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Downloaded from https://academic.oup.com/jid/article/218/5/814/4972819 by DeepDyve user on 13 July 2022 Longitudinal Study of Cellular and Systemic Cytokine Signatures to Define the Dynamics of a Balanced Immune Environment During Disease Manifestation in Zika Virus–Infected Patients Fok-Moon Lum, David C B Lye, Jeslin J L Tan, Bernett Lee, Po-Ying Chia, Tze- Kwang Chua, Siti N Amrun, Yiu-Wing Kam, Wearn-Xin Yee, Wei-Ping Ling, Vanessa W X Lim, Vincent J X Pang, Linda K Lee, Esther W H Mok, Chia-Yin Chong, Yee-Sin Leo, Lisa F P Ng Downloaded from https://academic.oup.com/jid/article/218/5/814/4972819 by DeepDyve user on 13 July 2022 The Journal of Infectious Diseases MAJOR ARTICLE Longitudinal Study of Cellular and Systemic Cytokine Signatures to Define the Dynamics of a Balanced Immune Environment During Disease Manifestation in Zika Virus–Infected Patients 1,a 2,3,4,a 1,a 1 2 1 1 1 1 Fok-Moon Lum, David C. B. Lye, Jeslin J. L. Tan, Bernett Lee, Po-Ying Chia, Tze-Kwang Chua, Siti N. Amrun, Yiu-Wing Kam, Wearn-Xin Yee, 2 2 2,6 2 1 5 2,3,4,6 1,7,8,9 Wei-Ping Ling, Vanessa W. X. Lim, Vincent J. X. Pang, Linda K. Lee, Esther W. H. Mok, Chia-Yin Chong, Yee-Sin Leo, and Lisa F. P. Ng 1 2 Singapore Immunology Network, Agency for Science, Technology, and Research, Communicable Diseases Centre, Institute of Infectious Diseases and Epidemiology, Tan Tock 3 4 5 Seng Hospital, Lee Kong Chian School of Medicine, Nanyang Technological University, Department of Medicine, Yong Loo Lin School of Medicine, KK Women’s and Children’s 6 7 8 Hospital, and Saw Swee Hock School of Public Health and Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; National Institute of Health Research, Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, United Kingdom; and Institute of Infection and Global Health, University of Liverpool, United Kingdom Background. Since its unexpected reemergence, Zika virus (ZIKV) has caused numerous outbreaks globally. This study charac- terized the host immune responses during ZIKV infection. Methods. Patient samples were collected longitudinally during the acute, convalescence and recovery phases of ZIKV infection over 6 months during the Singapore outbreak in late 2016. Plasma immune mediators were profiled via multiplex microbead assay, while changes in blood cell numbers were determined with immunophenotyping. Results. Data showed the involvement of various immune mediators during acute ZIKV infection accompanied by a general reduction in blood cell numbers for all immune subsets except CD14 monocytes. Importantly, viremic patients experiencing mod- erate symptoms had significantly higher quantities of interferon γ–induced protein 10, monocyte chemotactic protein 1, interleukin 1 receptor antagonist, interleukin 8, and placental growth factor 1, accompanied by reduced numbers of peripheral CD8 T cells, September CD4 T cells, and double-negative T cells. Levels of T-cell associated mediators, including interferon γ–induced protein 10, inter- feron γ, and interleukin 10, were high in recovery phases of ZIKV infection, suggesting a functional role for T cells. The identification of different markers at specific disease phases emphasizes the dynamics of a balanced cytokine environment in disease progression. Conclusions. This is the first comprehensive study that highlights specific cellular changes and immune signatures during ZIKV disease progression, and it provides valuable insights into ZIKV immunopathogenesis. Keywords. Zika virus; patient cohort; cytokines; immunophenotyping; viremia. Zika virus (ZIKV) was an obscure flavivirus until it reemerged in complications, such as Guillain-Barré syndrome in adults and con- OA-CC-BY 2015, with accompanying unexpected severe complications [1–3]. genital fetal growth abnormalities in newborns [1, 3, 9, 10]. ZIKV is an arbovirus transmitted via the bite of infected Aedes mos- While efforts have been made to study ZIKV immunopatho- quitoes, although non–vector‐borne transmissions such as sexual, genesis, there is still a gap in knowledge of how patients respond maternal-fetal, and blood transfusion transmissions have been immunologically to the infection. Previous investigation into reported [4–7]. Typically, ZIKV infection is rarely life threatening, ex vivo CD14 monocytes  and monocyte-derived macro- manifesting as a transient fever accompanied by headache, arthral- phages showed significant differences in messenger RNA tran- gia, conjunctivitis, fatigue, and rash, with many patients being script abundance aer ZIKV inf ft ection [11–13]. However, the asymptomatic [3, 8]. ZIKV has been associated with neurological overall regulation of other immune subsets during disease pro- gression and their associations with soluble immune mediators remain to be elucidated. In this study, the focus is on characterizing immune markers Received 21 February 2018; editorial decision 11 April 2018; accepted 13 April 2018; published online April 16, 2018. in a cohort of ZIKV-infected patients recruited from the first F.-M. L., D. C. B. L., and J. J. L. T. contributed equally to this report. ZIKV outbreak in Singapore, in 2016 [14, 15]. While none of Correspondence: Lisa F. P. Ng, PhD, Singapore Immunology Network, Agency for Science, Technology and Research, 8A Biomedical Grove, #04-06 Immunos, Biopolis, Singapore 138648 the patients had severe disease, some presented with moderate (email@example.com). symptoms. In-depth investigation was performed with longi- The Journal of Infectious Diseases 2018;218:814–24 tudinal profiling of immune mediators present in the patients’ © The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative Commons plasma collected during the acute to recovery phases of infec- Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted tion. This was further complemented with extensive immuno- reuse, distribution, and reproduction in any medium, provided the original work is properly cited. DOI: 10.1093/infdis/jiy225 phenotyping of whole-blood specimens collected from patients. 