PT  - JOURNAL ARTICLE
AU  - Sumit Mukherjee
AU  - Laura Heath
AU  - Christoph Preuss
AU  - Suman Jayadev
AU  - Gwenn A. Garden
AU  - Anna K Greenwood
AU  - Solveig K Sieberts
AU  - Phillip L De Jager
AU  - Nilufer Ertekin-Taner
AU  - Gregory W Carter
AU  - Lara M Mangravite
AU  - Benjamin A Logsdon
TI  - Molecular estimation of neurodegeneration pseudotime in older brains
AID  - 10.1101/686824
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 686824
4099  - http://biorxiv.org/content/early/2020/10/23/686824.short
4100  - http://biorxiv.org/content/early/2020/10/23/686824.full
AB  - The temporal molecular changes that lead to disease onset and progression in Alzheimer’s disease 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 LOAD. 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.Competing Interest StatementThe authors have declared no competing interest.