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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.
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.