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Metabolic engineering of murine cytotoxic T cells by solute carrier Slc2a1/GLUT1 overexpression to enhance anti-tumor activity
Get PDFIn the last decade, adoptive T-Cell therapy (ACT) has emerged as a successful treatment of hematological malignancies. However, solid tumors pose challenges created by various factors, including poor infiltration, immunosuppressive factors, and the lack of nutrient viability. In combination with the low vascularisation, the nutrient-deprived tumor microenvironment (TME) is mainly created by the elevated aerobic glycolysis in tumor cells. As one of the main precursor metabolites, glucose is used in vast amounts by the tumor, therefore causing a massive concentration drop in the surrounding matrix. The increased surface expression of facilitative glucose transporters from the solute carrier family 2 (GLUT1/Slc2) on tumor cells plays an essential role in this process, giving them a selective advantage in the created TME. Infiltrating T cells would face a deserted tumor landscape that heavily interferes with their metabolism and consequently, their metabolic need to be functionally active could not be fulfilled. This new paradigm of immune escape mechanism has long been ignored but was more and more shifted into the spotlight, recently. Herein, we propose a novel strategy for ACT of metabolically engineered cytotoxic CD8+ T cells to adapt immune cells to the prevalent glucose concentrations in the TME. In this study, we could reveal that the ectopic overexpression of the glucose transporter GLUT1 encoded by Slc2a1 increased the fitness of primary murine CD8+ T cells (CD8+Slc2a1) in hypoglycemic conditions. Additionally, our results showed augmented functional activity and anti-tumor efficacy, in vitro and partially in vivo. We observed CD8+Slc2a1 undergo a changed metabolic reprogramming affecting their transcriptional landscape, oxidative state, and memory formation. These findings set the foundation for future studies on ACT in combination with GLUT1 overexpression in pre-clinical settings.Im letzten Jahrzehnt hat sich Adoptive T-Zell Therapy (ACT) als erfolgreiche Therapie gegen Blutkrebserkranungen bewährt. Jedoch bergen solide Krebsarten eine größere Herausforderung, welche durch die schlechte Immunzellinfiltration, immunsuppressive Faktoren und nicht zuletzt dem Mangel an Nährstoffen definiert werden. In Kombination mit der schlechten Vaskularisierung kreieren Tumorzellen ein nährstoffarmes Tumormikromillieu (TME) durch ihre hochregulierte glykolytische Aktivität. Als eine der wichtigsten Grundmetabolite wird Glukose in großen Mengen vom Tumor entzogen, wodurch ein substanzieller Konzentrationsabfall in der umliegenden Matrix folgt. Die Überexpression des Glukosetransporters GLUT1/SLC2A1 an der Oberfläche der Tumorzellen spielt eine zentrale Rolle in diesem Prozess, indem es diesen einen selektiven Vorteil im entstandenen TME verleiht. Einwandernde T-Zellen sind daher mit einer nährstoffverlassenen Tumorlandschaft konfrontiert, welche signifikant ihren Stoffwechsel beeinträchtigt und folglich ihre metabolischen Bedürfnisse nicht erfüllen kann. Dieses neue Paradigma der Immunflucht würde lange ignoriert aber findet mehr und mehr Beachtung in diesem Kontext. In dieser Arbeit präsentieren wir eine neue Strategie der ACT indem wir metabolisch modifizierte zytotoxische T-Zellen an die vorherrschenden Glukosekonzentrationen des TME anpassen. Wir konnten zeigen, dass die ektope Überexpression des Glukosetransporters GLUT1 (codiert in Slc2a1) die Fitness von primären murinen CD8+ T-Zellen (CD8+Slc2a1) in hypoglykämischen Bedingungen erhöht. Unsere in vitro und in vivo Ergebnisse zeigen zusätzlich eine erhöhte funktionelle Aktivität und Zeichen verbesserter anti-tumor Effektivität. Außerdem konnten wir beobachten, dass CD8+Slc2a1 Zellen eine metabolische Reprogrammierung durchlaufen, welche zur Veränderung des transkriptionellen und oxidativen Zustandes führt und die Gedächtnisbildung beeinflusst. Diese Ergebnisse sollen als einen Grundstein für künftige präklinische Studien der ACT in Kombination mit GLUT1 Überexpression dienen
Postoperative Dimensionsstabilität der relativen Augmentation des zahnlosen Alveolarfortsatzes durch eine externe Sinusbodenelevation mit einem xenogenen Knochenaufbaumaterial (BioOss®)
Get PDFDie Rolle des Inkretins Glukagon-like Peptide-1 in der Regulation des Glukosemetabolismus und sein therapeutischer Nutzen bei Typ 2 Diabetes
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Self-organization pathways in active filament bundles
Get PDFLiving cells rely on the active remodeling of cytoskeletal structures. This remodeling is mediated by the interaction of filaments with a variety of different associated proteins, most importantly motor proteins. Motor proteins operate by using chemical energy to generate force and movement. Thus, filament-motor-mixtures are a paradigmatic example of out-of-equilibrium physics. The transduction of chemical energy enables filament-motor-mixtures to obtain spatial and temporal organization. How interactions between motor proteins and cytoskeletal filaments, which happen on the nanometer scale, can give rise to spatial organization on the scale of up to hundreds of micrometers is the main focus of this thesis. To approach this question, I studied two typical interactions between motor proteins and cytoskeletal filaments: First, motor-mediated length regulation of filaments in chapter 2, and second, motor-mediated force generation in chapter 3.
