Biology subjects

Meier-Schellersheim, M.

Publications and source records attributed to Meier-Schellersheim, M..

8 recordsLinked to original sources

bioRxiv2026

Quantitative Modeling of TLR Signaling Reveals Missing Negative Feedback Guiding Identification of TANK-IKKε Checkpoint

Toll-like receptor (TLR) signaling must be activated rapidly and then terminated to support host defense without sustained inflammation. We developed a rule-based model of mouse macrophage TLR4 signaling at the molecular-interaction level using measured protein copy numbers, RNA-seq-based abundance estimates, literature- and structure-informed reaction rates, and 979 dynamic experimental constraints. The trained model reproduced much of the TLR4-induced NF-{kappa}B and MAP kinase response but consistently failed to capture deactivation of MyD88, TRAF6-associated species, and IKK/{beta}. The recurrent model failure conveyed important biological information, localizing missing regulation to the proximal MyD88-IRAK-TRAF6 module and guiding experimental evaluation of IKK{varepsilon} and its scaffold TANK. Loss of IKK{varepsilon} enhanced transcriptional, cytokine, MAP kinase, and NF-{kappa}B responses to MyD88-specific TLR ligands. TANK deficiency produced a similar cellular phenotype and abolished stimulus-induced IKK{varepsilon} phosphorylation. Deficiency of either protein increased IRAK1 and TRAF6 ubiquitination without increasing MyD88 ubiquitination, placing the inhibitory checkpoint at or immediately downstream of the IRAK1-TRAF6 ubiquitin-signaling node. Overlapping but non-identical in vivo phenotypes further supported a shared regulatory axis with additional protein-specific functions. Our study presents a model-experiment discovery cycle where quantitative pathway discordance identifies missing biology and reveals a TANK-dependent IKK{varepsilon} checkpoint that restrains MyD88-driven inflammation.

systems biology↗

bioRxiv2025

Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments

Minimizing experimental noise is integral to robust data generation in single-cell science. Experimental processing of different samples as a single pool, made possible by hashtag-assisted pooling, helps minimize batch-effects, but the computational demultiplexing of the data can also lead to loss of cells whose hashtags cannot be resolved accurately. Here, we examine four alternate experimental designs that could be used instead of a single-pool approach and quantify the batch effects as well as cell loss in each case. While a reference design offers the best performance, this study can help individual investigators choose one suited for their biological questions.

bioinformatics↗

bioRxiv2024

Kinetic patterns of single cell gene expression discriminate between the murine cellular responses to live attenuated and inactivated Yellow Fever vaccines

The success of the live attenuated Yellow Fever vaccine (YF17D) that elicits immunity lasting over thirty years has made it a widely used model to understand the generation of durable protection. We compare the early single-cell level transcriptional response in mice to YF17D and an adjuvanted-inactivated, but less effective version (InYF). Within the first week, we identify 70 kinetic patterns in 45 cellular clusters, majority of which discriminate between the two formulations, some in a tissue and sex-specific manner. Intriguingly, differential transcripts fall into two categories, one whose association with YF17D or InYF is maintained even when decoupled from their cell-type of expression and the other where such cell-plus-gene pairing is critical to maintain differential marker status. We demonstrate applications of this resource, by identifying B cells with varied interferon and antigen responsiveness in relation to each vaccine. This high-resolution dataset is amenable to further biomarker discovery and hypothesis generation.

immunology↗

bioRxiv2024

CountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq Data

Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. To address this, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences.

bioinformatics↗

bioRxiv2024

IL-10 dependent adaptation allows macrophages to adjust inflammatory responses to TLR4 stimulation history

Macrophages must scale inflammation to the trajectories of infections, escalating as pathogen loads rise, resolving as they fall. The cytokine IL-10 and its chromatin-level effector BCL-3 are critically important anti-inflammatory agents, yet how they avoid muting newly recruited cells before those cells read their own pathogen input has been unclear. Applying systematically varied consecutive TLR4 (Kdo2-Lipid A) stimuli to bone marrow-derived macrophages, we identify two coupled features. First, macrophages retain a quantitative memory of prior stimulation: only when a secondary TLR stimulus matches or exceeds a prior one, I{kappa}B degradation, NF-{kappa}B/MAPK activation, and cytokine output increase. Second, IL-10 susceptibility is itself gated by TLR4 history: even a 100-fold IL-10 excess fails to suppress TNF- in weakly primed cells. Both trace to history- and IL-10-dependent BCL-3 recruitment with p65 displacement at the Tnf {kappa}B site. Modeling shows how this licensing logic enables trajectory-aware responses that clear pathogens while limiting tissue damage.

immunology↗

bioRxiv2022

Conserved Biophysical Compatibility Among the Highly Variable Germline-Encoded Regions Shapes TCR-MHC Interactions

T cells are critically important components of the adaptive immune system primarily responsible for identifying and responding to pathogenic challenges. This recognition of pathogens is driven by the interaction between membrane-bound T cell receptors (TCRs) and antigenic peptides presented on major histocompatibility complex (MHC) molecules. The formation of the TCR-peptide-MHC complex (TCR-pMHC) involves interactions among germline-encoded and hypervariable amino acids. Germline-encoded and hypervariable regions can form contacts critical for complex formation, but only interactions between germline-encoded contacts are likely to be shared across many of all the possible productive TCR-pMHC complexes. Despite this, experimental investigation of these interactions have focused on only a small fraction of the possible interaction space. To address this, we analyzed every possible germline-encoded TCR-MHC contact in humans, thereby generating the first comprehensive characterization of these largely antigen-independent interactions. Our analysis shows that germline-encoded TCR-MHC interactions that are conserved at the sequence level are rare due to the high amino acid diversity of the TCR CDR1 and CDR2 loops, and that such conservation is unlikely to dominate the dynamic protein-protein binding interface. Instead, we find that binding properties such as the docking orientation are defined by regions of biophysical compatibility between these loops and the MHC surface.

immunology↗

bioRxiv2022

An Integrated Approach to the Characterization of Immune Repertoires Using AIMS: An Automated Immune Molecule Separator

The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments - collectively referred to as antigens - initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS - Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.

immunology↗

bioRxiv2020

The Roles of Space and Stochasticity in Computational Simulations of Cellular Biochemistry: Quantitative Analysis and Qualitative Insights

Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multi-molecular structures and rapidly propagating molecular signals embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computational simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However it is often difficult to reconcile conflicting computational results that use different simulation approaches (for example partial differential equations versus particle-based stochastic methods) to describe the same phenomenon. Moreover, the details of the computational implementation of any particular algorithm may give rise to quantitatively or even qualitatively different results for the same set of starting assumptions and parameters. In an effort to address this issue systematically, we have defined a series of computational test cases ranging from very simple (bimolecular binding in solution) to moderately complex (spatial and temporal oscillations generated by proteins binding to membranes) that represent building blocks for comprehensive three-dimensional models of cellular function. Having used two or more distinct computational approaches to solve each of these test cases with consistent parameter sets, we generally find modest but measurable differences in the solutions of the same problem, and a few cases where significant deviations arise. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision-making by researchers wishing to develop new models for cell biology. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.

systems biology↗