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. 2024 Sep 9;14(1):20930.
doi: 10.1038/s41598-024-71419-x.

Immunologic mediators profile in COVID-19 convalescence

Alexander Leonardo Silva-Junior  1   2 , Lucas Silva Oliveira  1   2 , Stephanny Dias  2   3 , Thaina Cristina Cardoso Costa  2 , Lilyane Amorim Xabregas  2 , Fabíola Silva Alves-Hanna  2   4 , Cláudia Maria Moura Abrahim  2 , Walter Luiz Lima Neves  2 , Myuki Alfaia Esashika Crispim  2 , Diana Mota Toro  4 , Pedro Vieira Silva-Neto  4 , Danielle Costa Marques Aponte  5 , Tatiana Campos Oliveira  5 , Maria Carmo Costa Silva  6 , Miharu Maguinoria Matsuura Matos  5 , Maria Perpétuo Socorro Sampaio Carvalho  2 , Andrea Monteiro Tarragô  2   3   6 , Nelson Abrahim Fraiji  2 , Lúcia Helena Faccioli  7 , Carlos Artério Sorgi  4   8 , Ester Cerdeira Sabino  9 , Andrea Teixeira-Carvalho  10 , Olindo Assis Martins-Filho  10 , Allyson Guimarães Costa  11   12   13   14 , Adriana Malheiro  15   16   17   18   19
Affiliations

Immunologic mediators profile in COVID-19 convalescence

Alexander Leonardo Silva-Junior et al. Sci Rep. .

Abstract

SARS-CoV-2 caused the pandemic situation experienced since the beginning of 2020, and many countries faced the rapid spread and severe form of the disease. Mechanisms of interaction between the virus and the host were observed during acute phase, but few data are available when related to immunity dynamics in convalescents. We conducted a longitudinal study, with 51 healthy donors and 62 COVID-19 convalescent patients, which these had a 2-month follow-up after symptoms recovery. Venous blood sample was obtained from all participants to measure blood count, subpopulations of monocytes, lymphocytes, natural killer cells and dendritic cells. Serum was used to measure cytokines, chemokines, growth factors, anti-N IgG and anti-S IgG/IgM antibodies. Statistic was performed by Kruskal-Wallis test, and linear regression with days post symptoms and antibody titers. All analysis had confidence interval of 95%. Less than 35% of convalescents were anti-S IgM+, while more than 80% were IgG+ in D30. Anti-N IgG decreased along time, with loss of seroreactivity of 13%. Eosinophil count played a distinct role on both antibodies during all study, and the convalescence was orchestrated by higher neutrophil-to-lymphocyte ratio and IL-15, but initial stages were marked by increase in myeloid DCs, B1 lymphocytes, inflammatory and patrolling monocytes, G-CSF and IL-2. Later convalescence seemed to change to cytotoxicity mediated by T lymphocytes, plasmacytoid DCs, VEGF, IL-9 and CXCL10. Anti-S IgG antibodies showed the longest perseverance and may be a better option for diagnosis. The inflammatory pattern is yet present on initial stage of convalescence, but quickly shifts to a reparative dynamic. Meanwhile eosinophils seem to play a role on anti-N levels in convalescence, although may not be the major causative agent. We must highlight the importance of immunological markers on acute clinical outcomes, but their comprehension to potentialize adaptive system must be explored to improve immunizations and further preventive policies.

Keywords: Antibody; Brazil; Immune hallmarks; Severe acute respiratory syndrome (SARS).

