@article{LuReichetzederPrehnetal.2018, author = {Lu, Yong-Ping and Reichetzeder, Christoph and Prehn, Cornelia and von Websky, Karoline and Slowinski, Torsten and Chen, You-Peng and Yin, Liang-Hong and Kleuser, Burkhard and Yang, Xue-Song and Adamski, Jerzy and Hocher, Berthold}, title = {Fetal serum metabolites are independently associated with Gestational diabetes mellitus}, series = {Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry and pharmacology}, volume = {45}, journal = {Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry and pharmacology}, number = {2}, publisher = {Karger}, address = {Basel}, issn = {1015-8987}, doi = {10.1159/000487119}, pages = {625 -- 638}, year = {2018}, abstract = {Background/Aims: Gestational diabetes (GDM) might be associated with alterations in the metabolomic profile of affected mothers and their offspring. Until now, there is a paucity of studies that investigated both, the maternal and the fetal serum metabolome in the setting of GDM. Mounting evidence suggests that the fetus is not just passively affected by gestational disease but might play an active role in it. Metabolomic studies performed in maternal blood and fetal cord blood could help to better discern distinct fetal from maternal disease interactions. Methods: At the time of birth, serum samples from mothers and newborns (cord blood samples) were collected and screened for 163 metabolites utilizing tandem mass spectrometry. The cohort consisted of 412 mother/child pairs, including 31 cases of maternal GDM. Results: An initial non-adjusted analysis showed that eight metabolites in the maternal blood and 54 metabolites in the cord blood were associated with GDM. After Benjamini-Hochberg (BH) procedure and adjustment for confounding factors for GDM, fetal phosphatidylcholine acyl-alkyl C 32:1 and proline still showed an independent association with GDM. Conclusions: This study found metabolites in cord blood which were associated with GDM, even after adjustment for established risk factors of GDM. To the best of our knowledge, this is the first study demonstrating an independent association between fetal serum metabolites and maternal GDM. Our findings might suggest a potential effect of the fetal metabolome on maternal GDM. (c) 2018 The Author(s) Published by S. Karger AG, Basel}, 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{WatanabeTohgeBalazadehetal.2018, author = {Watanabe, Mutsumi and Tohge, Takayuki and Balazadeh, Salma and Erban, Alexander and Giavalisco, Patrick and Kopka, Joachim and Mueller-Roeber, Bernd and Fernie, Alisdair R. and Hoefgen, Rainer}, title = {Comprehensive Metabolomics Studies of Plant Developmental Senescence}, series = {Plant Senescence: Methods and Protocols}, volume = {1744}, journal = {Plant Senescence: Methods and Protocols}, publisher = {Humana Press}, address = {Totowa}, isbn = {978-1-4939-7672-0}, issn = {1064-3745}, doi = {10.1007/978-1-4939-7672-0_28}, pages = {339 -- 358}, year = {2018}, abstract = {Leaf senescence is an essential developmental process that involves diverse metabolic changes associated with degradation of macromolecules allowing nutrient recycling and remobilization. In contrast to the significant progress in transcriptomic analysis of leaf senescence, metabolomics analyses have been relatively limited. A broad overview of metabolic changes during leaf senescence including the interactions between various metabolic pathways is required to gain a better understanding of the leaf senescence allowing to link transcriptomics with metabolomics and physiology. In this chapter, we describe how to obtain comprehensive metabolite profiles and how to dissect metabolic shifts during leaf senescence in the model plant Arabidopsis thaliana. Unlike nucleic acid analysis for transcriptomics, a comprehensive metabolite profile can only be achieved by combining a suite of analytic tools. Here, information is provided for measurements of the contents of chlorophyll, soluble proteins, and starch by spectrophotometric methods, ions by ion chromatography, thiols and amino acids by HPLC, primary metabolites by GC/TOF-MS, and secondary metabolites and lipophilic metabolites by LC/ESI-MS. These metabolite profiles provide a rich catalogue of metabolic changes during leaf senescence, which is a helpful database and blueprint to be correlated to future studies such as transcriptome and proteome analyses, forward and reverse genetic studies, or stress-induced senescence studies.