Changes in the avids of IgG antibodies to the S protein of SARS-CoV-2 after coronavirus infection in medical workers of a temporary infections hospital

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BACKGROUND: The effector capabilities of humoral immunity are determined not only by the amount of specific antibodies produced in response to an antigenic effect, but also by their qualitative characteristics, which include avidity ― the total strength of binding to the antigen, which determines the duration and effectiveness of post-infectious immunity to SARS-CoV-2.

AIM: Is a selective study of the quantity and avidity of IgG antibodies to SARS-CoV-2 over time among medical workers of a temporary infectious diseases hospital in Kazan ― convalescents of COVID-19, during the period from July 2020 to July 2021.

MATERIALS AND METHODS: Determination of IgG to the S antigen of SARS-CoV-2 by ELISA was carried out using the test system SARS-CoV-2-IgG quantitative-ELISA-BEST (Vector-Best, Russia) and expressed in BAU/ml (binding antibody units). Antibody avidity was determined using a 4.0 M urea solution and expressed as avidity indices. 1, 4 and 7 months after COVID-19 asymptomatic (n=34); mild severity (n=42); moderate severity (n=29); reinfected (n=34). When statistically processing the data, descriptive statistics methods and the Wilcoxon matched data test were used. Differences were considered significant at p <0.05.

RESULTS: IgG avidity to SARS-CoV-2 depended on the severity of COVID-19. The highest rates of avidity indices were found in the group of those who had a moderate form of COVID-19. If in mild and asymptomatic forms there was a parallel decrease in avidity indices and IgG titer, then in moderate forms an increase in antibody titer was accompanied by a decrease in their avidity 4 months after the infection. 7 months after seroconversion, the IgG level decreased almost twofold, both in mild, asymptomatic and moderate forms. In the group of medical workers who had COVID-19 repeatedly, the initially low levels of avidity indices and antibody titers increased in parallel, while avidity indices after 7 months did not decrease, but remained high. Differences in avidity indices determined the subsequent formation of different trends in the development of the humoral immune response, which were mainly characterized by an uneven decrease in IgG and persistence of IgM antibodies for more than 1 month.

CONCLUSIONS: The research results expand the understanding of the mechanisms of formation of the humoral immune response and the avidity of IgG antibodies against SARS-CoV-2 in the risk group ― medical workers. The level of humoral immunity decreases in the first six months and varies depending on the severity of COVID-19. The data obtained can be used to identify categories of increased risk of SARS-CoV-2 infection among healthcare workers, make decisions about immunorehabilitation and revaccination against COVID-19.

作者简介

Irina Reshetnikova

Kazan Research Institute of Epidemiology and Microbiology; Kazan Federal University

编辑信件的主要联系方式.
Email: reshira@mail.ru
ORCID iD: 0000-0002-3584-6861

MD, Cand. Sci. (Med.), Associate Professor

俄罗斯联邦, Kazan; Kazan

Yuri Tyurin

Kazan Research Institute of Epidemiology and Microbiology; Kazan State Medical University

Email: tyurin.yurii@yandex.ru
ORCID iD: 0000-0002-2536-3604

MD, Dr. Sci. (Med.)

俄罗斯联邦, Kazan; Kazan

Elena Agafonova

Kazan Research Institute of Epidemiology and Microbiology; Kazan State Medical University

Email: agafono@mail.ru
ORCID iD: 0000-0002-4411-8786

MD, Cand. Sci. (Med.)

