Biology subjects

Meyer-Hermann, M.

Publications and source records attributed to Meyer-Hermann, M..

29 records · Page 2Linked to original sources

bioRxiv2020

In silico characterization of mechanisms positioning costimulatory and checkpoint complexes in immune synapses

Integrin and small immunoglobulin superfamily (sIGSF) adhesion complexes function physiologically in human immunological synapses (IS) wherein sIGSF complexes form a corolla of microdomains around an integrin ring and secretory core. The corolla recruits and retains the major costimulatory and checkpoint complexes that regulate the response to T cell receptor (TCR) engagement, making forces that govern corolla formation of particular interest. We developed a phenomenological agent-based model in order to test different hypotheses concerning the mechanisms underlying molecular reorganization during IS formation. The model showed that sIGSF complexes are passively excluded to the distal aspect of the IS as long as their interaction with the ramified F-actin transport network is absent or weaker than that of integrins. An attractive force between sIGSF adhesion and costimulatory/checkpoint complexes relocates the latter from the centre of the IS to the corolla. The simulations suggest that size based sorting interactions with large glycocalyx components as well as a short-range self-attraction between sIGSF complexes explain the corolla "petals". These molecular and mechanistic features establish a general model that can recapitulate complex pattern formation processes observed in cell-bilayer and cell-cell interfaces. One Sentence SummaryComputer simulations of immunological synapses reveal the localization mechanisms of immunoglobulin superfamily adhesion and costimulatory/checkpoint complexes.

immunology↗

bioRxiv2019

Evaluation of CD8 T cell killing models with computer simulations of 2-photon imaging experiments

In vivo imaging of cytotoxic T lymphocyte (CTL) killing activity revealed that infected cells have a higher observed probability of dying after multiple contacts with CTLs, suggesting memory effect in CTLs or infected cells. We developed a three-dimensional agent-based model of CTL killing activity to discriminate different hypotheses about how infected cells get killed based on quantitative 2-photon in vivo observations. We compared a constant CTL killing probability with mechanisms of signal integration in CTL or infected cells. The most likely scenario implied increased susceptibility of infected cells with increasing number of CTL contacts where the total number of contacts was a critical factor as opposed to signal integration over many contacts. However, when allowing in silico T cells to interact with apoptotic target cells (zombie contacts), a contact history independent killing mechanism was also in agreement with the experimental datasets. We showed that contacts that take place between CTLs and dying infected cells impact the observed killing dynamics because even in absence of modulation of cell properties, we saw an increase of the observed probability of killing infected cells with more interactions. The duration taken by an infected cell to die and the per capita killing rate (PCKR) of CTLs, parameters hard to measure directly, were determined from the model and turned out predictive to distinguish the different CTL killing models in future experiments. The comparison of observed datasets to simulation results, revealed limitations in interpreting 2-photon data, and provided prediction for additional measurements to distinguish CTL killing models. HighlightsO_LIKilling of infected cells by cytotoxic T cells typically involves more than a single contact. C_LIO_LICytotoxic T cells or infected cells integrate signals from multiple interactions. C_LIO_LIT cell contacts with dying infected cells have a major impact on in vivo data interpretation. C_LI Significance StatementDespite having a clear understanding of cytotoxic T lymphocyte (CTL) mediated cytotoxicity mechanisms, the quantitative dynamics remain unexplored at a cellular level. We developed an agent-based model to compare different hypotheses for mechanisms of CTL mediated cytotoxicity that could lead to an increase in observed probability of killing infected cells at higher interactions with CTLs as seen in vivo. We showed that this behaviour can be explained by modulation of properties by infected cells or CTLs with increasing number of contacts. For the modulation, we compared two modes of signal integration and showed that time is not a relevant parameter in signal integration. We also studied the impact of contacts between CTLs and apoptotic infected cells on observed killing properties.

systems biology↗

bioRxiv2019

A 3D structural affinity model for multi-epitope in silico germinal center simulations

Vaccine development for mutating pathogens is challenged by their fast evolution, the complexity of immunodominance, and the heterogeneous immune history of individuals. Mathematical models are critical for predicting successful vaccine conditions or designing potent antibodies. Existing models are limited by their abstract and poorly structural representations of antigen epitopes. Here, we propose a structural lattice-based model for antibody-antigen affinity. An efficient algorithm is given that predicts the best binding structure of an antibodys amino acid sequence around an antigen with shortened computational time. This structural representation contains key physiological properties, such as affinity jumps and cross-reactivity, and successfully reflects the topology of antigen epitopes, such as pockets and shielded residues. It is suitable for large simulations of affinity maturation. We perform in silico immunizations via germinal center simulations and show that our model can explain complex phenomena like recognition of the same epitope by unrelated clones. We show that the use of cocktails of similar epitopes promotes the development of cross-reactive antibodies. This model opens a new avenue for optimizing multivalent vaccines with combined antigen cocktails or sequential immunizations, and to reveal reasons for vaccine success or failure on a structural basis.

