Methods detecting rhythmic gene expression are biologically relevant only for strong signal

Single-Cell Multiomic Analysis of Circadian Rhythmicity in Mouse Liver

From bacteria to humans, most organisms showcase inherent 24-hour circadian rhythms, best exemplified by the sleep-wake cycle. These rhythms are remarkably widespread, governing hormonal, metabolic, physiological, and behavioral oscillations, and are driven by "molecular clocks" that orchestrate the rhythmic expression of thousands of genes throughout the body. Here, we generate single-cell RNA and ATAC multiomic data to simultaneously characterize gene expression and chromatin accessibility of ~33,000 mouse liver cells across the 24-hour day. Our study yields several key insights, including: (i) detecting circadian rhythmicity in both discretized liver cell types and transient sub-lobule cell states, capturing space-time RNA and ATAC profiles in a cell-type- and cell-state-specific manner; (ii) delving beyond mean cyclic patterns to characterize distributions, accounting for gene expression stochasticity due to transcriptional bursting; (iii) interrogating multimodal circadian rhythmicity, encompassing RNAs, DNA regulatory elements, and transcription factors (TFs), while examining priming and lagging effects across modalities; and (iv) inferring spatiotemporal gene regulatory networks involving target genes, TFs, and cis-regulatory elements that controls circadian rhythmicity and liver physiology. Our findings apply to existing single-cell data of mouse and Drosophila brains and are further validated by time-series single molecule fluorescence in situ hybridization, as well as vast amounts of existing and orthogonal high-throughput data from chromatin immunoprecipitation followed by sequencing, capture Hi-C, and TF knockout experiments. Altogether, our study constructs a comprehensive map of the time-series transcriptomic and epigenomic landscapes that elucidate the function and mechanism of the liver peripheral clocks.

Time to start taking time seriously: how to investigate unexpected biological rhythms within infectious disease research

The discovery of rhythmicity in host and pathogen activities dates back to the Hippocratic era, but the causes and consequences of these biological rhythms have remained poorly understood. Rhythms in infection phenotypes or traits are observed across taxonomically diverse hosts and pathogens, suggesting general evolutionary principles. Understanding these principles may enable rhythms to be leveraged in manners that improve drug and vaccine efficacy or disrupt pathogen timekeeping to reduce virulence and transmission. Explaining and exploiting rhythms in infections require an integrative and multidisciplinary approach, which is a hallmark of research within chronobiology. Many researchers are welcomed into chronobiology from other fields after observing an unexpected rhythm or time-of-day effect in their data. Such findings can launch a rich new research topic, but engaging with the concepts, approaches and dogma in a new discipline can be daunting. Fortunately, chronobiology has well-developed frameworks for interrogating rhythms that can be readily applied in novel contexts. Here, we provide a 'how to' guide for exploring unexpected daily rhythms in infectious disease research. We outline how to establish: whether the rhythm is circadian, to what extent the host and pathogen are responsible, the relevance for host-pathogen interactions, and how to explore therapeutic potential.This article is part of the Theo Murphy meeting issue 'Circadian rhythms in infection and immunity'.

A Bayesian Framework for Genome-wide Circadian Rhythmicity Biomarker Detection

Circadian rhythms are endogenous ∼24-hour cycles that significantly influence physiological and behavioral processes. These rhythms are governed by a transcriptional-translational feedback loop of core circadian genes and are essential for maintaining overall health. The study of circadian rhythms has expanded into various omics datasets, necessitating accurate analytical methodology for circadian biomarker detection. Here, we introduce a novel Bayesian framework for the genome-wide detection of circadian rhythms that is capable of incorporating prior biological knowledge and adjusting for multiple testing issue via a false discovery rate approach. Our framework leverages a Bayesian hierarchical model and employs a reverse jump Markov chain Monte Carlo (rjMCMC) technique for model selection. Through extensive simulations, our method, BayesCircRhy, demonstrated superior false discovery rate control over competing methods, robustness against heavier-tailed error distributions, and better performance compared to existing approaches. The method's efficacy was further validated in two RNA-Sequencing data, including a human resitrcted feeding data and a mouse aging data, where it successfully identified known and novel circadian genes. R package "BayesianCircadian" for the method is publicly available on GitHub https://github.com/jxncdhc/BayesianCircadian .

