variants of unknown significance in a spinocerebellar ataxia patient using an unsupervised machine learning pipeline |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010413/ https://www.ncbi.nlm.nih.gov/pubmed/35422034 http://dx.doi.org/10.1038/s41439-022-00188-8 |
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