13C-metabolic flux analysis of lipid accumulation in the green microalgae Tetradesmus obliquus under nitrogen deficiency stress.

Metabolic fluxes (MF) serve as the functional phenotypes of biochemical processes and are crucial to describe the distribution of precursors within metabolic networks. There is a lack of experimental observations for carbon flux towards lipids, which is important for biodiesel generation. Here, the accumulation of lipid, and MF in Tetradesmus obliquus under nitrogen deficiency stress (NF) using a 13C isotope tracer at different time intervals was investigated. The 13C based MF showed enhanced de novo synthesis of G3P and PEP, indicating increased carbon flux from CO2 into lipid synthesis. An increase in palmitic acid (3500 µmol/mg), linoleic acid (2100 µmol/mg), and oleic acid (2000 µmol/mg) was observed. The accumulation of C16:0 under NF was mainly related to de novo synthesis while C18:3 was accumulated through a non de novo pathway. Under NF stress, T. obliquus had higher flux in PPP and glycolysis pathway, together, it might provide more NADPH and substrate acetyl-CoA for fatty acid synthesis.

Rapid quantitative characterization of tea seedlings under lead-containing aerosol particles stress using Vis-NIR spectra.

The problem of excessive lead content in tea has become more and more serious with the development of society and industry. This paper investigated the ability of visible and near-infrared (Vis-NIR) spectroscopy to evaluate foliar lead uptake by tea plants through simulating real air pollution. Lead content of tea leaves in different treatment groups during stress time was measured by inductively coupled plasma mass spectrometry (ICP-MS). It was determined that stomata can be a channel for lead particles in the air and most of the lead entering through the stomata accumulates in the leaves. The spectral variation of treated samples was measured, and it was found that a combination of partial least squares-discriminant analysis (PLS-DA) and spectral responses can perfectly classify the tea samples under different lead concentrations stress with an overall accuracy of 0.979. Then the Vis-NIR spectra were used for fast monitoring physiological and biochemical indicators in tea leaves under atmospheric deposition. Relevant spectra pretreatment methods and characteristic wavelength selection approaches were evaluated for quantitative analysis and then optimal prediction models to instantly detect quality indicators in tea samples were built. Among predictive models, PLS had the best results (RMSE = 0.139 mg/g, 0.663 mmol/g, and 1.494 µmol/g) for the prediction of chlorophyll a (Chl-a), ascorbic acid (ASA), and glutathione (GSH), respectively. Also, principal component regression (PCR) gave the best results (RMSE = 0.053 mg/g, 0.024 mg/g, and 0.011%) for prediction of chlorophyll b (Chl-b), carotenoid (Car) and moisture content (MC), respectively. Results of this study can be applied for developing an effective and reliable approach for monitoring atmospheric deposition in plants.