CBCB Seminar

February 26, 2024 3:30 PM

DBI, 15 Innovation Way, Conference room 102

Exploring Disease Mechanisms through Gene Expression Analysis: Case Studies on Autoimmune Thyroid Disease and Prader-Willi Syndrome

Karen Ross, PhD

Associate Professor, Department of Biochemistry and Molecular & Cellular Biology
Senior Bioinformatics Scientist in the Protein Information Resource
Georgetown University Medical Center

Abstract: With the advent of reliable and relatively inexpensive high-throughput sequencing technology, it has become routine to collect data on global gene expression in biological systems. This talk will describe two studies in which gene expression analysis has yielded interesting insights into two very different diseases—autoimmune thyroid disease and Prader-Willi Syndrome (PWS). Autoimmune thyroid diseases, such as Hashimoto’s Thyroiditis and Graves’ Disease, ultimately lead to destruction of the thyroid, leaving patients dependent on lifelong thyroid hormone supplementation. We compared gene expression (RNAseq) data from patients with autoimmune thyroid disease and thyroid cancer and discovered a gene expression signature in a subset of autoimmune patients consistent with low-level chronic viral infection. Viral infection is a well-known trigger of autoimmunity and persistent viral infections have been implicated to a variety of other autoimmune diseases. Our results suggest that viral infection may play a role in autoimmune thyroid diseases as well. PWS is a complex genetic disease caused by loss of expression of genes in a region of Chromosome 15. Symptoms of PWS include uncontrollable appetite resulting in obesity, mild developmental delay, and poor muscle tone. The connection between the genetic defect and the disease symptoms is poorly understood. To identify proteins affected in PWS, we constructed protein-protein interaction networks with interactions from the STRING database and edge weights based on gene expression (RNAseq) data from either PWS cell lines or isogenic controls. We calculated embeddings for the proteins in the PWS and control networks using the deterministic embedding algorithm, GraphWave, and then calculated the distance between the PWS and control embeddings for each protein. We selected proteins with large distances, hypothesizing that they may be strongly affected in PWS and may play a role in PWS symptoms. We are currently evaluating these proteins’ relevance to PWS.

Bio: Dr. Ross is an Associate Professor in the Department of Biochemistry and Molecular & Cellular Biology and a Senior Bioinformatics Scientist in the Protein Information Resource group at Georgetown University Medical Center. Her research interests include omics data analysis, data integration, and graph learning. She also co-directs the Master’s program in Bioinformatics at Georgetown and teaches a variety of courses on bioinformatics and systems biology. She got her PhD in Cell Biology from Yale University in 2000, studying cell cycle regulation in yeast under the supervision of Mark Solomon. She continued her cell cycle studies as a post-doctoral fellow in the laboratory of Orna Cohen-Fix at the NIH. From 2012-2015, supported by an NIH Career Re-Entry Fellowship, she did post-doctoral work on biomedical ontologies and knowledge graphs at the University of Delaware supervised by Cathy Wu.