Mechanisms composing Drosophila's clock are conserved within the animal kingdom. To learn how such clocks influence behavioral and physiological rhythms, we determined the complement of circadian transcripts in adult Drosophila heads. High-density oligonucleotide arrays were used to collect data in the form of three 12-point time course experiments spanning a total of 6 days. Analyses of 24 hr Fourier components of the expression patterns revealed significant oscillations for approximately 400 transcripts. Based on secondary filters and experimental verifications, a subset of 158 genes showed particularly robust cycling and many oscillatory phases. Circadian expression was associated with genes involved in diverse biological processes, including learning and memory/synapse function, vision, olfaction, locomotion, detoxification, and areas of metabolism. Data collected from three different clock mutants (per(0), tim(01), and Clk(Jrk)), are consistent with both known and novel regulatory mechanisms controlling circadian transcription.

This review examines the connections between circadian and metabolic rhythms. Examples from a wide variety of well-studied organisms are used to illustrate some of the genetic and molecular pathways linking circadian timekeeping to metabolism. The principles underlying biological timekeeping by intrinsic circadian clocks are discussed briefly. Genetic and molecular studies have unambiguously identified the importance of gene expression feedback circuits to the generation of overt circadian rhythms. This is illustrated particularly well by the results of genome-wide expression studies, which have uncovered hundreds of clock-controlled genes in cyanobacteria, fungi, plants, and animals. The potential connections between circadian oscillations in gene expression and circadian oscillations in metabolic activity are a major focus of this review.

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them.

Many transcription factors, particularly those involved in the control of cell growth, are unstable proteins destroyed by ubiquitin-mediated proteolysis. In a previous study of sequences targeting the transcription factor Myc for destruction, we observed that the region in Myc signaling ubiquitin-mediated proteolysis overlaps closely with the region in Myc that activates transcription. Here, we present evidence that the overlap of these two activities is not unique to Myc, but reflects a more general phenomenon. We show that a similar overlap of activation domains and destruction elements occurs in other unstable transcription factors and report a close correlation between the ability of an acidic activation domain to activate transcription and to signal proteolysis. We also show that destruction elements from yeast cyclins, when tethered to a DNA-binding domain, activate transcription. The intimate overlap of activation domains and destruction elements reveals an unexpected convergence of two very different processes and suggests that transcription factors may be destroyed because of their ability to activate transcription.