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Nicolai E, Tomassetti F, Pelagalli M, Sarubbi S, Minieri M, Nisini A, Nuccetelli M, Ciotti M, Pieri M, Bernardini S. The Antibodies' Response to SARS-CoV-2 Vaccination: 1-Year Follow Up. Biomedicines 2023; 11:2661. [PMID: 37893035 PMCID: PMC10604657 DOI: 10.3390/biomedicines11102661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/29/2023] Open
Abstract
The use of vaccines has allowed the containment of coronavirus disease 2019 (COVID-19) at a global level. The present work aims to add data on vaccination by evaluating the level of neutralizing antibodies in individuals who have received a three-vaccination series. For this purpose, we ran a surveillance program directed at measuring the level of IgG Abs against the Receptor Binding Domain (RBD) and surrogate virus neutralizing Ab (sVNT) anti-SARS-CoV-2 in the serum of individuals undergoing vaccination. This study was performed on employees from the University of Rome Tor Vergata and healthcare workers from the University Hospital who received the Vaxzevria vaccine (n = 56) and Comirnaty vaccine (n = 113), respectively. After the second dose, an increase in both RBD and sVNT Ab values was registered. In individuals who received the Comirnaty vaccine, the antibody titer was about one order of magnitude higher after 6 months from the first dose. All participants in this study received the Comirnaty vaccine as the third dose, which boosted the antibody response. Five months after the third dose, nearly one year from the first injection, the antibody level was >1000 BAU/mL (binding antibody units/mL). According to the values reported in the literature conferring protection against SARS-CoV-2 infection, our data indicate that individuals undergoing three vaccine doses present a low risk of infection.
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Affiliation(s)
- Eleonora Nicolai
- Department of Experimental Medicine, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy; (E.N.); (F.T.); (M.P.); (S.S.); (M.M.); (S.B.)
| | - Flaminia Tomassetti
- Department of Experimental Medicine, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy; (E.N.); (F.T.); (M.P.); (S.S.); (M.M.); (S.B.)
| | - Martina Pelagalli
- Department of Experimental Medicine, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy; (E.N.); (F.T.); (M.P.); (S.S.); (M.M.); (S.B.)
| | - Serena Sarubbi
- Department of Experimental Medicine, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy; (E.N.); (F.T.); (M.P.); (S.S.); (M.M.); (S.B.)
| | - Marilena Minieri
- Department of Experimental Medicine, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy; (E.N.); (F.T.); (M.P.); (S.S.); (M.M.); (S.B.)
- Department of Laboratory Medicine, Tor Vergata University Hospital, Viale Oxford 81, 00133 Rome, Italy;
| | - Alberto Nisini
- Department of Diagnostic Imaging and Radiology, Tor Vergata University Hospital, Viale Oxford 81, 00133 Rome, Italy;
| | - Marzia Nuccetelli
- Department of Laboratory Medicine, Tor Vergata University Hospital, Viale Oxford 81, 00133 Rome, Italy;
| | - Marco Ciotti
- Department of Laboratory Medicine, Virology Unit, Tor Vergata University Hospital, Viale Oxford 81, 00133 Rome, Italy;
| | - Massimo Pieri
- Department of Experimental Medicine, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy; (E.N.); (F.T.); (M.P.); (S.S.); (M.M.); (S.B.)
- Department of Laboratory Medicine, Tor Vergata University Hospital, Viale Oxford 81, 00133 Rome, Italy;
| | - Sergio Bernardini
- Department of Experimental Medicine, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy; (E.N.); (F.T.); (M.P.); (S.S.); (M.M.); (S.B.)