814 • JID 2018:218 (1 September) • Lum et al Downloaded from https://academic.oup.com/jid/article/218/5/814/4972819 by DeepDyve user on 13 July 2022 Whole-Blood Staining and Flow Cytometry This is the first comprehensive study that combines cellular Staining was performed on 100-μL whole-blood specimens from changes with specific immune signatures during ZIKV infec- 44 ZIKV-infected patients and 14 healthy donors. Antibodies tion, and it provides a better understanding on the pathobiol- were used to identify CD45 leukocytes (mouse anti-human ogy of the virus during infection. In addition, specific immune CD45; Biolegend), CD16 neutrophils (mouse anti-human signatures allow for the prospect of identifying key markers CD16; Biolegend), CD14 monocytes (mouse anti-human CD14; associated with different disease manifestations. BD Biosciences), CD3 T cells (mouse anti-human CD3; BD METHODS Biosciences), CD4 helper T cells (mouse anti-human CD4; eBio- science), CD8 cytotoxic T cells (mouse anti-human CD8; BD Standard Protocol Approvals, Registrations, and Patient Consent Biosciences), CD56 natural killer (NK) cells (mouse anti-human Written informed consent was obtained from all participants CD56; Miltenyi Biotec), and CD19 B cells (mouse anti-human in accordance with the Declaration of Helsinki for Human CD19; eBioscience). Subsequently, cell fixation and red blood Research. Study protocols were approved by the SingHealth cells lysis were performed with 1X FACS lysing solution (BD Centralized Institutional Review Board (reference 2016/2219) Biosciences). Permeabilization was achieved with 1X FACS per- and the National Healthcare Group Domain Specific Review meabilization solution 2 (BD Biosciences), aer w ft hich cells were Board (reference 2015/00528). Collection of blood samples stained with ZIKV NS3 protein–specific rabbit polyclonal antibody from healthy donors was done with written consent in accord- . Stained cells were counterstained with a uo fl rophore-tagged ance with guidelines from the Health Sciences Authority secondary goat anti-rabbit IgG (H+L) antibody (Invitrogen), of Singapore (study approval number National University before acquisition with LSR Fortessa analyzers, LSRII 4 lasers, and Singapore Institutional Review Board approval 10-250). LSRII 5 lasers (BD Biosciences). Since samples were transported on ice within 3 hours to the laboratory at the Singapore Immunology Patients and Sample Collection Network, live/dead staining was not implemented because freshly e fir Th st case of ZIKV infection was reported on 27 August 2016 collected whole-blood specimens typically contain very few dead , and detection was confirmed by ZIKV-specific quantita- cells . Numbers of peripheral blood cell immune subsets were tive reverse transcription polymerase chain reaction (qRT-PCR) subsequently obtained with the following formula: [percentage analysis . er Th eaer ft , suspected ZIKV cases were referred/ of a specific immune subset, obtained from immunophenotyp- admitted to the Communicable Disease Centre at Tan Tock Seng ing] × [total leukocyte count, obtained from complete blood Hospital for ZIKV testing. Hematological and biochemistry count] = the number of cells in a specific immune subset. laboratory tests were performed in parallel with ZIKV-specific RT-PCR analysis  upon admission. ZIKV infection was con- ZIKV Load Quantification firmed by a positive result of ZIKV-specific RT-PCR analysis of RNA samples were extracted from 140-μL whole-blood sam- whole-blood specimens. Subsequently, whole-blood specimens ples, using the QIAamp Viral RNA Mini Kit (Qiagen) accord- were obtained at 5 collection time points: (1) the acute phase ing to the manufacturer’s protocols. ZIKV quantification was (2–7 days aer i ft llness onset), (2) the early convalescent phase performed by 1-step TaqMan real-time RT-PCR analysis (10–14 days), (3) the late convalescent phase (25–35 days), (4) the (QuantiTect Probe RT-PCR Kit; Qiagen) as described previ- early recovery phase (2–4 months), and (5) the late recovery phase ously [18, 19]. (5–6 months). Whole-blood specimens were collected in eth- ylenediaminetetraacetic acid–coated Vacutainer tubes (Becton Multiplex Microbead Immunoassay for Cytokine Quantification Dickinson) aer p ft eripheral venipuncture. Two milliliters of Cytokine and chemokine levels in ZIKV-infected patients’ whole blood was first aliquoted for blood count analysis, whole- plasma were measured simultaneously, using a multiplex blood staining, and viral load quantification, and the remaining microbead-based immunoassay (ProcartaPlex Human sample was centrifuged at 240g for 10 minutes to collect plasma Cytokine/Chemokine/Growth Factor Panel 1; Thermo for storage at −80°C. Samples from healthy donors were included Scientific) as described previously . Preparation of plasma and prescreened for the presence of ZIKV viral RNA  and samples and reagents, as well as immunoassay procedures, were ZIKV-specific antibodies . All healthy donors were nonfe- performed according to manufacturers’ instructions. brile and had no signs of acute illness during recruitment. Data Processing and