Dataset Information

Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates.

ABSTRACT:

Objectives

Population-level measures of seropositivity are critical for understanding the epidemiology of an emerging pathogen, yet most antibody tests apply a strict cutoff for seropositivity that is not learnt in a data-driven manner, leading to uncertainty when classifying low-titer responses. To improve upon this, we evaluated cutoff-independent methods for their ability to assign likelihood of SARS-CoV-2 seropositivity to individual samples.

Methods

Using robust ELISAs based on SARS-CoV-2 spike (S) and the receptor-binding domain (RBD), we profiled antibody responses in a group of SARS-CoV-2 PCR+ individuals (n = 138). Using these data, we trained probabilistic learners to assign likelihood of seropositivity to test samples of unknown serostatus (n = 5100), identifying a support vector machines-linear discriminant analysis learner (SVM-LDA) suited for this purpose.

Results

In the training data from confirmed ancestral SARS-CoV-2 infections, 99% of participants had detectable anti-S and -RBD IgG in the circulation, with titers differing > 1000-fold between persons. In data of otherwise healthy individuals, 7.2% (n = 367) of samples were of uncertain serostatus, with values in the range of 3-6SD from the mean of pre-pandemic negative controls (n = 595). In contrast, SVM-LDA classified 6.4% (n = 328) of test samples as having a high likelihood (> 99% chance) of past infection, 4.5% (n = 230) to have a 50-99% likelihood, and 4.0% (n = 203) to have a 10-49% likelihood. As different probabilistic approaches were more consistent with each other than conventional SD-based methods, such tools allow for more statistically-sound seropositivity estimates in large cohorts.

Conclusion

Probabilistic antibody testing frameworks can improve seropositivity estimates in populations with large titer variability.

SUBMITTER: Castro Dopico X

PROVIDER: S-EPMC8891432 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates.

Castro Dopico Xaquin X Muschiol Sandra S Grinberg Nastasiya F NF Aleman Soo S Sheward Daniel J DJ Hanke Leo L Ahl Marcus M Vikström Linnea L Forsell Mattias M Coquet Jonathan M JM McInerney Gerald G Dillner Joakim J Bogdanovic Gordana G Murrell Ben B Albert Jan J Wallace Chris C Karlsson Hedestam Gunilla B GB

Clinical & translational immunology 20220302 3

<h4>Objectives</h4>Population-level measures of seropositivity are critical for understanding the epidemiology of an emerging pathogen, yet most antibody tests apply a strict cutoff for seropositivity that is not learnt in a data-driven manner, leading to uncertainty when classifying low-titer responses. To improve upon this, we evaluated cutoff-independent methods for their ability to assign likelihood of SARS-CoV-2 seropositivity to individual samples.<h4>Methods</h4>Using robust ELISAs based ...[more]

PMID: 35284072

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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific antibody responses to the spike (S) protein monomer, S protein native trimeric form, or the nucleocapsid (N) proteins were evaluated in cohorts of individuals with acute infection (n = 93) and in individuals enrolled in a postinfection seroprevalence population study (n = 578) in Switzerland. Commercial assays specific for the S1 monomer, for the N protein, or within a newly developed Luminex assay using the S protein trimer were found to be equally sensitive in antibody detection in the acute-infection-phase samples. Interestingly, compared to anti-S antibody responses, those against the N protein appear to wane in the postinfection cohort. Seroprevalence in a "positive patient contacts" group (n = 177) was underestimated by N protein assays by 10.9 to 32.2%, while the "randomly selected" general population group (n = 311) was reduced by up to 45% relative to the S protein assays. The overall reduction in seroprevalence targeting only anti-N antibodies for the total cohort ranged from 9.4 to 31%. Of note, the use of the S protein in its native trimer form was significantly more sensitive compared to monomeric S proteins. These results indicate that the assessment of anti-S IgG antibody responses against the native trimeric S protein should be implemented to estimate SARS-CoV-2 infections in population-based seroprevalence studies.IMPORTANCE In the present study, we have determined SARS-CoV-2-specific antibody responses in sera of acute and postinfection phase subjects. Our results indicate that antibody responses against viral S and N proteins were equally sensitive in the acute phase of infection, but that responses against N appear to wane in the postinfection phase where those against the S protein persist over time. The most sensitive serological assay in both acute and postinfection phases used the native S protein trimer as the binding antigen, which has significantly greater conformational epitopes for antibody binding compared to the S1 monomer protein used in other assays. We believe these results are extremely important in order to generate correct estimates of SARS-CoV-2 infections in the general population. Furthermore, the assessment of antibody responses against the trimeric S protein will be critical to evaluate the durability of the antibody response and for the characterization of a vaccine-induced antibody response.

Dataset Information

Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates.

Objectives

Methods

Results

Conclusion

Publications

Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates.

Similar Datasets

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