俄罗斯联邦, Kazan; Kazan

Rustem Fassakhov

Kazan Federal University

Email: farrus@mail.ru
ORCID iD: 0000-0001-9322-2689

MD, Dr. Sci. (Med.), Professor

俄罗斯联邦, Kazan

参考

  1. Briko NI, Kagramanyan IN, Nikiforov VV, et al. Pandemic COVID-19. Prevention Measures in the Russian Federation. Epidemiology and Vaccinal Prevention. 2020;19(2):4–12. doi: 10.31631/2073-3046-2020-19-2-4-12
  2. Kudryashova AM, Manuylov VA, Murzina AA, et al. Dynamics in maturation of SARS-CoV-2 RBD-specific IgG antibody avidity depending on immunization timeframe and type. Russ J Infection Immunity. 2023;13(1):67–74. doi: 10.15789/2220-7619-DIM-2049
  3. Popova AYu, Ezhlova EB, Melnikova AA, et al. Herd immunity of SARS-CoV-2 among the population of Kalinigrad region amid the COVID-19 epidemic. J Infectology. 2020;12(5):62–71. doi: 10.22625/2072-6732-2020-12-5-62-71
  4. Bauer G. The potential significance of high avidity immunoglobulin G (IgG) for protective immunity towards SARS-CoV-2. Int J Infect Dis. 2021;106:61–64. doi: 10.1016/j.ijid.2021.01.061
  5. Underwood РA. Problems and pitfalls with measurement of antibody affinity using solid phase binding in the ELISA. J Immunol Methods. 1993;164(1):119–130. doi: 10.1016/0022-1759(93)90282-c
  6. Dauner J, Pan Y, Hildesheim A, et al. Development and application of a GuHCl-modified ELISA to measure the avidity of anti-HPV L1 VLP antibodies in vaccinated individuals. Mol Cell Probes. 2012;26(2):73–80. doi: 10.1016/j.mcp.2012.01.002
  7. Dimitrov J, Lacroix-Desmazes S, Kaveri V. Important parameters for evaluation of antibody avidity by immunosorbent assay. Anal Biochem. 2011;418(1):149–151. doi: 10.1016/j.ab.2011.07.007
  8. Benner SE, Patel EU, Laeyendecker O, et al. SARS-CoV-2 antibody avidity responses in COVID-19 patients and convalescent plasma donors. J Infect Dis. 2020;222(12):1974–1984. doi: 10.1093/infdis/jiaa581
  9. Tang J, Grubbs G, Lee Y, et al. Impact of convalescent plasma therapy on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody profile in coronavirus disease 2019 (COVID-19) patients. Clin Infect Dis. 2022;74(2):327–334. doi: 10.1093/cid/ciab317
  10. Interim guidelines "Prevention, diagnosis and treatment of new coronavirus infection (COVID-19)" Ministry of Health of the Russian Federation. Version 6. (28.04.2020). 2020. 165 р. (In Russ).
  11. Piccoli L, Park Y, Tortorici M, et al. Mapping neutralizing and immunodominant sites on the SARS-CoV-2 spike receptor-binding domain by structure-guided high-resolution serology. Cell. 2020;183(4):1024–1042.e21. doi: 10.1016/j.cell.2020.09.037
  12. Toptygina AP, Afridonova ZE, Zakirov RSh, Semikina EL. Maintaining immunological memory to the SARS-CoV-2 virus during COVID-19 pandemic. Russ J Infection Immunity. 2023;13(1):55–66. doi: 10.15789/2220-7619-MIM-2009
  13. Reshetnikova ID, Agafonova EV, Khakimov NM, et al. Features of the formation of seroprevalence to SARS-CoV2 in the population of the republic of tatarstan during the spread of COVID-19. Epidemiology and Vaccinal Prevention. 2023;22(1):13–21. doi: 10.31631/2073-3046-2023-22-1-13-21
  14. Lynch KL, Whitman JD, Lacanienta NP, et al. Magnitude and kinetics of anti-SARS-CoV-2 antibody responses and their relationship to disease severity. Clin Infect Dis. 2020;72(2):301–308. doi: 10.1093/cid/ciaa979
  15. Stephens DS, McElrath MJ. COVID-19 and the path to immunity. JAMA. 2020;324(13):1279–1281. doi: 10.1001/jama.2020.16656
  16. Wang C, Li W, Drabek D, et al. A human monoclonal antibodyblocking SARS-CoV-2 infection. Nat Commun. 2020;11(1):2251. doi: 10.1038/s41467-020-16256-y
  17. Long Q, Liu B, Deng H, et al. Antibody responses to SARS-CoV-2 in patients with COVID-19. Nat Med. 2020;26(6):845–848. doi: 10.1038/s41591-020-0897-1
  18. Andreev IV, Nechay KO, Andreev AI, et al. Post-vaccination and post-infection humoral immune response to the SARS-CoV-2 infection. Immunologiya. 2022;43(1):18–32. doi: 10.33029/0206-4952-2022-43-1-18-32
  19. Nordström P, Ballin M, Nordström A. Risk of SARS-CoV-2 reinfection and COVID-19 hospitalisation in individuals with natural and hybrid immunity: A retrospective, total population cohort study in Sweden. Lancet Infect Dis. 2022;22(6):781–790. doi: 10.1016/S1473-3099(22)00143-8
  20. Turner JS, Kim W, Kalaidina E, et al. SARS-CoV-2 infection induces long-lived bone marrow plasma cells in humans. Nature. 2021;595(7867):421–425. doi: 10.1038/s41586-021-03647-4
  21. Toptygina AP, Mamaeva TA, Alioshkin VA. Peculiarities of specific humoral measles immune response. Russ J Infection Immunity. 2013;3(3):243–250. doi: 10.15789/2220-7619-2013-3-243-250
  22. Manuylov V, Burgasova O, Borisova O, et al. Avidity of IgG to SARS-CoV-2 RBD as a prognostic factor for the severity of COVID-19 reinfection. Viruses. 2022;14(3):617. doi: 10.3390/v14030617