Detecting Rhythmic Gene Expression in Single-cell Transcriptomics

An autonomous, environmentally synchronizable circadian rhythm is a ubiquitous feature of life on Earth. In multicellular organisms, this rhythm is generated by a transcription-translation feedback loop present in nearly every cell that drives daily expression of thousands of genes in a tissue-dependent manner. Identifying the genes that are under circadian control can elucidate the mechanisms by which physiological processes are coordinated in multicellular organisms. Today, transcriptomic profiling at the single-cell level provides an unprecedented opportunity to understand the function of cell-level clocks. However, while many cycling detection algorithms have been developed to identify genes under circadian control in bulk transcriptomic data, it is not known how best to adapt these algorithms to single-cell RNA seq data. Here, we benchmark commonly used circadian detection methods on their reliability and efficiency when applied to single-cell RNA seq data. Our results provide guidance on adapting existing cycling detection methods to the single-cell domain and elucidate opportunities for more robust and efficient rhythm detection in single-cell data. We also propose a subsampling procedure combined with harmonic regression as an efficient strategy to detect circadian genes in the single-cell setting.

Detecting Rhythmic Gene Expression in Single Cell Transcriptomics

An autonomous, environmentally-synchronizable circadian rhythm is a ubiquitous feature of life on Earth. In multicellular organisms, this rhythm is generated by a transcription-translation feedback loop present in nearly every cell that drives daily expression of thousands of genes in a tissue-dependent manner. Identifying the genes that are under circadian control can elucidate the mechanisms by which physiological processes are coordinated in multicellular organisms. Today, transcriptomic profiling at the single-cell level provides an unprecedented opportunity to understand the function of cell-level clocks. However, while many cycling detection algorithms have been developed to identify genes under circadian control in bulk transcriptomic data, it is not known how best to adapt these algorithms to single-cell RNAseq data. Here, we benchmark commonly used circadian detection methods on their reliability and efficiency when applied to single cell RNAseq data. Our results provide guidance on adapting existing cycling detection methods to the single-cell domain, and elucidate opportunities for more robust and efficient rhythm detection in single-cell data. We also propose a subsampling procedure combined with harmonic regression as an efficient strategy to detect circadian genes in the single-cell setting.

Effects of (Poly)phenols on Circadian Clock Gene-Mediated Metabolic Homeostasis in Cultured Mammalian Cells: A Scoping Review

Circadian clocks regulate metabolic homeostasis. Disruption to our circadian clocks, by lifestyle behaviors such as timing of eating and sleeping, has been linked to increased rates of metabolic disorders. There is now considerable evidence that selected dietary (poly)phenols, including flavonoids, phenolic acids and tannins, may modulate metabolic and circadian processes. This review evaluates the effects of (poly)phenols on circadian clock genes and linked metabolic homeostasis in vitro, and potential mechanisms of action, by critically evaluating the literature on mammalian cells. A systematic search was conducted to ensure full coverage of the literature and identified 43 relevant studies addressing the effects of (poly)phenols on cellular circadian processes. Nobiletin and tangeretin, found in citrus, (-)-epigallocatechin-3-gallate from green tea, urolithin A, a gut microbial metabolite from ellagitannins in fruit, curcumin, bavachalcone, cinnamic acid, and resveratrol at low micromolar concentrations all affect circadian molecular processes in multiple types of synchronized cells. Nobiletin emerges as a putative retinoic acid-related orphan receptor (RORα/γ) agonist, leading to induction of the circadian regulator brain and muscle ARNT-like 1 (BMAL1), and increased period circadian regulator 2 (PER2) amplitude and period. These effects are clear despite substantial variations in the protocols employed, and this review suggests a methodological framework to help future study design in this emerging area of research.

