Der Transkriptionsfaktor CCAAT/enhancer-binding Protein β (C/EBPβ) reguliert die Expression zahlreicher Gene, welche die Proliferation, Differenzierung und Seneszenz in hämatopoietischen Zellen, Adipozyten und Leukämiezellen kontrollieren. Um diese mannigfaltigen Aufgaben zu erfüllen interagiert C/EBPβ mit zahlreichen Kofaktoren und Proteinen der Transkriptionsregulations-Maschinerie. Da das funktionale Netzwerk von C/EBPβ und seinen zahlreichen Kooperationspartnern bis heute nicht vollständig entziffert ist, ist es das Ziel dieser Arbeit das Netzwerk aus Interaktionspartnern und C/EBPβ regulierten Proteinen in Leukämiezelllinien und darüber hinaus zu erforschen und aufzudecken. Das Interaktom von C/EBPβ wurde mittels einer Kombination aus einem membranbasierten Peptid-Interaktions Testverfahrens (APS) und endogener Immunprezipitationen mit gekoppelter MS-Analyse untersucht. Außerdem wurde die Proteinmenge von C/EBPβ und von potentiell von C/EBPβ regulierten Proteinen mittels proteomischer MS-Analyse in C/EBPβ Knock-out- und Leukämiezelllinien untersucht. Die Protein-Interaktionsversuche ergaben epigenetische und allgemeine transkriptionsregulierende Proteine, sowie Chromatinstruktur modellierende Faktoren, die mit C/EBPβ interagieren. Zusätzlich konnten neue Interaktionen von C/EBPβ mit Kondensin- und Kinetochorproteinen beobachtet werden. Die Versuchsergebnisse eröffnen überdies neue Interaktionen von C/EBPβ mit DNA Reparatur und Apoptose assoziierten Proteinen. Interessanterweise konnten auch Komponenten des Spliceosomes und RNA-prozessierende Proteine als Interaktoren von C/EBPβ identifiziert werden. Zusammenfassend ermöglicht diese Studie nicht nur die Verifikation von bereits bekannten Proteininteraktionen von C/EBPβ, sondern eröffnet zahlreiche weitere zukünftige Forschungsfelder bezüglich des Interaktionsnetzwerkes von C/EBPβ in Leukämien, sowie anderen Zellarten und Geweben. / The basic leucine zipper transcription factor CCAAT/enhancer-binding protein β (C/EBPβ) regulates the expression of various genes that control the proliferation, differentiation and senescence of haematopoietic cells, adipocytes and leukemia cells. To facilitate its multifaceted functions C/EBPβ interacts with a collection of cofactors and proteins of the transcription regulation machinery. As the functional network of C/EBPβ and its numerous cooperation partners is still incomplete this study attempted to analyze interaction partners and downstream proteins of C/EBPβ in leukemia cells and beyond. A combinatory approach of an array based peptide-interaction screening (APS) and endogenous shotgun IP-MS from leukemia cell lines was applied to elucidate the interactome of C/EBPβ. Moreover, C/EBPβ abundance and potential C/EBPβ regulated proteins were determined by MS proteomics in C/EBPβ knockout and leukemia cell lines. The interaction screenings revealed proteins associated with the general and epigenetic regulation of transcription, with chromatin remodeling and mitotic chromatin organization as well as cell cycle regulation. Additionally, new interactions of C/EBPβ with condensin and kinetochore proteins could be elucidated. The data reports of novel C/EBPβ interactors involved in DNA repair and apoptosis. In addition, components of the spliceosome and RNA-processing were detected. Altogether this study verifies known and reveals various novel interactions of the transcription factor C/EBPβ and augments the network of previous reported interactions and potential cooperation partners. The here collected data discloses new subjects for further research concerning the interaction network of C/EBPβ during cell differentiation and in leukemia.

We are currently faced with the global challenge of conserving biological diversity while also increasing food production to meet the demands of a growing human population. Land-use change, primarily resulting from conversion to production land, is currently the leading cause of biodiversity loss. This occurs through habitat loss, fragmentation of remaining natural habitats, and resulting edge effects. Land-sparing and land-sharing approaches have been discussed as alternative ways to engineer landscapes to mitigate biodiversity loss while meeting production objectives. However, these represent extremes on a continuum of real-world landscapes, and it will be important to understand the mechanisms by which adjacent land use affects natural remnant ecosystems in order to make local land-management decisions that achieve conservation, as well as production, objectives. This thesis investigates the impact of juxtaposing production and natural forest on the community-wide interactions between lepidopteran herbivores and their parasitoids, as mediated by parasitoid spillover between habitats. The first and overarching objective was to determine whether herbivore productivity drives asymmetrical spillover of predators and parasitoids, primarily from managed to natural habitats, and whether this spillover alters trophic interactions in the recipient habitat. The study of trophic interactions at a community level requires understanding of both direct and indirect interactions. However, community-level indirect interactions are generally difficult to predict and measure, and these have therefore remained understudied. Apparent competition is an indirect interaction mechanism thought to be very important in structuring host-parasitoid assemblages. However, this is known primarily from studies of single species pairs, and its community-wide impacts are less clear. Therefore, my second objective was to determine whether apparent competition could be predicted for all species pairs within an herbivore assemblage, based on a measure of parasitoid overlap. My third objective was to determine whether certain host or parasitoid species traits can predict the involvement of those species in apparent competition. My key findings were that there is a net spillover of generalist predators and parasitoids from plantation to native forest, and that for generalists, this depends on herbivore abundance in the plantation forest. Herbivore populations across the edge were linked by shared parasitoids in apparent competition. Consequently, an experimental reduction of herbivore density in the plantation forest changed parasitism rates in the natural forest, as predicted based on parasitoid overlap. Finally, several host and parasitoid traits were identified that can predict the degree to which host or parasitoid species will be involved in apparent competition, a finding which may have extensive application in biological control, as well as in predicting spillover edge effects. Overall, this work suggests that asymmetrical spillover between production and natural habitats occurs in relation to productivity differences, with greater movement of predators and parasitoids in the managed-to-natural forest direction. The degree to which this affected species interactions has implications for landscape design to achieve conservation objectives in production landscapes.

