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Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach

Authors :
Sandeep Amberkar
Solveig K. Sieberts
Joshua M. Shulman
Lei Yu
Mariet Allen
Christoph Preuss
Wenbin Wei
Yooree Chae
Benjamin A. Logsdon
Thanneer M. Perumal
David C. Airey
David A. Bennett
Phillip J. Ebert
Karol Estrada
Cory C. Funk
Nathan D. Price
Chris Gaiteri
Michael W. Decker
Phil Snyder
Nilufer Ertekin-Taner
Sumit Mukherjee
Allan I. Levey
Eric B. Dammer
Ayla Ergun
David A. Collier
Eric E. Schadt
Minghui Wang
Hans-Ulrich Klein
Kristen D. Dang
Gregory W. Carter
Winston Hide
James A. Eddy
Todd E. Golde
Sara Mostafavi
Phillip L. De Jager
Zhandong Liu
Bin Zhang
Xue Wang
Vivek Swarup
Larsson Omberg
Gyan Srivastava
Lara M. Mangravite
Publication Year :
2019
Publisher :
Cold Spring Harbor Laboratory, 2019.

Abstract

SUMMARYAlzheimer’s disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related biological processes. This complexity contributes, in part, to existing difficulties in the identification of successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research studies. From this analysis, five consensus clusters were identified that described the major sources of AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. AD genetic associations, previously studied AD-related biological processes, and AD targets under active investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated strong heterogeneity between males and females in AD-related expression that was consistently observed across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, which was specific for genes that showed the strongest association with changes in AD-related gene expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been previously identified and was enriched for genes related to proteostasis. This study provides an atlas to map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery efforts.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....6c6e18f8ec5f320c3a3226da15020924
Full Text :
https://doi.org/10.1101/510420