What time is it? Deep learning approaches for circadian rhythms

Maternal circadian rhythms during pregnancy dictate metabolic plasticity in offspring

Tissue-level oscillation is achieved by tissue-intrinsic clocks along with network-dependent signals originating from distal organs and organismal behavior. Yet, it remains unexplored whether maternal circadian rhythms during pregnancy influence fetal rhythms and impact long-term susceptibility to dietary challenges in offspring. Here, we demonstrate that circadian disruption during pregnancy decreased placental and neonatal weight yet retained transcriptional and structural maturation. Intriguingly, diet-induced obesity was exacerbated in parallel with arrhythmic feeding behavior, hypothalamic leptin resistance, and hepatic circadian reprogramming in offspring of chronodisrupted mothers. In utero circadian desynchrony altered the phase-relationship between the mother and fetus and impacted placental efficiency. Temporal feeding restriction in offspring failed to fully prevent obesity, whereas the circadian alignment of caloric restriction with the onset of the active phase virtually ameliorated the phenotype. Thus, maternal circadian rhythms during pregnancy confer adaptive properties to metabolic functions in offspring and provide insights into the developmental origins of health and disease.

Chronodisruption that dampens output of the central clock abolishes rhythms in metabolome profiles and elevates acylcarnitine levels in the liver of female rats

AIM

Exposure to light at night and meal time misaligned with the light/dark (LD) cycle-typical features of daily life in modern 24/7 society-are associated with negative effects on health. To understand the mechanism, we developed a novel protocol of complex chronodisruption (CD) in which we exposed female rats to four weekly cycles consisting of 5-day intervals of constant light and 2-day intervals of food access restricted to the light phase of the 12:12 LD cycle.

METHODS

We examined the effects of CD on behavior, estrous cycle, sleep patterns, glucose homeostasis and profiles of clock- and metabolism-related gene expression (using RT qPCR) and liver metabolome and lipidome (using untargeted metabolomic and lipidomic profiling).

RESULTS

CD attenuated the rhythmic output of the central clock in the suprachiasmatic nucleus via Prok2 signaling, thereby disrupting locomotor activity, the estrous cycle, sleep patterns, and mutual phase relationship between the central and peripheral clocks. In the periphery, CD abolished Per1,2 expression rhythms in peripheral tissues (liver, pancreas, colon) and worsened glucose homeostasis. In the liver, it impaired the expression of NAD+, lipid, and cholesterol metabolism genes and abolished most of the high-amplitude rhythms of lipids and polar metabolites. Interestingly, CD abolished the circadian rhythm of Cpt1a expression and increased the levels of long-chain acylcarnitines (ACar 18:2, ACar 16:0), indicating enhanced fatty acid oxidation in mitochondria.

CONCLUSION

Our data show the widespread effects of CD on metabolism and point to ACars as biomarkers for CD due to misaligned sleep and feeding patterns.

Designing and Evaluating Circadian Experiments on Mouse Skin

All skin layers and cutaneous appendages harbor a robust circadian clock, whose phase is under the influence of light through the central clock in the suprachiasmatic nucleus. The skin clock coordinates fundamental biological processes, including metabolism and stem cell activation. It also prominently modulates activity of skin-resident immune cells and the inflammatory response. Numerous diurnally regulated genes in the skin have been implicated in skin diseases in GWASs. Therefore, the mouse skin is a powerful model for understanding the diverse roles of circadian biology in maintaining tissue health and the initiation and propagation of disease states. When planning experiments to study the circadian biology of mouse skin, multiple technical and biological factors must be carefully considered. In this paper, we provide comprehensive guidance on the general circadian experimental design and associated housing for the mice. We highlight the importance of aligning sample collection with the desired hair cycle stage and animal age. We introduce methods to disrupt the clock in the skin, including altering light and feeding schedules as well as using transgenic mouse models. Finally, we discuss the use of transcriptomic data, both bulk and single cell, for circadian studies.

