Publication date: Nov 05, 2024
Immunomodulatory variants that lead to the loss or gain of specific protein interactions often manifest only as organismal phenotypes in infectious disease. Here, we propose a network-based approach to integrate genetic variation with a structurally resolved human protein interactome network to prioritize immunomodulatory variants in COVID-19. We find that, in addition to variants that pass genome-wide significance thresholds, variants at the interface of specific protein-protein interactions, even though they do not meet genome-wide thresholds, are equally immunomodulatory. The integration of these variants with single-cell epigenomic and transcriptomic data prioritizes myeloid and T cell subsets as the most affected by these variants across both the peripheral blood and the lung compartments. Of particular interest is a common coding variant that disrupts the OAS1-PRMT6 interaction and affects downstream interferon signaling. Critically, our framework is generalizable across infectious disease contexts and can be used to implicate immunomodulatory variants that do not meet genome-wide significance thresholds.
Open Access PDF
Concepts | Keywords |
---|---|
Downstream | CP: Immunology |
Genetic | GWAS |
Immunomodulatory | host-viral interactions |
Lead | machine learning |
Myeloid | protein networks |
systems immunology |
Semantics
Type | Source | Name |
---|---|---|
disease | IDO | cell |
disease | MESH | COVID-19 |
disease | IDO | protein |
disease | MESH | infectious disease |
pathway | REACTOME | Infectious disease |
disease | IDO | blood |
pathway | REACTOME | Interferon Signaling |
disease | IDO | host |