Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data

CircadiPy: An open-source toolkit for analyzing chronobiology time series

BACKGROUND

Chronobiology is the scientific field focused on studying periodicity in biological processes. In mammals, most physiological variables exhibit circadian rhythmicity, such as metabolism, body temperature, locomotor activity, and sleep. The biological rhythmicity can be statistically evaluated by examining the time series and extracting parameters that correlate to the period of oscillation, its amplitude, phase displacement, and overall variability.

NEW METHOD

We have developed a library called CircadiPy, which encapsulates methods for chronobiological analysis and data inspection, serving as an open-access toolkit for the analysis and interpretation of chronobiological data. The package was designed to be flexible, comprehensive and scalable in order to assist research dealing with processes affected or influenced by rhythmicity.

RESULTS

The results demonstrate the toolkit's capability to guide users in analyzing chronobiological data collected from various recording sources, while also providing precise parameters related to the circadian rhythmicity.

COMPARISON WITH EXISTING METHODS

The analysis methodology from this proposed library offers an opportunity to inspect and obtain chronobiological parameters in a straightforward and cost-free manner, in contrast to commercial tools.

CONCLUSIONS

Moreover, being an open-source tool, it empowers the community with the opportunity to contribute with new functions, analysis methods, and graphical visualizations given the simplified computational method of time series data analysis using an easy and comprehensive pipeline within a single Python object.

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.

Meta-analysis of Diurnal Transcriptomics in Mouse Liver Reveals Low Repeatability of Rhythm Analyses

To assess the consistency of biological rhythms across studies, 57 public mouse liver tissue timeseries totaling 1096 RNA-seq samples were obtained and analyzed. Only the control groups of each study were included, to create comparable data. Technical factors in RNA-seq library preparation were the largest contributors to transcriptome-level differences, beyond biological or experiment-specific factors such as lighting conditions. Core clock genes were remarkably consistent in phase across all studies. Overlap of genes identified as rhythmic across studies was generally low, with no pair of studies having over 60% overlap. Distributions of phases of significant genes were remarkably inconsistent across studies, but the genes that consistently identified as rhythmic had acrophase clustering near ZT0 and ZT12. Despite the discrepancies between single-study analyses, cross-study analyses found substantial consistency. Running compareRhythms on each pair of studies identified a median of only 11% of the identified rhythmic genes as rhythmic in only 1 of the 2 studies. Data were integrated across studies in a joint and individual variance estimate (JIVE) analysis, which showed that the top 2 components of joint within-study variation are determined by time of day. A shape-invariant model with random effects was fit to the genes to identify the underlying shape of the rhythms, consistent across all studies, including identifying 72 genes with consistently multiple peaks.

Keep It Simple: Using README Files to Advance Standardization in Chronobiology

Standardization plays a crucial role in ensuring the reliability, reproducibility, and interoperability of research data in the biomedical sciences. Metadata standards are one foundation for the FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data management. They facilitate data discovery, understanding, and reuse. However, the adoption of metadata standards in biological research lags in practice. Barriers such as complexity, lack of incentives, technical challenges, resource constraints, and resistance to change hinder widespread adoption. In the field of chronobiology, standardization is essential but faces particular challenges due to the longitudinal nature of experimental data, diverse model organisms, and varied measurement techniques. To address these challenges, we propose an approach that emphasizes simplicity and practicality: the development of README templates tailored for particular data types and species. Through this opinion article, our intention is to initiate a dialogue and commence a community-driven standardization process by engaging potential contributors and collaborators.

