Biology subjects

Lenaerts, A.

Publications and source records attributed to Lenaerts, A..

4 recordsLinked to original sources

bioRxiv2026

ATACofthesnake: A scalable framework for analyzing multifactorial and time course chromatin accessibility data

Advances in sample extraction techniques, improvement of library preparation methods and the dramatic reduction in sequencing costs have made complex, multifactorial experimental design the de facto standard in epigenomic studies. In contrast to the plethora of tools, workflows and libraries available to analyze gene expression data, consolidated workflows for chromatin accessibility data are lacking and analysis of such data often occurs on an ad hoc basis. Here, a scalable end-to-end workflow, ATACofthesnake, is presented that simplifies analysis of multifactorial chromatin accessibility data considerably and provides a newly created framework to handle time series data reproducibly.

bioinformatics↗

bioRxiv2026

Cognate T-cell labeling by conventional and unconventional antigen-presenting cells in vivo using a modular SrtA-based toolkit

While different antigen-presenting cell (APC) populations can drive divergent T-cell fates even when presenting identical antigens (Ags), our ability to trace these specific in vivo interaction histories remains constrained by a lack of compatible molecular tools. Here, we describe an enhanced Labeling Immune Partnerships by SorTagging Intercellular Contacts (LIPSTIC) platform utilizing the immunologically inert human CD58/CD2 receptor-ligand pair (SrtA-hCD58/G5-hCD2). This system provides a modular alternative to previous LIPSTIC animal models and enables the detection and recovery of T cells following Ag-dependent interactions with distinct APC populations in vitro and in vivo. We applied this approach to interrogate the role of type 3 innate lymphoid cells (ILC3s), an unconventional ROR{gamma}t Ag-presenting population whose role in shaping Ag-specific T cells in vivo remain poorly defined. Modified LIPSTIC successfully recorded cognate interactions between ILC3s and naive CD4 T cells in the spleen and enabled direct comparison with CD4+ T cells activated by conventional dendritic cells (cDCs). Although interactions with ILC3s resulted in lower frequency of in vivo-labeled T cells than cDC2s, CD4+ T cells showed comparable cell division and expression of activation markers irrespectively of an APC type. Together, these findings establish SrtA-hCD58/G5-hCD2 as a versatile LIPSTIC receptor-ligand pair for tracking cell-cell interactions in vivo and provide a robust tool for studying how APC identity influences Ag-specific T-cell responses.

immunology↗

bioRxiv2026

EBF1 regulates the blood-thymus barrier and ETP influx through Claudin-5 in thymic endothelial cells

The blood-thymus-barrier (BTB) is essential for regulating the entry of thymic-seeding progenitors into the thymus, an organ lacking self-renewing T cell progenitors. Here, we identify Early B-cell factor 1 (Ebf1), a transcription factor with established roles in hematopoiesis, as specifically expressed in thymic endothelial cells. Tie2-Cre-mediated deletion of Ebf1 results in a significant reduction of early thymic progenitors (ETPs), despite unaltered bone marrow lymphoid progenitor populations, unaltered thymic stromal subset frequencies, and intact downstream thymocyte differentiation. Transcriptome analysis of Ebf1-deficient thymic endothelial cells reveals increased expression of tight-junction-associated genes, in particular Cldn5, a key tight-junction component that restricts the BTB. Consistent with reduced BTB permeability, ETP subset analysis demonstrates a proportional reduction in the ETP1 fraction, the progenitor population that resides in closest proximity to thymic endothelial cells at the cortico-medullary junction. Together, our study implicates Ebf1 in the regulation of the BTB permeability, through modulation of tight-junction programs, thereby enabling thymus-seeding progenitor entry.

immunology↗

bioRxiv2021

Model-Based Meta-Analysis of Relapsing Mouse Model Studies from the Critical Path to Tuberculosis Drug Regimens Initiative Database

Tuberculosis (TB), the disease caused by Mycobacterium tuberculosis (Mtb), remains a leading infectious disease-related cause of death worldwide, necessitating the development of new and improved treatment regimens. Non-clinical evaluation of candidate drug combinations via the relapsing mouse model (RMM) is an important step in regimen development, through which candidate regimens that provide the greatest decrease in probability of relapse following treatment in mice may be identified for further development. Although RMM studies are a critical tool to evaluate regimen efficacy, making comprehensive "apples to apples" comparisons of regimen performance in the RMM has been a challenge, in large part due to the need to evaluate and adjust for variability across studies arising from differences in design and execution. To address this knowledge gap, we performed a model-based meta-analysis on data for 17 unique regimens obtained from a total of 1592 mice across 28 RMM studies. Specifically, a mixed-effects logistic regression model was developed that described the treatment duration-dependent probability of relapse for each regimen and identified relevant covariates contributing to inter-study variability. Using the model, covariate-normalized metrics of interest, namely treatment duration required to reach 50% and 10% relapse probability, were derived and used to compare relative regimen performance. Overall, the model-based meta-analysis approach presented herein enables cross-study comparison of efficacy in the RMM, and provides a framework whereby data from emerging studies may be analyzed in the context of historical data to aid in selecting candidate drug combinations for clinical evaluation as TB drug regimens.

pharmacology and toxicology↗