- Department of Laboratory Medicine, Tor Vergata University Hospital, Viale Oxford 81, 00133 Rome, Italy;
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Pisanic N, Antar AAR, Kruczynski KL, Gregory Rivera M, Dhakal S, Spicer K, Randad PR, Pekosz A, Klein SL, Betenbaugh MJ, Detrick B, Clarke W, Thomas DL, Manabe YC, Heaney CD. Methodological approaches to optimize multiplex oral fluid SARS-CoV-2 IgG assay performance and correlation with serologic and neutralizing antibody responses. J Immunol Methods 2023; 514:113440. [PMID: 36773929 PMCID: PMC9911157 DOI: 10.1016/j.jim.2023.113440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/25/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023]
Abstract
BACKGROUND Oral fluid (hereafter, saliva) is a non-invasive and attractive alternative to blood for SARS-CoV-2 IgG testing; however, the heterogeneity of saliva as a matrix poses challenges for immunoassay performance. OBJECTIVES To optimize performance of a magnetic microparticle-based multiplex immunoassay (MIA) for SARS-CoV-2 IgG measurement in saliva, with consideration of: i) threshold setting and validation across different MIA bead batches; ii) sample qualification based on salivary total IgG concentration; iii) calibration to U.S. SARS-CoV-2 serological standard binding antibody units (BAU); and iv) correlations with blood-based SARS-CoV-2 serological and neutralizing antibody (nAb) assays. METHODS The salivary SARS-CoV-2 IgG MIA included 2 nucleocapsid (N), 3 receptor-binding domain (RBD), and 2 spike protein (S) antigens. Gingival crevicular fluid (GCF) swab saliva samples were collected before December 2019 (n = 555) and after molecular test-confirmed SARS-CoV-2 infection from 113 individuals (providing up to 5 repeated-measures; n = 398) and used to optimize and validate MIA performance (total n = 953). Combinations of IgG responses to N, RBD and S and total salivary IgG concentration (μg/mL) as a qualifier of nonreactive samples were optimized and validated, calibrated to the U.S. SARS-CoV-2 serological standard, and correlated with blood-based SARS-CoV-2 IgG ELISA and nAb assays. RESULTS The sum of signal to cutoff (S/Co) to all seven MIA SARS-CoV-2 antigens and disqualification of nonreactive saliva samples with ≤15 μg/mL total IgG led to correct classification of 62/62 positives (sensitivity [Se] = 100.0%; 95% confidence interval [CI] = 94.8%, 100.0%) and 108/109 negatives (specificity [Sp] = 99.1%; 95% CI = 97.3%, 100.0%) at 8-million beads coupling scale and 80/81 positives (Se = 98.8%; 95% CI = 93.3%, 100.0%] and 127/127 negatives (Sp = 100%; 95% CI = 97.1%, 100.0%) at 20-million beads coupling scale. Salivary SARS-CoV-2 IgG crossed the MIA cutoff of 0.1 BAU/mL on average 9 days post-COVID-19 symptom onset and peaked around day 30. Among n = 30 matched saliva and plasma samples, salivary SARS-CoV-2 MIA IgG levels correlated with corresponding-antigen plasma ELISA IgG (N: ρ = 0.76, RBD: ρ = 0.83, S: ρ = 0.82; all p < 0.001). Correlations of plasma SARS-CoV-2 nAb assay area under the curve (AUC) with salivary MIA IgG (N: ρ = 0.68, RBD: ρ = 0.78, S: ρ = 0.79; all p < 0.001) and with plasma ELISA IgG (N: ρ = 0.76, RBD: ρ = 0.79, S: ρ = 0.76; p < 0.001) were similar. CONCLUSIONS A salivary SARS-CoV-2 IgG MIA produced consistently high Se (> 98.8%) and Sp (> 99.1%) across two bead coupling scales and correlations with nAb responses that were similar to blood-based SARS-CoV-2 IgG ELISA data. This non-invasive salivary SARS-CoV-2 IgG MIA could increase engagement of vulnerable populations and improve broad understanding of humoral immunity (kinetics and gaps) within the evolving context of booster vaccination, viral variants and waning immunity.
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Affiliation(s)
- Nora Pisanic
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
| | - Annukka A R Antar
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Kate L Kruczynski
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Magdielis Gregory Rivera
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Santosh Dhakal
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Kristoffer Spicer
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Pranay R Randad
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Andrew Pekosz
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Sabra L Klein
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of Biochemistry and Molecular Biology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Barbara Detrick
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - William Clarke
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David L Thomas
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Yukari C Manabe
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Christopher D Heaney
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