Statistical Analysis Blood Count Luminex assay–determined concentrations obtained via Bio- Complete blood count was performed using the Ac·T diff plex Manager software (using 5-parameter logistic curve fitting) hematology analyzer (Beckman Coulter) according to the man- were normalized using median centering to remove potential ufacturer’s instructions. Beckman Coulter 4C Plus Tri-Pack Cell plate effects. Each analyte was normalized separately, such that Controls (Beckman Coulter) were conducted to confirm instru- the median concentration for each plate was first determined ment accuracy and precision performance. and then the global median concentration from all plates was Immune Profiles of Patients With Zika • JID 2018:218 (1 September) • 815 Downloaded from https://academic.oup.com/jid/article/218/5/814/4972819 by DeepDyve user on 13 July 2022 computed. A scaling factor was then computed for each plate, with the highest incidence of ZIKV infection occurring among which adjusted the median concentration for the plate to that of patients aged 18–34 years (26 [47.3%]). Among the 55 patients, the global median concentration. e Th final adjusted concentra- 26 (47.3%) still had ZIKV RNA detected by ZIKV-specific tions were then logarithmically transformed to assume a nor- RT-PCR analysis (median, 16.8 viral copies/uL; IQR, 10.0–30.6 mal distribution before further data analysis and visualization. viral copies/uL) during the acute phase (Table 1). No neuro- Two-way analysis of variance (ANOVA) with the post hoc logical complications were observed in the patients during the Tukey test was used to detect differences between the various acute phase of infection. sample groups and collections. ANOVA results were corrected for multiple testing, using the method of Benjamini and Hochberg. Table 1. Demographic and Clinical Characteristics of 55 Patients Infected Nonparametric testing was done for cell percentages and viral load With Zika Virus (ZIKV) data, using the Mann-Whitney test or the Kruskal-Wallis test with Variable Value Dunn multiple comparison tests. Data processing was done in the Age, y R statistical language (version 3.3.1). The relationship between 16–34 26 (47.3) analytes at different collections was also determined using Pearson 35–54 22 (40.0) correlation analysis. All statistical analyses were performed using ≥55 7 (12.7) R version 3.3.1 or Prism 7.0 (GraphPad sowa ft re). Overall 35 (28–47) Hierarchical clustering and heat map visualization were done Sex using TM4-MeV . In the heat map presentation, the aver- Female 24 (43.6) Male 31 (56.4) age concentration was computed for each measured analyte in Ethnicity its respective group, and the average values were then scaled Chinese 44 (80.0) between 0 and 1 for visualization . Indian 3 (5.45) Receiver operating characteristic (ROC) curve analysis Malay 4 (7.27) for analytes that were differentially expressed between ZIKV- Others 4 (7.27) ZIKV viremia status infected patients and healthy controls was performed, and the Viremic 26 (47.3) areas under the curve (AUCs) were calculated. Analytes with Nonviremic 29 (52.7) AUCs of >0.65 and a P value of <.05 are considered to be poten- Symptoms tial markers of infection. Fever 39 (70.9) A multivariate model was built using patient demographic Rash 50 (90.9) characteristics, immune mediator profiles, and immunopheno- Myalgia 29 (52.7) Arthralgia 24 (43.6) typing data. The analysis was done using a logistic regression Headache 19 (34.5) t o fi ptimized for analysis by the Akaike information criterion Conjunctivitis 19 (34.5) (AIC) in a step-forward fashion. The number of steps was lim- Sore throat 9 (16.4) ited to a floor value (calculated as the number of samples/10), to Cough 8 (14.5) ensure that there were sufficient data for fitting. Leave-one-out a ZIKV RNA level, viral copies/uL 16.8 (10.0–30.6) cross-validation was then used to assess the predictive quality Hospitalization duration, d 2 (2–3) Laboratory test results of the model. Biological processes were predicted from differ - Na level, mmol/L 138 (136–140) entially expressed mediators with Ingenuity Pathway Analysis K level, mmol/L 3.6 (3.4–3.7) (IPA; Qiagen). Interaction networks of selected immune medi- Urea level, mmol/L 3.3 (2.8–3.7) ators were predicted with STRING (version 10.5; available at: Creatinine level, mmol/L 73 (57–85) https://string-db.org/). AST level, U/L 26 (20–30) ALT level, U/L 20 (16–31) Blood test results RESULTS Hemoglobin level, g/dL 14.1 (13.4–15.2) Hematocrit, % 42.8 (40.3–46.0) Demographic and Clinical Characteristics of ZIKV-Infected Patients Platelet count, ×10 platelets/µL 202.5 (176.8–238.5) A total of 55 patients were recruited for the study, based on 11 (9–14) Bilirubin level, µmol/L confirmatory results of ZIKV-specific PCR analysis upon hos- Albumin level, g/L 39 (37–43) pitalization, and were subsequently recruited into the study. Data are no. (%) of patients or median value (interquartile range). The majority of the patients returned for their follow-up Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase. consultations during the acute phase (55 patients), the early a The ZIKV RNA load was determined during acute phase of infection and is expressed as viral copies/uL of extracted viral RNA. convalescent phase (34), the late convalescent phase (36), Among the symptoms, rash (in 50 patients [90.9%]) and fever (in 39 [70.9%]) were the the early recovery phase (35), and the late recovery phase most prevalent during hospital admission, followed by myalgia (in 29 [52.7%]), arthral- gia (in 24 [43.6%]), headache (in 19 [34.5%]), cough (in 8 [14.5%]), and sore throat (in 9 (32). Patient age ranged from 16 to 65 years (mean, 38 years; [16.4%]). Conjunctivitis (of ocular conjunctiva) was observed in 19 patients (34.5%). median, 35.0 years; interquartile range [IQR], 28–47 years), Symptoms were defined as moderate if a patient presented with >4 of 8 symptoms. 