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- Abteilung für Mikrobiologie und Molekularbiologie (8)
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In the present work high density helicon plasma discharges are created and characterized as a promising concept towards the realization of plasma wakefield accelerators to build up electric fields in the order of GV/m to accelerate electrons to energies in the TeV range with proton driving bunches. For such a concept plasma sources are needed that are able to maintain discharges with plasma densities of n_e = 7E20 m^-3 over long distances with a low variation in plasma density. Measurements at the PROMETHEUS-A device are performed for variable parameters, like magnetic induction, RF heating power and filling gas pressure. A CO2 laser interferometer, a laser induced fluorescence (LIF) diagnostic and a reaction rate model are combined to give a full picture. It is shown that in most cases the plasma density is centrally peaked with a high density region +- 5 mm from the center. The peak plasma density increases with increasing filling gas pressure, RF heating power and magnetic induction, limited by the number of neutral particles in low pressure discharges, by the transferred heating power and the increasing recombination and electron quenching rates of argon ions in high filling pressure cases. The increase in plasma density with increasing magnetic induction correlates to the direct proportionality in the helicon dispersion relation. For all investigated operational parameters the time evolution of the helicon discharge shows the same characteristics and is reliably reproducable inside the error bars. The electron temperature is determined by combining the collisional radiative model with line ratio measurements of two spontaneously emitted LIF lines. The low electron temperature regime of 1.2 eV < T_e < 1.4 eV and the electron temperature profiles are consistent with helicon wave heating via collisional power dissipation. The maximum plasma density of n_e = (6 +- 1)E20 m^-3 is measured at high RF power of P_RF = 24 kW, p_0 = 9 Pa filling gas pressure and a magnetic induction of B = 105 mT with a maximum electron temperature at 1.4 eV. At these operational parameters the plasma density peaking time and width are determined to be 270E-6 s and 50E-6 s, respectively. This shows that specific plasma density requirements for the use of a wakefield accelerator are reachable and the duration of the peak plasma density is more than sufficient for a relativistic particle to pass a 1 km long plasma cell. Additionally time-resolved LIF profile measurements for neutral and singly ionized argon were conducted to complement the previously evaluated measurements. The time resolution of the LIF diagnostic was chosen in a way to adequately represent the evolution of densities and to allow full profile measurements over one day. A resolution of 200E-6 s was chosen. The time-resolved neutral and ion metastable densities show hollow profiles with high densities at the edges over the first ms indicating higher ionization levels and increasing electron quenching rates. The metastable densities are highly determined by electron temperature, RF heating power and filling neutral gas pressure and do not reflect the neutral argon evolution. To investigate the influence of neutral depletion on the density evolution and maximum plasma density, the argon neutral and ion ground state densities are determined. Both time-resolved density profiles show a hollow profile with highest densities at the edges over a longer time interval of 3-4 ms. The penetration depths (ionization mean-free paths) indicate increased ionization of neutral argon while dissipating inwards, corresponding well to the theoretical value of lambda = 20 mm. This results in a depletion of neutrals in the center of the discharge, leading to a limitation and a fast decrease of plasma density after the neutrals are partially ionized. The shown refilling effect of neutral argon is too slow to have an important impact. At operation parameters for highest plasma density, the calculated ground states also show a fast increase in density at the end of the discharge after the RF-heating is switched off. This indicates recombination effects to these atomic states and higher ionization levels than ArII in the helicon discharge.
Understanding the fundamental mechanisms in the extracellular matrix of cells (ECM) is crucial for the development of drugs and biomaterials. Therefore, an atomistic model of the extracellular matrix is a cost-efficient way to observe influences of drugs, test the effect of mutations or misfolds in proteins or study the properties of fibril or network-forming peptides.
With this thesis, a refined molecular model of an adhesion complex is proposed that contains collagen, fibronectin and the cell receptor integrin. During the building of the model, major new insights are given for each of these proteins and a powerful protein-folding algorithm is
developed.
