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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.
Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here, we present a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM, ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (-0.69%), Western Alaska (-2.82%), and Kolyma Lowland (-0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e., upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region.