System level analysis of cacao seed ripening reveals a sequential interplay of primary and secondary metabolism leading to polyphenol accumulation and preparation of stress resistance.

Theobroma cacao and its popular product, chocolate, are attracting attention due to potential health benefits including antioxidative effects by polyphenols, anti-depressant effects by high serotonin levels, inhibition of platelet aggregation and prevention of obesity-dependent insulin resistance. The development of cacao seeds during fruit ripening is the most crucial process for the accumulation of these compounds. In this study, we analyzed the primary and the secondary metabolome as well as the proteome during Theobroma cacao cv. Forastero seed development by applying an integrative extraction protocol. The combination of multivariate statistics and mathematical modelling revealed a complex consecutive coordination of primary and secondary metabolism and corresponding pathways. Tricarboxylic acid (TCA) cycle and aromatic amino acid metabolism dominated during the early developmental stages (stages 1 and 2; cell division and expansion phase). This was accompanied with a significant shift of proteins from phenylpropanoid metabolism to flavonoid biosynthesis. At stage 3 (reserve accumulation phase), metabolism of sucrose switched from hydrolysis into raffinose synthesis. Lipids as well as proteins involved in lipid metabolism increased whereas amino acids and N-phenylpropenoyl amino acids decreased. Purine alkaloids, polyphenols, and raffinose as well as proteins involved in abiotic and biotic stress accumulated at stage 4 (maturation phase) endowing cacao seeds the characteristic astringent taste and resistance to stress. In summary, metabolic key points of cacao seed development comprise the sequential coordination of primary metabolites, phenylpropanoid, N-phenylpropenoyl amino acid, serotonin, lipid and polyphenol metabolism thereby covering the major compound classes involved in cacao aroma and health benefits.

Heat-Treatment-Responsive Proteins in Different Developmental Stages of Tomato Pollen Detected by Targeted Mass Accuracy Precursor Alignment (tMAPA).

Recently, we have developed a quantitative shotgun proteomics strategy called mass accuracy precursor alignment (MAPA). The MAPA algorithm uses high mass accuracy to bin mass-to-charge (m/z) ratios of precursor ions from LC-MS analyses, determines their intensities, and extracts a quantitative sample versus m/z ratio data alignment matrix from a multitude of samples. Here, we introduce a novel feature of this algorithm that allows the extraction and alignment of proteotypic peptide precursor ions or any other target peptide from complex shotgun proteomics data for accurate quantification of unique proteins. This strategy circumvents the problem of confusing the quantification of proteins due to indistinguishable protein isoforms by a typical shotgun proteomics approach. We applied this strategy to a comparison of control and heat-treated tomato pollen grains at two developmental stages, post-meiotic and mature. Pollen is a temperature-sensitive tissue involved in the reproductive cycle of plants and plays a major role in fruit setting and yield. By LC-MS-based shotgun proteomics, we identified more than 2000 proteins in total for all different tissues. By applying the targeted MAPA data-processing strategy, 51 unique proteins were identified as heat-treatment-responsive protein candidates. The potential function of the identified candidates in a specific developmental stage is discussed.

Untargeted Metabolomic Analysis Combined With Multivariate Statistics Reveal Distinct Metabolic Changes in GPR40 Agonist-Treated Animals Related to Bile Acid Metabolism.

Metabolomics has been increasingly applied to biomarker discovery, as untargeted metabolic profiling represents a powerful exploratory tool for identifying causal links between biomarkers and disease phenotypes. In the present work, we used untargeted metabolomics to investigate plasma specimens of rats, dogs, and mice treated with small-molecule drugs designed for improved glycemic control of type 2 diabetes mellitus patients via activation of GPR40. The in vivo pharmacology of GPR40 is not yet fully understood. Compounds targeting this receptor have been found to induce drug-induced liver injury (DILI). Metabolomic analysis facilitating an integrated UPLC-TWIMS-HRMS platform was used to detect metabolic differences between treated and non-treated animals within two 4-week toxicity studies in rat and dog, and one 2-week toxicity study in mouse. Multivariate statistics of untargeted metabolomics data subsequently revealed the presence of several significantly upregulated endogenous compounds in the treated animals whose plasma level is known to be affected during DILI. A specific bile acid metabolite useful as endogenous probe for drug-drug interaction studies was identified (chenodeoxycholic acid-24 glucuronide), as well as a metabolic precursor indicative of acidic bile acid biosynthesis (7α-hydroxy-3-oxo-4-cholestenoic acid). These results correlate with typical liver toxicity parameters on the individual level.

