| Literature DB >> 34691016 |
Xunyan Ye1, Laura S Angelo1, Erin G Nicholson1, Obinna P Iwuchukwu1, Wanderson Cabral de Rezende1,2, Anubama Rajan1, Letisha O Aideyan1, Trevor J McBride1, Nanette Bond1, Patricia Santarcangelo1, Yolanda J Rayford1, Laura Ferlic-Stark1, Sonia Fragoso1, Zoha Momin1, Hongbing Liu1, Khanghy Truong1, Brianna Lopez1, Margaret E Conner1, Andrew P Rice1, Jason T Kimata1, Vasanthi Avadhanula1, Pedro A Piedra1,2,3.
Abstract
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in December 2019 in Wuhan, China, and then rapidly spread causing an unprecedented pandemic. A robust serological assay is needed to evaluate vaccine candidates and better understand the epidemiology of coronavirus disease (COVID-19).Entities:
Keywords: COVID-19; SARS-CoV-2; binding antibody; functional antibody; human serum; serology
Mesh:
Substances:
Year: 2021 PMID: 34691016 PMCID: PMC8531527 DOI: 10.3389/fimmu.2021.693462
Source DB: PubMed Journal: Front Immunol ISSN: 1664-3224 Impact factor: 7.561
Figure 1Reverse cumulative distribution curves of IgG anti-S ELISA. (A) Percentages of positive sera among the pre-pandemic sera with unknown infectious status to endemic coronaviruses (n = 234), pre-pandemic human endemic coronavirus PCR positive sera (n=35), and SARS-CoV-2 PCR positive sera (n=51) at different dilutions at an O.D. ≥ 0.5. (B, C) Percentages of positive sera at different O.D. values for all three groups of sera at 1:512 dilution and 1:1024 dilution, respectively.
Positive predictive value (PPV) and negative predictive value (NPV) of SARS-CoV-2 IgG at different SARS-CoV-2 seroprevalence rates.
| SARS-CoV-2 | 1:512 Dilution | 1:1,024 Dilution | ||
|---|---|---|---|---|
| Seroprevalence Rates | PPV % (95% CI) | NPV % (95% CI) | PPV % (95% CI) | NPV % (95% CI) |
| 1% | 33.8% (17.6, 55) | 99.9% (99.8, 100) | 71.5% (26.1, 94.7) | 99.9% (99.8, 100) |
|
| 72.7% (52.7, 86.4) | 99.7% (99.1, 99.9) |
|
|
| 7.5% | 80.4% (63.2, 90.7) | 99.5% (98.6, 99.8) | 95.3% (73.9, 99.3) | 99.4% (98.4, 99.8) |
| 10% | 84.9% (70.2, 93.1) | 99.3% (98, 99.8) | 96.5% (79.5,99.5) | 99.1% (97.8, 99.7) |
| 15% | 89.9% (78.9, 95.5) | 99% (96.9, 99.6) | 97.8% (86.1, 99.7) | 98.6% (99.6, 99.5) |
| 20% | 92.7% (84.1, 96.8) | 98.5% (95.7, 99.5) | 98.4% (89.7, 99.8) | 98.1% (95.2, 99.2) |
| 50% | 98.1% (95.5, 99.2) | 94.3% (84.8, 98) | 99.6% (97.2, 99.9) | 92.7% (83.2, 97) |
A Bayesian approach was used to estimate the PPV and NPV for a given prevalence.
The bold values they are the final numbers reported in the Abstract. They are the determined/optimal PPV and NPV that helped to determine the optimal dilution factors for IgG assay (1:1024) and IgA assay (1:128) at the seroprevalence of 5% calculated with the 320 assay development samples.
Figure 2Reverse cumulative distribution curves of IgA anti-S ELISA. (A) Percentages of positive sera among the pre-pandemic sera with unknown infectious status to endemic coronaviruses (n = 234), pre-pandemic human endemic coronavirus PCR positive sera (n=35), and SARS-CoV-2 PCR positive sera (n=51) at different dilutions at an O.D. ≥ 0.4. (B, C) Percentages of positive sera at different O.D. values for all three groups of sera at 1:64 dilution and 1:128 dilution, respectively.
