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Sonstige beteiligte Institutionen
- Fraunhofer-Institut für Silicatforschung ISC (2)
- Helmholtz Institute for RNA-based Infection Research (HIRI) (2)
- Department of Cellular Therapies, University of Navarra, Pamplona, Spain (1)
- Department of X-ray Microscopy, University of Würzburg, Würzburg, Germany (1)
- EMBL Heidelberg (1)
- Institut Ruđer Bošković, Zagreb, Croatia (1)
- Institut für Tierökologie und Tropenbiologie (1)
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- Klinik und Poliklinik für Anästhesiologie, Intensivmedizin, Notfallmedizin und Schmerztherapie des Universitätsklinikums Würzburg (1)
- Lehrstuhl für Fernerkundung der Uni Würzburg, in Kooperation mit dem Deutschen Fernerkundungsdatenzentrum (DFD) des Deutschen Zentrums für Luft- und Raumfahrt (DLR) (1)
EU-Project number / Contract (GA) number
- 320377 (1)
Anxiety disorders are the most prevalent group of neuropsychiatric disorders and go along with high personal suffering. They often arise during childhood and show a progression across the life span, thus making this age a specific vulnerable period during development. Still most research about these disorders is done in adults. In light of this, it seems of utmost importance to identify predictive factors of anxiety disorders in children and adolescents. Temperament or personality traits have been proclaimed as risk markers for the development of subsequent anxiety disorders, but their exact interplay is not clear. In this dissertation an effort is made to contribute to the understanding of how risk markers of early temperamental traits, in this case Trait Anxiety, Anxiety Sensitivity and Separation Anxiety are interplaying. While Trait Anxiety is regarded as a more general tendency to react anxiously to threatening situations or stimuli (Unnewehr, Joormann, Schneider, & Margraf, 1992), Anxiety Sensitivity is the tendency to react with fear to one’s own anxious sensations (Allan et al., 2014; S. Reiss, Peterson, Gursky, & McNally, 1986), and Separation Anxiety is referring to the extent to which the child is avoiding certain situations because of the fear of being separated from primary care givers (In-Albon & Schneider, 2011). In addition, it will be addressed how these measurements are associated with negative life events, as well as brain functioning and if they are malleable by a prevention program in children and adolescents. In study 1 the aim was to extend the knowledge about the interrelations of this anxiety dimensions and negative life events. Results indicated positive correlations of all three anxiety traits as well as with negative life events. Thus, a close connection of all three anxiety measures as well as with negative life events could be indicated. The closest association was found between Anxiety Sensitivity and Trait Anxiety and between Separation Anxiety and Anxiety Sensitivity. Furthermore, negative life events functioned as mediator between Anxiety Sensitivity and Trait Anxiety, indicating that a part of the association was explained by negative life events. In study 2 we extended the findings from study 1 with neurobiological parameters and examined the influence of anxiety traits on emotional brain activation by administering the “emotional face matching task”. This task activated bilateral prefrontal regions as well as both hippocampi and the right amygdala. Further analyses indicated dimension-specific brain activations: Trait Anxiety was associated with a hyperactivation of the left inferior frontal gyrus (IFG) and Separation Anxiety with a lower activation bilaterally in the IFG and the right middle frontal gyrus (MFG). Furthermore, the association between Separation Anxiety and Anxiety Sensitivity was moderated by bi-hemispheric Separation-Anxiety-related IFG activation. Thus, we could identify distinct brain activation patterns for the anxiety dimensions (Trait Anxiety and Separation Anxiety) and their associations (Separation Anxiety and Anxiety Sensitivity). The aim of study 3 was to probe the selective malleability of the anxiety dimensions via a prevention program in an at-risk population. We could identify a reduction of all three anxiety traits from pre- to post-prevention-assessment and that this effect was significant in Anxiety Sensitivity and Trait Anxiety scores. Furthermore, we found that pre-intervention Separation Anxiety and Anxiety Sensitivity post-intervention were associated. In addition, pre-interventive scores were correlated with the intervention-induced change within the measure (i.e., the higher the score before the intervention the higher the prevention-induced change) and pre-intervention Anxiety Sensitivity correlated with the change in Separation Anxiety scores. All relations, seemed to be direct, as mediation/moderation analyses with negative life events did not reveal any significant effect. These results are very promising, because research about anxiety prevention in children and adolescents is still rare and our results are indicating that cognitive-behavioural-therapy based prevention is gilding significant results in an indicated sample even when samples sizes are small like in our study.
In sum the present findings hint towards distinct mechanisms underlying the three different anxiety dimensions on a phenomenological and neurobiological level, though they are highly overlapping (Higa-McMillan, Francis, Rith-Najarian, & Chorpita, 2016; Taylor, 1998). Furthermore, the closest associations were found between Anxiety Sensitivity and Trait Anxiety, as well as between Separation Anxiety and Anxiety Sensitivity. Specifically, we were able to find a neuronal manifestation of the association between Separation Anxiety and Anxiety Sensitivity (Separation Anxiety-specific IFG activation) and a predictive potential on prevention influence. The results of these studies lead to a better understanding of the etiology of anxiety disorders and the interplay between different anxiety-related temperamental traits and could lead to further valuable knowledge about the intervention as well as further prevention strategies.
In mammals, a major fraction of the genome is transcribed as non-coding RNAs. An increasing amount of evidence has accumulated showing that non-coding RNAs play important roles both for normal cell function and in disease processes such as cancer or neurodegeneration. Interpreting the functions of non-coding RNAs and the molecular mechanisms through which they act is one of the most important challenges facing RNA biology today.
In my Ph.D. thesis, I have been investigating the role of 7SK, one of the most abundant non-coding RNAs, in the development and function of motoneurons. 7SK is a highly structured 331 nt RNA transcribed by RNA polymerase III. It forms four stem-loop (SL) structures that serve as binding sites for different proteins. Larp7 binds to SL4 and protects the 3' end from exonucleolytic degradation. SL1 serves as a binding site for HEXIM1, which recruits the pTEFb complex composed of CDK9 and cyclin T1. pTEFb has a stimulatory role for transcription and is regulated through sequestration by 7SK. More recently, a number of heterogeneous nuclear ribonucleoproteins (hnRNPs) have been identified as 7SK interactors. One of these is hnRNP R, which has been shown to have a role in motoneuron development by regulating axon growth. Taken together, 7SK’s function involves interactions with RNA binding proteins, and different RNA binding proteins interact with different regions of 7SK, such that 7SK can be considered as a hub for recruitment and release of different proteins. The questions I have addressed during my Ph.D. are as follows: 1) which region of 7SK interacts with hnRNP R, a main interactor of 7SK? 2) What effects occur in motoneurons after the protein binding sites of 7SK are abolished? 3) Are there additional 7SK binding proteins that regulate the functions of the 7SK RNP?
Using in vitro and in vivo experiments, I found that hnRNP R binds both the SL1 and SL3 region of 7SK, and also that pTEFb cannot be recruited after deleting the SL1 region but is able to bind to a 7SK mutant with deletion of SL3. In order to answer the question of how the 7SK mutations affect axon outgrowth and elongation in mouse primary motoneurons, we proceeded to conduct rescue experiments in motoneurons by using lentiviral vectors. The constructs were designed to express 7SK deletion mutants under the mouse U6 promoter and at the same time to drive expression of a 7SK shRNA from an H1 promoter for the depletion of endogenous 7SK. Using this system we found that 7SK mutants harboring deletions of either SL1 or SL3 could not rescue the axon growth defect of 7SK-depleted motoneurons suggesting that 7SK/hnRNP R complexes are integral for this process.
