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The earth’s ecosystems undergo considerable changes characterized by human-induced alterations of environmental factors. In order to develop conservation goals for vulnerable ecosystems, research on ecosystem functioning is required.. Therefore, it is crucial to explore organismal interactions, such as trophic interaction or competition, which are decisive for key processes in ecosystems. These interactions are determined by the performance responses of organisms to environmental changes, which in turn, are shaped by the organism’s functional traits. Exploring traits, their variation, and the environmental factors that act on them may provide insights on how ecological interactions affect
populations, community structures and dynamics, and thus ecosystem
functioning. In aquatic ecosystems, global warming intensifies
phytoplankton blooms, which are more frequently dominated by
cyanobacteria. As cyanobacteria are poor in polyunsaturated fatty acids
(PUFA) and sterols, this compositional change alters the biochemical
food quality of phytoplankton for consumer species with potential
effects on ecological interactions. Within this thesis, I studied the
effects of biochemical food quality on consumer traits and performance responses at the phytoplankton-zooplankton interface using different strains of two closely related generalist rotifer species Brachionus calyciflorus and Brachionus fernandoi and three phytoplankton species that differ in their biochemical food quality, i.e. in their content and composition of PUFA and sterols. In a series of laboratory feeding experiments I found that biochemical food quality affected rotifer’s performance, i.e. fecundity, survival, and population growth, across a broad range of food quantities. Biochemical food quality constraints,
which are often underestimated as influencing environmental factors, had strong impacts on performance responses. I further explored the potential of biochemical food quality in mediating consumer response variation between species and among strains of one species. Co-limitation by food quantity and biochemical food quality resulted in differences in performance responses, which were more pronounced within than between rotifer species. Furthermore, I demonstrated that the body PUFA compositions of rotifer species and strains were differently affected by the dietary PUFA supply, which indicates inter- and intraspecific differences in physiological traits, such as PUFA retention, allocation, and/or bioconversion capacity, within the genus Brachionus. This indicates that dietary PUFA are involved in shaping traits and performance responses of rotifers. This thesis reveals that biochemical food quality is an environmental factor with strong effects on individual traits and performance responses of consumers. Biochemical food quality constraints can further mediate trait and response variation among species or strains. Consequently, they carry the potential to shape ecological interactions and evolutionary processes with effects on community structures and dynamics. Trait-based approaches, which include food quality research, thus may provide further insights into the linkage between functional diversity and the maintenance of crucial ecosystem functions.
Recent advances in microscopy have led to an improved visualization of different cell processes. Yet, this also leads to a higher demand of tools which can process images in an automated and quantitative fashion. Here, we present two applications that were developed to quantify different processes in eukaryotic cells which rely on the organization and dynamics of the cytoskeleton.. In plant cells, microtubules and actin filaments form the backbone of the cytoskeleton. These structures support cytoplasmic streaming, cell wall organization and tracking of cellular material to and from the plasma membrane. To better understand the underlying mechanisms of cytoskeletal organization, dynamics and coordination, frameworks for the quantification are needed. While this is fairly well established for the microtubules, the actin cytoskeleton has remained difficult to study due to its highly dynamic behaviour. One aim of this thesis was therefore to provide an automated framework to quantify and describe actin organization and dynamics. We used the framework to represent actin structures as networks and examined the transport efficiency in Arabidopsis thaliana hypocotyl cells. Furthermore, we applied the framework to determine the growth mode of cotton fibers and compared the actin organization in wild-type and mutant cells of rice. Finally, we developed a graphical user interface for easy usage. Microtubules and the actin cytoskeleton also play a major role in the morphogenesis of epidermal leaf pavement cells. These cells have highly complex and interdigitated shapes which are hard to describe in a quantitative way. While the relationship between microtubules, the actin cytoskeleton and shape formation is the object of many studies, it is still not clear how and if the cytoskeletal components predefine indentations and protrusions in pavement cell shapes. To understand the underlying cell processes which coordinate cell morphogenesis, a quantitative shape descriptor is needed. Therefore, the second aim of this thesis was the development of a network-based shape descriptor which captures global and local shape features, facilitates shape comparison and can be used to evaluate shape complexity. We demonstrated that our framework can be used to describe and compare shapes from various domains. In addition, we showed that the framework accurately detects local shape features of pavement cells and outperform contending approaches. In the third part of the thesis, we extended the shape description framework to describe pavement cell shape features on tissue-level by proposing different network representations of the underlying imaging data.
