Systems-level analysis of mechanisms regulating yeast metabolic flux

Abstract

Cellular metabolic fluxes are determined by enzyme activities and metabolite abundances. Biochemical approaches reveal the impact of specific substrates or regulators on enzyme kinetics, but do not capture the extent to which metabolite and enzyme concentrations vary across physiological states, and therefore how cellular reactions are regulated. We measured enzyme and metabolite concentrations and metabolic fluxes across 25 steady-state yeast cultures. We then assessed the extent to which flux can be explained by a Michaelis-Menten relationship between enzyme, substrate, product, and potential regulator concentrations. This revealed three new instances of cross-pathway regulation, which we biochemically verified. These included inhibition of pyruvate kinase by citrate, which accumulated and thereby curtailed glycolytic outflow in nitrogen-limited yeast. Overall, substrate concentrations were the strongest driver of the net rates of cellular metabolic reactions, with metabolite concentrations collectively having more than double the physiological impact of enzymes.

RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli

“…This is most likely due to utilization of the Phosphoenolpyruvate Carboxylase (PPC) in MG1655 and W3110 (McCloskey et al, 2016a(McCloskey et al, , 2016b, and overall increased levels of TCA cycle intermediates in strain C. E. coli MG1655 and W3110 were also found to have the highest levels of L-arginine. It should be noted that when compared to the transcript levels or flux predictions of the amino acid producing pathways (Monk et al, 2016), little correlation between the absolute metabolite concentrations was found, which indicates the difficulty in predicting metabolite levels through indirect evidence as is consistent with previous works (Daran-Lapujade et al, 2007;Hackett et al, 2016). Levels of central metabolism intermediates differed vastly between the strains (Fig.…”

Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity

“…in order to induce flux reversal (Link et al, 2013). In silico efforts to model the response of central metabolism to nutrient perturbations, combined with experimental data, have highlighted the fact that our understanding of the intricate regulation of central metabolism is incomplete (Gerosa et al, 2015; Hackett et al, 2016; Kochanowski et al, 2013; Link et al, 2013; Xu et al, 2012a). …”

“…Recently, it has been established that properly accounting for the activation/inhibition of enzymes by endogenous small molecules can lead to metabolic models that explain experimental data better (Chandra et al, 2011; Hackett et al, 2016; Khodayari and Maranas, 2016; Link et al, 2013; Xu et al, 2012a), facilitate engineering of novel metabolic pathways (Chen et al, 2015; He et al, 2016), and improve our understanding of metabolic phenomena in health and disease (Christofk et al, 2008). So far, high-throughput experimental assays for discovering small molecule regulatory interactions have been technically limited (Feng et al, 2014; Li et al, 2013; Nikolaev et al, 2016; Orsak et al, 2012; Reinhard et al, 2015; Savitski et al, 2014), while hybrid approaches that integrate experimental data with computational models are not scalable and typically focus on central carbon metabolism (Hackett et al, 2016; Link et al, 2014, 2013; Schueler-Furman and Wodak, 2016).…”

“…So far, high-throughput experimental assays for discovering small molecule regulatory interactions have been technically limited (Feng et al, 2014; Li et al, 2013; Nikolaev et al, 2016; Orsak et al, 2012; Reinhard et al, 2015; Savitski et al, 2014), while hybrid approaches that integrate experimental data with computational models are not scalable and typically focus on central carbon metabolism (Hackett et al, 2016; Link et al, 2014, 2013; Schueler-Furman and Wodak, 2016). …”

Engineering Aspergillus niger for galactaric acid production: elimination of galactaric acid catabolism by using RNA sequencing and CRISPR/Cas9

“…This may be partially due to the different composition of the pre-cultivation medium (yeast extract peptone) and galactaric acid medium (minimal medium). Although not studied in detailed in aspergilli, the genes of the most central carbon metabolic pathways, such as glycolytic genes, are considered to be constitutively active at least in the model organism Saccharomyces cerevisiae [16]. In contrast, metabolic pathways for less abundant carbon sources, such as the catabolic pathways for d -galUA [17] and d -glucuronic acid [18] in A. niger , tend to be specifically activated in the presence of a particular carbon source.…”