Biology subjects

Willemsen, L.

Publications and source records attributed to Willemsen, L..

6 recordsLinked to original sources

bioRxiv2026

An Open Benchmark for Systems Vaccinology: Insights from the CMI-PB Challenges

Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.

immunology↗

bioRxiv2026

A microbial seedbank from a natural oil seep accelerates hydrocarbon degradation in freshwater oil spills

Anthropogenic oil spills are a major source of hydrocarbon pollution, yet freshwater oil spills are studied less than marine once. We hypothesize that microorganisms from freshwater natural oil seeps are adapted to use oil as carbon source and could serve as a seedbank to accelerate degradation of anthropogenic oil spills. To test this, microcosms were set up using water and oil from a natural oil seep in Germany, while light oil and oil naive river water simulated an anthropogenic spill. Seven conditions combined different water types (adapted, naive) and oil types (heavy, light, mixed). Oil degradation was monitored over 81 days via reverse stable isotope labeling, and pH and microbial communities were analyzed by 16S rRNA gene amplicon sequencing at the start and end. Microcosms with adapted microbes exhibited higher oil mineralization rates. In mixed-oil incubations, simulating the application of a microbial seedbank, degradation rates were 0.26 mM/day with adapted microbes versus 0.15 mM/day in naive water. Applying seep-derived microbes to naive light-oil microcosms increased hydrocarbon mineralization by 32%. These results suggest that leveraging natural microbial seedbanks could accelerate oil weathering and reduce the environmental impact of freshwater oil spills, offering a promising strategy for spill mitigation if applied safely. ImportanceFreshwater oil spills remain poorly studied despite their substantial contribution to global hydrocarbon pollution. This study demonstrates that microbial communities from freshwater natural oil seeps are pre-adapted to degrade crude oil and can significantly enhance the breakdown of anthropogenic oil spills. Applying such microbial seedbanks increased hydrocarbon mineralization by over 30%, highlighting their potential as an effective and environmentally relevant tool for oil spill mitigation. These findings support the development of nature-based strategies to accelerate oil weathering and reduce the environmental impact of freshwater oil spills.

microbiology↗

bioRxiv2024

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

immunology↗

bioRxiv2024

Th1 polarization in Bordetella pertussis vaccine responses is maintained through a positive feedback loop

Outbreaks of Bordetella pertussis (BP), the causative agent of whooping cough, continue despite broad vaccination coverage and have been increasing since vaccination switched from whole-BP (wP) to acellular BP (aP) vaccines. wP vaccination has been associated with more durable protective immunity and an induced Th1 polarized memory T cell response. Here, a multi-omics approach was applied to profile the immune response of 30 wP and 31 aP-primed individuals and identify correlates of T cell polarization before and after Tdap booster vaccination. We found that transcriptional changes indicating an interferon response on day 1 post-booster along with elevated plasma concentrations of IFN-{gamma} and interferon-induced chemokines that peaked at day 1-3 post-booster correlated best with the Th1 polarization of the vaccine-induced memory T cell response on day 28. Our studies suggest that wP-primed individuals maintain their Th1 polarization through this early memory interferon response. This suggests that stimulating the interferon pathway during vaccination could be an effective strategy to elicit a predominant Th1 response in aP-primed individuals that protects better against infection.

immunology↗

bioRxiv2023

A systems vaccinology resource to develop and test computational models of immunity

Computational models that predict an individuals response to a vaccine offer the potential for mechanistic insights and personalized vaccination strategies. These models are increasingly derived from systems vaccinology studies that generate immune profiles from human cohorts pre- and post-vaccination. Most of these studies involve relatively small cohorts and profile the response to a single vaccine. The ability to assess the performance of the resulting models would be improved by comparing their performance on independent datasets, as has been done with great success in other areas of biology such as protein structure predictions. To transfer this approach to system vaccinology studies, we established a prototype platform that focuses on the evaluation of Computational Models of Immunity to Pertussis Booster vaccinations (CMI-PB). A community resource, CMI-PB generates experimental data for the explicit purpose of model evaluation, which is performed through a series of annual data releases and associated contests. We here report on our experience with the first such dry run for a contest where the goal was to predict individual immune responses based on pre-vaccination multi-omic profiles. Over 30 models adopted from the literature were tested, but only one was predictive, and was based on age alone. The performance of new models built using CMI-PB training data was much better, but varied significantly based on the choice of pre-vaccination features used and the model building strategy. This suggests that previously published models developed for other vaccines do not generalize well to Pertussis Booster vaccination. Overall, these results reinforced the need for comparative analysis across models and datasets that CMI-PB aims to achieve. We are seeking wider community engagement for our first public prediction contest, which will open in early 2024.

systems biology↗

bioRxiv2020

Anti-SARS-CoV-2 IgG from severely ill COVID-19 patients promotes macrophage hyper-inflammatory responses

For yet unknown reasons, severely ill COVID-19 patients often become critically ill around the time of activation of adaptive immunity. Here, we show that anti-Spike IgG from serum of severely ill COVID-19 patients induces a hyper-inflammatory response by human macrophages, which subsequently breaks pulmonary endothelial barrier integrity and induces microvascular thrombosis. The excessive inflammatory capacity of this anti-Spike IgG is related to glycosylation changes in the IgG Fc tail. Moreover, the hyper-inflammatory response induced by anti-Spike IgG can be specifically counteracted in vitro by use of the active component of fostamatinib, an FDA- and EMA-approved therapeutic small molecule inhibitor of Syk. One sentence summaryAnti-Spike IgG promotes hyper-inflammation.

immunology↗