@article{StoesselSchultedosSantosetal.2018, author = {Stoessel, Daniel and Schulte, Claudia and dos Santos, Marcia C. Teixeira and Scheller, Dieter and Rebollo-Mesa, Irene and Deuschle, Christian and Walther, Dirk and Schauer, Nicolas and Berg, Daniela and da Costa, Andre Nogueira and Maetzler, Walter}, title = {Promising Metabolite Profiles in the Plasma and CSF of Early Clinical}, series = {Frontiers in Aging Neuroscience}, volume = {10}, journal = {Frontiers in Aging Neuroscience}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1663-4365}, doi = {10.3389/fnagi.2018.00051}, pages = {14}, year = {2018}, abstract = {Parkinson's disease (PD) shows high heterogeneity with regard to the underlying molecular pathogenesis involving multiple pathways and mechanisms. Diagnosis is still challenging and rests entirely on clinical features. Thus, there is an urgent need for robust diagnostic biofluid markers. Untargeted metabolomics allows establishing low-molecular compound biomarkers in a wide range of complex diseases by the measurement of various molecular classes in biofluids such as blood plasma, serum, and cerebrospinal fluid (CSF). Here, we applied untargeted high-resolution mass spectrometry to determine plasma and CSF metabolite profiles. We semiquantitatively determined small-molecule levels (<= 1.5 kDa) in the plasma and CSF from early PD patients (disease duration 0-4 years; n = 80 and 40, respectively), and sex-and age-matched controls (n = 76 and 38, respectively). We performed statistical analyses utilizing partial least square and random forest analysis with a 70/30 training and testing split approach, leading to the identification of 20 promising plasma and 14 CSF metabolites. The semetabolites differentiated the test set with an AUC of 0.8 (plasma) and 0.9 (CSF). Characteristics of the metabolites indicate perturbations in the glycerophospholipid, sphingolipid, and amino acid metabolism in PD, which underscores the high power of metabolomic approaches. Further studies will enable to develop a potential metabolite-based biomarker panel specific for PD}, language = {en} } @article{CastroWardelmannGruneetal.2018, author = {Castro, Jose Pedro and Wardelmann, Kristina and Grune, Tilman and Kleinridders, Andre}, title = {Mitochondrial Chaperones in the Brain}, series = {Frontiers in Endocrinology}, volume = {9}, journal = {Frontiers in Endocrinology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-2392}, doi = {10.3389/fendo.2018.00196}, pages = {13}, year = {2018}, abstract = {The brain orchestrates organ function and regulates whole body metabolism by the concerted action of neurons and glia cells in the central nervous system. To do so, the brain has tremendously high energy consumption and relies mainly on glucose utilization and mitochondrial function in order to exert its function. As a consequence of high rate metabolism, mitochondria in the brain accumulate errors over time, such as mitochondrial DNA (mtDNA) mutations, reactive oxygen species, and misfolded and aggregated proteins. Thus, mitochondria need to employ specific mechanisms to avoid or ameliorate the rise of damaged proteins that contribute to aberrant mitochondrial function and oxidative stress. To maintain mitochondria homeostasis (mitostasis), cells evolved molecular chaperones that shuttle, refold, or in coordination with proteolytic systems, help to maintain a low steady-state level of misfolded/aggregated proteins. Their importance is exemplified by the occurrence of various brain diseases which exhibit reduced action of chaperones. Chaperone loss (expression and/or function) has been observed during aging, metabolic diseases such as type 2 diabetes and in neurode-generative diseases such as Alzheimer's (AD), Parkinson's (PD) or even Huntington's (HD) diseases, where the accumulation of damage proteins is evidenced. Within this perspective, we propose that proper brain function is maintained by the joint action of mitochondrial chaperones to ensure and maintain mitostasis contributing to brain health, and that upon failure, alter brain function which can cause metabolic diseases.}, language = {en} } @phdthesis{Stoessel2018, author = {St{\"o}ßel, Daniel}, title = {Biomarker Discovery in Multiple Sclerosis and Parkinson's disease}, school = {Universit{\"a}t Potsdam}, pages = {135}, year = {2018}, abstract = {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.}, language = {en} } @article{KumarGoodwinUhouseetal.2015, author = {Kumar, Kevin K. and Goodwin, Cody R. and Uhouse, Michael A. and Bornhorst, Julia and Schwerdtle, Tanja and Aschner, Michael A. and McLean, John A. and Bowman, Aaron B.}, title = {Untargeted metabolic profiling identifies interactions between Huntington's disease and neuronal manganese status}, series = {Metallomics}, volume = {7}, journal = {Metallomics}, publisher = {RSC Publ.}, address = {Cambridge}, issn = {1756-591X}, doi = {10.1039/C4MT00223G}, pages = {363 -- 370}, year = {2015}, abstract = {Manganese (Mn) is an essential micronutrient for development and function of the nervous system. Deficiencies in Mn transport have been implicated in the pathogenesis of Huntington's disease (HD), an autosomal dominant neurodegenerative disorder characterized by loss of medium spiny neurons of the striatum. Brain Mn levels are highest in striatum and other basal ganglia structures, the most sensitive brain regions to Mn neurotoxicity. Mouse models of HD exhibit decreased striatal Mn accumulation and HD striatal neuron models are resistant to Mn cytotoxicity. We hypothesized that the observed modulation of Mn cellular transport is associated with compensatory metabolic responses to HD pathology. Here we use an untargeted metabolomics approach by performing ultraperformance liquid chromatography-ion mobility-mass spectrometry (UPLC-IM-MS) on control and HD immortalized mouse striatal neurons to identify metabolic disruptions under three Mn exposure conditions, low (vehicle), moderate (non-cytotoxic) and high (cytotoxic). Our analysis revealed lower metabolite levels of pantothenic acid, and glutathione (GSH) in HD striatal cells relative to control cells. HD striatal cells also exhibited lower abundance and impaired induction of isobutyryl carnitine in response to increasing Mn exposure. In addition, we observed induction of metabolites in the pentose shunt pathway in HD striatal cells after high Mn exposure. These findings provide metabolic evidence of an interaction between the HD genotype and biologically relevant levels of Mn in a striatal cell model with known HD by Mn exposure interactions. The metabolic phenotypes detected support existing hypotheses that changes in energetic processes underlie the pathobiology of both HD and Mn neurotoxicity.}, language = {en} }