Refine
Year of publication
- 2016 (58) (remove)
Document Type
- Doctoral Thesis (58) (remove)
Is part of the Bibliography
- yes (58)
Keywords
- Centrosom (2)
- Dictyostelium (2)
- centrosome (2)
- dictyostelium (2)
- mathematical modeling (2)
- mathematical modelling (2)
- mathematische Modellierung (2)
- Arabidopsis thaliana (1)
- Ausrichtung (1)
- Bildanalyse (1)
Institute
- Institut für Biochemie und Biologie (58) (remove)
Seit der Einführung von Antibiotika in die medizinische Behandlung von bakteriellen Infektionskrankheiten existiert ein Wettlauf zwischen der Evolution von Bakterienresistenzen und der Entwicklung wirksamer Antibiotika. Während bis in die 80er Jahre verstärkt an neuen Antibiotika geforscht wurde, gewinnen multiresistente Keime heute zunehmend die Oberhand. Um einzelne Pathogene erfolgreich nachzuweisen und zu bekämpfen, ist ein grundlegendes Wissen über den Erreger unumgänglich. Bakterielle Proteine, die bei einer Infektion vorrangig vom Immunsystem prozessiert und präsentiert werden, könnten für die Entwicklung von Impfstoffen oder gezielten Therapeutika nützlich sein. Auch für die Diagnostik wären diese immundominanten Proteine interessant. Allerdings herrscht ein Mangel an Wissen über spezifische Antigene vieler pathogener Bakterien, die eine eindeutige Diagnostik eines einzelnen Erregers erlauben würden.
Daher wurden in dieser Arbeit vier verschiedene Humanpathogene mittels Phage Display untersucht: Neisseria gonorrhoeae, Neisseria meningitidis, Borrelia burgdorferi und Clostridium difficile. Hierfür wurden aus der genomischen DNA der vier Erreger Bibliotheken konstruiert und durch wiederholte Selektion und Amplifikation, dem sogenannten Panning, immunogene Proteine isoliert. Für alle Erreger bis auf C. difficile wurden immunogene Proteine aus den jeweiligen Bibliotheken isoliert. Die identifizierten Proteine von N. meningitidis und B. burgdorferi waren größtenteils bekannt, konnten aber in dieser Arbeit durch Phage Display verifiziert werden. Für N. gonorrhoeae wurden 21 potentiell immunogene Oligopeptide isoliert, von denen sechs Proteine als neue zuvor unbeschriebene Proteine mit immunogenem Charakter identifiziert wurden. Von den Phagen-präsentierten Oligopeptide der 21 immunogenen Proteine wurden Epitopmappings mit verschiedenen polyklonalen Antikörpern durchgeführt, um immunogene Bereiche näher zu identifizieren und zu charakterisieren. Bei zehn Proteinen wurden lineare Epitope eindeutig mit drei polyklonalen Antikörpern identifiziert, von fünf weiteren Proteinen waren Epitope mit mindestens einem Antikörper detektierbar. Für eine weitere Charakterisierung der ermittelten Epitope wurden Alaninscans durchgeführt, die eine detaillierte Auskunft über kritische Aminosäuren für die Bindung des Antikörpers an das Epitop geben.
Ausgehend von dem neu identifizierten Protein mit immunogenem Charakter NGO1634 wurden 26 weitere Proteine aufgrund ihrer funktionellen Ähnlichkeit ausgewählt und mithilfe bioinformatischer Analysen auf ihre Eignung zur Entwicklung einer diagnostischen Anwendung analysiert. Durch Ausschluss der meisten Proteine aufgrund ihrer Lokalisation, Membrantopologie oder unspezifischen Proteinsequenz wurden scFv-Antikörper gegen acht Proteine mittels Phage Display generiert und anschließend als scFv-Fc-Fusionsantikörper produziert und charakterisiert.
Die hier identifizierten Proteine und linearen Epitope könnten einen Ansatzpunkt für die Entwicklung einer diagnostischen oder therapeutischen Anwendung bieten. Lineare Epitopsequenzen werden häufig für die Impfstoffentwicklung eingesetzt, sodass vor allem die in dieser Arbeit bestimmten Epitope von Membranproteinen interessante Kandidaten für weitere Untersuchungen in diese Richtung sind. Durch weitere Untersuchungen könnten möglicherweise unbekannte Virulenzfaktoren entdeckt werden, deren Inhibierung einen entscheidenden Einfluss auf Infektionen haben könnten.
