Investigation of Paraoxonase-1 Genotype and Enzyme-Kinetic Parameters in the Context of Cognitive Impairment in Parkinson's Disease.

Cognitive impairment is a common non-motor symptom of Parkinson's disease (PD), which often progresses to PD dementia. PD patients with and without dementia may differ in certain biochemical parameters, which could thus be used as biomarkers for PD dementia. The enzyme paraoxonase 1 (PON1) has previously been investigated as a potential biomarker in the context of other types of dementia. In a cohort of PD patients, we compared a group of 89 patients with cognitive impairment with a group of 118 patients with normal cognition. We determined the kinetic parameters Km and Vmax for PON1 for the reaction with dihydrocoumarin and the genotype of four single nucleotide polymorphisms in PON1. We found that no genotype or kinetic parameter correlated significantly with cognitive impairment in PD patients. However, we observed associations between PON1 rs662 and PON1 Km (p < 10-10), between PON1 rs662 and PON1 Vmax (p = 9.33 × 10-7), and between PON1 rs705379 and PON1 Vmax (p = 2.21 × 10-10). The present study is novel in three main aspects. (1) It is the first study to investigate associations between the PON1 genotype and enzyme kinetics in a large number of subjects. (2) It is the first study to report kinetic parameters of PON1 in a large number of subjects and to use time-concentration progress curves instead of initial velocities to determine Km and Vmax in a clinical context. (3) It is also the first study to calculate enzyme-kinetic parameters in a clinical context with a new algorithm for data point removal from progress curves, dubbed iFIT. Although our results suggest that in the context of PD, there is no clinically useful correlation between cognitive status on the one hand and PON1 genetic and enzyme-kinetic parameters on the other hand, this should not discourage future investigation into PON1's potential associations with other types of dementia.

iFIT: An automated web tool for determining enzyme-kinetic parameters based on the high-curvature region of progress curves.

The area where progress curve exhibits maximum curvature contains the most information about kinetic parameters. To determine these parameters more accurately from progress curves, we propose an iterative approach that calculates the area of maximum curvature based on an estimate of kinetic parameters and then recalculates the parameters based on time-concentration data points within this area. Based on this algorithm, we developed a computer script called iFIT as a free web application at http://www.i-fit.si. The benefits of working with iFIT are that it decreases the importance of initial substrate concentration and the impact of certain side reactions on the final calculated kinetic parameters.

The Removal of Time-Concentration Data Points from Progress Curves Improves the Determination of Km: The Example of Paraoxonase 1.

Several approaches for determining an enzyme's kinetic parameter Km (Michaelis constant) from progress curves have been developed in recent decades. In the present article, we compare different approaches on a set of experimental measurements of lactonase activity of paraoxonase 1 (PON1): (1) a differential-equation-based Michaelis-Menten (MM) reaction model in the program Dynafit; (2) an integrated MM rate equation, based on an approximation of the Lambert W function, in the program GraphPad Prism; (3) various techniques based on initial rates; and (4) the novel program "iFIT", based on a method that removes data points outside the area of maximum curvature from the progress curve, before analysis with the integrated MM rate equation. We concluded that the integrated MM rate equation alone does not determine kinetic parameters precisely enough; however, when coupled with a method that removes data points (e.g., iFIT), it is highly precise. The results of iFIT are comparable to the results of Dynafit and outperform those of the approach with initial rates or with fitting the entire progress curve in GraphPad Prism; however, iFIT is simpler to use and does not require inputting a reaction mechanism. Removing unnecessary points from progress curves and focusing on the area around the maximum curvature is highly advised for all researchers determining Km values from progress curves.

A Multi-Omics Analysis of PON1 Lactonase Activity in Relation to Human Health and Disease.

Paraoxonase 1 (PON1) enzyme has antioxidative properties and is present in mammalian blood and several other body fluids. In blood, PON1 is usually integrated into the high-density lipoprotein (HDL) cholesterol. PON1 is a highly versatile enzyme displaying diverse functions such as arylesterase, lactonase, and paraoxonase, among others. PON1 activities are usually investigated with artificial substrates, for example, dihydrocoumarin and thiobutyl butyrolactone for lactonase activity. The PON1 enzyme activities measured with different substrates tend to be falsely assumed as being equivalent in the literature, although there are poor or weak correlations among the PON1 enzyme activities with different substrates. In addition, and despite our knowledge of the factors influencing PON1 paraoxonase and arylesterase activities, there is little knowledge of PON1 lactonase activity variations and attendant mechanisms. This is important considering further that the lactonase activity is the native activity of PON1. We report here a multi-omics analysis of PON1 lactonase activity. The influence of genetic variations, particularly of single nucleotide polymorphisms and epigenetic, proteomic, and lipidomic variations on PON1 lactonase activity are reviewed. In addition, the influence of various environmental, clinical, and demographic variables on PON1 lactonase activity is discussed. Finally, we examine the associations between PON1 lactonase activity and health states and common complex diseases such as atherosclerosis, dementias, obesity, and diabetes. To the best of our knowledge, this is the first multi-omics analysis of PON1 lactonase activity with an eye to future applications in basic life sciences and translational medicine and the nuances of critically interpreting PON1 function with lactones as substrates.