Prediction of post-PCV13 pneumococcal evolution using invasive disease data enhanced by inverse-invasiveness weighting

Abstract

After introducing pneumococcal conjugate vaccines (PCVs), serotype replacement occurred in Streptococcus pneumoniae. Predicting which pneumococcal strains will become common in carriage after vaccination can enhance vaccine design, public health interventions, and understanding of pneumococcal evolution. Invasive pneumococcal isolates were collected during 1998-2018 by the Active Bacterial Core surveillance (ABCs). Carriage data from Massachusetts (MA) and Southwest United States were used to calculate weights. Using pre-vaccine data, serotype-specific inverse-invasiveness weights were defined as the ratio of the proportion of the serotype in carriage to the proportion in invasive data. Genomic data were processed under bioinformatic pipelines to define genetically similar sequence clusters (i.e., strains), and accessory genes (COGs) present in 5-95% of isolates. Weights were applied to adjust observed strain proportions and COG frequencies. The negative frequency-dependent selection (NFDS) model predicted strain proportions by calculating the post-vaccine strain composition in the weighted invasive disease population that would best match pre-vaccine COG frequencies. Inverse-invasiveness weighting increased the correlation of COG frequencies between invasive and carriage data in linear or logit scale for pre-vaccine, post-PCV7, and post-PCV13; and between different epochs in the invasive data. Weighting the invasive data significantly improved the NFDS model's accuracy in predicting strain proportions in the carriage population in the post-PCV13 epoch, with the adjusted R² increasing from 0.254 before weighting to 0.545 after weighting. The weighting system adjusted invasive disease data to better represent the pneumococcal carriage population, allowing the NFDS mechanism to predict strain proportions in carriage in the post-PCV13 epoch. Our methods enrich the value of genomic sequences from invasive disease surveillance.IMPORTANCEStreptococcus pneumoniae, a common colonizer in the human nasopharynx, can cause invasive diseases including pneumonia, bacteremia, and meningitis mostly in children under 5 years or older adults. The PCV7 was introduced in 2000 in the United States within the pediatric population to prevent disease and reduce deaths, followed by PCV13 in 2010, PCV15 in 2022, and PCV20 in 2023. After the removal of vaccine serotypes, the prevalence of carriage remained stable as the vacated pediatric ecological niche was filled with certain non-vaccine serotypes. Predicting which pneumococcal clones, and which serotypes, will be most successful in colonization after vaccination can enhance vaccine design and public health interventions, while also improving our understanding of pneumococcal evolution. While carriage data, which are collected from the pneumococcal population that is competing to colonize and transmit, are most directly relevant to evolutionary studies, invasive disease data are often more plentiful. Previously, evolutionary models based on negative frequency-dependent selection (NFDS) on the accessory genome were shown to predict which non-vaccine strains and serotypes were most successful in colonization following the introduction of PCV7. Here, we show that an inverse-invasiveness weighting system applied to invasive disease surveillance data allows the NFDS model to predict strain proportions in the projected carriage population in the post-PCV13/pre-PCV15 and pre-PCV20 epoch. The significance of our research lies in using a sample of invasive disease surveillance data to extend the use of NFDS as an evolutionary mechanism to predict post-PCV13 population dynamics. This has shown that we can correct for biased sampling that arises from differences in virulence and can enrich the value of genomic data from disease surveillance and advance our understanding of how NFDS impacts carriage population dynamics after both PCV7 and PCV13 vaccination.

Keywords: Streptococcus pneumoniae; carriage population; invasive disease surveillance data; inverse-invasiveness weighting; negative frequency-dependent selection.

