Dataset Information

Computational network analysis of host genetic risk variants of severe COVID-19.

ABSTRACT:

Background

Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks.

Results

We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction.

Conclusions

This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.

SUBMITTER: Alsaedi SB

PROVIDER: S-EPMC9977643 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Computational network analysis of host genetic risk variants of severe COVID-19.

Alsaedi Sakhaa B SB Mineta Katsuhiko K Gao Xin X Gojobori Takashi T

Human genomics 20230302 1

<h4>Background</h4>Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis ...[more]

PMID: 36859360

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Project description:BackgroundCoronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was varied in disease symptoms. We aim to explore the effect of host genetic factors and comorbidities on severe COVID-19 risk.MethodsA total of 20,320 COVID-19 patients in the UK Biobank cohort were included. Genome-wide association analysis (GWAS) was used to identify host genetic factors in the progression of COVID-19 and a polygenic risk score (PRS) consisted of 86 SNPs was constructed to summarize genetic susceptibility. Colocalization analysis and Logistic regression model were used to assess the association of host genetic factors and comorbidities with COVID-19 severity. All cases were randomly split into training and validation set (1:1). Four algorithms were used to develop predictive models and predict COVID-19 severity. Demographic characteristics, comorbidities and PRS were included in the model to predict the risk of severe COVID-19. The area under the receiver operating characteristic curve (AUROC) was applied to assess the models' performance.ResultsWe detected an association with rs73064425 at locus 3p21.31 reached the genome-wide level in GWAS (odds ratio: 1.55, 95% confidence interval: 1.36-1.78). Colocalization analysis found that two genes (SLC6A20 and LZTFL1) may affect the progression of COVID-19. In the predictive model, logistic regression models were selected due to simplicity and high performance. Predictive model consisting of demographic characteristics, comorbidities and genetic factors could precisely predict the patient's progression (AUROC = 82.1%, 95% CI 80.6-83.7%). Nearly 20% of severe COVID-19 events could be attributed to genetic risk.ConclusionIn this study, we identified two 3p21.31 genes as genetic susceptibility loci in patients with severe COVID-19. The predictive model includes demographic characteristics, comorbidities and genetic factors is useful to identify individuals who are predisposed to develop subsequent critical conditions among COVID-19 patients.

Project description:ImportanceSevere coronavirus disease 2019 (COVID-19) can occur in younger, predominantly male, patients without preexisting medical conditions. Some individuals may have primary immunodeficiencies that predispose to severe infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).ObjectiveTo explore the presence of genetic variants associated with primary immunodeficiencies among young patients with COVID-19.Design, setting, and participantsCase series of pairs of brothers without medical history meeting the selection criteria of young (age <35 years) brother pairs admitted to the intensive care unit (ICU) due to severe COVID-19. Four men from 2 unrelated families were admitted to the ICUs of 4 hospitals in the Netherlands between March 23 and April 12, 2020. The final date of follow-up was May 16, 2020. Available family members were included for genetic variant segregation analysis and as controls for functional experiments.ExposureSevere COVID-19.Main outcome and measuresResults of rapid clinical whole-exome sequencing, performed to identify a potential monogenic cause. Subsequently, basic genetic and immunological tests were performed in primary immune cells isolated from the patients and family members to characterize any immune defects.ResultsThe 4 male patients had a mean age of 26 years (range, 21-32), with no history of major chronic disease. They were previously well before developing respiratory insufficiency due to severe COVID-19, requiring mechanical ventilation in the ICU. The mean duration of ventilatory support was 10 days (range, 9-11); the mean duration of ICU stay was 13 days (range, 10-16). One patient died. Rapid clinical whole-exome sequencing of the patients and segregation in available family members identified loss-of-function variants of the X-chromosomal TLR7. In members of family 1, a maternally inherited 4-nucleotide deletion was identified (c.2129_2132del; p.[Gln710Argfs*18]); the affected members of family 2 carried a missense variant (c.2383G>T; p.[Val795Phe]). In primary peripheral blood mononuclear cells from the patients, downstream type I interferon (IFN) signaling was transcriptionally downregulated, as measured by significantly decreased mRNA expression of IRF7, IFNB1, and ISG15 on stimulation with the TLR7 agonist imiquimod as compared with family members and controls. The production of IFN-γ, a type II IFN, was decreased in patients in response to stimulation with imiquimod.Conclusions and relevanceIn this case series of 4 young male patients with severe COVID-19, rare putative loss-of-function variants of X-chromosomal TLR7 were identified that were associated with impaired type I and II IFN responses. These preliminary findings provide insights into the pathogenesis of COVID-19.

Dataset Information

Computational network analysis of host genetic risk variants of severe COVID-19.

Background

Results

Conclusions

Publications

Computational network analysis of host genetic risk variants of severe COVID-19.

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