Challenges and best practices in omics benchmarking

Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.

Extensive dynamic changes in the human transcriptome and its circadian organization during prolonged bed rest

Physiological and molecular processes including the transcriptome change across the 24-h day, driven by molecular circadian clocks and behavioral and systemic factors. It is not known how the temporal organization of the human transcriptome responds to a long-lasting challenge. This may, however, provide insights into adaptation, disease, and recovery. We investigated the human 24-h time series transcriptome in 20 individuals during a 90-day constant bed rest protocol. We show that the protocol affected 91% of the transcriptome with 76% of the transcriptome still affected after 10 days of recovery. Dimensionality-reduction approaches revealed that many affected transcripts were associated with mRNA translation and immune function. The number, amplitude, and phase of rhythmic transcripts, including clock genes, varied significantly across the challenge. These findings of long-lasting changes in the temporal organization of the transcriptome have implications for understanding the mechanisms underlying health consequences of conditions such as microgravity and bed rest.

Mapping the daily rhythmic transcriptome in the diabetic retina

Retinal function changes dramatically from day to night, yet clinical diagnosis, treatments, and experimental sampling occur during the day. To begin to address this gap in our understanding of disease pathobiology, this study investigates whether diabetes affects the retina's daily rhythm of gene expression. Diabetic, Ins2Akita/J mice, and non-diabetic littermates were kept under a 12 h:12 h light/dark cycle until 4 months of age. mRNA sequencing was conducted in retinas collected every 4 h throughout the 24 hr light/dark cycle. Computational approaches were used to detect rhythmicity, predict acrophase, identify differential rhythmic patterns, analyze phase set enrichment, and predict upstream regulators. The retinal transcriptome exhibited a tightly regulated rhythmic expression with a clear 12-hr transcriptional axis. Day-peaking genes were enriched for DNA repair, RNA splicing, and ribosomal protein synthesis, night-peaking genes for metabolic processes and growth factor signaling. Although the 12-hr transcriptional axis is retained in the diabetic retina, it is phase advanced for some genes. Upstream regulator analysis for the phase-shifted genes identified oxygen-sensing mechanisms and HIF1alpha, but not the circadian clock, which remained in phase with the light/dark cycle. We propose a model in which, early in diabetes, the retina is subjected to an internal desynchrony with the circadian clock and its outputs are still light-entrained whereas metabolic pathways related to neuronal dysfunction and hypoxia are phase advanced. Further studies are now required to evaluate the chronic implications of such desynchronization on the development of diabetic retinopathy.

LimoRhyde2: Genomic analysis of biological rhythms based on effect sizes

Genome-scale data have revealed daily rhythms in various species and tissues. However, current methods to assess rhythmicity largely restrict their focus to quantifying statistical significance, which may not reflect biological relevance. To address this limitation, we developed a method called LimoRhyde2 (the successor to our method LimoRhyde), which focuses instead on rhythm-related effect sizes and their uncertainty. For each genomic feature, LimoRhyde2 fits a curve using a series of linear models based on periodic splines, moderates the fits using an Empirical Bayes approach called multivariate adaptive shrinkage (Mash), then uses the moderated fits to calculate rhythm statistics such as peak-to-trough amplitude. The periodic splines capture non-sinusoidal rhythmicity, while Mash uses patterns in the data to account for different fits having different levels of noise. To demonstrate LimoRhyde2's utility, we applied it to multiple circadian transcriptome datasets. Overall, LimoRhyde2 prioritized genes having high-amplitude rhythms in expression, whereas a prior method (BooteJTK) prioritized "statistically significant" genes whose amplitudes could be relatively small. Thus, quantifying effect sizes using approaches such as LimoRhyde2 has the potential to transform interpretation of genomic data related to biological rhythms.

An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells

MOTIVATION

Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms (for instance in gene expression, eclosion, egg-laying, and feeding) tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets, and are also limited by their use of P-values in detecting oscillations.

