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As autotrophic organisms, plants capture light energy to convert carbon dioxide into ATP, nicotinamide adenine dinucleotide phosphate (NADPH), and sugars, which are essential for the biosynthesis of building blocks, storage, and growth. At night, metabolism and growth can be sustained by mobilizing carbon (C) reserves. In response to changing environmental conditions, such as light-dark cycles, the small-molecule regulation of enzymatic activities is critical for reprogramming cellular metabolism. We have recently demonstrated that proteogenic dipeptides, protein degradation products, act as metabolic switches at the interface of proteostasis and central metabolism in both plants and yeast. Dipeptides accumulate in response to the environmental changes and act via direct binding and regulation of critical enzymatic activities, enabling C flux distribution. Here, we provide evidence pointing to the involvement of dipeptides in the metabolic rewiring characteristics for the day-night cycle in plants. Specifically, we measured the abundance of 13 amino acids and 179 dipeptides over short- (SD) and long-day (LD) diel cycles, each with different light intensities. Of the measured dipeptides, 38 and eight were characterized by day-night oscillation in SD and LD, respectively, reaching maximum accumulation at the end of the day and then gradually falling in the night. Not only the number of dipeptides, but also the amplitude of the oscillation was higher in SD compared with LD conditions. Notably, rhythmic dipeptides were enriched in the glucogenic amino acids that can be converted into glucose. Considering the known role of Target of Rapamycin (TOR) signaling in regulating both autophagy and metabolism, we subsequently investigated whether diurnal fluctuations of dipeptides levels are dependent on the TOR Complex (TORC). The Raptor1b mutant (raptor1b), known for the substantial reduction of TOR kinase activity, was characterized by the augmented accumulation of dipeptides, which is especially pronounced under LD conditions. We were particularly intrigued by the group of 16 dipeptides, which, based on their oscillation under SD conditions and accumulation in raptor1b, can be associated with limited C availability or photoperiod. By mining existing protein-metabolite interaction data, we delineated putative protein interactors for a representative dipeptide Pro-Gln. The obtained list included enzymes of C and amino acid metabolism, which are also linked to the TORC-mediated metabolic network. Based on the obtained results, we speculate that the diurnal accumulation of dipeptides contributes to its metabolic adaptation in response to changes in C availability. We hypothesize that dipeptides would act as alternative respiratory substrates and by directly modulating the activity of the focal enzymes.
Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data
(2017)
Recent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been explored with regression-based approaches that only capture linear relationships, rendering it difficult to determine the extent to which the data reflect the underlying reaction rates and their couplings. Here we propose an approach termed Stoichiometric Correlation Analysis (SCA) based on correlation between positive linear combinations of log-transformed metabolic profiles. The log-transformation is due to the evidence that metabolic networks can be modeled by mass action law and kinetics derived from it. Unlike the existing approaches which establish a relation between pairs of metabolites, SCA facilitates the discovery of higherorder dependence between more than two metabolites. By using a paradigmatic model of the tricarboxylic acid cycle we show that the higher-order dependence reflects the coupling of concentration of reactant complexes, capturing the subtle difference between the employed enzyme kinetics. Using time-resolved metabolic profiles from Arabidopsis thaliana and Escherichia coli, we show that SCA can be used to quantify the difference in coupling of reactant complexes, and hence, reaction rates, underlying the stringent response in these model organisms. By using SCA with data from natural variation of wild and domesticated wheat and tomato accession, we demonstrate that the domestication is accompanied by loss of such couplings, in these species. Therefore, application of SCA to metabolomics data from natural variation in wild and domesticated populations provides a mechanistic way to understanding domestication and its relation to metabolic networks.