Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19

David Kartchner; Kevin McCoy; Janhvi Dubey; Dongyu Zhang; Kevin Zheng; Rushda Umrani; James J Kim; Cassie S Mitchell

doi:10.3390/biology12091269

Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19

Biology (Basel). 2023 Sep 21;12(9):1269. doi: 10.3390/biology12091269.

Authors

David Kartchner^{1

2}, Kevin McCoy^{1

2}, Janhvi Dubey^{1

2}, Dongyu Zhang^{1

2}, Kevin Zheng^{1

2}, Rushda Umrani^{1

3}, James J Kim¹, Cassie S Mitchell^{1

2

4}

Affiliations

¹ Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
² Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
³ College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.
⁴ Center for Machine Learning at Georgia Tech, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Abstract

Multiple studies have reported new or exacerbated persistent or resistant hypertension in patients previously infected with COVID-19. We used literature-based discovery to identify and prioritize multi-scalar explanatory biology that relates resistant hypertension to COVID-19. Cross-domain text mining of 33+ million PubMed articles within a comprehensive knowledge graph was performed using SemNet 2.0. Unsupervised rank aggregation determined which concepts were most relevant utilizing the normalized HeteSim score. A series of simulations identified concepts directly related to COVID-19 and resistant hypertension or connected via one of three renin-angiotensin-aldosterone system hub nodes (mineralocorticoid receptor, epithelial sodium channel, angiotensin I receptor). The top-ranking concepts relating COVID-19 to resistant hypertension included: cGMP-dependent protein kinase II, MAP3K1, haspin, ral guanine nucleotide exchange factor, N-(3-Oxododecanoyl)-L-homoserine lactone, aspartic endopeptidases, metabotropic glutamate receptors, choline-phosphate cytidylyltransferase, protein tyrosine phosphatase, tat genes, MAP3K10, uridine kinase, dicer enzyme, CMD1B, USP17L2, FLNA, exportin 5, somatotropin releasing hormone, beta-melanocyte stimulating hormone, pegylated leptin, beta-lipoprotein, corticotropin, growth hormone-releasing peptide 2, pro-opiomelanocortin, alpha-melanocyte stimulating hormone, prolactin, thyroid hormone, poly-beta-hydroxybutyrate depolymerase, CR 1392, BCR-ABL fusion gene, high density lipoprotein sphingomyelin, pregnancy-associated murine protein 1, recQ4 helicase, immunoglobulin heavy chain variable domain, aglycotransferrin, host cell factor C1, ATP6V0D1, imipramine demethylase, TRIM40, H3C2 gene, COL1A1+COL1A2 gene, QARS gene, VPS54, TPM2, MPST, EXOSC2, ribosomal protein S10, TAP-144, gonadotropins, human gonadotropin releasing hormone 1, beta-lipotropin, octreotide, salmon calcitonin, des-n-octanoyl ghrelin, liraglutide, gastrins. Concepts were mapped to six physiological themes: altered endocrine function, 23.1%; inflammation or cytokine storm, 21.3%; lipid metabolism and atherosclerosis, 17.6%; sympathetic input to blood pressure regulation, 16.7%; altered entry of COVID-19 virus, 14.8%; and unknown, 6.5%.

Keywords: COVID-19; SARS-CoV-2; adverse event; artificial intelligence; cardiovascular disease; knowledge graph; resistant hypertension; text mining; unsupervised machine learning.

Abstract

Grants and funding