In the chapter Collective filament length regulation in filament-motor mixtures, we study a minimal biophysical model for motor-mediated filament length regulation in an ensemble of kinesin-8 motors and microtubules. Importantly, we account explicitly for the diffusive redistribution of cytosolic tubulin and kinesin-8 motors. We derive a hydrodynamic description of the model on the basis of time and length scale separation arguments. Our theoretic description is accompanied by large-scale computer simulations. Strikingly, we find that, even though filaments interact only indirectly via a shared pool of resources, the filament-motor-mixture is capable of self-organizing into structures that span multiple filament lengths and show aster-like orientational order. In the subsequent section, we formalize our theoretical approach and perform a moment and gradient expansion to derive our hydrodynamic theory. Using agent-based simulations, we study the long term dynamics of the system on a phenomenological level and find spontaneous symmetry breaking in the orientational order, traveling wave solutions, coalescence and coarsening of the emerging filament structures.
In the chapter Collective filament motion in active filament bundles, we investigate emergent collective dynamics in filament bundles cross-linked by motor proteins that exert mechanical forces on the filaments. Starting from a microscopic model and based on a time-scale separation argument, we derive a coarse-grained filament-filament interaction. Based on this filament filament interaction, we study the collective interplay between motor-generated forces and filament bundle dynamics. In the first section, we study a bundle of filaments that are cross-linked by a set of motors and ask which mechanisms control the filament bundles’ propensity to contract or expand. Based on a generic model for motor cross-linkers, we derive a formalism to quantify the active tension in a bundle of cross-linked filaments. Using this generic model, we study a system composed of filaments, passive bundling agents, and cross-linking motors that can dwell at the filament tip. In this system, we identify three external control parameters that regulate the filament bundles’ propensity to contract or expand: First, the total number of motors in the system. Second, the total number of bundling agents, and third, the filament length. We validate our theoretical predictions and study the emergent long-term dynamics of the filament bundle using agent-based simulations. Our predictions are in accordance with recent in vitro experiments. In the following sections, we addressed the question of how motor cross-linkers control the filament sliding speed in a bundle of cross-linked filaments. On the microscopic scale, motor-generated filament motion seems to be inherently linked to the relative orientation of cross linked filaments. However, in vivo and in vitro observations in the mitotic spindle demonstrated that the speed of filament sliding is independent of the local number of interaction partners with equal and opposite orientations. Motivated by this apparent contradiction, we sought to understand which processes regulate collective filament sliding in active nematic networks. We identified a mechanism for collective filament sliding: Owing to the cross-linking in the filament network, the locally generated force can be propagated through the network over a characteristic length scale. This length scale is set by the antagonism between dissipation to the surrounding fluid and active motor-driven forces imposed on the filament. We then proceeded to study how the identified mechanism depends on the connectivity of the filament network with the help of large-scale computer simulations.
Taken together, we have studied how interactions of motor proteins and filaments, which take place on the nanometer scale, affect the emergent collective dynamics of filament-motor-mixtures on the scale of micrometers. We have shown that the active, motor-mediated depolymerization of filaments, in combination with mass conservation, does not only control the size of emergent filament structures but provides a self-organization pathway on its own. In bundles of filaments cross-linked by motor proteins capable of exerting mechanical force, we have shown how the active tension and the sliding speed of filaments depend on the kinetic and mechanical properties of the cross-linking motor proteins. Thereby, our work clarifies why some motor cross-linkers cause filament network contraction while others promote extensile tension in filament-motor mixtures. In the living cell, motor-mediated filament length regulation and motor-mediated mechanical interactions between filaments are no isolated processes – they take place simultaneously. It will be an interesting avenue for future research to understand the collective dynamics of filament-motor-mixtures where both motor-mediated mechanical filament-filament interactions and length regulation are present
Diagnosing and predicting clinical outcomes based on computational methods for immune microenvironment patterns: two examples
Get PDFThe immune system is a critical component of the delicate balance between human health and disease, particularly as immunotherapy gains popularity. In order to identify patients at an early stage and develop individualized disease prevention strategies, it is important to systematically and accurately describe the immune environment before disease onset. However, the immune environment is a complex system consisting of immune cells, antibodies, complement, and cytokines. Traditional methods of monitoring immune patterns in clinical settings are limited in accuracy and reliability, which have created a pressing need for more advanced technologies. Therefore, bioinformatics has become an important tool in the field of disease immunology research. In recent years, computational methods and approaches have been developed, which specifically enumerate the immune microenvironment and allow for the further quantification of the complex immune system.
The objective of this project is to explore the role of bioinformatics in the prediction of the diagnosis or prognosis of immune-related diseases. The focus of this dissertation is on two types of diseases, each with a different prediction model, which are described separately in two independent chapters due to their respective specificities. In Chapter 2, the immune cell compositions of blood samples from patients with Kawasaki disease (KD)—an immune-mediated inflammation in children—were enumerated. A novel algorithm was developed for predicting KD diagnosis based on this enumeration. Using the model, patients with KD and febrile controls could be well distinguished in the test set, with an AUC of 0.80. In Chapter 3, a study concerning a tumor disease, uveal melanoma (UVM), was conducted. The study integrates the patterns of basement membrane and immunogenic cell death to investigate the immune microenvironment patterns in UVM patients. On this basis, three models using different algorithms were constructed and the optimal one was selected after comparing them in validation set, which was the model generated by the IPF-LASSO algorithm, with an AUC of 0.740, 0.841 and 0.835 for 1-, 3-, and 5-year overall survival, respectively. Furthermore, we assessed its performance on the test sets with different survival outcomes and preliminary investigated its association with the response to UVM immunotherapy.
As such, this dissertation highlights how the integration of machine learning and high-throughput data can improve the characterization of the disease-associated immune microenvironment and the development of better prediction models. The study results demonstrate that the bioinformatics-based approach presented in this project holds great potential for predicting the diagnosis or prognosis of immune-related diseases, and could ultimately improve patient outcomes
