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The adaptive evolutionary potential of a species or population to cope with omnipresent environmental challenges is based on its genetic variation. Variability at immune genes, such as the major histocompatibility complex (MHC) genes, is assumed to be a very powerful and effective tool to keep pace with diverse and rapidly evolving pathogens. In my thesis, I studied natural levels of variation at the MHC genes, which have a key role in immune defence, and parasite burden in different small mammal species. I assessed the importance of MHC variation for parasite burden in small mammal populations in their natural environment. To understand the processes shaping different patterns of MHC variation I focused on evidence of selection through pathogens upon the host. Further, I addressed the issue of low MHC diversity in populations or species, which could potentially arise as a result from habitat fragmentation and isolation. Despite their key role in the mammalian evolution the marsupial MHC has been rarely investigated. Studies on primarily captive or laboratory bred individuals indicated very little or even no polymorphism at the marsupial MHC class II genes. However, natural levels of marsupial MHC diversity and selection are unknown to date as studies on wild populations are virtually absent. I investigated MHC II variation in two Neotropical marsupial species endemic to the threatened Brazilian Atlantic Forest (Gracilinanus microtarsus, Marmosops incanus) to test whether the predicted low marsupial MHC class II polymorphism proves to be true under natural conditions. For the first time in marsupials I confirmed characteristics of MHC selection that were so far only known from eutherian mammals, birds, and fish: Positive selection on specific codon sites, recombination, and trans-species polymorphism. Beyond that, the two marsupial species revealed considerable differences in their MHC class II diversity. Diversity was rather low in M. incanus but tenfold higher in G. microtarsus, disproving the predicted general low marsupial MHC class II variation. As pathogens are believed to be very powerful drivers of MHC diversity, I studied parasite burden in both host species to understand the reasons for the remarkable differences in MHC diversity. In both marsupial species specific MHC class II variants were associated to either high or low parasite load highlighting the importance of the marsupial MHC class II in pathogen defence. I developed two alternative scenarios with regard to MHC variation, parasite load, and parasite diversity. In the ‘evolutionary equilibrium’ scenario I assumed the species with low MHC diversity, M. incanus, to be under relaxed pathogenic selection and expected low parasite diversity. Alternatively, low MHC diversity could be the result of a recent loss of genetic variation by means of a genetic bottleneck event. Under this ‘unbalanced situation’ scenario, I assumed a high parasite burden in M. incanus due to a lack of resistance alleles. Parasitological results clearly reject the first scenario and point to the second scenario, as M. incanus is distinctly higher parasitised but parasite diversity is relatively equal compared to G. microtarsus. Hence, I suggest that the parasite load in M. incanus is rather the consequence than the cause for its low MHC diversity. MHC variation and its associations to parasite burden have been typically studied within single populations but MHC variation between populations was rarely taken into account. To gain scientific insight on this issue, I chose a common European rodent species. In the yellow necked mouse (Apodemus flavicollis), I investigated the effects of genetic diversity on parasite load not on the individual but on the population level. I included populations, which possess different levels of variation at the MHC as well as at neutrally evolving genetic markers (microsatellites). I was able to show that mouse populations with a high MHC allele diversity are better armed against high parasite burdens highlighting the significance of adaptive genetic diversity in the field of conservation genetics. An individual itself will not directly benefit from its population’s large MHC allele pool in terms of parasite resistance. But confronted with the multitude of pathogens present in the wild a population with a large MHC allele reservoir is more likely to possess individuals with resistance alleles. These results deepen our understanding of the complex causes and processes of evolutionary adaptations between hosts and pathogens.
