@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{StoesselStellmannWillingetal.2018,
  author    = {Stoessel, Daniel and Stellmann, Jan-Patrick and Willing, Anne and Behrens, Birte and Rosenkranz, Sina C. and Hodecker, Sibylle C. and Stuerner, Klarissa H. and Reinhardt, Stefanie and Fleischer, Sabine and Deuschle, Christian and Maetzler, Walter and Berg, Daniela and Heesen, Christoph and Walther, Dirk and Schauer, Nicolas and Friese, Manuel A. and Pless, Ole},
  title     = {Metabolomic Profiles for Primary Progressive Multiple Sclerosis Stratification and Disease Course Monitoring},
  series = {Frontiers in human neuroscienc},
  volume    = {12},
  journal   = {Frontiers in human neuroscienc},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1662-5161},
  doi       = {10.3389/fnhum.2018.00226},
  pages     = {13},
  year      = {2018},
  abstract  = {Primary progressive multiple sclerosis (PPMS) shows a highly variable disease progression with poor prognosis and a characteristic accumulation of disabilities in patients. These hallmarks of PPMS make it difficult to diagnose and currently impossible to efficiently treat. This study aimed to identify plasma metabolite profiles that allow diagnosis of PPMS and its differentiation from the relapsing remitting subtype (RRMS), primary neurodegenerative disease (Parkinson's disease, PD), and healthy controls (HCs) and that significantly change during the disease course and could serve as surrogate markers of multiple sclerosis (MS)-associated neurodegeneration over time. We applied untargeted high-resolution metabolomics to plasma samples to identify PPMS-specific signatures, validated our findings in independent sex- and age-matched PPMS and HC cohorts and built discriminatory models by partial least square discriminant analysis (PLS-DA). This signature was compared to sex- and age-matched RRMS patients, to patients with PD and HC. Finally, we investigated these metabolites in a longitudinal cohort of PPMS patients over a 24-month period. PLS-DA yielded predictive models for classification along with a set of 20 PPMS-specific informative metabolite markers. These metabolites suggest disease-specific alterations in glycerophospholipid and linoleic acid pathways. Notably, the glycerophospholipid LysoPC(20:0) significantly decreased during the observation period. These findings show potential for diagnosis and disease course monitoring, and might serve as biomarkers to assess treatment efficacy in future clinical trials for neuroprotective MS therapies.},
  language  = {en}
}
@misc{SprengerErbanSeddigetal.2018,
  author    = {Sprenger, Heike and Erban, Alexander and Seddig, Sylvia and Rudack, Katharina and Thalhammer, Anja and Le, Mai Q. and Walther, Dirk and Zuther, Ellen and K{\"o}hl, Karin I. and Kopka, Joachim and Hincha, Dirk K.},
  title     = {Metabolite and transcript markers for the prediction of potato drought tolerance},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {673},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42463},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-424630},
  pages     = {12},
  year      = {2018},
  abstract  = {Potato (Solanum tuberosum L.) is one of the most important food crops worldwide. Current potato varieties are highly susceptible to drought stress. In view of global climate change, selection of cultivars with improved drought tolerance and high yield potential is of paramount importance. Drought tolerance breeding of potato is currently based on direct selection according to yield and phenotypic traits and requires multiple trials under drought conditions. Marker-assisted selection (MAS) is cheaper, faster and reduces classification errors caused by noncontrolled environmental effects. We analysed 31 potato cultivars grown under optimal and reduced water supply in six independent field trials. Drought tolerance was determined as tuber starch yield. Leaf samples from young plants were screened for preselected transcript and nontargeted metabolite abundance using qRT-PCR and GC-MS profiling, respectively. Transcript marker candidates were selected from a published RNA-Seq data set. A Random Forest machine learning approach extracted metabolite and transcript markers for drought tolerance prediction with low error rates of 6\% and 9\%, respectively. Moreover, by combining transcript and metabolite markers, the prediction error was reduced to 4.3\%. Feature selection from Random Forest models allowed model minimization, yielding a minimal combination of only 20 metabolite and transcript markers that were successfully tested for their reproducibility in 16 independent agronomic field trials. We demonstrate that a minimum combination of transcript and metabolite markers sampled at early cultivation stages predicts potato yield stability under drought largely independent of seasonal and regional agronomic conditions.},
  language  = {en}
}
@misc{StoesselStellmannWillingetal.2018,
  author    = {Stoessel, Daniel and Stellmann, Jan-Patrick and Willing, Anne and Behrens, Birte and Rosenkranz, Sina C. and Hodecker, Sibylle C. and St{\"u}rner, Klarissa H. and Reinhardt, Stefanie and Fleischer, Sabine and Deuschle, Christian and Maetzler, Walter and Berg, Daniela and Heesen, Christoph and Walther, Dirk and Schauer, Nicolas and Friese, Manuel A. and Pless, Ole},
  title     = {Metabolomic profiles for primary progressive multiple sclerosis stratification and disease course monitoring},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {694},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42630},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-426307},
  pages     = {13},
  year      = {2018},
  abstract  = {Primary progressive multiple sclerosis (PPMS) shows a highly variable disease progression with poor prognosis and a characteristic accumulation of disabilities in patients. These hallmarks of PPMS make it difficult to diagnose and currently impossible to efficiently treat. This study aimed to identify plasma metabolite profiles that allow diagnosis of PPMS and its differentiation from the relapsing remitting subtype (RRMS), primary neurodegenerative disease (Parkinson's disease, PD), and healthy controls (HCs) and that significantly change during the disease course and could serve as surrogate markers of multiple sclerosis (MS)-associated neurodegeneration over time. We applied untargeted high-resolution metabolomics to plasma samples to identify PPMS-specific signatures, validated our findings in independent sex- and age-matched PPMS and HC cohorts and built discriminatory models by partial least square discriminant analysis (PLS-DA). This signature was compared to sex- and age-matched RRMS patients, to patients with PD and HC. Finally, we investigated these metabolites in a longitudinal cohort of PPMS patients over a 24-month period. PLS-DA yielded predictive models for classification along with a set of 20 PPMS-specific informative metabolite markers. These metabolites suggest disease-specific alterations in glycerophospholipid and linoleic acid pathways. Notably, the glycerophospholipid LysoPC(20:0) significantly decreased during the observation period. These findings show potential for diagnosis and disease course monitoring, and might serve as biomarkers to assess treatment efficacy in future clinical trials for neuroprotective MS therapies.},
  language  = {en}
}