}, language = {en} } @article{LuReichetzederPrehnetal.2018, author = {Lu, Yong-Ping and Reichetzeder, Christoph and Prehn, Cornelia and Yin, Liang-Hong and Yun, Chen and Zeng, Shufei and Chu, Chang and Adamski, Jerzy and Hocher, Berthold}, title = {Cord blood Lysophosphatidylcholine 16:1 is positively associated with birth weight}, series = {Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry and pharmacology}, volume = {45}, journal = {Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry and pharmacology}, number = {2}, publisher = {Karger}, address = {Basel}, issn = {1015-8987}, doi = {10.1159/000487118}, pages = {614 -- 624}, year = {2018}, abstract = {Background/Aims: Impaired birth outcomes, like low birth weight, have consistently been associated with increased disease susceptibility to hypertension in later life. Alterations in the maternal or fetal metabolism might impact on fetal growth and influence birth outcomes. Discerning associations between the maternal and fetal metabolome and surrogate parameters of fetal growth could give new insight into the complex relationship between intrauterine conditions, birth outcomes, and later life disease susceptibility. Methods: Using flow injection tandem mass spectrometry, targeted metabolomics was performed in serum samples obtained from 226 mother/child pairs at delivery. Associations between neonatal birth weight and concentrations of 163 maternal and fetal metabolites were analyzed. Results: After FDR adjustment using the Benjamini-Hochberg procedure lysophosphatidylcholines (LPC) 14:0, 16:1, and 18:1 were strongly positively correlated with birth weight. In a stepwise linear regression model corrected for established confounding factors of birth weight, LPC 16: 1 showed the strongest independent association with birth weight (CI: 93.63 - 168.94; P = 6.94x10(-11)). The association with birth weight was stronger than classical confounding factors such as offspring sex (CI: - 258.81- -61.32; P = 0.002) and maternal smoking during pregnancy (CI: -298.74 - -29.51; P = 0.017). Conclusions: After correction for multiple testing and adjustment for potential confounders, LPC 16:1 showed a very strong and independent association with birth weight. The underlying molecular mechanisms linking fetal LPCs with birth weight need to be addressed in future studies. (c) 2018 The Author(s) Published by S. Karger AG, Basel}, language = {en} } @article{deAbreueLimaLeifelsNikoloski2018, author = {de Abreu e Lima, Francisco Anastacio and Leifels, Lydia and Nikoloski, Zoran}, title = {Regression-based modeling of complex plant traits based on metabolomics data}, series = {Plant Metabolomics}, volume = {1778}, journal = {Plant Metabolomics}, publisher = {Humana Press Inc.}, address = {New York}, isbn = {978-1-4939-7819-9}, issn = {1064-3745}, doi = {10.1007/978-1-4939-7819-9_23}, pages = {321 -- 327}, year = {2018}, abstract = {Bridging metabolomics with plant phenotypic responses is challenging. Multivariate analyses account for the existing dependencies among metabolites, and regression models in particular capture such dependencies in search for association with a given trait. However, special care should be undertaken with metabolomics data. Here we propose a modeling workflow that considers all caveats imposed by such large data sets.}, language = {en} } @phdthesis{Wittenbecher2017, author = {Wittenbecher, Clemens}, title = {Linking whole-grain bread, coffee, and red meat to the risk of type 2 diabetes}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-404592}, school = {Universit{\"a}t Potsdam}, pages = {XII, 194, ii}, year = {2017}, abstract = {Background: Consumption of whole-grain, coffee, and red meat were consistently related to the risk of developing type 2 diabetes in prospective cohort studies, but potentially underlying biological mechanisms are not well understood. Metabolomics profiles were shown to be sensitive to these dietary exposures, and at the same time to be informative with respect to the risk of type 2 diabetes. Moreover, graphical network-models were demonstrated to reflect the biological processes underlying high-dimensional metabolomics profiles. Aim: The aim of this study was to infer hypotheses on the biological mechanisms that link consumption of whole-grain bread, coffee, and red meat, respectively, to the risk of developing type 2 diabetes. More specifically, it was aimed to consider network models of amino acid and lipid profiles as potential mediators of these risk-relations. Study population: Analyses were conducted in the prospective EPIC-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2731, including 692 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Concentrations of 126 metabolites (acylcarnitines, phosphatidylcholines, sphingomyelins, amino acids) were determined in baseline-serum samples. Incident type 2 diabetes cases were assed and validated in an active follow-up procedure. The median follow-up time was 6.6 years. Analytical design: The methodological approach was conceptually based on counterfactual causal inference theory. Observations on the network-encoded conditional independence structure restricted the space of possible causal explanations of observed metabolomics-data patterns. Given basic directionality assumptions (diet affects metabolism; metabolism affects future diabetes incidence), adjustment for a subset of direct neighbours was sufficient to consistently estimate network-independent direct effects. Further model-specification, however, was limited due to missing directionality information on the links between metabolites. Therefore, a multi-model approach was applied to infer the bounds of possible direct effects. All metabolite-exposure links and metabolite-outcome links, respectively, were classified into one of three categories: direct effect, ambiguous (some models indicated an effect others not), and no-effect. Cross-sectional and longitudinal relations were evaluated in