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Serum antibody analysis during convalescence. (A) Comparison of IgG anti-nucleocapsid protein (OD) and correlation under anti-N concentration and days post symptoms; (B) Comparison of IgG anti-Spike protein (AU/mL) and correlation between anti-S concentration and days post symptoms. The cutt-off 1.280 AU/mL was highlighted to demonstrate the participants that were eligible to convalescent plasma donation in D30 (n = 30/62 [48.4%]), D60 (n = 20/48 [41.7%]) and D90 (n = 14/47 [29.8%]); (C) Percentage of participants with a qualitative (pos/neg) antibody production among study period, based on anti-S immunochromatographic test, and CMIA anti-S and anti-N, with absolute and relative values on table below; (D) Fold change comparison between D60/D30, D90/D30 and D90/60 IgG anti-S antibody concentration, using quantitative result from patients with all follow-up (n = 45). Statistical analysis was performed with One-Way ANOVA followed by Turkey’s Multiple comparison test, considering significative when p < 0.05. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.
Fig. 2
Fig. 2
Phenotypic analysis of immune cells, comparing HD, D30, D60 and D90 groups. Data is expressed as median and interquartile range in percentage of cells. Bar graphs represent analysis of: (A) NK cells; (B) NKT cells; (C) Classical monocytes; (D) Inflammatory monocytes; (E) Patrolling monocytes; (F) T helper lymphocytes; (G) Activated T helper lymphocytes; (H) T cytotoxic lymphocytes; (I) Activated T cytotoxic lymphocytes; (J) Plasmacytoid dendritic cells; (K) Myeloid dendritic cells; (L) B lymphocytes; and (M) B1 lymphocytes. Statistical analysis was conducted with Kruskal–Wallis and Dunn’s Multiple Comparisons tests, considering significative when p < 0.05. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.
Fig. 3
Fig. 3
Circulating level of soluble molecules comparing HD, D30, D60 and D90 groups. Data is expressed as median and interquartile range in pg/mL. Circulating levels of chemokines (A), cytokines (B) and growth factors (C). Statistical analysis was conducted with Kruskal–Wallis and Dunn’s Multiple Comparisons tests, considering significative when p < 0.05. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.
Fig. 4
Fig. 4
Biomarker correlation matrix indicating difference in patter of healthy donors (A), and convalescence in D30 (B), D60 (C) and D90 (D). Networks were based on Spearman’s correlation indices (r). Association was significant when p < 0.05 between all markers analyzed. Blue scale, ranging from − 1.0 to 1.0, shows correlation strength, as represented on image. WBC white blood count, ANC absolute neutrophil count, ALC absolute lymphocyte count, AMC absolute monocyte count, AEC absolute eosinophil count, ABC absolute basophil count, NK natural killer, Chemokines CXCL8, CXCL10, CCL3, CCL4, CCL2, CCL5 and CCL11, Cytokines IL-1β, IL-1ra, IL-6, TNF-α, IL-12p70, IFN-γ, IL-2, IL-7, IL-9, IL-15, IL-4, IL-5, IL-13, IL-17A, IL-10, Growth factors VEGF, FGF basic, PDGF-BB, GM-CSF, G-CSF, HD healthy donors.
Fig. 5
Fig. 5
Biomarker signature of groups represented in a Venn Diagram. (A) Frequency of subjects with biomarker level above the Cut-off; (B) Venn Diagram representing the groups, intersections, and elements, suggesting potential hallmarks for immunomodulation under convalescence. Global median for each parameter was measured and used to characterize participants as low (< 50%) or higher (> 50%) producers. HD healthy donors.
Fig. 6
Fig. 6
Concluding remarks on immunological shifts related to acute (based on literature) and convalescence COVID-19 patients. DC dendritic cells, NLR neutrophil-to-lymphocyte ratio.
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References

    1. Cui, J., Li, F. & Shi, Z.-L. Origin and evolution of pathogenic coronaviruses. Nat. Rev. Microbiol.17, 181–192 (2019). 10.1038/s41579-018-0118-9 - DOI - PMC - PubMed
    1. Dousari, A. S., Moghadam, M. T. & Satarzadeh, N. COVID-19 (Coronavirus Disease 2019): A new coronavirus disease. Infect. Drug Resist.13, 2819–2828 (2020). 10.2147/IDR.S259279 - DOI - PMC - PubMed
    1. Zhang, Y.-Z. & Holmes, E. C. A Genomic perspective on the origin and emergence of SARS-CoV-2. Cell181, 223–227 (2020). 10.1016/j.cell.2020.03.035 - DOI - PMC - PubMed
    1. Harrison, A. G., Lin, T. & Wang, P. Mechanisms of SARS-CoV-2 transmission and pathogenesis. Trends Immunol.41, 1100–1115 (2020). 10.1016/j.it.2020.10.004 - DOI - PMC - PubMed
    1. Kevadiya, B. D. et al. Diagnostics for SARS-CoV-2 infections. Nat. Mater.20, 593–605 (2021). 10.1038/s41563-020-00906-z - DOI - PMC - PubMed