Seroconversion stages COVID19 into distinct pathophysiological states

Elife. 2021 Mar 16:10:e65508. doi: 10.7554/eLife.65508.


COVID19 is a heterogeneous medical condition involving diverse underlying pathophysiological processes including hyperinflammation, endothelial damage, thrombotic microangiopathy, and end-organ damage. Limited knowledge about the molecular mechanisms driving these processes and lack of staging biomarkers hamper the ability to stratify patients for targeted therapeutics. We report here the results of a cross-sectional multi-omics analysis of hospitalized COVID19 patients revealing that seroconversion status associates with distinct underlying pathophysiological states. Low antibody titers associate with hyperactive T cells and NK cells, high levels of IFN alpha, gamma and lambda ligands, markers of systemic complement activation, and depletion of lymphocytes, neutrophils, and platelets. Upon seroconversion, all of these processes are attenuated, observing instead increases in B cell subsets, emergency hematopoiesis, increased D-dimer, and hypoalbuminemia. We propose that seroconversion status could potentially be used as a biosignature to stratify patients for therapeutic intervention and to inform analysis of clinical trial results in heterogenous patient populations.

Keywords: COVID19; SARS; antibodies; complement; cytokines; human; immunology; inflammation; interferons.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers
  • COVID-19 / epidemiology*
  • COVID-19 / immunology
  • COVID-19 / metabolism
  • COVID-19 / virology*
  • Comorbidity
  • Complement Activation / immunology
  • Complement System Proteins / immunology
  • Hematopoiesis
  • Homeostasis
  • Hospitalization
  • Humans
  • Hypoalbuminemia
  • Interferons / metabolism
  • Models, Biological
  • SARS-CoV-2*
  • Seroconversion*
  • Seroepidemiologic Studies
  • Signal Transduction


  • Biomarkers
  • Complement System Proteins
  • Interferons

Associated data

  • GEO/GSE167000