Positive predictive value (PPV) and negative predictive value (NPV) of SARS-CoV-2 IgA at different SARS-CoV-2 seroprevalence rates.
| SARS-CoV-2 | 1:64 Dilution | 1:128 Dilution | ||
|---|---|---|---|---|
| Seroprevalence Rates | PPV % (95% CI) | NPV % (95% CI) | PPV % (95% CI) | NPV % (95% CI) |
| 1% | 18.2% (11.0, 28.6) | 99.9% (99.8, 100) | 40.9% (18.2, 68.3) | 99.8% (99.6, 99.9) |
|
| 53.7% (39.3, 67.6) | 99.5% (98.8, 99.8) |
|
|
| 7.5% | 64.1% (49.9, 76.3) | 99.2% (98.1, 99.6) | 84.8% (64.1, 94.5) | 98.1% (96.9, 98.8) |
| 10% | 71.0% (57.7, 81.5) | 98.9% (97.5, 99.5) | 88.4% (71.0, 96.0) | 97.4% (95.8, 98.4) |
| 15% | 79.6% (68.4, 87.5) | 98.2% (96.0, 99.2) | 92.4% (79.5, 97.4) | 96.0% (93.6, 97.5) |
| 20% | 84.6% (75.4, 90.8) | 97.5% (94.4, 98.9) | 94.5% (84.6, 98.2) | 94.4% (91.1, 96.5) |
| 50% | 95.7% (92.5, 97.5) | 90.7% (81, 95.7) | 98.6% (95.7, 99.5) | 80.8% (71.9, 87.3) |
A Bayesian approach was used to estimate the PPV and NPV for a given prevalence.
The bold values they are the final numbers reported in the Abstract. They are the determined/optimal PPV and NPV that helped to determine the optimal dilution factors for IgG assay (1:1024) and IgA assay (1:128) at the seroprevalence of 5% calculated with the 320 assay development samples.
Demographic data of the subjects during SARS-CoV-2 pandemic.
| Variables | # of SARS-CoV-2 PCR positive subjects (%) | # of surveillance subjects (%) | # of both subjects (%) |
|---|---|---|---|
|
| |||
| 18-34 | 16 (21.1) | 9 (36) | 25 (24.8) |
| 35-49 | 27 (35.5) | 10 (40) | 37 (36.6) |
| 50-64 | 22 (28.9) | 4 (16) | 26 (25.7) |
| ≥ 65 | 11 (14.5) | 2 (8) | 13 (12.9) |
|
| |||
| Male | 36 (47.4) | 9 (36) | 45 (44.6) |
| Female | 40 (52.6) | 16 (64) | 56 (55.4) |
|
| |||
| White | 50 (65.8) | 18 (72) | 68 (67.3) |
| Black | 7 (9.2) | 1 (4) | 8 (7.9) |
| Asian | 14 (18.4) | 2 (8) | 16 (15.8) |
| American Indian or Alaska Native | 0 (0.0) | 2 (8) | 2 (2.0) |
| Multiracial | 3 (3.9) | 1 (4) | 4 (4.0) |
| Declined | 2 (2.6) | 1 (4) | 3 (3.0) |
|
| |||
| Hispanic | 15 (19.7) | 8 (32) | 23 (22.8) |
| Non-Hispanic | 61 (80.3) | 17 (68) | 78 (77.2) |
|
| |||
| 0 | 45 (59.2) | 15 (60) | 60 (59.4) |
| 1 | 17 (22.4) | 9 (36) | 26 (25.7) |
| 2 | 12 (15.8) | 1 (4) | 13 (12.9) |
| ≥3 | 2 (2.6) | 0 (0) | 2 (2.0) |
|
| |||
| Healthcare | 24 (31.6) | 10 (40) | 34 (33.7) |
| Non-healthcare | 52 (68.4) | 15 (60) | 67 (66.3) |
|
| |||
| Yes | 14 (18.4) | 0 (0) | 14 (13.9) |
| No | 62 (81.6) | 25 (100) | 87 (86.1) |
|
| |||
| Asymptomatic/Mild | 2 (2.6) | 22 (88) | 24 (23.8) |
| Mild/Moderate | 60 (79) | 3 (12) | 63 (62.4) |
| Moderate/Severe | 14 (18.4) | 0 (0) | 14 (13.9) |
for the co-morbid conditions, 0=none, 1=one co-morbid condition, 2=two co-morbid conditions, ≥3=three or more co-morbid conditions.