In order to identify novel 7SK binding proteins and investigate their functions, I proceeded to conduct pull-down experiments by using a biotinylated RNA antisense oligonucleotide that targets the U17-C33 region of 7SK thereby purifying endogenous 7SK complexes. Following mass spectrometry of purified 7SK complexes, we identified a number of novel 7SK interactors. Among these is the Smn complex. Deficiency of the Smn complex causes the motoneuron disease spinal muscular atrophy (SMA) characterized by loss of lower motoneurons in the spinal cord. Smn has previously been shown to interact with hnRNP R. Accordingly, we found Smn as part of 7SK/hnRNP R complexes. These proteomics data suggest that 7SK potentially plays important roles in different signaling pathways in addition to transcription.
Background
Tobacco smoking is accountable for more than one in ten deaths in patients with cardiovascular disease. Thus, smoking cessation has a high priority in secondary prevention of coronary heart disease (CHD). The present study meant to assess smoking cessation patterns, identify parameters associated with smoking cessation and investigate personal reasons to change or maintain smoking habits in patients with established CHD.
Methods
Quality of CHD care was surveyed in 24 European countries in 2012/13 by the fourth European Survey of Cardiovascular Disease Prevention and Diabetes. Patients 18 to 79 years of age at the date of the CHD index event hospitalized due to first or recurrent diagnosis of coronary artery bypass graft, percutaneous coronary intervention, acute myocardial infarction or acute myocardial ischemia without infarction (troponin negative) were included. Smoking status and clinical parameters were iteratively obtained a) at the cardiovascular disease index event by medical record abstraction, b) during a face-to-face interview 6 to 36 months after the index event (i.e. baseline visit) and c) by telephone-based follow-up interview two years after the baseline visit. Parameters associated with smoking status at the time of follow-up interview were identified by logistic regression analysis. Personal reasons to change or maintain smoking habits were assessed in a qualitative interview and analyzed by qualitative content analysis.
Results
One hundred and four of 469 (22.2%) participants had been classified current smokers at the index event and were available for follow-up interview. After a median observation period of 3.5 years (quartiles 3.0, 4.1), 65 of 104 participants (62.5%) were classified quitters at the time of follow-up interview. There was a tendency of diabetes being more prevalent in quitters vs non-quitters (37.5% vs 20.5%, p=0.07). Higher education level (15.4% vs 33.3%, p=0.03) and depressed mood (17.2% vs 35.9%, p=0.03) were less frequent in quitters vs non-quitters. Quitters more frequently participated in cardiac rehabilitation programs (83.1% vs 48.7%, p<0.001). Cardiac rehabilitation appeared as factor associated with smoking cessation in multivariable logistic regression analysis (OR 5.19, 95%CI 1.87 to 14.46, p=0.002). Persistent smokers at telephone-based follow-up interview reported on addiction as wells as relaxation and pleasure as reasons to continue their habit. Those current and former smokers who relapsed at least once after a quitting attempt, stated future health hazards as their main reason to undertake quitting attempts. Prevalent factors leading to relapse were influence by their social network and stress. Successful quitters at follow-up interview referred to smoking-related harm done to their health having had been their major reason to quit.
Interpretation
Participating in a cardiac rehabilitation program was strongly associated with smoking cessation after a cardiovascular disease index event. Smoking cessation counseling and relapse prophylaxis may include alternatives for the pleasant aspects of smoking and incorporate effective strategies to resist relapse.
Experimental investigation of the effect of distal stress induction on threat conditioning in humans
(2022)
Stress constitutes a major risk factor for the development of psychiatric disorders, such as PTSD and anxiety disorders, by shifting the brain into a state of sensitization and makes it more vulnerable when being exposed to further aversive events. This was experimentally in-vestigated in rodents by examining the effect of a distal stress induction on threat conditioning, where stress impaired extinction learning and caused spontaneous recovery. However, this effect has never been experimentally investigated in humans, so far. Thus, the aim of this dissertation was to investigate the effect of distal stress on threat conditioning in humans.
Therefore, two subsequent studies were conducted. For both studies, the threat conditioning paradigm comprised threat acquisition, extinction learning, and re-extinction. In the threat acquisition phase, two geometrical shapes were used as conditioned stimulus (CS), from which one (CS+) was paired with a painful electric stimulus (unconditioned stimulus, US), but not the other one (CS-). During extinction learning 24 h later and re-extinction seventeen days later, CSs were again presented but without any US delivery.
In Study 1, 69 participants underwent either a stress (socially evaluated cold pressor test; SECPT) or sham protocol 10 days prior to threat conditioning. Furthermore, context effects were examined by placing the stress protocol in the same context (context-A stress, and sham group) or a different context (context-B stress group) than conditioning. Results revealed that the context-A, but not context-B, stress group displayed impaired safety learning (i.e. potenti-ation towards CS-) for startle response during threat acquisition. Moreover, the same stress group showed impaired threat extinction, evident in sustained CS discrimination in valence and arousal ratings during extinction learning, and memory recall. In sum, distal stress on the one hand impaired safety learning during threat conditioning on a level of startle response. On the other hand, stress impaired threat extinction on a level of ratings. Noteworthy, the effect of distal stress was only found when the stressor was placed in the same context as later threat learning. Hence, suggesting that the combination of stressor and stressor-associated context exerted the effect on threat extinction.
In Study 2, it was examined if distal stress induction could also have an impact on threat and extinction processes without the necessity of context association. Therefore, the same stress (n = 45) or sham protocol (n = 44) as in Study 1 was conducted in a different context than and 24 h prior to a threat conditioning paradigm. Similar to Study 1, weakened extinction learning was found in fear ratings for the stress (vs. sham) group, which was indicated by persistent CS+/CS- differentiation after the first block of extinction trials. Alterations in safety learning towards the CS- during threat acquisition were only supported by significant correlations between stress measures on the stress day and conditioned startle response of the CS- during acquisition.
Taken together, in two subsequent studies this dissertation provided first evidence of impaired threat extinction after distal stress induction in humans. Furthermore, impairments in safety learning, as can be observed in PTSD, were additionally demonstrated. Interestingly, the effects were boosted and more profound when associating the stressor to the later learning context. These results have clinical implications as they can be translated to the notion that prior stress exposure makes an individual more vulnerable for later aversive events.
The importance of proactive and timely prediction of critical events is steadily increasing, whether in the manufacturing industry or in private life. In the past, machines in the manufacturing industry were often maintained based on a regular schedule or threshold violations, which is no longer competitive as it causes unnecessary costs and downtime. In contrast, the predictions of critical events in everyday life are often much more concealed and hardly noticeable to the private individual, unless the critical event occurs. For instance, our electricity provider has to ensure that we, as end users, are always supplied with sufficient electricity, or our favorite streaming service has to guarantee that we can watch our favorite series without interruptions. For this purpose, they have to constantly analyze what the current situation is, how it will develop in the near future, and how they have to react in order to cope with future conditions without causing power outages or video stalling.
In order to analyze the performance of a system, monitoring mechanisms are often integrated to observe characteristics that describe the workload and the state of the system and its environment. Reactive systems typically employ thresholds, utility functions, or models to determine the current state of the system. However, such reactive systems cannot proactively estimate future events, but only as they occur. In the case of critical events, reactive determination of the current system state is futile, whereas a proactive system could have predicted this event in advance and enabled timely countermeasures. To achieve proactivity, the system requires estimates of future system states. Given the gap between design time and runtime, it is typically not possible to use expert knowledge to a priori model all situations a system might encounter at runtime. Therefore, prediction methods must be integrated into the system. Depending on the available monitoring data and the complexity of the prediction task, either time series forecasting in combination with thresholding or more sophisticated machine and deep learning models have to be trained.
