Biology subjects

van Dorp, C. H.

Publications and source records attributed to van Dorp, C. H..

6 recordsLinked to original sources

bioRxiv2026

Beyond the blood: Tissue-resident immunity shapes SARS-CoV-2 vaccine antibody responses

Infections and vaccinations elicit coordinated humoral and cellular adaptive immune responses that together provide protection. In addition to antibodies, pathogen-specific memory T and Bcells persist in blood and tissues, but it remains unclear how their composition and spatial distribution relate to serum antibody titers, the most common correlate of vaccine-induced protection. Understanding these relationships is essential for predicting vaccine efficacy and optimizing immunization strategies. We analyzed tissues from 58 adult human organ donors vaccinated against SARS-CoV-2, including individuals with and without prior infection. Using multivariate imputation, dimensionality reduction, and correlation, regression, and causal analyses, we identified immune signatures linking memory B cell, CD4 T cell, and CD8 T cell subsets in spleen, lung, and lung-draining lymph nodes with antibody titers and neutralizing activity. Our analyses indicate that humoral immunity is driven primarily by virus-specific B cellsand CD4 T cells in lymphoid tissues rather than blood, whereas tissue-localized CD8 T cell responses, although correlated with antibody levels, develop independently. These findings demonstrate that cross-sectional immune profiling across multiple tissues recapitulates established immunological principles and reveal that serum antibody responses emerge from coordinated cellular immune responses distributed throughout the body.

immunology↗

bioRxiv2024

A variational deep-learning approach to modeling memory T cell dynamics

Mechanistic models of dynamic, interacting cell populations have yielded many insights into the growth and resolution of immune responses. Historically these models have described the behavior of pre-defined cell types based on small numbers of phenotypic markers. The ubiquity of deep pheno-typing therefore presents a new challenge; how do we confront tractable and interpretable mathematical models with high-dimensional data? To tackle this problem, we studied the development and persistence of lung-resident memory CD4 and CD8 T cells (TRM) in mice infected with influenza virus. We developed an approach in which dynamical model parameters and the population structure are inferred simultaneously. This method uses deep learning and stochastic variational inference and is trained on the single-cell flow-cytometry data directly, rather than on the kinetics of pre-identified clusters. We show that during the resolution phase of the immune response, memory CD4 and CD8 T cells within the lung are phenotypically diverse, with subsets exhibiting highly distinct and time-dependent dynamics. TRM heterogeneity is maintained long-term by ongoing differentiation of relatively persistent Bcl-2hi CD4 and CD8 TRM subsets which resolve into distinct functional populations. Our approach yields new insights into the dynamics of tissue-localized immune memory, and is a novel basis for interpreting time series of high-dimensional data, broadly applicable to diverse biological systems.

immunology↗

bioRxiv2020

Models of SIV rebound after treatment interruption that involve multiple reactivation events

In order to assess the efficacy of novel HIV-1 treatments leading to a functional cure, the time to viral rebound is frequently used as a surrogate endpoint. The longer the time to viral rebound, the more efficacious the therapy. In support of such an approach, mathematical models serve as a connection between the size of the latent reservoir and the time to HIV-1 rebound after treatment interruption. The simplest of such models assumes that a single successful latent cell reactivation event leads to observable viremia after a period of exponential viral growth. Here we consider a generalization developed by Pinkevych et al. and Hill et al. of this simple model in which multiple reactivation events can occur, each contributing to the exponential growth of the viral load. We formalize and improve the previous derivation of the dynamics predicted by this model, and use the model to estimate relevant biological parameters from SIV rebound data. We confirm a previously described effect of very early antiretroviral therapy (ART) initiation on the rate of recrudescence and the viral load growth rate after treatment interruption. We find that every day ART initiation is delayed results in a 39% increase in the recrudescence rate, and a 11% decrease of the viral growth rate. We show that when viral rebound occurs early relative to the viral load doubling time, a model with multiple successful reactivation events fits the data better than a model with only a single successful reactivation event. Author SummaryHIV-1 persists during suppressive antiretroviral therapy (ART) due to a reservoir of latently infected cells. When ART is stopped, HIV generally rebounds within a few weeks. However, there is a small fraction of patients that do not rebound over a period of months or years. A variety of treatments are being tested for their ability to reduce the size of the latent reservoir, to induce effective immune responses against the virus, or to prevent or prolong the time to viral rebound after ART interruption. These novel treatments are typically first tested in SIV infected macaques, and the efficacy of the treatment assessed by interrupting ART and measuring the time to viral rebound. Here, we develop and test a mathematical and statistical model that describes the process of viral rebound. The model can be used for statistical inference of the efficacy of newly developed treatments. Importantly, the model takes into account that multiple recrudescence events can precede rebound. We test the model using data from early treated SIV infected macaques.

