Determinants of intra-specific variation in basal metabolic rate

Abstract

Basal metabolic rate (BMR) provides a widely accepted benchmark of metabolic expenditure for endotherms under laboratory and natural conditions. While most studies examining BMR have concentrated on inter-specific variation, relatively less attention has been paid to the determinants of within-species variation. Even fewer studies have analysed the determinants of within-species BMR variation corrected for the strong influence of body mass by appropriate means (e.g. ANCOVA). Here, we review recent advancements in studies on the quantitative genetics of BMR and organ mass variation, along with their molecular genetics. Next, we decompose BMR variation at the organ, tissue and molecular level. We conclude that within-species variation in BMR and its components have a clear genetic signature, and are functionally linked to key metabolic process at all levels of biological organization. We highlight the need to integrate molecular genetics with conventional metabolic field studies to reveal the adaptive significance of metabolic variation. Since comparing gene expressions inter-specifically is problematic, within-species studies are more likely to inform us about the genetic underpinnings of BMR. We also urge for better integration of animal and medical research on BMR; the latter is quickly advancing thanks to the application of imaging technologies and 'omics' studies. We also suggest that much insight on the biochemical and molecular underpinnings of BMR variation can be gained from integrating studies on the mammalian target of rapamycin (mTOR), which appears to be the major regulatory pathway influencing the key molecular components of BMR.

Fig. 1

Schematic representation of regulation of BMR variation by the mTOR pathway. The mTOR–raptor complex responds to nutrient availability by up- or down-regulating mitochondrial oxidation. It also controls cell growth, which in turn generates metabolic costs of biosynthesis directly, and indirectly affects the metabolic costs of maintenance of the membrane ionic gradients being the function of the cell size

Fig. 2

Schematic representation of phenotypic variation in BMR. Quantitative genetics studies indicate that ca. 40 % of phenotypic variance can be attributed to additive genetic effects (Table 1 in White and Kearney 2012). Thus, it is likely that in most populations the frequency of alleles underlying BMR is somewhere between two extremes: (1) the loss of genetic variation due to genetic drift or purifying selection, (2) the fixation of alleles due to long-term directional selection. Assuming a 15 % measurement error of BMR (Konarzewski et al. 2005), ca. 45 % of the total BMR variation can be due to environmental effects and non-additive gene expression. This points to the need to examine BMR variation using functional genomics tools