To meet the demands of a growing world population while reducing carbon dioxide (CO2) emissions, it is necessary to capture CO2 and convert it into value-added compounds. In recent years, metabolic engineering of microbes has gained strong momentum as a strategy for the production of valuable chemicals. As common microbial feedstocks like glucose directly compete with human consumption, the one carbon (C1) compound formate was suggested as an alternative feedstock. Formate can be easily produced by various means including electrochemical reduction of CO2 and could serve as a feedstock for microbial production, hence presenting a novel entry point for CO2 to the biosphere and a storage option for excess electricity. Compared to the gaseous molecule CO2, formate is a highly soluble compound that can be easily handled and stored. It can serve as a carbon and energy source for natural formatotrophs, but these microbes are difficult to cultivate and engineer. In this work, I present the results of several projects that aim to establish efficient formatotrophic growth of E. coli – which cannot naturally grow on formate – via synthetic formate assimilation pathways. In the first study, I establish a workflow for growth-coupled metabolic engineering of E. coli. I demonstrate this approach by presenting an engineering scheme for the PFL-threonine cycle, a synthetic pathway for anaerobic formate assimilation in E. coli. The described methods are intended to create a standardized toolbox for engineers that aim to establish novel metabolic routes in E. coli and related organisms. The second chapter presents a study on the catalytic efficiency of C1-oxidizing enzymes in vivo. As formatotrophic growth requires generation of both energy and biomass from formate, the engineered E. coli strains need to be equipped with a highly efficient formate dehydrogenase, which provides reduction equivalents and ATP for formate assimilation. I engineered a strain that cannot generate reducing power and energy for cellular growth, when fed on acetate. Under this condition, the strain depends on the introduction of an enzymatic system for NADH regeneration, which could further produce ATP via oxidative phosphorylation. I show that the strain presents a valuable testing platform for C1-oxidizing enzymes by testing different NAD-dependent formate and methanol dehydrogenases in the energy auxotroph strain. Using this platform, several candidate enzymes with high in vivo activity, were identified and characterized as potential energy-generating systems for synthetic formatotrophic or methylotrophic growth in E. coli. In the third chapter, I present the establishment of the serine threonine cycle (STC) – a synthetic formate assimilation pathway – in E. coli. In this pathway, formate is assimilated via formate tetrahydrofolate ligase (FtfL) from Methylobacterium extorquens (M. extorquens). The carbon from formate is attached to glycine to produce serine, which is converted into pyruvate entering central metabolism. Via the natural threonine synthesis and cleavage route, glycine is regenerated and acetyl-CoA is produced as the pathway product. I engineered several selection strains that depend on different STC modules for growth and determined key enzymes that enable high flux through threonine synthesis and cleavage. I could show that expression of an auxiliary formate dehydrogenase was required to achieve growth via threonine synthesis and cleavage on pyruvate. By overexpressing most of the pathway enzymes from the genome, and applying adaptive laboratory evolution, growth on glycine and formate was achieved, indicating the activity of the complete cycle. The fourth chapter shows the establishment of the reductive glycine pathway (rGP) – a short, linear formate assimilation route – in E. coli. As in the STC, formate is assimilated via M. extorquens FtfL. The C1 from formate is condensed with CO2 via the reverse reaction of the glycine cleavage system to produce glycine. Another carbon from formate is attached to glycine to form serine, which is assimilated into central metabolism via pyruvate. The engineered E. coli strain, expressing most of the pathway genes from the genome, can grow via the rGP with formate or methanol as a sole carbon and energy source.
