TY - GEN A1 - Alker, Wiebke A1 - Schwerdtle, Tanja A1 - Schomburg, Lutz A1 - Haase, Hajo T1 - A Zinpyr-1-based fluorimetric microassay for free zinc in human serum T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Zinc is an essential trace element, making it crucial to have a reliable biomarker for evaluating an individual’s zinc status. The total serum zinc concentration, which is presently the most commonly used biomarker, is not ideal for this purpose, but a superior alternative is still missing. The free zinc concentration, which describes the fraction of zinc that is only loosely bound and easily exchangeable, has been proposed for this purpose, as it reflects the highly bioavailable part of serum zinc. This report presents a fluorescence-based method for determining the free zinc concentration in human serum samples, using the fluorescent probe Zinpyr-1. The assay has been applied on 154 commercially obtained human serum samples. Measured free zinc concentrations ranged from 0.09 to 0.42 nM with a mean of 0.22 ± 0.05 nM. It did not correlate with age or the total serum concentrations of zinc, manganese, iron or selenium. A negative correlation between the concentration of free zinc and total copper has been seen for sera from females. In addition, the free zinc concentration in sera from females (0.21 ± 0.05 nM) was significantly lower than in males (0.23 ± 0.06 nM). The assay uses a sample volume of less than 10 µL, is rapid and cost-effective and allows us to address questions regarding factors influencing the free serum zinc concentration, its connection with the body’s zinc status, and its suitability as a future biomarker for an individual’s zinc status. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1086 KW - zinc KW - free zinc KW - serum KW - biomarker KW - fluorescent probe KW - Zinypr-1 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-472833 SN - 1866-8372 IS - 1086 ER - TY - GEN A1 - Stoessel, Daniel A1 - Stellmann, Jan-Patrick A1 - Willing, Anne A1 - Behrens, Birte A1 - Rosenkranz, Sina C. A1 - Hodecker, Sibylle C. A1 - Stürner, Klarissa H. A1 - Reinhardt, Stefanie A1 - Fleischer, Sabine A1 - Deuschle, Christian A1 - Maetzler, Walter A1 - Berg, Daniela A1 - Heesen, Christoph A1 - Walther, Dirk A1 - Schauer, Nicolas A1 - Friese, Manuel A. A1 - Pless, Ole T1 - Metabolomic profiles for primary progressive multiple sclerosis stratification and disease course monitoring T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 694 KW - untargeted metabolomics KW - biomarker KW - PPMS KW - MS neurodegeneration KW - LysoPC(20:0) Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-426307 SN - 1866-8372 IS - 694 ER - TY - GEN A1 - Hische, Manuela A1 - Larhlimi, Abdelhalim A1 - Schwarz, Franziska A1 - Fischer-Rosinský, Antje A1 - Bobbert, Thomas A1 - Assmann, Anke A1 - Catchpole, Gareth S. A1 - Pfeiffer, Andreas F. H. A1 - Willmitzer, Lothar A1 - Selbig, Joachim A1 - Spranger, Joachim T1 - A distinct metabolic signature predictsdevelopment of fasting plasma glucose T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - Background High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods. Methods We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort. Results We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis. Conclusions We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 850 KW - prediction KW - fasting glucose KW - type 2 diabetes KW - metabolomics KW - plasma KW - random forest KW - metabolite KW - regression KW - biomarker Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-427400 SN - 1866-8372 IS - 850 ER -