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Review 1: "SARS-CoV-2 antibody signatures for predicting the outcome of COVID-19"

Reviewers: Brenda M. Juan-Guardela, Jose D. Herazo-Maya (University of South Florida) | 📗📗📗📗◻️

Published onMay 05, 2022
Review 1: "SARS-CoV-2 antibody signatures for predicting the outcome of COVID-19"
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key-enterThis Pub is a Review of
Anti-SARS-CoV-2 IgG responses are powerful predicting signatures for the outcome of COVID-19 patients

AbstractThe COVID-19 global pandemic is far from ending. There is an urgent need to identify applicable biomarkers for early predicting the outcome of COVID-19. Growing evidences have revealed that SARS-CoV-2 specific antibodies evolved with disease progression and severity in COIVD-19 patients. We assumed that antibodies may serve as biomarkers for predicting disease outcome. By taking advantage of a newly developed SARS-CoV-2 proteome microarray, we surveyed IgG responses against 20 proteins of SARS-CoV-2 in 1,034 hospitalized COVID-19 patients on admission and followed till 66 days. The microarray results were further correlated with clinical information, laboratory test results and patient outcomes. Cox proportional hazards model was used to explore the association between SARS-CoV-2 specific antibodies and COVID-19 mortality. We found that nonsurvivors induced higher levels of IgG responses against most of non-structural proteins than survivors on admission. In particular, the magnitude of IgG antibodies against 8 non-structural proteins (NSP1, NSP4, NSP7, NSP8, NSP9, NSP10, RdRp, and NSP14) and 2 accessory proteins (ORF3b and ORF9b) possessed significant predictive power for patient death, even after further adjustments for demographics, comorbidities, and common laboratory biomarkers for disease severity (all with p trend < 0.05). Additionally, IgG responses to all of these 10 non-structural/accessory proteins were also associated with the severity of disease, and differential kinetics and serum positive rate of these IgG responses were confirmed in COVID-19 patients of varying severities within 20 days after symptoms onset. The AUCs for these IgG responses, determined by computational cross-validations, were between 0.62 and 0.71. Our findings have important implications for improving clinical management, and especially for developing medical interventions and vaccines.

RR:C19 Evidence Scale rating by reviewer:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.



Lei and colleagues performed a SARS-CoV-2 proteome microarray of 20 SARS-CoV-2 proteins to detect serum IgM/IgG responses to predict COVID-19 survival in 1,034 eligible participants. Out of this cohort, 955 patients survived and 79 died. COVID-19 was diagnosed based on a positive SARS-CoV-2 nucleic acid test from respiratory tract specimens or based on clinical diagnosis with clinical symptoms and imaging features of pneumonia on chest computed tomographic (CT).  The investigators used a total of 2973 samples for time-course analysis to determine time course trends of selected antibodies. A series of statistical tests including Two-Tailed t-test, Spearman Rank correlation, Cox Proportional-Hazards model, Kaplan-Meier curves, Principal Component analysis, ROC curves, and Cross-Validation (among others), were used to identify significant associations. 

I will summarize the most relevant results of the study

  1. High ORF7b IgM level is an independent predictor of COVID-19 survival

  2. High IgG levels of NSP4, NSP7, NSP9, NSP10, RdRp (NSP12), NSP14 and 1 accessory protein ORF3b, (non-structural SARS-CoV-2 proteins)  are predictive of COVID-19 mortality

  3. IgM response to ORF7b is negatively correlated with pro-inflammatory factors such as procalcitonin, C-reactive protein, lactate dehydrogenase, D-dimer, IL-2R, and IL-6

  4. ORF7b IgM provided the highest AUC (0.74) for outcome prediction (survival) of all studied antibodies 

  5. Levels of ORF7b IgM, NSP9 IgG, and NSP10 IgG were relatively stable over time in the study population 

Overall, this manuscript provides important insights regarding IgM and IgG antibody responses in COVID-19 and their association with disease outcomes, the most relevant being the prediction of survival based on high ORF7b IgM levels and low NSP4, NSP7, NSP9, NSP10, RdRp (NSP12), NSP14 and 1 accessory protein ORF3b, IgG levels. The most intriguing result of this study is the discordancy between IgM and IgG antibody response and their association with survival. Mechanistically, it makes sense that high ORF7b IgM levels are associated with COVID-19 survival but the association between high IgG levels of non-structural SARS-CoV-2 proteins and increased mortality is less clear. Future mechanistic studies will be required to further investigate the association between high IgG antibody responses against non-structural SARS-CoV-2 proteins and COVID-19 survival.


  1. The authors need to outline the number of patients who were diagnosed based on nasal swab PCR versus clinical and radiological diagnosis to identify potential confounders

  2. This manuscript will improve significantly if the authors simplify the statistical analysis and present only the most relevant results.   

  3. I am concerned about the time course analysis and results. The authors should specify the time interval between sample collection and the number of samples available per time point. The results may be biased by unbalanced sample collection between time intervals. The authors should also split the analysis into groups (survivors versus non-survivors) so that time course differences in antibody responses between these two groups can be better evaluated. 


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