Data integration and analysis for circadian medicine

Data integration, data sharing, and standardized analyses are important enablers for data-driven medical research. Circadian medicine is an emerging field with a particularly high need for coordinated and systematic collaboration between researchers from different disciplines. Datasets in circadian medicine are multimodal, ranging from molecular circadian profiles and clinical parameters to physiological measurements and data obtained from (wearable) sensors or reported by patients. Uniquely, data spanning both the time dimension and the spatial dimension (across tissues) are needed to obtain a holistic view of the circadian system. The study of human rhythms in the context of circadian medicine has to confront the heterogeneity of clock properties within and across subjects and our inability to repeatedly obtain relevant biosamples from one subject. This requires informatics solutions for integrating and visualizing relevant data types at various temporal resolutions ranging from milliseconds and seconds to minutes and several hours. Associated challenges range from a lack of standards that can be used to represent all required data in a common interoperable form, to challenges related to data storage, to the need to perform transformations for integrated visualizations, and to privacy issues. The downstream analysis of circadian rhythms requires specialized approaches for the identification, characterization, and discrimination of rhythms. We conclude that circadian medicine research provides an ideal environment for developing innovative methods to address challenges related to the collection, integration, visualization, and analysis of multimodal multidimensional biomedical data.

Pathways from the clinical learning environment and ego identity to professional identity: A cross-sectional study

BACKGROUND

The clinical learning environment and ego identity are positively related to professional identity. However, the pathways from these factors to professional identity are unknown. Aim This study explores the pathways from the clinical learning environment and ego identity to professional identity.

METHODS

The study used a convenience sampling method in a comprehensive hospital in Hunan Province, China to enrol 222 nursing interns between April and May 2021. General information questionnaires and scales with good psychometric properties (e.g., Environment Evaluation Scale for Clinical Nursing Internship, Ego Identity Scale, and Professional Identification Scale) were used to collect data. A structural equation model was used to explore the relationships between the clinical learning environment, ego identity, and professional identity among nursing interns.

RESULTS

The professional identity of nursing interns was positively correlated with the clinical learning environment and ego identity. The clinical learning environment had a direct effect (Effect = -0.052, P < 0.05) and an indirect effect through ego identity (Effect = -0.042, P < 0.05) on nursing interns' professional identity.

CONCLUSION

The clinical learning environment and ego identity are important influencing factors of professional identity among nursing interns. Therefore, clinical teaching hospitals and teachers should pay attention to the improvement in the clinical learning environment and the cultivation of nursing interns' ego identity.

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.

Dietary Restriction Impacts Peripheral Circadian Clock Output Important for Longevity in Drosophila

Circadian clocks may mediate lifespan extension by caloric or dietary restriction (DR). We find that the core clock transcription factor Clock is crucial for a robust longevity and fecundity response to DR in Drosophila. To identify clock-controlled mediators, we performed RNA-sequencing from abdominal fat bodies across the 24 h day after just 5 days under control or DR diets. In contrast to more chronic DR regimens, we did not detect significant changes in the rhythmic expression of core clock genes. Yet we discovered that DR induced de novo rhythmicity or increased expression of rhythmic clock output genes. Network analysis revealed that DR increased network connectivity in one module comprised of genes encoding proteasome subunits. Adult, fat body specific RNAi knockdown demonstrated that proteasome subunits contribute to DR-mediated lifespan extension. Thus, clock control of output links DR-mediated changes in rhythmic transcription to lifespan extension.

Integrative Analysis of Rhythmicity: From Biology to Urban Environments and Sustainability

From biological to socio-technical systems, rhythmic processes are pervasive in our environment. However, methods for their comprehensive analysis are prevalent only in specific fields that limit the transfer of knowledge across scientific disciplines. This hinders interdisciplinary research and integrative analyses of rhythms across different domains and datasets. In this paper, we review recent developments in cross-disciplinary rhythmicity research, with a focus on the importance of rhythmic analyses in urban planning and biomedical research. Furthermore, we describe the current state of the art of (integrative) computational methods for the investigation of rhythmic data. Finally, we discuss the further potential and propose necessary future developments for cross-disciplinary rhythmicity analysis to foster integration of heterogeneous datasets across different domains, as well as guide data-driven decision making beyond the boundaries of traditional intradisciplinary research, especially in the context of sustainable and healthy cities.

