Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome
Video
Team Information
Team Members
Tiffany Callahan, Postdoctoral Fellow, Department of Biomedical Informatics, CUIMC
Faculty Advisor: George Hripcsak, Vivian Beaumont Allen Professor of Biomedical Informatics and Department Chair, Vagelos College of Physicians and Surgeons
Abstract
Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). DEGs are identified using unbiased assays, however the decisions to investigate DEGs experimentally are biased by many factors (e.g., investigator interests, available reagents, knowledge of gene function), causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association to the disease in the literature are known as the ignorome. The combination of having an extensive body of scientific literature, a pool of DEG data, and only one definitive treatment, makes preeclampsia a prime candidate for knowledge-based analysis to potentially identify additional treatment options and possibly even preventative measures. Our goal was to demonstrate how a PheKnowLator knowledge graph could be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Using the keyword “preeclampsia”, 68 publicly available human gene expression experiments deposited in the Gene Expression Omnibus were identified and reviewed. Meta-analysis of the 12 experiments meeting inclusion criteria generated a list of 548 DEGs. The relative complement of the annotation- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445). Experimentally investigated DEGs were then identified from published literature based on semantic and syntactic annotations to PubMed abstracts. Using PheKnowLator-derived node embeddings to investigate relevant DEGs revealed 53 relevant and actionable mechanistic associations of preeclampsia, thus potentially identifying additional targets for prevention/intervention.
Team Lead Contact
Tiffany Callahan: tc3206@cumc.columbia.edu