Fig 1

Weight value for each serotype. The weight was developed from the serotype distribution in the pre-vaccine epoch in the United States (CDC ABCs IPD data from 1998 to 2000 and carriage data collected in Massachusetts and Southwest United States from 1998 to 2001). There are 65 different serotypes, including the not-assigned (NA) group. (A) Linear scale. The orange bars represent PCV7-covered serotypes, and the blue bars represent the additional six serotypes in PCV13. The red dotted line indicates the weight value is equal to 1, and the blue dotted line indicates the weight value is equal to 15. (B) Log scale. These vaccine-covered serotypes generally had weight values less than or close to 1. Many non-vaccine types (NVTs) had weight values greater than 1. PCV7, 7-valent pneumococcal conjugate vaccine; PCV13, 13-valent pneumococcal conjugate vaccine; NVT, non-vaccine types; NA, serotype not assigned.

Fig 2

Correlations of accessory gene frequencies between the carriage data and the IPD data before and after weights. The upper panels (A–C) represent the correlations between the carriage data and the IPD data before the application of weights for pre-vaccine (A), post-PCV7 (B), and post-PCV13 (C). The lower panels (D–F) show the correlations for each epoch after the application of weights. We observed both visually tighter scatterplots and improved correlations after weights. COG, accessory clusters of orthologous genes; PCV7, 7-valent pneumococcal conjugate vaccine; PCV13, 13-valent pneumococcal conjugate vaccine; R, correlation coefficient.

Fig 3

Correlations of accessory gene frequencies between different epochs in the IPD data before and after application of weights. (A–C) show the correlations before weights in the IPD data for pre-vaccine versus post-PCV7 (A), pre-vaccine versus post-PCV13 (B), and post-PCV7 versus post-PCV13 (C). (D–F) show the correlations after weights in the IPD data for different epochs. We observed that the weights improved the correlations in the IPD data with both visually tighter scatterplots and improved correlation coefficients. COG, accessory clusters of orthologous genes; PCV7, 7-valent pneumococcal conjugate vaccine; PCV13, 13-valent pneumococcal conjugate vaccine; R, correlation coefficient.

Fig 4

Strain proportion before and after application of weights. (A) is the strain proportion before the application of weights, and (B) is after. The name of the strain indicated the original GPSC called by PopPUNK. Sixty-three strains with >5 isolates were shown. The weights generally reduced the vaccine types in the strain and increased non-vaccine types. The mixed strains could be increased or decreased depending on the proportion of vaccine/non-vaccine serotypes in a mixed strain. The pre-vaccine and post-vaccine indicated pre-PCV13 and post-PCV13, respectively.

Fig 5

The NFDS model predicted strain proportions before and after applying weights. Strains (SCs) were GPSC clusters with >5 isolates. There was a total of 63 SCs with GPSC >5 isolates, among which 53 strains contained at least one NVT isolate pre-vaccine or were imputed with one isolate pre-vaccine. The scatterplot of observed versus predicted proportions of 53 strains at post-PCV13 equilibrium is based on quadratic programming. These perfect predictions fall on the dotted line of equality (1:1 line). The shaded gray region shows the confidence interval from the linear regression model used to test for deviation of the observed versus predicted values compared with the 1:1 line. (A) is the post-PCV13 E3 strain proportion prediction based on the IPD data with 2009 as E1 and 2017–2018 as E3 before applying weights, and (B) is after applying weights. Outliers are defined as the difference between their predicted and observed proportion that is >1.5 times the interquartile range of the distribution of predicted and observed proportion differences. There were no outliers in the pre-weighting prediction. The SC labels in (A) showed where the outliers from post-weighting prediction were located in the pre-weighting prediction plot. In the prediction after applying weights, the outliers with higher observed proportion than the predicted are SC-7 and SC-12. The SC-7 contained 38.8% of serotype 23A and 60.1% of 23B, and the SC-12 contained 93.9% of serotype 15A. The outliers with higher predicted proportion than the observed included SC-4 (60.2% of 15BC and 37.7% of 19A), SC-15 (84.1% of 6C), and SC-78 (94.8% of 6C). Adj. R2, adjusted R-squared; Mixed, strain containing both vaccine type and non-vaccine type; NFDS, negative frequency-dependent selection; NVT, non-vaccine type; PCV13, 13-valent pneumococcal conjugate vaccine; RMSE, root mean square error; SC, sequence cluster or strain; SSE, sum of squares due to error.