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Multi-Omic Directed Networks Describe Features of Gene Regulation in Aged Brains and Expand the Set of Genes Driving Cognitive Decline

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Multi-Omic Directed Networks Describe Features of Gene Regulation in Aged Brains and Expand the Set of Genes Driving Cognitive Decline fgene-09-00294 August 9, 2018 Time: 12:22 # 1 ORIGINAL RESEARCH published: 09 August 2018 doi: 10.3389/fgene.2018.00294 Multi-omic Directed Networks Describe Features of Gene Regulation in Aged Brains and Expand the Set of Genes Driving Cognitive Decline Shinya Tasaki1*, Chris Gaiteri1, Sara Mostafavi2, Lei Yu1, Yanling Wang1, Philip L. De Jager3,4 and David A. Bennett1 1 Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States, 2 Department of Statistics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada, 3 Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, United States, 4 Cell Circuits Program, Broad Institute, Cambridge, MA, United States Multiple aspects of molecular regulation, including genetics, epigenetics, and mRNA collectively influence the development of age-related neurologic diseases. Therefore, with the ultimate goal of understanding molecular systems associated with cognitive Edited by: decline, we infer directed interactions among regulatory elements in the local regulatory Ruzong Fan, Georgetown University, United States vicinity of individual genes based on brain multi-omics data from 413 individuals. These Reviewed by: local regulatory networks (LRNs) capture the influences of genetics and epigenetics Kui Zhang, on gene expression in older adults. LRNs were confirmed through correspondence to Michigan Technological University, known transcription biophysics. To relate LRNs to age-related neurologic diseases, we United States Wan-Yu Lin, then incorporate common neuropathologies and measures of cognitive decline into this National Taiwan University, Taiwan framework. This step identifies a specific set of largely neuronal genes, such as STAU1 *Correspondence: and SEMA3F, predicted to control cognitive decline in older adults. These predictions Shinya Tasaki [email protected] are validated in separate cohorts by comparison to genetic associations for general cognition. LRNs are shared through www.molecular.network on the Rush Alzheimer’s Specialty section: Disease Center Resource Sharing Hub (www.radc.rush.edu). This article was submitted to Statistical Genetics and Methodology, Keywords: Alzheimer’s dementia, cognitive decline, multi-omics data integration, gene regulatory network, xQTL, a section of the journal expression quantitative trait DNA methylation, expression quantitative trait histone acetylation, GWAS Frontiers in Genetics Received: 18 April 2018 Accepted: 13 July 2018 INTRODUCTION Published: 09 August 2018 Citation: The repeated failure of traditional drug discoveries for Alzheimer’s dementia (AD) (Cummings Tasaki S, Gaiteri C, Mostafavi S, Yu L, et al., 2014; Gauthier et al., 2016) indicate the necessity of a paradigm shift toward precision Wang Y, De Jager PL and Bennett DA medicine that aims to perturb the right targets in specific people at the right time (Collins (2018) Multi-omic Directed Networks and Varmus, 2015). To pursue this goal, big biomedical data including genomes, epigenomes, Describe Features of Gene Regulation transcriptomes, and proteomes have been generated by community aging studies and consortium in Aged Brains and Expand the Set of Genes Driving Cognitive Decline. efforts (Hodes and Buckholtz, 2016). In theory, the integration of multi-omics data provides the Front. Genet. 9:294. basis for a more complete and accurate understanding of complex molecular regulation and thus doi: 10.3389/fgene.2018.00294 increases the odds of identifying effective therapeutic targets for patients with cognitive decline. Frontiers in Genetics| www.frontiersin.org 1 August 2018| Volume 9| Article 294 fgene-09-00294 August 9, 2018 Time: 12:22 # 2 Tasaki et al. Local Gene Regulatory Networks in Brains However, in practice, the elucidation of integrated molecular from measurements provided by this cohort. All phenotypes regulatory mechanisms remains rare, especially in the context of and omics data are shared freely through the RADC hub the aging human brain. Generating such integrated mechanisms www.radc.rush.edu. requires phenotypes and multiple omics assayed in the same set of individuals, the mathematical and biological frameworks to Standard Protocol Approvals, integrate these data, and external validation of results. To provide integrated multi-omic molecular networks that are Registrations, and Patient Consents relevant to the aging brain, it is necessary to first quantify and The parent cohort studies and substudies were approved validate cross-omics interactions from multiple omics gathered by Rush University Medical Center Institutional Review in