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Genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease
Hundreds of mutations in a single gene result in rare diseases, but why mutations induce severe or attenuated states remains poorly understood. Defect in glycine decarboxylase (GLDC) causes Non-ketotic Hyperglycinemia (NKH), a neurological disease associated with elevation of plasma glycine. We unif...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7850488/ https://www.ncbi.nlm.nih.gov/pubmed/33524012 http://dx.doi.org/10.1371/journal.pgen.1009307 |
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author | Farris, Joseph Alam, Md Suhail Rajashekara, Arpitha Mysore Haldar, Kasturi |
author_facet | Farris, Joseph Alam, Md Suhail Rajashekara, Arpitha Mysore Haldar, Kasturi |
author_sort | Farris, Joseph |
collection | PubMed |
description | Hundreds of mutations in a single gene result in rare diseases, but why mutations induce severe or attenuated states remains poorly understood. Defect in glycine decarboxylase (GLDC) causes Non-ketotic Hyperglycinemia (NKH), a neurological disease associated with elevation of plasma glycine. We unified a human multiparametric NKH mutation scale that separates severe from attenuated neurological disease with new in silico tools for murine and human genome level-analyses, gathered in vivo evidence from mice engineered with top-ranking attenuated and a highly pathogenic mutation, and integrated the data in a model of pre- and post-natal disease outcomes, relevant for over a hundred major and minor neurogenic mutations. Our findings suggest that highly severe neurogenic mutations predict fatal, prenatal disease that can be remedied by metabolic supplementation of dams, without amelioration of persistent plasma glycine. The work also provides a systems approach to identify functional consequences of mutations across hundreds of genetic diseases. Our studies provide a new framework for a large scale understanding of mutation functions and the prediction that severity of a neurogenic mutation is a direct measure of pre-natal disease in neurometabolic NKH mouse models. This framework can be extended to analyses of hundreds of monogenetic rare disorders where the underlying genes are known but understanding of the vast majority of mutations and why and how they cause disease, has yet to be realized. |
format | Online Article Text |
id | pubmed-7850488 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78504882021-02-09 Genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease Farris, Joseph Alam, Md Suhail Rajashekara, Arpitha Mysore Haldar, Kasturi PLoS Genet Research Article Hundreds of mutations in a single gene result in rare diseases, but why mutations induce severe or attenuated states remains poorly understood. Defect in glycine decarboxylase (GLDC) causes Non-ketotic Hyperglycinemia (NKH), a neurological disease associated with elevation of plasma glycine. We unified a human multiparametric NKH mutation scale that separates severe from attenuated neurological disease with new in silico tools for murine and human genome level-analyses, gathered in vivo evidence from mice engineered with top-ranking attenuated and a highly pathogenic mutation, and integrated the data in a model of pre- and post-natal disease outcomes, relevant for over a hundred major and minor neurogenic mutations. Our findings suggest that highly severe neurogenic mutations predict fatal, prenatal disease that can be remedied by metabolic supplementation of dams, without amelioration of persistent plasma glycine. The work also provides a systems approach to identify functional consequences of mutations across hundreds of genetic diseases. Our studies provide a new framework for a large scale understanding of mutation functions and the prediction that severity of a neurogenic mutation is a direct measure of pre-natal disease in neurometabolic NKH mouse models. This framework can be extended to analyses of hundreds of monogenetic rare disorders where the underlying genes are known but understanding of the vast majority of mutations and why and how they cause disease, has yet to be realized. Public Library of Science 2021-02-01 /pmc/articles/PMC7850488/ /pubmed/33524012 http://dx.doi.org/10.1371/journal.pgen.1009307 Text en © 2021 Farris et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Farris, Joseph Alam, Md Suhail Rajashekara, Arpitha Mysore Haldar, Kasturi Genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease |
title | Genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease |
title_full | Genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease |
title_fullStr | Genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease |
title_full_unstemmed | Genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease |
title_short | Genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease |
title_sort | genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7850488/ https://www.ncbi.nlm.nih.gov/pubmed/33524012 http://dx.doi.org/10.1371/journal.pgen.1009307 |
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