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Cyber Physical Systems (CPS) are growing more and more complex due to the availability of cheap hardware, sensors, actuators and communication links. A network of cooperating CPSs (CPN) additionally increases the complexity. This poses challenges as well as it offers chances: the increasing complexity makes it harder to design, operate, optimize and maintain such CPNs. However, on the other side an appropriate use of the increasing resources in computational nodes, sensors, actuators can significantly improve the system performance, reliability and flexibility. Therefore, self-X features like self-organization, self-adaptation and self-healing are key principles for such systems.
Additionally, CPNs are often deployed in dynamic, unpredictable environments and safety-critical domains, such as transportation, energy, and healthcare. In such domains, usually applications of different criticality level exist. In an automotive environment for example, the brake has a higher criticality level regarding safety as the infotainment. As a result of mixed-criticality, applications requiring hard real-time guarantees compete with those requiring soft real-time guarantees and best-effort application for the given resources within the overall system. This leads to the need to accommodate multiple levels of criticality while ensuring safety and reliability, which increases the already high complexity even more.
This thesis deals with the question on how to conveniently, effectively and efficiently handle the management and complexity of mixed-critical CPNs (MC-CPNs). Since this cannot be done by the system developer without the assistance of the system itself any longer, it is essential to develop new approaches and techniques to ensure that such systems can operate under a range of conditions while meeting stringent requirements.
Based on five research hypothesis, this thesis introduces a comprehensive adaptive mixed-criticality supporting middleware for Cyber-Physical Networks (Chameleon), which efficiently and autonomously takes care of the management and complexity of CPNs with regard to the mixed-criticality aspect.
Chameleon contributes to the state-of-art by introducing and combining the following concepts:
- A comprehensive self-adaption mechanism on all levels of the system model is provided.
- This mechanism allows a flexible combination of parametric and structural adaptation actions (relocation, scheduling, tuning, ...) to modify the behavior of the system.
- Real-time constraints of mixed-critical applications (hard real-time, soft real-time, best-effort) are considered in all possible adaptation conditions and actions by the use of the importance parameter.
- CPNs are supported by the introduction of different scopes (local, system, global) for the adaptation conditions and actions. This also enables the combination of different scopes for conditions and actions.
- The realization of the adaptation with a MAPE-K loop instantiated by a distributed LCS allows for real-time capable reasoning of adaptation actions which also works on resource-spare systems.
- The developed rule language Rango offers an intuitive way to specify an initial rule set for LCS in the context of CPS/CPNs and supports the system administrators in the process of rule set generation.
Proteins are biological macromolecules playing essential roles in all living organisms.
Proteins often bind with each other forming complexes to fulfill their function. Such protein complexes assemble along an ordered pathway. An assembled protein complex can often be divided into structural and functional modules. Knowing the order of assembly and the modules of a protein complex is important to understand biological processes and treat diseases related to misassembly.
Typical structures of the Protein Data Bank (PDB) contain two to three subunits and a few thousand atoms. Recent developments have led to large protein complexes being resolved. The increasing number and size of the protein complexes demand for computational assistance for the visualization and analysis. One such large protein complex is respiratory complex I accounting for 45 subunits in Homo sapiens.
Complex I is a well understood protein complex that served as case study to validate our methods.
Our aim was to analyze time-resolved Molecular Dynamics (MD) simulation data, identify modules of a protein complex and generate hypotheses for the assembly pathway of a protein complex. For that purpose, we abstracted the topology of protein complexes to Complex Graphs of the Protein Topology Graph Library (PTGL). The subunits are represented as vertices, and spatial contacts as edges. The edges are weighted with the number of contacts based on a distance threshold. This allowed us to apply graph-theoretic methods to visualize and analyze protein complexes.
We extended the implementations of two methods to achieve a computation of Complex Graphs in feasible runtimes. The first method skipped checks for contacts using the information which residues are sequential neighbors. We extended the method to protein complexes and structures containing ligands. The second method introduced spheres encompassing all atoms of a subunit and skipped the check for contacts if the corresponding spheres do not overlap. Both methods combined allowed skipping up to 93 % of the checks for contacts for sample complexes of 40 subunits compared to up to 10 % of the previous implementation. We showed that the runtime of the combined method scaled linearly with the number of atoms compared to a non-linear scaling of the previous implementation We implemented a third method fixing the assignment of an orientation to secondary structure elements. We placed a three-dimensional vector in each secondary structure element and computed the angle between secondary structure elements to assign an orientation. This method sped up the runtime especially for large structures, such as the capsid of human immunodeficiency virus, for which the runtime decreased from 43 to less than 9 hours.