<p>Protein malfunction can cause human diseases, which makes the protein a target in the process of drug discovery. In-depth knowledge of how protein functions can widely contribute to the understanding of the mechanism of these diseases. Protein functions are determined by protein structures and their dynamic properties. Protein dynamics refers to the constant physical movement of atoms in a protein, which may result in the transition between different conformational states of the protein. These conformational transitions are critically important for the proteins to function. Understanding protein dynamics can help to understand and interfere with the conformational states and transitions, and thus with the function of the protein. If we can understand the mechanism of conformational transition of protein, we can design molecules to regulate this process and regulate the protein functions for new drug discovery. Protein Dynamics can be simulated by Molecular Dynamics (MD) Simulations.</p> <p>The MD simulation data generated are spatial-temporal and therefore very high dimensional. To analyze the data, distinguishing various atomic interactions within a protein by interpreting their 3D coordinate values plays a significant role. Since the data is humongous, the essential step is to find ways to interpret the data by generating more efficient algorithms to reduce the dimensionality and developing user-friendly visualization tools to find patterns and trends, which are not usually attainable by traditional methods of data process. The typical allosteric long-range nature of the interactions that lead to large conformational transition, pin-pointing the underlying forces and pathways responsible for the global conformational transition at atomic level is very challenging. To address the problems, Various analytical techniques are performed on the simulation data to better understand the mechanism of protein dynamics at atomic level by developing a new program called Probing Long-distance interactions by Tapping into Paired-Distances (PLITIP), which contains a set of new tools based on analysis of paired distances to remove the interference of the translation and rotation of the protein itself and therefore can capture the absolute changes within the protein.</p> <p>Firstly, we developed a tool called Decomposition of Paired Distances (DPD). This tool generates a distance matrix of all paired residues from our simulation data. This paired distance matrix therefore is not subjected to the interference of the translation or rotation of the protein and can capture the absolute changes within the protein. This matrix is then decomposed by DPD</p> <p>using Principal Component Analysis (PCA) to reduce dimensionality and to capture the largest structural variation. To showcase how DPD works, two protein systems, HIV-1 protease and 14-3-3 σ, that both have tremendous structural changes and conformational transitions as displayed by their MD simulation trajectories. The largest structural variation and conformational transition were captured by the first principal component in both cases. In addition, structural clustering and ranking of representative frames by their PC1 values revealed the long-distance nature of the conformational transition and locked the key candidate regions that might be responsible for the large conformational transitions.</p> <p>Secondly, to facilitate further analysis of identification of the long-distance path, a tool called Pearson Coefficient Spiral (PCP) that generates and visualizes Pearson Coefficient to measure the linear correlation between any two sets of residue pairs is developed. PCP allows users to fix one residue pair and examine the correlation of its change with other residue pairs.</p> <p>Thirdly, a set of visualization tools that generate paired atomic distances for the shortlisted candidate residue and captured significant interactions among them were developed. The first tool is the Residue Interaction Network Graph for Paired Atomic Distances (NG-PAD), which not only generates paired atomic distances for the shortlisted candidate residues, but also display significant interactions by a Network Graph for convenient visualization. Second, the Chord Diagram for Interaction Mapping (CD-IP) was developed to map the interactions to protein secondary structural elements and to further narrow down important interactions. Third, a Distance Plotting for Direct Comparison (DP-DC), which plots any two paired distances at user’s choice, either at residue or atomic level, to facilitate identification of similar or opposite pattern change of distances along the simulation time. All the above tools of PLITIP enabled us to identify critical residues contributing to the large conformational transitions in both HIV-1 protease and 14-3-3σ proteins.</p> <p>Beside the above major project, a side project of developing tools to study protein pseudo-symmetry is also reported. It has been proposed that symmetry provides protein stability, opportunities for allosteric regulation, and even functionality. This tool helps us to answer the questions of why there is a deviation from perfect symmetry in protein and how to quantify it.</p>