Percent agreement of positivity and negativity of IgA binding and functional antibodies to IgG anti-S binding antibody.
| IgG Anti-S | |||
|---|---|---|---|
| Negative (titer <10 log2) | Positive (titer ≥10 log2) | ||
|
| Negative (titer < 7 log2) | 28/30 (93.3%) | 16/71 (22.5%) |
| Positive (titer ≥ 7 log2) | 2/30 (6.7%) | 55/71 (77.5%) | |
|
| Negative (5 log2% = 0) | 26/30 (86.7%) | 3/71 (4.2%) |
| Positive (5 log2% > 0) | 4/30 (13.3%) | 68/71 (95.8) | |
|
| Negative (IC50 = 0) | 14/15 (93.3%) | 0/64 (0%) |
| Positive (IC50 > 0) | 1/15 (6.7%) | 64/64 (100%) | |
|
| Negative (titer ≤ 2 log2) | 29/30 (96.7%) | 1/71 (1.4%) |
| Positive (titer > 2 log2) | 1/30 (3.3%) | 70/71 (98.6%) | |
Figure 3Correlation between IgG anti-S and IgA anti-S (A) concentrations (log2 ng/mL) and (B) titers (log2) in SARS-CoV-2 PCR positive (red open circles) and pandemic surveillance sera (green open circles). Gray dash lines represent the cut-offs of antibody titers (log2) or concentrations (log2 ng/mL). Pearson’s correlation coefficient was calculated to measure the strength of the linear association. *Correlation was significant at the 0.05 level (2-tailed). **Correlation was significant at the 0.01 level (2-tailed). A total of 101 serum samples including 76 from SARS-CoV-2 PCR positive adults and 25 from adults who participated in pandemic surveillance study were tested by each assay.
Figure 4Correlation between IgG anti-S levels and (A, B) ACE-2 receptor blocking antibody levels (5 log2%), (C, D) lentipseudovirus-S neutralizing antibody titers (log2 IC50) and (E, F) SARS-CoV-2 neutralizing antibody titers (log2). A total of 101 serum samples including 76 SARS-CoV-2 PCR positive sera and 25 SARS-CoV-2 surveillance sera were tested in the IgG anti-S ELISA, ACE-2 receptor blocking antibody assay, and SARS-CoV-2 microneutralization assay. A total of 79 serum samples (73 SARS-CoV-2 PCR positive sera and 6 SARS-CoV-2 surveillance sera) were tested in the lentipseudovirus-S neutralizing antibody assay. SARS-CoV-2 PCR positive sera represented by red open circles and SARS-CoV-2 surveillance sera by green open circles. Gray dash lines represent the cut-offs of antibody titers (log2) or concentrations (log2 ng/mL). Pearson’s correlation coefficient was calculated to measure the strength of the linear association. *Correlation was significant at the 0.05 level (2-tailed). **Correlation was significant at the 0.01 level (2-tailed).
Figure 5Comparison of IgG anti-S geometric mean levels by (A) age, (B) gender, and (C) disease severity. A one-way ANOVA was used for the statistical analysis of mean differences between age comparison, and an independent Student’s t test was employed for mean differences in gender and disease severity. A total of 74 SARS-CoV-2 PCR positive sera was analyzed in the comparison. Error bars represent standard deviations.
Figure 6Comparison of IgA anti-S geometric mean levels by (A) age, (B) gender, and (C) disease severity. A one-way ANOVA was used for the statistical analysis of mean differences between age comparison, and an independent Student’s t test was employed for mean differences in gender and disease severity. A total of 74 SARS-CoV-2 PCR positive sera were analyzed in the comparison. Error bars represent standard deviations.