The feasible runtimes allowed us to investigate two data sets of MD trajectories of respiratory complex I of Thermus thermophilus that we received. The data sets differ only by whether ubiquinone is bound to the complex. We implemented a pipeline, PTGLdynamics, to compute the contacts and Complex Graphs for all time steps of the trajectories. We investigated different methods to track changes of contacts during the simulation and created a heat map put onto the three-dimensional structure visualizing the changes. We also created line plots to visualize the changes of contacts over the course of the simulation. Both visualizations helped spotting outstandingly flexible or rigid regions of the structure or time points of the simulation in which major dynamics occur.
We introduced normalizations of the edge weights of Complex Graphs for identi-fying modules and predicting the assembly pathway. The idea is to normalize the number of contacts for the number of residues of a subunit. We defined five different normalizations.
To identify structural and functional modules, we applied the Leiden graph clustering algorithm to the Complex Graphs of respiratory complex I and the respiratory supercomplex. We examined the results for the different normalizations of the weights of the Complex Graphs. The absolute edge weight produced the best result identifying three of four modules that have been defined in the literature for respiratory complex I.
We applied agglomerative hierarchical clustering to the edges of a Complex Graph to create hypotheses of the assembly pathway. The rationale was that subunits with an extensive interface in the final structure assemble early. We tested our method against two existing methods on a data set of 21 proteins with reported assembly pathways. Our prediction outperformed the other methods and ran in feasible runtimes of a few minutes at most.
We also tested our method on respiratory complex I, the respiratory supercomplex and the respiratory megacomplex. We compared the results for the different normalizations with an assembly pathway of respiratory complex I described in the literature. We transformed the assembly pathways to dendrograms and compared the predictions to the reference using the Robinson-Foulds distance and clustering information distance. We analyzed the landscape of the clustering information distance by generating random dendrograms and showed that our result is far better than expected at random. We showed in a detailed analysis that the assembly prediction using one normalization was able to capture key features of the assembly pathway that has been proposed in the literature.
In conclusion, we presented different applications of graph theory to automatically analyze the topology of protein complexes. Our programs run in feasible runtimes even for large complexes. We showed that graph-theoretic modeling of the protein structure can be used to analyze MD simulation data, identify modules of protein complexes and predict assembly pathways.
Hintergrund: Epilepsie bezeichnet eine Erkrankung, welche durch eine anhaltende Prädisposition für Symptome, die im Zusammenhang mit einer ungewöhnlich starken oder synchronisierten elektrischen Aktivität des Gehirns auftreten (=epileptischer Anfall) charakterisiert ist. Mutationen in dem Gen DEPDC5 (Dishevelled, Egl-10 and Pleckstrin (DEP) domain-containing protein 5) sind mit fokalen Epilepsien assoziiert und führen in Tiermodellen und humanen Modellen zu einer Überaktivierung des mTOR-Signalweges. Auf neuromorphologischer Ebene zeigt sich die mTOR-Überaktivierung durch eine Vergrößerung des Zelldurchmessers und einer zunehmenden Verästelung der Neuriten. Ziel dieser Studie war es, die morphologischen Auswirkungen der DEPDC5-assoziierten mTOR-Überaktivierung in der SH-SY5Y-Neuroblastomzelllinie zu untersuchen. Dadurch soll eine Einschätzung getroffen werden, ob das im Zusammenhang mit neurodegenerativen Erkrankungen bereits gut etablierte SH-SY5Y-Zellmodell auch bei der Untersuchung epilepsieassoziierter Pathomechanismen zum Einsatz kommen kann.
