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Blood and Brain Gene Expression Trajectories Underlying Neuropathology and Cognitive Impairment in Neurodegeneration

Yasser Iturria-Medina, Ahmed F. Khan, Quadri Adewale, Alzheimer’s Disease Neuroimaging Initiative
doi: https://doi.org/10.1101/548974
Yasser Iturria-Medina
1McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada
2Ludmer Centre for NeuroInformatics and Mental Health, Montreal, Canada
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  • For correspondence: iturria.medina@gmail.com
Ahmed F. Khan
1McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada
2Ludmer Centre for NeuroInformatics and Mental Health, Montreal, Canada
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Quadri Adewale
1McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada
2Ludmer Centre for NeuroInformatics and Mental Health, Montreal, Canada
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SUMMARY

Neurodegenerative disorders take decades to develop and their early detection is challenged by confounding non-pathological aging processes. For all neurodegenerative conditions, we lack longitudinal gene expression (GE) data covering their large temporal evolution, which hinders the fully understanding of the underlying dynamic molecular mechanisms. Here, we aimed to overcome this limitation by introducing a novel GE 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 studies), this unsupervised machine learning algorithm strongly predicts neuropathological severity (e.g. Braak, Amyloid and Vonsattel stages). Furthermore, when applied to in-vivo blood samples (ADNI), it predicts 97% of the variance in memory deterioration and its future declining rate, supporting the identification of a powerful and minimally invasive (blood-based) tool for early clinical screening and disease prevention. This technique also allows the discovery of genes and molecular pathways, in both peripheral and brain tissues, that are highly predictive of disease evolution. Eighty percent of the most predictive molecular pathways identified in the brain were also top predictors in the blood. The GE-cTI is a promising tool for revealing complex neuropathological mechanisms, with direct implications for implementing personalized dynamic treatments in neurology.

  • - Unsupervised learning detects enriched gene expression (GE) trajectories in disease

  • - These plasma and brain GE trajectories predict neuropathology and future cognitive impairment

  • - Most predictive molecular functions/pathways in the brain are also top predictors in the plasma

  • - By identifying plasma GE trajectories, patients can be easily screened and follow dynamic treatments

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 13, 2019.
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Blood and Brain Gene Expression Trajectories Underlying Neuropathology and Cognitive Impairment in Neurodegeneration
Yasser Iturria-Medina, Ahmed F. Khan, Quadri Adewale, Alzheimer’s Disease Neuroimaging Initiative
bioRxiv 548974; doi: https://doi.org/10.1101/548974
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Blood and Brain Gene Expression Trajectories Underlying Neuropathology and Cognitive Impairment in Neurodegeneration
Yasser Iturria-Medina, Ahmed F. Khan, Quadri Adewale, Alzheimer’s Disease Neuroimaging Initiative
bioRxiv 548974; doi: https://doi.org/10.1101/548974

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