systems biology↗

bioRxiv2019

Mathematical model shows how sleep may affect amyloid β fibrillization

Deposition of amyloid {beta} (A{beta}) fibers in extra-cellular matrix of the brain is a ubiquitous feature associated with several neurodegenerative disorders, especially Alzheimers disease (AD). While many of the biological aspects that contribute to the formation of A{beta} plaques are well addressed at the intra- and inter-cellular level in short timescales, an understanding of how A{beta} fibrillization usually starts to dominate at a longer timescale in spite of the presence of mechanisms dedicated to A{beta} clearance, is still lacking. Furthermore, no existing mathematical model integrates the impact of diurnal neural activity as emanated from circadian regulation to predict disease progression due to a disruption in sleep-wake cycle. In this study, we develop a minimal model of A{beta} fibrillization to investigate the onset of AD over a long time-scale. Our results suggest that the diseased state is a manifestation of a phase change of the system from soluble A{beta} (sA{beta}) to fibrillar A{beta} (fA{beta}) domination upon surpassing a threshold in the production rate of soluble A{beta}. By incorporating the circadian rhythm into our model, we reveal that fA{beta} accumulation is crucially dependent on the regulation of sleep-wake cycle, thereby indicating the importance of a good sleep hygiene in averting AD onset. We also discuss potential intervention schemes to reduce fA{beta} accumulation in the brain by modification of the critical sA{beta} production rate.

systems biology↗

bioRxiv2019

Modeling bacteria-based therapy in tumor spheroids

Tumor-targeting bacteria elicit anticancer effects by infiltrating hypoxic regions, releasing toxic agents and inducing immune responses. Although current research has largely focused on the influence of chemical and immunological aspects on the mechanisms of bacterial therapy, the impact of physical effects is still elusive. Here, we propose a mathematical model for the anti-tumor activity of bacteria in avascular tumors that takes into account the relevant chemo-mechanical effects. We consider a time-dependent administration of bacteria and analyze the impact of bacterial chemotaxis and killing rate. We show that active bacterial migration towards tumor hypoxic regions provides optimal infiltration and that high killing rates combined with high chemotactic values provide the smallest tumor volumes at the end of the treatment. We highlight the emergence of steady states in which a small population of bacteria is able to constrain tumor growth. Finally, we show that bacteria treatment works best in the case of tumors with high cellular proliferation and low oxygen consumption.

biophysics↗

bioRxiv2019

CD2 expression acts as a quantitative checkpoint for immunological synapse structure and T-cell activation.

The CD2 receptor has been described as an adhesion and costimulatory receptor on T cells. Here, transcriptional profiling of colorectal cancers (CRC) revealed a negative correlation between CD2 expression and \"exhausted CD8+ T-cells\" gene signatures. Furthermore, we detected reduced surface CD2 levels in exhausted CD127lowPD-1hi CD3+CD8+ tumour infiltrating lymphocytes (TILs) in CRC. We describe a CD2 expression-level-dependent switch in CD2-CD58 localization between central and peripheral domains in the immunological synapse (IS). A peripheral \"CD2 corolla\" formed when CD2 surface expression was sufficiently high and its cytoplasmic domain intact. The corolla recruited other ligated receptors like CD28, boosted recruitment of activated Src-family kinases (pSrc), LAT and PLC-{gamma} in the IS and consequently T-cell activation in response to a tumour antigen. Corolla formation and pSrc in the IS increased linearly with CD2 expression, whereas pSrc signals were reduced by high, \"exhausted-like\" levels of PD-1, which invaded the corolla. These results suggest two levels of inhibition of Src-family kinases in CD3+CD8+ TILs: reduced CD2 expression and high PD-1 expression.

immunology↗

bioRxiv2018

Injection of antibodies against immunodominant epitopes tunes germinal centres to generate broadly neutralizing antibodies

The development of broadly neutralizing antibodies is critical for the control of many life-threatening viral infections like HIV, influenza, or hepatitis. Elite neutralizers are generated by natural germinal centre (GC) reactions in a very small subset of patients. The best strategy of how to promote the generation of broadly neutralizing antibodies is not known. Here, computer simulations of GC reactions are used to predict a feedback loop based on memory B cells from previous GC reactions that promotes GCs to focus on new epitopes. The simulations suggest that the injection of antibodies against immunodominant epitopes fosters affinity maturation of antibodies specific for rare or hidden epitopes. This principle can be used for the design and testing of future therapies and vaccination protocols.

