{"doi":"10.1093/infdis/jiac022","title":"IgG Against Human Betacoronavirus Spike Proteins Correlates With SARS-CoV-2 Anti-Spike IgG Responses and COVID-19 Disease Severity","abstract":"BACKGROUND: A protective antibody response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial to decrease morbidity and mortality from severe coronavirus disease 2019 (COVID-19) disease. The effects of preexisting anti-human coronavirus (HCoV) antibodies on the SARS-CoV-2-specific immunoglobulin G (IgG) responses and severity of disease are currently unclear. METHODS: We profiled anti-spike (S), S1, S2, and receptor-binding domain IgG antibodies against SARS-CoV-2 and 6 HCoVs using a multiplex assay (mPLEX-CoV) with serum samples from SARS-CoV-2 infected (n = 155) and pre-COVID-19 (n = 188) cohorts. RESULTS: COVID-19 subjects showed significantly increased anti-S SARS-CoV-2 IgG levels that were highly correlated with IgG antibodies against OC43 and HKU1 S proteins. However, OC43 and HKU1 anti-S antibodies in pre-COVID-19 era sera did not cross-react with SARS-CoV-2. Unidirectional cross-reactive antibodies elicited by SARS-CoV-2 infection were distinct from the bidirectional cross-reactive antibodies recognizing homologous strains RaTG13 and SARS-CoV-1. High anti-OC43 and anti-S2 antibody levels were associated with both a rapid anti-SARS-CoV-2 antibody response and increased disease severity. Subjects with increased sequential organ failure assessment (SOFA) scores developed a higher ratio of S2- to S1-reactive antibodies. CONCLUSIONS: Early and rapid emergence of OC43 S- and S2-reactive IgG after SARS-CoV-2 infection correlates with COVID-19 disease severity.","journal":"The Journal of Infectious Diseases","year":2022,"id":268740,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":19,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9712,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":278344,"name":"Dongmei Li","orcid":"0000-0001-9140-2483","position":1,"is_corresponding":false},{"id":930936,"name":"Andrew Cameron","orcid":"0000-0003-4038-921X","position":2,"is_corresponding":false},{"id":721911,"name":"Qian Zhou","orcid":"0000-0003-3954-5449","position":3,"is_corresponding":false},{"id":576790,"name":"Alexander Wiltse","orcid":null,"position":4,"is_corresponding":false},{"id":322561,"name":"Jennifer L. Nayak","orcid":"0000-0002-5042-3031","position":5,"is_corresponding":false},{"id":378546,"name":"Nicole Pecora","orcid":"0000-0001-5633-8746","position":6,"is_corresponding":false},{"id":378547,"name":"Martin S. Zand","orcid":"0000-0002-7095-8682","position":7,"is_corresponding":false},{"id":408834,"name":"Jiong Wang","orcid":"0000-0002-4938-5074","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":null,"created_at":"2026-07-19T00:27:14.088663Z","pmid":"35091739","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}