Aging disrupts circadian gene regulation and function in macrophages

Aging is characterized by an increased vulnerability to infection and the development of inflammatory diseases, such as atherosclerosis, frailty, cancer and neurodegeneration. Here, we find that aging is associated with the loss of diurnally rhythmic innate immune responses, including monocyte trafficking from bone marrow to blood, response to lipopolysaccharide and phagocytosis. This decline in homeostatic immune responses was associated with a striking disappearance of circadian gene transcription in aged compared to young tissue macrophages. Chromatin accessibility was significantly greater in young macrophages than in aged macrophages; however, this difference did not explain the loss of rhythmic gene transcription in aged macrophages. Rather, diurnal expression of Kruppel-like factor 4 (Klf4), a transcription factor (TF) well established in regulating cell differentiation and reprogramming, was selectively diminished in aged macrophages. Ablation of Klf4 expression abolished diurnal rhythms in phagocytic activity, recapitulating the effect of aging on macrophage phagocytosis. Examination of individuals harboring genetic variants of KLF4 revealed an association with age-dependent susceptibility to death caused by bacterial infection. Our results indicate that loss of rhythmic Klf4 expression in aged macrophages is associated with disruption of circadian innate immune homeostasis, a mechanism that may underlie age-associated loss of protective immune responses.

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.

Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function

The circadian clock is an internal molecular 24-h timer that is critical to life on Earth. We describe a series of artificial intelligence (AI)– and machine learning (ML)–based approaches that enable more cost-effective analysis and insight into circadian regulation and function. Throughout the manuscript, we illuminate what is inside the ML “black box” via explanation or interpretation of predictive ML models. Using this interpretation of our models, we derive biological insights into why a prediction was made, alongside accurate predictions. Most innovatively, we use only DNA sequence features for accurate circadian gene expression prediction. Using explainable AI, we define possible, responsible regulatory elements as we make these predictions; this critically requires no prior knowledge of regulatory elements.

The circadian clock is an important adaptation to life on Earth. Here, we use machine learning to predict complex, temporal, and circadian gene expression patterns in Arabidopsis. Most significantly, we classify circadian genes using DNA sequence features generated de novo from public, genomic resources, facilitating downstream application of our methods with no experimental work or prior knowledge needed. We use local model explanation that is transcript specific to rank DNA sequence features, providing a detailed profile of the potential circadian regulatory mechanisms for each transcript. Furthermore, we can discriminate the temporal phase of transcript expression using the local, explanation-derived, and ranked DNA sequence features, revealing hidden subclasses within the circadian class. Model interpretation/explanation provides the backbone of our methodological advances, giving insight into biological processes and experimental design. Next, we use model interpretation to optimize sampling strategies when we predict circadian transcripts using reduced numbers of transcriptomic timepoints. Finally, we predict the circadian time from a single, transcriptomic timepoint, deriving marker transcripts that are most impactful for accurate prediction; this could facilitate the identification of altered clock function from existing datasets.

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.

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.

Comment on "Circadian rhythms in the absence of the clock gene Bmal1"

Ray et al (Reports, 14 February 2020, p. 800) report apparent transcriptional circadian rhythms in mouse tissues lacking the core clock component BMAL1. To better understand these surprising results, we reanalyzed the associated data. We were unable to reproduce the original findings, nor could we identify reliably cycling genes. We conclude that there is insufficient evidence to support circadian transcriptional rhythms in the absence of Bmal1.

Applications of cosinor rhythmometry in pharmacology

Study design and data analysis are two important aspects relevant to chronopharmacometrics. Blunders can be avoided by recognizing that most physiological variables are circadian periodic. Both ill health and treatment can affect the amplitude, phase, and/or period of circadian (and other) rhythms, in addition to their mean. The involvement of clock genes in molecular pathways related to important physiological systems underlies the bidirectional relationship often seen between circadian rhythm disruption and disease risk. Circadian rhythm characteristics of marker rhythms interpreted in the light of chronobiologic reference values represent important diagnostic tools. A set of cosinor-related programs is presented. They include the least squares fit of multiple-frequency cosine functions to model the time structure of individual records; a cosinor-based spectral analysis to detect periodic signals; the population-mean cosinor to generalize inferences; the chronobiologic serial section to follow the time course of changing rhythm parameters over time; and parameter tests to assess differences among populations. Relative merits of other available cosinor and non-parametric algorithms are reviewed. Parameter tests to compare individual records and a self-starting cumulative sum (CUSUM) make personalized chronotherapy possible, where the treatment of each patient relies on an N-of-1 design. Methods are illustrated in a few examples relevant to endocrinology, cancer and cardiology. New sensing technology yielding large personal data sets is likely to change the healthcare system. Chronobiologic concepts and methods should become an integral part of these evolving systems.