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Pisanic N, Antar AAR, Kruczynski K, Rivera MG, Dhakal S, Spicer K, Randad PR, Pekosz A, Klein SL, Betenbaugh MJ, Detrick B, Clarke W, Thomas DL, Manabe YC, Heaney CD. Methodological approaches to optimize multiplex oral fluid SARS-CoV-2 IgG assay performance and correlation with serologic and neutralizing antibody responses. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.12.22.22283858. [PMID: 36597525 PMCID: PMC9810233 DOI: 10.1101/2022.12.22.22283858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Background Oral fluid (hereafter, saliva) is a non-invasive and attractive alternative to blood for SARS-CoV-2 IgG testing; however, the heterogeneity of saliva as a matrix poses challenges for immunoassay performance. Objectives To optimize performance of a magnetic microparticle-based multiplex immunoassay (MIA) for SARS-CoV-2 IgG measurement in saliva, with consideration of: i) threshold setting and validation across different MIA bead batches; ii) sample qualification based on salivary total IgG concentration; iii) calibration to U.S. SARS-CoV-2 serological standard binding antibody units (BAU); and iv) correlations with blood-based SARS-CoV-2 serological and neutralizing antibody (nAb) assays. Methods The salivary SARS-CoV-2 IgG MIA included 2 nucleocapsid (N), 3 receptor-binding domain (RBD), and 2 spike protein (S) antigens. Gingival crevicular fluid (GCF) swab saliva samples were collected before December, 2019 (n=555) and after molecular test-confirmed SARS-CoV-2 infection from 113 individuals (providing up to 5 repeated-measures; n=398) and used to optimize and validate MIA performance (total n=953). Combinations of IgG responses to N, RBD and S and total salivary IgG concentration (μg/mL) as a qualifier of nonreactive samples were optimized and validated, calibrated to the U.S. SARS-CoV-2 serological standard, and correlated with blood-based SARS-CoV-2 IgG ELISA and nAb assays. Results The sum of signal to cutoff (S/Co) to all seven MIA SARS-CoV-2 antigens and disqualification of nonreactive saliva samples with ≤15 μg/mL total IgG led to correct classification of 62/62 positives (sensitivity [Se]=100.0%; 95% confidence interval [CI]=94.8%, 100.0%) and 108/109 negatives (specificity [Sp]=99.1%; 95% CI=97.3%, 100.0%) at 8-million beads coupling scale and 80/81 positives (Se=98.8%; 95% CI=93.3%, 100.0%] and 127/127 negatives (Sp=100%; 95% CI=97.1%, 100.0%) at 20-million beads coupling scale. Salivary SARS-CoV-2 IgG crossed the MIA cutoff of 0.1 BAU/mL on average 9 days post-COVID-19 symptom onset and peaked around day 30. Among n=30 matched saliva and plasma samples, salivary SARS-CoV-2 MIA IgG levels correlated with corresponding-antigen plasma ELISA IgG (N: ρ=0.67, RBD: ρ=0.76, S: ρ=0.82; all p <0.0001). Correlations of plasma SARS-CoV-2 nAb assay area under the curve (AUC) with salivary MIA IgG (N: ρ=0.68, RBD: ρ=0.78, S: ρ=0.79; all p <0.0001) and with plasma ELISA IgG (N: ρ=0.76, RBD: ρ=0.79, S: ρ=0.76; p <0.0001) were similar. Conclusions A salivary SARS-CoV-2 IgG MIA produced consistently high Se (>98.8%) and Sp (>99.1%) across two bead coupling scales and correlations with nAb responses that were similar to blood-based SARS-CoV-2 IgG ELISA data. This non-invasive salivary SARS-CoV-2 IgG MIA could increase engagement of vulnerable populations and improve broad understanding of humoral immunity (kinetics and gaps) within the evolving context of booster vaccination, viral variants and waning immunity.
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Affiliation(s)
- Nora Pisanic
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Annukka A. R. Antar
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kate Kruczynski
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Magdielis Gregory Rivera
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Santosh Dhakal
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kristoffer Spicer
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Pranay R. Randad
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Andrew Pekosz
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sabra L. Klein
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Biochemistry and Molecular Biology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Michael J. Betenbaugh
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Barbara Detrick
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - William Clarke
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - David L. Thomas
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yukari C. Manabe
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Christopher D. Heaney
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
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T-Cell Assay after COVID-19 Vaccination Could Be a Useful Tool? A Pilot Study on Interferon-Gamma Release Assay in Healthcare Workers. Diseases 2022; 10:diseases10030049. [PMID: 35997354 PMCID: PMC9396988 DOI: 10.3390/diseases10030049] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/20/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022] Open
Abstract
Background: SARS-CoV-2 T-cells are crucial for long-term protection against reinfection. The aim was to demonstrate the Interferon-gamma Release Assay (IGRA) test could be useful for vaccination monitoring. Methods: In a prospective cohort of 98 vaccinated healthcare workers for SARS-CoV-2, we selected 23 people in low-antibodies (Group 1, N = 8), high-antibodies (Group 2, N = 9), and negative control groups (Group 3, N = 6). SARS-CoV-2-specific humoral and cellular responses were analyzed at 8 months after two doses of Pfizer BioNTech, evaluating anti-RBD (Receptor Binding Domain) and RBD-ACE2 (Angiotensin Converting Enzyme-2) blocking antibodies in sera through a Chemiluminescence Immunoassay (CLIA) and T-cells through the IGRA test in heparinized plasma. Moreover, lymphocyte subtyping was executed by a flow cytometer. Statistical analysis was performed. Results: The data confirmed that RBD and RBD-ACE2 blocking ACE2 antibody levels of Group 1 were significantly lower than Group 2; p < 0.001. However, T-cells showed no significant difference between Group 1 and Group 2. Conclusions: This work suggests the need for new strategies for booster doses administration.
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