Pectic polysaccharides, a class of plant cell wall polymers, form one of the most complex networks known in nature. Despite their complex structure and their importance in plant biology, little is known about the molecular mechanism of their biosynthesis, modification, and turnover, particularly their structure-function relationship. One way to gain insight into pectin metabolism is the identification of mutants with an altered pectin structure. Those were obtained by a recently developed pectinase-based genetic screen. Arabidopsis thaliana seedlings grown in liquid medium containing pectinase solutions exhibited particular phenotypes: they were dwarfed and slightly chlorotic. However, when genetically different A. thaliana seed populations (random T-DNA insertional populations as well as EMS-mutagenized populations and natural variations) were subjected to this treatment, individuals were identified that exhibit a different visible phenotype compared to wild type or other ecotypes and may thus contain a different pectin structure (pec-mutants). After confirming that the altered phenotype occurs only when the pectinase is present, the EMS mutants were subjected to a detailed cell wall analysis with particular emphasis on pectins. This suite of mutants identified in this study is a valuable resource for further analysis on how the pectin network is regulated, synthesized and modified. Flanking sequences of some of the T-DNA lines have pointed toward several interesting genes, one of which is PEC100. This gene encodes a putative sugar transporter gene, which, based on our data, is implicated in rhamnogalacturonan-I synthesis. The subcellular localization of PEC100 was studied by GFP fusion and this protein was found to be localized to the Golgi apparatus, the organelle where pectin biosynthesis occurs. Arabidopsis ecotype C24 was identified as a susceptible one when grown with pectinases in liquid culture and had a different oligogalacturonide mass profile when compared to ecotype Col-0. Pectic oligosaccharides have been postulated to be signal molecules involved in plant pathogen defense mechanisms. Indeed, C24 showed elevated accumulation of reactive oxygen species upon pectinase elicitation and had altered response to the pathogen Alternaria brassicicola in comparison to Col-0. Using a recombinant inbred line population three major QTLs were identified to be responsible for the susceptibility of C24 to pectinases. In a reverse genetic approach members of the qua2 (putative pectin methyltransferase) family were tested for potential target genes that affect pectin methyl-esterification. The list of these genes was determined by in silico study of the pattern of expression and co-expression of all 34 members of this family resulting in 6 candidate genes. For only for one of the 6 analyzed genes a difference in the oligogalacturonide mass profile was observed in the corresponding knock-out lines, confirming the hypothesis that the methyl-esterification pattern of pectin is fine tuned by members of this gene family. This study of pectic polysaccharides through forward and reverse genetic screens gave new insight into how pectin structure is regulated and modified, and how these modifications could influence pectin mediated signalling and pathogenicity.
Background: To improve the understanding of consequences of climate change for annual plant communities, I used a detailed, grid-based model that simulates the effect of daily rainfall variability on individual plants in five climatic regions on a gradient from 100 to 800 mm mean annual precipitation (MAP). The model explicitly considers moisture storage in the soil. I manipulated daily rainfall variability by changing the daily mean rain (DMR, rain volume on rainy days averaged across years for each day of the year) by ± 20%. At the same time I adjusted intervals appropriately between rainy days for keeping the mean annual volume constant. In factorial combination with changing DMR I also changed MAP by ± 20%. Results: Increasing MAP generally increased water availability, establishment, and peak shoot biomass. Increasing DMR increased the time that water was continuously available to plants in the upper 15 to 30 cm of the soil (longest wet period, LWP). The effect of DMR diminished with increasing humidity of the climate. An interaction between water availability and density-dependent germination increased the establishment of seedlings in the arid region, but in the more humid regions the establishment of seedlings decreased with increasing DMR. As plants matured, competition among individuals and their productivity increased, but the size of these effects decreased with the humidity of the regions. Therefore, peak shoot biomass generally increased with increasing DMR but the effect size diminished from the semiarid to the mesic Mediterranean region. Increasing DMR reduced via LWP the annual variability of biomass in the semiarid and dry Mediterranean regions. Conclusion: More rainstorms (greater DMR) increased the recharge of soil water reservoirs in more arid sites with consequences for germination, establishment, productivity, and population persistence. The order of magnitudes of DMR and MAP overlapped partially so that their combined effect is important for projections of climate change effects on annual vegetation.