Methoden: Unter Einsatz der CRISPR/Cas9-Methode wurden Knockout(KO)-Mutationen in Exon 2 und Exon 3 des DEPDC5-Gens erzeugt und diese mittels Sanger-Sequenzierung bestätigt. Danach wurden die Knockouts auf RNA- und Proteinebene, durch Real-time-RT-PCR und Western Blot validiert. Die bestätigten homozygoten DEPDC5-KO-Zelllinen wurden anschließend mittels Western Blot auf eine mTOR-Überaktivierung untersucht. Zuletzt erfolgte die neuromorphologische Validierung des DEPDC5-KO. Die Zellgröße proliferierender SH-SY5Y wurden mittels Durchflusszytometrie (FACS) untersucht. Zudem bestimmten wir die neuronale Architektur differenzierter SH-SY5Y unter Einsatz der Sholl-Analyse.
Ergebnisse: Es konnten vier unabhängige DEPDC5-KO-SH-SY5Y-Zelllinien mit homozygoten Indel-Mutationen und vorzeitigem Stoppcodon in Exon 3 generiert werden. Die erwartete Reduktion an DEPDC5-mRNA konnte mittels Real-time 10 RT-PCR nicht festgestellt werden. Die Abwesenheit des Proteins konnte durch Western Blot aber gezeigt werden. Funktionell konnte für alle Zelllinien eine mTOR-Überaktivierung mittels Western Blot nachgewiesen werden. Dabei konnte phosphoryliertes AKT (AKT serine/threonine kinase 1) als stabilster Marker etabliert werden. Auf neuromorphologischer Ebene ließ sich ein Trend in Richtung vergrößertem Zelldurchmesser bei verlängertem Auswachsen der Neuriten feststellen, wobei sich für das Modell Unterschiede zwischen den einzelnen Klonen ergaben.
Diskussion: In dieser Studie gelang es erstmals, den Zusammenhang zwischen DEPDC5-KO und einer mTOR-Überaktivierung in der onkogenen SH-SY5Y-Zelllinie zu replizieren. Das verlängerte Auswachsen der Neuriten, bei jedoch gleichbleibender Anzahl peripherer Verästelungen, stellt dabei einen neuen Befund dar und könnte durch die frühe neuronale Entwicklungsstufe des SH-SY5Y-Zellmodells erklärt werden. Auf Grundlage der Ergebnisse dieser Arbeit lässt sich sagen, dass das robuste und kostengünstige SH-SY5Y-Zellmodell insbesondere für high-throughput Methoden und Screeningassays ein geeignetes Modell ist. Durch die Kombination mit reiferen Zellmodellen, wie beispielsweise iPSCs (induced pluripotent stem cells), könnte der Phänotyp eines DEPDC5-KO und anderer mTOR-assoziierter Epilepsien, möglichst umfassend in-vitro dargestellt werden.
Background: Hepatectomy is a complex procedure with high morbidity and mortality.
Early prediction/prevention of major complications is highly valuable for patient care.
Surgical APGAR score (SAS) has been validated to predict post-surgical complications (PCs).
Goal: We aimed to define a simple complications classification following hepatectomy based on a therapy-oriented severity Clavien-Dindo classification (CDC).
Methods: 119 patients undergoing liver resection were included. PCs were determined at follow-up based on CDC. Clinicopathological factors were used to calculate SAS. A receiver-operator characteristic (ROC) curve analysis estimated the predictive value of SAS for PCs. Circulating markers levels of liver injury were analyzed as critical elements on PCs.
Results: SAS (P=0.008), estimated blood-loss (P=0.018) and operation time (P=0.0008) were associated with PCs. SAS was reduced in patients with (+) compared to those without (-) complications (6.64±1.84 vs 5.70±1.79, P=0.0079). The area-under-the-curve was 0.646 by ROC, indicating an acceptable discrimination with 65% possibility to distinguish (-) and (+) groups (P=0.004).
Best cutoff value for SAS was ≤6/≥7, at which sensitivity and specificity were maximal.
ALT/ASL levels were significantly different within the group with 9-10 SAS points (P=0.01 and 0.02).
Conclusion: SAS provides accurate risk stratification for major PCs after hepatectomy, and might help improving the overall patient outcome.
Facial expression recognition is linked to clinical and neurofunctional differences in autism
(2022)
Background: Difficulties in social communication are a defining clinical feature of autism. However, the underlying neurobiological heterogeneity has impeded targeted therapies, and requires new approaches to identifying clinically relevant bio-behavioural subgroups. In the largest autism cohort to date, we comprehensively examined difficulties in facial expression recognition, a key process in social communication, as a bio-behavioural stratification biomarker, and validated them against clinical features and neurofunctional responses.
