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Desiccation is a severe survival problem for organisms. We have been studying the desiccation tolerance mechanisms in the true slime mold Physarum polycephalum. We measured the trehalose content of P. polycephalum vegetative cells (plasmodia) and drought cells (sclerotia). Surprisingly, we found that the content in sclerotia was about 473-fold greater than in the plasmodia. We then examined trehalose metabolism-related genes via RNAseq, and consequently found that trehalose 6-phosphate phosphorylase (T6pp) expression levels increased following desiccation. Next, we cloned and expressed the genes for T6pp, trehalose 6-phosphate synthase/phosphatase (Tps/Tpp), maltooligosyltrehalose trehalohydrolase (TreZ), and maltooligosyltrehalose synthase (TreY) in E. coli. Incidentally, TreY and TreZ clones have been reported in several prokaryotes, but not in eukaryotes. This report in P. polycephalum is the first evidence of their presence in a eukaryote species. Recombinant T6pp, TreY, and TreZ were purified and confirmed to be active. Our results showed that these enzymes catalyze reactions related to trehalose production, and their reaction kinetics follow the Michaelis-Menten equation. The t6pp mRNA levels of the sclerotia were about 15-fold higher than in the plasmodia. In contrast, the expression levels of TreZ and TreY showed no significant change between the sclerotia and plasmodia. Thus, T6pp is probably related to desiccation tolerance, whereas the contribution of TreY and TreZ is insufficient to account for the considerable accumulation of trehalose in sclerotia. • Study for the desiccation tolerance mechanisms in a true slime mold Physarum polycephalum. • A large amount of trehalose was accumulated in the drought cells (sclerotia). • The genes (t6pp , tps/tpp , treZ , treY) related to trehalose metabolism were cloned. • T6pp is probably related to desiccation tolerance. • This is the first report of treY and treZ genes from eukaryotic species. [ABSTRACT FROM AUTHOR]

Microscopic randomness and the small volumes of living cells combine to generate random fluctuations in molecule concentrations called “noise”. Here I investigate the effect of noise on biochemical reactions obeying Michaelis–Menten kinetics, concluding that substrate noise causes these reactions to slow. I derive a general expression for the time evolution of the joint probability density of chemical species in arbitrarily connected networks of non-linear chemical reactions in small volumes. This equation is a generalization of the chemical master equation (CME), a common tool for investigating stochastic chemical kinetics, extended to reaction networks occurring in small volumes, such as living cells. I apply this equation to a generalized Michaelis-Menten reaction in an open system, deriving the following general result: 〈 p 〉 ≤ p ¯ and 〈 s 〉 ≥ s ¯ , where s ¯ and p ¯ denote the deterministic steady-state concentration of reactant and product species, respectively, and 〈 s 〉 and 〈 p 〉 denote the steady-state ensemble average over independent realizations of a stochastic reaction. Under biologically realistic conditions, namely when substrate is degraded or diluted by cell division, 〈 p 〉 ≤ p ¯ . Consequently, noise slows the rate of in vivo Michaelis–Menten reactions. These predictions are validated by extensive stochastic simulations using Gillespie's exact stochastic simulation algorithm. I specify the conditions under which these effects occur and when they vanish, therefore reconciling discrepancies among previous theoretical investigations of stochastic biochemical reactions. Stochastic slowdown of reaction flux caused by molecular noise in living cells may have functional consequences, which the present theory may be used to quantify. [ABSTRACT FROM AUTHOR]

We propose that the time course of an enzyme reaction following the Michaelis-Menten reaction mechanism can be conveniently described by a newly derived algebraic equation, which includes the Lambert Omega function. Following Northrop's ideas [ Anal. Biochem. 321 , 457–461, 1983], the integrated rate equation contains the Michaelis constant ( K M ) and the specificity number ( k S ≡ k cat / K M ) as adjustable parameters, but not the turnover number k cat . A modification of the usual global-fit approach involves a combinatorial treatment of nominal substrate concentrations being treated as fixed or alternately optimized model parameters. The newly proposed method is compared with the standard approach based on the “initial linear region” of the reaction progress curves, followed by nonlinear fit of initial rates to the hyperbolic Michaelis-Menten equation. A representative set of three chelation-enhanced fluorescence EGFR kinase substrates is used for experimental illustration. In one case, both data analysis methods (linear and nonlinear) produced identical results. However, in another test case, the standard method incorrectly reported a finite (50–70 μM) K M value, whereas the more rigorous global nonlinear fit shows that the K M is immeasurably high. [ABSTRACT FROM AUTHOR]