Biology subjects

Bogaert, D.

Publications and source records attributed to Bogaert, D..

4 recordsLinked to original sources

bioRxiv2025

Integrative Mapping of Pre-existing Immune Landscapes for Vaccine Response Prediction

Predicting individual vaccine responses remains a significant challenge due to the complexity and variability of immune processes. To address this gap, we developed immunaut, an open-source, data-driven framework implemented as an R package specifically designed for all systems vaccinologists seeking to analyze and predict immunological outcomes across diverse vaccination settings. Leveraging one of the most comprehensive live attenuated influenza vaccine (LAIV) datasets to date - 244 Gambian children enrolled in a phase 4 immunogenicity study - immunaut integrates humoral, mucosal, cellular, transcriptomic, and microbiological parameters collected before and after vaccination, providing an unprecedentedly holistic view of LAIV-induced immunity. Through advanced dimensionality reduction, clustering, and predictive modeling, immunaut identifies distinct immunophenotypic responder profiles and their underlying baseline determinants. In this study, immunaut delineated three immunophenotypes: (1) CD8 T-cell responders, marked by strong baseline mucosal immunity and extensive prior influenza virus exposure that boosts memory CD8 T-cell responses, without generating influenza virus-specific antibody responses; (2) Mucosal responders, characterized by pre-existing systemic influenza A virus immunity (specifically to H3N2) and stable epithelial integrity, leading to potent mucosal IgA expansions and subsequent seroconversion to influenza B virus; and (3) Systemic, broad influenza A virus responders, who start with relatively naive immunity and leverage greater initial viral replication to drive broad systemic antibody responses against multiple influenza A virus variants beyond those included in the LAIV vaccine. By integrating pathway-level analysis, model-derived contribution scores, and hierarchical decision rules, immunaut elucidates how distinct immunological landscapes shape each response trajectory and how key baseline features, including pre-existing immunity, mucosal preparedness, and cellular support, dictate vaccine outcomes. Collectively, these findings emphasize the power of integrative, predictive frameworks to advance precision vaccinology, and highlight immunaut as a versatile, community-available resource for optimizing immunization strategies across diverse populations and vaccine platforms. One-Sentence SummaryUsing one of the most comprehensive LAIV datasets compiled to date, immunaut, an integrative machine learning framework, identifies distinct immunophenotypic responder groups shaped by baseline immune landscapes, advancing precision vaccinology and guiding more effective, personalized immunization strategies. Graphical abstractImmunaut, an automated framework for mapping and predicting vaccine response immunotypes. Step 1 outlines the identification of vaccine response outcomes using pre- and post-vaccination data integration across immune features, including antibodies, flu-specific T-cells, and immunophenotyping at mucosal and systemic sites. Clustering methods define the vaccine response landscape, stability, and validation through t-SNE-based visualization. Step 2 leverages an automated machine learning modeling approach, to enhance the accuracy and interpretability of vaccine response predictions, enabling stratification and targeted intervention strategies for personalized vaccine immunogenicity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/634302v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@62276corg.highwire.dtl.DTLVardef@a5f25org.highwire.dtl.DTLVardef@eca34borg.highwire.dtl.DTLVardef@1d4999b_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

General factors of white matter microstructure from DTI and NODDI in the developing brain