Methods: Between 255 and 488 participants aged 6-30 years with autism, typical development and/or mild intellectual disability completed the Karolinska Directed Emotional Faces task, the Reading the Mind in the Eyes Task and/or the Films Expression Task. We first examined mean-group differences on each test. Then we used a novel intersection approach that compares two centroid and connectivity-based clustering methods to derive subgroups based on the combined performance across the three tasks. Measures and subgroups were then related to clinical features and neurofunctional differences measured using fMRI during a fearful face-matching task.
Results: We found significant mean-group differences on each expression recognition test. However, cluster analyses showed that these were driven by a low-performing autistic subgroup (~30% of autistic individuals who performed below 2SDs of the neurotypical mean on at least one test), while a larger subgroup (~70%) performed within 1SD on at least 2 tests. The low-performing subgroup also had on average significantly more social-communication difficulties and lower activation in the amygdala and fusiform gyrus than the high-performing subgroup.
Limitations: Findings of autism expression recognition subgroups and their characteristics require independent replication. This is currently not possible, as there is no other existing data set that includes all relevant measures. However, we demonstrated high internal robustness (91.6%) of findings between two clustering methods with fundamentally different assumptions, which is a critical pre-condition for independent replication.
Conclusions: We identified a subgroup of autistic individuals with expression recognition difficulties and showed that this related to clinical and neurobiological characteristics. If replicated, expression recognition may serve as bio-behavioural stratification biomarker and aid in the development of targeted interventions for a subgroup of autistic individuals.
For thousands of years, S. cerevisiae has been employed by humans in brewing and baking. Nowadays, this budding yeast is more than that: it is a well investigated model organism and an established workhorse in biotechnology. S. cerevisiae serves as a production host for various applications such as i) bioethanol production ii) the biosynthesis of hormones including insulin or iii) cannabinoid biosynthesis. Hereby, the robustness of S. cerevisiae and its high tolerances regarding pH and salt concentrations qualifies it for a wide range of industrial applications. Moreover, products of S. cerevisiae are generally recognised as safe (GRAS), enabling diverse biotechnological applications. Various mechanisms for genetic engineering of S. cerevisiae are applicable and the engineering process itself is straightforward since methods are established and widely known. Due to the wide range of industrial applications of S. cerevisiae, this organism is an ideal candidate for applied research and implementation of the recombinant biosynthesis of tocochromanols in this study.
Tocochromanols encompass tocotrienols and tocopherols, which are lipid-soluble compounds that are commonly associated with vitamin E activity. Hereby, α-tocopherol is the most prevalent form, as it is an essential nutrient in the diet of humans and animals. Naturally, tocochromanols are almost exclusively synthesised by photoautotrophic organisms such as plants or cyanobacteria. They consist of an aromatic head group and a polyprenyl side chain which is saturated in tocopherols and 3-fold unsaturated in tocotrienols. The methylation status of the chromanol ring distinguishes α-, β-, γ- and δ-tocochromanol. All forms of tocochromanols represent a group of powerful antioxidants, scavenging reactive oxygen species (ROS) and preventing the propagation of lipid oxidation in lipophilic environments. Recently, attention has been drawn to tocotrienols, due to their benefits in neuroprotection as well as cholesterol-lowering and anti-cancer properties. Consequently, tocochromanols are valuable additives in the food, feed, cosmetic and pharmaceutical industries.
The metabolic engineering strategy of S. cerevisiae to enable tocochromanol biosynthesis was started in a preceding master thesis with the provision of the aromatic moiety, homogentisic acid (HGA), from the aromatic amino acid biosynthesis. Hereby, the upregulation and redirection of the native pathway was essential. Therefore, a strain with an engineered aromatic amino acid pathway for improved 4 hydroxyphenylpyruvate (HPP) production (MRY33) was utilised from Reifenrath and Boles (2018). Furthermore, a heterologous hydroxyphenylpyruvate dioxygenase (HPPD) was required to convert HPP into HGA. Thus, several heterologous HPPDs were expressed and characterised regarding their HGA production within the previous study. The best variant originated from Yarrowia lipolytica, YlHPPD, and was integrated into the genome of MRY33. The resulting strain JBY2, produced 435 mg/L HGA in a shake flask fermentation.
