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Zellmembranen sind hochspezialisierte Mehrkomponentenlegierungen, welche sowohl die Zelle selbst als auch ihre Organellen umgeben. Sie spielen eine entscheidende Rolle bei vielen biologisch relevanten Prozessen wie die Signaltransduktion und die Zellbewegung. Aus diesem Grund ist eine genaue Charakterisierung ihrer Eigenschaften der Schlüssel zum Verständnis der Bausteine des Lebens sowie ihrer Erkrankungen. Besonders Krebs steht im engen Zusammenhang mit Veränderungen der biomechanischen Eigenschaften vom Gewebe, Zellen und ihren Organellen. Während Veränderungen des Zytoskeletts von Krebszellen im Fokus vieler Biophysiker stehen, ist die Bedeutung der Biomechanik von Zellmembran weitgehend unklar. Zellmembranen faszinieren Wissenschaftler jedoch nicht nur wegen ihrer biomechanischen Eigenschaften. Sie sind auch Beispiele für eine selbstorganisierte und heterogene Landschaft, in der Prozesse fernab des Gleichgewichtes, wie z.B. räumliche und zeitliche Musterbildungen, auftreten. Die vorgelegte Dissertation untersucht erstmals umfassend die zentrale Rolle der Zellmembran und ihrer molekularen Architektur für die Signalübertragung, die Biomechanik und die Zellmigration. Hierfür werden einfache Modellmembranen aber auch komplexere Vesikel und ganze Zellen mittels etablierter physikalischer Methoden analysiert. Diese reichen von Fourier- Analysen zur Charakterisierung von thermisch angeregten Membranundulationen über Massenspektrometrie und ‘Optical Stretcher’ Messungen von ganzen Zellen bis hin zur Filmwaagentechnik. Des Weiteren wird ein Modellsystem vorgestellt, welches sowohl einen experimentellen als auch einen mathematischen Zugang zum ‘ME-switch’ ermöglicht. Die vorgelegte Dissertation bietet neue Einblicke in wichtige Funktionen von Zellmembranen und zeigt neue therapeutische Perspektiven in der Membran- und Krebsforschung auf.:1 Introduction 2 Background 2.1 The Cell Membrane 2.1.1 Lipids in Cell Membranes 2.1.2 Membrane Proteins 2.1.3 An Overview on Membrane Models 2.1.4 Lipid Rafts 2.2 Model Membranes – An Experimental Access to Cell Membranes 2.2.1 Surface Tension and Thermodynamic Equilibrium 2.2.2 Langmuir Monolayer 2.2.3 The Polymorphism of Langmuir Monolayers 2.2.4 Membrane Vesicles 2.3 Biological Membranes as Semiflexible Shells 2.3.1 Elasticity of Soft Shells 2.3.2 Helfrichs Theory About Bending Deformations 2.3.3 Membrane Undulation 2.4 Membranes in Cell Signaling 2.4.1 Signal Transduction Fundamentals 2.4.2 Phosphoinositides 2.4.3 Phosphatidylinositol Signaling Pathway 2.4.4 The Myristoyl-Electrostatic Switch 2.5 Reaction-Diffusion Systems 2.5.1 Diffusion 2.5.2 Michaelis-Menten Kinetics 2.5.3 Reaction-Diffusion Systems 3 Methods, Materials and Theory 3.1 Optical Microscopy 3.1.1 Fluorescence Microscopy 3.1.2 Phase Contrast Microscopy 3.2 Cell Culture and GPMV Formation 3.2.1 Tumor Dissociation and Cell Culturing of Primary Cells 3.2.2 Cell Lines and Cell Culturing 3.2.3 Preparation of Giant Plasma Membrane Vesicles 3.3 Optical Stretcher 3.4 Fourier Analysis of Thermally Excited Membrane Fluctuations 3.4.1 The Quasi-Spherical Model – Membrane Fluctuations 3.4.2 Determination of the Bending Rigidity 3.5 Mass Spectrometry 3.5.1 MALDI-TOF Mass Spectrometry 3.5.2 ESI Mass Spectrometry 3.6 Migration, Invasion and Cell Death Assays 3.7 Langmuir-Blodgett Technique 3.7.1 Langmuir Troughs and Film Balances 3.7.2 Experimental Setup and Monolayer Preperation 3.7.3 Phospholipids, Dyes and Buffer Solutions 4 Fluctuations in Cell Membranes 4.1 Cell Membrane Softening in Human Breast and Cervical Cancer Cells 4.1.1 Bending Rigidity of Human Beast and Cervical Cell Membranes 4.1.2 MALDI-TOF Analysis of Lipid Composition 4.1.3 Summary and Discussion 4.2 Targeting of Membrane Rigidity – Implications on Migration 4.2.1 ESI Tandem Analysis of Lipid Composition 4.2.2 