Although numerous forecasting methods have been proposed in the literature, these methods have their advantages and disadvantages depending on the characteristics of the time series under consideration. Therefore, expert knowledge is required to decide which forecasting method to choose. However, since the time series observed at runtime cannot be known at design time, such expert knowledge cannot be implemented in the system. In addition to selecting an appropriate forecasting method, several time series preprocessing steps are required to achieve satisfactory forecasting accuracy. In the literature, this preprocessing is often done manually, which is not practical for autonomous computing systems, such as Self-Aware Computing Systems. Several approaches have also been presented in the literature for predicting critical events based on multivariate monitoring data using machine and deep learning. However, these approaches are typically highly domain-specific, such as financial failures, bearing failures, or product failures. Therefore, they require in-depth expert knowledge. For this reason, these approaches cannot be fully automated and are not transferable to other use cases. Thus, the literature lacks generalizable end-to-end workflows for modeling, detecting, and predicting failures that require only little expert knowledge.
To overcome these shortcomings, this thesis presents a system model for meta-self-aware prediction of critical events based on the LRA-M loop of Self-Aware Computing Systems. Building upon this system model, this thesis provides six further contributions to critical event prediction. While the first two contributions address critical event prediction based on univariate data via time series forecasting, the three subsequent contributions address critical event prediction for multivariate monitoring data using machine and deep learning algorithms. Finally, the last contribution addresses the update procedure of the system model. Specifically, the seven main contributions of this thesis can be summarized as follows:
First, we present a system model for meta self-aware prediction of critical events. To handle both univariate and multivariate monitoring data, it offers univariate time series forecasting for use cases where a single observed variable is representative of the state of the system, and machine learning algorithms combined with various preprocessing techniques for use cases where a large number of variables are observed to characterize the system’s state. However, the two different modeling alternatives are not disjoint, as univariate time series forecasts can also be included to estimate future monitoring data as additional input to the machine learning models. Finally, a feedback loop is incorporated to monitor the achieved prediction quality and trigger model updates.
We propose a novel hybrid time series forecasting method for univariate, seasonal time series, called Telescope. To this end, Telescope automatically preprocesses the time series, performs a kind of divide-and-conquer technique to split the time series into multiple components, and derives additional categorical information. It then forecasts the components and categorical information separately using a specific state-of-the-art method for each component. Finally, Telescope recombines the individual predictions. As Telescope performs both preprocessing and forecasting automatically, it represents a complete end-to-end approach to univariate seasonal time series forecasting. Experimental results show that Telescope achieves enhanced forecast accuracy, more reliable forecasts, and a substantial speedup. Furthermore, we apply Telescope to the scenario of predicting critical events for virtual machine auto-scaling. Here, results show that Telescope considerably reduces the average response time and significantly reduces the number of service level objective violations.
For the automatic selection of a suitable forecasting method, we introduce two frameworks for recommending forecasting methods. The first framework extracts various time series characteristics to learn the relationship between them and forecast accuracy. In contrast, the other framework divides the historical observations into internal training and validation parts to estimate the most appropriate forecasting method. Moreover, this framework also includes time series preprocessing steps. Comparisons between the proposed forecasting method recommendation frameworks and the individual state-of-the-art forecasting methods and the state-of-the-art forecasting method recommendation approach show that the proposed frameworks considerably improve the forecast accuracy.
With regard to multivariate monitoring data, we first present an end-to-end workflow to detect critical events in technical systems in the form of anomalous machine states. The end-to-end design includes raw data processing, phase segmentation, data resampling, feature extraction, and machine tool anomaly detection. In addition, the workflow does not rely on profound domain knowledge or specific monitoring variables, but merely assumes standard machine monitoring data. We evaluate the end-to-end workflow using data from a real CNC machine. The results indicate that conventional frequency analysis does not detect the critical machine conditions well, while our workflow detects the critical events very well with an F1-score of almost 91%.
To predict critical events rather than merely detecting them, we compare different modeling alternatives for critical event prediction in the use case of time-to-failure prediction of hard disk drives. Given that failure records are typically significantly less frequent than instances representing the normal state, we employ different oversampling strategies. Next, we compare the prediction quality of binary class modeling with downscaled multi-class modeling. Furthermore, we integrate univariate time series forecasting into the feature generation process to estimate future monitoring data. Finally, we model the time-to-failure using not only classification models but also regression models. The results suggest that multi-class modeling provides the overall best prediction quality with respect to practical requirements. In addition, we prove that forecasting the features of the prediction model significantly improves the critical event prediction quality.
We propose an end-to-end workflow for predicting critical events of industrial machines. Again, this approach does not rely on expert knowledge except for the definition of monitoring data, and therefore represents a generalizable workflow for predicting critical events of industrial machines. The workflow includes feature extraction, feature handling, target class mapping, and model learning with integrated hyperparameter tuning via a grid-search technique. Drawing on the result of the previous contribution, the workflow models the time-to-failure prediction in terms of multiple classes, where we compare different labeling strategies for multi-class classification. The evaluation using real-world production data of an industrial press demonstrates that the workflow is capable of predicting six different time-to-failure windows with a macro F1-score of 90%. When scaling the time-to-failure classes down to a binary prediction of critical events, the F1-score increases to above 98%.
Finally, we present four update triggers to assess when critical event prediction models should be re-trained during on-line application. Such re-training is required, for instance, due to concept drift. The update triggers introduced in this thesis take into account the elapsed time since the last update, the prediction quality achieved on the current test data, and the prediction quality achieved on the preceding test data. We compare the different update strategies with each other and with the static baseline model. The results demonstrate the necessity of model updates during on-line application and suggest that the update triggers that consider both the prediction quality of the current and preceding test data achieve the best trade-off between prediction quality and number of updates required.
We are convinced that the contributions of this thesis constitute significant impulses for the academic research community as well as for practitioners. First of all, to the best of our knowledge, we are the first to propose a fully automated, end-to-end, hybrid, component-based forecasting method for seasonal time series that also includes time series preprocessing. Due to the combination of reliably high forecast accuracy and reliably low time-to-result, it offers many new opportunities in applications requiring accurate forecasts within a fixed time period in order to take timely countermeasures. In addition, the promising results of the forecasting method recommendation systems provide new opportunities to enhance forecasting performance for all types of time series, not just seasonal ones. Furthermore, we are the first to expose the deficiencies of the prior state-of-the-art forecasting method recommendation system.
Concerning the contributions to critical event prediction based on multivariate monitoring data, we have already collaborated closely with industrial partners, which supports the practical relevance of the contributions of this thesis. The automated end-to-end design of the proposed workflows that do not demand profound domain or expert knowledge represents a milestone in bridging the gap between academic theory and industrial application. Finally, the workflow for predicting critical events in industrial machines is currently being operationalized in a real production system, underscoring the practical impact of this thesis.
Ongoing research to fight cancer, one of the dominant diseases of the 21st century has led to big progress especially when it comes to understanding the tumor growth and metastasis. This includes the discovery of the molecular mechanisms of tumor vascularization, which is critically required for establishment of tumor metastasis.
Formation of new blood vessels is the first step in tumor vascularization. Therefore, understanding the molecular and cellular basis of tumor vascularization attracted a significant effort studying in biomedical research. The blood vessels for supplying tumor can be formed by sprouting from pre-existing vessels, a process called angiogenesis, or by vasculogenesis, that is de novo formation of blood vessels from not fully differentiated progenitor cell populations. Vasculogenic endothelial progenitor cells (EPCs) can either be activated from populations in the bone marrow reaching the pathological region via the circulation or they can be recruited from local reservoirs. Neovessel formation influences tumor progression, hence therapeutic response model systems of angiogenesis/vasculogenesis are necessary to study the underlying mechanisms. Although, initially the research in this area focused more on angiogenesis, it is now well understood that both angiogenesis and postnatal vasculogenesis contribute to neovessel formation in adult under both most pathological as well as physiological conditions. Studies in the last two decades demonstrate that in addition to the intimal layer of fully differentiated mature endothelial cells (ECs) and various smaller supplying vessels (vasa vasorum) that can serve as a source for new vessels by angiogenesis, especially the adventitia of large and medium size blood vessels harbors various vascular wall-resident stem and progenitor cells (VW-SPCs) populations that serve as a source for new vessels by postnatal vasculogenesis. However, little is known about the potential role of VW-SPCs in tumor vascularization.
