Biology subjects

Bouman, J. A.

Publications and source records attributed to Bouman, J. A..

3 recordsLinked to original sources

bioRxiv2022

Per-Pathogen Virulence of HIV-1 subtypes A, C and D

HIV-1 subtypes differ, among other things, in their clinical manifestations and the speed in which they spread. In particular, the frequency of subtype C is increasing relative to subtype A and D. We aim to investigate whether HIV-1 subtype A, C and D differ in their per-pathogen virulence and to what extend this can explain the difference in spread between these subtypes. We use data from the Hormonal Contraception and HIV-1 Genital Shedding and Disease Progression among Women with Primary HIV Infection (GS) Study. For each study participant, we determine the set-point viral load value, CD4+ T cell level after primary infection and CD4+ T cell decline. Based on both the CD4+ T cell count after primary infection and CD4+ T cell decline, we estimate the time until AIDS for each individual. We then obtain our newly introduced measure of virulence as the inverse of the estimated time until AIDS. This new measure of virulence has an improved correlation with the set-point viral load compared to the decline of CD4+ T cells alone. After fitting a model to the measured virulence and set-point viral load values, we tested if this relation varies per subtype. We found that subtype C has a significantly higher per-pathogen virulence than subtype A. Based on an evolutionary model, we then hypothesize that differences in the primary length of infection period cause the observed variation in the speed of spread of the subtypes. Author summaryHIV-1 subtype C is currently spreading relatively fast in various parts of the world. Data from a study that followed many women infected with different HIV-1 subtypes (A, C and D) before they started treatment shows that neither their viral load nor the disease duration are increased for subtype C compared to subtype A and D. Thus, it seems that subtype C does not have a transmission advantage, neither per contact nor due to longer infection, making the observed relative rise in subtype C a puzzle. We used the same data to test if subtype C has optimized its potential to spread by decreasing the disease duration per unit of viral load (per-pathogen virulence) compared to subtype A and D. However, we find that subtype C has a significantly higher per-pathogen virulence than subtype A and D. This result makes the rise of subtype C even more counter-intuitive. In a last step, we develop an evolutionary model, in which we synthesize all our empirical results. With this model we can show that the most likely explanation for the global spread of subtype C is a difference in the duration of primary infection between the subtypes.

evolutionary biology↗

bioRxiv2021

Diversity of vaccination-induced immune responses can prevent the spread of vaccine escape mutants

Pathogens that are resistant against drug treatment are widely observed. In contrast, pathogens that escape the immune response elicited upon vaccination are rare. Previous studies showed that the prophylactic character of vaccines, the multiplicity of epitopes to which the immune system responds within a host, and their diversity between hosts delay the evolution and emergence of escape mutants in a vaccinated population. By extending previous mathematical models, we find that, depending on the cost of the escape mutations, there even exist critical levels of immune response diversity that completely prevent vaccine escape. Furthermore, to quantify the potential for vaccine escape below these critical levels, we propose a concept of escape depth which measures the fraction of escape mutants that can spread in a vaccinated population. Determining this escape depth for a vaccine could help to predict its sustainability in the face of pathogen evolution.

evolutionary biology↗

bioRxiv2020

Estimating seroprevalence with imperfect serological tests: a cutoff-free approach

Large-scale serological testing in the population is essential to determine the true extent of the current SARS-CoV-2 pandemic. Serological tests measure antibody responses against pathogens and use predefined cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives and use this as a proxy for past infection. With the imperfect assays that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of the cumulative incidence and is usually corrected to account for the sensitivity and specificity. Here we use an inference method -- referred to as mixture-model approach -- for the estimation of the cumulative incidence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that the mixture model outperforms the methods based on cutoffs, leading to less bias and error in estimates of the cumulative incidence. We illustrate how the mixture model can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the tests ambiguity sufficiently to enable the reliable estimation of the cumulative incidence. Lastly, we show how this approach can be used to estimate the cumulative incidence of classes of infections with an unknown distribution of quantitative test measures. This is a very promising application of the mixture-model approach that could identify the elusive fraction of asymptomatic SARS-CoV-2 infections. An R-package implementing the inference methods used in this paper is provided. Our study advocates using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods at exactly the low cumulative incidence levels and test accuracies that we are currently facing in SARS-CoV-2 serosurveys. Author SummaryAs other pathogens, SARS-CoV-2 elicits antibody responses in infected people that can be detected in their blood serum as early as a week after the infection until long after recovery. The presence of SARS-CoV-2 specific antibodies can therefore be used as a marker of past infection, and the prevalence of seropositive people, i.e. people with specific antibodies, is a key measure to determine the extent of the SARS-CoV-2 pandemic. The serological tests, however, are usually not perfect, yielding false positive and false negative results. Here we exploit an approach that refrains from classifying people as seropositive or negative, but rather compares the antibody level of an individual to that of confirmed cases and controls. This approach leads to more reliable estimates of cumulative incidence, especially for the low prevalence and low test accuracies that we face during the current SARS-CoV-2 pandemic. We also show how this approach can be extended to infer the presence of specific types of cases that have not been used for validating the test, such as people that underwent a mild or asymptomatic infection.

immunology↗