immunology↗

bioRxiv2020

Modeling the immunological pre-adaptation of HIV-1

It is becoming increasingly evident that the evolution of HIV-1 is to a large extent determined by the immunological background of the host. On the population-level this results in associations between specific human leukocyte antigen (HLA) alleles and polymorphic loci of the virus. Furthermore, some HLA alleles that were previously associated with slow progression to AIDS have been shown to lose their protective effect, because HLA-specific immunological escape variants have spread through the population. This phenomenon is known as immunological pre-adaptation. Apart from adapting to human immune responses, the set-point virus load (SPVL) of HIV-1 is thought to have evolved to values that optimize the population-level fitness of the virus. This suggestion is supported by considerable heritability of the SPVL. Previous modeling studies show that whether or not SPVL optimization is expected to occur depends sensitively on the underlying assumptions with respect to the extent of within-versus between-host selection. Here we use a detailed and semi-realistic multi-level HIV-1 model in which immunological pre-adaptation and SPVL evolution can emerge from the underlying interactions of the virus with the immune system of the host. This enables us to study the effect of immunological escape on disease progression, and how disease progression may be molded by SPVL evolution. We find that the time to AIDS could decrease significantly (0.5-1.0 years) in a HLA-dependent manner by immunological pre-adaptation over the long-term course of the epidemic (> 100 years). We find that SPVL is not expected to evolve to optimize the population-level fitness of HIV-1, even though high heritability of the SPVL emerges from continual selection of immune-escape mutations.

evolutionary biology↗

bioRxiv2018

Estimation of age-specific susceptibility to influenza in the Netherlands and its relation to loss of CD8+ T-cell memory

The magnitude of influenza epidemics is largely determined by the number of susceptible individuals at the start of the influenza season. Susceptibility, in turn, is influenced by antigenic drift. The evolution of influenzas B-cell epitopes has been charted thoroughly, and only recently evidence for T-cell driven evolution is accumulating. We investigate the relation between susceptibility to influenza, and antigenic drift at CD8+ T-cell epitopes over a 45-year timespan. We estimate age-specific susceptibility with data reported by general practitioners, using a disease-transmission model in a Bayesian framework. We find large variation in susceptibility, both between seasons and age classes. Although it is often assumed that antigenic drift drives the variation in susceptibility, we do not find evidence for a relation between drift and susceptibility in our data. This suggests that other factors determining the variation in susceptibility play a dominating role, or that complex influenza-infection histories obscure any direct effects.\n\nPreface to this bioR{chi}iv pre-printWe are currently in the process of making this manuscript ready for re-submission, and are resolving some issues brought forward by our referees. Most importantly, we aim to better incorporate the co-circulation of the various influenza A and B subtypes during the different seasons, both in the estimation of susceptibility and antigenic drift.

epidemiology↗

bioRxiv2017

Infectious Reactivation of Cytomegalovirus Explaining Age-and Sex-Specific Patterns of Seroprevalence

Human cytomegalovirus is a herpes virus with poorly understood transmission dynamics. We here provide quantitative estimates of the transmissibility of primary infection, reactivation, and re-infection using age-and sex-specific antibody response data. The data are optimally described by three distributions of antibody measurements, i.e. uninfected, infected, and infected after reactivation/re-infection. Estimates of seroprevalence increase gradually with age, such that at 80 years 73% (95%CrI: 64%-78%) of females and 62% (95%CrI: 55%-68%) of males is infected, while 57% (95%CrI: 47%-67%) of females and 37% (95%CrI: 28%-46%) of males has experienced a reactivation or re-infection episode. Merging the statistical analyses with transmission models, we find that infectious reactivation is key to provide a good fit fit to the data. Estimated reactivation rates increase from low values in children to 2%-6% per year older women. The results advance a hypothesis in which adult-to-adult transmission after infectious reactivation is the main driver of infection.

epidemiology↗