Circadian regulation of endoplasmic reticulum calcium response in cultured mouse astrocytes

The circadian clock, an internal time-keeping system orchestrates 24 hr rhythms in physiology and behavior by regulating rhythmic transcription in cells. Astrocytes, the most abundant glial cells, play crucial roles in CNS functions, but the impact of the circadian clock on astrocyte functions remains largely unexplored. In this study, we identified 412 circadian rhythmic transcripts in cultured mouse cortical astrocytes through RNA sequencing. Gene Ontology analysis indicated that genes involved in Ca2+ homeostasis are under circadian control. Notably, Herpud1 (Herp) exhibited robust circadian rhythmicity at both mRNA and protein levels, a rhythm disrupted in astrocytes lacking the circadian transcription factor, BMAL1. HERP regulated endoplasmic reticulum (ER) Ca2+ release by modulating the degradation of inositol 1,4,5-trisphosphate receptors (ITPRs). ATP-stimulated ER Ca2+ release varied with the circadian phase, being more pronounced at subjective night phase, likely due to the rhythmic expression of ITPR2. Correspondingly, ATP-stimulated cytosolic Ca2+ increases were heightened at the subjective night phase. This rhythmic ER Ca2+ response led to circadian phase-dependent variations in the phosphorylation of Connexin 43 (Ser368) and gap junctional communication. Given the role of gap junction channel (GJC) in propagating Ca2+ signals, we suggest that this circadian regulation of ER Ca2+ responses could affect astrocytic modulation of synaptic activity according to the time of day. Overall, our study enhances the understanding of how the circadian clock influences astrocyte function in the CNS, shedding light on their potential role in daily variations of brain activity and health.

Circadian cilia transcriptome in mouse brain across physiological and pathological states

Primary cilia are dynamic sensory organelles that continuously undergo structural modifications in response to environmental and cellular signals, many of which exhibit rhythmic patterns. Building on our previous findings of rhythmic cilia-related gene expression in diurnal primates (baboon), this study extends the investigation to the nocturnal mouse brain to identify circadian patterns of cilia gene expression across brain regions. We used computational techniques and transcriptomic data from four publicly available databases, to examine the circadian expression of cilia-associated genes within six brain areas: brainstem, cerebellum, hippocampus, hypothalamus, striatum, and suprachiasmatic nucleus. Our analysis reveals that a substantial proportion of cilia transcripts exhibit circadian rhythmicity across the examined regions, with notable overrepresentation in the striatum, hippocampus, and cerebellum. We also demonstrate region-specific variations in the abundance and timing of circadian cilia genes' peaks, indicating an adaptation to the distinct physiological roles of each brain region. Additionally, we show that the rhythmic patterns of cilia transcripts are shifted under various physiological and pathological conditions, including modulation of the dopamine system, high-fat diet, and epileptic conditions, indicating the adaptable nature of cilia transcripts' oscillation. While limited to a few mouse brain regions, our study provides initial insights into the distinct circadian patterns of cilia transcripts and highlights the need for future research to expand the mapping across wider brain areas to fully understand the role of cilia's spatiotemporal dynamics in brain functions.

Environmental circadian disruption re-writes liver circadian proteomes

Circadian gene expression is fundamental to the establishment and functions of the circadian clock, a cell-autonomous and evolutionary-conserved timing system. Yet, how it is affected by environmental-circadian disruption (ECD) such as shiftwork and jetlag are ill-defined. Here, we provided a comprehensive and comparative description of male liver circadian gene expression, encompassing transcriptomes, whole-cell proteomes and nuclear proteomes, under normal and after ECD conditions. Under both conditions, post-translation, rather than transcription, is the dominant contributor to circadian functional outputs. After ECD, post-transcriptional and post-translational processes are the major contributors to whole-cell or nuclear circadian proteome, respectively. Furthermore, ECD re-writes the rhythmicity of 64% transcriptome, 98% whole-cell proteome and 95% nuclear proteome. The re-writing, which is associated with changes of circadian regulatory cis-elements, RNA-processing and protein localization, diminishes circadian regulation of fat and carbohydrate metabolism and persists after one week of ECD-recovery.

The epidermal circadian clock integrates and subverts brain signals to guarantee skin homeostasis

In mammals, the circadian clock network drives daily rhythms of tissue-specific homeostasis. To dissect daily inter-tissue communication, we constructed a mouse minimal clock network comprising only two nodes: the peripheral epidermal clock and the central brain clock. By transcriptomic and functional characterization of this isolated connection, we identified a gatekeeping function of the peripheral tissue clock with respect to systemic inputs. The epidermal clock concurrently integrates and subverts brain signals to ensure timely execution of epidermal daily physiology. Timely cell-cycle termination in the epidermal stem cell compartment depends upon incorporation of clock-driven signals originating from the brain. In contrast, the epidermal clock corrects or outcompetes potentially disruptive feeding-related signals to ensure the optimal timing of DNA replication. Together, we present an approach for cataloging the systemic dependencies of daily temporal organization in a tissue and identify an essential gate-keeping function of peripheral circadian clocks that guarantees tissue homeostasis.

