@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} } @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{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{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} } @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} } @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} }