Background: Circadian rhythms have a profound effect on human health. Their disruption can lead to serious pathologies, such as cancer and obesity. Gene expression studies in these pathologies are often studied in different mouse strains by quantitative real time polymerase chain reaction (qPCR). Selection of reference genes is a crucial step of qPCR experiments. Recent studies show that reference gene stability can vary between species and tissues, but none has taken circadian experiments into consideration. Results: In the present study the expression of ten candidate reference genes (Actb, Eif2a, Gapdh, Hmbs, Hprt1, Ppib, Rn18s, Rplp0, Tbcc and Utp6c) was measured in 131 liver and 97 adrenal gland samples taken from three mouse strains (C57BL/6JOlaHsd, 129Pas plus C57BL/6J and Crem KO on 129Pas plus C57BL/6J background) every 4 h in a 24 h period. Expression stability was evaluated by geNorm and NormFinder programs. Differences in ranking of the most stable reference genes were observed both between individual mouse strains as well as between tissues within each mouse strain. We show that selection of reference gene (Actb) that is often used for analyses in individual mouse strains leads to errors if used for normalization when different mouse strains are compared. We identified alternative reference genes that are stable in these comparisons. Conclusions: Genetic background and circadian time influence the expression stability of reference genes. Differences between mouse strains and tissues should be taken into consideration to avoid false interpretations. We show that the use of a single reference gene can lead to false biological conclusions. This manuscript provides a useful reference point for researchers that search for stable reference genes in the field of circadian biology.

Serum paraoxonase-1 (PON1) is the most studied member of the group of paraoxonases (PONs). This enzyme possesses three enzymatic activities: lactonase, arylesterase, and paraoxonase activity. PON1 and its isoforms play an important role in drug metabolism as well as in the prevention of cardiovascular and neurodegenerative diseases. Although all three members of the PON family have the same origin and very similar amino acid sequences, they have different functions and are found in different locations. PONs exhibit substrate promiscuity, and their true physiological substrates are still not known. However, possible substrates include homocysteine thiolactone, an analogue of natural quorum-sensing molecules, and the recently discovered derivatives of arachidonic acid—bioactive δ-lactones. Directed evolution, site-directed mutagenesis, and kinetic studies provide comprehensive insights into the active site and catalytic mechanism of PON1. However, there is still a whole world of mystery waiting to be discovered, which would elucidate the substrate promiscuity of a group of enzymes that are so similar in their evolution and sequence yet so distinct in their function.

The Michaelis-Menten rate equation can be found in most general biochemistry textbooks, where the time derivative of the substrate is a hyperbolic function of two kinetic parameters (the limiting rate V, and the Michaelis constant K_M) and the amount of substrate. However, fundamental concepts of enzyme kinetics can be difficult to understand fully, or can even be misunderstood, by students when based only on the differential form of the Michaelis-Menten equation, and the variety of methods available to calculate the kinetic constants from rate versus substrate concentration “textbook data.” Consequently, enzyme kinetics can be confusing if an analytical solution of the Michaelis-Menten equation is not available. Therefore, the still rarely known exact solution to the Michaelis-Menten equation is presented here through the explicit closed-form equation in terms of the Lambert W(x) function. Unfortunately, as the W(x) is not available in standard curve-fitting computer programs, the practical use of this direct solution is limited for most life-science students. Thus, the purpose of this article is to provide analytical approximations to the equation for modeling Michaelis-Menten kinetics. The elementary and explicit nature of these approximations can provide students with direct and simple estimations of kinetic parameters from raw experimental time-course data. The Michaelis-Menten kinetics studied in the latter context can provide an ideal alternative to the 100-year-old problems of data transformation, graphical visualization, and data analysis of enzyme-catalyzed reactions. Hence, the content of the course presented here could gradually become an important component of the modern biochemistry curriculum in the 21st century. Biochemistry and Molecular Biology Education Vol. 39, No. 2, pp. 117–125, 2011

A recent article of Johnson and Goody (Biochemistry, 2011;50:8264–8269) described the almost-100-years-old paper of Michaelis and Menten. Johnson and Goody translated this classic article and presented the historical perspective to one of incipient enzyme-reaction data analysis, including a pioneering global fit of the integrated rate equation in its implicit form to the experimental time-course data. They reanalyzed these data, although only numerical techniques were used to solve the model equations. However, there is also the still little known algebraic rate-integration equation in a closed form that enables direct fitting of the data. Therefore, in this commentary, I briefly present the integral solution of the Michaelis-Menten rate equation, which has been largely overlooked for three decades. This solution is expressed in terms of the Lambert W function, and I demonstrate here its use for global nonlinear regression curve fitting, as carried out with the original time-course dataset of Michaelis and Menten.

Tyrosinase (EC 1.14.18.1) catalyzes the oxidation of L‐3,4‐dihydroxyphenylalanine (L‐DOPA) to 2,3,5,6‐tetrahydro‐5,6‐dioxo‐1H‐indole‐2‐carboxylate (dopachrome), according to the classical Michaelis‐Menten kinetic mechanism. The enzyme is strongly but slowly inhibited by α‐amino‐β‐[N‐(3‐hydroxy‐4‐pyridone)] propionic acid (L‐mimosine), a toxic plant amino acid. Easily available reagents and simple spectrophotometric detection of the product make the experimental characterization and kinetic analysis of tyrosinase action convenient and interesting for teaching purposes. In the present article, we present a theoretical and practical guide to the kinetic analysis of slow‐binding inhibition. The effect of L‐mimosine on tyrosinase is established by progress curve measurements, carried out on a conventional spectrophotometer equipped with a rapid kinetic accessory. In the analysis, we recommend a classical linearization approach but we also took advantage of a more reliable nonlinear regression method to avoid subjective bias. A multistep procedure starts by careful inspection of the curves to discriminate between candidate mechanisms. Next, the evaluation of initial and steady‐state velocities provides information on the enzyme catalytic and Michaelis‐Menten constants, as well as the corresponding inhibition constants. Subsequently, an appropriate mathematical derivation enables estimation of the isomerization rate constants characteristic for a slow‐binding inhibitor. To conclude, we suggest simultaneous multivariable regression, using all the progress curve data, to cross‐check the proposed reaction mechanism and evaluated kinetic constants.