To this end, the present work started first to establish a modified aortic ring assay (ARA) using mouse aorta in order to study the contribution of vascular adventitia-resident VW-SPCs to neovascularization in general and in presence of tumor cells. ARA is already established an ex vivo model for neovascularization allows to study the morphogenetic events of complex new vessel formation that includes all layers of mature blood vessels, a significant advantage over the assays that employ monolayer endothelial cell cultures. Moreover, in contrast to assays employing endothelial cells monocultures, both angiogenic and vasculogenic events take place during new vessel formation in ARA although the exact contribution of these two processes to new vessel formation cannot be easily distinguished in conventional ARA. Thus, in this study, a modified protocol for the ARA (mdARA) was established by either removing or keeping the aortic adventitia in place. The mdARA allows to distinguish the role of VW-SPCs from those of other aortic layers. The present data show that angiogenic sprouting from mature aortic endothelium was markedly delayed when the adventitial layer was removed. Furthermore, the network between the capillary-like sprouts was significantly reduced in absence of aortic adventitia. Moreover, the stabilization of new sprouts by assembling the NG2+ pericyte-like cells that enwrapped the endothelial sprouts from the outside was improved when the adventitial layer remained in place.
Next, mimicking the tumor-vessel adventitia-interaction, multicellular tumor spheroids (MCTS) and aortic rings (ARs) with or without adventitia of C57BL/6-Tg (UBC-GFP) mice were confronted within the collagen gel and cultured ex vivo. This 3D model enabled analysis of the mobilization, migration and capillary-like sprouts formation by VW-SPCs within tumor-vessel wall-interface in comparison to tumor-free side of the ARs. Interestingly, while MCTS preferred the uptake of single vascular adventitia-derived cells, neural spheroids were directly penetrated by capillary-like structures that were sprouted from the aortic adventitia. In summary, the model established in this work allows to study new vessel formation by both postnatal vasculogenesis and angiogenesis under same conditions. It can be applied in various mouse models including reporter mouse models, e.g. Cxcr1 CreER+/mTmG+/- mice, in which GFP-marked macrophages of the vessel wall were directly observed as they mobilized from their niche and migrated into collagen gel. Another benefit of the model is that it can be used for testing different factors such as small molecules, growth factors, cytokines, and drugs with both pro- and anti-angiogenic/vasculogenic effects.
The interaction of bacterial pathogens and the human host is a complex process that has shaped both organisms on a molecular, cellular and population level. When pathogenic bacteria infect the human body, a battle ensues between the host immune system and the pathogen. In order to escape an immune response and to colonize the host, pathogenic bacteria have developed diverse virulence strategies and some pathogens even replicate within host cells. For survival and propagation within the dynamic environment of a host cell, these bacteria interfere with the regulation of host pathways, such as the cell cycle, for their own benefit.
The intracellular pathogen Salmonella Typhimurium invades eukaryotic cells and resides and replicates in a modified vacuolar compartment in which it is protected from the innate immune response. To this end, it employs a set of virulence factors that help to invade cells (SPI-1 effectors) and to hijack and modify the host endolysosomal system, in order to stabilize and mature its vacuolar niche (SPI-2 effectors). Previous studies have shown that Salmonella arrests host cells in G2/M phase and that Salmonella infected cells progress faster from G1 into S phase, suggesting that the G1 phase is disadvantageous for Salmonella infection. In fact, it has already been observed that Salmonella replication is impaired in G1 arrested cells. However, the reason for this impairment remained unclear.
The current study addressed this question for the first time and revealed that the highly adapted, intracellular lifestyle of Salmonella is drastically altered upon G1 arrest of the host cell. It is shown that proteasomal degradation in G1 arrested cells is delayed and endolysosomal and autophagosomal trafficking is compromised. Accordingly, processing of lysosomal proteins is insufficient and lysosomal activity is decreased; resulting in uneven distribution and accumulation of endolysosomes and autophagosomes, containing undegraded cargo. The deregulation of these cellular signaling pathways affects maturation of the Salmonella containing vacuole (SCV). For the first time it is shown that acidification of SCVs is impaired upon G1 arrest. Thus, an important environmental factor for the switch from SPI-1 to SPI-2 gene expression is
missing and the SPI-2 system is not activated. Consequently, targeting and modification of host cell structures by SPI-2 effectors e.g. recruitment of endolysosomal membrane proteins, like LAMP1, or exchange of endosomal cargo, is compromised.
In addition, degradation of Salmonella SPI-1 effectors by the host proteasome is delayed. Their prolonged presence sustained the recruitment of early endosomes and contributed to the SCV remaining in an early, vulnerable maturation stage. Finally, it was shown that SCV membrane integrity is compromised; the early SCV ruptures and bacteria are released into the cytoplasm. Depending on the host cell type, SPI-2 independent, cytoplasmic replication is promoted. This might favor bacterial spreading, dissemination into the tissue and provide an advantage in host colonization.
Overall, the present study establishes a link between host cell cycle regulation and the outcome of Salmonella infection. It fills the gap of knowledge as to why the host cell cycle stage is of critical importance for Salmonella infection and sheds light on a key aspect of host-pathogen interaction.
Agrochemicals like systemic active ingredients (AI) need to penetrate the outermost barrier of the plant, known as the plant cuticle, to reach its right target site. Therefore, adjuvants are added to provide precise and efficient biodelivery by i.a. modifying the cuticular barrier and increasing the AI diffusion. This modification process is depicted as plasticization of the cuticular wax which mainly consists of very long-chain aliphatic (VLCA) and cyclic compounds. Plasticization of cuticular waxes is pictured as an increase of amorphous domains and/or a decrease of crystalline fractions, but comprehensive, experimental proof is lacking to date. Hence, the objective of this thesis was to i) elucidate the permeation barrier of the plant cuticle to AIs in terms of the different wax fractions and ii) holistically investigate the modification of this barrier using selected oil and surface active adjuvants, an aliphatic leaf wax and an artificial model wax. Therefore, the oil adjuvant methyl oleate (MeO) and other oil derivatives like methyl linolenate (MeLin), methyl stearate (MeSt) and oleic acid (OA) were selected. Three monodisperse, non-ionic alcohol ethoxylates with increasing ethylene oxide monomer (EO) number (C10E2, C10E5, C10E8) were chosen as representatives of the group of surface active agents (surfactants). Both adjuvant classes are commonly used as formulation aids for agrochemicals which are known for its penetration enhancing effect. The aliphatic leaf wax of Schefflera elegantissima was selected, as well as a model wax comprising the four most abundant cuticular wax compounds of this species. Permeation, transpiration and penetration studies were conducted using enzymatically isolated cuticles of Prunus laurocerasus and Garcinia xanthochymus.
Cuticular permeability to the three organic solutes theobromine, caffeine and azoxystrobin differing in lipophilicity was measured using a steady-state two-chamber system separated by the isolated leaf cuticles of the evergreen species P. laurocerasus and G. xanthochymus. Treating the isolated cuticles with methanol selectively removed the cyclic fraction, and membrane permeability to the organic compounds was not altered. In contrast, fully dewaxing the membranes using chloroform resulted in a statistically significant increase in permeance for all compounds and species, except caffeine with cuticles of G. xanthochymus due to a matrix-specific influence on the semi-hydrophilic compound. Crystalline regions may reduce the accessibility to the lipophilic pathway across the waxes and also block hydrophilic domains in the cuticle.
