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Electrosynthesis and characterization of molecularly imprinted polymers for peptides and proteins
(2019)
Die funktionelle Charakterisierung von therapeutisch relevanten Proteinen kann bereits durch die Bereitstellung des Zielproteins in adäquaten Mengen limitierend sein. Dies trifft besonders auf Membranproteine zu, die aufgrund von zytotoxischen Effekten auf die Produktionszelllinie und der Tendenz Aggregate zu bilden, in niedrigen Ausbeuten an aktivem Protein resultieren können. Der lebende Organismus kann durch die Verwendung von translationsaktiven Zelllysaten umgangen werden- die Grundlage der zellfreien Proteinsynthese. Zu Beginn der Arbeit wurde die ATP-abhängige Translation eines Lysates auf der Basis von kultivierten Insektenzellen (Sf21) analysiert. Für diesen Zweck wurde ein ATP-bindendes Aptamer eingesetzt, durch welches die Translation der Nanoluziferase reguliert werden konnte. Durch die dargestellte Applizierung von Aptameren, könnten diese zukünftig in zellfreien Systemen für die Visualisierung der Transkription und Translation eingesetzt werden, wodurch zum Beispiel komplexe Prozesse validiert werden können.
Neben der reinen Proteinherstellung können Faktoren wie posttranslationale Modifikationen sowie eine Integration in eine lipidische Membran essentiell für die Funktionalität des Membranproteins sein. Im zweiten Abschnitt konnte, im zellfreien Sf21-System, für den G-Protein-gekoppelten Rezeptor Endothelin B sowohl eine Integration in die endogen vorhandenen Endoplasmatisch Retikulum-basierten Membranstrukturen als auch Glykosylierungen, identifiziert werden.
Auf der Grundlage der erfolgreichen Synthese des ET-B-Rezeptors wurden verschiedene Methoden zur Fluoreszenzmarkierung des Adenosin-Rezeptors A2a (Adora2a) angewandt und optimiert. Im dritten Abschnitt wurde der Adora2a mit Hilfe einer vorbeladenen tRNA, welche an eine fluoreszierende Aminosäure gekoppelt war, im zellfreien Chinesischen Zwerghamster Ovarien (CHO)-System markiert. Zusätzlich konnte durch den Einsatz eines modifizierten tRNA/Aminoacyl-tRNA-Synthetase-Paares eine nicht-kanonische Aminosäure an Position eines integrierten Amber-Stopcodon in die Polypeptidkette eingebaut und die funktionelle Gruppe im Anschluss an einen Fluoreszenzfarbstoff gekoppelt werden. Aufgrund des offenen Charakters eignen sich zellfreie Proteinsynthesesysteme besonders für eine Integration von exogenen Komponenten in den Translationsprozess. Mit Hilfe der Fluoreszenzmarkierung wurde eine ligandvermittelte Konformationsänderung im Adora2a über einen Biolumineszenz-Resonanzenergietransfer detektiert. Durch die Etablierung der Amber-Suppression wurde darüber hinaus das Hormon Erythropoetin pegyliert, wodurch Eigenschaften wie Stabilität und Halbwertszeit des Proteins verändert wurden.
Zu guter Letzt wurde ein neues tRNA/Aminoacyl-tRNA-Synthetase-Paar auf Basis der Methanosarcina mazei Pyrrolysin-Synthetase etabliert, um das Repertoire an nicht-kanonischen Aminosäuren und den damit verbundenen Kopplungsreaktionen zu erweitern. Zusammenfassend wurden die Potenziale zellfreier Systeme in Bezug auf der Herstellung von komplexen Membranproteinen und der Charakterisierung dieser durch die Einbringung einer positionsspezifischen Fluoreszenzmarkierung verdeutlicht, wodurch neue Möglichkeiten für die Analyse und Funktionalisierung von komplexen Proteinen geschaffen wurden.