CosinorPy: a python package for cosinor-based rhythmometry

Background

Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats.

Results

We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats.

Conclusion

CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy. CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.

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.

TimeTrial: An Interactive Application for Optimizing the Design and Analysis of Transcriptomic Time-Series Data in Circadian Biology Research

The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues, coordinating physiological processes. The effect of this rhythm on health has generated increasing interest in discovering genes under circadian control by searching for periodic patterns in transcriptomic time-series experiments. While algorithms for detecting cycling transcripts have advanced, there remains little guidance quantifying the effect of experimental design and analysis choices on cycling detection accuracy. We present TimeTrial, a user-friendly benchmarking framework using both real and synthetic data to investigate cycle detection algorithms’ performance and improve circadian experimental design. Results show that the optimal choice of analysis method depends on the sampling scheme, noise level, and shape of the waveform of interest and provides guidance on the impact of sampling frequency and duration on cycling detection accuracy. The TimeTrial software is freely available for download and may also be accessed through a web interface. By supplying a tool to vary and optimize experimental design considerations, TimeTrial will enhance circadian transcriptomics studies.

Circadian Clocks Function in Concert with Heat Shock Organizing Protein to Modulate Mutant Huntingtin Aggregation and Toxicity

Neurodegenerative diseases commonly involve the disruption of circadian rhythms. Studies indicate that mutant Huntingtin (mHtt), the cause of Huntington’s disease (HD), disrupts circadian rhythms often before motor symptoms are evident. Yet little is known about the molecular mechanisms by which mHtt impairs circadian rhythmicity and whether circadian clocks can modulate HD pathogenesis. To address this question, we used a Drosophila HD model. We found that both environmental and genetic perturbations of the circadian clock alter mHtt-mediated neurodegeneration. To identify potential genetic pathways that mediate these effects, we applied a behavioral platform to screen for clock-regulated HD suppressors, identifying a role for Heat Shock Protein 70/90 Organizing Protein (Hop). Hop knockdown paradoxically reduces mHtt aggregation and toxicity. These studies demonstrate a role for the circadian clock in a neurodegenerative disease model and reveal a clock-regulated molecular and cellular pathway that links clock function to neurodegenerative disease.

Disruption of circadian rhythms is frequently observed across a range of neurodegenerative diseases. Here, Xu et al. demonstrate that perturbation of circadian clocks alters the toxicity of the mutant Huntingtin protein, the cause of Huntington’s disease (HD). Moreover, they reveal a key mechanistic link between the clock and HD.

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

The nycthemeral transcriptome embodies all genes displaying a rhythmic variation of their mRNAs periodically every 24 hours, including but not restricted to circadian genes. In this study, we show that the nycthemeral rhythmicity at the gene expression level is biologically functional and that this functionality is more conserved between orthologous genes than between random genes. We used this conservation of the rhythmic expression to assess the ability of seven methods (ARSER, Lomb Scargle, RAIN, JTK, empirical-JTK, GeneCycle, and meta2d) to detect rhythmic signal in gene expression. We have contrasted them to a naive method, not based on rhythmic parameters. By taking into account the tissue-specificity of rhythmic gene expression and different species comparisons, we show that no method is strongly favored. The results show that these methods designed for rhythm detection, in addition to having quite similar performances, are consistent only among genes with a strong rhythm signal. Rhythmic genes defined with a standard p-value threshold of 0.01 for instance, could include genes whose rhythmicity is biologically irrelevant. Although these results were dependent on the datasets used and the evolutionary distance between the species compared, we call for caution about the results of studies reporting or using large sets of rhythmic genes. Furthermore, given the analysis of the behaviors of the methods on real and randomized data, we recommend using primarily ARS, empJTK, or GeneCycle, which verify expectations of a classical distribution of p-values. Experimental design should also take into account the circumstances under which the methods seem more efficient, such as giving priority to biological replicates over the number of time-points, or to the number of time-points over the quality of the technique (microarray vs RNAseq). GeneCycle, and to a lesser extent empirical-JTK, might be the most robust method when applied to weakly informative datasets. Finally, our analyzes suggest that rhythmic genes are mainly highly expressed genes.