Knowing that the aliphatic wax fraction builds the cuticular diffusion barrier, the influence of the adjuvants on the phase behaviour of an aliphatic cuticular wax as well as the influence on the cuticular penetration of AIs were investigated. Differential scanning calorimetry (DSC) and Fourier-transform infrared spectroscopy (FTIR) were selected to investigate the phase behaviour and thus possible plasticization of pure Schefflera elegantissima leaf wax, its artificial model wax comprising the four most abundant compounds (n-nonacosane, n-hentriacontane, 1-triacontanol and 1-dotriacontanol) and wax adjuvant mixtures. DSC thermograms showed a shift of the melting ranges to lower temperatures and decreased absolute values of the total enthalpy of transition (EOT) for all adjuvant leaf wax blends at 50 % (w/w) adjuvant proportion. The highest decrease was found for C10E2 followed by MeO > OA and C10E8 > MeLin > MeSt. The aliphatic crystallinity determined by FTIR yielded declined values for the leaf and the artificial wax with 50 % MeO. All other adjuvant leaf wax blends did not show a significant decrease of crystallinity. As it is assumed that the cuticular wax is formed by crystalline domains which consist of aliphatic hydrocarbon chains and an amorphous fraction comprising aliphatic chain ends and functional groups, the plasticizers are depicted as wax disruptors influencing amorphization and/or crystallization. The adjuvants can increase crystalline domains using the aliphatic tail whereas their more hydrophilic head is embedded in the amorphous wax fraction. DSC and FTIR showed similar trends using the leaf wax and the model wax in combination with the adjuvants.
In general, cuticular transpiration increased after adding the pure adjuvants to the surface of isolated cuticles or leaf envelopes. As waxes build the cuticular permeation barrier not only to AIs but also to water, the adjuvant wax interaction might affect the cuticular barrier properties leading to increased transpiration. Direct evidence for increased AI penetration with the adjuvants was given using isolated cuticles of P. laurocerasus in combination with the non-steady-state setup simulation of foliar penetration (SOFP) and caffeine at relative humidity levels (RH) of 30, 50 and 80 %. The increase in caffeine penetration was much more pronounced using C10E5 and C10E8 than MeO but always independent of RH. Only C10E2 exhibited an increased penetration enhancing effect positively related to RH. The role of the molecular structure of adjuvants in terms of humectant and plasticizer properties are discussed.
Hence, the current work shows for the first time that the cuticular permeation barrier is associated with the VLCAs rather than the cyclic fraction and that adjuvants structurally influence this barrier resulting in penetration enhancing effects. Additionally, this work demonstrates that an artificial model wax is feasible to mimic the wax adjuvant interaction in conformity with a leaf wax, making it feasible for in-vitro experiments on a larger scale (e.g. screenings). This provides valuable knowledge about the cuticular barrier modification to enhance AI penetration which is a crucial factor concerning the optimization of AI formulations in agrochemistry.
A graph is an abstract network that represents a set of objects, called vertices, and relations between these objects, called edges. Graphs can model various networks. For example, a social network where the vertices correspond to users of the network and the edges represent relations between the users. To better see the structure of a graph it is helpful to visualize it. A standard visualization is a node-link diagram in the Euclidean plane. In such a representation the vertices are drawn as points in the plane and edges are drawn as Jordan curves between every two vertices connected by an edge. Edge crossings decrease the readability of a drawing, therefore, Crossing Optimization is a fundamental problem in Computer Science. This book explores the research frontiers and introduces novel approaches in Crossing Optimization.
The defense against invading pathogens is, amongst other things, mediated via the action of antibodies. Class-switched antibodies and antibodies of high affinity are produced by plasma cells descending from germinal center B (GCB) cells. GCB cells develop in the germinal center (GC), a specialized microstructure found in the B-cell follicle of secondary lymphoid organs. GCB-cell maturation and proliferation are supported by follicular T- helper (Tfh) cells. On the other hand, follicular regulatory T (Tfr) cells control this process in quantity and quality preventing, for instance, the formation of autoantibodies directed against endogenous structures. The development of GCB, Tfh and Tfr cells essentially depends on the migration into the GC, which is mediated via the expression of the chemokine receptor CXCR5.
One transcription factor highly expressed in follicular T cells, comprising Tfh and Tfr cells, is NFATc1. Tfr cells additionally express the transcriptional repressor Blimp-1, which is not expressed in Tfh cells. We found that NFATc1 is transactivating Cxcr5 via response elements in the promoter and enhancer in vitro. Blimp-1 binds to the same elements, transactivating Cxcr5 expression in cooperation with NFATc1, whilst mediating Cxcr5- repression on its own. In Tfr cells Blimp-1 suppresses CXCR5 expression in the absence of NFATc1. Blimp-1 itself is necessary to restrict Tfr-cell frequencies and to mediate Tfr- cell function as in mice with Blimp-1-ablated Tregs high frequencies of Tfr cells do not reduce GCB- or Tfh cell frequencies. NFATc1 and Blimp-1 double deficient Tfr cells show additional loss of function, which becomes visible in clearly expanded antibody titers.
To evaluate the function of NFATc1 in Tfr cells, we not only deleted it, but also overexpressed a constitutive active form of NFATc1/aA (caNFATc1/aA) in regulatory T cells (Tregs). The latter is leading to an upregulation of CXCR5 per cell, without changing Tfh or Tfr-cell frequencies. However, the high density of surface CXCR5 enhances the migration of Tfr cells deep into the GC, which results in a tighter control of the antigen- specific humoral immune response. Additionally, caNFATc1/aA increases the expression of genes coding for Tfr effector molecules like Il1rn, Il10, Tigit and Ctla4. Interestingly, this part of the transcriptional change is dependent on the presence of Blimp-1. Furthermore, Blimp-1 regulates the expression of multiple chemokine receptor genes on the background of caNFATc1/aA.
In contrast, when caNFATc1/aA is overexpressed in all T cells, the frequencies of Tfh- and GCB cells are dominantly reduced. This effect seems to stem from the conventional T- cell (Tcon) side, most probably originating from increased secretion of interleukin-2 (IL- 2) via the caNFATc1/aA overexpressing Tcons. IL-2 is known to hinder the germinal center reaction (GCR) and it might in its abundance not be neutralizable by Tfr cells.
Taken together, NFATc1 and Blimp-1 cooperate to control the migration of Tfr cells into the GC. Tfr cells in the GC depend on NFATc1 and Blimp-1 to perform their proper function. Overexpression of caNFATc1 in Tregs strengthens Tfr function in a Blimp-1-dependent manner, whilst overexpression of caNFATc1 in all T cells dominantly diminishes the GCR.
In this work, accelerated non-Cartesian Magnetic Resonance Imaging (MRI) methods were established and applied to cardiovascular imaging (CMR) at different magnetic field strengths (3T and 7T).
To enable rapid data acquisition, highly efficient spiral k-space trajectories were created. In addition, hybrid sampling patterns such as the twisting radial lines (TWIRL) k-space trajectory were studied.
Imperfections of the dynamic gradient system of a MR scanner result in k-space sampling errors. Ultimately, these errors can lead to image artifacts in non-Cartesian acquisitions.
Among other reasons such as an increased reconstruction complexity, they cause the lack of spiral sequences in clinical routine compared to standard Cartesian imaging.
Therefore, the Gradient System Transfer Functions (GSTFs) of both scanners were determined and used for k-space trajectory correction in post-correction as well as in terms of a pre-emphasis.
The GSTF pre-emphasis was implemented as a fully automatic procedure, which enabled a precise correction of arbitrary gradient waveforms for double-oblique slice orientations.
Consequently, artifacts due to trajectory errors could be mitigated, which resulted in high image quality in non-Cartesian MRI.
Additionally, the GSTF correction was validated by measuring pre-emphasized spiral gradient outputs, which showed high agreement with the theoretical gradient waveforms.
Furthermore, it could be demonstrated that the performance of the GSTF correction is superior to a simple delay compensation approach.