This work presents mathematical and computational approaches to cover various aspects of metabolic network modelling, especially regarding the limited availability of detailed kinetic knowledge on reaction rates. It is shown that precise mathematical formulations of problems are needed i) to find appropriate and, if possible, efficient algorithms to solve them, and ii) to determine the quality of the found approximate solutions. Furthermore, some means are introduced to gain insights on dynamic properties of metabolic networks either directly from the network structure or by additionally incorporating steady-state information. Finally, an approach to identify key reactions in a metabolic networks is introduced, which helps to develop simple yet useful kinetic models. The rise of novel techniques renders genome sequencing increasingly fast and cheap. In the near future, this will allow to analyze biological networks not only for species but also for individuals. Hence, automatic reconstruction of metabolic networks provides itself as a means for evaluating this huge amount of experimental data. A mathematical formulation as an optimization problem is presented, taking into account existing knowledge and experimental data as well as the probabilistic predictions of various bioinformatical methods. The reconstructed networks are optimized for having large connected components of high accuracy, hence avoiding fragmentation into small isolated subnetworks. The usefulness of this formalism is exemplified on the reconstruction of the sucrose biosynthesis pathway in Chlamydomonas reinhardtii. The problem is shown to be computationally demanding and therefore necessitates efficient approximation algorithms. The problem of minimal nutrient requirements for genome-scale metabolic networks is analyzed. Given a metabolic network and a set of target metabolites, the inverse scope problem has as it objective determining a minimal set of metabolites that have to be provided in order to produce the target metabolites. These target metabolites might stem from experimental measurements and therefore are known to be produced by the metabolic network under study, or are given as the desired end-products of a biotechological application. The inverse scope problem is shown to be computationally hard to solve. However, I assume that the complexity strongly depends on the number of directed cycles within the metabolic network. This might guide the development of efficient approximation algorithms. Assuming mass-action kinetics, chemical reaction network theory (CRNT) allows for eliciting conclusions about multistability directly from the structure of metabolic networks. Although CRNT is based on mass-action kinetics originally, it is shown how to incorporate further reaction schemes by emulating molecular enzyme mechanisms. CRNT is used to compare several models of the Calvin cycle, which differ in size and level of abstraction. Definite results are obtained for small models, but the available set of theorems and algorithms provided by CRNT can not be applied to larger models due to the computational limitations of the currently available implementations of the provided algorithms. Given the stoichiometry of a metabolic network together with steady-state fluxes and concentrations, structural kinetic modelling allows to analyze the dynamic behavior of the metabolic network, even if the explicit rate equations are not known. In particular, this sampling approach is used to study the stabilizing effects of allosteric regulation in a model of human erythrocytes. Furthermore, the reactions of that model can be ranked according to their impact on stability of the steady state. The most important reactions in that respect are identified as hexokinase, phosphofructokinase and pyruvate kinase, which are known to be highly regulated and almost irreversible. Kinetic modelling approaches using standard rate equations are compared and evaluated against reference models for erythrocytes and hepatocytes. The results from this simplified kinetic models can simulate acceptably the temporal behavior for small changes around a given steady state, but fail to capture important characteristics for larger changes. The aforementioned approach to rank reactions according to their influence on stability is used to identify a small number of key reactions. These reactions are modelled in detail, including knowledge about allosteric regulation, while all other reactions were still described by simplified reaction rates. These so-called hybrid models can capture the characteristics of the reference models significantly better than the simplified models alone. The resulting hybrid models might serve as a good starting point for kinetic modelling of genome-scale metabolic networks, as they provide reasonable results in the absence of experimental data, regarding, for instance, allosteric regulations, for a vast majority of enzymatic reactions.
The study of biological interaction networks is a central theme in systems biology. Here, we investigate common as well as differentiating principles of molecular interaction networks associated with different levels of molecular organization. They include metabolic pathway maps, protein-protein interaction networks as well as kinase interaction networks. First, we present an integrated analysis of metabolic pathway maps and protein-protein interaction networks (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzyme complexes. Inspecting high-throughput PIN data, it has been shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In this study, we expanded this line of research to include comparisons of the respective underlying network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency. Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and hence might be essential for the structural integrity of several biosynthetic systems. Besides metabolic aspects of PINs, we investigated the characteristic topological properties of protein interactions involved in signaling and regulatory functions mediated by kinase interactions. Characteristic topological differences between PINs associated with metabolism, and those describing phosphorylation networks were revealed and shown to reflect the different modes of biological operation of both network types. The construction of phosphorylation networks is based on the identification of specific kinase-target relations including the determination of the actual phosphorylation sites (P-sites). The computational prediction of P-sites as well as the identification of involved kinases still suffers from insufficient accuracies and specificities of the underlying prediction algorithms, and the experimental identification in a genome-scale manner is not (yet) doable. Computational prediction methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding P-sites. However the recognition of such motifs by the respective kinases is a spatial event. Therefore, we characterized the spatial distributions of amino acid residue types around P-sites and extracted signature 3D-profiles. We then tested the added value of spatial information on the prediction performance. When compared to sequence-only based predictors, a consistent performance gain was obtained. The availability of reliable training data of experimentally determined P-sites is critical for the development of computational prediction methods. As part of this thesis, we provide an assessment of false-positive rates of phosphoproteomic data.