Biomechanical Behavior of Whole Cells and Membranes 4.2.3 Migration and Invasion Behavior 4.2.4 Summary and Discussion 5 Oscillations in Cell Membranes 5.1 Mimicking the ME-switch 5.1.1 DPPC/PIP2 monolayers at the presence of MARCKS 5.1.2 Lateral organization of PIP2 in DPPC/PIP2 monolayers 5.1.3 Translocation of MARCKS 5.1.4 Phosphorylation of MARCKS by PKC 5.1.5 Summary and Discussion 5.2 Dynamic Membrane Structure Induces Temporal Pattern Formation 5.2.1 Mechanism of the Oscillation 5.2.2 Modeling the ME-switch 5.2.3 Time Evolution 5.2.4 Phase Diagrams and Open Systems 5.2.5 Summary and Discussion 6 Conclusion and Outlook Appendix Bibliography List of Figures List of Abbreviations Acknowledgement

Cancer is a leading cause of death worldwide. Development of new cancer drugs is increasingly costly and time-consuming. By exploiting massive amounts of biological data, computational repositioning proposes new uses for old drugs to reduce these development hurdles. A promising approach is the systematic analysis of structural data for identification of shared binding pockets and modes of action. In this thesis, I developed the Protein-Ligand Interaction Profiler (PLIP), which characterizes and indexes protein-ligand interactions to enable comparative analyses and searching in all available structures. Following, I applied PLIP to identify new treatment options in cancer: the heat shock protein Hsp27 confers resistance to drugs in cancer cells and is therefore an attractive target with a postulated drug binding site. Starting from Hsp27, I used PLIP to define an interaction profile to screen all structures from the Protein Data Bank (PDB). The top prediction was experimentally validated in vitro. It inhibits Hsp27 and significantly reduces resistance of multiple myeloma cells against the chemotherapeutic agent bortezomib. Besides computational repositioning, PLIP is used in docking, binding mode analysis, quantification of interactions and many other applications as evidenced by over 12,000 users so far. PLIP is provided to the community online and as open source.

Alcoholic beverages have been classified as carcinogenic to humans. As alcoholic beverages are multicomponent mixtures containing several carcinogenic compounds, a quantitative approach is necessary to compare the risks. Fifteen known and suspected human carcinogens (acetaldehyde, acrylamide, aflatoxins, arsenic, benzene, cadmium, ethanol, ethyl carbamate, formaldehyde, furan, lead, 4-methylimidazole, N-nitrosodimethylamine, ochratoxin A and safrole) occurring in alcoholic beverages were identified based on monograph reviews by the International Agency for Research on Cancer. The margin of exposure (MOE) approach was used for comparative risk assessment. MOE compares a toxicological threshold with the exposure. MOEs above 10,000 are judged as low priority for risk management action. MOEs were calculated for different drinking scenarios (low risk and heavy drinking) and different levels of contamination for four beverage groups (beer, wine, spirits and unrecorded alcohol). The lowest MOEs were found for ethanol (3.1 for low risk and 0.8 for heavy drinking). Inorganic lead and arsenic have average MOEs between 10 and 300, followed by acetaldehyde, cadmium and ethyl carbamate between 1,000 and 10,000. All other compounds had average MOEs above 10,000 independent of beverage type. Ethanol was identified as the most important carcinogen in alcoholic beverages, with clear dose response. Some other compounds (lead, arsenic, ethyl carbamate, acetaldehyde) may pose risks below thresholds normally tolerated for food contaminants, but from a cost-effectiveness point of view, the focus should be on reducing alcohol consumption in general rather than on mitigative measures for some contaminants that contribute only to a limited extent (if at all) to the total health risk.