The circadian clock in the choroid plexus drives rhythms in multiple cellular processes under the control of the suprachiasmatic nucleus

Choroid plexus (ChP), the brain structure primarily responsible for cerebrospinal fluid production, contains a robust circadian clock, whose role remains to be elucidated. The aim of our study was to [1] identify rhythmically controlled cellular processes in the mouse ChP and [2] assess the role and nature of signals derived from the master clock in the suprachiasmatic nuclei (SCN) that control ChP rhythms. To accomplish this goal, we used various mouse models (WT, mPer2Luc, ChP-specific Bmal1 knockout) and combined multiple experimental approaches, including surgical lesion of the SCN (SCNx), time-resolved transcriptomics, and single cell luminescence microscopy. In ChP of control (Ctrl) mice collected every 4 h over 2 circadian cycles in darkness, we found that the ChP clock regulates many processes, including the cerebrospinal fluid circadian secretome, precisely times endoplasmic reticulum stress response, and controls genes involved in neurodegenerative diseases (Alzheimer's disease, Huntington's disease, and frontotemporal dementia). In ChP of SCNx mice, the rhythmicity detected in vivo and ex vivo was severely dampened to a comparable extent as in mice with ChP-specific Bmal1 knockout, and the dampened cellular rhythms were restored by daily injections of dexamethasone in mice. Our data demonstrate that the ChP clock controls tissue-specific gene expression and is strongly dependent on the presence of a functional connection with the SCN. The results may contribute to the search for a novel link between ChP clock disruption and impaired brain health.

tauFisher predicts circadian time from a single sample of bulk and single-cell pseudobulk transcriptomic data

As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tauFisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher's performance in adding timestamps to both bulk and single-cell transcriptomic samples collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNAseq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNAseq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.

Timely Questions Emerging in Chronobiology: The Circadian Clock Keeps on Ticking

Chronobiology investigations have revealed much about cellular and physiological clockworks but we are far from having a complete mechanistic understanding of the physiological and ecological implications. Here we present some unresolved questions in circadian biology research as posed by the editorial staff and guest contributors to the Journal of Circadian Rhythms. This collection of ideas is not meant to be comprehensive but does reveal the breadth of our observations on emerging trends in chronobiology and circadian biology. It is amazing what could be achieved with various expected innovations in technologies, techniques, and mathematical tools that are being developed. We fully expect strengthening mechanistic work will be linked to health care and environmental understandings of circadian function. Now that most clock genes are known, linking these to physiological, metabolic, and developmental traits requires investigations from the single molecule to the terrestrial ecological scales. Real answers are expected for these questions over the next decade. Where are the circadian clocks at a cellular level? How are clocks coupled cellularly to generate organism level outcomes? How do communities of circadian organisms rhythmically interact with each other? In what way does the natural genetic variation in populations sculpt community behaviors? How will methods development for circadian research be used in disparate academic and commercial endeavors? These and other questions make it a very exciting time to be working as a chronobiologist.

Ror homolog nhr-23 is essential for both developmental clock and circadian clock in C. elegans

Animals have internal clocks that generate biological rhythms. In mammals, clock genes such as Period form the circadian clock to generate approximately 24-h biological rhythms. In C. elegans, the clock gene homologs constitute the "developmental clock", which has an 8-h period during larval development to determine the timing of molting. Thus, the ancestral circadian clock has been believed to evolve into the oscillator with a shorter period in C. elegans. However, circadian rhythms have also been observed in adult C. elegans, albeit relatively weak. This prompts the question: if the clock gene homologs drive the developmental rhythm with 8-h period, which genes generate the circadian rhythms in C. elegans? In this study, we discovered that nhr-23, a homolog of the mammalian circadian clock gene Ror, is essential for circadian transcriptional rhythms in adult C. elegans. Interestingly, nhr-23 was also known to be essential for the molting clock. The bilaterian ancestral circadian clock genes might have evolved to function over multiple periods depending on developmental contexts rather than a single 8-h period in C. elegans.

Chronotherapeutic Approaches

The chapter provides a comprehensive review of current approaches to personalized chronodiagnosis and chronotherapy. We discuss circadian clock drug targets that aim to affect cellular clock machinery, circadian mechanisms of pharmacokinetics/pharmacodynamics, and chronotherapeutic approaches aimed at increasing treatment efficacy and minimizing its side effects. We explore how chronotherapy can combat acquired and compensatory drug resistance. Non-pharmacological interventions for clock preservation and enhancement are also overviewed, including light treatment, melatonin, sleep scheduling, time-restricted feeding, physical activity, and exercise.

