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Blood transcriptomic signatures associated with molecular changes in the brain and clinical outcomes in Parkinson's disease.

ABSTRACT: The ability to use blood to predict the outcomes of Parkinson's disease, including disease progression and cognitive and motor complications, would be of significant clinical value. We undertook bulk RNA sequencing from the caudate and putamen of postmortem Parkinson's disease (n = 35) and control (n = 40) striatum, and compared molecular profiles with clinical features and bulk RNA sequencing data obtained from antemortem peripheral blood. Cognitive and motor complications of Parkinson's disease were associated with molecular changes in the caudate (stress response) and putamen (endothelial pathways) respectively. Later and earlier-onset Parkinson's disease were molecularly distinct, and disease duration was associated with changes in caudate (oligodendrocyte development) and putamen (cellular senescence), respectively. Transcriptome patterns in the postmortem Parkinson's disease brain were also evident in antemortem peripheral blood, and correlated with clinical features of the disease. Together, these findings identify molecular signatures in Parkinson's disease patients' brain and blood of potential pathophysiologic and prognostic importance.

SUBMITTER: Irmady K

PROVIDER: S-EPMC10322907 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Blood transcriptomic signatures associated with molecular changes in the brain and clinical outcomes in Parkinson's disease.

Irmady Krithi K Hale Caryn R CR Qadri Rizwana R Fak John J Simelane Sitsandziwe S Carroll Thomas T Przedborski Serge S Darnell Robert B RB

Nature communications 20230705 1

The ability to use blood to predict the outcomes of Parkinson's disease, including disease progression and cognitive and motor complications, would be of significant clinical value. We undertook bulk RNA sequencing from the caudate and putamen of postmortem Parkinson's disease (n = 35) and control (n = 40) striatum, and compared molecular profiles with clinical features and bulk RNA sequencing data obtained from antemortem peripheral blood. Cognitive and motor complications of Parkinson's diseas ...[more]

PMID: 37407548

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Project description:Brain tissue gene expression from donors with and without Alzheimer's disease has been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyse RNA-seq data from 1114 brain donors from the Accelerating Medicines Project for Alzheimer's Disease consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-β plaque, tau neurofibrillary tangles and clinical severity in multiple Alzheimer's disease dementia populations. Starting from the cross-sectional data in the Religious Orders Study and Memory and Aging Project cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors Alzheimer's disease progression. A severity index was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this data set, severity indexes were found to be very closely correlated with all Alzheimer's disease neuropathology biomarkers (R ∼ 0.5, P < 1e-11) and global cognitive function (R = -0.68, P < 2.2e-16). We then applied the model to additional transcriptomic data sets from different brain regions (MAYO, n = 266; Mount Sinai Brain Bank, n = 214), and observed that the model remained significantly predictive (P < 1e-3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with Alzheimer's disease co-expression regulatory networks, resolving four discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types, respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with Alzheimer's disease, other age-related neurological disorders and controls, and revealed that the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyse heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of neurodegenerative diseases like Alzheimer's disease.

Dataset Information

Blood transcriptomic signatures associated with molecular changes in the brain and clinical outcomes in Parkinson's disease.

Publications

Blood transcriptomic signatures associated with molecular changes in the brain and clinical outcomes in Parkinson's disease.

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