RESULTS

We introduce a new method, ODeGP (Oscillation Detection using Gaussian Processes), which combines Gaussian Process regression and Bayesian inference to incorporate measurement errors, non-uniformly sampled data, and a recently developed non-stationary kernel to improve detection of oscillations. By using Bayes factors, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses, thus providing an advantage over P-values. Using synthetic datasets, we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary symmetric oscillations. Next, by analyzing existing qPCR datasets, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak and noisy oscillations. Finally, we generate new qPCR data on mouse embryonic stem cells. Surprisingly, we discover using ODeGP that increasing cell-density results in rapid generation of oscillations in the Bmal1 gene, thus highlighting our method's ability to discover unexpected and new patterns. In its current implementation, ODeGP is meant only for analyzing single or a few time-trajectories, not genome-wide datasets.

AVAILABILITY AND IMPLEMENTATION

ODeGP is available at https://github.com/Shaonlab/ODeGP.

Dominant constraints on the evolution of rhythmic gene expression

Although the individual transcriptional regulators of the core circadian clock are distinct among different organisms, the autoregulatory feedback loops they form are conserved. This unified design principle explains how daily physiological activities oscillate across species. However, it is unknown whether analogous design principles govern the gene expression output of circadian clocks. In this study, we performed a comparative analysis of rhythmic gene expression in eight diverse species and identified four common distribution patterns of cycling gene expression across these species. We hypothesized that the maintenance of reduced energetic costs constrains the evolution of rhythmic gene expression. Our large-scale computational simulations support this hypothesis by showing that selection against high-energy expenditure completely regenerates all cycling gene patterns. Moreover, we find that the peaks of rhythmic expression have been subjected to this type of selective pressure. The results suggest that selective pressure from circadian regulation efficiently removes unnecessary gene products from the transcriptome, thereby significantly impacting its evolutionary path.

CIRCUST: A novel methodology for temporal order reconstruction of molecular rhythms; validation and application towards a daily rhythm gene expression atlas in humans

The circadian system drives near-24-h oscillations in behaviors and biological processes. The underlying core molecular clock regulates the expression of other genes, and it has been shown that the expression of more than 50 percent of genes in mammals displays 24-h rhythmic patterns, with the specific genes that cycle varying from one tissue to another. Determining rhythmic gene expression patterns in human tissues sampled as single timepoints has several challenges, including the reconstruction of temporal order of highly noisy data. Previous methodologies have attempted to address these challenges in one or a small number of tissues for which rhythmic gene evolutionary conservation is assumed to be preserved. Here we introduce CIRCUST, a novel CIRCular-robUST methodology for analyzing molecular rhythms, that relies on circular statistics, is robust against noise, and requires fewer assumptions than existing methodologies. Next, we validated the method against four controlled experiments in which sampling times were known, and finally, CIRCUST was applied to 34 tissues from the Genotype-Tissue Expression (GTEx) dataset with the aim towards building a comprehensive daily rhythm gene expression atlas in humans. The validation and application shown here indicate that CIRCUST provides a flexible framework to formulate and solve the issues related to the analysis of molecular rhythms in human tissues. CIRCUST methodology is publicly available at https://github.com/yolandalago/CIRCUST/.

Experimental design and power calculation in omics circadian rhythmicity detection using the cosinor model

Circadian clocks are 24-h endogenous oscillators in physiological and behavioral processes. Though recent transcriptomic studies have been successful in revealing the circadian rhythmicity in gene expression, the power calculation for omics circadian analysis have not been fully explored. In this paper, we develop a statistical method, namely CircaPower, to perform power calculation for circadian pattern detection. Our theoretical framework is determined by three key factors in circadian gene detection: sample size, intrinsic effect size and sampling design. Via simulations, we systematically investigate the impact of these key factors on circadian power calculation. We not only demonstrate that CircaPower is fast and accurate, but also show its underlying cosinor model is robust against variety of violations of model assumptions. In real applications, we demonstrate the performance of CircaPower using mouse pan-tissue data and human post-mortem brain data, and illustrate how to perform circadian power calculation using mouse skeleton muscle RNA-Seq pilot as case study. Our method CircaPower has been implemented in an R package, which is made publicly available on GitHub ( https://github.com/circaPower/circaPower).