NADPH is an essential cofactor that drives biosynthetic reactions in all living organisms. It is a reducing agent and thus electron donor of anabolic reactions that produce major cellular components as well as many products in biotechnology. Indeed, the engineering of metabolic pathways for the production of many products is often limited by the availability of NADPH. One common strategy to address this issue is to swap cofactor specificity from NADH to NADPH of enzymes. However, this process is time consuming and challenging because multiple parameters need to be engineered in parallel. Therefore, the first aim of this project is to establish an efficient metabolic biosensor to select enzymes that can reduce NADP+. An NADPH auxotroph strain was constructed by deleting major reactions involved in NADPH biosynthesis in E. coli’s central carbon metabolism with the exception of 6-phosphogluconate dehydrogenase. To validate this strain, two enzymes were tested in the presence of several carbon sources: a dihydrolipoamide dehydrogenase variant of E. coli harboring seven mutations and a formate dehydrogenase (FDH) from Mycobacterium vaccae N10 harboring four mutations were found to support NADPH biosynthesis and growth. The strain was subjected to adaptive laboratory evolution with the goal of testing its robustness under different carbon sources. Our evolution experiment resulted in the random mutagenesis of the malic enzyme (maeA), enabling it to produce NADPH. The additional deletion of maeA rendered a more robust second-generation biosensor strain for NADP+ reduction. We devised a structure-guided directed evolution approach to change cofactor specificity in Pseudomonas sp. 101 FDH. To this end, a library of >106 variants was tested using in vivo selection. Compared to the best engineered enzymes reported, our best variant carrying five mutations shows 5-fold higher catalytic efficiency and 13-fold higher specificity towards NADP+, as well as 2-fold higher affinity towards formate. In conclusion, we demonstrate the potential of in vivo selection and evolution-guided approaches to develop better NADPH biosensors and to engineer cofactor specificity by the simultaneous improvement of multiple parameters (kinetic efficiency with NADP+, specificity towards NADP+, and affinity towards formate), which is a major challenge in protein engineering due to the existence of tradeoffs and epistasis.
Escaping the plant cell
(2020)
Due to continuously intensifying human usage of the marine environment worldwide ranging cetaceans face an increasing number of threats. Besides whaling, overfishing and by-catch, new technical developments increase the water and noise pollution, which can negatively affect marine species. Cetaceans are especially prone to these influences, being at the top of the food chain and therefore accumulating toxins and contaminants. Furthermore, they are extremely noise sensitive due to their highly developed hearing sense and echolocation ability. As a result, several cetacean species were brought to extinction during the last century or are now classified as critically endangered. This work focuses on two odontocetes. It applies and compares different molecular methods for inference of population status and adaptation, with implications for conservation. The worldwide distributed sperm whale (Physeter macrocephalus) shows a matrilineal population structure with predominant male dispersal. A recently stranded group of male sperm whales provided a unique opportunity to investigate male grouping for the first time. Based on the mitochondrial control region, I was able to infer that male bachelor groups comprise multiple matrilines, hence derive from different social groups, and that they represent the genetic variability of the entire North Atlantic. The harbor porpoise (Phocoena phocoena) occurs only in the northern hemisphere. By being small and occurring mostly in coastal habitats it is especially prone to human disturbance. Since some subspecies and subpopulations are critically endangered, it is important to generate and provide genetic markers with high resolution to facilitate population assignment and subsequent protection measurements. Here, I provide the first harbour porpoise whole genome, in high quality and including a draft annotation. Using it for mapping ddRAD seq data, I identify genome wide SNPs and, together with a fragment of the mitochondrial control region, inferred the population structure of its North Atlantic distribution range. The Belt Sea harbors a distinct subpopulation oppose to the North Atlantic, with a transition zone in the Kattegat. Within the North Atlantic I could detect subtle genetic differentiation between western (Canada-Iceland) and eastern (North Sea) regions, with support for a German North Sea breading ground around the Isle of Sylt. Further, I was able to detect six outlier loci which show isolation by distance across the investigated sampling areas. In employing different markers, I could show that single maker systems as well as genome wide data can unravel new information about population affinities of odontocetes. Genome wide data can facilitate investigation of adaptations and evolutionary history of the species and its populations. Moreover, they facilitate population genetic investigations, providing a high resolution, and hence allowing for detection of subtle population structuring especially important for highly mobile cetaceans.