Determining the relationship between genotype and phenotype is the key to understand the plasticity and robustness of phenotypes in nature. While the directly observable plant phenotypes (e.g. agronomic, yield and stress resistance traits) have been well-investigated, there is still a lack in our knowledge about the genetic basis of intermediate phenotypes, such as metabolic phenotypes. Dissecting the links between genotype and phenotype depends on suitable statistical models. The state-of-the-art models are developed for directly observable phenotypes, regardless the characteristics of intermediate phenotypes. This thesis aims to fill the gaps in understanding genetic architecture of intermediate phenotypes, and how they tie to composite traits, namely plant growth. The metabolite levels and reaction fluxes, as two aspects of metabolic phenotypes, are shaped by the interrelated chemical reactions formed in genome-scale metabolic network. Here, I attempt to answer the question: Can the knowledge of underlying genome-scale metabolic network improve the model performance for prediction of metabolic phenotypes and associated plant growth? To this end, two projects are investigated in this thesis. Firstly, we propose an approach that couples genomic selection with genome-scale metabolic network and metabolic profiles in Arabidopsis thaliana to predict growth. This project is the first integration of genomic data with fluxes predicted based on constraint-based modeling framework and data on biomass composition. We demonstrate that our approach leads to a considerable increase of prediction accuracy in comparison to the state-of-the-art methods in both within and across environment predictions. Therefore, our work paves the way for combining knowledge on metabolic mechanisms in the statistical approach underlying genomic selection to increase the efficiency of future plant breeding approaches. Secondly, we investigate how reliable is genomic selection for metabolite levels, and which single nucleotide polymorphisms (SNPs), obtained from different neighborhoods of a given metabolic network, contribute most to the accuracy of prediction. The results show that the local structure of first and second neighborhoods are not sufficient for predicting the genetic basis of metabolite levels in Zea mays. Furthermore, we find that the enzymatic SNPs can capture most the genetic variance and the contribution of non-enzymatic SNPs is in fact small. To comprehensively understand the genetic architecture of metabolic phenotypes, I extend my study to a local Arabidopsis thaliana population and their hybrids. We analyze the genetic architecture in primary and secondary metabolism as well as in growth. In comparison to primary metabolites, compounds from secondary metabolism were more variable and show more non-additive inheritance patterns which could be attributed to epistasis. Therefore, our study demonstrates that heterozygosity in local Arabidopsis thaliana population generates metabolic variation and may impact several tasks directly linked to metabolism. The studies in this thesis improve the knowledge of genetic architecture of metabolic phenotypes in both inbreed and hybrid population. The approaches I proposed to integrate genome-scale metabolic network with genomic data provide the opportunity to obtain mechanistic insights about the determinants of agronomically important polygenic traits.
Predators can have numerical and behavioral effects on prey animals. While numerical effects are well explored, the impact of behavioral effects is unclear. Furthermore, behavioral effects are generally either analyzed with a focus on single individuals or with a focus on consequences for other trophic levels. Thereby, the impact of fear on the level of prey communities is overlooked, despite potential consequences for conservation and nature management. In order to improve our understanding of predator-prey interactions, an assessment of the consequences of fear in shaping prey community structures is crucial.
In this thesis, I evaluated how fear alters prey space use, community structure and composition, focusing on terrestrial mammals. By integrating landscapes of fear in an existing individual-based and spatially-explicit model, I simulated community assembly of prey animals via individual home range formation. The model comprises multiple hierarchical levels from individual home range behavior to patterns of prey community structure and composition. The mechanistic approach of the model allowed for the identification of underlying mechanism driving prey community responses under fear.
My results show that fear modified prey space use and community patterns. Under fear, prey animals shifted their home ranges towards safer areas of the landscape. Furthermore, fear decreased the total biomass and the diversity of the prey community and reinforced shifts in community composition towards smaller animals. These effects could be mediated by an increasing availability of refuges in the landscape. Under landscape changes, such as habitat loss and fragmentation, fear intensified negative effects on prey communities. Prey communities in risky environments were subject to a non-proportional diversity loss of up to 30% if fear was taken into account. Regarding habitat properties, I found that well-connected, large safe patches can reduce the negative consequences of habitat loss and fragmentation on prey communities. Including variation in risk perception between prey animals had consequences on prey space use. Animals with a high risk perception predominantly used safe areas of the landscape, while animals with a low risk perception preferred areas with a high food availability. On the community level, prey diversity was higher in heterogeneous landscapes of fear if individuals varied in their risk perception compared to scenarios in which all individuals had the same risk perception.
Overall, my findings give a first, comprehensive assessment of the role of fear in shaping prey communities. The linkage between individual home range behavior and patterns at the community level allows for a mechanistic understanding of the underlying processes. My results underline the importance of the structure of the landscape of fear as a key driver of prey community responses, especially if the habitat is threatened by landscape changes. Furthermore, I show that individual landscapes of fear can improve our understanding of the consequences of trait variation on community structures. Regarding conservation and nature management, my results support calls for modern conservation approaches that go beyond single species and address the protection of biotic interactions.
This is a publication-based dissertation comprising three original research stud-ies (one published, one submitted and one ready for submission; status March 2019). The dissertation introduces a generic computer model as a tool to investigate the behaviour and population dynamics of animals in cyclic environments. The model is further employed for analysing how migratory birds respond to various scenarios of altered food supply under global change. Here, ecological and evolutionary time-scales are considered, as well as the biological constraints and trade-offs the individual faces, which ultimately shape response dynamics at the population level. Further, the effect of fine-scale temporal patterns in re-source supply are studied, which is challenging to achieve experimentally. My findings predict population declines, altered behavioural timing and negative carry-over effects arising in migratory birds under global change. They thus stress the need for intensified research on how ecological mechanisms are affected by global change and for effective conservation measures for migratory birds. The open-source modelling software created for this dissertation can now be used for other taxa and related research questions. Overall, this thesis improves our mechanistic understanding of the impacts of global change on migratory birds as one prerequisite to comprehend ongoing global biodiversity loss. The research results are discussed in a broader ecological and scientific context in a concluding synthesis chapter.