In freshwater sciences, nitrogen gained increasing attention in the past as an important resource potentially influencing phytoplankton growth and thus eutrophication. Most studies and all management approaches, however, are still restricted to dissolved inorganic nitrogen (DIN = nitrate + nitrite + ammonium) since dissolved organic nitrogen (DON) was considered to be refractory for most of the photoautotrophs. In the meantime this assumption has been disproved for all aquatic systems. While research on DON in marine ecosystems substantially increased, in freshwater a surprisingly small number of investigations has been carried out on DON utilization by phytoplankton or even the occurrence and seasonal development of total DON or its compounds in lakes. Therefore, our present knowledge on DON utilization by phytoplankton is often based on single species experiments using a sole, usually low molecular weight DON component, often in unnaturally high amounts mainly carried out with marine phytoplankton species. Thus, we know that some phytoplankton species can take up different DON fractions if they are available in high concentrations and as sole nitrogen source. This does not necessarily imply that phytoplankton would perform likewise in natural environments. In addition, it will be difficult to draw conclusions on the behavior of freshwater phytoplankton from experiments with marine phytoplankton since the nutrient regime in marine environments differs from that of freshwater. In the light of the parallel availability of inorganic and organic nitrogen species in natural freshwater ecosystems, several questions must be raised: "If inorganic nitrogen is available, would phytoplankton really rely on an organic nitrogen source? Could a connection be detected between the seasonal development of DON and changes in the phytoplankton community composition as found for inorganic nitrogen? And if we reduce the input of inorganic nitrogen in lakes and rivers would the importance of DON as nitrogen source for phytoplankton increase, counteracting all management efforts or even leading to undesired effects due to changes in phytoplankton physiology and biodiversity?" I experimentally addressed the questions whether those DON compounds differentially influence growth, physiology and composition of phytoplankton both as sole available nitrogen source and in combination with other nitrogen compounds. I hypothesized that all offered DON - compounds (urea, natural organic matter (NOM), dissolved free and combined amino acids (DFAA, DCAA)) could be utilized by phytoplankton at natural concentrations. However, I assumed that the availability would decrease with increasing compound complexity. I furthermore hypothesized that the occurrence of low DIN concentrations would not affect the utilization of DON negatively. The nitrogen source, whatsoever, would have an impact on phytoplankton physiology as well as community composition. To investigate these questions and assumptions I conducted bioassays with algae monocultures as well as phytoplankton communities testing the utilization of various DON compounds by several freshwater phytoplankton species. Especially the potential utilization of NOM, a complex DON compound mainly consisting of humic substances is of interest, since it is usually regarded to be refractory. In order to be able to use natural concentrations of DON - compounds for my experiments the concentration of total DON and some DON - compounds (urea, humic substances, heigh molecular weight substances) was assessed in Lake Müggelsee. All compounds were able to support algae growth in the low natural concentrations supplied. However, I found that the offered DON compounds differ in their availability to various algae species, both, as sole nitrogen source or in combination with low DIN concentrations. As expected, the availability decreased with increasing complexity of the nitrogen compound. Furthermore, I could show that changes in algal physiology (nitrogen storage, metabolism) occur depending on the utilized nitrogen source. Especially the secondary photosynthetic pigment composition, heterocyst frequency and C:N - ratio of the algae were affected. The uptake and usage of certain nitrogen compounds might be more costly, potentially resulting in those physiology changes. Whereas laboratory experiments with single species revealed strong effects of DON, algal responses to DON in a multi-species situation remain unclear. Experiments with phytoplankton communities from Lake Müggelsee revealed that the nitrogen pool composition does influence the phytoplankton community structure. The findings furthermore show that several species combined might utilize the supplied nitrogen completely different than monocultures in the laboratory. Thus, besides the actual ability of algae to use the offered nitrogen sources other factors, such as interspecific competition, may be of importance. I further investigated, if the results of the laboratory experiments, can be verified in the field. Here, I surveyed the seasonal development of several dissolved organic matter (DOM) components (urea, high molecular weight substances (HMWS), humic substances (HS)) and associated parameters (Specific UV-absorption (SUVA), C:N - ratio) in Lake Müggelsee between 2011 and 2013. Furthermore, data from the long term measurements series of Lake Müggelsee such as physical (temperature, light, pH, O2) and chemical parameters (nitrogen, phosphorous, silica, inorganic carbon), zooplankton and phytoplankton data were used to investigate how much of the variability of the phytoplankton composition in Lake Müggelsee can be explained by DON/DOM concentration and composition, relative to the other groups of explanatory variables. The results show that DON mainly consists of rather complex compounds such as humic substances and biopolymers (80 %) and that only slight seasonal trends are detectable. Using variance partitioning I could show, that the usually investigated nutrients (DIN, silica, inorganic carbon, phosphorous) and abiotic factors together explain most of the algae composition as was to be expected (57.1 % of modeled variance). However, DOM and the associated parameters uniquely explain 10.3 % of the variance and thus slightly more than zooplankton with 9.3 %. I could therefore prove, that the composition of DOM (nitrogen and carbon) is connected to the algae composition in an eutrophic lake such as Lake Müggelsee. DON - compounds such as urea, however, could not be correlated with the occurrence of specific phytoplankton species. Overall, the results of this study imply that DON can be a valuable nitrogen source for freshwater phytoplankton. DON is used by various species even when DIN is available in low concentrations. Through the reduction of DIN in lakes and rivers, the DON:DIN ratio might be changed, resulting even in an increased importance of DON as phytoplankton nitrogen source. My work suggests that not only N2-fixation but also DON utilization might compensate for reduced N - input. Changes from DIN to DON as main nitrogen source might also promote certain, potentially undesired algae species and influence the biodiversity of a limnic ecosystem through changes in the phytoplankton community structure. Thus, DON, especially urea, should be included in calculations concerning total available nitrogen and when determining nitrogen threshold values. Furthermore, the input-reduction of DON, for example from waste-water treatment plants should also be evaluated and the results of my thesis should find consideration when planning to reduce the nitrogen input in freshwater.
The human immunodeficiency virus (HIV) has resisted nearly three decades of efforts targeting a cure. Sustained suppression of the virus has remained a challenge, mainly due
to the remarkable evolutionary adaptation that the virus exhibits by the accumulation of drug-resistant mutations in its genome. Current therapeutic strategies aim at achieving and maintaining a low viral burden and typically involve multiple drugs. The choice of optimal combinations of these drugs is crucial, particularly in the background of treatment failure having occurred previously with certain other drugs. An understanding of the dynamics of viral mutant genotypes aids in the assessment of treatment failure with a certain drug
combination, and exploring potential salvage treatment regimens.
Mathematical models of viral dynamics have proved invaluable in understanding the viral life cycle and the impact of antiretroviral drugs. However, such models typically use simplified and coarse-grained mutation schemes, that curbs the extent of their application to drug-specific clinical mutation data, in order to assess potential next-line therapies. Statistical
models of mutation accumulation have served well in dissecting mechanisms of resistance evolution by reconstructing mutation pathways under different drug-environments. While these models perform well in predicting treatment outcomes by statistical learning, they do not incorporate drug effect mechanistically. Additionally, due to an inherent lack of
temporal features in such models, they are less informative on aspects such as predicting mutational abundance at treatment failure. This limits their application in analyzing the
pharmacology of antiretroviral drugs, in particular, time-dependent characteristics of HIV therapy such as pharmacokinetics and pharmacodynamics, and also in understanding the impact of drug efficacy on mutation dynamics.
In this thesis, we develop an integrated model of in vivo viral dynamics incorporating drug-specific mutation schemes learned from clinical data. Our combined modelling
approach enables us to study the dynamics of different mutant genotypes and assess mutational abundance at virological failure. As an application of our model, we estimate in vivo
fitness characteristics of viral mutants under different drug environments. Our approach also extends naturally to multiple-drug therapies. Further, we demonstrate the versatility of our model by showing how it can be modified to incorporate recently elucidated mechanisms of drug action including molecules that target host factors.
Additionally, we address another important aspect in the clinical management of HIV disease, namely drug pharmacokinetics. It is clear that time-dependent changes in in vivo
drug concentration could have an impact on the antiviral effect, and also influence decisions on dosing intervals. We present a framework that provides an integrated understanding
of key characteristics of multiple-dosing regimens including drug accumulation ratios and half-lifes, and then explore the impact of drug pharmacokinetics on viral suppression.
Finally, parameter identifiability in such nonlinear models of viral dynamics is always a concern, and we investigate techniques that alleviate this issue in our setting.