Chloroplasts as bioreactors : high-yield production of active bacteriolytic protein antibiotics
(2008)
Plants, more precisely their chloroplasts with their bacterial-like expression machinery inherited from their cyanobacterial ancestors, can potentially offer a cheap expression system for proteinaceous pharmaceuticals. This system would be easily scalable and provides appropriate safety due to chloroplasts maternal inheritance. In this work, it was shown that three phage lytic enzymes (Pal, Cpl-1 and PlyGBS) could be successfully expressed at very high levels and with high stability in tobacco chloroplasts. PlyGBS expression reached an amount of foreign protein accumulation (> 70% TSP) that has never been obtained before. Although the high expression levels of PlyGBS caused a pale green phenotype with retarded growth, presumably due to exhaustion of plastid protein synthesis capacity, development and seed production were not impaired under greenhouse conditions. Since Pal and Cpl-1 showed toxic effects when expressed in E. coli, a special plastid transformation vector (pTox) was constructed to allow DNA amplification in bacteria. The construction of the pTox transformation vector allowing a recombinase-mediated deletion of an E. coli transcription block in the chloroplast, leading to an increase of foreign protein accumulation to up to 40% of TSP for Pal and 20% of TSP for Cpl-1. High dose-dependent bactericidal efficiency was shown for all three plant-derived lytic enzymes using their pathogenic target bacteria S. pyogenes and S. pneumoniae. Confirmation of specificity was obtained for the endotoxic proteins Pal and Cpl-1 by application to E. coli cultures. These results establish tobacco chloroplasts as a new cost-efficient and convenient production platform for phage lytic enzymes and address the greatest obstacle for clinical application. The present study is the first report of lysin production in a non-bacterial system. The properties of chloroplast-produced lysins described in this work, their stability, high accumulation rate and biological activity make them highly attractive candidates for future antibiotics.
Adenylates are metabolites with essential function in metabolism and signaling in all living organisms. As Cofactors, they enable thermodynamically unfavorable reactions to be catalyzed enzymatically within cells. Outside the cell, adenylates are involved in signalling processes in animals and emerging evidence suggests similar signaling mechanisms in the plants’ apoplast. Presumably, apoplastic apyrases are involved in this signaling by hydrolyzing the signal mediating molecules ATP and ADP to AMP. This PhD thesis focused on the role of adenylates on metabolism and development of potato (Solanum tuberosum) by using reverse genetics and biochemical approaches. To study the short and long term effect of cellular ATP and the adenylate energy charge on potato tuber metabolism, an apyrase from Escherichia coli targeted into the amyloplast was expressed inducibly and constitutively. Both approaches led to the identification of adaptations to reduced ATP/energy charge levels on the molecular and developmental level. These comprised a reduction of metabolites and pathway fluxes that require significant amounts of ATP, like amino acid or starch synthesis, and an activation of processes that produce ATP, like respiration and an immense increase in the surface-to-volume ratio. To identify extracellular enzymes involved in adenylate conversion, green fluorescent protein and activity localization studies in potato tissue were carried out. It was found that extracellular ATP is imported into the cell by an apoplastic enzyme complement consisting of apyrase, unspecific phosphatase, adenosine nucleosidase and an adenine transport system. By changing the expression of a potato specific apyrase via transgenic approaches, it was found that this enzyme has strong impact on plant and particular tuber development in potato. Whereas metabolite levels were hardly altered, transcript profiling of tubers with reduced apyrase activity revealed a significant upregulation of genes coding for extensins, which are associated with polar growth. The results are discussed in context of adaptive responses of plants to changes in the adenylate levels and the proposed role of apyrase in apoplastic purinergic signaling and ATP salvaging. In summary, this thesis provides insight into adenylate regulated processes within and outside non-photosynthetic plant cells.
