Collaboration on Alzheimer\u2019s Disease Research<\/a> (CADRE):\u00a0CADRE aims to\u00a0<\/span>analyze whole genome sequence data (>60,000) generated by the <\/span>ADSP<\/a> incorporating large cohorts of patients and healthy individuals of diverse population groups. \u00a0Comprehensive analyses of the combined ADSP data of case-control cohorts and family study designs, the CADRE project aims to enhance understanding of the genetic architecture of Alzheimer\u2019s Disease and to accelerate the search for new therapeutic targets.<\/span><\/p>\n 1. Dr. Zhang’s lab is leading this sub-aim by developing methods for calling mitochondria variants, copy numbers from the WGS data, and understanding the role of mitochondria in AD pathology, cognitive decline, and how mitochondrial genetics contribute to AD risk.<\/p>\n 2. Dr.\u00a0 Zhang’s lab is also leading an NIA-funded project, \u201ctRNA modifications regulating mitochondrial function and the mitochondrial proteome in AD\/ADRD,<\/strong><\/span>\u201d to investigate the bidirectional MT-nuclear crosstalk.<\/span><\/p>\n The\u00a0<\/span>Alzheimer\u2019s Disease Sequencing Project<\/a>\u00a0(ADSP) Functional Genomics Consortium (FunGen-AD<\/a>) aims to apply cutting-edge genomics technologies and high-throughput genetic screening to understand the functional impacts underlying the genetic basis of susceptibility and resilience of Alzheimer\u2019s Disease, and to identify genetics-guided targets for the prevention, diagnosis, and treatment of Alzheimer\u2019s disease and related dementias (AD\/ADRD). <\/span>As one of the Core Projects of FunGen-AD<\/a>, Dr. Zhang’s lab will conduct research about<\/p>\n Goals: This U01 will leverage large rRNA-depleted RNA-seq data to identify and characterize disease-linked circRNAs and RNA-binding proteins (RBPs), and functional changes in circRNAs or circRNA-RBP interactions that modify AD neuropathology or neurodegeneration.<\/p>\n The <\/span>xQTL<\/strong> project is a collaborative effort across the ADSP-FGC, the AMP-AD, the NIH CARD, and the <\/span>ADSP. The goal of the\u00a0<\/span>xQTL project is to generate a reference map of AD-related quantitative trait loci (QTLs).<\/span><\/p>\n A summary of\u00a0a variety of molecular xQTL studies in human tissues and cells<\/span> can be found here<\/a>.<\/p>\n An analysis xQTL pipeline\/the ADSP FunGen-xQTL protocol can be found here.<\/a><\/p>\n Major goals of the FHS-BAP<\/a> (U19-AG068753) are to continue surveillance, perform cognitive and MRI exams, and orchestrate the brain donor programs and repository,\u00a0<\/span>perform research related to risk, resilience, and inflammatory and vascular basis of AD & Dementia.<\/span><\/p>\n In Project 2 (co-led by Drs. Qiu<\/a> and Zhang), we will evaluate the hypothesis that the link between AD genetic vulnerabilities and chronic peripheral inflammation plays an important role in AD pathogenesis.<\/p>\n This R01 proposal will investigate a specific type of RNA modification, termed methylation<\/span>. We will study how RNA methylation changes in Alzheimer\u2019s disease, and whether reducing excessive methylation can delay disease progression and help patients with the disease. Specifically, we will determine how the m6A transcriptome<\/span> over the disease course in mouse models of AD\/ADRD, examining both coding and non-coding RNA (Aim 1), then we will determine how the m6A transcriptome varies by disease severity and type in human pathological cases (Aim 2).\u00a0 Finally, Aim 3 will determine how m6A contributes to the pathophysiology of tau and neurodegeneration in AD.<\/p>\n \u00a0 \u00a0 \u00a0 \u00a0 \u00a02.\u00a0 tRNA Epitranscriptome and Translational Dysfunction\u00a0<\/span><\/strong>in Alzheimer\u2019s disease and related disorders (AD\/ADRD)<\/span><\/p>\n This NSF supported project will develop a mathematical theory for construction of novel features for identifying underlying stochastic behavior of input data, and analyze and optimize the parameters of the multilevel feature construction to dramatically improve the performance of machine learning algorithms given complex and difficult data with application to identify ML based Alzheimer\u2019s Disease (AD) subtypes from massive AD human datasets.<\/p>\n <\/p>\n Alzheimer\u2019s Disease Sequencing Project (ADSP) Collaboration on Alzheimer\u2019s Disease Research (CADRE):\u00a0CADRE aims to\u00a0analyze whole genome sequence data (>60,000) generated by the ADSP incorporating large cohorts of patients and healthy individuals of diverse population groups. \u00a0Comprehensive analyses of the combined ADSP data of case-control cohorts and family study designs, the CADRE project aims to enhance understanding […]<\/p>\n","protected":false},"author":21018,"featured_media":0,"parent":0,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/zhanglab\/wp-json\/wp\/v2\/pages\/34"}],"collection":[{"href":"https:\/\/sites.bu.edu\/zhanglab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.bu.edu\/zhanglab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/zhanglab\/wp-json\/wp\/v2\/users\/21018"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/zhanglab\/wp-json\/wp\/v2\/comments?post=34"}],"version-history":[{"count":50,"href":"https:\/\/sites.bu.edu\/zhanglab\/wp-json\/wp\/v2\/pages\/34\/revisions"}],"predecessor-version":[{"id":697,"href":"https:\/\/sites.bu.edu\/zhanglab\/wp-json\/wp\/v2\/pages\/34\/revisions\/697"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/zhanglab\/wp-json\/wp\/v2\/media?parent=34"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\u00a0Mitochondria variation with AD, ADRD, and Aging:<\/h4>\n
ADSP Functional Genomics Consortium (ADSP-FGC)<\/h3>\n
1. Circular RNAs and their interactions with RNA-binding proteins to modulate AD-related neuropathology (PIs: Dr. Zhang & Dr. Wolozin<\/a>)<\/h4>\n
\u00a0 \u00a0 \u00a0 \u00a0 2. FunGen-xQTL: Unraveling the genetic basis of molecular functions in AD<\/strong><\/span><\/h4>\n
Framingham Heart Study-Brain Aging Program (FHS-BAP) – Project 2<\/h3>\n
<\/h3>\n
Epi-transcriptomic (Methylated RNA) Study in <\/span>Alzheimer’s Disease\u00a0<\/span><\/h3>\n
1. The role of N6-methyladenosine (m6A) modified RNA in Alzheimer’s disease (PIs:<\/span>\u00a0Dr. Wolozin<\/a> & Dr. Zhang)<\/h4>\n
AD and Biomedical Data Machine Learning Project:<\/h3>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 DMS\/NIGMS 1: Multilevel stochastic orthogonal subspace transformations for robust machine learning with applications to biomedical data and Alzheimer’s disease subtyping\u00a0 (PIs:<\/span> Dr. Castrillon<\/a>, Dr. Kon<\/a>, Dr.\u00a0 Zhang)<\/h4>\n
DEIA Statements Added to BU Profiles (link<\/a>)<\/h5>\n","protected":false},"excerpt":{"rendered":"