@misc{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 = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {637}, issn = {1866-8372}, doi = {10.25932/publishup-42458}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-424585}, pages = {14}, 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} } @misc{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 = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {631}, issn = {1866-8372}, doi = {10.25932/publishup-42456}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-424566}, pages = {11}, 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} } @phdthesis{Schwahn2018, author = {Schwahn, Kevin}, title = {Data driven approaches to infer the regulatory mechanism shaping and constraining levels of metabolites in metabolic networks}, doi = {10.25932/publishup-42324}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-423240}, school = {Universit{\"a}t Potsdam}, pages = {109}, year = {2018}, abstract = {Systems biology aims at investigating biological systems in its entirety by gathering and analyzing large-scale data sets about the underlying components. Computational systems biology approaches use these large-scale data sets to create models at different scales and cellular levels. In addition, it is concerned with generating and testing hypotheses about biological processes. However, such approaches are inevitably leading to computational challenges due to the high dimensionality of the data and the differences in the dimension of data from different cellular layers. This thesis focuses on the investigation and development of computational approaches to analyze metabolite profiles in the context of cellular networks. This leads to determining what aspects of the network functionality are reflected in the metabolite levels. With these methods at hand, this thesis aims to answer three questions: (1) how observability of biological systems is manifested in metabolite profiles and if it can be used for phenotypical comparisons; (2) how to identify couplings of reaction rates from metabolic profiles alone; and (3) which regulatory mechanism that affect metabolite levels can be distinguished by integrating transcriptomics and metabolomics read-outs. I showed that sensor metabolites, identified by an approach from observability theory, are more correlated to each other than non-sensors. The greater correlations between sensor metabolites were detected both with publicly available metabolite profiles and synthetic data simulated from a medium-scale kinetic model. I demonstrated through robustness analysis that correlation was due to the position of the sensor metabolites in the network and persisted irrespectively of the experimental conditions. Sensor metabolites are therefore potential candidates for phenotypical comparisons between conditions through targeted metabolic analysis. Furthermore, I demonstrated that the coupling of metabolic reaction rates can be investigated from a purely data-driven perspective, assuming that metabolic reactions can be described by mass action kinetics. Employing metabolite profiles from domesticated and wild wheat and tomato species, I showed that the process of domestication is associated with a loss of regulatory control on the level of reaction rate coupling. I also found that the same metabolic pathways in Arabidopsis thaliana and Escherichia coli exhibit differences in the number of reaction rate couplings. I designed a novel method for the identification and categorization of transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approach determines the partial correlation of metabolites with control by the principal components of the transcript levels. The principle components contain the majority of the transcriptomic information allowing to partial out the effect of the transcriptional layer from the metabolite profiles. Depending whether the correlation between metabolites persists upon controlling for the effect of the transcriptional layer, the approach allows us to group metabolite pairs into being associated due to post-transcriptional or transcriptional regulation, respectively. I showed that the classification of metabolite pairs into those that are associated due to transcriptional or post-transcriptional regulation are in agreement with existing literature and findings from a Bayesian inference approach. The approaches developed, implemented, and investigated in this thesis open novel ways to jointly study metabolomics and transcriptomics data as well as to place metabolic profiles in the network context. The results from these approaches have the potential to provide further insights into the regulatory machinery in a biological system.}, language = {en} }