816 • JID 2018:218 (1 September) • Lum et al Downloaded from https://academic.oup.com/jid/article/218/5/814/4972819 by DeepDyve user on 13 July 2022 ZIKV Infection Triggers High Levels of Proinflammatory Mediators IFN-γ, interleukin 1β [IL-1β], interleukin 18 [IL-18], interleukin 6 To define the acute host immune markers during ZIKV infection, [IL-6], tumor necrosis factor α [TNF-α], interleukin 17A [IL-17A], levels of immune mediators present in patients’ plasma obtained interleukin 9 [IL-9], interleukin 22 [IL-22], and interleukin 2 [IL- during the acute phase were quantified by a 45-plex microbead 2]), and antiinflammatory cytokines (ie, IL-1 receptor antagonist assay (Figure 1A). It was observed that patients with acute ZIKV [IL-1RA], interleukin 10 [IL-10], interleukin 4 [IL-4], interleu- infection had significantly higher levels of CCL/CXC chemokines kin 5 [IL-5], and interleukin 21 [IL-21]) than healthy controls (ie, interferon γ [IFN-γ]–induced protein 10 [IP-10], regulated on (Figure 1B). Further categorization of ZIKV-infected patients on activation normal T-cell expressed and secreted [RANTES], mono- the basis of viremic status (Table 1) revealed differences between cyte chemoattractant protein 1 [MCP-1], stromal cell–derived fac- viremic and nonviremic patients (Figure 1A and Supplementary tor 1α [SDF-1α], macrophage inflammatory protein 1β [MIP-1β], Table 1). Collectively, these data highlight the presence of unique and growth-regulated oncogene α [GRO-α]), growth factors (ie, cytokine proles a fi mong ZIKV-infected patients. brain-derived neurotrophic factor [BDNF], epidermal growth fac- tor [EGF], platelet-derived growth factor ββ [PDGF-ββ], placenta ZIKV Infection Reduces the Peripheral Blood Cell Count growth factor 1 [PIGF-1], hepatocyte growth factor [HGF], and Blood cell count and subset proles a fi re important criteria for granulocyte-macrophage colony-stimulating factor [GM-CSF]), determining the diagnosis and prognosis of viral infections . + + proinflammatory cytokines (ie, interleukin 12p70 [IL-12p70], Changes in the number of neutrophils (CD45 CD16 ), monocytes AB Proinﬂammatory Antiinﬂammatory CCL CXCGrowth Factors ZIKV-infected CCL/CXC chemokines Growth factors patients (n=55) *** 4 6 Healthy controls *** *** (n=31) * * 3 4 *** 2 2 1 0 0 –2 Proinﬂammatory Antiinﬂammatory 4 6 *** 3 *** *** ** ** 4 ** *** *** ** 2 *** *** –1 –2 Low (0.0) (1.0) High Figure 1. Profiling of immune mediators in plasma from patients with acute Zika virus (ZIKV) infection. A microbead assay was performed to quantify the levels of immune mediators in plasma specimens obtained from 55 patients during the acute phase of infection. A, Levels of 45 immune mediators were analyzed and presented in a heat map of normalized scores (0–1). B, Box-and-whisker plots of levels of 27 mediators that were statistically significantly different between ZIKV-infected patients and 31 healthy controls. Concentrations of immune mediators are expressed on a log scale. *P < .05, **P < .01, and ***P < .001 by t tests conducted on the logarithmically transformed concentrations, with correction for multiple testing by the Benjamini-Hochberg method. BDNF, brain-derived neurotrophic factor; bNGF, β nerve growth factor; EGF, epidermal growth factor; FGF-2, fibroblast growth factor 2; GM-CSF, granulocyte-macrophage colony-stimulating factor; GRO-α, growth-regulated protein α; HGF, hepatocyte growth factor; IFN-α, interferon α; IFN-γ, interferon γ; IL-1RA, interleukin 1 receptor antagonist; IL-1α, interleukin 1α; IL-1β, interleukin 1β; IL-2, interleukin 2; IL-4, interleukin 4; IL-5, interleukin 5; IL-6, interleukin 6; IL-7, interleukin 7; IL-8, interleukin 8; IL-9, interleukin 9; IL-10, interleukin 10; IL-12p70, interleukin 12p70; IL-13, interleukin 13; IL-15, interleukin 15; IL-17A, interleukin 17A; IL-18, interleukin 18; IL-21, interleukin 21; IL-22, interleukin 22; IL-23, interleukin 23; IL-27, interleukin 27; IL-31, interleukin 31; IP-10, interferon γ–induced protein 10; LIF, leukemia inhibitory factor; MCP-1, monocyte chemotactic protein 1; MIP-1α, macrophage inflammatory protein 1α; MIP-1β, macrophage inflammatory protein 1β; PDGF-ββ, platelet-derived growth factor ββ; PIGF-1, placental growth factor 1; RANTES, regulated on activation normal T-cell expressed and secreted; SCF, stem cell factor; SDF-1α, stromal cell–derived factor 1α; TNF-α, tumor necrosis factor α; TNF-β, tumor necrosis factor β; VEGF-A, vascular endothelial growth factor A; VEGF-D, vascular endothelial growth factor D. Immune Profiles of Patients With Zika • JID 2018:218 (1 September) • 817 IP-10 RANTES MCP-1 SDF-1α MIP-1β GRO-α IL-12p70 BDNF IFN-γ EGF IL-1β PDGF-ββ IL-18 PIGF-1 IL-6 HGF TNF-α GM-CSF IL-17A IL-9 IL-22 IL-2 IL1-RA IL-10 IL-4 IL-5 IL-21 ZIKV-infected patients Healthy controls Nonviremic Viremic IFN-α IFN-γ IL-1α IL-1β IL-12p70 IL-15 IL-17A IL-18 IL-2 IL-22 IL-23 IL-27 IL-6 IL-7 IL-9 TNF-α TNF-β IL-1RA IL-21 IL-31 IL-4 IL-5 IL-10 IL-13 Eotaxin MCP-1 MIP-1α MIP-1β RANTES GRO-α IL-8 IP-10 SDF-1α BDNF bNGF EGF FGF-2 GM-CSF HGF LIF PDGF-ββ PIGF-1 SCF VEGF-A VEGF-D Log concentration (pg/mL) 10 Downloaded from https://academic.oup.com/jid/article/218/5/814/4972819 by DeepDyve user on 13 July 2022 + + + + + + (CD14 ), B cells (CD19 ), T cells (CD3 CD4 , CD3 CD8 ), dou- data, comorbidity data, hospitalization details, travel history + - - + ble-negative (CD3 CD4 CD8 ) T (DNT) cells, NK cells (CD56 and sick contact, pregnancy and vaccination status, signs and hi + + + + and CD56 ) NK T cells (CD56 CD3 ), and CD14 CD56 cells symptoms, and laboratory data on complete blood count; renal during acute ZIKV infection were prole fi d (Figure 2A). and liver function test results; diagnostic test results for ZIKV, It was observed that, during acute ZIKV infection, the total num- dengue virus, and chikungunya virus; and clinical outcomes ber of blood leukocytes in the patients was significantly lower than (ie, full recovery, sequelae, and death). In this study, symptoms in healthy controls, owing to the overall reduction of all immune were defined as moderate if a patient presented with >4 of 8 populations except monocytes (Figure 2B). It was observed that symptoms presented in Table 1. Patients who did not fulfill this ZIKV-infected patients generally had decreased numbers of B cells, criterion were classified as having mild symptoms (Figure 3A). DNT cells, and neutrophils. The presence of detectable viremia It was observed that patients with moderate ZIKV disease expe- ae ff cted the numbers of other immune subsets (Figure 2B), while rienced higher incidences of myalgia, arthralgia, conjunctivitis, in nonviremic patients, an intermediate phenotype (ie, a non- and cough (Figure 3B). Rash was present in >90% of patients significant drop in cell number) was observed. Notably, although from both groups (Figure 3B). ZIKV-infected patients had leukopenia, the number of cells in each To further identify specific unique immune profiles, ZIKV- immune subset was still within the normal range . infected patients were categorized on the basis of both their viremic status and symptom manifestation, as follows: (1) non- Unique Cellular and Immune Mediator Signatures Associated With viremic with mild symptoms, (2) nonviremic with moderate Disease Symptoms symptoms, (3) viremic with mild symptoms, and (4) viremic Because the ZIKV epidemic was anticipated, a study was with moderate symptoms. Viremic patients with moderate planned with a standardized care path to collect demographic symptoms had significantly higher levels of IL-1RA, MCP-1, 22.8 Neutrophils 55.8 3.17 + CD4 B cells 0.39 36.7 Monocytes 24.2 T cells + + CD14 CD56 NKT cells 2.48 cells hi CD56 CD8 25.0 NK cells T cells 73.0 35.9 NK 67.6 62.7 70.8 7.40 8.94 cells DNT cells CD16 CD56 CD56 CD3 CD8 Monocytes Neutrophils B cells DNT cells CD4 T cells *** *** * 8.0 1.0 0.3 1.5 ** 0.8 * * * 0.8 0.6 6.0 1.0 0.2 0.6 0.4 4.0 0.4 0.1 0.5 0.2 2.0 0.2 0.0 0.0 0.0 0.0 0.0 + hi + + CD8 T cells NKT cells NK cells CD56 NK cells CD56 CD14 cells 0.3 0.6 0.1 0.2 0.8 ** ** * * * 0.6 0.2 0.4 0.0 0.1 0.4 0.1 0.2 0.0 0.1 0.2 0.0 0.0 0.0 0.0 0.0 Viremic (n=21) Nonviremic (n=23) Healthy controls (n=14) Figure 2. Blood immunophenotyping of patients with acute Zika virus (ZIKV) infection. Percentages of specific immune subsets were assessed via immunophenotyping of whole-blood specimens. A, Gating strategy of immunophenotyping. Briefly, immune cells were identified with a combination of CD45, CD14, CD3, CD4, CD8, CD19, CD16, and CD56 antibodies. Data in the plots shown were obtained from a representative donor. B, The cellular numbers of each identified immune subset in the peripheral blood were subsequently determined by the following formula: [percentages of specific immune subset] × [total leukocyte numbers] = cellular numbers of specific immune subset. Total leukocyte numbers were obtained with a hematology analyzer. Data were obtained from 21 viremic and 23 nonviremic patients. Values obtained from 14 healthy controls are plotted alongside for comparison. *P < .05, **P < .01, and ***P < .001 by 2-tailed nonparametric Mann-Whitney tests. DNT, double-negative T; NK, natural killer; NKT, natural killer T; SSC, side scatter. 818 • JID 2018:218 (1 September) • Lum et al Total cells/μL blood (× 1000) SSC-A CD14 CD3 CD19 CD4 Downloaded from https://academic.oup.com/jid/article/218/5/814/4972819 by DeepDyve user on 13 July 2022 A C IL-1RA MCP-1 IP-10 Disease severity 4.5 3.5 * 4.5 ** ** ** 30 * ** 4 * 4 ** 8 2 2.5 3.5 3.5 3 3 1.5 2.5 2.5 7 3 0 2 0.5 2 Patients, % PIGF-1 IL-8 Moderate Viremic/moderate Mild ** 4 2.5 (n=8) 6 4 * ** Viremic/mild (n=21) 1.5 Nonviremic/moderate (n=7) Symptoms associated with severity Nonviremic/mild (*) 0 0.5 (n=19) Fever (*) + + Cough Rash CD4 T cells CD8 T cells DNT cells 1.5 0.8 0.3 0.6 (***) 1.0 0.2 Sore throat Myalgia 0 0.4 0.5 0.1 0.2 Patients, % Moderate 0.0 0.0 0.0 (***) (***) Mild Conjunctivitis Arthralgia Viremic/moderate (n=6) Nonviremic/moderate (n=6) Viremic/mild (n=15) Nonviremic/mild (n=17) Headache Figure 3. Specific immune response in Zika virus (ZIKV)–infected patients categorized by symptom manifestations. A and B, Radar charts showing the percentages of patients experiencing either mild or moderate symptoms, based on their clinical scores (A), and the differences in the prevalence of symptom manifestations between patients in the 2 groups (B). Clinical scores reflect the numbers of symptoms experienced during admission. *P < .05, **P < .01, and ***P < .001 by 2-sided Fisher exact tests for comparison of the prevalence of symptoms between groups. C and D, Box-and-whisker plots of levels of specific cytokines (C) and immune subsets (D) that were significantly different between patients in the viremic/moderate group and patients from the other 3 groups (ie, viremic/mild, nonviremic/moderate, and nonviremic/mild). *P < .05 and **P < .01 by 1-way analysis of variance, with correction for multiple testing by the Benjamini-Hochberg method with post hoc Tukey test (for cytokines), or by the Kruskal-Wallis test, with the Dunn multiple comparison test (for immune subsets). DNT, double-negative T; IL-1RA, interleukin 1 receptor antagonist; IL-8, interleukin 8; IP-10, interferon γ–induced protein 10; MCP-1, monocyte chemotactic protein 1; PIGF-1, placental growth factor 1. IP-10, and PIGF-1, compared with other patients. These patients ZIKV-infected patients (Figure 4A). Levels of 14 immune also had increased levels of IL-8 (Figure 3C and Supplementary mediators were found to be significantly higher than those Table 2). Levels of these immune mediators were relatively sim- in healthy controls during acute ZIKV infection. Of these, 13 ilar among the other 3 groups. could be potential markers for acute ZIKV infection, as cal- In terms of total blood profiling, viremic patients experienc- culated by ROC analyses that evaluated their diagnostic val- ing moderate symptoms of ZIKV infection had reduced num- ues (Supplementary Table 4). Uniquely, there exists a group of + + bers of DNT cells, CD4 T cells, and CD8 T cells (Figure 3D). immune mediators that remain differentially expressed from To further understand the link between the host immune the acute phase through the postacute phases of ZIKV infection response and ZIKV disease progression, multivariate prediction (Table 2). Although levels in the majority (ie, in IP-10, IL-10, on ZIKV viremic status and disease outcome was conducted IL-1RA, IL-12p70, IL-1β, RANTES, and IFN-γ) peaked during using data from both the microbead assay and immunophe- the acute phase, levels peaked in later phases for some (ie, IL-18 notyping. The analysis predicted that HGF could influence dis- [convalescent phase] and SDF-1α, IL-21, and MIP-1β [recov- ease outcome (Supplementary Table 3). On the other hand, the ery phase]). These immune mediators could potentially be use- hi percentages of monocytes and CD56 NK cells were predicted ful markers of these phases, based on ROC analyses (Table 2). to influence symptom severity, whereas neutrophils were pre- Interestingly, levels of IL-21 were significantly lower during the dicted to influence viremia (Supplementary Table 3). acute phase and peaked only during the recovery phase, making it a potential marker for the recovery phase (Table 2). Levels of T-Cell–Associated Cytokines and Chemokines Remain High To further understand the consequences of these differen- After the Acute Phase of ZIKV Infection tially expressed immune mediators in ZIKV-infected patients, Blood immunophenotyping findings and immune markers cellular developmental processes were predicted using IPA. It were further profiled longitudinally during the acute, early and was observed that processes involving proliferation and expan- late convalescent, and early and late recovery phases among sion of T cells were important (Figure 4A). However, blood Immune Profiles of Patients With Zika • JID 2018:218 (1 September) • 819 Total cells/μL blood Log concentration (pg/mL) (× 1000) 10 Downloaded from https://academic.oup.com/jid/article/218/5/814/4972819 by DeepDyve user on 13 July 2022 Top 3 cellular developmental processes predicted by IPA Convalescent phase Proliferation of eector Proliferation of T lymphocytes Expansion of T lymphocytes IL-18 T lymphocytes IL-1β P =: 3.45E-10 P =: 1.45E-09 P =: 3.31E-08 RANTES, IL-21, IFN-γ, IL-18, IL-1β, SDF-1α IFN-γ, IL-1β, IL-21 IL-18, IFN-γ, IL-1β, IL-21 IP-10 BDNF SDF-1α IL-1RA IFN-γ IL-21 RANTES Proliferation of lymphocytes Proliferation of B lymphocytes Proliferation of T lymphocytes TNF-β IL-12p70 P =: 1.04E-12 P =: 7.57E-10 P =: 2.52E-09 IFN-α MIP-1β EGF IL-10 SCF MIP-1β, RANTES, IL-21, IFN-γ, IL-21, IFN-γ, IL-10, SDF-1α, MIP-1β, RANTES, IL-21, IFN-γ, IL-10, IL-12p70, SDF-1α, TNF-β IL-10, SDF-1α TNF-β Chronic phase IFN-α IP-10 SDF-1α SCF EGF MIP-1β BDNF TNF-β IFN-γ IL-10 RANTES IL-21 IL-12p70 Activation Reaction Positive Negative Inhibition Catalysis Unspeciﬁed Binding Transcriptional regulation IL-1RA IL-1β IL-18 Figure 4. Immune response in Zika virus (ZIKV)–infected patients after the acute phase of infection. Immune mediators during the convalescent and recovery phases were assessed via a microbead-based assay. A, Venn diagram displaying the mediators that are differentially expressed in ZIKV-infected patients as compared to healthy controls during the convalescent and recovery phases of infection. Shown are the top 3 cellular developmental processes and associated mediators, as predicted with Ingenuity Pathway Analysis (IPA). B, Interactive relationships between the immune mediators were determined by STRING analysis, with a confidence threshold of .85. BDNF, brain-derived neurotrophic factor; EGF, epidermal growth factor; IFN-α, interferon α; IFN-γ, interferon γ; IL-1RA, interleukin 1 receptor antagonist; IL-1β, interleukin 1β; IL-10, interleukin 10; IL-12p70, interleukin 12p70; IL-18, interleukin 18; IL-21, interleukin 21; IP-10, interferon γ–induced protein 10; MIP-1β, macrophage inflammatory protein 1β; RANTES, regulated on activation normal T-cell expressed and secreted; SCF, stem cell factor; SDF-1α, stromal cell–derived factor 1α; TNF-β, tumor necrosis factor β. cell counts at both the convalescent and recovery phases did note that high levels of IFN-γ, IL-18, IL-10, IP-10, and TNF-α are not indicate any significant differences between ZIKV-infected consistent with our earlier report detailing the immune mediator profile from a Brazilian patient cohort . Uniquely, high