Identifiability of Enzyme Kinetic Parameters in Substrate Competition: a Case Study of CD39/NTPDase1

Overview

Journal Purinergic Signal

Publisher Springer

Date 2023 May 10

PMID 37165287

Authors

Anna N McGuinness

Aman Tahir

Nadia R Sutton

Andrew D Marquis

Affiliations

Soon will be listed here.

Abstract

CD39 (NTPDase1-nucleoside triphosphate diphosphohydrolase 1) is a membrane-tethered ectonucleotidase that hydrolyzes extracellular ATP to ADP and ADP to AMP. This enzyme is expressed in a variety of cell types and tissues and has broadly been recognized within vascular tissue to have a protective role in converting "danger" ligands (ATP) into neutral ligands (AMP). In this study, we investigate the enzyme kinetics of CD39 using a Michaelis-Menten modeling framework. We show how the unique situation of having a reaction product also serving as a substrate (ADP) complicates the determination of the governing kinetic parameters. Model simulations using values for the kinetic parameters reported in the literature do not align with corresponding time-series data. This dissonance is explained by CD39 kinetic parameters previously being determined by graphical/linearization methods, which have been shown to distort the underlying error structure and lead to inaccurate parameter estimates. Modern methods of estimating these kinetic parameters using nonlinear least squares are still challenging due to unidentifiable parameter interactions. We propose a workflow to accurately determine these parameters by isolating the ADPase and ATPase reactions and estimating the respective ADPase parameters and ATPase parameters with independent data sets. Theoretically, this ensures all kinetic parameters are identifiable and reliable for future prospective model simulations involving CD39. These kinds of mathematical models can be used to understand how circulating purinergic nucleotides affect disease etiology and potentially inform the development of corresponding therapies.

References

Zhao H, Bo C, Kang Y, Li H . What Else Can CD39 Tell Us?. Front Immunol. 2017; 8:727. PMC: 5479880. DOI: 10.3389/fimmu.2017.00727. View

Laliberte J, St-Jean P, BEAUDOIN A . Kinetic effects of Ca2+ and Mg2+ on ATP hydrolysis by the purified ATP diphosphohydrolase. J Biol Chem. 1982; 257(7):3869-74. View

Jin J, Kunapuli S . Coactivation of two different G protein-coupled receptors is essential for ADP-induced platelet aggregation. Proc Natl Acad Sci U S A. 1998; 95(14):8070-4. PMC: 20930. DOI: 10.1073/pnas.95.14.8070. View

Schnell S, Mendoza C . Time-dependent closed form solutions for fully competitive enzyme reactions. Bull Math Biol. 2000; 62(2):321-36. DOI: 10.1006/bulm.1999.0156. View

Kukulski F, Levesque S, Lavoie E, Lecka J, Bigonnesse F, Knowles A . Comparative hydrolysis of P2 receptor agonists by NTPDases 1, 2, 3 and 8. Purinergic Signal. 2008; 1(2):193-204. PMC: 2096530. DOI: 10.1007/s11302-005-6217-x. View

Hanson S, Schnell S . Reactant stationary approximation in enzyme kinetics. J Phys Chem A. 2008; 112(37):8654-8. DOI: 10.1021/jp8026226. View

Johnson K . A century of enzyme kinetic analysis, 1913 to 2013. FEBS Lett. 2013; 587(17):2753-66. PMC: 4624389. DOI: 10.1016/j.febslet.2013.07.012. View

Simoni G, Reali F, Priami C, Marchetti L . Stochastic simulation algorithms for computational systems biology: Exact, approximate, and hybrid methods. Wiley Interdiscip Rev Syst Biol Med. 2019; 11(6):e1459. DOI: 10.1002/wsbm.1459. View

Chou T, Talalay P . A simple generalized equation for the analysis of multiple inhibitions of Michaelis-Menten kinetic systems. J Biol Chem. 1977; 252(18):6438-42. View

10.

Ottesen J, Mehlsen J, Olufsen M . Structural correlation method for model reduction and practical estimation of patient specific parameters illustrated on heart rate regulation. Math Biosci. 2014; 257:50-9. PMC: 4252605. DOI: 10.1016/j.mbs.2014.07.003. View

11.

Hofstee B . Non-inverted versus inverted plots in enzyme kinetics. Nature. 1959; 184:1296-8. DOI: 10.1038/1841296b0. View

12.

Goudar C, Sonnad J, Duggleby R . Parameter estimation using a direct solution of the integrated Michaelis-Menten equation. Biochim Biophys Acta. 1999; 1429(2):377-83. DOI: 10.1016/s0167-4838(98)00247-7. View

13.

Sutton N, Hayasaki T, Hyman M, Anyanwu A, Liao H, Petrovic-Djergovic D . Ectonucleotidase CD39-driven control of postinfarction myocardial repair and rupture. JCI Insight. 2017; 2(1):e89504. PMC: 5213916. DOI: 10.1172/jci.insight.89504. View

14.

Sutton N, Bouis D, Mann K, Rashid I, McCubbrey A, Hyman M . CD73 Promotes Age-Dependent Accretion of Atherosclerosis. Arterioscler Thromb Vasc Biol. 2019; 40(1):61-71. PMC: 7956240. DOI: 10.1161/ATVBAHA.119.313002. View

15.

Marquis A, Arnold A, Dean-Bernhoft C, Carlson B, Olufsen M . Practical identifiability and uncertainty quantification of a pulsatile cardiovascular model. Math Biosci. 2018; 304:9-24. DOI: 10.1016/j.mbs.2018.07.001. View

16.

Dowd J, RIGGS D . A COMPARISON OF ESTIMATES OF MICHAELIS-MENTEN KINETIC CONSTANTS FROM VARIOUS LINEAR TRANSFORMATIONS. J Biol Chem. 1965; 240:863-9. View

17.

Sevigny J, Sundberg C, Braun N, Guckelberger O, Csizmadia E, Qawi I . Differential catalytic properties and vascular topography of murine nucleoside triphosphate diphosphohydrolase 1 (NTPDase1) and NTPDase2 have implications for thromboregulation. Blood. 2002; 99(8):2801-9. DOI: 10.1182/blood.v99.8.2801. View

18.

Mahdi A, Meshkat N, Sullivant S . Structural identifiability of viscoelastic mechanical systems. PLoS One. 2014; 9(2):e86411. PMC: 3921126. DOI: 10.1371/journal.pone.0086411. View

19.

Sevigny J, Cote Y, BEAUDOIN A . Purification of pancreas type-I ATP diphosphohydrolase and identification by affinity labelling with the 5'-p-fluorosulphonylbenzoyladenosine ATP analogue. Biochem J. 1995; 312 ( Pt 2):351-6. PMC: 1136270. DOI: 10.1042/bj3120351. View

20.

Laliberte J, BEAUDOIN A . Sequential hydrolysis of the gamma- and beta-phosphate groups of ATP by the ATP diphosphohydrolase from pig pancreas. Biochim Biophys Acta. 1983; 742(1):9-15. DOI: 10.1016/0167-4838(83)90352-7. View