Chronobiological Study Designs

The chapter describes experimental designs for various chronobiological studies aimed at basic research and clinical trials, with an emphasis on circadian rhythms. In the first part, various methods of data collection, particularly longitudinal and transverse sampling and their relative merits, are discussed. Thereafter, specific methods and their constraints for monitoring marker rhythms are presented. Variables that are most effective in characterizing the endogenous pacemaker and those of clinical relevance are discussed. Besides melatonin and core body temperature rhythms, which are widely accepted as the gold standard for representing the circadian clock, rhythms of cortisol concentration, physical activity, sleep parameters and chronotypes are considered. The relevance of stable rhythms with appropriate internal and external phase relationships for health and wellbeing, as well as adverse effects of certain rhythm alterations are discussed. The last part describes two experimental designs that allow separating endogenous and exogenous components of biological rhythms, the constant routine and the forced desynchronization protocols.

TimeTeller: A tool to probe the circadian clock as a multigene dynamical system

Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart diseases. Therefore, there is a need for tools to measure the functional state of the molecular circadian clock and its downstream targets in patients. Moreover, the clock is a multi-dimensional stochastic oscillator and there are few tools for analysing it as a noisy multigene dynamical system. In this paper we consider the methodology behind TimeTeller, a machine learning tool that analyses the clock as a noisy multigene dynamical system and aims to estimate circadian clock function from a single transcriptome by modelling the multi-dimensional state of the clock. We demonstrate its potential for clock systems assessment by applying it to mouse, baboon and human microarray and RNA-seq data and show how to visualise and quantify the global structure of the clock, quantitatively stratify individual transcriptomic samples by clock dysfunction and globally compare clocks across individuals, conditions and tissues thus highlighting its potential relevance for advancing circadian medicine.

Diurnal Rhythms in the Red Seaweed Gracilariopsis chorda are Characterized by Unique Regulatory Networks of Carbon Metabolism

Cellular and physiological cycles are driven by endogenous pacemakers, the diurnal and circadian rhythms. Key functions such as cell cycle progression and cellular metabolism are under rhythmic regulation, thereby maintaining physiological homeostasis. The photoreceptors phytochrome and cryptochrome, in response to light cues, are central input pathways for physiological cycles in most photosynthetic organisms. However, among Archaeplastida, red algae are the only taxa that lack phytochromes. Current knowledge about oscillatory rhythms is primarily derived from model species such as Arabidopsis thaliana and Chlamydomonas reinhardtii in the Viridiplantae, whereas little is known about these processes in other clades of the Archaeplastida, such as the red algae (Rhodophyta). We used genome-wide expression profiling of the red seaweed Gracilariopsis chorda and identified 3,098 rhythmic genes. Here, we characterized possible cryptochrome-based regulation and photosynthetic/cytosolic carbon metabolism in this species. We found a large family of cryptochrome genes in G. chorda that display rhythmic expression over the diurnal cycle and may compensate for the lack of phytochromes in this species. The input pathway gates regulatory networks of carbon metabolism which results in a compact and efficient energy metabolism during daylight hours. The system in G. chorda is distinct from energy metabolism in most plants, which activates in the dark. The green lineage, in particular, land plants, balance water loss and CO2 capture in terrestrial environments. In contrast, red seaweeds maintain a reduced set of photoreceptors and a compact cytosolic carbon metabolism to thrive in the harsh abiotic conditions typical of intertidal zones.

tauFisher accurately predicts circadian time from a single sample of bulk and single-cell transcriptomic data

As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tau-Fisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher's out-standing performance in both bulk and single-cell transcriptomic data collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNA-seq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNA-seq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.

Reanalysis of primate brain circadian transcriptomics reveals connectivity-related oscillations

Research shows that brain circuits controlling vital physiological processes are closely linked with endogenous time-keeping systems. In this study, we aimed to examine oscillatory gene expression patterns of well-characterized neuronal circuits by reanalyzing publicly available transcriptomic data from a spatiotemporal gene expression atlas of a non-human primate. Unexpectedly, brain structures known for regulating circadian processes (e.g., hypothalamic nuclei) did not exhibit robust cycling expression. In contrast, basal ganglia nuclei, not typically associated with circadian physiology, displayed the most dynamic cycling behavior of its genes marked by sharp temporally defined expression peaks. Intriguingly, the mammillary bodies, considered hypothalamic nuclei, exhibited gene expression patterns resembling the basal ganglia, prompting reevaluation of their classification. Our results emphasize the potential for high throughput circadian gene expression analysis to deepen our understanding of the functional synchronization across brain structures that influence physiological processes and resulting complex behaviors.

