Literature Survey and Ontological Modeling of COVID-19 Related Acute Kidney Injury Manifestations – UROP Spring Symposium 2021

Literature Survey and Ontological Modeling of COVID-19 Related Acute Kidney Injury Manifestations

Easheta Shah

Easheta Shah

Pronouns: She/Her

Research Mentor(s): Yongqun He, Associate Professor
Research Mentor School/College/Department: Lab Animal Medicine, Microbiology and Immunology, & Bioinformatics, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 4 (2pm-2:50pm)
Breakout Room: Room 13
Presenter: 4

Event Link


COVID-19, caused by the novel coronavirus known as the SARS-CoV-2, has many different phenotypic outcomes, or observable characteristics that result from molecular and cellular host-coronavirus interactions. One of these outcomes is Acute Kidney Injury, or AKI. When the virus enters the host cell by binding to the ACE2 receptor, the regulatory Renin-Angiotensin (RAS) hormone system is activated resulting in untoward effects on multiple organs, of which those pertaining to the kidney are of interest. As reported in existing literature, pathophysiological mechanisms associate the novel coronavirus to outcomes related to kidney damage closely following the RAS system as well as signaling pathways like complement activation, yet much of the specific interactions are still under close study. These mechanisms are central to understanding how SAR-CoV-2 directly and indirectly affects the kidney. Since the interactions of infectious diseases are quite complex, ontology-based modeling can be used to describe the relationships between different interactors within a specific domain of interest. One main coronavirus ontology established as the framework for our own modeling is the Coronavirus Infectious Disease Ontology (CIDO) initiated by our group. This project focuses on annotating existing and hypothesized mechanistic interactions between the SARS-CoV-2 virus and the host’s kidneys drawing from an extensive literature survey of ongoing AKI related coronavirus publications. With our own proposed model from this data, we have represented the interactions in visual webs and Excel groupings to emulate the ontological organization of the mentioned databases. Standardization of this specific set of interactions using ontology and machine learning helps us to better understand the pathophysiology of SARS-CoV-2 in its relation to kidney related outcomes. Since AKI pathways with relation to COVID-19 are still under intensive study in literature, our working data proposes broader relationships between phenotypic interactors to guide any hypothesized relationships between genotypic interactors. While these findings can then provide foundational understanding of this etiology, further research may be required at the molecular level for additional application in diagnosis and risk assessment of co-morbidities.

Authors: Easheta Shah, Oliver He
Research Method: Computer Programming

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