Studies on the triglyceride - fatty acid cycle

The triglyceride-free fatty acid (TG-FFA) cycle was studied in white adipose tissue. The major aims of the study were 1) to see if the rate of TG-FFA cycling (i.e. FFA reesterification) and the sensitivity properties (see Newsholme and Crabtree, 1976, Biochem. Soc. Symp. 41, 61-109) were affected by various treatments, and 2) to measure the rate of cycling in vivo and assess its contribution to the metabolic rate of an animal. There are two ways of estimating the rate of TG-FFA cycling; the first is based on the release of glycerol and FFA from the tissue, and the second on the synthesis of the glycerol and FFA moieties of triglyceride. Experimental agreement between the two methods is very good. It is shown that the rate of TG glycerol synthesis can be estimated by measuring the incorporation of tritium from tritiated water into the TG-glycerol moiety; this method is used to study the TG-FFA cycle in vivo. Experimental results indicated that the rate of TG-FFA cycling in white adipose tissue in vitro and in vivo is affected by various short- and long-term treatments. However, the reesterification of FFA in adipose tissue can only account for perhaps ~1% of the basal metabolic rate of a mouse, and perhaps 4% of the increase in osygen consumption observed in fenoterol-treated mice. The equations of Newsholme and Crabtree (1976) describing the sensitivity properties of substrate cycles are extended and used to show that the TG-FFA cycle increases the sensitivity of control of FFA release from adipose tissue. The degree of sensitivity attainable is variable depending on the treatment used. The use of tritiated water for estimating TG-FFA cycling is tentatively extended to brown adipose tissue. It is suggested that the rate of cycling could be used as an indicator of sympathetic activity in brown and white adipose tissue.</p

A guide to analysis of mouse energy metabolism.

We present a consolidated view of the complexity and challenges of designing studies for measurement of energy metabolism in mouse models, including a practical guide to the assessment of energy expenditure, energy intake and body composition and statistical analysis thereof. We hope this guide will facilitate comparisons across studies and minimize spurious interpretations of data. We recommend that division of energy expenditure data by either body weight or lean body weight and that presentation of group effects as histograms should be replaced by plotting individual data and analyzing both group and body-composition effects using analysis of covariance (ANCOVA)