Combined Metabolomic Analysis of Plasma and Urine Reveals AHBA, Tryptophan and Serotonin Metabolism as Potential Risk Factors in Gestational Diabetes Mellitus (GDM).

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.

Cajanus cajan- a source of PPARγ activators leading to anti-inflammatory and cytotoxic effects.

Cajanus cajan is an important legume crop in the human diet in many parts of the world. Due to its pharmacological properties, C. cajan is, moreover, used in traditional medicine for treating skin diseases, diabetes, inflammatory disorders and various other dysfunctions. In this study, we focused on the role of peroxisome proliferator-activated receptor gamma (PPARγ) as a potential therapeutic target of Cajanus cajan and its main compounds for the treatment of cancer, inflammation and inflammation-related disorders. The anti-inflammatory potential of C. cajan and its bioactive compounds and their cytotoxicity on the human cervical adenocarcinoma cell line HeLa, the human colorectal adenocarcinoma cell line CaCo-2 and the human breast adenocarcinoma cell line MCF-7 were elucidated. C. cajan and its compounds exerted significant anti-inflammatory activity on lipopolysaccharide-stimulated macrophages, showed good cytotoxic effects on the 3 different cancer cell lines and proved PPARγ activity in vitro. The main active compounds were orientin, pinostrobin and vitexin. Cajaninstilbene acid and pinosylvin monomethylether were identified as novel PPARγ activators. Based on these data, C. cajan provides excellent beneficial medicinal attributes and may be used as a potential food or a pharmaceutical supplement.

mzGroupAnalyzer--predicting pathways and novel chemical structures from untargeted high-throughput metabolomics data.

The metabolome is a highly dynamic entity and the final readout of the genotype x environment x phenotype (GxExP) relationship of an organism. Monitoring metabolite dynamics over time thus theoretically encrypts the whole range of possible chemical and biochemical transformations of small molecules involved in metabolism. The bottleneck is, however, the sheer number of unidentified structures in these samples. This represents the next challenge for metabolomics technology and is comparable with genome sequencing 30 years ago. At the same time it is impossible to handle the amount of data involved in a metabolomics analysis manually. Algorithms are therefore imperative to allow for automated m/z feature extraction and subsequent structure or pathway assignment. Here we provide an automated pathway inference strategy comprising measurements of metabolome time series using LC- MS with high resolution and high mass accuracy. An algorithm was developed, called mzGroupAnalyzer, to automatically explore the metabolome for the detection of metabolite transformations caused by biochemical or chemical modifications. Pathways are extracted directly from the data and putative novel structures can be identified. The detected m/z features can be mapped on a van Krevelen diagram according to their H/C and O/C ratios for pattern recognition and to visualize oxidative processes and biochemical transformations. This method was applied to Arabidopsis thaliana treated simultaneously with cold and high light. Due to a protective antioxidant response the plants turn from green to purple color via the accumulation of flavonoid structures. The detection of potential biochemical pathways resulted in 15 putatively new compounds involved in the flavonoid-pathway. These compounds were further validated by product ion spectra from the same data. The mzGroupAnalyzer is implemented in the graphical user interface (GUI) of the metabolomics toolbox COVAIN (Sun & Weckwerth, 2012, Metabolomics 8: 81-93). The strategy can be extended to any biological system.

Granger causality in integrated GC-MS and LC-MS metabolomics data reveals the interface of primary and secondary metabolism.

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.