Compared to adults, children with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have predominantly mild or asymptomatic infections, but the underlying immunological differences remain unclear. Here, we describe clinical features, virology, longitudinal cellular, and cytokine immune profile, SARS-CoV-2-specific serology and salivary antibody responses in a family of two parents with PCR-confirmed symptomatic SARS-CoV-2 infection and their three children, who tested repeatedly SARS-CoV-2 PCR negative. Cellular immune profiles and cytokine responses of all children are similar to their parents at all timepoints. All family members have salivary anti-SARS-CoV-2 antibodies detected, predominantly IgA, that coincide with symptom resolution in 3 of 4 symptomatic members. Plasma from both parents and one child have IgG antibody against the S1 protein and virus-neutralizing activity detected. Using a systems serology approach, we demonstrate higher levels of SARS-CoV-2-specific antibody features of these family members compared to healthy controls. These data indicate that children can mount an immune response to SARS-CoV-2 without virological confirmation of infection, raising the possibility that immunity in children can prevent the establishment of SARS-CoV-2 infection. Relying on routine virological and serological testing may not identify exposed children, with implications for epidemiological and clinical studies across the life-span.
RNA was manually extracted from 140 μL of NP swabs and saliva, 280 μL of urine, and plasma and 140 μL of 20% (w/v) fecal suspension24 and then eluted in 50 to 60 μL sterile, molecular water (Life Technologies, Australia), using the QIAamp viral RNA kit (QIAgen GmbH, Hilden, Germany) according to the manufacturer’s instructions. A previously published RT-PCR protocol targeting the RdRp gene was used on an ABI 750025. The nucleotide sequences of primers and probe are shown in Supplementary Table 2. SARS-CoV-2 standard (Exact Diagnostic, US) was used as a positive control for the PCR. Respiratory panel testing was by Ausdiagnostic viral panel.
Plasma S1 and RBD ELISA
The ELISA method used to measure IgG, IgM, and IgA levels to SARS-COV-2 S1 and RBD protein was based on Amanat et al.26. Briefly, 96-well high-binding plates (Thermo Fisher Scientific) were coated with S1 or RBD (Sino Biological) diluted in PBS at 2 µg/mL and then incubated at 4 °C overnight. The following day, plates were washed with PBS containing 0.1% (v/v) Tween20 (PBS-T) and blocked with PBS containing 0.1% Tween and 10% (w/v) skim milk (PBS-TSM) for 1 h at room temperature (RT). Serial dilutions (3-fold) of plasma samples were prepared in PBS-TSM starting at 1:50. A positive control (convalescent sample) and negative control (pre-pandemic) sample were used in all assays. The blocking solution was removed and 100 µl of each serial dilution was added to the plates for 2 h at RT. The plates were then washed three times with 200 µl per well of PBS-T. Goat anti-human IgG- (1:10,000) or IgM- (1:5,000) horseradish peroxidase (HRP) conjugated secondary antibody (Southern Biotech) was prepared in PBS-TSM, and 50 µl of this secondary antibody was added to each well for 1 h. For IgA, 50 µL of biotinylated IgA (1:5000) was diluted in PBS-T and added to each well for 1 h, followed by the addition of Streptavidin-HRP to each well for 30 min. Plates were washed with PBS-T followed by distilled water and 50 µL of 3.3′, 5.5′-tetramethylbenzidine (TMB, Sera Care) substrate solution was added for 9 min. The reaction was stopped by the addition of 50 µL of 1 M phosphoric acid and optical densities measured using a microplate reader (Bio-Tek) at 450 nm (630 nm reference filter). Endpoint titers were calculated following background correction of the respective negative control reactivity in each assay.
Saliva S1 protein ELISA
Saliva pooled under the tongue was drooled into a 50 mL tube and stored at −80 °C until analyzed. Immuno MaxiSorp 96-well ELISA plates (Thermo Fisher Scientific) were coated overnight at 4 °C with 2 µg/mL recombinant SARS-CoV-2/2019-nCoV S1 protein (Sino Biologicals) diluted in PBS. Wells were blocked with 10% skim milk in PBST (PBS + 0.1% Tween 20) at room temperature for 1 h. Two-fold serial dilutions of saliva samples in PBST were transferred to the ELISA plates (in duplicate) and incubated at room temperature for 1 h. Saliva from an asymptomatic individual confirmed negative for SARS-CoV-2 by clinical testing was used as a negative control. Saliva from a convalescent individual recently infected with SARS-CoV-2 was used as a positive control. Antibody binding was detected with biotinylated anti-human IgA (1:5000; Sigma-Aldrich) and IgG (1:10,000; Assay Matrix) for 1 hour at room temperature, then Streptavidin-HRP (1:5000; Life technologies) in PBST for 45 min at room temperature. Color was developed with TMB solution (Sigma-Aldrich) and H2O2 with the reaction stopped using 2 M H2SO4. Absorbance at 450 nm was read on a microplate reader. Examples of titrations are shown in Supplementary Fig. 2. OD values with negative control saliva were subtracted from the test samples at each dilution, then endpoint titers calculated.
