Biology subjects

van Dorp, C.

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

3 recordsLinked to original sources

bioRxiv2024

Tissue resident memory CD4+ T cells are sustained by site-specific levels of self-renewal and replacement from precursors

Tissue-resident memory T cells (TRM) protect from repeat infections within organs and barrier sites. The breadth and duration of such protection is defined at minimum by three quantities; the rate at which new TRM are generated from precursors, their rate of self-renewal, and their rate of loss through death, egress, or differentiation. Quanti-fying these processes individually is challenging. Here we combine genetic fate mapping tools and mathematical models to untangle these basic homeostatic properties of CD4+ TRM in the skin and gut lamina propria (LP) of healthy adult mice. We show that CD69+CD4+ TRM in skin reside for [~]24 days and self-renew more slowly, such that clones halve in size approximately every 5 weeks; and approximately 2% of cells are replaced daily from precursors. CD69+CD4+ TRM in LP have shorter residencies ([~]14 days) and are maintained largely by immigration (4-6% per day). We also find evidence that the continuous replacement of CD69+CD4+ TRM at both sites derives from circulating effector-memory CD4+ T cells, in skin possibly via a local CD69- intermediate. Our approach maps the ontogeny of CD4+ TRM in skin and LP and exposes their dynamic and distinct behaviours, with continuous seeding and erosion potentially impacting the duration of immunity at these sites.

immunology↗

bioRxiv2023

Cell age, not chronological age, governs the dynamics and longevity of circulating CD4+ memory T cells

Quantifying the kinetics with which memory T cell populations are generated and maintained is essential for identifying the determinants of the duration of immunity. The quality and persistence of circulating CD4+ effector memory (TEM) and central memory (TCM) T cells in mice appear to shift with age, but it is unclear whether these changes are driven by the aging host environment, by cell age effects, or both. Here we address these issues by combining DNA labelling methods, established fate-mapping systems, a novel reporter mouse strain, and mathematical models. Together, these allow us to quantify the dynamics of both young and established circulating memory CD4+ T cell subsets, within both young and old mice. We show that that these cells and their descendents become more persistent the longer they reside within the TCM and TEM pools. This behaviour may limit memory CD4 T cell diversity by skewing TCR repertoires towards clones generated early in life, but may also compensate for functional defects in new memory cells generated in old age. Author summaryOur long-term protection against infections depends in part on the maintenance of diverse populations of memory CD4 T cells, which are made in response to the initial exposure to the pathogen or a vaccine. These cells are not long-lived, but instead are maintained dynamically at a clonal level through loss and division. Understanding how immune memory persists therefore requires measuring these rates of these processes, and how they might change with age. Here we combine experiments in mice with mathematical models to show that memory CD4 T cells exhibit complex dynamics but increase their capacity to survive as they age. This dynamic implies that as individuals age, their memory CD4 T cell populations become enriched for older clones. This established memory may compensate for functional defects in new T cell responses generated later in life.

immunology↗

bioRxiv2018

Estimating HLA disease associations using similarity trees

The human leukocyte antigen (HLA) is associated with many (infectious) disease outcomes. These associations are perhaps best documented for HIV-1. For example, the HLA-B*58:01 allele is associated with control of the virus, while HLA-B*18:01 is considered detrimental. In HLA disease association studies, it is often ignored that certain HLA molecules are functionally very similar to others. For instance, HLA-B*18:03 differs \"only\" at 3 positions in its peptide binding site from HLA-B*18:01, and not surprisingly, HLA-B*18:03 is also associated with fast progression to AIDS. Here, we present a Bayesian method that takes functional HLA similarities into account to find HLA associations with quantitative traits such as HIV-1 viral load. The method is based on the so-called phylogenetic mixed model (a model for the evolution of a quantitative trait on the branches of a phylogeny), and can easily be modified to study a wide range of research questions, like the role of the heterozygote advantage, or KIR ligands on disease outcomes. We show that in the case of HIV-1, our model is significantly better at predicting set-point virus load than a model that ignores HLA similarities altogether. Furthermore, our method provides a comprehensible visualization of HLA associations. The software is available online at www.github.com/chvandorp/MHCshrubs

immunology↗