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- 2018 (34) (entfernen)
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- biodiversity (2)
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- Institut für Biochemie und Biologie (34) (entfernen)
The sequencing of the human genome in the early 2000s led to an increased interest in cheap and fast sequencing technologies. This interest culminated in the advent of next generation sequencing (NGS). A number of different NGS platforms have arisen since then all promising to do the same thing, i.e. produce large amounts of genetic information for relatively low costs compared to more traditional methods such as Sanger sequencing. The capabilities of NGS meant that researchers were no longer bound to species for which a lot of previous work had already been done (e.g. model organisms and humans) enabling a shift in research towards more novel and diverse species of interest. This capability has greatly benefitted many fields within the biological sciences, one of which being the field of evolutionary biology. Researchers have begun to move away from the study of laboratory model organisms to wild, natural populations and species which has greatly expanded our knowledge of evolution. NGS boasts a number of benefits over more traditional sequencing approaches. The main benefit comes from the capability to generate information for drastically more loci for a fraction of the cost. This is hugely beneficial to the study of wild animals as, even when large numbers of individuals are unobtainable, the amount of data produced still allows for accurate, reliable population and species level results from a small selection of individuals.
The use of NGS to study species for which little to no previous research has been carried out on and the production of novel evolutionary information and reference datasets for the greater scientific community were the focuses of this thesis. Two studies in this thesis focused on producing novel mitochondrial genomes from shotgun sequencing data through iterative mapping, bypassing the need for a close relative to serve as a reference sequence. These mitochondrial genomes were then used to infer species level relationships through phylogenetic analyses. The first of these studies involved reconstructing a complete mitochondrial genome of the bat eared fox (Otocyon megalotis). Phylogenetic analyses of the mitochondrial genome confidently placed the bat eared fox as sister to the clade consisting of the raccoon dog and true foxes within the canidae family. The next study also involved reconstructing a mitochondrial genome but in this case from the extinct Macrauchenia of South America. As this study utilised ancient DNA, it involved a lot of parameter testing, quality controls and strict thresholds to obtain a near complete mitochondrial genome devoid of contamination known to plague ancient DNA studies. Phylogenetic analyses confidently placed Macrauchenia as sister to all living representatives of Perissodactyla with a divergence time of ~66 million years ago. The third and final study of this thesis involved de novo assemblies of both nuclear and mitochondrial genomes from brown and striped hyena and focussed on demographic, genetic diversity and population genomic analyses within the brown hyena. Previous studies of the brown hyena hinted at very low levels of genomic diversity and, perhaps due to this, were unable to find any notable population structure across its range. By incorporating a large number of genetic loci, in the form of complete nuclear genomes, population structure within the brown hyena was uncovered. On top of this, genomic diversity levels were compared to a number of other species. Results showed the brown hyena to have the lowest genomic diversity out of all species included in the study which was perhaps caused by a continuous and ongoing decline in effective population size that started about one million years ago and dramatically accelerated towards the end of the Pleistocene.
The studies within this thesis show the power NGS sequencing has and its utility within evolutionary biology. The most notable capabilities outlined in this thesis involve the study of species for which no reference data is available and in the production of large amounts of data, providing evolutionary answers at the species and population level that data produced using more traditional techniques simply could not.
East Africa is a natural laboratory: Studying its unique geological and biological history can help us better inform our theories and models. Studying its present and future can help us protect its globally important biodiversity and ecosystem services. East African vegetation plays a central role in all these aspects, and this dissertation aims to quantify its dynamics through computer simulations.
Computer models help us recreate past settings, forecast into the future or conduct simulation experiments that we cannot otherwise perform in the field. But before all that, one needs to test their performance. The outputs that the model produced using the present day-inputs, agreed well with present-day observations of East African vegetation. Next, I simulated past vegetation for which we have fossil pollen data to compare. With computer models, we can fill the gaps of knowledge between sites where we have fossil pollen data from, and create a more complete picture of the past. Good level of agreement between model and pollen data where they overlapped in space further validated our model performance.
Once the model was tested and validated for the region, it became possible to probe one of the long standing questions regarding East African vegetation: How did East Africa lose its tropical forests? The present-day vegetation in the tropics is mainly characterized by continuous forests worldwide except in tropical East Africa, where forests only occur as patches. In a series of simulation experiments, I was able to show under which conditions these forest patches could have been connected and fragmented in the past. This study showed the sensitivity of East African vegetation to climate change and variability such as those expected under future climate change.
El Niño Southern Oscillation (ENSO) events that result from the fluctuations in temperature between the ocean and atmosphere, bring further variability to East African climate and are predicted to increase in intensity in the future. But climate models are still not good at capturing the pattens of these events. In a study where I quantified the influence of ENSO events on East African vegetation, I showed how different the future vegetation could be from what we currently predict with these climate models that lack accurate ENSO contribution. Consideration of these discrepancies is important for our future global carbon budget calculations and management decisions.
Systems biology aims at investigating biological systems in its entirety by gathering and analyzing large-scale data sets about the underlying components. Computational systems biology approaches use these large-scale data sets to create models at different scales and cellular levels. In addition, it is concerned with generating and testing hypotheses about biological processes. However, such approaches are inevitably leading to computational challenges due to the high dimensionality of the data and the differences in the dimension of data from different cellular layers.