levels patients and healthy controls for all immune subsets. of GM-CSF, IL-2, IL-4, IL-5, IL-6 IL-9, IL-17A, IL-22, MCP-1, DISCUSSION HGF, and TNF-α and low levels of PIGF-1 and PGDF-ββ were identified only in the acute phase, making them useful markers In this study, ZIKV-infected patients from the Singapore cohort for acute ZIKV infection. As such, the prognostic and predictive had high levels of proinflammatory mediators, antiinflammatory values on the combinatorial assessment of these markers should mediators, CCL/CXC chemokines, and growth factors. While be further understood to improve patient management. this robust immune response was expected, it is worthwhile to 820 • JID 2018:218 (1 September) • Lum et al Downloaded from https://academic.oup.com/jid/article/218/5/814/4972819 by DeepDyve user on 13 July 2022 Table 2. Analysis of Immune Mediator Levels Among Healthy Controls and Zika Virus (ZIKV)–Infected Patients During the Postacute Phases of ZIKV Infection Level, Log pg/mL, Mean ROC Analysis, a b Phase, Immune Mediator Healthy Controls ZIKV-Infected Patients Adjusted P AUC (95% CI) P Early convalescent IP-10 2.5866 2.8702 <.0001 0.8833 (.8037–.9629) <.0001 IL-1RA 2.6719 2.8447 .0025 0.7571 (.6362–.8780) .0004 IL-21 0.2904 -0.6542 .0155 0.5705 (.4080–.7330) .4326 IL-18 1.7089 1.9178 .0182 0.6917 (.5621–.8212) .0080 SDF-1α 2.4688 2.5328 .0357 0.6667 (.5309–.8024) .0220 1.8324 1.9940 .0427 0.7097 (.5809–.8384) .0037 IFN-γ Late convalescent IFN-γ 1.8324 2.0241 .0027 0.7608 (.6464–.8751) .0003 IL-1RA 2.6719 2.8271 .0027 0.7401 (.6204–.8599) .0008 IL-18 1.7089 1.9431 .0027 0.7401 (.6204–.8599) .0008 RANTES 2.6539 2.7768 .0063 0.6254 (.4871–.7638) .0783 BDNF 3.4277 3.2368 .0118 0.7384 (.6153–.8614) .0008 IL-1β 0.9149 1.1365 .0439 0.5021 (.3438–.6604) .9791 Early recovery IP-10 2.5866 2.7809 <.0001 0.8700 (.7831–.9570) <.0001 -0.3277 -1.1561 .0003 0.7300 (.6033–.8566) .0013 TNF-β IL-1RA 2.6719 2.9355 .0008 0.7862 (.6750–.8974) <.0001 IL-21 0.2904 0.9240 .0143 0.7972 (.6421–.9523) .0019 IFN-γ 1.8324 2.0087 .0182 0.6931 (.5654–.8208) .0071 RANTES 2.6539 2.7696 .0182 0.6986 (.5693–.8280) .0056 -0.2438 -0.6617 .0182 0.6645 (.5282–.8008) .0273 IFN-α SDF-1α 2.4688 2.5734 .0236 0.7088 (.5838–.8337) .0036 IL-12p70 0.9016 1.0991 .0236 0.6793 (.5493–.8092) .0125 1.7129 1.8926 .0236 0.6857 (.5578–.8136) .0096 MIP-1β BDNF 3.4277 3.2787 .0272 0.7318 (.6064–.8572) .0012 IL-10 0.6270 0.8388 .0441 0.6537 (.5173–.7901) .0334 Late recovery TNF-β -0.3277 -1.1325 .0022 0.7211 (.5900–.8523) .0028 IFN-α -0.2438 -0.7634 .0072 0.7071 (.5737–.8404) .0051 IP-10 2.5866 2.7448 .0072 0.8216 (.7172–.9260) .0001 EGF 2.3015 2.1273 .0159 0.6744 (.5415–.8073) .0174 IL-1RA 2.6719 2.8726 .0159 0.747 (.6222–.8718) .0008 BDNF 3.4277 3.2442 .0202 0.7419 (.6154–.8685) .0010 SCF 0.9699 0.8801 .0241 0.6855 (.5540–.8170) .0114 Abbreviations: AUC, area under the curve; BDNF, brain-derived neurotrophic factor; CI, confidence interval; EGF, epidermal growth factor; IFN-α, interferon α; IFN-γ, interferon γ; IP-10, inter - feron γ–induced protein 10; IL-1RA, interleukin 1 receptor antagonist; IL-1β, interleukin 1β; IL-10, interleukin 10; IL-12p70, interleukin 12p70; IL-18, interleukin 18; IL-21, interleukin 21; MIP-1β, macrophage inflammatory protein 1β; RANTES, regulated on activation normal T-cell expressed and secreted; ROC, receiver operating characteristic; SCF, stem cell factor; SDF-1α, stromal cell–derived factor 1α; TNF-α, tumor necrosis factor α; TNF-β, tumor necrosis factor β. By the Student t test, with correction for multiple testing, using the Benjamini-Hochberg method. Immune mediators with a P value < .05 are considered to exhibit a reasonable AUC, signifying their potential as a marker during the different phases of ZIKV infection. e d Th uration of detectable acute ZIKV viremia is relatively significant aer t ft he acute phase (Supplementary Table 2). While short . With this in mind, ZIKV-infected patients were fur- the majority of the aforementioned immune mediators have ther grouped on the basis of both viremic status and symptom been previously associated with dengue virus infection [24, 25], manifestations. Viremic patients with moderate disease exhib- PIGF-1 could have an important role in ZIKV-induced congen- ited high levels of IL-1RA, MCP-1, IP-10, PIGF-1, and IL-8. IL-8 ital abnormalities, because placental PIGF-1 has roles in vas- could be a symptom severity marker only in such patients, indi- culogenesis and angiogenesis in placenta . Coincidentally, cating that subtle unique differences could be missed if detailed levels of MCP-1, IP-10, IL-1RA, and IL-8 were higher in patients clinical and laboratory parameters are incomplete. However, with severe dengue [24, 27], similar to what was observed in in this study, it was observed that the window for detecting this cohort of ZIKV-infected patients. viremic patients with moderate symptoms is short, because lev- Hematological changes have commonly been reported for els of IL-1RA, IL-8, PIGF-1, MCP-1, and IP-10 were no longer other arboviral infections, such as those due to DENV and Immune Profiles of Patients With Zika • JID 2018:218 (1 September) • 821 Downloaded from https://academic.oup.com/jid/article/218/5/814/4972819 by DeepDyve user on 13 July 2022 chikungunya virus, but not for ZIKV infection. In this cohort, In this cohort, fever, conjunctivitis, arthralgia, and myalgia it was observed that the patients experienced transient leu- were the main symptoms in moderately symptomatic patients, kopenia and neutropenia, possibly due to transmigration of in contrast to those reported for dengue