No abstract

Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC–MS) and liquid chromatography coupled to mass spectrometry (LC–MS). Each platform has a specific performance detecting subsets of metabolites. GC–MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC–MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC–MS and LC–MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC–LC–MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality, a subset of secondary metabolites was detected with significant correlations to primary metabolites such as sugars and amino acids. These metabolic subsets were compiled into a stoichiometric matrix N. Using N the inverse calculation of a differential Jacobian J from metabolomics data was possible. Key points of regulation at the interface of primary and secondary metabolism were identified.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-012-0470-0) contains supplementary material, which is available to authorized users.

In maize, a series of seed mutants with starchy endosperm could increase the lysine content by decreased amount of zeins, the main storage proteins in endosperm. Cloning and characterization of these mutants could reveal regulatory mechanisms for zeins accumulation in maize endosperm. Opaque7 (o7) is a classic maize starchy endosperm mutant with large effects on zeins accumulation and high lysine content. In this study, the O7 gene was cloned by map-based cloning and confirmed by transgenic functional complementation and RNAi. The o7-ref allele has a 12-bp in-frame deletion. The four-amino-acid deletion caused low accumulation of o7 protein in vivo. The O7 gene encodes an acyl-activating enzyme with high similarity to AAE3. The opaque phenotype of the o7 mutant was produced by the reduction of protein body size and number caused by a decrease in the a-zeins concentrations. Analysis of amino acids and metabolites suggested that the O7 gene might affect amino acid biosynthesis by affecting a-ketoglutaric acid and oxaloacetic acid. Transgenic rice seeds containing RNAi constructs targeting the rice ortholog of maize O7 also produced lower amounts of seed proteins and displayed an opaque endosperm phenotype, indicating a conserved biological function of O7 in cereal crops. The cloning of O7 revealed a novel regulatory mechanism for storage protein synthesis and highlighted an effective target for the genetic manipulation of storage protein contents in cereal seeds.T HE texture and protein quality of maize (Zea mays L.) endosperm are important factors affecting grain shipping, insect and fungal pathogen resistance, and nutritional quality. Much evidence indicates that the reduction in the amount of zeins in the endosperm leads to a decrease in the endosperm hardness and an increase in lysine content (Mertz et al. 1964;Misra et al. 1972;Schmidt et al. 1987;Dombrink-Kurtzman and Bietz 1993;Holding and Larkins 2006;. Maize have a number of opaque or floury endosperm mutants that affect the texture and protein quality of endosperm by altering zeins accumulation. Our understanding of the underlying mechanisms determining zeins accumulation comes from the study of seed mutants.There are .18 mutants that can exhibit an opaque or floury endosperm (Thompson and Larkins 1994;Hunter et al. 2002). Among them are the recessive opaque mutants (o1, o2, o5, o7, o9-o11, and o13-o17), the semidominant floury mutants (fl1, fl2, and fl3), and the dominant mutants Mucronate (Mc) and Defective endosperm B30 (De-B30) (Motto et al. 1996;Gibbon and Larkins 2005). The cloning and characterization of some of the opaque mutants has revealed important regulatory mechanisms for zeins accumulation in maize endosperm. The O2 gene, which encodes a defective basic-domain-leucine-zipper transcription factor, regulates several endosperm-expressed genes, in particular the 22-kDa a-zeins (Schmidt et al. 1987(Schmidt et al. , 1990Damerval and De Vienne

Highlights d Metabolomics and inverse modeling reveal a Tsc2/mTORC1dependent checkpoint in macrophages d M2 macrophages have high Phgdh activity d Phgdh activity promotes M2 polarization d Phgdh activity supports macrophage proliferation

Gestational diabetes mellitus during pregnancy has severe implications for the health of the mother and the fetus. Therefore, early prediction and an understanding of the physiology are an important part of prenatal care. Metabolite profiling is a long established method for the analysis and prediction of metabolic diseases. Here, we applied untargeted and targeted metabolomic protocols to analyze plasma and urine samples of pregnant women with and without GDM. Univariate and multivariate statistical analyses of metabolomic profiles revealed markers such as 2-hydroxybutanoic acid (AHBA), 3-hydroxybutanoic acid (BHBA), amino acids valine and alanine, the glucose-alanine-cycle, but also plant-derived compounds like sitosterin as different between control and GDM patients. PLS-DA and VIP analysis revealed tryptophan as a strong variable separating control and GDM. As tryptophan is biotransformed to serotonin we hypothesized whether serotonin metabolism might also be altered in GDM. To test this hypothesis we applied a method for the analysis of serotonin, metabolic intermediates and dopamine in urine by stable isotope dilution direct infusion electrospray ionization mass spectrometry (SID-MS). Indeed, serotonin and related metabolites differ significantly between control and GDM patients confirming the involvement of serotonin metabolism in GDM. Clustered correlation coefficient visualization of metabolite correlation networks revealed the different metabolic signatures between control and GDM patients. Eventually, the combination of selected blood plasma and urine sample metabolites improved the AUC prediction accuracy to 0.99. The detected GDM candidate biomarkers and the related systemic metabolic signatures are discussed in their pathophysiological context. Further studies with larger cohorts are necessary to underpin these observations.