The developed pulse sequences were applied to gated as well as real-time CMR. Special focus lied on the implementation of a spiral imaging protocol to resolve the beating heart of animals and humans in real time and free breathing.
In order to achieve real-time CMR with high spatiotemporal resolution, k-space undersampling was performed. For this reason, efficient sampling strategies were developed with the aim to facilitate compressed sensing (CS) during image reconstruction.
The applied CS approach successfully removed aliasing artifacts and yielded high-resolution cardiac image series. Image reconstruction was performed offline in all cases such that the images were not available immediately after acquisition at the scanner.
Spiral real-time CMR could be performed in free breathing, which led to an acquisition time of less than 1 minute for a whole short-axis stack.
At 3T, the results were compared to the gold standard of electrocardiogram-gated Cartesian CMR in breath hold, which revealed similar values for important cardiovascular functional and volumetric parameters.
This paves the way to an application of the developed framework in clinical routine of CMR.
In addition, the spiral real-time protocol was transferred to swallowing and speech imaging at 3T, and first images were presented.
The results were of high quality and confirm the straightforward utilization of the spiral sequence in other fields of MRI.
In general, the GSTF correction yielded high-quality images at both field strengths, 3T and 7T.
Off-resonance related blurring was mitigated by applying non-Cartesian readout gradients of short duration. At 7T, however, B1-inhomogeneity led to image artifacts in some cases.
All in all, this work demonstrated great advances in accelerating the MRI process by combining efficient, undersampled non-Cartesian k-space coverage with CS reconstruction.
Trajectory correction using the GSTF can be implemented at any scanner model and enables non-Cartesian imaging with high image quality.
Especially MRI of dynamic processes greatly benefits from the presented rapid imaging approaches.
The motivation for this work has been contributing a step to the advancement of technology. A next leap in technology would be the realization of a scalable quantum computer. One potential route is via topological quantum computing. A profound understanding of topological materials is thus essential. My work contributes by the investigation of the exemplary topological material HgTe. The focus lies on the understanding of the topological surface states (TSS) and new possibilities to manipulate them appropriately. Traditionally top gate electrodes are used to adjust the carrier density in such semi-conductor materials. We found that the electric field of the top gate can further alter the properties of the HgTe layer. The formation of additional massive Volkov-Pankratov states limits the accessibility of the TSS. The understanding of these states and their interplay with the TSS is necessary to appropriately design devices and to ensure their desired properties. Similarly, I observed the existence and stability of TSSs even without a bandgap in the bulk band structure in the inversion induced Dirac semi-metal phase of compressively strained HgTe. The finding of topological surface states in inversion-induced Dirac semi-metals provides a consistent and simple explanation for the observation reported for \(\text{Cd}_3\text{As}_2\).
These observations have only been possible due to the high quality of the MBE grown HgTe layers and the access of different phases of HgTe via strain engineering. As a starting point I performed Magneto-transport measurements on 67 nm thick tensilely strained HgTe layers grown on a CdTe substrate. We observed multiple transport channels in this three-dimensional topological insulator and successfully identified them. Not only do the expected topological surface states exist, but also additional massive surface states have been observed. These additional massive surface states are formed due to the electrical field applied at the top gate, which is routinely used to vary the carrier density in the HgTe layer. The additional massive surface states are called Volkov-Pankratov states after B. A. Volkov and O. A. Pankratov. They predicted the existence of similar massive surface states at the interface of materials with mutually inverted bands. We first found indications for such massive Volkov-Pankratov states in high-frequency compressibility measurements for very high electron densities in a fruitful collaboration with LPA in Paris. Magneto-transport measurements and \(k \cdot p\) calculations revealed that such Volkov-Pankratov states are also responsible for the observed whole transport. We also found indications for similar massive VPS in the electron regime, which coexist with the topological surface states. The topological surface states exist over the full investigated gate range including a regime of pure topological insulator transport. To increase the variability of the topological surface states we introduced a modulation doping layer in the buffer layer. This modulation doping layer also enabled us to separate and identify the top and bottom topological surface states.
We used the variability of the bulk band structure of HgTe with strain to engineer the band structure of choice using virtual substrates. The virtual substrates enable us to grow compressively strained HgTe layers that do not possess a bandgap, but instead linear crossing points. These layers are predicted to beDirac semi-metals. Indeed I observed also topological surface states and massive Volkov-Pankratov states in the compressively strained Dirac semi-metal phase. The observation of topological surfaces states also in the Dirac semi-metal phase has two consequences: First, it highlights that no bulk bandgap is necessary to observe topological surface states. Second, the observation of TSS also in the Dirac semi-metal phase emphasizes the importance of the underlying band inversion in this phase. I could not find any clear signatures of the predicted disjoint topological surface states, which are typically called Fermi-arcs. The presence of topological surface states and massive Volkov-Pankratov states offer a simple explanation for the observed quantum Hall effect and other two-dimensional transport phenomena in the class of inversion induced Dirac semi-metals, as \(\text{Cd}_3\text{As}_2\). This emphasizes the importance of the inherent bulk band inversion of different topological materials and provides a consistent and elegant explanation for the observed phenomena in these materials. Additionally, it offers a route to design further experiments, devices, and thus the foundation for the induction of superconductivity and thus topological quantum computing.
Another possible path towards quantum computing has been proposed based on the chiral anomaly. The chiral anomaly is an apparent transport anomaly that manifests itself as an additional magnetic field-driven current in three-dimensional topological semimetals with a linear crossing point in their bulk band structure. I observed the chiral anomaly in compressively strained HgTe samples and performed multiple control experiments to identify the observed reduction of the magnetoresistance with the chiral anomaly. First, the dependence of the so-called negative magnetoresistance on the angle and strength of the magnetic field has been shown to fit the expectation for the chiral anomaly. Second, extrinsic effects as scattering could be excluded as a source for the observed negative MR using samples with different mobilities and thus impurity concentrations. Third, the necessity of the linear crossing point has been shown by shifting the electrochemical potential away from the linear crossing points, which diminished the negative magnetoresistance. Fourth, I could not observe a negative magnetoresistance in the three-dimensional topological insulator phase of HgTe. These observations together prove the existence of the chiral anomaly and verify compressively strained HgTe as Dirac semi-metal. Surprisingly, the chiral anomaly is also present in unstrained HgTe samples, which constitute a semi-metal with a quadratic band touching point. This observation reveals the relevance of the Zeeman effect for the chiral anomaly due to the lifting of the spin-degeneracy in these samples. Additionally to the chiral anomaly, the Dirac semi-metal phase of compressively strained HgTe showed other interesting effects. For low magnetic fields, a strong weak-antilocalization has been observed. Such a strong weak-anti-localization correction in a three-dimensional layer is surprising and interesting. Additionally, non-trivial magnetic field strength and direction dependencies have been observed. These include a strong positive magnetoresistance for high magnetic fields, which could indicate a metal-insulator transition. On a more device-oriented note, the semi-metal phase of unstrained HgTe constitutes the lower limit of the by strain engineering adjustable minimal carrier density of the topological surface states and thus of very high mobility.
To sum up, topological surface states have been observed in the three-dimensional topological insulator phase and the Dirac semi-metal phase of HgTe. The existence and accessibility of topological surface states are thus independent of the existence of a bandgap in the bulk band structure. The topological surface states can be accompanied by massive Volkov-Pankratov states. These VPS are created by electric fields, which are routinely applied to adjust the carrier density in semiconductor devices. The theoretical predicted chiral anomaly has been observed in the Dirac semi-metal phase of HgTe. In contrast to theoretical predictions, no indications for the Fermi-arc called disjoint surface states have been observed, but instead the topological and massive Volkov-Pankratov surface states have been found. These states are thus expected for all inversion-induced topological materials.
