Mapping brain gene coexpression in daytime transcriptomes unveils diurnal molecular networks and deciphers perturbation gene signatures

Wang, Nan; Langfelder, Peter; Stricos, Matthew; Ramanathan, Lalini; Richman, Jeffrey; Vaca, Raymond; Plascencia, Mary; Gu, Xiaofeng; Zhang, Shasha; Tamai, T. Katherine; Zhang, Liguo; Gao, Fuying; Ouk, Koliane; Lü, Xiang; Іванов, Л. В.; Vogt, Thomas; Lü, Qing; Morton, A. Jennifer; Colwell, Christopher S.; Aaronson, Jeffrey S.; Rosinski, Jim; Horvath, Steve; Yang, Xia

doi:10.1016/j.neuron.2022.09.028

Cited by 8 publications

(1 citation statement)

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“…Indeed, there is growing evidence that circadian disruption in HD is widespread. Most recently a study mapping brain gene coexpression in daytime transcriptomes revealed diurnal molecular networks that showed perturbations in gene signatures in HD mice [53]. The mechanisms underlying the changes we see in the rhythms of metabolites in HD are unknown.…”

Section: Discussionmentioning

confidence: 97%

Metabolomic Analysis of Plasma in Huntington’s Disease Transgenic Sheep (Ovis aries) Reveals Progressive Circadian Rhythm Dysregulation

Spick

Hancox

Chowdhury

et al. 2023

JHD

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Background: Metabolic abnormalities have long been predicted in Huntington’s disease (HD) but remain poorly characterized. Chronobiological dysregulation has been described in HD and may include abnormalities in circadian-driven metabolism. Objective: Here we investigated metabolite profiles in the transgenic sheep model of HD (OVT73) at presymptomatic ages. Our goal was to understand changes to the metabolome as well as potential metabolite rhythm changes associated with HD. Methods: We used targeted liquid chromatography mass spectrometry (LC-MS) metabolomics to analyze metabolites in plasma samples taken from female HD transgenic and normal (control) sheep aged 5 and 7 years. Samples were taken hourly across a 27-h period. The resulting dataset was investigated by machine learning and chronobiological analysis. Results: The metabolic profiles of HD and control sheep were separable by machine learning at both ages. We found both absolute and rhythmic differences in metabolites in HD compared to control sheep at 5 years of age. An increase in both the number of disturbed metabolites and the magnitude of change of acrophase (the time at which the rhythms peak) was seen in samples from 7-year-old HD compared to control sheep. There were striking similarities between the dysregulated metabolites identified in HD sheep and human patients (notably of phosphatidylcholines, amino acids, urea, and threonine). Conclusion: This work provides the first integrated analysis of changes in metabolism and circadian rhythmicity of metabolites in a large animal model of presymptomatic HD.

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Section: Discussionmentioning

confidence: 97%