In order to function properly, organisms have a complex control mechanism, in which a given gene is expressed at a particular time and place. One way to achieve this control is to regulate the initiation of transcription. This step requires the assembly of several components, i.e., a basal/general machinery common to all expressed genes, and a specific/regulatory machinery, which differs among genes and is the responsible for proper gene expression in response to environmental or developmental signals. This specific machinery is composed of transcription factors (TFs), which can be grouped into evolutionarily related gene families that possess characteristic protein domains. In this work we have exploited the presence of protein domains to create rules that serve for the identification and classification of TFs. We have modelled such rules as a bipartite graph, where families and protein domains are represented as nodes. Connections between nodes represent that a protein domain should (required rule) or should not (forbidden rule) be present in a protein to be assigned into a TF family. Following this approach we have identified putative complete sets of TFs in plant species, whose genome is completely sequenced: Cyanidioschyzon merolae (red algae), Chlamydomonas reinhardtii (green alga), Ostreococcus tauri (green alga), Physcomitrella patens (moss), Arabidopsis thaliana (thale cress), Populus trichocarpa (black cottonwood) and Oryza sativa (rice). The identification of the complete sets of TFs in the above-mentioned species, as well as additional information and reference literature are available at http://plntfdb.bio.uni-potsdam.de/. The availability of such sets allowed us performing detailed evolutionary studies at different levels, from a single family to all TF families in different organisms in a comparative genomics context. Notably, we uncovered preferential expansions in different lineages, paving the way to discover the specific biological roles of these proteins under different conditions. For the basic leucine zipper (bZIP) family of TFs we were able to infer that in the most recent common ancestor (MRCA) of all green plants there were at least four bZIP genes functionally involved in oxidative stress and unfolded protein responses that are bZIP-mediated processes in all eukaryotes, but also in light-dependent regulations. The four founder genes amplified and diverged significantly, generating traits that benefited the colonization of new environments. Currently, following the approach described above, up to 57 TF and 11 TR families can be identified, which are among the most numerous transcription regulatory families in plants. Three families of putative TFs predate the split between rhodophyta (red algae) and chlorophyta (green algae), i.e., G2-like, PLATZ, and RWPRK, and may have been of particular importance for the evolution of eukaryotic photosynthetic organisms. Nine additional families, i.e., ABI3/VP1, AP2-EREBP, ARR-B, C2C2-CO-like, C2C2-Dof, PBF-2-like/Whirly, Pseudo ARR-B, SBP, and WRKY, predate the split between green algae and streptophytes. The identification of putative complete list of TFs has also allowed the delineation of lineage-specific regulatory families. The families SBP, bHLH, SNF2, MADS, WRKY, HMG, AP2-EREBP and FHA significantly differ in size between algae and land plants. The SBP family of TFs is significantly larger in C. reinhardtii, compared to land plants, and appears to have been lost in the prasinophyte O. tauri. The families bHLH, SNF2, MADS, WRKY, HMG, AP2-EREBP and FHA preferentially expanded with the colonisation of land, and might have played an important role in this great moment in evolution. Later, after the split of bryophytes and tracheophytes, the families MADS, AP2-EREBP, NAC, AUX/IAA, PHD and HRT have significantly larger numbers in the lineage leading to seed plants. We identified 23 families that are restricted to land plants and that might have played an important role in the colonization of this new habitat. Based on the list of TFs in different species we have started to develop high-throughput experimental platforms (in rice and C. reinhardtii) to monitor gene expression changes of TF genes under different genetic, developmental or environmental conditions. In this work we present the monitoring of Arabidopsis thaliana TFs during the onset of senescence, a process that leads to cell and tissue disintegration in order to redistribute nutrients (e.g. nitrogen) from leaves to reproductive organs. We show that the expression of 185 TF genes changes when leaves develop from half to fully expanded leaves and finally enter partial senescence. 76% of these TFs are down-regulated during senescence, the remaining are up-regulated. The identification of TFs in plants in a comparative genomics setup has proven fruitful for the understanding of evolutionary processes and contributes to the elucidation of complex developmental programs.
