UCLA

Uterine disorders are common and can be debilitating, but they are poorly understood. Endometriosis is the most common cause of secondary dysmenorrhea, yet existing treatments have substantial side effects and are not able to fully resolve pain for most patients. Many studies have shown that endometrial tissue from individuals with endometriosis has differential gene expression and DNA methylation patterns compared to endometrial tissue from controls. Data sharing efforts have made large amounts of genetic and epigenetic data available to researchers. However, it is technically challenging to compare datasets from different platforms and study designs, and these resources are underutilized. In this dissertation, I explore methods of combining DNA methylation and gene expression data to gain additional insights into endometrial disorders. First, I demonstrate that DNA methylation age can be used to understand ectopic endometriosis lesions. Then, I correlate DNA methylation age changes caused by hormonal treatment with differential gene expression in endometriosis. Given a candidate pathway, I then use datasets of infertility to determine whether these expression changes could provide insight into clinical phenotypes. Finally, I explore whether clinical phenotypes of menstrual pain resistant to non-steroidal anti-inflammatory drugs (NSAIDs) can predict altered signaling of related pathways in menstrual-derived tissues. This work explores methods of utilizing the vast amounts of genetic and epigenetic data that have already been generated, while taking into consideration both the unique dynamic properties of uterine tissue and the aspects of uterine dysfunction that are most disruptive to quality of life.