In my thesis, I characterized aGPCRs Adgrl1 and Adgrl3, tight junction proteins and the blood-DRG-barrier in rats’ lumbar dorsal root ganglions after traumatic neuropathy. In contrast to the otherwise tightly sealed barriers shielding neural tissues, the dorsal root ganglion’s neuron rich region is highly permeable in its healthy state. Furthermore, the DRG is a source of ectopic signal generation during neuropathy; the exact origin of which is still unclear. I documented expression of Adgrl1 and Adgrl3 in NF200 + , CGRP + and IB4 + neurons. One week after CCI, I observed transient downregulation of Adgrl1 in non-peptidergic nociceptors (IB4+). In the context of previous data, dCirl deletion causing an allodynia-like state in Drosophila, our research hints to a possible role of Adgrl1 nociceptive signal processing and pain resolution in neuropathy. Furthermore, I demonstrated similar claudin-1, claudin-12, claudin-19, and ZO-1 expression of the dorsal root ganglion’s neuron rich and fibre rich region. Claudin-5 expression in vessels of the neuron rich region was lower compared to the fibre rich region. Claudin-5 expression was decreased one week after nerve injury in vessels of the neuron rich region while permeability for small and large injected molecules remained unchanged. Nevertheless, we detected more CD68+ cells in the neuron rich region one week after CCI. As clinically relevant conclusion, we verified the high permeability of the neuron rich regions barrier as well as a vessel specific claudin-5 downregulation after CCI. We observed increased macrophage invasion into the neuron rich region after CCI. Furthermore, we identified aGPCR as potential target for further research and possible treatments for neuropathy, which should be easily accessible due to the blood-DRG-barriers leaky nature. Its precise function in peripheral tissues, its mechanisms of activation, and its role in pain resolution should be evaluated further.
Cellular proteome profiling revealed that most biomolecules do not exist in isolation, but rather are incorporated into modular complexes. These assembled complexes are usually very large, consisting of 10 subunits on an average and include either proteins alone, or proteins and nucleic acids. Consequently, such macromolecular assemblies rather than individual biopolymers perform the vast majority of cellular activities. The faithful assembly of such molecular assemblies is often aided by trans-acting factors in vivo, to preclude aggregation of complex components and/or non-cognate interactions. A paradigm for an assisted assembly of a macromolecular machine is the formation of the common Sm/LSm core of spliceosomal and histone-mRNA processing U snRNPs. The key assembly factors united in the Protein Arginine Methyltransferase 5 (PRMT5) and the Survival Motor Neuron (SMN) complexes orchestrate the assembly of the Sm/LSm core on the U snRNAs. Assembly is initiated by the PRMT5-complex subunit pICln, which pre-arranges the Sm/LSm proteins into spatial positions occupied in the mature U snRNPs. The SMN complex subsequently binds these Sm/LSm units, displaces pICln and catalyses the Sm ring closure on the Sm-site of the U snRNA.
The SMN complex consists of the eponoymous SMN protein linked in a modular network of interactions with eight other proteins, termed Gemins 2-8 and Unrip. Despite functional and structural characterisation of individual protein components and/or sub-complexes of this assembly machinery, coherent understanding of the structural framework of the core SMN complex remained elusive. The current work, employing a combined approach of biochemical and structural studies, aimed to contribute to the understanding of how distinct modules within the SMN complex coalecse to form the macromolecular SMN complex.
A novel atomic resolution (1.5 Å) structure of the human Gemin8:7:6 sub-complex, illustrates how the peripheral Gemin7:6 module is tethered to the SMN complex via Gemin8’s C-terminus. In this model, Gemin7 engages with both Gemin6 and Gemin8 via the N- and C-termini of its Sm-fold like domain. This highly conserved interaction mode is reflected in the pronounced sequence conservation and identical biochemical behaviour of similar sub-complexes from divergent species, namely S. pombe and C. elegans.
Despite lacking significant sequence similarity to the Sm proteins, the dimeric Gemin7:6 complex share structural resemblance to the Sm heteromers. The hypothesis that the dimeric Gemin7:6 functions as a Sm-surrogate during Sm core assembly could not be confirmed in this work. The functional relevance of the structural mimicry of the dimeric Gemin7:6 sub-complex with the Sm heterodimers therefore still remains unclear.
Reduced levels of functional SMN protein is the cause of the devastating neurodegenerative disease, Spinal Muscular Atrophy (SMA). The C-terminal YG-zipper motif of SMN is a major hot-spot for most SMA patient mutations. In this work, adding to the existing inventory of the human and fission yeast YG-box models, a novel 2.2 Å crystal structure of the nematode SMN’s YG-box domain adopting the glycine zipper motif has been reported. Furthermore, it could be assessed that SMA patient mutations mapping to this YG-box domain greatly influences SMN’s self-association competency, a property reflected in both the human and nematode YG-box biochemical handles. The shared molecular architecture and biochemical behaviour of the nematode SMN YG-box domain with its human and fission yeast counterparts, reiterates the pronounced conservation of this oligomerisation motif across divergent organisms.
Apart from serving as a multimerization domain, SMN’s YG-box also acts as interaction platform for Gemin8. A systematic investigation of SMA causing missense mutations uncovered that Gemin8’s incorporation into the SMN complex is influenced by the presence of certain SMA patient mutations, albeit independent of SMN’s oligomerisation status. Consequently, loss of Gemin8 association in the presence of SMA patient mutations would also affect the incorporation of Gemin7:6 sub-complex. Gemin8, therefore sculpts the heteromeric SMN complex by bridging the Gemin7:6 and SMN:Gemin2 sub-units, a modular feature shared in both the human and nematode SMN complexes.
These findings provide an important foundation and a prospective structural framework for elucidating the core architecture of the SMN complex in the ongoing Cryo-EM studies.
The dissertation investigates the wide class of Epstein zeta-functions in terms of uniform distribution modulo one of the ordinates of their nontrivial zeros. Main results are a proof of a Landau type theorem for all Epstein zeta-functions as well as uniform distribution modulo one for the zero ordinates of all Epstein zeta-functions asscoiated with binary quadratic forms.
In the central nervous system, excitatory and inhibitory signal transduction processes are mediated by presynaptic release of neurotransmitters, which bind to postsynaptic receptors. Glycine receptors (GlyRs) and GABAA receptors (GABAARs) are ligand-gated ion channels that enable synaptic inhibition. One part of the present thesis elucidated the role of the GlyRα1 β8 β9 loop in receptor expression, localization, and function by means of amino acid substitutions at residue Q177. This residue is underlying a startle disease phenotype in the spontaneous mouse model shaky and affected homozygous animals are dying 4-6 weeks after birth. The residue is located in the β8 β9 loop and thus part of the signal transduction unit essential for proper ion channel function. Moreover, residue Q177 is involved in a hydrogen network important for ligand binding. We observed no difference in ion channel trafficking to the cellular membrane for GlyRα1Q177 variants. However, electrophysiological measurements demonstrated reduced glycine, taurine, and β alanine potency in comparison to the wildtype protein. Modeling revealed that some GlyRα1Q177 variants disrupt the hydrogen network around residue Q177. The largest alterations were observed for the Q177R variant, which displayed similar effects as the Q177K mutation present in shaky mice. Exchange with structurally related amino acids to the original glutamine preserved the hydrogen bond network. Our results underlined the importance of the GlyR β8 β9 loop for proper ion channel gating.
GlyRs as well as GABAARs can be modulated by numerous allosteric substances. Recently, we focused on monoterpenes from plant extracts and showed positive allosteric modulation of GABAARs. Here, we focused on the effect of 11 sesquiterpenes and sesquiterpenoids (SQTs) on GABAARs. SQTs are compounds naturally occurring in plants. We tested SQTs of the volatile fractions of hop and chamomile, including their secondary metabolites generated during digestion. Using the patch-clamp technique on transfected cells and neurons, we were able to observe significant GABAAR modulation by some of the compounds analyzed. Furthermore, a possible binding mechanism of SQTs to the neurosteroid binding site of the GABAAR was revealed by modeling and docking studies. We successfully demonstrated GABAAR modulation by SQTs and their secondary metabolites.
