SARS-CoV-2 specific IgM / IgG responses accurately predict the outcome of COVID-19

Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 in Wuhan, China, and has rapidly progressed into a devastating pandemic affecting global public health and paralyzing economies. So far, the COVID-19 pandemic has caused over 55.4 million infections and over 1.33 million deaths worldwide.

COVID-19 affects the upper and lower respiratory systems and causes flu-like symptoms in most infected people. Although many COVID-19 patients experience only mild symptoms, some patients have severe symptoms that lead to massive lung damage. Treatment options for COVID-19 are limited, and the WHO-estimated crude death rate is about 2.9%. Although a preventative vaccine for COVID-19 could eventually become available, unless sufficient herd immunity is achieved, COVID-19 could potentially cause significant morbidity and mortality in the coming years.

This highlights the importance of understanding the role of the immune system in the progression and clinical outcome of patients with COVID-19 to improve clinical management and develop effective vaccines and therapeutic interventions. A better understanding of the role of immunity could help identify applicable biomarkers that can predict the clinical outcome of COVID-19.

IgM, IgG, and IgA antibodies rise and remain elevated during the progression of COVID-19

SARS-CoV-2, like SARS-CoV and MERS-CoV, is part of the betacoronavirus family and its genome encodes 4 main structural proteins: envelope (E), spike (S), membrane (M) and nucleocapsid (N); 15 non-structural proteins – Nsp1-10 and Nsp12-16; and 9 accessory proteins. Protein S has the N-terminal S1 peptide with a key receptor binding domain region and a C-terminal S2 fragment. It plays an important role in viral attachment, fusion and entry into host cells with the viral receptor angiotensin converting enzyme 2.

Rapidly growing serological tests show that IgM, IgG, and IgA antibodies to S or N proteins evolve rapidly in the serum of asymptomatic and symptomatic COVID-19 patients within one week of infection or symptom onset and remain elevated with the progression of the disease. However, not much is known about humoral immune responses to the rest of structural and non-structural SARS-CoV-2 proteins during disease progression.

New proteome microarray with 20 SARS-CoV-2 proteins to understand IgM / IgG responses

Researchers from Huazhong University of Science and Technology and Shanghai Jiao Tong University, China, recently constructed a proteome microarray with 20 of the 28 expected SARS-CoV-2 proteins to help understand SARS-specific IgM / IgG responses. -CoV-2. Their study is published on the prepress server medRxiv* before undergoing the peer review process.

The team hypothesized that IgM and IgG antibodies to SARS-CoV-2 may serve as biomarkers that can predict prognosis and outcome in COVID-19 patients. Using the SARS-CoV-2 proteome microarray, they analyzed IgM / IgG responses in 1,034 hospitalized COVID-19 patients against 20 SARS-CoV-2 proteins. The analysis was performed on admission and continued for 66 days. Microarray results were correlated with laboratory test results, clinical information, and patient outcomes. They used Cox’s proportional hazards model to determine the association between SARS-CoV-2 specific antibodies and mortality in COVID-19 patients.

IgM / IgG response levels to SARS-CoV-2 proteins are important predictors of patient survival and mortality

The team found that a high IgM level against ORF7b at admission is an independent predictor of patient survival, while IgG response levels to 6 non-structural proteins – NSP4, NSP7, NSP9, NSP10, RdRp (NSP12 ), NSP14 – and 1 accessory protein – ORF3b – is a significant predictor of patient mortality.

“Our results demonstrate that a high level of IgM antibodies against ORF7b at admission is an independent predictor of patient survival, while IgG responses to NSP9 and NSP10 possess significant predictive power for patient death.”

These results were accurate even after adjustments for comorbidities, demographics, and common laboratory markers for disease severity. Spline regression analysis showed that the correlation between NSP9 IgG, ORF7b IgM and NSP10 IgG and COVID-19 mortality risk is linear. Their areas under the curve for predictions were determined using computational cross-validation and were 0.74, 0.66, and 0.68, respectively.

Further validations conducted in the serial samples of these cases showed a high accuracy of the prediction for clinical outcome. The authors believe these findings have significant implications for improving clinical management and the development of therapeutic interventions and vaccines.

“Our research could improve clinical management and guide the development of effective medical interventions and vaccines by improving further understanding of the pathogenesis of COVID-19.”

*Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and therefore should not be considered conclusive, guide clinical practice / health-related behaviors, or treated as consolidated information.