This work was started with the genetically highly modified strain JBY2, whose genome already contained a large number of genes artificially expressed behind strong promoters. For further strain development, it was advantageous to maintain a high degree of sequence variability in order to prevent genomic instabilities due to sequence homologies. Thus, 17 artificial promoters (AP1-AP17) were characterised regarding their strength of expression by the yellow fluorescent protein (YFP). These sequences were also part of a patent that was filed during this work (WO2023094429A1).
The key point of this study was the development of a metabolic engineering strategy for the strain JBY2. First, the sufficient supply of the second precursor, the polyprenyl side chain, was investigated. Natively, S. cerevisiae produces the precursor, geranylgeranyl diphosphate (GGPP), from the isopentenyl diphosphate pathway. However, without further engineering, GGPP was barely detectable in JBY2 (< 0.1 mg/L). Thus, engineering of the isopentenyl diphosphate biosynthesis was necessary. The limiting enzyme of the mevalonate pathway was the 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), which is encoded by HMG1. Therefore, a truncation for feedback-resistance and its overexpression by a promoter exchange was performed. Furthermore, the promoter of the gene for the squalene synthase (pERG9) was exchanged by the ergosterol sensitive promoter pERG1 to limit the metabolic flux of the mevalonate pathway into the ergosterol pathway. The native GGPP synthase (BTS1) was another limitation that was observed throughout this study. To overcome this bottleneck, plasmid-based and integrative overexpression of the native BTS1 and a codon optimised BTS1 were investigated. Other strategies to improve GGPP production were the deletion of the gene for the diacylglycerol pyrophosphate phosphatase (DPP1) to prevent excessive dephosphorylation of GGPP to geranylgeraniol (GGOH), and the overexpression of the farnesyl pyrophosphate synthetase, encoded by ERG20. However, the best improvements of the GGPP biosynthesis, inferred through GGOH measurements, were achieved from the screening of several heterologous GGPP synthases in S. cerevisiae. The best performing strain was JBY61 (JBY2, hmg1Δ::pTDH3-HMG1tr[1573–3165], pERG9Δ::pERG1, ChrIV-49293-49345Δ::pTDH3-XdcrtE-tSSA1_LEU2), bearing the heterologous GGPP synthase crtE of Xanthophyllomyces dendrorhous and produced 64.23 mg/L GGOH. Consequently, this engineering strategy improved the GGOH production by a factor of 642 compared to the parent strain JBY2.
The capacity of pathogenic bacteria to adhere to host cells and to avoid subsequent clearance by the host´s immune response is the initial and most decisive step leading to infections. Human pathogenic bacteria circulating in the bloodstream need to find ways to interact with endothelial cells (ECs) lining the blood vessels to infect and colonise the host. The extracellular matrix (ECM) of ECs might represent an attractive initial target for bacterial interaction, as many bacterial adhesins have reported affinities to ECM proteins, particularly fibronectin (Fn). Trimeric autotransporter adhesins (TAA) have been described as important pathogenicity factors of Gram-negative bacteria. The TAA from human pathogenic Bartonella henselae, Bartonella adhesin A (BadA), is one of the longest and best characterised adhesin and represents a prototypic TAA due to its domain architecture. B. henselae, the causative agent of cat scratch disease, endocarditis, and bacillary angiomatosis, adheres to ECs and ECM proteins via BadA interaction.
In this research, it was determined that the interaction between BadA and Fn is essential for B. henselae host cell adhesion. BadA interactions were identified within the heparin-binding domains of Fn, and the exact binding sites were revealed by mass spectrometry analysis of chemically crosslinked whole-cell bacteria and Fn. It turned out that specific BadA interactions with defined Fn regions represent the molecular basis for bacterial adhesion to ECs. These data were confirmed by using BadA-deficient bacteria and CRISPR-Cas FN1 knockout ECs. It was also identified that BadA binds to Fn from both cellular and plasma origin, suggesting that B. henselae binding to Fn might possibly take part in other infection processes apart from bacterial adherence, e.g. evasion from the host cell immune system.