the same set of individuals from longitudinal studies of Boards. Participants provided written informed consent and all aging. Then, utilizing these cross-omics interactions such as participants signed an Anatomic Gift Act for brain donation. relationships of DNA methylation and histone acetylation to gene expression, we can accurately determine the relationships of Tau and b-amyloid Measurement genes to AD-related neuropathologies and cognitive decline. The To quantify the burden of parenchymal deposition of b-amyloid caveat to cross-omic interactions from correlation-based analyses and the density of abnormally phosphorylated paired helical is that they are not necessarily causal relationships. Approaches filament tau (PHFtau)-positive neurofibrillary tangles, tissue which combine genetics with gene expression address this issue was dissected from midfrontal cortex. 20 mm sections were to provide directed gene interaction networks (Chaibub Neto stained with antibodies to the b-amyloid protein and the tau et al., 2010; Zhang et al., 2013), while related approaches extend protein, and quantified with image analysis and stereology, as causality from genetics to disease phenotypes (Schadt et al., previously described (Bennett et al., 2006, 2012b; Schneider 2005). et al., 2012; Boyle et al., 2013). Briefly, b-amyloid was We further develop an analytical framework for combining labeled with an antibody for b-amyloid (10D5; Elan, Dublin, genetic and multiple types of omics data to infer mechanisms Ireland; 1:1,000). Immunohistochemistry was performed using regulating gene expression levels in aged brains. This approach diaminobenzidine as the reporter, with 2.5% nickel sulfate infers a series of biophysically based links from genetic to enhance immunoreaction product contrast. Between 20 variants, through multiple molecular traits to dementia-related and 90 video images of stained sections were sampled and phenotypes, by modeling local regulatory networks (LRNs), in processed to determine the average percent area positive for the vicinity of individual genes. We extensively validate the b-amyloid. PHFtau-tangles were labeled with an antibody specific output of these models in terms of known biological relationships for phosphorylated tau (AT8; Innogenetics, San Ramon, CA, between regulatory elements. Leveraging the inferred structure United States; 1:1,000). Between 120 and 700 grid interactions of the LRNs, we find the epigenetic modifications predicted to were sampled and processed, using the stereological mapping affect gene expression levels and show strong enhancer/repressor station, to determine the average density (per mm2) of PHFtau- activities of those modifications by assessing overlaps with a tangles. variety of genomic annotations. Moreover, LRNs predict genes upstream of dementia-related phenotypes and we validate their genetic associations with general cognition in separate cohorts. Cognitive Function Assessment Overall, this approach begins to address the multiple data For each participant, comprehensive cognitive assessments were integration challenges and the multi-layer regulation around administered at baseline and during each annual follow-up visit. genes with predicted associations to ongoing disease phenotypes. Details on cognitive assessment have been described previously The results can increase the efficiency of experimental work (Wilson et al., 2002, 2003, 2015; Bennett et al., 2006, 2012a). by directing it toward upstream regulators that are likely Briefly, the battery contains a total of 17 cognitive performance to control cognitive decline and neuropathology in older tests which assess 5 dissociable cognitive domains including, individuals. episodic memory (7 measures), semantic memory (3 measures), working memory (3 measures), perceptual speed (2 measures), and visuospatial ability (2 measures). To minimize the floor and MATERIALS AND METHODS ceiling effects, composite measures were used to examine the longitudinal cognitive decline. For each test, raw scores were Cohort Summary standardized using the baseline mean and standard deviation We infer LRN’s in the context of two longitudinal, community- across the cohorts. The z-scores were subsequently averaged based aging studies: the Religious Orders Study (ROS) and across all the 17 tests to obtain a summary measure representing the Rush Memory and Aging Project (MAP), collectively global cognition. Similarly, summary measures for individual referred to as ROSMAP (Bennett et al., 2018). Together, cognitive domains were obtained by averaging z scores from these ongoing studies have enrolled ∼3500 older persons the corresponding tests. The longitudinal rate of decline was without dementia, all of whom have agreed to brain donation computed for each participant using linear mixed models, which and annual detailed clinical evaluation, cognitive
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