Document Type

Article

Publication Date

11-13-2020

Keywords

JMG; Algorithms, Alzheimer Disease, Brain, Disease Progression, Gene Expression Profiling, Gene Expression Regulation, Humans, Nerve Degeneration, Prefrontal Cortex, Time Factors, Unsupervised Machine Learning

JAX Source

Nat Commun 2020 Nov 13; 11(1):5781

PMID

33188183

DOI

https://doi.org/10.1038/s41467-020-19622-y

Abstract

The temporal molecular changes that lead to disease onset and progression in Alzheimer's disease (AD) are still unknown. Here we develop a temporal model for these unobserved molecular changes with a manifold learning method applied to RNA-Seq data collected from human postmortem brain samples collected within the ROS/MAP and Mayo Clinic RNA-Seq studies. We define an ordering across samples based on their similarity in gene expression and use this ordering to estimate the molecular disease stage-or disease pseudotime-for each sample. Disease pseudotime is strongly concordant with the burden of tau (Braak score, P = 1.0 × 10-5), Aβ (CERAD score, P = 1.8 × 10-5), and cognitive diagnosis (P = 3.5 × 10-7) of late-onset (LO) AD. Early stage disease pseudotime samples are enriched for controls and show changes in basic cellular functions. Late stage disease pseudotime samples are enriched for late stage AD cases and show changes in neuroinflammation and amyloid pathologic processes. We also identify a set of late stage pseudotime samples that are controls and show changes in genes enriched for protein trafficking, splicing, regulation of apoptosis, and prevention of amyloid cleavage pathways. In summary, we present a method for ordering patients along a trajectory of LOAD disease progression from brain transcriptomic data.

Comments

This article is licensed under a Creative Commons Attribution 4.0 International License.

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