This thesis describes experiments with clusters stored in an electrostatic ion trap called Multi-reflection time-of-flight (MR-ToF) analyzer. These devices are established as mass separators and analyzers with high resolving powers and fast processing times. The objective was to characterize an experiment that utilizes such analyzer for cluster research, to this end a laser-ablation ion source was combined with an MR-ToF analyzer.
In the first part, an experiment scheme that combines two operating modes, namely in-trap lift operation and mirror operation, is presented and characterized for the present setup. For ion capture in-trap lift switching was employed and exit-side mirror switching for ejection with higher information content. Measurements were performed with small lead clusters to illustrate individual advantages of both techniques and the gain of combining them with focus on the ions’ ToF ejection window.
In the second part, a recently introduced method of ion separation by transversal ejection of unwanted species inside the trap was studied for the present setup. The ejection is performed by appropriate pulses of the potentials of deflector electrodes located in the trap. The various parameters affecting the selection effectivity and resolving power are illustrated with tin-cluster measurements, with resolving powers of up to several tens of thousands.
The third part presents the experiment in detail, with the construction of each component and measurements for its various performance parameters. Because the heart of the setup is the MR-ToF analyzer the characterization focuses on the trap. In addition, cluster ions were mass selected in the MR-ToF device and photodissociated. The charged fragments were stored and mass analyzed in a proof-of principle MS/MS experiment where both MS steps were performed in the MR-ToF operation mode.
Hyperoxia is a well-known cause of cerebral white matter injury in preterm infants with male sex being an independent and critical risk factor for poor neurodevelopmental outcome. We investigated the underlying mechanisms behind such a sex dependent difference in oligodendrocyte progenitor cells (OPCs). Our findings demonstrate that oxidative stress severely affects cellular functions related to energy metabolism, stress response, and maturation in male derived oligodendrocyte progenitor cells (OPCs) whereas the female cells remain largely unaffected. This impairment of maturation is accompanied by the downregulation of nucleoporin and nuclear lamina proteins. We identify Nup133, which regulates OPC maturation as a major target protein affected by hyperoxia in male cells and that this differential response is mediated by an inverse Nup133 regulation in the male and female cells. It also regulates mitochondrial function and oxidative stress response through its downstream target Nuclear respiratory factor 1 (Nrf1). Additionally, the presence of 17-β estradiol and higher amounts of fetal zone steroids (precursors for maternal estrogen synthesis during fetal development) confer resistance to the female cells mediated by the estrogen receptor alpha (ERα) along with Nup133. Both Nup133 and ERα regulate mitochondrial function and oxidative stress response by transcriptional regulation of Nrf1. These findings establish prominent sex based differences and the molecular mechanisms involved in differential response of OPCs towards oxidative stress and the important role of Nup133 in mediating a severe negative outcome in the male cells.
Microbial cell factories have been largely exploited for the controlled production of recombinant proteins, including industrial enzymes and biopharmaceuticals. The advent of high-throughput ‘-omics’ techniques have boosted the design of these production systems due to their valuable contribution to the field of systems metabolic engineering, a discipline integrating metabolic engineering with systems and synthetic biology. In order to thrive, the field of systems metabolic engineering needs absolute proteomics data to be generated, as proteins are the central players in the complex metabolic and adaptational networks. Due to advent of mass spectrometry-based proteomics, a substantial amount of absolute proteomic data became available in the past decade. However, membrane proteins remained inaccessible to these efforts.
Nonetheless, comparative studies targeting the membrane proteome have been quite successful in characterizing physiological processes. Hence, label-free proteomics was used in a study (Quesada-Ganuza et al, 2019 – Article I) to identify and optimize PrsA in Bacillus subtilis, for improved yield of amylase. Amylase is one of the most relevant enzymes in the biotechnological sector. By employing a label-free mass spectrometry approach targeting the membrane proteome of this bacterium, relative changes in heterologous and native levels of PrsA could be quantified. The results of this study evidenced that each PrsA shows different relative abundancies, but with no relevant impact in the yield of amylase.