Interactions between TAAs and Fn represent a key step for adherence of B. henselae to ECs. Still, Fn-mediated binding is of more significant importance for pathogenic bacteria than broadly recognised. Fn removal from the ECM environment of ECs, also reduced adherence of Staphylococcus aureus, Borrelia burgdorferi, and Acinetobacter baumannii to host cells Interactions between adhesins and Fn might therefore represent a crucial step for the adhesion of human-pathogenic Gram-negative and Gram-positive bacteria targeting the ECs as a niche of infection or as means for persistence.
This research demonstrated that combining large-scale analysis approaches to describe protein-protein interactions with supportive functional readouts (binding assays) allows for the discrimination of crucial interactions involved in bacterial adhesion to the host. The herein-described experimental approaches and tools might guide future research for other pathogenic bacteria and represent an initial point for the future generation of anti-virulence strategies to inhibit bacterial binding to host cells.
Trauma ist in Deutschland und weltweit eine der häufigsten Todesursache bei Personen unter 55 Jahren. Eine traumatische Verletzung von Gewebe führt zur Freisetzung von sogenannten damage-associated molecular patterns (DAMPs), die eine Entzündungskaskade auslösen, welche die Organfunktion negativ beeinträchtigt. Dies führt bei anhaltender Entzündung zunächst zu Organdysfunktion, was im weiteren Verlauf zu Organversagen führt und im Spätstadium im multiplen Organversagen (MOF) enden kann. In den meisten Fällen ist die inflammatorische Antwort des Immunsystems auf das Trauma adäquat und entspricht einem gut koordinierten Netzwerk von Immunzellen, Zytokinen und Chemokinen, welches zur Wiederherstellung des geschädigten Gewebes führt. Wenn dieses Netzwerk jedoch nicht im Gleichgewicht ist, kann die Entzündungsreaktion durch eine sogenannte feed-forward-loop von Inflammation und Gewebeschäden verstärkt werden. Wenn dieser Prozess systemisch wird, spricht man vom systemic inflammatory response syndrome (SIRS). Auf der anderen Seite gibt es ein Gegenstück zu SIRS, nämlich das sogenannte compensatory anti-inflammatory response syndrome (CARS). Auch ein Überschießen von CARS kann den Verlauf der posttraumatischen Inflammation negativ beeinträchtigen. Sowohl eine Verschiebung in Richtung von SIRS als auch in Richtung von CARS kann zu Organfunktionsstörungen, nosokomialen Infektionen und letztendlich zum Tod führen. Daher ist eine ausgeglichene, frühe posttraumatische Immunantwort für ein gutes Outcome von entscheidender Bedeutung.
Monozyten nehmen eine kritische Stellung sowohl in der primären Immunantwort nach Trauma als auch nach Infektion ein. Durch die Oberflächenexpression von diversen sogenannten pattern-recocnition receptors (PRRs), insbesondere Toll-Like-Rezeptoren (TLRs), können Monozyten Pathogene und sogenannte pathogen-associated molecular patterns (PAMPs) erkennen und neutralisieren. Darüber hinaus können Monozyten PAMPs über major histocompatibility complex class II -Moleküle (MHC-II) dem adaptiven Immunsystem präsentieren und somit als zelluläre Verbindung zwischen dem angeborenen und dem adaptiven Immunsystem fungieren. TLR2, eine Untergruppe der TLRs, ist einer der Hauptrezeptoren für PAMPs von grampositiven Bakterien wie S. aureus, dem Hauptkeim für posttraumatische Wundinfektionen und nosokomiale Infektionen.
Es gibt immer noch keinen Konsens über den Einfluss von Trauma auf die Funktion von Monozyten und deren Expressionsprofil von PRRs und MHC-II-Molekülen nach einem Trauma. Verschiedene Studien berichten von einer beeinträchtigten TLR2-Expression bei Monozyten nach Trauma, während sich in anderen Studien eine konstante oder sogar erhöhte TLR2-Expression bei Monozyten nach Trauma gezeigt hat. Die Daten sind auch in Bezug auf die posttraumatische Phagozytoseleistung von Monozyten unbeständig. Während in einigen Studien im frühen posttraumatischen Verlauf im Vergleich zu gesunden Probanden von einer erhöhten Anzahl zirkulierender Monozyten mit erhaltener Phagozytoseleistung berichtet wird, zeigten andere Studien eine verminderte Phagozytoseleistung von Monozyten nach Trauma. Darüber hinaus gibt es widersprüchliche Ergebnisse bezüglich der Fähigkeit von Monozyten, proinflammatorische Zytokine wie Interleukin (IL)-1β oder Tumor necrosis factor (TNF)-α freizusetzen.
