Journal of Huntington's disease · 2021
A Multi-Omic Huntington's Disease Transgenic Sheep-Model Database for Investigating Disease Pathogenesis.
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Mears Emily R, Handley Renee R, Grant Matthew J, Reid Suzanne J, Day Benjamin T, Rudiger Skye R, McLaughlan Clive J, Verma Paul J, Bawden Simon C, Patassini Stefano, Unwin Richard D, Cooper Garth J S, Gusella James F, MacDonald Marcy E, Brauning Rudiger, Maclean Paul, Pearson John F, Waldvogel Henry J, Faull Richard L M, Snell Russell G
Аннотация
BACKGROUND: The pathological mechanism of cellular dysfunction and death in Huntington's disease (HD) is not well defined. Our transgenic HD sheep model (OVT73) was generated to investigate these mechanisms and for therapeutic testing. One particular cohort of animals has undergone focused investigation resulting in a large interrelated multi-omic dataset, with statistically significant changes observed comparing OVT73 and control 'omic' profiles and reported in literature.
OBJECTIVE: Here we make this dataset publicly available for the advancement of HD pathogenic mechanism discovery.
METHODS: To enable investigation in a user-friendly format, we integrated seven multi-omic datasets from a cohort of 5-year-old OVT73 (n = 6) and control (n = 6) sheep into a single database utilising the programming language R. It includes high-throughput transcriptomic, metabolomic and proteomic data from blood, brain, and other tissues.
RESULTS: We present the 'multi-omic' HD sheep database as a queryable web-based platform that can be used by the wider HD research community (https://hdsheep.cer.auckland.ac.nz/). The database is supported with a suite of simple automated statistical analysis functions for rapid exploratory analyses. We present examples of its use that validates the integrity relative to results previously reported. The data may also be downloaded for user determined analysis.
CONCLUSION: We propose the use of this online database as a hypothesis generator and method to confirm/refute findings made from patient samples and alternate model systems, to expand our understanding of HD pathogenesis. Importantly, additional tissue samples are available for further investigation of this cohort.
OBJECTIVE: Here we make this dataset publicly available for the advancement of HD pathogenic mechanism discovery.
METHODS: To enable investigation in a user-friendly format, we integrated seven multi-omic datasets from a cohort of 5-year-old OVT73 (n = 6) and control (n = 6) sheep into a single database utilising the programming language R. It includes high-throughput transcriptomic, metabolomic and proteomic data from blood, brain, and other tissues.
RESULTS: We present the 'multi-omic' HD sheep database as a queryable web-based platform that can be used by the wider HD research community (https://hdsheep.cer.auckland.ac.nz/). The database is supported with a suite of simple automated statistical analysis functions for rapid exploratory analyses. We present examples of its use that validates the integrity relative to results previously reported. The data may also be downloaded for user determined analysis.
CONCLUSION: We propose the use of this online database as a hypothesis generator and method to confirm/refute findings made from patient samples and alternate model systems, to expand our understanding of HD pathogenesis. Importantly, additional tissue samples are available for further investigation of this cohort.