Enzyme kinetics from circular dichroism of insulin reveals mechanistic insights into the regulation of insulin-degrading enzyme

Ivancic, Valerie A.; Krasinski, Claire A.; Zheng, Qingyuan; Meservier, Rebecca; Spratt, Donald E.; Lazo, Noel D.

doi:10.1042/bsr20181416

Cited by 11 publications

(41 citation statements)

References 45 publications

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“…The bacterial expression vector encoding IDE fused to GST was kindly provided by Dr. Malcolm A. Leissring of the University of California at Irvine. GST-IDE was overexpressed in E. coli BL21-CodonPlus RIL cells (EMD Biosciences Inc., San Diego, CA, USA), and purified as previously described [ 25 , 26 , 27 ]. Cleavage of the GST tag was accomplished using GST PreScission protease and further purification of IDE was accomplished by standard gel filtration [ 25 , 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

“…GST-IDE was overexpressed in E. coli BL21-CodonPlus RIL cells (EMD Biosciences Inc., San Diego, CA, USA), and purified as previously described [ 25 , 26 , 27 ]. Cleavage of the GST tag was accomplished using GST PreScission protease and further purification of IDE was accomplished by standard gel filtration [ 25 , 26 , 27 ]. UV absorbance at 280 nm was used to determine the concentration of IDE using an extinction coefficient of ε 280nm = 113,570 M −1 cm −1 [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

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Differential Effects of Polyphenols on Insulin Proteolysis by the Insulin-Degrading Enzyme

et al. 2021

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The insulin-degrading enzyme (IDE) possesses a strong ability to degrade insulin and Aβ42 that has been linked to the neurodegeneration in Alzheimer’s disease (AD). Given this, an attractive IDE-centric strategy for the development of therapeutics for AD is to boost IDE’s activity for the clearance of Aβ42 without offsetting insulin proteostasis. Recently, we showed that resveratrol enhances IDE’s activity toward Aβ42. In this work, we used a combination of chromatographic and spectroscopic techniques to investigate the effects of resveratrol on IDE’s activity toward insulin. For comparison, we also studied epigallocatechin-3-gallate (EGCG). Our results show that the two polyphenols affect the IDE-dependent degradation of insulin in different ways: EGCG inhibits IDE while resveratrol has no effect. These findings suggest that polyphenols provide a path for developing therapeutic strategies that can selectively target IDE substrate specificity.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Differential Effects of Polyphenols on Insulin Proteolysis by the Insulin-Degrading Enzyme

et al. 2021

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“…Thus, ATP and other nucleoside polyphosphates, as well as polyphosphate alone, induce dose-dependent allosteric inhibition of insulin degradation and, concomitantly, activation of short fluorogenic peptide substrates at physiologically relevant concentrations (1–5 mM) [ 50 , 183 ]. Interestingly, the activating effect is stronger for nucleoside triphosphates than nucleoside di- or monophosphates, and is attenuated in the presence of Mg 2+ [ 50 , 184 ]. Polyphosphate binding has been shown to occur at a specific region within IDE, known as the polyanion-binding domain [ 144 ], and appears to mediate activation of the protease towards short substrates by facilitating the transition from the closed state to the open conformation [ 49 , 50 ].…”

Section: Introductionmentioning

confidence: 99%

Modulation of Insulin Sensitivity by Insulin-Degrading Enzyme

et al. 2021

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Insulin-degrading enzyme (IDE) is a highly conserved and ubiquitously expressed metalloprotease that degrades insulin and several other intermediate-size peptides. For many decades, IDE had been assumed to be involved primarily in hepatic insulin clearance, a key process that regulates availability of circulating insulin levels for peripheral tissues. Emerging evidence, however, suggests that IDE has several other important physiological functions relevant to glucose and insulin homeostasis, including the regulation of insulin secretion from pancreatic β-cells. Investigation of mice with tissue-specific genetic deletion of Ide in the liver and pancreatic β-cells (L-IDE-KO and B-IDE-KO mice, respectively) has revealed additional roles for IDE in the regulation of hepatic insulin action and sensitivity. In this review, we discuss current knowledge about IDE’s function as a regulator of insulin secretion and hepatic insulin sensitivity, both evaluating the classical view of IDE as an insulin protease and also exploring evidence for several non-proteolytic functions. Insulin proteostasis and insulin sensitivity have both been highlighted as targets controlling blood sugar levels in type 2 diabetes, so a clearer understanding the physiological functions of IDE in pancreas and liver could led to the development of novel therapeutics for the treatment of this disease.

