Abstract
Infection of humans by the SARS-CoV-2 virus leads to highly variable host responses and diverse clinical outcomes, ranging from asymptomatic to hospitalization, intensive care unit (ICU) admission and death. 10% of those with acute infections continue to display post-acute sequelae of coronavirus disease (PASC), now colloquially termed Post-COVID Syndrome (PCS). There is an acute unmet need for unbiased diagnostic biomarkers to predict outcomes before or during the early stages of acute infection, to discover more about PCS and to enable targeting of therapeutics to individual patients. Here, starting with whole blood taken at the time of diagnosis, a predictive classifier model containing six 3-dimensional (3D)-genomic biomarkers able to identify individuals at the highest risk of acute severe COVID disease with a positive predictive value of 93% and balanced accuracy of 88% was developed. The discovery process started with a whole 3D-genome microarray generating 964,631 data points per patient. Mapping the position of the most informative 3D markers to nearby genes revealed associations with ACE2, olfactory, Gby, Ca2+ and nitric oxide signalling; innate and adaptive immunity; programme death ligand 1 (PD-L1); prostaglandin E2 (PGE2); and the inflammatory cytokine CCL5, confirming variability in host immune responses, rather than viral genetics or load, as the primary determinant of disease outcomes, and supporting the use of mammalian target of rapamycin (mTOR) inhibitors and immunosuppressants to treat acute severe disease. Using the 3D genomics knowledgebase, with >1 billion 3D genomic datapoints derived from clinical studies, a subset of 77 of the acute COVID-associated prognostic 3D biomarkers were found close to 10 loci genetically linked to fatigue-dominant PCS, and to be informative biomarkers in 6 diseases with fatigue as a symptom. Network analysis linked individual 3D genomic markers to pathways, diseases and therapies. 3D-genomic profiling, as an integrator of multi-omic molecular regulation, offers a new approach for better understanding the complex heterogeneous clinical outcomes triggered by infectious agents.