In a recent study published in Scientific Reports, researchers performed a protein diffusion network analysis using tissue-specific gene regulatory networks (GRNs) to identify predispositions and comorbidities of coronavirus disease 2019 (COVID-19) outcomes and the linking mechanisms.
Study: Comorbidity genetic risk and pathways impact SARS-CoV-2 infection outcomes. Image Credit: Gorodenkoff/Shutterstock.com
Background
Genome-wide association studies (GWAS) findings indicate genetic links between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and an intricate genetic influence on vulnerability to infection and its severity.
Studies have linked COVID-19 to comorbidities such as obesity, diabetes, active malignancy, hypertension, and cardiovascular disease (CVD), exacerbating the COVID-19 burden on health and healthcare facilities.
However, the relationships between the genomic factors linked to these comorbid conditions and variations in risks of COVID-19 outcomes remain unknown.
Knowing the genetic risks and processes by which COVID-19 outcomes and related comorbidities combine to influence immediate and long-term consequences is crucial to reducing the COVID-19 burden.
About the study
In the present study, researchers examined putative mechanisms that underlie the associations between COVID-19 outcomes and comorbid conditions.
The Host Genetics Initiative (HGI) for COVID-19 was used to obtain GWAS information on the clinical phenotypes of COVID-19.
Hospitalized vs. severe (hospitalized versus ventilation support requirements and deaths) SNPs were retrieved from the sixth coronavirus disease 2019 HGI release. Assumed transcription-related functions were assigned to COVID-19-related SNPs.
Hospitalization-requiring and severe COVID-19-related SNPs were analyzed separately to detect spatially limited expression quantitative trait loci (eQTLs) and the genetic targets specific to phenotypes.
Hi-C nuclear chromatin information extracted from human pulmonary tissues, blood (B lymphocytes, helper T lymphocytes, and cytotoxic T lymphocytes), the coronary arterial smooth muscle cells, and the brain's dorsolateral prefrontal cortical cells were used to identify the eQTLs.
Expression quantitative trait loci SNPs were identified using single nucleotide polymorphism-gene combinations querying the lung, brain, coronary artery, and blood cells of the gene expression (GTEx) database.
The team performed linkage disequilibrium (LD) analysis for the eQTL-gene pairs, including parameters such as dbSNP15433 single nucleotide polymorphisms (SNPs), the European population, and the 1,000 Genomes Project's phase III genotyping data.
GRNs were generated, including eQTLs for known dbSNP15433 SNPs with minor allelic frequency (MAF) ≥0.05, followed by an analysis of inter-protein interaction networks based on data obtained from the STRING and PROPER-Seq databases and the human umbilical vein endothelial cells (HUVECs), human embryonic kidney (HEK)293T cells, and Jurkat cells.
Hypergeometric distribution analysis was performed to identify significant eQTL enrichments, and bootstrapping analysis was performed to ascertain traits identified by the protein interaction networks that were uniquely related to SARS-CoV-2.
The team also performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, analyzing data from the WikiPathways (WP), Reactome (REAC), mirTarBase (MIRNA), Transfac (TF), Human Protein Atlas (HPA), Human Phenotype Ontology (HP), and CORUM databases.
Results
The analysis identified known comorbid traits such as coronary artery disease (CAD), mood disorders, asthma, and type 1 diabetes. Evidence for genetic predispositions for traits that either have not been or have been weakly associated with COVID-19, Alzheimer's disease, Parkinson's disease, inflammatory bowel disease, and Hirschsprung disease was also obtained.
New target genes were identified, including the SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 4 (SMARCA4, eQTLs rs7247198 and rs1041607), which is only second to the angiotensin-converting enzyme 2 (ACE2) gene in SARS-CoV-2 pro-viral activity.
The study found 28 genes [such as KAT8 regulatory NSL complex subunit 1 (KANSL1), microtubule-associated protein Tau (MAPT), and corticotropin-releasing hormone receptor gene (CRHR1)] and 26 variants associated with the link between Parkinson's disease and COVID-19, involving the 17q21.31 genetic locus and human leukocyte antigen (HLA) regional variants.
Four variants and two pleiotropic genes [farnesyl-diphosphate farnesyltransferase 1 (FDFT1) and tousled-like kinase 1 (TLK1)] were identified in the blood beyond 6p21 and 17q21.31 related to both traits. The protein interactions and genetic architectures may represent therapeutic avenues to prevent the development of Parkinson's disease following COVID-19.
Type 1 diabetes was linked to severe and hospitalized phenotypes in COVID-19 patients based on 27 genes, such as neurogenic locus notch homolog 4 (NOTCH4), with the CVD burden increasing concomitantly with COVID-19 severity.
In total, 32 eQTLs and 34 genes were enriched for CVD in the protein interaction networks of the pulmonary tissues, and 30.0 eQTLs and 18.0 genes were enriched for coronary artery disease in the protein networks of the human brain.
Detection of variants in germline cells could increase adverse COVID-19 outcomes risks. Receptor protein-tyrosine kinase ErbB-4 (ERBB4, expression quantitative trait loci rs582384) gene-encoded proteins were detected in the brain to function within CAD-activated pathways.
ERBB4 interacts significantly with neuregulin-1, an ErbB4 receptor agonist, crucial for mitigating heart failure and inhibiting atherogenesis.
Conclusion
The study findings revealed genetic impacts on traits affecting COVID-19 susceptibility, highlighting complex relationships and a potential post-acute COVID-19 burden.
Therapeutics targeting comorbidities may be clinically viable for individuals with predisposing genetic susceptibilities.
Link between COVID-19 and long-term risk of autoimmune and autoinflammatory connective tissue disorders