An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells

Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms in time series (for instance gene expression, eclosion, egg-laying and feeding) datasets tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets. Here we introduce a new method, ODeGP ( O scillation De tection using G aussian P rocesses), which combines Gaussian Process (GP) regression with Bayesian inference to provide a flexible approach to the problem. Besides naturally incorporating measurement errors and non-uniformly sampled data, ODeGP uses a recently developed kernel to improve detection of non-stationary waveforms. An additional advantage is that by using Bayes factors instead of p-values, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses. Using a variety of synthetic datasets we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary oscillations. Next, on analyzing existing qPCR datasets that exhibit low amplitude and noisy oscillations, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak oscillations. Finally, we generate new qPCR time-series datasets on pluripotent mouse embryonic stem cells, which are expected to exhibit no oscillations of the core circadian clock genes. Surprisingly, we discover using ODeGP that increasing cell density can result in the rapid generation of oscillations in the Bmal1 gene, thus highlighting our method’s ability to discover unexpected patterns. In its current implementation, ODeGP (available as an R package) is meant only for analyzing single or a few time-trajectories, not genome-wide datasets.

LimoRhyde2: genomic analysis of biological rhythms based on effect sizes

Genome-scale data have revealed daily rhythms in various species and tissues. However, current methods to assess rhythmicity largely restrict their focus to quantifying statistical significance, which may not reflect biological relevance. To address this limitation, we developed a method called LimoRhyde2 (the successor to our method LimoRhyde), which focuses instead on rhythm-related effect sizes and their uncertainty. For each genomic feature, LimoRhyde2 fits a curve using a series of linear models based on periodic splines, moderates the fits using an Empirical Bayes approach called multivariate adaptive shrinkage (Mash), then uses the moderated fits to calculate rhythm statistics such as peak-to-trough amplitude. The periodic splines capture non-sinusoidal rhythmicity, while Mash uses patterns in the data to account for different fits having different levels of noise. To demonstrate LimoRhyde2's utility, we applied it to multiple circadian transcriptome datasets. Overall, LimoRhyde2 prioritized genes having high-amplitude rhythms in expression, whereas a prior method (BooteJTK) prioritized "statistically significant" genes whose amplitudes could be relatively small. Thus, quantifying effect sizes using approaches such as LimoRhyde2 has the potential to transform interpretation of genomic data related to biological rhythms.

Rhythmicity is linked to expression cost at the protein level but to expression precision at the mRNA level

Many genes have nycthemeral rhythms of expression, i.e. a 24-hours periodic variation, at either mRNA or protein level or both, and most rhythmic genes are tissue-specific. Here, we investigate and discuss the evolutionary origins of rhythms in gene expression. Our results suggest that rhythmicity of protein expression could have been favored by selection to minimize costs. Trends are consistent in bacteria, plants and animals, and are also supported by tissue-specific patterns in mouse. Unlike for protein level, cost cannot explain rhythm at the RNA level. We suggest that instead it allows to periodically reduce expression noise. Noise control had the strongest support in mouse, with limited evidence in other species. We have also found that genes under stronger purifying selection are rhythmically expressed at the mRNA level, and we propose that this is because they are noise sensitive genes. Finally, the adaptive role of rhythmic expression is supported by rhythmic genes being highly expressed yet tissue-specific. This provides a good evolutionary explanation for the observation that nycthemeral rhythms are often tissue-specific.