Zusammengefasst ist die derzeitige Studienlage widersprüchlich. Zusätzlich wurde es in bisherigen Studien verpasst, die Kombination verschiedener Aspekte wie die Funktionalität der Monozyten in Bezug auf Phänotypisierung oder funktioneller Assays und deren Beobachtung über einen längeren Zeitraum miteinzubeziehen.
Die unterschiedlichen Ergebnisse der bisherigen Studien könnte durch die Natur des Polytraumas bedingt sein, beispielsweise durch verschiedene Verletzungsmuster und Verletzungsschwere, die unterschiedliche Erstversorgung am Unfallort, die Zeit bis zur Ankunft in der Notaufnahme oder die unterschiedliche primäre und definitive Versorgung der Verletzungen. All das kann zu einer unterschiedlichen Antwort des Immunsystems und somit zu unterschiedlichen Outcomes führen.
Daher war es Ziel unserer Studie, die oben genannten Variablen zu eliminieren, und eine kontrollierte Polytraumastudie am Schweinemodell durchzuführen, um die Funktionalität von Schweinemonozyten über einen Zeitraum von 72 Stunden nach Trauma zu untersuchen.
Zusätzlich und im Gegensatz zu früheren Studien haben wir die Phänotypisierung von Monozyten (TLR2 und MHC-II [SLA-DR]) mit einem funktionellen Phagozytose-Assay kombiniert und deren direkte Assoziation in einem unabhängigen Assay analysiert.
Peripheres Blut wurde vor (-1h) und direkt nach der Induktion des Polytraumas (PT) (0h) entnommen, sowie 3,5h, 5,5h, 24h und 72h später. Die Expression von H(S)LA-DR und TLR2 auf Schweine-Monozyten wurde untersucht. Außerdem wurde die Phagozytierungsaktivität von Schweinemonozyten gemessen.
Darüber hinaus wurden aus mechanistischen Gründen Blutproben von 10 gesunden Schweinen zunächst einem TLR2-neutralisierenden Antikörper und anschließend S. aureus-Partikeln ausgesetzt, bevor die Phagozytoseleistung der Monozyten untersucht wurde.
Die Anzahl der CD14 + -Monozyten aller zirkulierenden Leukozyten blieb während des Beobachtungszeitraums konstant, während der Prozentsatz der CD14 + H(S)LA-DR + -Monozyten direkt, 3,5h und 5,5h nach dem Trauma signifikant abnahm. Der Prozentsatz von TLR2 + exprimierenden Zellen aus allen Monozyten verringerte sich direkt, 3,5h und 5,5h nach dem Trauma signifikant. Der Prozentsatz der phagozytierenden Monozyten nahm direkt nach Trauma ab und blieb in den ersten 3,5 Stunden nach dem Trauma niedriger, stieg jedoch nach 24 Stunden an. Die Antagonisierung von TLR2 verringerte signifikant die Phagozytoseleistung der Monozyten.
Sowohl der verringerte Prozentsatz der aktivierten als auch der TLR2-exprimierenden Monozyten blieb bestehen, solange die verringerte Phagozytoseleistung beobachtet wurde.
Darüber hinaus führte auch die Neutralisation von TLR2 zu einer verminderten Phagozytoseleistung. Daher nehmen wir an, dass eine verringerte TLR2-Expression für die verringerte Phagozytoseleistung verantwortlich ist.
