To characterize the role of the circadian clock in mouse physiology and behavior, we used RNA-seq and DNA arrays to quantify the transcriptomes of 12 mouse organs over time. We found 43% of all protein coding genes showed circadian rhythms in transcription somewhere in the body, largely in an organ-specific manner. In most organs, we noticed the expression of many oscillating genes peaked during transcriptional "rush hours" preceding dawn and dusk. Looking at the genomic landscape of rhythmic genes, we saw that they clustered together, were longer, and had more spliceforms than nonoscillating genes. Systems-level analysis revealed intricate rhythmic orchestration of gene pathways throughout the body. We also found oscillations in the expression of more than 1,000 known and novel noncoding RNAs (ncRNAs). Supporting their potential role in mediating clock function, ncRNAs conserved between mouse and human showed rhythmic expression in similar proportions as protein coding genes. Importantly, we also found that the majority of best-selling drugs and World Health Organization essential medicines directly target the products of rhythmic genes. Many of these drugs have short half-lives and may benefit from timed dosage. In sum, this study highlights critical, systemic, and surprising roles of the mammalian circadian clock and provides a blueprint for advancement in chronotherapy.circadian | genomics | gene networks | noncoding RNA | chronotherapy C ircadian rhythms are endogenous 24-h oscillations in behavior and biological processes found in all kingdoms of life. This internal clock allows an organism to adapt its physiology in anticipation of transitions between night and day. The circadian clock drives oscillations in a diverse set of biological processes, including sleep, locomotor activity, blood pressure, body temperature, and blood hormone levels (1, 2). Disruption of normal circadian rhythms leads to clinically relevant disorders including neurodegeneration and metabolic disorders (3, 4). In mammals, the molecular basis for these physiological rhythms arises from the interactions between two transcriptional/translational feedback loops (reviewed in ref. 5). Many members of the core clock regulate the expression of other transcripts. These clock-controlled genes mediate the molecular clock's effect on downstream rhythms in physiology.In an effort to map these connections between the core clock and the diverse biological processes it regulates, researchers have devoted significant time and effort to studying transcriptional rhythms (6-10). Although extremely informative, most circadian studies of this nature have analyzed one or two organs by using microarrays, and little work has been done to analyze either clock control at the organism level or regulation of the noncoding transcriptome. To address these gaps in our knowledge, we used RNA-sequencing (RNA-seq) and DNA arrays to profile the transcriptomes of 12 different mouse organs: adrenal gland, aorta, brainstem, brown fat, cerebellum, heart, hypothalamus, ki...

Circadian rhythms are oscillations of physiology, behavior, and metabolism that have period lengths of 24 hours. In several model organisms and man, circadian clock genes have been characterized and found to be transcription factors. Because of this, researchers have used microarrays to characterize global regulation of gene expression and algorithmic approaches to detect cycling. Here we present a new algorithm, JTK_CYCLE, designed to efficiently identify and characterize cycling variables in large datasets. Compared to COSOPT and the Fisher’s G test, two commonly used methods for detecting cycling transcripts, JTK_CYCLE distinguishes between rhythmic and non-rhythmic transcripts more reliably and efficiently. We also show that JTK_CYCLE’s increased resistance to outliers results in considerably greater sensitivity and specificity. Moreover, JTK_CYCLE accurately measures the period, phase, and amplitude of cycling transcripts, facilitating downstream analyses. Finally, it is several orders of magnitude faster than COSOPT, making it ideal for large scale data sets. We used JTK_CYCLE to analyze legacy data sets including NIH3T3 cells, which have comparatively low amplitude. JTK_CYCLE’s improved power led to the identification of a novel cluster of RNA-interacting genes whose abundance is under clear circadian regulation. These data suggest that JTK_CYCLE is an ideal tool for identifying and characterizing oscillations in genome-scale datasets.

The circadian clock is a molecular and cellular oscillator found in most mammalian tissues that regulates rhythmic physiology and behavior. Numerous investigations have addressed the contribution of circadian rhythmicity to cellular, organ, and organismal physiology. We recently developed a method to look at transcriptional oscillations with unprecedented precision and accuracy using high-density time sampling. Here, we report a comparison of oscillating transcription from mouse liver, NIH3T3, and U2OS cells. Several surprising observations resulted from this study, including a 100-fold difference in the number of cycling transcripts in autonomous cellular models of the oscillator versus tissues harvested from intact mice. Strikingly, we found two clusters of genes that cycle at the second and third harmonic of circadian rhythmicity in liver, but not cultured cells. Validation experiments show that 12-hour oscillatory transcripts occur in several other peripheral tissues as well including heart, kidney, and lungs. These harmonics are lost ex vivo, as well as under restricted feeding conditions. Taken in sum, these studies illustrate the importance of time sampling with respect to multiple testing, suggest caution in use of autonomous cellular models to study clock output, and demonstrate the existence of harmonics of circadian gene expression in the mouse.

In a longitudinal clinical study to compare two groups, the primary end point is often the time to a specific event (eg, disease progression, death). The hazard ratio estimate is routinely used to empirically quantify the between-group difference under the assumption that the ratio of the two hazard functions is approximately constant over time. When this assumption is plausible, such a ratio estimate may capture the relative difference between two survival curves. However, the clinical meaning of such a ratio estimate is difficult, if not impossible, to interpret when the underlying proportional hazards assumption is violated (ie, the hazard ratio is not constant over time). Although this issue has been studied extensively and various alternatives to the hazard ratio estimator have been discussed in the statistical literature, such crucial information does not seem to have reached the broader community of health science researchers. In this article, we summarize several critical concerns regarding this conventional practice and discuss various well-known alternatives for quantifying the underlying differences between groups with respect to a time-to-event end point. The data from three recent cancer clinical trials, which reflect a variety of scenarios, are used throughout to illustrate our discussions. When there is not sufficient information about the profile of the between-group difference at the design stage of the study, we encourage practitioners to consider a prespecified, clinically meaningful, model-free measure for quantifying the difference and to use robust estimation procedures to draw primary inferences.