Biology subjects

Van Deuren, V.

Publications and source records attributed to Van Deuren, V..

4 recordsLinked to original sources

bioRxiv2026

Longitudinal Profiling of CD4⁺ T Cell Responses Following de novo Yellow Fever Vaccination

The yellow fever 17D vaccine is one of the most successful live-attenuated viral vaccines, yet the cellular mechanisms underlying its long-term protection remain not fully understood. This study provides a longitudinal analysis of the human CD4+ T cell and IgG response following de novo yellow fever vaccination by focusing. Peripheral blood mononuclear cells (PBMCs) from 49 vaccinated individuals were stimulated with yellow fever (YF) and control peptide pools across four timepoints: pre-vaccination/baseline (D1), day 22 (D22), day 43 (D43), and one year (D365) post-vaccination. Activation-induced marker (AIM) assays confirmed robust activation of CD4+ T cells following yellow fever peptide stimulation, peaking at day 22 post-vaccination and subsequently declining. T-cell receptor (TCR{beta}) sequencing of AIM-sorted CD4+ T cells showed a transient increase in clonal diversity at D22, consistent with broad epitope targeting and early polyclonal expansion. This was followed by repertoire contraction, which could indicate the persistence of a limited set of dominant clonotypes responsible for immune memory formation. TCR repertoires remained largely private over time, indicating a mostly individualized immune response. Next, serological analyses revealed a robust and highly yellow fever virus (YFV)-specific IgG response. Antibody levels peaked within the D22-D43 window and remained elevated at one year post-vaccination. Cross-reactivity toward other flaviviruses was limited, suggesting an antigen-specific humoral response. Together, these findings characterize the longitudinal dynamics of the CD4+ T cell and IgG response following de novo yellow fever vaccination and provide insights into the mechanisms contributing to durable antiviral immunity.

immunology↗

bioRxiv2025

NanoBlocks: creating fluorescent biosensors from affinity binders using competitive binding

Genetically-encoded fluorescent biosensors have revolutionized our understanding of complex systems by permitting the in situ observation of chemical activities. However, only a comparatively small set of chemical activities can be monitored, largely due to the need to identify protein domains that undergo conformational and/or association changes in response to a stimulus. Here, we present a strategy that can convert simple affinity binders such as nanobodies into biosensors for their innate targets by introducing a peptide sequence that competes for the binding site. We demonstrate proof-of-concept implementations of this NanoBlock design, developing sensors based on the ALFA nanobody and on the PDZ domain of Erbin. We show that these sensors can reliably detect their targets in vitro, in mammalian cells, and as part of fluorescence-activated cell sorting (FACS) experiments. In doing so, our strategy offers a way to strongly expand the range of cellular processes that can be probed using fluorescent biosensors.

biochemistry↗

bioRxiv2024

Bridging immunotypes and enterotypes using a systems immunology approach

Unveiling the systemic effects of disease and health requires an holistic approach that has mainly revolved around well established, directly determinable molecular relationships such as the protein synthesis cascade and epigenetic mechanisms. In this study, involving 394 individuals, we found direct linkage of branches spanning human biological functions often not studied in conjunction, using clinical data, gut microbial abundances, blood immune cell repertoires, blood transcriptomic and blood T cell receptor data. Contrary to current paradigms, we demonstrate that immunotypes and enterotypes are orthogonal, likely fulfilling distinct roles in maintaining homeostasis, only bridged via the blood transcriptome. We also identified two distinct inflammatory profiles: the first driven by interferon signalling and the other characterised by non-viral, NF-kB and IL-6 markers. Lastly, we present compelling data showing strong associations of the Ruminococcaceae and Christensenellaceae bacteria with a healthy immunotype and transcriptomic pattern, highlighting their potential role in immune health. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=194 HEIGHT=200 SRC="FIGDIR/small/625344v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@c6b5eorg.highwire.dtl.DTLVardef@15d0c3forg.highwire.dtl.DTLVardef@1cf29eeorg.highwire.dtl.DTLVardef@1c9ff8d_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2024

Revealing the hidden sequence distribution of epitope-specific TCR repertoires and its influence on machine learning model performance

Numerous efforts have been made to decipher the epitope-T cell receptor (TCR) recognition code. Both simple machine learning techniques and deep learning strategies have been used to train models to predict the binding of epitopes by TCR sequences. A good training data set rests at the basis of every accurate prediction model, yet little attention has been given to the composition of these data sets. In this paper, we studied the natural distribution of TCR sequences within epitope-specific TCR repertoires, i.e. a set of TCRs binding the same epitope, and its impact on the predictability of TCR-epitope interactions. We found that the observed diversity of these repertoires can result from a smaller set of core binding motifs constrained by TCR generation. Moreover, a clear relationship was found between the sequence distribution of the training data and performance metrics, emphasizing the importance of the used ground-truth data when using machine learning models in this domain. Taken together, these findings inform data set composition to help push epitope-TCR prediction models to the next level.

bioinformatics↗