SARS-CoV-2 isolate CoV/Australia/VIC01/202027 passaged in Vero cells was stored at −80 °C.
Serial two-fold dilutions of heat-inactivated plasma were incubated with 100 TCID50 of SARS-CoV-2 for 1 h and residual virus infectivity was assessed in quadruplicate wells of Vero cells; viral cytopathic effect was read on day 5. The neutralizing antibody titer is calculated using the Reed/Muench method.
Healthy participants. Age-matched children undergoing elective tonsillectomy (age 5–9) were recruited at the Launceston General Hospital (Tasmania) and, apart from fulfilling the criteria for tonsillectomy, they were considered otherwise healthy, showing no signs of immune compromise. Healthy adult donors (age 36–48) were recruited via the University of Melbourne. All healthy donors were recruited prior to SARS-CoV-2 pandemic. Heparinised blood was centrifuged for 10 min at 300 g to collect plasma, which was frozen at −20 °C until required.
Coupling of carboxylated beads. A custom CoV multiplex assay was designed, with SARS-CoV-2 Spike 1 (Sino Biological), SARS-CoV-2 Spike 2, SARS-CoV Spike 1 (ACRO Biosystems, USA), and hCoV (229E, NL63, OC43) spikes (Sino Biologicals), as well as SARS-CoV-2 RBD (produced under HHSN272201400008C and obtained through BEI Resources, NIAID, NIH USA), SARS-CoV RBD (gift from Dale Godfrey) and both SARS-CoV-2 and HKU1 Trimeric Spikes (gift from Adam Wheatley). Tetanus toxoid (Sigma Aldrich) and influenza hemagglutinin (H1Cal2009; Sino Biological) were also added to the assay as positive controls. Antigens were covalently coupled to magnetic carboxylated beads (Bio Rad) using a two-step carbodiimide reaction and blocked with 0.1% BSA, before being resuspended and stored in PBS 0.05% sodium azide for use.
Luminex bead-based multiplex assay. The isotypes and subclasses of pathogen-specific antibodies present in collected plasma were assessed using the above multiplex assay. Briefly, 20 µl of working bead mixture (1000 beads per bead region) and 20 µl of diluted plasma (final dilution 1:100) were added per well and incubated overnight at 4 °C on a shaker. Pathogen-specific antibodies were detected using 14 different detectors. One-step detection was done using phycoerythrin (PE)-conjugated mouse anti-human pan-IgG, IgG1-4, IgA1-2 (Southern Biotech; 1.3 µg/ml, 25 µl/well), where detectors were added to the beads, washed then read by the MagPix. C1q protein (MP Biomedicals, USA) was first biotinylated (Thermo Fisher Scientific, USA), then tetramerized with Streptavidin R-PE (SAPE; Thermo Fisher Scientific) before dimers or tetrameric C1q-PE were being used in one-step detection. For the detection of FcγR-binding, two-step detection was done by first adding soluble recombinant FcγR dimers (higher affinity polymorphisms FcγRIIa-H131, lower affinity polymorphisms FcγRIIa-R131, FcγRIIb, higher affinity polymorphisms FcγRIIIa-V158, lower affinity polymorphisms FcγRIIIa-F158; 1.3 µg/ml, 25 µl/well; gift from Bruce Wines and Mark Hogarth) to the beads, washing, followed by the addition of SAPE. Likewise, for IgM, two-step detection was done using biotinylated mouse anti-human IgM (mAb MT22; MabTech; 1.3 µg/ml, 25 µl/well;), followed by SAPE. Assays were repeated in duplicate.
Data Pre-processing for Systems Serology Analysis. In the multivariate analysis, positive control antigens (Tetanus and H1Cal2009) were removed. All visit days were used for each individual. Data was right-shifted and then log-transformed (log10(x + 1)). Right shifting was performed on each feature (detector-antigen pair) that contained negative values individually, by adding the minimum value for that feature to all samples within that feature. For all multivariate analysis the data were mean-centered and variance scaled for each feature using the z-score function in Matlab.
