2020 SRF Summer Scholar Profile: Dennis Gong

Dennis Gong

SRF Summer Scholar, Class of 2020

Stanford University

My name is Dennis Gong. I am a Biomedical Engineering and Applied Math & Statistics major at Johns Hopkins University. At Johns Hopkins, I work in Dr. Jordan Green’s immunoengineering and drug delivery lab, working on improving the potency of artificial antigen presenting cells (aAPCs) to improve immune stimulation with graduate student Savannah Est Witte. This summer, I am working in Dr. Michael Snyder’s multi-omics focused lab with Dr. Lihua Jiang to study the proteomic signatures of aging. In general, the Snyder Lab works to develop and use a variety of approaches to analyze genomes (DNA), transcriptomes (RNA), proteomes (protein), and regulatory networks, applying these approaches to understand human variation and health. My particular project involved understanding upregulated and downregulated protein pathways in aging to develop organ-specific maps of aging regulatory networks.

Aging is a dominant risk factor for many diseases, including cancer, cardiovascular disease, and neurodegeneration, and is associated with a variety of molecular changes including telomere attrition, DNA damage, mitochondrial dysfunction, and immune impairment. While exploratory studies in model animals have demonstrated the possibility of health and life extension with various intervention strategies, a targeted investigation of the molecular signature of aging may provide new insights. High-throughput -omics datasets have provided a more comprehensive map of molecular changes and have shown that aging is distinct at molecular, cellular and tissue levels. My project provides a proteome level characterization of age-related molecular changes in order to identify age-associated proteins and relevant biological pathways.

The Genotype-Tissue Expression (GTEx) project has sequenced human tissue samples from 1000+ people and 56 organ types, with 12,000+ measured proteins. The GTEx cohort contains all age groups, and includes transcriptome, genome, and proteomic data. Using linear regression methods, associations between protein expression and age can be developed. The result of this correlation analysis will be a list of aging-associated proteins that can be cross-referenced with similar analyses of genomic and transcriptomic data to elucidate aging-related pathways and develop aging biomarkers. These biomarkers are essential for developing a quantitative clinical understanding of disease, which can be used to measure the efficacy of therapeutics in clinical trials and give clinicians a quantitative understanding of the ‘molecular age’ of their patients to inform risk of developing disease. Additional uses might include identifying appropriate trial groups to segment the patient population as well as developing therapeutic targets.

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