immunology↗

bioRxiv2018

On the impact of chemo-mechanically induced phenotypic transitions in gliomas

Tumor microenvironment is a critical player in glioma progression and novel therapies for its targeting have been recently proposed. In particular, stress-alleviation strategies act on the tumor by reducing its stiffness, decreasing solid stresses and improving blood perfusion. However, these microenvironmental changes trigger chemo-mechanically induced cellular phenotypic transitions whose impact on therapy outcomes is not completely understood. In this work, we perform experiments to analyze the effects of mechanical compression on migration and proliferation of two glioma cell lines. From these experiments, we derive a mathematical model of glioma progression focusing on cellular phenotypic plasticity. The model reveals a trade-off between tumor infiltration and cellular content as a consequence of stress-alleviation approaches. We discuss how these findings can improve the current understanding of glioma/microenvironment interactions, and suggest strategies to improve therapeutic outcomes.

biophysics↗

bioRxiv2018

Estimation of the cancer risk induced by rejuvenation therapy with young blood and treatment recommendations

In recent years the transfer of blood from young to old individuals was shown to bear the potential of rejuvenation of stem cell activity. While this process might increase life expectancy by prolonging functionality of organs, higher cell replication rates bear also the risk of cancer. The extent of this risk is not known.\n\nWhile it is difficult to evaluate this cancer risk in experiments, this is possible with a mathematical model for tissue homeostasis by stem cell replication and associated cancer risk. The model suggests that young blood treatments can induce a substantial delay of organ failure with only minor increase in cancer risk. The benefit of rejuvenation therapy as well as the impact on cancer risk depend on the biological age at the time of treatment and on the overall cell turnover rate of the organs. Different organs have to be considered separately in the planning of the systemic treatment. In particular, the model predicts that the treatment schedules successfully applied in mice are not directly transferable to humans and guidelines for successful protocols are proposed. The model presented here may be used as a guidance for the development of treatment protocols.\n\nAdditional informationThere is NO competing interests.

systems biology↗

bioRxiv2018

MoonFit, a minimal interface for fitting ODE dynamical models, bridging simulation by experimentalists and customization by C++ programmers

The modelling of biological systems often consists into differential equation models that need to be fitted to experimental data. During this complex process, the practical experience of the biologist and the theoretical abstraction of the modeller require back-and-forth refinements of the model, design of new experiments and inclusion of more data-points into the fitting procedure. Available optimization interfaces rarely simultaneously allow customizations by the programmer and the capacity for the biologist to perform simulations or optimizations with a simple interface.\n\nHere, we provide the C++ code of a graphical user interface based on a user defined minimal C++ ODE model class. The graphical interface allows to perform simulations and optimizations without any knowledge in programming. The code was designed minimal and modular to be easily modified, with maximal freedom to link customized optimization libraries, solver or hand-made scripts. Moonfit is powerful enough to fit and compare models with high dimensionality, multiple datasets, to automatize optimizations, and to perform iterative fittings using data interpolation. We believe this will ease the interaction between modellers and experimental partners.\n\nAvailability: Moonfit is freely available via c++ source code and accompanying scripts from gitlab.com/Moonfit/MoonLight.

systems biology↗

bioRxiv2017

Quantitative characterization of CTLA4 trafficking and turnover using a combined in vitro and in silico approach

CTLA4 is an essential negative regulator of T cell immune responses and is a key checkpoint regulating autoimmunity and anti-tumour immunity. Genetic mutations resulting in a quantitative defect in CTLA4 are associated with the development of an immune dysregulation syndrome. Endocytosis of CTLA4 is rapid and continuous with subsequent degradation or recycling. CTLA4 has two natural ligands, the surface transmembrane proteins CD80 and CD86 that are shared with the T cell co-stimulatory receptor CD28. Upon ligation with CD80/CD86, CTLA4 can remove these ligands from the opposing cells by transendocytosis. The efficiency of ligand removal is thought to be highly dependent on the processes involved in CTLA4 trafficking. With a combined in vitro-in silico study, we quantify the rates of CTLA4 internalization, recycling and degradation. We incorporate experimental data from cell lines and primary human T cells. Our model provides a framework for exploring the impact of altered affinity of natural ligands or therapeutic anti-CTLA4 antibodies and for predicting the effect of clinically relevant CTLA4 pathway mutations. The presented methodology for extracting trafficking rates can be transferred to the study of other transmembrane proteins.

immunology↗