Background Cancer is one of the leading causes of death in Sweden. In order to develop better treatments against cancer we need to better understand it. One area of special interest is cancer metabolism and the metabolic fluxes. As these fluxes cannot be directly measured, modeling is required to determine them. Due to the complexity of cell metabolism, some limitations in the metabolism model are required. As the TCA-cycle (TriCarboxylic Acid cycle) is one of the most important parts of cell metabolism, it was chosen as a starting point. The primary goal of this project has been to evaluate the previously constructed TCA-cycle model. The first step of the evaluation was to determine the CI (Confidence Interval) of the model parameters, to determine the parameters’ identifiability. The second step was to validate the model to see if the model could predict data for which the model had not been trained for. The last step of the evaluation was to determine the uncertainty of the model simulation. Method The TCA-cycle model was created using Isotopicaly labeled data and EMUs (ElementaryMetabolic Units) in OpenFlux, an open source toolbox. The CIs of the TCA-cycle model parameters were determined using both OpenFlux’s inbuilt functionality for it as well as using amethod called PL (Profile Likelihood). The model validation was done using a leave one out method. In conjunction with using the leave on out method, a method called PPL (Prediction Profile Likelihood) was used to determine the CIs of the TCA-cycle model simulation. Results and Discussion Using PL to determine CIs had mixed success. The failures of PL are most likely caused by poor choice of settings. However, in the cases in which PL succeeded it gave comparable results to those of OpenFLux. However, the settings in OpenFlux are important, and the wrong settings can severely underestimate the confidence intervals. The confidence intervals from OpenFlux suggests that approximately 30% of the model parameters are identifiable. Results from the validation says that the model is able to predict certain parts of the data for which it has not been trained. The results from the PPL yields a small confidence interval of the simulation. These two results regarding the model simulation suggests that even though the identifiability of the parameters could be better, that the model structure as a whole is sound. Conclusion The majority of the model parameters in the TCA-cycle model are not identifiable, which is something future studies needs to address. However, the model is able to to predict data for which it has not been trained and the model has low simulation uncertainty.

Chemotherapien verursachen DNA-Schäden in Krebszellen, was zur Apoptose der meisten Zellen führt. Die überlebenden Zellen können in eine Therapie-induzierte Seneszenz (TIS) eintreten, die zum Zellzyklusarrest führt. Zellen in TIS weisen einen Seneszenz-assoziierten sekretorischen Phänotyp (SASP) auf – eine erhöhte Proteinproduktion, die proteotoxischen Stress bedingt. Verbliebene seneszente Zellen könnten jedoch wieder in den Zellzyklus eintreten und ihr SASP kann entzündungsfördernd wirken. Da seneszente Zellen besonders auf Proteinabbau angewiesen sind, um eine zu hohe Proteotoxizität zu vermeiden, zielten wir auf Proteindegradierungswege mit einer sekundären Therapie ab, um TIS Zellen zu eliminieren. Mittels Bcl2-überexprimierender Lymphome aus dem transgenen Eμ-myc Maus-Modell, die nach Chemotherapie in Seneszenz eintreten, und des Vergleichs mit Lymphomen, die aufgrund einer genetischen Läsion nicht seneszent werden können. identifizierten wir eine verringerte proteasomale Aktivität in TIS, die mit Kennzeichen von proteotoxischem Stress korrelierte. Dabei hängt die daraus folgende Empfindlichkeit von TIS Zellen für proteasomale Hemmung von NF-κB regulierter SASP Produktion ab. Die Kombination aus proteasomaler Hemmung durch Bortezomib und einem