Evolution of the innate and adaptive immune response in women with acute Zika virus infection

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Zika virus (ZIKV) is a flavivirus that is closely related to other human pathogens, such as dengue virus (DENV)1. Primary transmission usually involves Aedes aegypti, which has expanded its distribution range considerably2, although rarer infection routes, including mother-to-fetus transmission, sexual contact and blood transfusion, have also been observed3,4,5,6,7. Primary ZIKV infection is usually asymptomatic or mild in adults, with quickly resolved blood viraemia, but ZIKV might persist for months in saliva, urine, semen, breast milk and the central nervous system8,9,10,11,12. During a recent ZIKV outbreak in South America, substantial numbers of neurological complications, such as Guillain–Barré syndrome, were reported13,14 together with cases of microcephaly and associated developmental problems in infants born to women infected with ZIKV during pregnancy15,16,17,18,19,20, highlighting the clinical importance of this infection. Analyses of the human immune response to ZIKV are lacking21,22,23,24,25,26,27,28, but the recent outbreak has provided an opportunity to assess ZIKV immunity using current immunological methods. Here, we comprehensively assess the acute innate and adaptive immune response to ZIKV infection in ten women who were recruited during early infection and followed through reconvalescence. We define a cascade of events that lead to immunological control of ZIKV, with previous exposure to DENV impacting some, but not all, mediators of antiviral immunity.

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Fig. 1: Clinical and immunological features of ten women acutely infected with ZIKV.
Fig. 2: Dynamics of the humoral response to acute ZIKV infection.
Fig. 3: Dynamics of the cellular immune response to ZIKV infection.
Fig. 4: Memory differentiation and polyfunctionality of ZIKV-specific CD4+ and CD8+ T cells.

Data availability

The data that support the findings of this study are available from the corresponding authors on reasonable request.


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We thank the patients as well as the physicians and medical staff of the Viral Hepatitis Clinic in Brazil who provided care to the patients; the Flavivirus Laboratory staff at FIOCRUZ as well as J. Quick and N. Loman from the University of Birmingham for their assistance in analysing patient samples for ZIKV; B. da Silva Baptista as well as the Massachusetts General Hospital HSCI CRM flow cytometry core facility members for their technical assistance. This work was supported in part by National Institutes of Health grants U01 AI131314 (to G.M.L. and L.L.L.-X.), U19 AI066345 (to G.M.L. and L.L.L.-X.), HHSN27220140045C (to A.S.), 1PO1AI106695-01A1 (to A.S.), U19AI118626-01 (to A.S.), 75N9301900065 (to A.S.), the EU grant 734584 (to A.S.), Conselho Nacional de Desenvolvimento Tecnológico (CNPq 470092/2014-9; 200099/2016-7), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (Faperj E-26/202.930/2016) and ‘Sicherheit von Blut(produkten) und Geweben hinsichtlich der Abwesenheit von Zikaviren’ from the German Ministry of Health.

Author information

P.T., J.G.M. and A.T.-C. conceived and designed the study. P.T., J.G.M., A.T.-C. and R.C.H. performed immunophenotyping and intracellular cytokine stainings by flow cytometry. J.G.M., P.S.F.d.S., V.d.M.d.M. and L.L.L.-X. collected clinical data and biological samples. C.Y., A.M.B.d.F. and S.A.B. performed ZIKV-RNA detection assays. J.G.M., C.Y., M.A.P, J.M. and S.A.B. performed anti-ZIKV antibody ELISA assays. C.Y. and J.B. performed ZIKV neutralizing antibody assays. D.W., A.G., A.S., D.H.B., S.A.B. and L.L.L.-X. contributed to the study design and data interpretation. G.M.L. conceived and supervised the study, and provided funding. P.T. and G.M.L. analysed the data and wrote the manuscript.

Correspondence to Georg M. Lauer or Lia L. Lewis-Ximenez.

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Competing interests

J.M. is the owner of Dr. Julio Moran Laboratories, a company owning and, for research purposes only, distributing the ELISA assays DIACHECK for the detection of anti-human ZIKV antibodies. The other authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Patient’s symptoms and ZIKV-RNA detection data.

(a) Patient’s symptoms information and associated plasma ZIKV-RNA detection, over time. (b) Quantification of ZIKV RNA in the plasma. Each patient and previous exposure to DENV status, as defined by the positive detection of anti-DENV IgG at symptom onset, is displayed through unique symbols and connecting lines. X-axis, Time (days) represents the time from onset of symptoms. Gray dashed line notes the assay’s detection limit. Data are representative of n=2 independent experiments.

Extended Data Fig. 2 Gating strategies for blood immunophenotyping.

Flow cytometry gating strategies for the identification of the different cell subsets of monocytes (NC= non-classical; I= intermediate; C= classical), dendritic cells (pDCs= plasmacytoid dendritic cells; mDCs= myeloid dendritic cells) and MDSCs (myeloid-derived suppressor cells) (a), as well as plasmablasts and activated CD8+ T cells (b).

Extended Data Fig. 3 Changes in immune cell frequencies following acute ZIKV infection.

