Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process

Chao Wang, Samantha M. Scott, Kanagaraj Subramanian, Salvatore Loguercio, Pei Zhao, Darren M. Hutt, Nicole Y. Farhat, Forbes D. Porter and William E. Balch ()
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Chao Wang: Scripps Research
Samantha M. Scott: Scripps Research
Kanagaraj Subramanian: Scripps Research
Salvatore Loguercio: Scripps Research
Pei Zhao: Scripps Research
Darren M. Hutt: Scripps Research
Nicole Y. Farhat: National Institutes of Health
Forbes D. Porter: National Institutes of Health
William E. Balch: Scripps Research

Nature Communications, 2019, vol. 10, issue 1, 1-15

Abstract: Abstract 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.

Date: 2019
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DOI: 10.1038/s41467-019-12969-x

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