Blood and brain gene expression trajectories mirror neuropathology and clinical deterioration in neurodegeneration.

ABSTRACT: Most prevalent neurodegenerative disorders take decades to develop and their early detection is challenged by confounding non-pathological ageing processes. For all neurodegenerative conditions, we continue to lack longitudinal gene expression data covering their large temporal evolution, which hinders the understanding of the underlying dynamic molecular mechanisms. Here, we overcome this key limitation by introducing a novel gene expression contrastive trajectory inference (GE-cTI) method that reveals enriched temporal patterns in a diseased population. Evaluated on 1969 subjects in the spectrum of late-onset Alzheimer's and Huntington's diseases (from ROSMAP, HBTRC and ADNI datasets), this unsupervised machine learning algorithm strongly predicts neuropathological severity (e.g. Braak,

SUBMITTER: Iturria-Medina Y 

PROVIDER: S-EPMC7009530 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature