Mounting an effective antibody response against SARS-CoV-2 is key to fight off the infection. However,
not all antibodies produced against the virus are protective. Recent studies show that high titers of
immunoglobulin G (IgG) against the SARS-CoV-2 Spike (S) protein during natural infection correlate with disease
severity. The S protein, highlighted due to its receptor-binding role in host cell infection, represents only one of
potentially 29 proteins that are encoded by the SARS-CoV-2 virus. In fact, recent studies show that SARS-CoV-
2 non-structural proteins, including ORF3b, ORF8, ORF9b, and ORF9c, have the ability to suppress host antiviral
type-I interferon (IFN) and elicit sustained antibody responses. These findings raise the question of whether
antibodies produced against these immunomodulatory viral proteins early during the infection could provide
protection against severe COVID-19.
In addition to the antibody specificity to different SARS-CoV-2 proteins, the antibody isotype and
subclass can also influence the disease outcome. However, much of the serology data from SARS-CoV-2-
infected patients mainly focus on IgG and IgM responses to S and nucleocapsid proteins only. In this proposal,
we aim to tackle this problem by developing a new technology that will allow for simultaneous measurement of
8 antibody isotypes and subclasses (IgM, IgG1, IgG2, IgG3, IgG4, IgA1, IgA2, IgE) against 24 SARS-CoV-2
and endemic HCoV-encoded proteins, including against proteins from emerging virus variants, all in a single
multiplexed assay (Aim 1). We will apply this technology, namely flowBEAT (flow cytometry-based BEads
assay to detect Antigen-specific antibody isoTypes), to 600 serum samples collected from a longitudinal study
(6 timepoints; = 6 months) of a well-characterized cohort of 100 COVID-19 patients presenting with either mild
or severe symptoms (Aim 2). We hypothesize that mild, but not severe, COVID-19 patients will show an early
and sustained antibody subclass against immunomodulatory viral proteins, particularly against viral proteins
known to suppress IFN responses.
We expect this study to provide an in-depth view of the breadth (type, specificity, and longevity) of
antibody responses mounted against 24 SARS-CoV-2- and HCoV-encoded proteins that are associated with
efficient recovery from COVID-19. Our study can also inform on ongoing and future vaccine development by
identifying proteins that generate long-lasting and protective antibody subclasses in the recovered patients.
Finally, the flexible modular feature of flowBEAT can quickly be adapted to study the breadth of antibody
responses in virtually any disease.