Seed development dedicates to reserve synthesis and accumulation and uncovering its genetic and biochemical mechanisms has been a major research focus. Although proteomic and transcriptomic analyses revealed dynamic changes of genes and enzymes involved, the information regarding concomitant metabolic changes is missing. Here we investigated the dynamic metabolic changes along the rice grain development of two japonica and two indica cultivars using non-targeted metabolomics approach, in which we successfully identified 214 metabolites. Statistical analyses revealed both cultivar and developmental stage dependent metabolic changes in rice grains. Generally, the stage specific metabolic kinetics corresponded well to the physiological status of the developing grains, and metabolic changes in developing rice grain are similar to those of dicot Arabidopsis and tomato at reserve accumulation stage but are different from those of dicots at seed desiccation stage. The remarkable difference in metabolite abundances between japonica and indica rice grain was observed at the reserve accumulation stage. Metabolite-metabolite correlation analysis uncovered potential new pathways for several metabolites. Taken together, this study uncovered both conserved and diverse development associated metabolic kinetics of rice grains, which facilitates further study to explore fundamental questions regarding the evolution of seed metabolic capabilities as well as their potential applications in crop improvement.

Nicotine is a highly toxic tobacco alkaloid that is ubiquitous in wastewater effluent. For the first time, we report the identification of the products and the pathways for the photodegradation of nicotine in an effluent matrix under simulated solar irradiation. Nicotine was found to be degraded by triplet-state organic matter (OM*), thus indicating that electron transfer is a preferred reaction mechanism. Using the multivariate statistical strategies orthogonal projection to latent structures discriminant analysis (OPLS-DA) and hierarchical clustering, 49 potential transformation products (TPs) of nicotine were successfully extracted from the water matrix via high-resolution ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS). Overall, 30 TPs, including 4 groups of nonseparated isomeric photo TPs, were identified with various levels of confidence based on the tandem mass spectrometry information on standard compounds and the isotope-labeling method (using rac-nicotine-2',3',3'-D, rac-nicotine-CD, and rac-nicotine-D) under air-saturated conditions. The pyrrolidine ring of nicotine was found to be the reactive site under sunlight irradiation. Pseudooxynicotine was the main primary TP from nicotine, with a maximum transformation ratio of 64%. Nicotinic acid, cotinine, 3'-hydroxycotinine, and myosmine were the final stable TPs after 72 h of solar irradiation, with yields of 13%, 3%, 5%, and 5%, respectively.

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Few literatures have evaluated the exact role of metabolomics in the identification process of potential biomarkers for acute kidney injury among the patients receiving renal transplantation. On top of this, the success of metabolomics in biomarker translation seems to lie in the robust quantitative method. As such, a single-center retrospective observational study was conducted enrolling 42 patients underwent renal transplantation with/without acute kidney injury, as well as 24 healthy volunteers, in Shanghai Changzheng Hospital. Plasma amino acid metabolic patterns for the participants were investigated by targeted UHPLC-MS/MS metabolic profiling. The most significant changes of the explored metabolites were related to the disturbance of tryptophan metabolism and arginine metabolism. Abnormal circulating tryptophan and symmetric dimethylarginine were identified to be potential biomarkers of acute kidney injury, combination of which showed a higher area under receiver-operator curve value (AUC = 0.901), improved sensitivity (0.889) and specificity (0.831) compared with creatinine only. Overall, these results revealed that targeted metabolomics analysis would be a potent and promising strategy for identification and pre-validation of biomarkers of acute kidney injury in renal transplantation patients.

Microfluidic chip-based high-performance-liquid-chromatography coupled to mass spectrometry (chip-HPLC-MS) has been widely used in proteomic research due to its enhanced sensitivity. We employed a chip-HPLC-MS system for determining small molecules such as drug metabolites in biological fluids. This chip-HPLC-MS system integrates a microfluidic switch, a 2-dimensional column design including an enrichment column (160 nL) for sample pre-concentration and an analytical column for chromatographic separation, as well as a nanospray emitter on a single polyimide chip. In this study, a relatively large sample volume (500 nL) was injected into the enrichment column for pre-concentration and an additional 4 μL of the initial mobile phase was applied to remove un-retained components from the sample matrix prior to chromatographic separation. The 2-dimensional column design provides the advantages of online sample concentration and reducing matrix influence on MS detection. 7-Aminoflunitrazepam (7-aminoFM2), a major metabolite of flunitrazepam (FM2), was determined in urine samples using the integrated chip-HPLC-MS system. The linear range was 0.1-10 ng mL(-1) and the method detection limit (signal-to-noise ratio of 3) was 0.05 ng mL(-1) for 7-aminoFM2. After consecutive liquid-liquid extraction (LLE) and solid-phase extraction (SPE), the chip-HPLC-MS exhibited high correlation between 7-aminoFM2 spiked Milli-Q water and 7-aminoFM2 spiked urine samples. This system also showed good precision (n = 5) and recovery for spiked urine samples at the levels of 0.1, 1.0, and 10 ng mL(-1). Intra-day and inter-day precision were 2.0-7.1% and 4.3-6.0%, respectively. Clinical urine samples were also analyzed by this chip-HPLC-MS system and acceptable relative differences (-1.3 to -13.0%) compared with the results using a GC-MC method were determined. Due to its high sensitivity and ease of operation, the chip-HPLC-MS system can be utilized for the determination of small molecules such as drug metabolites and neurotransmitters in biological fluids for clinical diagnosis.