For many genes, their expression, i.e. the production of RNA and proteins, is rhythmic with a 24-hour period. Here, we study and discuss the evolutionary origins of these rhythms. Our analyses of data from different species suggest that the rhythmicity of protein level may have been favored by selection for cost minimization. Furthermore, we have shown that cost cannot explain the rhythmic variations in RNA levels. Instead, we suggest that it periodically reduces the stochasticity of gene expression. We also found that genes under stronger purifying selection are rhythmically expressed at the mRNA level, and propose that this is because they are noise-sensitive genes. Finally, rhythmic expression involves genes that are often highly expressed and tissue-specific. This provides a good evolutionary explanation for the tissue-specificity of these rhythms.

Time-course RNASeq of Camponotus floridanus forager and nurse ant brains indicate links between plasticity in the biological clock and behavioral division of labor

Background

Circadian clocks allow organisms to anticipate daily fluctuations in their environment by driving rhythms in physiology and behavior. Inter-organismal differences in daily rhythms, called chronotypes, exist and can shift with age. In ants, age, caste-related behavior and chronotype appear to be linked. Brood-tending nurse ants are usually younger individuals and show “around-the-clock” activity. With age or in the absence of brood, nurses transition into foraging ants that show daily rhythms in activity. Ants can adaptively shift between these behavioral castes and caste-associated chronotypes depending on social context. We investigated how changes in daily gene expression could be contributing to such behavioral plasticity in Camponotus floridanus carpenter ants by combining time-course behavioral assays and RNA-Sequencing of forager and nurse brains.

Results

We found that nurse brains have three times fewer 24 h oscillating genes than foragers. However, several hundred genes that oscillated every 24 h in forager brains showed robust 8 h oscillations in nurses, including the core clock genes Period and Shaggy. These differentially rhythmic genes consisted of several components of the circadian entrainment and output pathway, including genes said to be involved in regulating insect locomotory behavior. We also found that Vitellogenin, known to regulate division of labor in social insects, showed robust 24 h oscillations in nurse brains but not in foragers. Finally, we found significant overlap between genes differentially expressed between the two ant castes and genes that show ultradian rhythms in daily expression.

Conclusion

This study provides a first look at the chronobiological differences in gene expression between forager and nurse ant brains. This endeavor allowed us to identify a putative molecular mechanism underlying plastic timekeeping: several components of the ant circadian clock and its output can seemingly oscillate at different harmonics of the circadian rhythm. We propose that such chronobiological plasticity has evolved to allow for distinct regulatory networks that underlie behavioral castes, while supporting swift caste transitions in response to colony demands. Behavioral division of labor is common among social insects. The links between chronobiological and behavioral plasticity that we found in C. floridanus, thus, likely represent a more general phenomenon that warrants further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08282-x.

CRYPTOCHROMES promote daily protein homeostasis

The daily organisation of most mammalian cellular functions is attributed to circadian regulation of clock-controlled protein expression, driven by daily cycles of CRYPTOCHROME-dependent transcriptional feedback repression. To test this, we used quantitative mass spectrometry to compare wild-type and CRY-deficient fibroblasts under constant conditions. In CRY-deficient cells, we found that temporal variation in protein, phosphopeptide, and K+ abundance was at least as great as wild-type controls. Most strikingly, the extent of temporal variation within either genotype was much smaller than overall differences in proteome composition between WT and CRY-deficient cells. This proteome imbalance in CRY-deficient cells and tissues was associated with increased susceptibility to proteotoxic stress, which impairs circadian robustness, and may contribute to the wide-ranging phenotypes of CRY-deficient mice. Rather than generating large-scale daily variation in proteome composition, we suggest it is plausible that the various transcriptional and post-translational functions of CRY proteins ultimately act to maintain protein and osmotic homeostasis against daily perturbation.

