Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome. (arXiv:2207.14294v1 [q-bio.GN])

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,
causing many DEGs to remain uninvestigated. A set of DEGs which are associated
with a disease experimentally, but which have no known association with the
disease in the literature is known as the ignorome. Preeclampsia has an
extensive body of scientific literature, a large pool of DEG data, and only one
definitive treatment. Tools facilitating knowledge-based analyses, which are
capable of combining disparate data from many sources in order to suggest
underlying mechanisms of action, may be a valuable resource to support
discovery and improve our understanding of this disease. In this work we
demonstrate how a biomedical knowledge graph (KG) can 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. Experimentally investigated genes associated with
preeclampsia were identified from PubMed abstracts using text-mining
methodologies. The relative complement of the text-mined- and
meta-analysis-derived lists were identified as the uninvestigated
preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using
the KG to investigate relevant DEGs revealed 53 novel clinically relevant and
biologically actionable mechanistic associations.



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