Antigenic distance between primary and secondary dengue infections correlates with disease risk

Abstract

Many pathogens continuously change their protein structure in response to immune-driven selection, resulting in weakened protection even in previously exposed individuals. In addition, for some pathogens, such as dengue virus, poorly targeted immunity is associated with increased risk of severe disease through a mechanism known as antibody-dependent enhancement. However, it remains unclear whether the antigenic distances between an individual's first infection and subsequent exposures dictate disease risk, explaining the observed large-scale differences in dengue hospitalizations across years. Here, we develop a framework that combines detailed antigenic and genetic characterization of viruses with details on hospitalized cases from 21 years of dengue surveillance in Bangkok, Thailand, to identify the role of the antigenic profile of circulating viruses in determining disease risk. We found that the risk of hospitalization depended on both the specific order of infecting serotypes and the antigenic distance between an individual's primary and secondary infections, with risk maximized at intermediate antigenic distances. These findings suggest that immune imprinting helps determine dengue disease risk and provide a pathway to monitor the changing risk profile of populations and to quantifying risk profiles of candidate vaccines.

Figure 1.. Long-term hospital-based dengue case data along with antigenic and genetic characterization of dengue viruses in Bangkok, Thailand.

(A) Shown is the monthly number of secondary dengue cases requiring hospitalization in the Queen Sirikit National Institute of Child Health in Bangkok, Thailand, from 1997 to 2014. Infecting age and year were known for each case, with 69.7% of cases diagnosed with the infecting serotype. The inset illustrates the aggregation of the original case linelist data into the serotype and age-specific case counts per year. Table S7 summarizes the age groups analyzed for each year. ND, not determined. (B) Shown is a two-dimensional antigenic map of 2,594 DENV viruses across four serotypes isolated in Thailand, colored by year of isolation. Sequenced DENV viruses were placed onto the original antigenic map as shown in (13) to provide a representative description of the changing antigenic profile of the DENV population. Each colored circle indicates one of the 348 dengue viruses antigenically characterized using a PRNT assay. The size of each circle indicates the number of sequenced viruses placed onto the corresponding map location. Serotype clusters are labelled. Each grid square side in any direction represents one unit of antigenic distance, which is equivalent to a two-fold dilution of antiserum in the PRNT assay. (C) Shown are time-calibrated maximum clade credibility phylogenies built with sequenced viruses from each serotype. Colored circles at the tips of each phylogeny indicate viruses selected for antigenic characterization. In the inset, colored bars indicate the distribution of the amino acid (AA) differences across the whole genome of each sequenced virus as compared to its genetically closest virus used for antigenic characterization, and the gray curve indicates the same distribution but compares each sequenced virus to an antigenically characterized virus randomly selected from those of the same serotype. (D) Shown are distributions of antigenic distances separating viruses that are possibly responsible for an individual’s primary and secondary infections, which is stratified by the serotypes of the possible primary and secondary infecting viruses (serotype pair) and colored by the identity of the secondary serotype. Vertical line in each distribution indicates the corresponding mean antigenic distance. Fig. S2, A and B, present the same distributions but use antigenic distance data derived from the original 3D antigenic map and the 3D antigenic map enhanced with sequenced viruses, respectively.

Figure 2.. Probability of disease following a secondary infection depends on the serotype pair and antigenic distance.

(A) Shown is the probability of disease determined by serotype pair. Primary DENV-1 followed by secondary DENV-3 serves as the reference for comparison. In each boxplot, the central horizontal line, edges of box, and whiskers indicate the median, interquartile range (IQR), and 1.5 * IQR of the posterior distribution, respectively. Color indicates the identity of the secondary serotype. (B) Shown is the relationship between the relative probability of disease and the antigenic distance between the two viruses that are possibly responsible for an individual’s primary and secondary infections. Color indicates the density of the data points in the posterior estimates, using all antigenic distance data for 13,170,100 pairwise potential primary and secondary infecting viruses. Darker color indicates higher density.

Figure 3.. Transmission intensity by serotype and year and model fit.

(A) Annual serotype-specific force of infection, estimated using the full model. Dots and error bars indicate the median and 95% credible interval (CrI) of the posterior estimates, colored by the infecting serotype. (B) Reconstruction of the yearly hospitalized secondary dengue cases by serotype and age. Results for 2008 and 2013, two years with distinct serotype patterns, are shown to illustrate the adequacy of model estimations. Fig. S7 provides results for each year from 1997 to 2014. Vertical bars show the serotype and age-specific counts of secondary cases observed in our surveillance hospital. Dots and error bars indicate the median and 95% CrI of the corresponding case counts estimated by simulating the infection histories of individuals using posteriors inferred from the full model (Materials and Methods). The observed and estimated case counts are colored by the identity of the secondary serotype. D1 to D4 indicate the secondary cases of each serotype. ND indicates cases without serotype information.

Figure 4.. Population disease risk evolves by year and age.

(A) Shown is the relative disease risk by year and age with respect to the overall average of disease risks across all years and ages in the study. The estimation averages over 2,000 stochastic simulation realizations of the infection histories of individuals in Bangkok, using 40 randomly selected posteriors inferred from the full model. In each year from 1997 to 2007, older age groups without antigenic distance data were excluded from the analysis (table S7). Color in the heat map corresponds to the disease risk, which is estimated by adjusting the marginal probability of hospitalization with the marginal probability of secondary infection for each cohort of individuals based on their year and age of secondary infections (Materials and Methods). (B) Shown is the mean relative disease risk for individuals of each age across years from 2008 to 2014. (C) Shown is the mean relative disease risk for individuals acquiring secondary infection in each year, which averages the estimated relative disease risk over different ages in the same year. In (B) and (C), colors indicate the estimates using different models, with the line and shaded regions indicating the respective mean and 95% confidence interval. CV, coefficient of variation.