Time-restricted feeding prevents deleterious metabolic effects of circadian disruption through epigenetic control of β cell function

Circadian rhythm disruption (CD) is associated with impaired glucose homeostasis and type 2 diabetes mellitus (T2DM). While the link between CD and T2DM remains unclear, there is accumulating evidence that disruption of fasting/feeding cycles mediates metabolic dysfunction. Here, we used an approach encompassing analysis of behavioral, physiological, transcriptomic, and epigenomic effects of CD and consequences of restoring fasting/feeding cycles through time-restricted feeding (tRF) in mice. Results show that CD perturbs glucose homeostasis through disruption of pancreatic β cell function and loss of circadian transcriptional and epigenetic identity. In contrast, restoration of fasting/feeding cycle prevented CD-mediated dysfunction by reestablishing circadian regulation of glucose tolerance, β cell function, transcriptional profile, and reestablishment of proline and acidic amino acid–rich basic leucine zipper (PAR bZIP) transcription factor DBP expression/activity. This study provides mechanistic insights into circadian regulation of β cell function and corresponding beneficial effects of tRF in prevention of T2DM.

Likelihood-based tests for detecting circadian rhythmicity and differential circadian patterns in transcriptomic applications

Circadian rhythmicity in transcriptomic profiles has been shown in many physiological processes, and the disruption of circadian patterns has been found to associate with several diseases. In this paper, we developed a series of likelihood-based methods to detect (i) circadian rhythmicity (denoted as LR_rhythmicity) and (ii) differential circadian patterns comparing two experimental conditions (denoted as LR_diff). In terms of circadian rhythmicity detection, we demonstrated that our proposed LR_rhythmicity could better control the type I error rate compared to existing methods under a wide variety of simulation settings. In terms of differential circadian patterns, we developed methods in detecting differential amplitude, differential phase, differential basal level and differential fit, which also successfully controlled the type I error rate. In addition, we demonstrated that the proposed LR_diff could achieve higher statistical power in detecting differential fit, compared to existing methods. The superior performance of LR_rhythmicity and LR_diff was demonstrated in four real data applications, including a brain aging data (gene expression microarray data of human postmortem brain), a time-restricted feeding data (RNA sequencing data of human skeletal muscles) and a scRNAseq data (single cell RNA sequencing data of mouse suprachiasmatic nucleus). An R package for our methods is publicly available on GitHub https://github.com/diffCircadian/diffCircadian.

TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data

MOTIVATION

The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics, coupled with the significant implications of the circadian clock for human health, has sparked an interest in circadian profiling studies to discover genes under circadian control.

RESULT

We present TimeCycle: a topology-based rhythm detection method designed to identify cycling transcripts. For a given time-series, the method reconstructs the state space using time-delay embedding, a data transformation technique from dynamical systems theory. In the embedded space, Takens' theorem proves that the dynamics of a rhythmic signal will exhibit circular patterns. The degree of circularity of the embedding is calculated as a persistence score using persistent homology, an algebraic method for discerning the topological features of data. By comparing the persistence scores to a bootstrapped null distribution, cycling genes are identified. Results in both synthetic and biological data highlight TimeCycle's ability to identify cycling genes across a range of sampling schemes, number of replicates, and missing data. Comparison to competing methods highlights their relative strengths, providing guidance as to the optimal choice of cycling detection method.

AVAILABILITY

A fully documented open-source R package implementing TimeCycle is available at: https://nesscoder.github.io/TimeCycle/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Optimal Time for Treatment-Predicting Circadian Time by Machine Learning and Mathematical Modelling

Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient's internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient's internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity.

Single-cell transcriptomics allows novel insights into aging and circadian processes

Aging and circadian rhythms are two biological processes that affect an organism, although at different time scales. Nevertheless, due to the overlap of their actions, it was speculated that both interfere or interact with each other. However, to address this question, a much deeper insight into these processes is necessary, especially at the cellular level. New methods such as single-cell RNA-sequencing (scRNA-Seq) have the potential to close this gap in our knowledge. In this review, we analyze applications of scRNA-Seq from the aging and circadian rhythm fields and highlight new findings emerging from the analysis of single cells, especially in humans or rodents. Furthermore, we judge the potential of scRNA-Seq to identify common traits of both processes. Overall, this method offers several advantages over more traditional methods analyzing gene expression and will become an important tool to unravel the link between these biological processes.