The second part of the thesis investigated three-dimensional (3D) in vitro cell culture models which are becoming more and more important in different part of natural sciences. The third dimension allows developing of complex models closer to the natural environment of cells, but also requires materials with mechanical and biological properties comparable to the native tissue of the encapsulated cells. This is especially challenging for 3D in vitro cultures of primary neurons and astrocytes as the brain is one of the softest tissues found in the body. Ultra-soft matrices that mimic the neuronal in vivo environment are difficult to handle. We have overcome these challenges using fiber scaffolds created by melt electrowriting to reinforce ultra-soft matrigel. Hence, the scaffolds enabled proper handling of the whole composites and thus structural and functional characterizations requiring movement of the composites to different experimental setups. Using these scaffold-matrigel composites, we successfully established methods necessary for the characterization of neuronal network formation. Before starting with neurons, a mouse fibroblast cell line was seeded in scaffold-matrigel composites and transfected with the GlyR. 3D cultured cells displayed high viability, could be immunocytochemically stained, and electrophysiologically analyzed.
In a follow-up study, primary mouse cortical neurons in fiber-reinforced matrigel were grown for up to 21 days in vitro. Neurons displayed high viability, and quantification of neurite lengths and synapse density revealed a fully formed neuronal network already after 7 days in 3D culture. Calcium imaging and patch clamp experiments demonstrated spontaneous network activity, functional voltage-gated sodium channels as well as action potential firing. By combining ultra-soft hydrogels with fiber scaffolds, we successfully created a cell culture model suitable for future work in the context of cell-cell interactions between primary cells of the brain and tumor cells, which will help to elucidate the molecular pathology of aggressive brain tumors and possibly other disease mechanisms.
Since the first CubeSat launch in 2003, the hardware and software complexity of the nanosatellites was continuosly increasing.
To keep up with the continuously increasing mission complexity and to retain the primary advantages of a CubeSat mission, a new approach for the overall space and ground software architecture and protocol configuration is elaborated in this work.
The aim of this thesis is to propose a uniform software and protocol architecture as a basis for software development, test, simulation and operation of multiple pico-/nanosatellites based on ultra-low power components.
In contrast to single-CubeSat missions, current and upcoming nanosatellite formation missions require faster and more straightforward development, pre-flight testing and calibration procedures as well as simultaneous operation of multiple satellites.
A dynamic and decentral Compass mission network was established in multiple active CubeSat missions, consisting of uniformly accessible nodes.
Compass middleware was elaborated to unify the communication and functional interfaces between all involved mission-related software and hardware components.
All systems can access each other via dynamic routes to perform service-based M2M communication.
With the proposed model-based communication approach, all states, abilities and functionalities of a system are accessed in a uniform way.
The Tiny scripting language was designed to allow dynamic code execution on ultra-low power components as a basis for constraint-based in-orbit scheduler and experiment execution.
The implemented Compass Operations front-end enables far-reaching monitoring and control capabilities of all ground and space systems.
Its integrated constraint-based operations task scheduler allows the recording of complex satellite operations, which are conducted automatically during the overpasses.
The outcome of this thesis became an enabling technology for UWE-3, UWE-4 and NetSat CubeSat missions.
One third of all market approved drugs target G protein coupled receptors (GPCRs), covering a highly diverse spectrum of indications reaching from acute anti-allergic treatment over bloodpressure regulation, Parkinson's disease, schizophrenia up to the treatment of severe pain. GPCRs are key signaling proteins that mostly function as monomers, but for several receptors constitutive dimer formation has been described and in some cases is essential for function. I have investigated this problem using the μ-opioid receptor (µOR) as a model system - based both on its pharmacological importance and on specific biochemical data suggesting that it may present a particularly intriguing case of mono- vs- dimerization. The µOR is the prime target for the treatment of severe pain. In its inactive conformation it crystallizes as homodimer when bound to the antagonist β- funaltrexamine (β-FNA), whereas the active, agonist-bound receptor crystallizes as a monomer. Using single-molecule microscopy combined with superresolution techniques on intact cells, I describe here a dynamic monomer-dimer equilibrium of µORs where dimer formation is driven by specific agonists. The agonist DAMGO, but not morphine, induces dimer formation in a process that correlates temporally and, in its agonist, and phosphorylation dependence with β-arrestin2 binding to the receptors. This dimerization is independent from but may precede µOR internalization. Furthermore, the results show that the μOR tends to stay, on the cell surface, within compartments defined by actin fibers and its mobility is modulated by receptor activation. These data suggest a new level of GPCR regulation that links receptor compartmentalization and dimer formation to specific agonists and their downstream signals.
Neurons are specialized cells dedicated to transmit the nerve impulses throughout the human body across specialized structures called synapses. At the synaptic terminals, a crosstalk between multiple macromolecules regulates the structure and function of the presynaptic nerve endings and the postsynaptic recipient sites.
Gephyrin is the central organizer at inhibitory postsynaptic specializations and plays a crucial role in the organization of these structures by anchoring GABAA receptors (GABAAR) and glycine receptors (GlyR) to the postsynaptic membrane. This 93 kDa protein features an N-terminal G domain and a C-terminal E domain and the latter interacts directly with the intracellular loop between transmembrane helices 3 and 4 of certain subunits of the GlyRs and GABAARs. Biochemical and structural analyses have already provided valuable insights into the gephyrin-GlyR interaction. Interestingly, biochemical studies on the gephyrin-GABAAR interaction demonstrated that the GABAARs also depend on the same binding site as the GlyRs for the interaction with the gephyrin, but the molecular basis for this receptor specific interaction of gephyrin was still unknown. Co-crystal structures of GephE-GABAAR α3- derived peptides with supporting biochemical data presented in this study deciphered the receptor-specific interactions of gephyrin in atomic detail.
In its moonlighting function, gephyrin also catalyzes the terminal step of the evolutionarily conserved molybdenum cofactor biosynthesis. Molybdenum, an essential transition element has to be complexed with a pterin-based cofactor resulting in the formation of the molybdenum cofactor (Moco). Moco is an essential component at the active site of all molybdenum-containing enzymes with the exception of nitrogenase. Mutations in enzymes involved in this pathway lead to a rare yet severe disease called Moco deficiency, which manifest itself in severe neurodevelopmental abnormalities and early childhood death. Moco biosynthesis follows a complex multistep pathway, where in the penultimate step, the N-terminal G domain of gephyrin activates the molybdopterin to form an adenylated molybdopterin intermediate. In the terminal step, this intermediate is then transferred to the C-terminal E domain of gephyrin, which catalyzes the metal insertion and deadenylation reaction to form active Moco. Previous biochemical and structural studies provided valuable insights into the penultimate step of the Moco biosynthesis but the terminal step remained elusive. Through the course of my dissertation, I crystallized the C-terminal E domain in the apo-form as well as in complex with ADP and AMP. These structures shed lightonto the deadenylation reaction and the formation of a ternary E-domain-ADP-Mo/W complex and thus provide structural insight into the metal insertion mechanism. Moreover, the structures also provided molecular insights into a mutation leading to Moco deficiency. Finally, ternary
complexes of GephE, ADP and receptor-derived peptides provided first clues regarding the integration of gephyrin’s dual functionality.
In summary, during the course of the dissertation I was able to derive high resolution structural insights into the interactions between gephyrin and GABAARs, which explain the receptor-specific interaction of gephyrin and, furthermore, these studies can be extended in the future to understand GABAAR subunit-specific interactions of gephyrin. Finally, the understanding of Moco biosynthesis shed light on the molecular basis of the fatal Moco deficiency.