multivariable-adjusted linear regression and Cox proportional hazard regression models, respectively. Models were comprehensively adjusted for age, sex, body mass index, prevalence of hypertension, dietary and lifestyle factors, and medication. Results: Consumption of whole-grain bread was related to lower levels of several lipid metabolites with saturated and monounsaturated fatty acids. Coffee was related to lower aromatic and branched-chain amino acids, and had potential effects on the fatty acid profile within lipid classes. Red meat was linked to lower glycine levels and was related to higher circulating concentrations of branched-chain amino acids. In addition, potential marked effects of red meat consumption on the fatty acid composition within the investigated lipid classes were identified. Moreover, potential beneficial and adverse direct effects of metabolites on type 2 diabetes risk were detected. Aromatic amino acids and lipid metabolites with even-chain saturated (C14-C18) and with specific polyunsaturated fatty acids had adverse effects on type 2 diabetes risk. Glycine, glutamine, and lipid metabolites with monounsaturated fatty acids and with other species of polyunsaturated fatty acids were classified as having direct beneficial effects on type 2 diabetes risk. Potential mediators of the diet-diabetes links were identified by graphically overlaying this information in network models. Mediation analyses revealed that effects on lipid metabolites could potentially explain about one fourth of the whole-grain bread effect on type 2 diabetes risk; and that effects of coffee and red meat consumption on amino acid and lipid profiles could potentially explain about two thirds of the altered type 2 diabetes risk linked to these dietary exposures. Conclusion: An algorithm was developed that is capable to integrate single external variables (continuous exposures, survival time) and high-dimensional metabolomics-data in a joint graphical model. Application to the EPIC-Potsdam cohort study revealed that the observed conditional independence patterns were consistent with the a priori mediation hypothesis: Early effects on lipid and amino acid metabolism had the potential to explain large parts of the link between three of the most widely discussed diabetes-related dietary exposures and the risk of developing type 2 diabetes.}, language = {en} } @article{LiLuReichetzederetal.2016, author = {Li, Jian and Lu, Yong Ping and Reichetzeder, Christoph and Kalk, Philipp and Kleuser, Burkhard and Adamski, Jerzy and Hocher, Berthold}, title = {Maternal PCaaC38:6 is Associated With Preterm Birth - a Risk Factor for Early and Late Adverse Outcome of the Offspring}, series = {Journal of European public policy}, volume = {41}, journal = {Journal of European public policy}, publisher = {Karger}, address = {Basel}, issn = {1420-4096}, doi = {10.1159/000443428}, pages = {250 -- 257}, year = {2016}, abstract = {Background/Aims: Preterm birth (PTB) and low birth weight (LBW) significantly influence mortality and morbidity of the offspring in early life and also have long-term consequences in later life. A better understanding of the molecular mechanisms of preterm birth could provide new insights regarding putative preventive strategies. Metabolomics provides a powerful analytic tool to readout complex interactions between genetics, environment and health and may serve to identify relevant biomarkers. In this study, the association between 163 targeted maternal blood metabolites and gestational age was investigated in order to find candidate biomarkers for PTB. Methods: Five hundred twenty-three women were included into this observational study. Maternal blood was obtained before delivery. The concentration of 163 maternal serum metabolites was measured by flow injection tandem mass spectrometry. To find putative biomarkers for preterm birth, a three-step analysis was designed: bivariate correlation analysis followed by multivariable regression analysis and a comparison of mean values among gestational age groups. Results: Bivariate correlation analysis showed that 2 acylcarnitines (C16:2, C2), 1 amino acids (xLeu), 8 diacyl-PCs (PCaaC36:4, PCaaC38:4, PCaaC38:5, PCaaC38:6, PCaaC40:4, PCaaC40:5, PCaaC40:6, PCaaC42:4), and 1 Acylalkyl-PCs (PCaeC40:5) were inversely correlated with gestational age. Multivariable regression analysis confounded for PTB history, maternal body mass index (BMI) before pregnancy, systolic blood pressure at the third trimester, and maternal body weight at the third trimester, showed that the diacyl-PC PCaaC38:6 was the only metabolite inversely correlated with gestational age. Conclusions: Maternal blood concentrations of PCaaC38:6 are independently associated with gestational age. (C) 2016 The Author(s) Published by S. Karger AG, Basel}, language = {en} } @phdthesis{Kopka2008, author = {Kopka, Joachim}, title = {Applied metabolome analysis : exploration, development and application of gas chromatography-mass spectrometry based metabolite profiling technologies}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-40597}, school = {Universit{\"a}t Potsdam}, year = {2008}, abstract = {The uptake of nutrients and their subsequent chemical conversion