Presentation Title
Association analysis of endogenous retrovirus expression with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)
Abstract
Sophie Kogut1, and Dawei Li1
1Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT
Abstract:
Despite the high prevalence and debilitating effects of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), no biomarkers or effective treatments have been discovered. As a result, ME/CFS remains difficult to diagnose and manage, and the mechanisms that drive ME/CFS progression remain largely unknown. Our hypothesis is that certain endogenous retroviruses (ERVs) (distinct elements in our genome that are the remnants of ancient retroviral infection) may induce an abnormal immune response that may trigger the development of ME/CFS or otherwise influence the progression of the disease. This project was conducted using RNA sequencing data from ME/CFS patients and healthy controls to examine the association of ERV expression with ME/CFS diagnosis and severity levels. The goal was to establish a list of the top 10% of ERVs that show a strong association and predictive potential for ME/CFS using a linear model, and from there, look for genes that show association with altered ERV expression. For the purposes of this analysis, only the largest cohort of Caucasian femalesand only ERVs with a log2 fold change of greater than |1| were selected (resulting in 141 ERVs, 58 Caucasian females.The first linear model identified a list of 10 ERVs that should be considered for further in-depth study. We have identified several genes (associated with methylation and regulation) that demonstrate strong association with differential ERV expression in cases and controls and developed a new hypothesis, which posits that genes like DNMT1 may be induced by the expression (or lack thereof) of an ERV that is associated with ME/CFS. Going forward, we will establish a network of notable co-expressed ERVs and continue refining this method of analyzing ERV count data, including a more comprehensive association analysis using all available gene transcripts.
Primary Faculty Mentor Name
Dr. Dawei Li
Faculty/Staff Collaborators
Dr. Dawei Li
Status
Undergraduate
Student College
College of Arts and Sciences
Program/Major
Biological Science
Primary Research Category
Biological Sciences
Association analysis of endogenous retrovirus expression with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)
Sophie Kogut1, and Dawei Li1
1Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT
Abstract:
Despite the high prevalence and debilitating effects of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), no biomarkers or effective treatments have been discovered. As a result, ME/CFS remains difficult to diagnose and manage, and the mechanisms that drive ME/CFS progression remain largely unknown. Our hypothesis is that certain endogenous retroviruses (ERVs) (distinct elements in our genome that are the remnants of ancient retroviral infection) may induce an abnormal immune response that may trigger the development of ME/CFS or otherwise influence the progression of the disease. This project was conducted using RNA sequencing data from ME/CFS patients and healthy controls to examine the association of ERV expression with ME/CFS diagnosis and severity levels. The goal was to establish a list of the top 10% of ERVs that show a strong association and predictive potential for ME/CFS using a linear model, and from there, look for genes that show association with altered ERV expression. For the purposes of this analysis, only the largest cohort of Caucasian femalesand only ERVs with a log2 fold change of greater than |1| were selected (resulting in 141 ERVs, 58 Caucasian females.The first linear model identified a list of 10 ERVs that should be considered for further in-depth study. We have identified several genes (associated with methylation and regulation) that demonstrate strong association with differential ERV expression in cases and controls and developed a new hypothesis, which posits that genes like DNMT1 may be induced by the expression (or lack thereof) of an ERV that is associated with ME/CFS. Going forward, we will establish a network of notable co-expressed ERVs and continue refining this method of analyzing ERV count data, including a more comprehensive association analysis using all available gene transcripts.