Intelligenz ist einer der stärksten Einzelprädikatoren für berufliche Leistungen. Hochintelligente Personen, sogenannte Hochbegabte, müssten daher generell hohe berufliche Leistungen erbringen. Dies steht jedoch im Widerspruch zu Darstellungen von Schwierigkeiten Hochbegabter im Beruf. Sollten die negativen Stereotype zutreffen, wäre dies aus ressourcenorientierter Sicht problematisch, da Organisationen das der Hochbegabung zugrunde liegende Potenzial der Mitarbeiter*innen nicht hinreichend nutzen. Die vorliegende Arbeit leistet einen Beitrag dazu, diese Diskrepanz aufzuklären, indem sie untersucht wie Hochbegabte ihre berufliche Situation (erfolgreich) gestalten. Nach einer Begriffsklärung und Darstellung des Zusammenhangs von Intelligenz und Beruf wird im ersten Beitrag der internationale Forschungsstand mithilfe eines Literature Reviews systematisiert. Aufbauend darauf werden im zweiten Beitrag mithilfe einer Fragebogenerhebung die überwiegend internationalen Beiträge durch eine deutsche Stichprobe und die bisher wenig betrachtete Gruppe von Hochbegabten gemäß Kompetenzdefinition ergänzt. Schließlich fokussiert der dritte Beitrag den Widerspruch, dass Hochbegabte generell erfolgreich und zufrieden im Beruf sind, jedoch individuell von Schwierigkeiten berichten. Dazu werden teilstrukturierte Interviews durchgeführt, um ein tieferes Verständnis für den Einfluss der Hochbegabung auf den Beruf sowie etwaige bestehende Unterschiede zu nicht-hochbegabten Personen zu gewinnen. Zudem werden Strategien erfasst, die Hochbegabte nutzen, um Unterschiede und eventuell daraus resultierende Schwierigkeiten zu bewältigen. Abschließend werden Implikationen für die Berufspraxis und Forschung diskutiert.
In our rapidly changing world, land use has been recognized as having one of the strongest impacts on species and genetic diversity. The present state of temperate forests in Europe is a product of decisions made by former and current management and policy actions, rather than natural factors. Alterations of crown projection areas, structural complexity of the forest stand caused by thinning and cuttings, and changes in tree species composition caused by regeneration or plantings not only affect forest interior buffering against warming, but also the understorey light environment and nutrient availability. Ultimately, current silvicultural management practices have deep impact on the forest ecosystems, microenvironmental changes and forest floor understorey herbs. In response to environmental changes, plants rely on genetically heritable phenotypic variation, an important level of variation in the population, as it is prerequisite for adaptation. However, until now most studies on plant adaptation to land use focus on grassland management. Yet, studies on the adaptation of forest understorey herbs to forest management have been absent so far. This is important because understanding adaptation of understorey herbs is crucial for biodiversity conservation, forest restoration, and climate change mitigation. Studying current adaptation of understorey herbs to forest management yields insights into the evolutionary consequences of management practices, which could be employed to improve sustainable use of forest habitat.
In sum, my conducted experiments complement each other well and managed to fill in research gaps on the topic of genetically heritable phenotypic variation in understorey herbs and how it is affected by forest management and related microenvironmental variables. I showed that forest management has direct evolutionary consequences on the genetic basis of understorey herbs, but also indirectly through the microenvironment. Furthermore, I revealed that local adaptation and phenotypic plasticity of understorey herbs to forest structural attributes act along continuous gradients. And lastly, I highlighted the important role of intra-individual variation by revealing plastic responses to drought and shading, urging researchers to not ignore this important level of trait variation. Ultimately, understorey herbs in temperate forests employ phenotypic plasticity as a flexible strategy to adapt to varying environmental conditions. By adjusting their leaf characteristics, reproductive investment, and phenology, they can optimize their fitness and survival in response to changes in light availability, resource availability, and seasonal cues. The anthropogenic impact on temperate forests and understorey herbs will continue and likely increase in the future. This should urge foresters to adapt their silvicultural management decisions towards the long-term preservation of genetic diversity and, through this, the evolvability and adaptability of forest understorey herbs and associated organisms. Based on the results shown in my dissertation, variation in forest management regimes and types could be beneficial for promoting genetic diversity within several species of forest understorey herbs. Lastly, in the face of future climatic changes, the mechanisms by which plants can cope with increasing stressful environmental conditions might very well rely heavily on intra-individual variation, providing the necessary rapid plastic adjustment to changing microclimatic conditions within populations and thus increase climate change resilience.