To be active, genes have to be transcribed to RNA. For some genes, the transcription rate follows a circadian rhythm with a periodicity of approximately 24 hours; we call these genes “rhythmic”. In this study, we compared methods designed to detect rhythmic genes in gene expression data. The data are measures of the number of RNA molecules for each gene, given at several time-points, usually spaced 2 to 4 hours, over one or several periods of 24 hours. There are many such methods, but it is not known which ones work best to detect genes whose rhythmic expression is biologically functional. We compared these methods using a reference group of evolutionarily conserved rhythmic genes. We compared data from baboon, mouse, rat, zebrafish, fly, and mosquitoes. Surprisingly, no method was particularly effective. Furthermore, we found that only very strong rhythmic signals were relevant with each method. More precisely, when we use a usual cut-off to define rhythmic genes, the group of genes considered as rhythmic contains many genes whose rhythmicity cannot be confirmed to be biologically relevant. We also show that rhythmic genes mainly contain highly expressed genes. Finally, based on our results, we provide recommendations on which methods to use and how, and suggestions for future experimental designs.

Circadian Proteomic Analysis Uncovers Mechanisms of Post-Transcriptional Regulation in Metabolic Pathways

Transcriptional and translational feedback loops in fungi and animals drive circadian rhythms in transcript levels that provide output from the clock, but post-transcriptional mechanisms also contribute. To determine the extent and underlying source of this regulation, we applied newly developed analytical tools to a long-duration, deeply sampled, circadian proteomics time course comprising half of the proteome. We found a quarter of expressed proteins are clock regulated, but >40% of these do not arise from clock-regulated transcripts, and our analysis predicts that these protein rhythms arise from oscillations in translational rates. Our data highlighted the impact of the clock on metabolic regulation, with central carbon metabolism reflecting both transcriptional and post-transcriptional control and opposing metabolic pathways showing peak activities at different times of day. The transcription factor CSP-1 plays a role in this metabolic regulation, contributing to the rhythmicity and phase of clock-regulated proteins.

LimoRhyde: A Flexible Approach for Differential Analysis of Rhythmic Transcriptome Data

Unraveling the effects of genetic or environmental perturbations on biological rhythms requires detecting changes in rhythmicity across multiple conditions. Although methods to detect rhythmicity in genome-scale data are well established, methods to detect changes in rhythmicity or changes in average expression between experimental conditions are often ad hoc and statistically unreliable. Here we present LimoRhyde (linear models for rhythmicity, design), a flexible approach for analyzing transcriptome data from circadian systems. Borrowing from cosinor regression, LimoRhyde decomposes circadian or zeitgeber time into multiple components to fit a linear model to the expression of each gene. The linear model can accommodate any number of additional experimental variables, whether discrete or continuous, making it straightforward to detect differential rhythmicity and differential expression using state-of-the-art methods for analyzing microarray and RNA-seq data. In this approach, differential rhythmicity corresponds to a statistical interaction between an experimental variable and circadian time, whereas differential expression corresponds to the main effect of an experimental variable while accounting for circadian time. To validate LimoRhyde’s performance, we applied it to simulated data. To demonstrate LimoRhyde’s versatility, we applied it to murine and human circadian transcriptome datasets acquired under various experimental designs. Our results show how LimoRhyde systematizes the analysis of such data, and suggest that LimoRhyde could prove valuable for assessing how circadian systems respond to perturbations.