Autophagieblocker zeigte einen additiven Effekt auf den Zelltod von TIS Zellen. Diese Ergebnisse konnten in vivo anhand verbesserten Überlebens durch TIS-nachfolgende Sekundärtherapie von mit Lymphomen inokulierten Mäusen bestätigt werden. Schließlich konnte das Prinzip der Seneszenz-spezifischen Beseitigung von Tumorzellen unter Einsatz von Bortezomib in zwei humanen Lymphomzelllinien untermauert werden. Diese Ergebnisse zeigen, dass seneszente Zellen auf eine intakte Proteinabbau-Maschinerie angewiesen sind und auf Veränderungen in proteasomaler Aktivität empfindlich reagieren. Diese Angreifbarkeit könnte in einer neuartigen, synthetisch lethalen Strategie ausgenutzt werden, um maligne Zellen effektiv zu eliminieren. / Chemotherapy causes DNA damage in cancer cells, causing apoptosis in most, but not all cells. Surviving cells can undergo therapy-induced senescence (TIS), an emergency mechanism that arrests the cell cycle. Cells in TIS exhibit a senescence-associated secretory phenotype (SASP) - a massive increase in protein production leading to proteotoxic stress. Sustained senescent cells might reenter the cell cycle, and their SASP can stimulate dangerous inflammation. Aiming to eliminate remaining TIS cells, we reasoned that they need to reduce their excessive protein burden and avoid proteotoxicity and we therefore targeted protein degradation pathways with a secondary treatment. Using Bcl2-overexpressing lymphomas from the Eμ-myc mouse transgenic model, which become senescent after chemotherapy, and comparing them to lymphomas that do not become senescent due to a genetic impairment, we found reduced proteasomal activity in TIS, correlating to hallmarks of proteotoxic stress, such as accumulation of protein aggregates and oxidized proteins. The consequential sensitivity of TIS cells to proteasome inhibition was dependent on NF-κB governed SASP production. Very significantly, combining proteasome inhibition by bortezomib with autophagy inhibition had an additive effect on susceptibility to rapid death after TIS. These findings were confirmed by in vivo treatments improving survival of mice carrying lymphomas that had been treated into TIS. Lastly, the senescent-specific clearance of cells by bortezomib following TIS induction was confirmed using two human, lymphoid cell lines, supporting the proof-of-principle. This study suggests that senescent cells are highly dependent on protein disposal and are hypersensitive to changes in proteasome activity. This vulnerability might be exploited in a novel synthetic lethality strategy to fully eliminate malignant cells.

Hohe Telomeraseaktivität bedingt durch genomische TERT-Rearrangements definiert eine Gruppe an Hochrisiko-Neuroblastompatienten mit ungünstiger Prognose. Das Abzielen auf Telomerase ist ein hochpriorisierter Ansatzpunkt in der Therapie, für die es bislang keine klinisch erfolgreichen Inhibitoren gibt. Der Einsatz von epigenetisch wirksamen Histondeacetylase Inhibitoren (HDACi) stellt dabei eine interessante Therapieoption dar. In TERT-rearrangierten Neuroblastomzellen erzielte die Behandlung mit verschiedenen pan-, Klasse I oder spezifischen HDAC1/2 Inhibitoren eine Supprimierung der TERT mRNA Expression und der Telomeraseaktivität. RNA-Interferenz Studien bestätigten, dass HDAC1 und HDAC2 die TERT Expression positiv regulieren. Die transiente Überexpression von TERT zeigte einen partiellen Rescue des HDACi-bedingten anti-proliferativen Effekts. Der präventive und therapeutische Einsatz von HDACi Panobinostat verlangsamte das Xenografttumorwachstum, die TERT-Expression und Telomeraseaktivität in subkutanen NMRI-Foxn1nu/nu Mausmodellen des TERT-rearrangierten Neuroblastoms bei klinisch relevanten Dosen. Dies zeigt das translationale Potential und die klinische Durchführbarkeit der Panobinostat-Behandlung. ChIP Sequenzierung und Methylierungsanalyse zeigten keine bedeutenden Unterschiede der Histonmodifikationen und der