Next-generation Sequence Data Analysis for Dissecting Molecular Mechanisms in Late-onset Alzheimer’s

Friday, December 20, 2019

2:30 PM-4:30 PM

BIOMED PhD Thesis Defense

Title:
Next-generation Sequence Data Analysis for Dissecting Molecular Mechanisms in Late-onset Alzheimer’s Disease

Speaker:
John Stephen Malamon, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisor:
Andres Kriete, PhD
Associate Dean for Academic Affairs
School of Biomedical Engineering, Science and Health Systems
Drexel University

Abstract:
Late-onset Alzheimer’s disease is a devastating and complex condition which presents one of the biggest healthcare challenges this country will face in the twenty-first century. Progress in understanding the many molecular mechanism and biological systems involved in LOAD is in part determined by our ability to fully integrate models that combine several modes of next-generation sequence (NGS) data to build comprehensive models and descriptions of the many genetic and regulatory factors influencing LOAD. With the increasing availability, variety, and quality of NGS data, we have the unique and exciting opportunity to test hypotheses concerning genetic risk factors, regulatory processes, compensatory mechanisms, etiology, and potential therapeutic targets. Also, to improve upon traditional functional association testing methods, we developed an entirely novel and extensible functional genomics framework called SECRETs for functional association hypothesis testing, robust variable importance detection, and flexible phenotype construction. SECRETs fill several critical gaps in functional genomics by reducing the number of functional associations and their p-values along with increasing biological precision and relevance.

Our combined approach identified significant systematic reductions in the transcriptomic organization and functional dynamics of a previously identified immune system gene network in association with clinical disease progression. We also identified expression quantitative trait loci (eQTLs) in the microtubule-associated protein tau (MAPT) and human leukocyte antigen (HLA) regions. Immune network architectures account for desirable immune system properties such as inducibility, adaptability, and robustness. Our results are consistent with a growing body of evidence implicating diseased microglia and immune system dysfunctions as critical drivers for LOAD onset and progression. Additionally, microglia offer a promising target of therapy. In summary, we have successfully combined statistical, engineering, and systems biology approaches to identify several biological pathways and potential therapeutic targets aim at treating this dreadful condition.

Contact Information

Ken Barbee
215-895-1335
barbee@drexel.edu