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Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process
To understand the impact of epigenetics on human misfolding disease, we apply Gaussian-process regression (GPR) based machine learning (ML) (GPR-ML) through variation spatial profiling (VSP). VSP generates population-based matrices describing the spatial covariance (SCV) relationships that link gene...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6838179/ https://www.ncbi.nlm.nih.gov/pubmed/31699992 http://dx.doi.org/10.1038/s41467-019-12969-x |
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author | Wang, Chao Scott, Samantha M. Subramanian, Kanagaraj Loguercio, Salvatore Zhao, Pei Hutt, Darren M. Farhat, Nicole Y. Porter, Forbes D. Balch, William E. |
author_facet | Wang, Chao Scott, Samantha M. Subramanian, Kanagaraj Loguercio, Salvatore Zhao, Pei Hutt, Darren M. Farhat, Nicole Y. Porter, Forbes D. Balch, William E. |
author_sort | Wang, Chao |
collection | PubMed |
description | To understand the impact of epigenetics on human misfolding disease, we apply Gaussian-process regression (GPR) based machine learning (ML) (GPR-ML) through variation spatial profiling (VSP). VSP generates population-based matrices describing the spatial covariance (SCV) relationships that link genetic diversity to fitness of the individual in response to histone deacetylases inhibitors (HDACi). Niemann-Pick C1 (NPC1) is a Mendelian disorder caused by >300 variants in the NPC1 gene that disrupt cholesterol homeostasis leading to the rapid onset and progression of neurodegenerative disease. We determine the sequence-to-function-to-structure relationships of the NPC1 polypeptide fold required for membrane trafficking and generation of a tunnel that mediates cholesterol flux in late endosomal/lysosomal (LE/Ly) compartments. HDACi treatment reveals unanticipated epigenomic plasticity in SCV relationships that restore NPC1 functionality. GPR-ML based matrices capture the epigenetic processes impacting information flow through central dogma, providing a framework for quantifying the effect of the environment on the healthspan of the individual. |
format | Online Article Text |
id | pubmed-6838179 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68381792019-11-12 Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process Wang, Chao Scott, Samantha M. Subramanian, Kanagaraj Loguercio, Salvatore Zhao, Pei Hutt, Darren M. Farhat, Nicole Y. Porter, Forbes D. Balch, William E. Nat Commun Article To understand the impact of epigenetics on human misfolding disease, we apply Gaussian-process regression (GPR) based machine learning (ML) (GPR-ML) through variation spatial profiling (VSP). VSP generates population-based matrices describing the spatial covariance (SCV) relationships that link genetic diversity to fitness of the individual in response to histone deacetylases inhibitors (HDACi). Niemann-Pick C1 (NPC1) is a Mendelian disorder caused by >300 variants in the NPC1 gene that disrupt cholesterol homeostasis leading to the rapid onset and progression of neurodegenerative disease. We determine the sequence-to-function-to-structure relationships of the NPC1 polypeptide fold required for membrane trafficking and generation of a tunnel that mediates cholesterol flux in late endosomal/lysosomal (LE/Ly) compartments. HDACi treatment reveals unanticipated epigenomic plasticity in SCV relationships that restore NPC1 functionality. GPR-ML based matrices capture the epigenetic processes impacting information flow through central dogma, providing a framework for quantifying the effect of the environment on the healthspan of the individual. Nature Publishing Group UK 2019-11-07 /pmc/articles/PMC6838179/ /pubmed/31699992 http://dx.doi.org/10.1038/s41467-019-12969-x Text en © The Author(s) 2019 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/. |
spellingShingle | Article Wang, Chao Scott, Samantha M. Subramanian, Kanagaraj Loguercio, Salvatore Zhao, Pei Hutt, Darren M. Farhat, Nicole Y. Porter, Forbes D. Balch, William E. Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process |
title | Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process |
title_full | Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process |
title_fullStr | Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process |
title_full_unstemmed | Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process |
title_short | Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process |
title_sort | quantitating the epigenetic transformation contributing to cholesterol homeostasis using gaussian process |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6838179/ https://www.ncbi.nlm.nih.gov/pubmed/31699992 http://dx.doi.org/10.1038/s41467-019-12969-x |
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