Methylierung von CpG Dinukleotiden am TERT Lokus nach Panobinostatbehandlung. Die Inhibierung der de novo RNA Synthese zeigte, dass die Stabilität des TERT mRNA Transkripts nach Panobinostatbehandlung verringert war. Dies deutet darauf hin, dass die reduzierte Transkriptstabilität der zugrundeliegende molekulare Mechanismus ist. Zusammenfassend konnte gezeigt werden, dass die hohe Telomeraseaktivität in TERT-rearrangierten Neuroblastommodellen durch den Einsatz zugelassener HDACi supprimiert werden kann. / Telomerase activation by genomic TERT-rearrangements defines a subgroup of high-risk neuroblastomas with adverse outcome. Accordingly, telomerase activity presents a high-priority drug target with no currently available clinical inhibitors. It was assessed whether telomerase activity could be inhibited through histone deacetylase (HDAC) inhibition in models of TERT-rearranged neuroblastoma. Treatment with a panel of seven pan-, class I- or specific HDAC1/2 inhibitors suppressed TERT mRNA expression and telomerase activity in TERT-rearranged neuroblastoma cells at clinically achievable concentrations. RNA interference-based studies confirmed that HDAC1 and HDAC2 positively regulate TERT transcript levels. Enforced TERT expression partly rescued the anti-proliferative effect of HDAC inhibition indicating a causal role of TERT suppression in the HDAC inhibitormediated tumor-suppressive phenotype. Panobinostat treatment, in preventive and therapeutic settings, considerably attenuated tumor growth in subcutaneous TERT-rearranged neuroblastoma xenograft models in NMRI-Foxn1nu/nu mice and suppressed TERT transcript levels and telomerase activity at clinically relevant doses, thus demonstrating translational potential and clinical feasibility. ChIP sequencing detected no major differences in the chromatin context of the TERT locus between HDAC inhibitor-treated and control cells. Likewise, HDAC inhibition did not substantially alter the methylation profile in the TERT region. Blocking de novo RNA synthesis, however, reduced TERT mRNA transcript levels in HDAC inhibitor-treated cells, suggesting reduced TERT transcript stability as the underlying molecular mechanism. In summary, high-level telomerase activity caused by genomic rearrangements in neuroblastoma models is suppressed by treatment with clinically approved HDAC inhibitors, suggesting indirect druggability and a potential molecular rationale for therapeutic intervention.

The principle of trained immunity represents innate immune memory due to sustained, mainly epigenetic, changes triggered by endogenous or exogenous stimuli in bone marrow (BM) progenitors (central trained immunity) and their innate immune cell progeny, thereby triggering elevated responsiveness against secondary stimuli. BM progenitors can respond to microbial and sterile signals, thereby possibly acquiring trained immunity-mediated long-lasting alterations that may shape the fate and function of their progeny, for example, neutrophils. Neutrophils, the most abundant innate immune cell population, are produced in the BM from committed progenitor cells in a process designated granulopoiesis. Neutrophils are the first responders against infectious or inflammatory challenges and have versatile functions in immunity. Together with other innate immune cells, neutrophils are effectors of peripheral trained immunity. However, given the short lifetime of neutrophils, their ability to acquire immunological memory may lie in the central training of their BM progenitors resulting in generation of reprogrammed, that is, “trained”, neutrophils. Although trained immunity may have beneficial effects in infection or cancer, it may also mediate detrimental outcomes in chronic inflammation. Here, we review the emerging research area of trained immunity with a particular emphasis on the role of neutrophils and granulopoiesis.

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