by reactions which provide energy and building blocks for growth and propagation is a fundamental property of life. This property is termed metabolism. In the course of evolution life has been dependent on chemical reactions which generate molecules that are common and indispensable to all life forms. These molecules are the so-called primary metabolites. In addition, life has evolved highly diverse biochemical reactions. These reactions allow organisms to produce unique molecules, the so-called secondary metabolites, which provide a competitive advantage for survival. The sum of all metabolites produced by the complex network of reactions within an organism has since 1998 been called the metabolome. The size of the metabolome can only be estimated and may range from less than 1,000 metabolites in unicellular organisms to approximately 200,000 in the whole plant kingdom. In current biology, three additional types of molecules are thought to be important to the understanding of the phenomena of life: (1) the proteins, in other words the proteome, including enzymes which perform the metabolic reactions, (2) the ribonucleic acids (RNAs) which constitute the so-called transcriptome, and (3) all genes of the genome which are encoded within the double strands of desoxyribonucleic acid (DNA). Investigations of each of these molecular levels of life require analytical technologies which should best enable the comprehensive analysis of all proteins, RNAs, et cetera. At the beginning of this thesis such analytical technologies were available for DNA, RNA and proteins, but not for metabolites. Therefore, this thesis was dedicated to the implementation of the gas chromatography - mass spectrometry technology, in short GC-MS, for the in-parallel analysis of as many metabolites as possible. Today GC-MS is one of the most widely applied technologies and indispensable for the efficient profiling of primary metabolites. The main achievements and research topics of this work can be divided into technological advances and novel insights into the metabolic mechanisms which allow plants to cope with environmental stresses. Firstly, the GC-MS profiling technology has been highly automated and standardized. The major technological achievements were (1) substantial contributions to the development of automated and, within the limits of GC-MS, comprehensive chemical analysis, (2) contributions to the implementation of time of flight mass spectrometry for GC-MS based metabolite profiling, (3) the creation of a software platform for reproducible GC-MS data processing, named TagFinder, and (4) the establishment of an internationally coordinated library of mass spectra which allows the identification of metabolites in diverse and complex biological samples. In addition, the Golm Metabolome Database (GMD) has been initiated to harbor this library and to cope with the increasing amount of generated profiling data. This database makes publicly available all chemical information essential for GC-MS profiling and has been extended to a global resource of GC-MS based metabolite profiles. Querying the concentration changes of hundreds of known and yet non-identified metabolites has recently been enabled by uploading standardized, TagFinder-processed data. Long-term technological aims have been pursued with the central aims (1) to enhance the precision of absolute and relative quantification and (2) to enable the combined analysis of metabolite concentrations and metabolic flux. In contrast to concentrations which provide information on metabolite amounts, flux analysis provides information on the speed of biochemical reactions or reaction sequences, for example on the rate of CO2 conversion into metabolites. This conversion is an essential function of plants which is the basis of life on earth. Secondly, GC-MS based metabolite profiling technology has been continuously applied to advance plant stress physiology. These efforts have yielded a detailed description of and new functional insights into metabolic changes in response to high and low temperatures as well as common and divergent responses to salt stress among higher plants, such as Arabidopsis thaliana, Lotus japonicus and rice (Oryza sativa). Time course analysis after temperature stress and investigations into salt dosage responses indicated that metabolism changed in a gradual manner rather than by stepwise transitions between fixed states. In agreement with these observations, metabolite profiles of the model plant Lotus japonicus, when exposed to increased soil salinity, were demonstrated to have a highly predictive power for both NaCl accumulation and plant biomass. Thus, it may be possible to use GC-MS based metabolite profiling as a breeding tool to support the selection of individual plants that cope best with salt stress or other environmental challenges.}, language = {en} } @phdthesis{Lisec2008, author = {Lisec, Jan}, title = {Identification and characterization of metabolic Quantitative Trait Loci (QTL) in Arabidopsis thaliana}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-25903}, school = {Universit{\"a}t Potsdam}, year = {2008}, abstract = {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.}, language = {en} }