Circadian oscillation of body temperature is a basic, evolutionary-conserved feature of mammalian biology1. Additionally, homeostatic pathways allow organisms to protect their core temperatures in response to cold exposure2. However, the mechanism responsible for coordinating daily body temperature rhythm and adaptability to environmental challenges is unknown. Here we show that the nuclear receptor Rev-erbα, a powerful transcriptional repressor, links circadian and thermogenic networks through the regulation of brown adipose tissue (BAT) function. Mice exposed to cold fare dramatically better at 5 AM (Zeitgeber time 22) when Rev-erbα is barely expressed than at 5 PM (ZT10) when Rev-erbα is abundant. Deletion of Rev-erbα markedly improves cold tolerance at 5 PM, indicating that overcoming Rev-erbα-dependent repression is a fundamental feature of the thermogenic response to cold. Physiological induction of uncoupling protein 1 (UCP1) by cold temperatures is preceded by rapid down-regulation of Rev-erbα in BAT. Rev-erbα represses UCP1 in a brown adipose cell-autonomous manner and BAT UCP1 levels are high in Rev-erbα-null mice even at thermoneutrality. Genetic loss of Rev-erbα also abolishes normal rhythms of body temperature and BAT activity. Thus, Rev-erbα acts as a thermogenic focal point required for establishing and maintaining body temperature rhythm in a manner that is adaptable to environmental demands.

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.