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“…Most of the effort in these studies has been directed toward investigating the impact of diffusion in fractal media on the reaction kinetics (22), with little focus on characterizing the effect of crowding on the mean effective enzyme kinetics. However, because it has now been shown that in vitro, some enzymes might not be limited by their translational diffusion but by their apparent association rate constants (24)(25)(26)(27)(28), the reevaluation of crowding in these reactions is important. In this work, therefore, we introduced computational methods for studying spatial effects of any kind, applying our work to the effects of crowding on reaction-limited enzymes with the goal of bridging the discrepancy between the in vitro measurement of kinetic parameters and the actual in vivo conditions.…”

Section: Introductionmentioning

confidence: 99%

Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism

Weilandt

Hatzimanikatis

2019

Biophysical Journal

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Many computational models for analyzing and predicting cell physiology rely on in vitro data collected in dilute and controlled buffer solutions. However, this can mislead models because up to 40% of the intracellular volume-depending on the organism, the physiology, and the cellular compartment-is occupied by a dense mixture of proteins, lipids, polysaccharides, RNA, and DNA. These intracellular macromolecules interfere with the interactions of enzymes and their reactants and thus affect the kinetics of biochemical reactions, making in vivo reactions considerably more complex than the in vitro data indicates. In this work, we present a new, to our knowledge, type of kinetics that captures and quantifies the effect of volume exclusion and other spatial phenomena on the kinetics of elementary reactions. We further developed a framework that allows for the efficient parameterization of these kinetics using particle simulations. Our formulation, entitled generalized elementary kinetics, can be used to analyze and predict the effect of intracellular crowding on enzymatic reactions and was herein applied to investigate the influence of crowding on phosphoglycerate mutase in Escherichia coli, which exhibits prototypical reversible Michaelis-Menten kinetics. Current research indicates that many enzymes are reaction limited and not diffusion limited, and our results suggest that the influence of fractal diffusion is minimal for these reaction-limited enzymes. Instead, increased association rates and decreased dissociation rates lead to a strong decrease in the effective maximal velocities V max and the effective Michaelis-Menten constants K M under physiologically relevant volume occupancies. Finally, the effects of crowding were explored in the context of a linear pathway, with the finding that crowding can have a redistributing effect on the effective flux responses in the case of twofold enzyme overexpression. We suggest that this framework, in combination with detailed kinetics models, will improve our understanding of enzyme reaction networks under nonideal conditions. SIGNIFICANCE Kinetic models are essential for understanding and designing biochemical and biophysical processes in living organisms. Currently, kinetic models rely on the in vitro characterization of biochemical reactions, although intracellular reactions are taking place in crowded, nonideal conditions. The interactions of the enzymes and their reactants with other macromolecules in a cell alter the enzyme kinetics significantly, but little has been done to model and quantify the impact of these interactions on the in vivo reaction rates. We present a computational framework that allows us for the first time, to our knowledge, to estimate the in vivo apparent kinetic parameters of an enzyme that follows Michaelis-Menten kinetics. Interestingly, crowding conditions similar to those in Escherichia coli can reduce the maximal enzyme activity 10fold.

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Enzyme kinetics from circular dichroism of insulin reveals mechanistic insights into the regulation of insulin-degrading enzyme

Cited by 11 publications

References 45 publications

Differential Effects of Polyphenols on Insulin Proteolysis by the Insulin-Degrading Enzyme

Differential Effects of Polyphenols on Insulin Proteolysis by the Insulin-Degrading Enzyme

Modulation of Insulin Sensitivity by Insulin-Degrading Enzyme

Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism

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