Our goal is to identify variants in the human genome and corresponding changes in the arterial proteome that are correlated with premature atherosclerosis. To accomplish this goal we plan detailed molecular characterization of arterial tissue from subjects in the Pathobiologic Determinants of Atherosclerosis in Youth (PDAY) repository. We will integrate genomic and proteomic data from the PDAY samples and use additional systems biology tools and data from other NIH funded genomic resources to optimize the search for molecular correlates of early disease.
Our specific aims are:
Aim 1. To identify genetic variants associated with premature atherosclerosis in PDAY, including: a. rare variants in genes identified through PDAY case-control exome and promoter sequencing, and b. common variants identified through a (previously conducted) PDAY GWAS. Nominally significant exome sequencing and GWAS results will be combined with similar sequencing and GWAS data from the ESP Early Onset MI Project (N=2,400) and the MIGen Consortium (N=6,402) using meta- analysis to refine the list of candidate gene products for protein validation.
(Aim 3) Aim 2. To expand and prioritize the list of candidate proteins from Aim 1 using statistical methods to combining evidence from SNPs, rare variants and structural variants, gene-pathway enrichment techniques based on a priori knowledge of gene networks (eg. KEGG, Ingenuity, PPI, GO, etc.), and functional evaluation of specific genetic markers using Polyphen-2 and related tools.
Aim 3. To measure case-control differences in arterial wall concentration of proteins identified in Aims 1 and 2. For this aim we will exploit the multiplex capability of a new quantitative mass-spectrometry method, multiple reaction monitoring (MRM), to evaluate ~150 arterial wall proteins in all 1,100 PDAY case-control subjects.
Aim 4. To further evaluate genetic variants and proteins with the most compelling evidence for association using genotyping, specific protein quantitation and complementary immunohistochemical analyses in de-novo post-mortem arterial specimens from subjects with and without extensive atherosclerosis (N=150).
Aim 5. To provide harmonized nomenclature and annotation for the data from Specific Aims 1-4 and share the entire data set with the scientific community through the appropriate publically accessible NCBI databases. This combination of highly specific and precise histopathologic phenotyping with state-of-the-art molecular and analytic methods creates an unprecedented opportunity to construct the genomic and proteomic architecture of premature atherosclerosis and will add fundamentally new knowledge about the linkage between genome and proteome in the artery wall.
The data which result from this study may provide new insight about the interdependent networks of genes and proteins that put some people at risk for premature clinical cardiovascular events. Ultimately, such information could lead to new screening strategies and help identify new therapeutic targets to reduce the human and financial costs of cardiovascular disease worldwide.
|Lam, Maggie P Y; Venkatraman, Vidya; Xing, Yi et al. (2016) Data-Driven Approach To Determine Popular Proteins for Targeted Proteomics Translation of Six Organ Systems. J Proteome Res 15:4126-4134|
|Holewinski, Ronald J; Parker, Sarah J; Matlock, Andrea D et al. (2016) Methods for SWATHâ„¢: Data Independent Acquisition on TripleTOF Mass Spectrometers. Methods Mol Biol 1410:265-79|
|Wang, Niya; Hoffman, Eric P; Chen, Lulu et al. (2016) Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues. Sci Rep 6:18909|
|Tian, Ye; Zhang, Bai; Hoffman, Eric P et al. (2015) KDDN: an open-source Cytoscape app for constructing differential dependency networks with significant rewiring. Bioinformatics 31:287-9|
|Fu, Yi; Yu, Guoqiang; Levine, Douglas A et al. (2015) BACOM2.0 facilitates absolute normalization and quantification of somatic copy number alterations in heterogeneous tumor. Sci Rep 5:13955|
|Wang, Niya; Gong, Ting; Clarke, Robert et al. (2015) UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples. Bioinformatics 31:137-9|
|Zhang, Bai; Tian, Ye; Zhang, Zhen (2014) Network biology in medicine and beyond. Circ Cardiovasc Genet 7:536-47|
|Tian, Ye; Zhang, Bai; Hoffman, Eric P et al. (2014) Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks. BMC Syst Biol 8:87|
|Tian, Ye; Wang, Sean S; Zhang, Zhen et al. (2014) Integration of Network Biology and Imaging to Study Cancer Phenotypes and Responses. IEEE/ACM Trans Comput Biol Bioinform 11:1009-19|
|Perricone, Adam J; Vander Heide, Richard S (2014) Novel therapeutic strategies for ischemic heart disease. Pharmacol Res 89:36-45|
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