PNNL

Abstract

It is unclear how SARS-CoV-2 infection leads to the strong but ineffective inflammatory response that characterizes severe COVID-19, with amplified immune activation in diverse cell types, including cells without ACE2 receptors necessary for infection. Proteolytic degradation of SARSCoV-2 virions is a milestone in host viral clearance, but the impact of remnant viral peptide fragments from high viral loads is not known. Here, we examine the inflammatory capacity of fragmented viral components from the perspective of self-assembly in the infected host environment. Interestingly, we identify exposed SARS-CoV-2 peptide fragments with architectures cognate to host antimicrobial peptides (AMPs) in tracheal aspirates of critical COVID-19 patients. To assess effects of AMP-like viral fragments on host cells, we use machine learning to map out all sequence motifs in the SARS-CoV-2 proteome that mimic host antimicrobial peptides (xenoAMPs), especially highly cationic human cathelicidin LL-37. Such xenoAMPs are strongly enriched in SARS-CoV-2 relative to low-pathogenicity coronaviruses. Moreover, xenoAMPs from SARS-CoV-2 but not low-pathogenicity homologs assemble dsRNA into nanocrystalline complexes with lattice constants commensurate with the steric size of Toll-like receptor (TLR)-3 and therefore capable of multivalent binding. Such complexes amplify cytokine secretion in diverse uninfected cell types in culture (epithelial cells, endothelial cells, monocytes, and macrophages), similar to cathelicidin’s role in rheumatoid arthritis and lupus. The induced transcriptome matches well with the global gene expression pattern in COVID-19, despite using