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2. Fig. 1. Dynamics of the average avidity index IgG to SARS-CoV-2 (avidity index dynamics at three points of the blood test ― 1, 2, 3), depending on the severity of COVID-19 in different groups of medical workers: a ― Group I with asymptomatic COVID-19; b ― group II with mild severity of COVID-19; c ― Group III with a moderately severe form of COVID-19; d ― Group IV, who had a repeated history of the new coronavirus infection COVID-19 within 7 months of the initial infection. The abscissa axis is the research point; Y-axis ― avidity index (%).

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3. Fig. 2. Line plot. Dynamics of the IgG avidity index to SARS-CoV-2 in medical workers who suffered from an asymptomatic and mild form of COVID 19, depending on the type of humoral immune response, expressed in trends. On the X-axis: trend of the immune response: G1M1 ― rapid trend of decrease in IgG, retention of IgM for 1–2 months. followed by a rapid decline; G2MO ― an uneven trend of decreasing IgG, while IgM was not detected in the blood serum; G2M1 ― uneven trend of decrease in IgG, persistence of IgM for 1–2 months; G2M2 ― uneven trend of decrease in IgG with persistence of IgM in the blood serum for three or more months; G2M3 ― uneven trend of decrease in IgG, with persistence of IgM for up to 1 month; GOMO ― slow trend of decreasing IgG titer in which IgM was not detected in the blood serum; GOM1 ― a slow trend of decreasing IgG titer with persistence of IgM for 1–2 months. Y-axis: avidity index (%).

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4. Fig. 3. Line plot. Dynamics of the avidity index of IgG to SARS-CoV-2 in medical workers with moderate disease and in those who have recovered from COVID-19 again, depending on the type of humoral immune response expressed in trends. On the X-axis: trend of the immune response: G1M1 ― rapid trend of decrease in IgG, retention of IgM for 1–2 months. Followed by a rapid decline; G2MO ― an uneven trend of decreasing IgG, while IgM was not detected in the blood serum; G2M1 ― uneven trend of decrease in IgG, persistence of IgM for 1–2 months; G2M2 ― uneven trend of decrease in IgG with persistence of IgM in the blood serum for three or more months; G2M3 ― uneven trend of decrease in IgG, with persistence of IgM for up to 1 month; GOMO ― slow trend of decreasing IgG titer in which IgM was not detected in the blood serum; GOM1 ― a slow trend of decreasing IgG titer with persistence of IgM for 1–2 months. Y-axis: avidity index (%).

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