Understanding the interactions of predators and their prey and their responses to environmental changes is one of the striking features of ecological research. In this thesis, spring dynamics of phytoplankton and its consumers, zooplankton, were considered in dependence on the environmental conditions in a deep lake (Lake Constance) and a shallow marine water (mesocosms from Kiel Bight), using descriptive statistics, multiple regression models, and process-oriented dynamic simulation models. The development of the spring phytoplankton bloom, representing a dominant feature in the plankton dynamics in temperate and cold oceans and lakes, may depend on temperature, light, and mixing intensity, and the success of over-wintering phyto- and zooplankton. These factors are often correlated in the field. Unexpectedly, irradiance often dominated algal net growth rather than vertical mixing even in deep Lake Constance. Algal net losses from the euphotic layer to larger depth were induced by vertical mixing, but were compensated by the input from larger depth when algae were uniformly distributed over the water column. Dynamics of small, fast-growing algae were well predicted by abiotic variables, such as surface irradiance, vertical mixing intensity, and temperature. A simulation model additionally revealed that even in late winter, grazing may represent an important loss factor of phytoplankton during calm periods when losses due to mixing are small. The importance of losses by mixing and grazing changed rapidly as it depended on the variable mixing intensity. Higher temperature, lower global irradiance and enhanced mixing generated lower algal biomass and primary production in the dynamic simulation model. This suggests that potential consequences of climate change may partly counteract each other. The negative effect of higher temperatures on phytoplankton biomass was due to enhanced temperature-sensitive grazing losses. Comparing the results from deep Lake Constance to those of the shallow mesocosm experiments and simulations, confirmed the strong direct effect of light in contrast to temperature, and the importance of grazing already in early spring as soon as moderate algal biomasses developed. In Lake Constance, ciliates dominated the herbivorous zooplankton in spring. The start of ciliate net growth in spring was closely linked to that of edible algae, chlorophyll a and the vertical mixing intensity but independent of water temperature. The duration of ciliate dominance in spring was largely controlled by the highly variable onset of the phytoplankton bloom, and little by the less variable termination of the ciliate bloom by grazing of meta-zooplankton. During years with an extended spring bloom of algae and ciliates, they coexisted at relatively high biomasses over 15-30 generations, and internally forced species shifts were observed in both communities. Interception feeders alternated with filter feeders, and cryptomonads with non-cryptomonads in their relative importance. These dynamics were not captured by classical 1-predator-1-prey models which consistently predict pronounced predator-prey cycles or equilibria with either the predator or the prey dominating or suppressed. A multi-species predator-prey model with predator species differing in their food selectivity, and prey species in their edibility reproduced the observed patterns. Food-selectivity and edibility were related to the feeding and growth characteristics of the species, which represented ecological trade-offs. For example, the prey species with the highest edibility also had the highest maximum growth rate. Data and model revealed endogenous driven ongoing species alternations, which yielded a higher variability in species-specific biomasses than in total predator and prey biomass. This holds for a broad parameter space as long as the species differ functionally. A more sophisticated model approach enabled the simulation of a continuum of different functional types and adaptability of predator and prey communities to altered environmental conditions, and the maintenance of a rather low model complexity, i.e., low number of equations and free parameters. The community compositions were described by mean functional traits --- prey edibility and predator food-selectivity --- and their variances. The latter represent the functional diversity of the communities and thus, the potential for adaptation. Oscillations in the mean community trait values indicated species shifts. The community traits were related to growth and grazing characteristics representing similar trade-offs as in the multi-species model. The model reproduced the observed patterns, when nonlinear relationships between edibility and capacity, and edibility and food availability for the predator were chosen. A constant minimum amount of variance represented ongoing species invasions and thus, preserved a diversity which allows adaptation on a realistic time-span.
Plants are the primary producers of biomass and thereby the basis of all life. Many varieties are cultivated, mainly to produce food, but to an increasing amount as a source of renewable energy. Because of the limited acreage available, further improvements of cultivated species both with respect to yield and composition are inevitable. One approach to further progress in developing improved plant cultivars is a systems biology oriented approach. This work aimed to investigate the primary metabolism of the model plant A.thaliana and its relation to plant growth using quantitative genetics methods. A special focus was set on the characterization of heterosis, the deviation of hybrids from their parental means for certain traits, on a metabolic level. More than 2000 samples of recombinant inbred lines (RILs) and introgression lines (ILs) developed from the two accessions Col-0 and C24 were analyzed for 181 metabolic traces using gas-chromatography/ mass-spectrometry (GC-MS). The observed variance allowed the detection of 157 metabolic quantitative trait loci (mQTL), genetic regions carrying genes, which are relevant for metabolite abundance. By analyzing several hundred test crosses of RILs and ILs it was further possible to identify 385 heterotic metabolic QTL (hmQTL). Within the scope of this work a robust method for large scale GC-MS analyses was developed. A highly significant canonical correlation between biomass and metabolic profiles (r = 0.73) was found. A comparable analysis of the results of the two independent experiments using RILs and ILs showed a large agreement. The confirmation rate for RIL QTL in ILs was 56 % and 23 % for mQTL and hmQTL respectively. Candidate genes from available databases could be identified for 67 % of the mQTL. To validate some of these candidates, eight genes were re-sequenced and in total 23 polymorphisms could be found. In the hybrids, heterosis is small for most metabolites (< 20%). Heterotic QTL gave rise to less candidate genes and a lower overlap between both populations than was determined for mQTL. This hints that regulatory loci and epistatic effects contribute to metabolite heterosis. The data described in this thesis present a rich source for further investigation and annotation of relevant genes and may pave the way towards a better understanding of plant biology on a system level.