Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity

Profiling coronaviruses

Among the coronaviruses that infect humans, four cause mild common colds, whereas three others, including the currently circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), result in severe infections. Shrock et al. used a technology known as VirScan to probe the antibody repertoires of hundreds of coronavirus disease 2019 (COVID-19) patients and pre–COVID-19 era controls. They identified hundreds of antibody targets, including several antibody epitopes shared by the mild and severe coronaviruses and many specific to SARS-CoV-2. A machine-learning model accurately classified patients infected with SARS-CoV-2 and guided the design of an assay for rapid SARS-CoV-2 antibody detection. The study also looked at how the antibody response and viral exposure history differ in patients with diverging outcomes, which could inform the production of improved vaccine and antibody therapies.

Science, this issue p. eabd4250

Abstract

Deep serological profiling reveals discriminatory epitopes that could be used for SARS-CoV-2 antibody detection.

Abstract

INTRODUCTION

A systematic characterization of the humoral response to severe acute respiratory system coronavirus 2 (SARS-CoV-2) epitopes has yet to be performed. This analysis is important for understanding the immunogenicity of the viral proteome and the basis for cross-reactivity with the common-cold coronaviruses.

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is notable for its variable course, with some individuals remaining asymptomatic whereas others experience fever, respiratory distress, or even death. A comprehensive investigation of the antibody response in individuals with severe versus mild COVID-19—as well as an examination of past viral exposure history—is needed.

RATIONALE

An understanding of humoral responses to SARS-CoV-2 is critical for improving diagnostics and vaccines and gaining insight into variable clinical outcomes. To this end, we used VirScan, a high-throughput method to analyze epitopes of antiviral antibodies in human sera. We supplemented the original VirScan library with additional libraries of peptides spanning the proteomes of SARS-CoV-2 and all other human coronaviruses. These libraries enabled us to precisely map epitope locations and investigate cross-reactivity between SARS-CoV-2 and other coronavirus strains. The original VirScan library allowed us to simultaneously investigate antibody responses to prior infections and viral exposure history.

RESULTS

We screened sera from 232 COVID-19 patients and 190 pre–COVID-19 era controls against the original VirScan and supplemental coronavirus libraries, assaying more than 10 ⁸ antibody repertoire–peptide interactions. We identified epitopes ranging from “private” (recognized by antibodies in only a small number of individuals) to “public” (recognized by antibodies in many individuals) and detected SARS-CoV-2–specific epitopes as well as those that cross-react with common-cold coronaviruses. Several of these cross-reacting antibodies are present in pre–COVID-19 era samples. We developed a machine learning model that predicted SARS-CoV-2 exposure history with 99% sensitivity and 98% specificity from VirScan data. We used the most discriminatory SARS-CoV-2 peptides to produce a Luminex-based serological assay, which performed similarly to gold-standard enzyme-linked immunosorbent assays. We stratified the COVID-19 patient samples by disease severity and found that patients who had required hospitalization exhibited stronger and broader antibody responses to SARS-CoV-2 but weaker overall responses to past infections compared with those who did not need hospitalization. Further, the hospitalized group had higher seroprevalence rates for cytomegalovirus and herpes simplex virus 1. These findings may be influenced by differences in demographic compositions between the two groups, but they raise hypotheses that may be tested in future studies. Using alanine scanning mutagenesis, we precisely mapped 823 distinct epitopes across the entire SARS-CoV-2 proteome, 10 of which are likely targets of neutralizing antibodies. One cross-reactive antibody epitope in S2 has been previously suggested to be neutralizing and, as it exists in pre–COVID-19 era samples, could affect the severity of COVID-19.

CONCLUSION

We present a highly detailed view of the epitope landscape within the SARS-CoV-2 proteome. This knowledge may be used to produce diagnostics with improved specificity and can provide a stepping stone to the isolation and functional dissection of both neutralizing antibodies and antibodies that might exacerbate patient outcomes through antibody-dependent enhancement or immune distraction.

Our study reveals notable correlations between COVID-19 severity and both viral exposure history and overall strength of the antibody response to past infections. These findings are likely influenced by demographic covariates, but they generate hypotheses that may be tested with larger patient cohorts matched for age, gender, race, and other demographic variables.

SARS-CoV-2 epitope mapping.

VirScan detects antibodies against SARS-CoV-2 in COVID-19 patients with severe and mild disease. Heatmap color represents the strength of the antibody response in each sample (columns) to each protein (rows, left) or peptide (rows, right). VirScan reveals the precise positions of epitopes, which can be mapped onto the structure of the spike protein (S). Examination of SARS-CoV-2 and seasonal coronavirus sequence conservation explains epitope cross-reactivity. A, Ala; D, Asp; E, Glu; F, Phe; I, Ile; K, Lys; L, Leu; N, Asn; P, Pro; Q, Gln; R, Arg; S, Ser; T, Thr; V, Val; W, Trp; Y, Tyr.

Abstract

Understanding humoral responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical for improving diagnostics, therapeutics, and vaccines. Deep serological profiling of 232 coronavirus disease 2019 (COVID-19) patients and 190 pre–COVID-19 era controls using VirScan revealed more than 800 epitopes in the SARS-CoV-2 proteome, including 10 epitopes likely recognized by neutralizing antibodies. Preexisting antibodies in controls recognized SARS-CoV-2 ORF1, whereas only COVID-19 patient antibodies primarily recognized spike protein and nucleoprotein. A machine learning model trained on VirScan data predicted SARS-CoV-2 exposure history with 99% sensitivity and 98% specificity; a rapid Luminex-based diagnostic was developed from the most discriminatory SARS-CoV-2 peptides. Individuals with more severe COVID-19 exhibited stronger and broader SARS-CoV-2 responses, weaker antibody responses to prior infections, and higher incidence of cytomegalovirus and herpes simplex virus 1, possibly influenced by demographic covariates. Among hospitalized patients, males produce stronger SARS-CoV-2 antibody responses than females.