Gene and genome parameters of mammalian liver circadian genes (LCGs)

Genome-wide studies of time of day in the brain: Design and analysis

Transcriptome profiling at different times of day is powerful for studying circadian regulation in model organisms and humans. To date, 24 h profiles from many tissue types suggest that about half of all genes are circadian-expressed somewhere in the body. However, few of these studies focused on the brain. Thus, despite known links between circadian disruption and neurological disease, we have virtually no mechanistic understanding. In the coming decade, we expect more genome-wide studies of time of day in different brain diseases, regions, and cell types. We expect just as many different approaches to the design and analysis of these studies. This review considers key principles of circadian tran scriptomics, with the goal of maximizing utility and reproducibility of future studies in the nervous system.

Genome-wide circadian rhythm detection methods: systematic evaluations and practical guidelines

Circadian rhythms are oscillations of behavior, physiology and metabolism in many organisms. Recent advancements in omics technology make it possible for genome-wide profiling of circadian rhythms. Here, we conducted a comprehensive analysis of seven existing algorithms commonly used for circadian rhythm detection. Using gold-standard circadian and non-circadian genes, we systematically evaluated the accuracy and reproducibility of the algorithms on empirical datasets generated from various omics platforms under different experimental designs. We also carried out extensive simulation studies to test each algorithm's robustness to key variables, including sampling patterns, replicates, waveforms, signal-to-noise ratios, uneven samplings and missing values. Furthermore, we examined the distributions of the nominal $P$-values under the null and raised issues with multiple testing corrections using traditional approaches. With our assessment, we provide method selection guidelines for circadian rhythm detection, which are applicable to different types of high-throughput omics data.

A single factor dominates the behavior of rhythmic genes in mouse organs

Background

Circadian rhythm, regulated by both internal and external environment of the body, is a multi-scale biological oscillator of great complexity. On the molecular level, thousands of genes exhibit rhythmic transcription, which is both organ- and species-specific, but it remains a mystery whether some common factors could potentially explain their rhythmicity in different organs. In this study we address this question by analyzing the transcriptome data in 12 mouse organs to determine such major impacting factors.

Results

We found a strong positive correlation between the transcriptional level and rhythmic amplitude of circadian rhythmic genes in mouse organs. Further, transcriptional level could explain over 70% of the variation in amplitude. In addition, the functionality and tissue specificity were not strong predictors of amplitude, and the expression level of rhythmic genes was linked to the energy consumption associated with transcription.

Conclusion

Expression level is a single major factor impacts the behavior of rhythmic genes in mouse organs. This single determinant implicates the importance of rhythmic expression itself on the design of the transcriptional system. So, rhythmic regulation of highly expressed genes can effectively reduce the energetic cost of transcription, facilitating the long-term adaptive evolution of the entire genetic system.

Time-Dependent Molecular Responses Differ between Gastric Bypass and Dieting but Are Conserved Across Species

The effectiveness of Roux-en-Y gastric bypass (RYGB) against obesity and its comorbidities has generated excitement about developing new, less invasive treatments that use the same molecular mechanisms. Although controversial, RYGB-induced improvement of metabolic function may not depend entirely upon weight loss. To elucidate the differences between RYGB and dieting, we studied several individual organ molecular responses and generated an integrative, interorgan view of organismal physiology. We also compared murine and human molecular signatures. We show that, although dieting and RYGB can bring about the same degree of weight loss, post-RYGB physiology is very different. RYGB induces distinct, organ-specific adaptations in a temporal pattern that is characterized by energetically demanding processes, which may be coordinated by HIF1a activation and the systemic repression of growth hormone receptor signaling. Many of these responses are conserved in rodents and humans and may contribute to the remarkable ability of surgery to induce and sustain metabolic improvement.

Cycling Transcriptional Networks Optimize Energy Utilization on a Genome Scale

Genes expressing circadian RNA rhythms are enriched for metabolic pathways, but the adaptive significance of cyclic gene expression remains unclear. We estimated the genome-wide synthetic and degradative cost of transcription and translation in three organisms and found that the cost of cycling genes is strikingly higher compared to non-cycling genes. Cycling genes are expressed at high levels and constitute the most costly proteins to synthesize in the genome. We demonstrate that metabolic cycling is accelerated in yeast grown under higher nutrient flux and the number of cycling genes increases ∼40%, which are achieved by increasing the amplitude and not the mean level of gene expression. These results suggest that rhythmic gene expression optimizes the metabolic cost of global gene expression and that highly expressed genes have been selected to be downregulated in a cyclic manner for energy conservation.

Evaluation of five methods for genome-wide circadian gene identification

Identification of circadian-regulated genes based on temporal transcriptome data is important for studying the regulation mechanism of the circadian system. However, various computational methods adopting different strategies for the identification of cycling transcripts usually yield inconsistent results even for the same dataset, making it challenging to choose the optimal method for a specific circadian study. To address this challenge, we evaluate 5 popular methods, including ARSER (ARS), COSOPT (COS), Fisher's G test (FIS), HAYSTACK (HAY), and JTK_CYCLE (JTK), based on both simulated and empirical datasets. Our results show that increasing the number of total samples (through improving sampling frequency or lengthening the sampling time window) is beneficial for computational methods to accurately identify circadian transcripts and measure circadian phase. For a given number of total samples, higher sampling frequency is more important for HAY and JTK, and the longer sampling time window is more crucial for ARS and COS, as testified on simulated and empirical datasets from which circadian signals are computationally identified. In addition, the preference of higher sampling frequency or the longer sampling time window is also obvious for JTK, ARS, and COS in estimating circadian phases of simulated periodic profiles. Our results also indicate that attention should be paid to the significance threshold that is used for each method in selecting circadian genes, especially when analyzing the same empirical dataset with 2 or more methods. To summarize, for any study involving genome-wide identification of circadian genes from transcriptome data, our evaluation results provide suggestions for the selection of an optimal method based on specific goal and experimental design.

A circadian gene expression atlas in mammals: implications for biology and medicine

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.