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Root-microbe interaction and its specialized root nodule structures and functions are well studied. In contrast, leaf nodules harboring microbial endophytes in special glandular leaf structures have only recently gained increased interest as plant-microbe phyllosphere interactions. Here, we applied a comprehensive metabolomics platform in combination with natural product isolation and characterization to dissect leaf and leaf nodule metabolism and functions in Ardisia crenata (Primulaceae) and Psychotria punctata (Rubiaceae). The results indicate that abiotic stress resilience plays an important part within the leaf nodule symbiosis of both species. Both species showed metabolic signatures of enhanced nitrogen assimilation/dissimilation pattern and increased polyamine levels in nodules compared to leaf lamina tissue potentially involved in senescence processes and photosynthesis. Multiple links to cytokinin and REDOX-active pathways were found. Our results further demonstrate that secondary metabolite production by endophytes is a key feature of this symbiotic system. Multiple anhydromuropeptides (AhMP) and their derivatives were identified as highly characteristic biomarkers for nodulation within both species. A novel epicatechin derivative was structurally elucidated with NMR and shown to be enriched within the leaf nodules of A. crenata. This enrichment within nodulated tissues was also observed for catechin and other flavonoids indicating that flavonoid metabolism may play an important role for leaf nodule symbiosis of A. crenata. In contrast, pavettamine was only detected in P. punctata and showed no nodule specific enrichment but a developmental effect. Further natural products were detected, including three putative unknown depsipeptide structures in A. crenata leaf nodules. The analysis presents a first metabolomics reference data set for the intimate interaction of microbes and plants in leaf nodules, reveals novel metabolic processes of plant-microbe interaction as well as the potential of natural product discovery in these systems.

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.

The study aim was to test the applicability of pooling of nanomaterials-induced in vitro data for identifying the toxic capacity of specific (SiO2, TiO2, ZnO, CuO, CeO2 and carbon nanotubes, [CNT]) nanoparticles (NP) and to test the usefulness for grouping purposes. Publication selection was based on specific criteria regarding experimental conditions. Two relevant biological endpoints were selected; generation of intracellular reactive oxygen species (ROS) and viability above 90%. The correlations of the ROS ratios with the NP parameters’ size, concentration, and exposure time were analysed. The obtained data sets were then analysed with multiple regression analysis of variance (ANOVA) and the Tukey post-hoc test. The results show that this method is applicable for the selected metal oxide NP, but might need reconsideration and a larger data set for CNT. Several statistically significant correlations and results were obtained, thus validating the method. Furthermore, the relevance of the combination of ROS release with a cell viability test was shown. The data also show that it is advisable to compare ROS production of professional phagocytic with non-phagocytic cells. In conclusion, this is the first systematic analysis showing that pooling of available data into groups is a useful method for evaluation of data regarding NP induced toxicity in vitro.