Representative flow-cytometry plots showing changes in the frequency of monocytes (a), dendritic cells (b), plasmablasts (c) and activated CD8+ T cells (d), at different time points following ZIKV infection. Numerical values of the measured frequencies in a total n=10 patients, at different time points (acute n=10; recovery n=5; follow-up n=4) are displayed in Fig. 1d. Times (in days) from onset of symptoms are indicated.

Extended Data Fig. 4 Humoral immune response to acute ZIKV infection.

Linear regression analysis to model the relationship between plasma anti-ZIKV IgM and IgA detection signals (n=41) using DIACHECK ELISA assays. (b) Plasma detection of anti-ZIKV IgM antibodies using EUROIMMUNE ELISA assay (left chart) and linear regression analysis to model the relationship between plasma anti-ZIKV IgM detection signals obtained with EUROIMMUNE and DIACHECK ELISA assays (n=41) (right chart). (c) Plasma detection of anti-ZIKV IgG antibodies using EUROIMMUNE ELISA assay (left chart) and linear regression analysis to model the relationship between plasma anti-ZIKV IgG detection signals obtained with EUROIMMUNE and DIACHECK ELISA assays (n=41) (right chart). (a-c) Each patient and previous exposure to DENV status, as defined by the positive detection of anti-DENV IgG at symptom onset, is displayed through unique symbols and connecting lines. Gray dashed lines note assay’s detection limits. X-axis, (Time (days)) represents the time from onset of symptoms. Data are expressed as mean values of OD/CO ratios from two independent experiments. Pearson correlation coefficient R and significance p (two-sided) values are reported from the linear regression analysis performed with GraphPad Prism v.6 software.

Extended Data Fig. 5 Titration of anti-ZIKV neutralizing antibodies.

Representative titration assays for the detection of anti-ZIKV neutralizing antibodies, overtime. Titers were measured by endpoint titration. A color mapping of the OD/CO ratio values for the detection anti-ZIKV IgM and IgG using EUROIMUNE ELISA assays are indicated. The data are representative of n=2 (patients CR8587, CR8592, CR8602, CR8663, CR4434, CR8597 and CR8622), n=3 (patients CR4965 and 8603) or n=4 (CR8623) independent experiments.

Extended Data Fig. 6 ZIKV-specific T cell memory differentiation following acute ZIKV infection.

T cell memory differentiation based on CCR7 and CD45RA co-expression (naïve: CCR7+CD45RA+; CM: CCR7+CD45RA-; EM: CCR7-CD45RA-; TEMRA: CCR7-CD45RA+). Frequencies of ZIKV-specific CD4+ (a) and CD8+ (b) T cells across the different memory subsets over time, from 5 different patients, are indicated. The analysis was performed on a total of n=6 patients, at different time points (Data from patient CR4965 are available in Fig. 4a, b).

Extended Data Fig. 7 ZIKV-specific T cell functional profiles overtime.

Detailed representation of the overlapping pie charts presented in Fig. 4d, e. The data represent the different sub-groups of cytokine secreting and cytotoxic CD154+CD4+ (a) and CD69+CD8+ (b) T cells after stimulation with 15-mer overlapping peptide pools covering all ZIKV-proteins, by ex vivo intracellular cytokine stainings (ICSs). Frequencies of IL-2, TNFa, IFNγ and CD107a co-expressing cells are indicated. Baseline signals of IL-2, TNFα, IFNγ and CD107a-expressing cells from unstimulated controls have been subtracted to the stimulated conditions to allow the visualization of ZIKV-specific CD4+ and CD8+ T cell signals. Only time points with detectable CD154+IFNγ+CD4+ (acute n=24, recovery n=26, follow-up n=29) and CD69+IFNγ+CD8+ T cells (acute n=17, recovery n=19, follow-up n=24) from patients CR4965, CR8623, CR8603 and CR8622, as depicted in Fig. 3a, b, have been used for this analysis. Black bars correspond to the median of expression in each condition.

Extended Data Fig. 8 ZIKV-specific T cell functional profiles across the different viral proteins targeted.

Overlapping pie charts describing the polyfunctionality of ZIKV-specific CD4+ (a) and CD8+ (b) T cells according to the ZIKV-overlapping peptide pools used for T cell stimulation and determined by ex vivo intracellular cytokine staining (ICS) as defined in Fig. 3. Baseline signals of TNFα, IL-2, CD107a and IFNγ-producing cells in unstimulated controls have been subtracted from ZIKV-stimulated assays to allow the visualization of ZIKV-specific CD4+ and CD8+ T cell signals. Only time points with detectable CD154+IFNγ+CD4+ (acute n=24, recovery n=26, follow-up n=29) and CD69+IFNγ+CD8+ T cells (acute n=17, recovery n=19, follow-up n=24) from patients CR4965, CR8623, CR8603 and CR8622, as depicted in Fig. 3a, b, were used for this analysis. Distribution of the numbers (n) of T cell responses across the different ZIKV-peptide pools are reported.

Extended Data Fig. 9

General overview of the dynamics of immune responses following acute ZIKV infection in human.

Extended Data Fig. 10

Patient’s demographics and HLA-types.

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Tonnerre, P., Melgaço, J.G., Torres-Cornejo, A. et al. Evolution of the innate and adaptive immune response in women with acute Zika virus infection. Nat Microbiol (2019) doi:10.1038/s41564-019-0618-z

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