Even though relative protein quantification can already provide a good visualization of the physiological changes occurring between different conditions, they are not sufficient to understand how resources are allocated in the cell under certain physiological conditions. Therefore, a global method for absolute membrane protein quantification remains the biggest requirement for systems metabolic engineering.
Hence, with this work, we successfully developed a mass spectrometry-based approach enabling the absolute quantification of membrane proteins (Antelo-Varela et al, 2019 – Article II). This study was also performed in the Gram-positive model organism Bacillus subtilis, regarded as a prolific microbial cell factory. The method developed in this work combines the comprehensiveness of shotgun proteomics with the sensitivity and accuracy of targeted mass spectrometry. Fundamental to the method is that it relies on the application of a correction and an enrichment factor to calibrate absolute membrane protein abundances derived from shotgun mass spectrometry. This has permitted, for the first time reported, the calculation of absolute membrane protein abundances in a living organism.
The newly developed approach enabled to accurately quantify ~40% of the predicted proteome of this bacterium, offering a clear visualization of the physiological rearrangements occurring upon the onset of osmotic stress. In addition, this work also provides evidence for new membrane protein stoichiometries.
Overall, this study enabled the development of a straightforward methodology long-needed in the scientific and biotechnological community and, for the first time reported, providing absolute abundances of one of the most puzzling fractions of the cell – the membrane proteome.
The next step of the work summarized here was to implement the afore described method to a biotechnological relevant strain, as absolute membrane protein abundances are essential to understand the fundamental principles of protein secretion and production stress. Hence, this work was applied in a genome-reduced B. subtilis strain, ‘midiBacillus’, expressing the major staphylococcal antigen IsaA (Antelo-Varela et al, submitted – Article III). The employed absolute membrane protein quantification methodology enabled the analysis of physiological rearrangements occurring upon the induction of heterologous protein production. This work showed that, even though IsaA was successfully secreted into the growth medium, one of the main requirements for the biotechnological sector, it was still partly accumulated in the cell membrane of this bacterium. This led to an exacerbated physiological response where membrane proteins involved in the management of secretion stress were activated. In addition, this study also showed that a rearrangement of the cell’s translocation machinery occurs upon induction of production, where a ‘game’ of in- and decrease of transporters takes place.
Anticipating the impact of genetic and environmental insults, such as the ones caused by production stress, is essential for the field of systems metabolic engineering. Thus, the highly accurate and comprehensive dataset generated during this work can be implemented in predictive mathematical models, thereby contributing in the rational design of next-generation secretion systems.
In phylogenetics, evolutionary relationships of different species are represented by phylogenetic trees.
In this thesis, we are mainly concerned with the reconstruction of ancestral sequences and the accuracy of this reconstruction given a rooted binary phylogenetic tree.
For example, we wish to estimate the DNA sequences of the ancestors given the observed DNA sequences of today living species.
In particular, we are interested in reconstructing the DNA sequence of the last common ancestor of all species under consideration. Note that this last common ancestor corresponds to the root of the tree.
There exist various methods for the reconstruction of ancestral sequences.
A widely used principle for ancestral sequence reconstruction is the principle of parsimony (Maximum Parsimony).
This principle means that the simplest explanation it the best.
Applied to the reconstruction of ancestral sequences this means that a sequence which requires the fewest evolutionary changes along the tree is reconstructed.
Thus, the number of changes is minimized, which explains the name of Maximum Parsimony.
Instead of estimating a whole DNA sequence, Maximum Parsimony considers each position in the sequence separately. Thus in the following, each sequence position is regarded separately, and we call a single position in a sequence state.
It can happen that the state of the last common ancestor is reconstructed unambiguously, for example as A. On the other hand, Maximum Parsimony might be indecisive between two DNA nucleotides, say for example A and C.
In this case, the last common ancestor will be reconstructed as {A,C}.
Therefore we consider, after an introduction and some preliminary definitions, the following question in Section 3: how many present-day species need to be in a certain state, for example A, such that the Maximum Parsimony estimate of the last common ancestor is also {A}?