virus infection . The immune cells into the tissues during acute infection. The role finding that viremic and moderately symptomatic patients had of blood immune cells in ZIKV infection is understudied, but higher levels of IL-8, MCP-1, and IP-10 could possibly explain monocytes are a reported cellular target for ZIKV [11–14, 19]. the higher incidence of conjunctivitis, through the recruitment Moreover, monocytes have been shown ex vivo to modulate the of immune cells to the conjunctiva . Moreover, these cyto- host immune response during ZIKV infection . High levels kines were reported to be associated with acute arthralgia and of MCP-1 and GM-CSF detected in patients during the acute myalgia in chikungunya virus–infected patients [49, 50]. phase further confirm the role of monocytes during infection. e m Th arked differences in disease presentation between + + Lower numbers of DNT cells, CD4 T cells, and CD8 T cells mildly and moderately symptomatic patients reported in this were observed in viremic patients with moderate symptoms study are useful in developing a more robust definition of ZIKV during the acute phase. This could be due to high levels of IP-10 infection. Importantly, the presence of viremia does not nec- in these patients, because IP-10 is responsible for recruitment of T essarily equate to more-severe disease. Likewise, symptom- cells to infection sites . CD8 T cells have been demonstrated atic patients can be nonviremic, which suggests the interplay to have a protective role during ZIKV infection in immunocom- between symptoms, viremia, and host immune response in petent animals , and their cytotoxicity can be influenced by defining disease severity. Collectively, ZIKV infection stands their responsiveness to IL-8 . Likewise, higher IL-1RA levels out on its own apart from other flaviviruses, and the findings in these ZIKV-infected patients could also signal regulated IL-1 reported here will open up new research angles that can only signaling in the Th17 subset of the CD4 T cells [31, 32]. On the increase knowledge about ZIKV immunopathogenesis. other hand, little is known about the function of DNT cells in Supplementary Data viral infections. DNT cells are commonly associated with autoim- mune lymphoproliferative syndrome  and are known to share Supplementary materials are available at e Th Journal of Infectious some characteristics with regulatory T cells and CD8 T cells . Diseases online. Consisting of data provided by the authors to eir f Th unctional role in ZIKV infection should therefore not be benefit the reader, the posted materials are not copyedited and neglected. The overall importance of T cells during acute ZIKV are the sole responsibility of the authors, so questions or com- infection is further highlighted by the general presence of T-helper ments should be addressed to the corresponding author. type 1 (Th1)–related (IL-2 and IFN-γ), Th2-related (IL-4), Th17- Notes related (IL-17A), and Th9-related (IL-9) cytokines detected in this study, an observation that has been previously reported . Acknowledgments. We thank Cheryl Y. P. Lee, Jonathan Functional prediction by IPA suggested active roles for T Cox, Yi-Hao Chan, Guillaume Carissimo, Farhana Abu Bakar, cells during the convalescent and recovery phases of ZIKV Nicholas Q. R. Kng, Kia-Joo Puan, and Nurhashikin Binte Yusof disease, while STRING analysis (performed with a high con- (Singapore Immunology Network [SIgN]), for their help in pro- fidence threshold of 0.85) revealed key interactions between cessing patient samples; Ivy Low, Seri Mustafah, and Anis Larbi 11 of these immune mediators (Figure 4B). As expected, the (SIgN flow cytometry team) and the SIgN immunomonitoring predicted interactions are linked to T cells. IFN-γ, a known group, for their assistance; Laurent Rénia, Teck-Hui Teo, and T-cell cytokine , was predicted to positively trigger the Kai-Er Eng, for critical discussion and proofreading of the man- activity of IP-10 and IL-1β, which are mediators involved in uscript; and all study participants and healthy volunteers, for T-cell recruitment and activation [28, 31, 36]. It was also pre- their participation. dicted to interact with IL-10, an antiinflammatory cytokine Financial support. This work was supported by the , resulting in reciprocal inhibition. IFN-γ activity could in Singapore Biomedical Research Council (BMRC; core research turn be triggered by IL-12p70 , IFN-α , and IL-18 . grants to the Singapore Immunology Network), the Singapore IL-10, a regulator of immunity , was predicted to inhibit National Public Health Laboratory, the University of Tartu, the activity of IP-10, RANTES , and IL-1β. Interestingly, the and the BMRC and Singapore Agency for Science, Technology, balance between IFN-γ and IL-10 has been reported to be criti- and Research–led Zika Virus Consortium Fund (project cal in the pathogenesis of autoimmune diseases [43, 44]. Other 15/1/82/27/001). interactions predicted by STRING analysis suggest binding Potential conifl cts of interest. All authors: No reported and catalytic relationships between other T-cell chemokines, conflicts. RANTES, SDF-1α and IP-10 . Taken together, these find- All authors have submitted the ICMJE Form for Disclosure ings emphasize the importance of a balanced cytokine environ- of Potential Conflicts of Interest. 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The Journal of Infectious Diseases – Oxford University Press
Published: Jul 24, 2018
Keywords: cytokine; zika virus infections; immune mediator; immunophenotyping
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