This thesis focuses on the investigation and development of computational approaches to analyze metabolite profiles in the context of cellular networks. This leads to determining what aspects of the network functionality are reflected in the metabolite levels. With these methods at hand, this thesis aims to answer three questions: (1) how observability of biological systems is manifested in metabolite profiles and if it can be used for phenotypical comparisons; (2) how to identify couplings of reaction rates from metabolic profiles alone; and (3) which regulatory mechanism that affect metabolite levels can be distinguished by integrating transcriptomics and metabolomics read-outs.
I showed that sensor metabolites, identified by an approach from observability theory, are more correlated to each other than non-sensors. The greater correlations between sensor metabolites were detected both with publicly available metabolite profiles and synthetic data simulated from a medium-scale kinetic model. I demonstrated through robustness analysis that correlation was due to the position of the sensor metabolites in the network and persisted irrespectively of the experimental conditions. Sensor metabolites are therefore potential candidates for phenotypical comparisons between conditions through targeted metabolic analysis.
Furthermore, I demonstrated that the coupling of metabolic reaction rates can be investigated from a purely data-driven perspective, assuming that metabolic reactions can be described by mass action kinetics. Employing metabolite profiles from domesticated and wild wheat and tomato species, I showed that the process of domestication is associated with a loss of regulatory control on the level of reaction rate coupling. I also found that the same metabolic pathways in Arabidopsis thaliana and Escherichia coli exhibit differences in the number of reaction rate couplings.
I designed a novel method for the identification and categorization of transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approach determines the partial correlation of metabolites with control by the principal components of the transcript levels. The principle components contain the majority of the transcriptomic information allowing to partial out the effect of the transcriptional layer from the metabolite profiles. Depending whether the correlation between metabolites persists upon controlling for the effect of the transcriptional layer, the approach allows us to group metabolite pairs into being associated due to post-transcriptional or transcriptional regulation, respectively. I showed that the classification of metabolite pairs into those that are associated due to transcriptional or post-transcriptional regulation are in agreement with existing literature and findings from a Bayesian inference approach.
The approaches developed, implemented, and investigated in this thesis open novel ways to jointly study metabolomics and transcriptomics data as well as to place metabolic profiles in the network context. The results from these approaches have the potential to provide further insights into the regulatory machinery in a biological system.
Biological invasions are the dispersal and following establishment of species outside their native habitat. Due to globalisation, connectivity of regions and climate changes the number of invasive species and their successful establishment is rising. The impact of these species is mostly negative, can induce community and habitat alterations, and is one main cause for biodiversity loss. This impact is particularly high and less researched in aquatic systems and microbial organisms and despite the high impact, the knowledge about overall mechanisms and specific factors affecting invasions are not fully understood. In general, the characteristics of the habitat, native community and invader determine the invasiveness.
In this thesis, I aimed to provide a better understanding of aquatic invasions focusing on the invader and its traits and identity. This thesis used a set of 12 strains of the invasive cyanobacterium <i>Cylindrospermopsis raciborskii</i> to examine the effect and impact of the invaders’ identity and genetic diversity. Further, the effect of timing on the invasion potential and success was determined, because aquatic systems in particular undergo seasonal fluctuations.
Most studies revealed a higher invasion success with increasing genetic diversity. Here, the increase of the genetic diversity, by either strain richness or phylogenetic dissimilarity, is not firstly driving the invasion, but the strain-identity. The high variability among the strains in traits important for invasions led to the highly varying strain-specific invasion success. This success was most dependent on nitrogen uptake and efficient resource use. The lower invasion success into communities comprising further N-fixing species indicates <i>C. raciborskii</i> can use this advantage only without the presence of competitive species. The relief of grazing pressure, which is suggested to be more important in aquatic invasions, was only promoting the invasion when unselective and larger consumers were present. High abundances of unselective consumers hampered the invasion success.
This indicates a more complex and temporal interplay of competitive and consumptive resistance mechanisms during the invasion process. Further, the fluctuation abundance and presence of competitors (= primary producers) and consumers (= zooplankton) in lakes can open certain ‘invasion windows’.
Remarkably, the composition of the resident community was also strain-specific affected and altered, independent of a high or low invasion success. Prior, this was only documented on the species level. Further, investigations on the population of invasive strains can reveal more about the invasion patterns and how multiple strain invasions change resident communities.
The present dissertation emphasises the importance of invader-addition experiments with a community context and the importance of the strain-level for microbial invasions and in general, e.g. for community assemblies and the outcome of experiments. The strain-specific community changes, also after days, may explain some sudden changes in communities, which have not been explained yet. This and further knowledge may also facilitate earlier and less cost-intensive management to step in, because these species are rarely tracked until they reach a high abundance or bloom, because of their small size.
Concluded for <i>C. raciborskii</i>, it shows that this species is no ‘generalistic’ invader and its invasion success depends more on the competitor presence than grazing pressure. This may explain its, still unknown, invasion pattern, as <i>C. raciborskii</i> is not found in all lakes of a region.