The answer of this question depends on the tree topology as well as on the number of different states.
In Section 4, we provide a sufficient condition for Maximum Parsimony to recover the ancestral state at the root correctly from the observed states at the leaves.
The so-called reconstruction accuracy for the reconstruction of ancestral states is introduced in Section 5. The reconstruction accuracy is the probability that the true root state is indeed reconstructed and always takes two processes into account: on the one hand the approach to reconstruct ancestral states, and on the other hand the way how the states evolve along the edges of the tree. The latter is given by an evolutionary model.
In the present thesis, we focus on a simple symmetric model, the Neyman model.
The symmetry of the model means for example that a change from A to C is equally likely than a change from C to A.
Intuitively, one could expect that the reconstruction accuracy it the highest when all present-day species are taken into account. However, it has long been known that the reconstruction accuracy improves when some taxa are disregarded for the estimation.
Therefore, the question if there exits at least a lower bound for the reconstruction accuracy arises, i.e. if it is best to consider all today living species instead of just one for the reconstruction.
This is bad news for Maximum Parsimony as a criterion for ancestral state reconstruction, and therefore the question if there exists at least a lower bound for the reconstruction accuracy arises.
In Section 5, we start with considering ultrametric trees, which are trees where the expected number of substitutions from the root to each leaf is the same.
For such trees, we investigate a lower bound for the reconstruction accuracy, when the number of different states at the leaves of the tree is 3 or 4.
Subsequently in Section 6, in order to generalize this result, we introduce a new method for ancestral state reconstruction: the coin-toss method.
We obtain new results for the reconstruction accuracy of Maximum Parsimony by relating Maximum Parsimony to the coin-toss method.
Some of these results do not require the underlying tree to be ultrametric.
Then, in Section 7 we investigate the influence of specific tree topologies on the reconstruction accuracy of Maximum Parsimony. In particular, we consider balanced and imbalanced trees as the balance of a tree may have an influence on the reconstruction accuracy.
We end by introducing the Colless index in Section 8, an index which measures the degree of balance a rooted binary tree can have, and analyze its extremal properties.
Brain aging even in healthy older adults is characterized by a decline in cognitive functions including memory, learning and attention. Among others, memory is one of the major cognitive functions affected by aging. Understanding the mechanisms underlying age-related memory decline may help pave the road for novel treatment strategies. Here, we tried to elucidate the neural correlates associated with memory decline using structural and functional neuroimaging and neuromodulation with transcranial direct current stimulation (tDCS).
Over the course of three studies, we investigated 1) the influence of white matter integrity and grey matter volume on memory performance in healthy older adults, 2) the role of functional coupling within the memory network in predicting memory performance and the impact of tDCS in modulating retrieval performance in healthy older adults, 3) the effect of tDCS over the sensorimotor cortex on cognitive performance in young adults.
MRI was used to study associations of cognitive performance with white matter integrity and grey matter volume, and examine their causal relationship in the course of aging. White matter integrity was assessed by acquiring diffusion tensor imaging (DTI) and performing deterministic tractography based on constrained spherical deconvolution. Grey matter volume was estimated using fully automated segmentation. Both white matter integrity and grey matter volume were correlated with behavioral data of a verbal episodic memory task. Percentage of correct answers at retrieval was used to measure memory performance (Manuscript 1). In addition, anodal tDCS (atDCS) (1 mA, 20 min) was applied over CP5 (left temporoparietal cortex) to modulate memory formation in healthy older adults. Participants underwent resting-state fMRI before the stimulation. Functional connectivity analysis was performed to determine whether functional coupling within the memory network predicted initial memory performance, and to examine its association to tDCS-induced enhancement effect (Manuscript 2). Finally, atDCS (1 mA, 20 min) was applied over C3 (left sensorimotor cortex) to explore the effect of tDCS over the sensorimotor cortex on cognitive performance in young adults. During the stimulation, participants performed three tasks; gestural task, attentional load task and simple reaction time task (Manuscript 3).