Feature Selection. To determine the minimal set of features (signatures) needed to classify the various cohorts, a three-step process was used based on. First, the data were randomly sampled without replacement to generate 2000 subsets. All classes were resampled at the size of the smallest class for categorical outcomes, which corrected for any potential effects of class size imbalances during regularization. Elastic-Net regularization was then applied to each of the 2000 resampled subsets to select features most associated with cohort classifications. The Elastic-Net hyperparameter, alpha, was set to have equal weights between the L1 norm and L2 norm associated with the penalty function for the least absolute shrinkage and selection (LASSO) and ridge regression, respectively which allows for better analysis of collinear data, which may be eliminated in LASSO regression. The frequency at which each feature was selected across the 2000 iterations was used to determine the signatures by using a sequential step-forward algorithm that iteratively added a single feature into a PLSDA model starting with the feature that had the highest frequency of selection, to the lowest frequency of selection. Model prediction performance was assessed at each step and evaluated by 10-fold cross-validation classification error. The model with the lowest classification error within a 0.01 difference between the minimum classification error was selected as the minimum signature. If only one feature was selected, the next best set of features was chosen. If consecutive feature sets were all equivalent, either the smallest or the largest set of features was chosen based on interpretability
PLSDA. Partial Least Squares Discriminant Analysis (PLSDA), performed in Eigenvectors PLS toolbox in Matlab, was used in conjunction with Elastic-Net, described above, to identify and visualize signatures that distinguish cohorts. This supervised method assigns a loading to each feature within a given signature, and identifies the linear combination of loadings (a latent variable) that best separates the categorical groups. A feature with a high loading magnitude indicates greater importance for separating the groups from one another. Each sample is then scored and plotted using their individual response measurements expressed through the latent variables (LVs). The scores and loadings can then be cross-referenced to determine which features are loaded in association with which categorical groups (positively loaded features are higher in positively scoring groups etc). All models are created with 10 fold cross-validation, where iteratively 10% of the data is left out as the test set, and the rest is used to train the model. Model performance is measured through calibration error (average error in the training set) as well as cross-validation error (average error in the test set), with values near zero being best. All models were othronogonalized to enable clear visualization of results.
Hierarchical Clustering. Cohort classification clustering was visualized for the Healthy vs. Household Cohort and based on their feature selected signatures described above, using unsupervised average linkage hierarchical clustering of z-scored data. Euclidean distance was used as the distance metric.
Software. PLSDA models were completed using the Eigenvector PLS toolbox in Matlab. Hierarchical Clustering was completed using MATLAB 2017b (MathWorks, Natick, MA). PLSDA scores and loadings plots were plotted in Prism version 8.0.0.
Flow cytometry of PBMC and whole blood
Blood was collected in EDTA tubes from each participant at day 12, 37, and 88. Immediately following collection, 100 µl of whole blood was aliquoted for flow cytometry analysis. The remaining EDTA blood samples were processed into plasma and PBMC. For flow cytometry analysis of whole blood samples, whole blood was lysed with 1 mL of red cell lysis buffer for 10 min at room temperature. Cells were washed with 1 mL PBS and centrifuged at 350 × g for 5 min. Following two more washes, cells were resuspended in PBS for viability staining using near infra-red viability dye according to manufacturer’s instructions. For flow cytometry analysis of freshly isolated PBMC, cells were washed in 1 mL PBS prior to viability staining using BV510 viability dye according to manufacturer’s instructions. For both whole blood and PBMC samples, the viability dye reaction was stopped by the addition of FACS buffer (2% heat-inactivated FCS in 2 mM PBS EDTA) and cells were centrifuged at 350 × g for 5 minutes. Cells were then resuspended in human FC-block according to manufacturer’s instructions for 5 min at room temperature. The whole blood or PBMC antibody cocktails (Supplementary Table 1) made up at 2× concentration were added 1:1 with the cells and incubated for 30 min on ice. Following staining, cells were washed with 2 mL FACS buffer and centrifuged at 350 × g for 5 min. Cells were then resuspended in 2% PFA for a 20 min fixation on ice, washed, and resuspended in 150 µl FACS buffer for acquisition using the BD LSR X-20 Fortessa. For all flow cytometry experiments, compensation was performed at the time of sample acquisition using compensation beads. Supplementary Fig. 1 depicts the manual gating strategy for PBMC and whole blood samples.
Results were analyzed (manual gating and tSNE analysis) using FlowJo Version 10.6 software. The tSNE plots were generated from a concatenated file containing 300,000 events (20,000 randomly selected live single cells per patient per time point). Manually gated results are presented as proportion of live cells or as proportion of parent gate (for PBMC) or as proportion of leukocyes (for whole blood). Data were plotted in Prism version 8.0.0.
Plasma was diluted 1:2 and 1:4 for assessment of cytokines using the human soluble protein cytometric bead array flex sets (BD Biosciences) according to manufacturer’s instructions. Cytometric bead array data were acquired on a BD LSR II X-20 Fortessa and analyzed using the FCAP Array Software. The following 18 cytokines were quantified: IL-1α, IL-1β, IL-6, IFNα, TNFα, MIP-1α, MCP-1, IL-8, RANTES, IL-12p70, IL-10, IL-2, IL-5, IL-5, IL-9, IL-13, IFNγ, and IL-17A. All cytokines except for IL-8, MCP-1, and RANTES fell below the limit of detection of the assay at both dilutions and were excluded from future analysis. Results are reported in pg/mL and plotted using Prism version 8.0.0.
Human experimental work was conducted according to the Declaration of Helsinki principles and according to the Australian National Health and Medical Research Council Code of Practice. All donors or their legal guardians provided written informed consent. The study was approved by the Human Research Ethics Committee (HREC) of the University of Melbourne (Ethics ID #1443389.4, #2056761, #1647326, #2056689, #1955465) for healthy adults, Tasmanian Health and Medical HREC (H0017479) for healthy child donors.
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