The metabolic state of an organism reflects the entire phenotype that is jointly affected by genetic and environmental changes. Due to the complexity of metabolism, system-level modelling approaches have become indispensable tools to obtain new insights into biological functions. In particular, simulation and analysis of metabolic networks using constraint-based modelling approaches have helped the analysis of metabolic fluxes. However, despite ongoing improvements in prediction of reaction flux through a system, approaches to directly predict metabolite concentrations from large-scale metabolic networks remain elusive. In this thesis, we present a computational approach for inferring concentration ranges from genome-scale metabolic models endowed with mass action kinetics. The findings specify a molecular mechanism underling facile control of concentration ranges for components in large-scale metabolic networks. Most importantly, an extended version of the approach can be used to predict concentration ranges without knowledge of kinetic parameters, provided measurements of concentrations in a reference state. We show that the approach is applicable with large-scale kinetic and stoichiometric metabolic models of organisms from different kingdoms of life. By challenging the predictions of concentration ranges in the genome-scale metabolic network of Escherichia coli with real-world data sets, we further demonstrate the prediction power and limitations of the approach. To predict concentration ranges in other species, e.g. model plant species Arabidopsis thaliana, we would rely on estimates of kinetic parameters (i.e. enzyme catalytic rates) since plant-specific enzyme catalytic rates are poorly documented. Using the constraint-based approach of Davidi et al. for estimation of enzyme catalytic rates, we obtain values for 168 plant enzymes. The approach depends on quantitative proteomics data and flux estimates obtained from constraint-based model of plant leaf metabolism integrating maximal rates of selected enzymes, plant-specific constraints on fluxes through canonical pathways, and growth measurements from Arabidopsis thaliana rosette under ten conditions. We demonstrate a low degree of plant enzyme saturation, supported by the agreement between concentrations of nicotinamide adenine dinucleotide, adenosine triphosphate, and glyceraldehyde 3-phosphate, based on our maximal in vivo catalytic rates, and available quantitative metabolomics data. Hence, our results show genome-wide estimation for plant-specific enzyme catalytic rates is feasible. These can now be readily employed to study resource allocation, to predict enzyme and metabolite concentrations using recent constrained-based modelling approaches. Constraint-based methods do not directly account for kinetic mechanisms and corresponding parameters. Therefore, a number of workflows have already been proposed to approximate reaction kinetics and to parameterize genome-scale kinetic models. We present a systems biology strategy to build a fully parameterized large-scale model of Chlamydomonas reinhardtii accounting for microcompartmentalization in the chloroplast stroma. Eukaryotic algae comprise a microcompartment, the pyrenoid, essential for the carbon concentrating mechanism (CCM) that improves their photosynthetic performance. Since the experimental study of the effects of microcompartmentation on metabolic pathways is challenging, we employ our model to investigate compartmentation of fluxes through the Calvin-Benson cycle between pyrenoid and stroma. Our model predicts that ribulose-1,5-bisphosphate, the substrate of Rubisco, and 3-phosphoglycerate, its product, diffuse in and out of the pyrenoid. We also find that there is no major diffusional barrier to metabolic flux between the pyrenoid and stroma. Therefore, our computational approach represents a stepping stone towards understanding of microcompartmentalized CCM in other organisms. This thesis provides novel strategies to use genome-scale metabolic networks to predict and integrate metabolite concentrations. Therefore, the presented approaches represent an important step in broadening the applicability of large-scale metabolic models to a range of biotechnological and medical applications.