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- Institut für Biochemie und Biologie (19) (remove)
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.
Neuroinflammatory and neurodegenerative diseases such as Parkinson's (PD) and multiple sclerosis (MS) often result in a severe impairment of the patient´s quality of life. Effective therapies for the treatment are currently not available, which results in a high socio-economic burden. Due to the heterogeneity of the disease subtypes, stratification is particularly difficult in the early phase of the disease and is mainly based on clinical parameters such as neurophysiological tests and central nervous imaging. Due to good accessibility and stability, blood and cerebrospinal fluid metabolite markers could serve as surrogates for neurodegenerative processes. This can lead to an improved mechanistic understanding of these diseases and further be used as "treatment response" biomarkers in preclinical and clinical development programs. Therefore, plasma and CSF metabolite profiles will be identified that allow differentiation of PD from healthy controls, association of PD with dementia (PDD) and differentiation of PD subtypes such as akinetic rigid and tremor dominant PD patients. In addition, plasma metabolites for the diagnosis of primary progressive MS (PPMS) should be investigated and tested for their specificity to relapsing-remitting MS (RRMS) and their development during PPMS progression.
By applying untargeted high-resolution metabolomics of PD patient samples and in using random forest and partial least square machine learning algorithms, this study identified 20 plasma metabolites and 14 CSF metabolite biomarkers. These differentiate against healthy individuals with an AUC of 0.8 and 0.9 in PD, respectively. We also identify ten PDD specific serum metabolites, which differentiate against healthy individuals and PD patients without dementia with an AUC of 1.0, respectively. Furthermore, 23 akinetic-rigid specific plasma markers were identified, which differentiate against tremor-dominant PD patients with an AUC of 0.94 and against healthy individuals with an AUC of 0.98. These findings also suggest more severe disease pathology in the akinetic-rigid PD than in tremor dominant PD. In the analysis of MS patient samples a partial least square analysis yielded predictive models for the classification of PPMS and resulted in 20 PPMS specific metabolites. In another MS study unknown changes in human metabolism were identified after administration of the multiple sclerosis drug dimethylfumarate, which is used for the treatment of RRMS. These results allow to describe and understand the hitherto completely unknown mechanism of action of this new drug and to use these findings for the further development of new drugs and targets against RRMS.
In conclusion, these results have the potential for improved diagnosis of these diseases and improvement of mechanistic understandings, as multiple deregulated pathways were identified. Moreover, novel Dimethylfumarate targets can be used to aid drug development and treatment efficiency. Overall, metabolite profiling in combination with machine learning identified as a promising approach for biomarker discovery and mode of action elucidation.