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PGC-1alpha plays essential and diverse functions in the control of metabolism ranging from mitochondrial biogenesis and respiration to hepatic gluconeogenesis and muscle fiber-type switching. In a paper in this issue of Cell, the characterization of PGC-1alpha(-/-) mice illustrates these pleiotropic functions and reveals an unexpected role for PGC-1alpha in the brain.

The pyruvate dehydrogenase complex (PDC) catalyzes the conversion of pyruvate into acetyl-CoA, a critical step in metabolism. Sutendra et al. now demonstrate that PDC can translocate from the mitochondria to the nucleus to provide acetyl-CoA necessary for histone acetylation, suggesting a new pathway for mitochondrial-nuclear communication.

PGC-1alpha plays essential and diverse functions in the control of metabolism ranging from mitochondrial biogenesis and respiration to hepatic gluconeogenesis and muscle fiber-type switching. In a paper in this issue of Cell, the characterization of PGC-1alpha(-/-) mice illustrates these pleiotropic functions and reveals an unexpected role for PGC-1alpha in the brain.

SummaryThe manner by which genotype and environment affect complex phenotypes is one of the fundamental questions in biology. In this study, we quantified the transcriptome—a subset of the metabolome—and, using targeted proteomics, quantified a subset of the liver proteome from 40 strains of the BXD mouse genetic reference population on two diverse diets. We discovered dozens of transcript, protein, and metabolite QTLs, several of which linked to metabolic phenotypes. Most prominently, Dhtkd1 was identified as a primary regulator of 2-aminoadipate, explaining variance in fasted glucose and diabetes status in both mice and humans. These integrated molecular profiles also allowed further characterization of complex pathways, particularly the mitochondrial unfolded protein response (UPRmt). UPRmt shows strikingly variant responses at the transcript and protein level that are remarkably conserved among C. elegans, mice, and humans. Overall, these examples demonstrate the value of an integrated multilayered omics approach to characterize complex metabolic phenotypes.