Uncovering cell-type-specific immunomodulatory variants and molecular phenotypes in COVID-19 using structurally resolved protein networks.

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.

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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

Original Article

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