Preterm birth is closely associated with diffuse white matter dysmaturation inferred from diffusion MRI and neurocognitive impairment in childhood. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are distinct dMRI modalities, yet metrics derived from these two methods share variance across tracts. This raises the hypothesis that dimensionality reduction approaches may provide efficient whole-brain estimates of white matter microstructure that capture (dys)maturational processes. To investigate the optimal model for accurate classification of generalised white matter dysmaturation in preterm infants we assessed variation in DTI and NODDI metrics across 16 major white matter tracts using principal component analysis and structural equation modelling, in 79 term and 141 preterm infants at term equivalent age. We used logistic regression models to evaluate performances of single-metric and multimodality general factor frameworks for efficient classification of preterm infants based on variation in white matter microstructure. Single-metric general factors from DTI and NODDI capture substantial shared variance (41.8-72.5%) across 16 white matter tracts, and two multimodality factors captured 93.9% of variance shared between DTI and NODDI metrics themselves. General factors associate with preterm birth and a single model that includes all seven DTI and NODDI metrics provides the most accurate prediction of microstructural variations associated with preterm birth. This suggests that despite global covariance of dMRI metrics in neonates, each metric represents information about specific (and additive) aspects of the underlying microstructure that differ in preterm compared to term subjects. HighlightsO_LIWe measured variation of 7 DTI and NODDI metrics across 16 major tracts C_LIO_LIGeneral factors for DTI and NODDI capture substantial shared variance across tracts C_LIO_LIGeneral factors also capture substantial shared variance between DTI and NODDI C_LIO_LISingle-metric and multimodality factors associate with gestational age at birth C_LIO_LIThe best preterm prediction model contains all 7 single-metric g-factors C_LI

neuroscience↗

bioRxiv2018

Inflammation induced by influenza virus impairs innate control of human pneumococcal carriage

Secondary bacterial pneumonia following influenza infection is a significant cause of mortality worldwide. Upper respiratory tract pneumococcal carriage is important as both determinants of disease and population transmission. The immunological mechanisms that contain pneumococcal carriage are well-studied in mice but remain unclear in humans. Loss of this control of carriage following influenza infection is associated with secondary bacterial pneumonia during seasonal and pandemic outbreaks. We used a human type 6B pneumococcal challenge model to show that carriage acquisition induces early degranulation of resident neutrophils and recruitment of monocytes to the nose. Monocyte function associated with clearance of pneumococcal carriage. Prior nasal infection with live attenuated influenza virus induced inflammation, impaired innate function and altered genome-wide nasal gene responses to pneumococcal carriage. Levels of the cytokine IP-10 promoted by viral infection at the time of pneumococcal encounter was positively associated with bacterial density. These findings provide novel insights in nasal immunity to pneumococcus and viral-bacterial interactions during co-infection.

immunology↗

bioRxiv2018

Effect of Live Attenuated Influenza Vaccine on Pneumococcal Carriage

The widely used nasally-administered Live Attenuated Influenza Vaccine (LAIV) alters the dynamics of naturally occurring nasopharyngeal carriage of Streptococcus pneumoniae in animal models. Using a human experimental model (serotype 6B) we tested two hypotheses: 1) LAIV increased the density of S. pneumoniae in those already colonised; 2) LAIV administration promoted colonisation. Randomised, blinded administration of LAIV or nasal placebo either preceded bacterial inoculation or followed it, separated by a 3-day interval. The presence and density of S. pneumoniae was determined from nasal washes by bacterial culture and PCR. Overall acquisition for bacterial carriage were not altered by prior LAIV administration vs. controls (25/55 [45.5%] vs 24/62 [38.7%] respectively, p=0.46). Transient increase in acquisition was detected in LAIV recipients at day 2 (33/55 [60.0%] vs 25/62 [40.3%] in controls, p=0.03). Bacterial carriage densities were increased approximately 10-fold by day 9 in the LAIV recipients (2.82 vs 1.81 log10 titers, p=0.03). When immunisation followed bacterial acquisition (n=163), LAIV did not change area under the bacterial density-time curve (AUC) at day 14 by conventional microbiology (primary endpoint), but significantly reduced AUC to day 27 by PCR (p=0.03). These studies suggest that LAIV may transiently increase nasopharyngeal density of S. pneumoniae. Transmission effects should therefore be considered in the timing design of vaccine schedules.\n\nTrial registrationThe study was registered on EudraCT (2014-004634-26)\n\nFundingThe study was funded by the Bill and Melinda Gates Foundation and the UK Medical Research Council.

immunology↗