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

Abstract

52Alzheimer's disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related 53 biological processes. This complexity contributes, in part, to existing difficulties in the identification of 54 successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to 55 characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular 56 models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-57 analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression 58 methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research 59 studies. From this analysis, five consensus clusters were identified that described the major sources of 60 AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. 61AD genetic associations, previously studied AD-related biological processes, and AD targets under active 62 investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated 63 strong heterogeneity between males and females in AD-related expression that was consistently observed 64 across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams 65 were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, 66 which was specific for genes that showed the strongest association with changes in AD-related gene 67 expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules 68 that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been 69 previously identified and was enriched for genes related to proteostasis. This study provides an atlas to 70 map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery 71 efforts.

Genetic control of the human brain proteome

“…Briefly, RNA was extracted from cortically dissected sections of dPFC grey matter and samples with RNA integrity numbers (RIN) over 5 were used to prepare RNA-Seq libraries using strand-specific dUTP method with poly-A selection [27,28] using the Illumina HiSeq with 101-bp paired-end reads to a target coverage of 50 million reads per library. Raw RNA-Seq reads were aligned to a GRCh38 reference genome and gene counts were computed using STAR [29] as described in reference [30]. We obtained RNA-Seq data from synapse (ID: syn17010685) and performed the following quality control measures in a subset of individuals with normal cognition defined by a clinical diagnosis of no cognitive impairment rendered at death.…”

Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

“…One effort, the Genotype-Tissue Expression (GTEx) project 21,22 , has profiled a broad range of tissues (42 distinct) for eQTL discovery, however, samples sizes in brain have been small (typically 100-150). Recently, efforts to understand gene expression changes in neuropsychiatric [23][24][25] and neurodegenerative diseases [26][27][28][29][30][31][32][33] have generated brain RNAseq from disease and normal tissue, as well as genomewide genotypes. These analyses have found little evidence for widespread disease-specific eQTL, as well as high cross-cohort overlap 24,34 , meaning that most eQTL detected are disease-condition independent.…”

“…Here we generate a public eQTL resource from cerebral cortical tissue using 1433 samples from 4 cohorts from the CommonMind Consortium (CMC) 23,24 and Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) Consortium 29,30 , as well as eQTL for cerebellum using 261 samples from AMP-AD. We show that eQTL discovered in GTEx, which consists of control individuals (without disease) only, are replicated in this larger brain eQTL resource.…”

Identification of conserved proteomic networks in neurodegenerative dementia

“…One powerful approach to provide a data-driven framework for connecting genetic risk to molecular processes disrupted in disease is gene network analysis, which leverages gene coexpression to improve mechanistic models of pathophysiology (Parikshak et al, 2015). To accelerate this process, the National Institutes of Health (NIH) developed the Target Identification and Preclinical Validation Project of the Accelerating Medicines Project -Alzheimer's Disease (AMP-AD) consortium (Hodes and Buckholtz, 2016) whose goal is to integrate high-throughput genomic and molecular data from disease brain within a network driven structure (Hodes and Buckholtz, 2016;Logsdon et al, 2019). Within this context, several recent large-scale RNA sequencing projects have been conducted (Allen et al, 2018;De Jager et al, 2018;Gaiteri et al, 2016;Logsdon et al, 2019;Mostafavi et al, 2018;Readhead et al, 2018;Zhang et al, 2013), identifying transcriptomic networks and splicing events altered in the cerebral cortex from patients with AD and comparing them to the aging brain.…”