Results showed that volumes of the left dentate gyrus (DG) and tractography-based fractional anisotropy (FA) of individual fornix pathways were positively related to memory retrieval in older adults. Brain-behavior associations were observed for correct rejections rather than hits of memory performance, indicating specificity of memory network functioning for detecting false associations. Thus, the data suggested a particular role of neural integrity that promotes successful memory retrieval in older adults. Subsequent mediation analysis showed that left DG volume mediated the effect of fornix FA on memory performance (48%), corrected for age, revealing a crucial role of hippocampal pathway microstructure in modulating memory performance in older adults (Manuscript 1). tDCS results showed that atDCS led to better retrieval performance and increasing learning curves, indicating that brain stimulation can induce plasticity of episodic memory processes in older adults. Combining tDCS and fMRI, hippocampo-temporoparietal functional connectivity was positively associated with initial memory performance in healthy older adults and was positively correlated with the magnitude of individual tDCS-induced enhancement, suggesting that individual tDCS responsiveness may be determined by intrinsic network coupling (Manuscript 2). Finally, our findings suggested that atDCS over left sensorimotor cortex reduced reaction times in the gestural-verbal integration task, specifically for incongruent pairs of gestures and verbal expressions, indicating the role of sensorimotor cortex in gestural-verbal integration in young adults (Manuscript 3).
The results of all three studies may help to elucidate age-related structural deterioration and functional coupling network underlying cognitive processes in healthy adults. Furthermore, these studies emphasized the importance of interventions like tDCS in modulating cognitive performance, specifically episodic verbal memory and gestural-verbal integration. By unveiling the specific role of brain structures and functional network coupling as well as the role of tDCS in modulating cognitive performance, our results contribute to a better understanding of brain-behavior associations, and may help to develop clinical interventional approaches, tailored for specific cognitive functions in aging.
In this thesis, we elaborate upon Bayesian changepoint analysis, whereby our focus is on three big topics: approximate sampling via MCMC, exact inference and uncertainty quantification. Besides, modeling matters are discussed in an ongoing fashion. Our findings are underpinned through several changepoint examples with a focus on a well-log drilling data.
Staphylococcus aureus is one of the commonly encountered bacteria of the human microbiome. Although mostly a seemingly harmless commensal microbe, S. aureus can act as an invasive pathogen with seriously devastating effects on its host’s health and wellbeing. A wide range of infections caused by this bacterium has been reported to affect diverse parts of the human body, including the skin, soft tissues and bones, as well as important organs like the heart, kidneys and lungs. Particularly, S. aureus is infamous for being a major causative agent of respiratory tract infections that may escalate up to necrotizing pneumonia. Due to its clinical relevance, this pathogen has been intensively studied for many years. Nonetheless, further research in this field is still needed, because of the high capacity of S. aureus to evolve drug resistance, its high genomic plasticity and adaptability and, not in the last place, the plethora of niches within the human body where it can thrive and survive. In this regard, there are still many uncertainties concerning the specific adaptations carried out by S. aureus during colonization and infection of the human body, the transition between both stages, and upon the invasion of different types of host cells. To shed more light on some of these adaptations, the research described in this thesis has employed in vitro models of infection that mimic particular conditions during the infectious process with special focus on the lung epithelium. The adaptations displayed by S. aureus were monitored using advanced proteomics. Furthermore, the analyses documented in this thesis included S. aureus strains with diverse backgrounds and epidemiology to take into account the genetic diversity encountered in this species.
The aim of the present dissertation was to investigate the biological and chemical potential of two European mushroom species: Fomitopsis betulina and Calvatia gigantea. For this purpose, different extracts of both fungi were tested for: antimicrobial, antifungal, cytotoxic, in vitro wound healing, and anti-adhesive properties. Bioassay-guided fractionation led to the isolation of bioactive compounds, altogether 20 compounds were isolated and identified. The compounds were obtained from the ethyl acetate extracts, they included triterpenes, sterols and aromatic compounds. The separated substances from both fungi were proved for biological activities, some of them showed antimicrobial and cytotoxic activities.