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.

Integrated 3D genome, epigenome and transcriptome analyses reveal transcriptional coordination of circadian rhythm in rice

Photoperiods integrate with the circadian clock to coordinate gene expression rhythms and thus ensure plant fitness to the environment. Genome-wide characterization and comparison of rhythmic genes under different light conditions revealed delayed phase under constant darkness (DD) and reduced amplitude under constant light (LL) in rice. Interestingly, ChIP-seq and RNA-seq profiling of rhythmic genes exhibit synchronous circadian oscillation in H3K9ac modifications at their loci and long non-coding RNAs (lncRNAs) expression at proximal loci. To investigate how gene expression rhythm is regulated in rice, we profiled the open chromatin regions and transcription factor (TF) footprints by time-series ATAC-seq. Although open chromatin regions did not show circadian change, a significant number of TFs were identified to rhythmically associate with chromatin and drive gene expression in a time-dependent manner. Further transcriptional regulatory networks mapping uncovered significant correlation between core clock genes and transcription factors involved in light/temperature signaling. In situ Hi-C of ZT8-specific expressed genes displayed highly connected chromatin association at the same time, whereas this ZT8 chromatin connection network dissociates at ZT20, suggesting the circadian control of gene expression by dynamic spatial chromatin conformation. These findings together implicate the existence of a synchronization mechanism between circadian H3K9ac modifications, chromatin association of TF and gene expression, and provides insights into circadian dynamics of spatial chromatin conformation that associate with gene expression rhythms.

Environmental circadian disruption re-programs liver circadian gene expression

Circadian gene expression is fundamental to the establishment and functions of the circadian clock, a cell-autonomous and evolutionary-conserved timing system. Yet, how it is affected by environmental-circadian disruption (ECD) such as shiftwork and jetlag, which impact millions of people worldwide, are ill-defined. Here, we provided the first comprehensive description of liver circadian gene expression under normal and after ECD conditions. We found that post-transcription and post-translation processes are dominant contributors to whole-cell or nuclear circadian proteome, respectively. Furthermore, rhythmicity of 64% transcriptome, 98% whole-cell proteome and 95% nuclear proteome is re-written by ECD. The re-writing, which is associated with changes of circadian cis-regulatory elements, RNA-processing and protein trafficking, diminishes circadian regulation of fat and carbohydrate metabolism and persists after one week of ECD-recovery.

Characterizing the circadian connectome of Ocimum tenuiflorum using an integrated network theoretic framework

Across the three domains of life, circadian clock is known to regulate vital physiological processes, like, growth, development, defence etc. by anticipating environmental cues. In this work, we report an integrated network theoretic methodology comprising of random walk with restart and graphlet degree vectors to characterize genome wide core circadian clock and clock associated raw candidate proteins in a plant for which protein interaction information is available. As a case study, we have implemented this framework in Ocimum tenuiflorum (Tulsi); one of the most valuable medicinal plants that has been utilized since ancient times in the management of a large number of diseases. For that, 24 core clock (CC) proteins were mined in 56 template plant genomes to build their hidden Markov models (HMMs). These HMMs were then used to identify 24 core clock proteins in O. tenuiflorum. The local topology of the interologous Tulsi protein interaction network was explored to predict the CC associated raw candidate proteins. Statistical and biological significance of the raw candidates was determined using permutation and enrichment tests. A total of 66 putative CC associated proteins were identified and their functional annotation was performed.

Spatiotemporal Mapping of Brain Cilia Reveals Region-Specific Oscillation of Length and Orientation

In this study, we conducted high-throughput spatiotemporal analysis of primary cilia length and orientation across 22 mouse brain regions. We developed automated image analysis algorithms, which enabled us to examine over 10 million individual cilia, generating the largest spatiotemporal atlas of cilia. We found that cilia length and orientation display substantial variations across different brain regions and exhibit fluctuations over a 24-hour period, with region-specific peaks during light-dark phases. Our analysis revealed unique orientation patterns of cilia at 45 degree intervals, suggesting that cilia orientation within the brain is not random but follows specific patterns. Using BioCycle, we identified circadian rhythms of cilia length in five brain regions: nucleus accumbens core, somatosensory cortex, and three hypothalamic nuclei. Our findings present novel insights into the complex relationship between cilia dynamics, circadian rhythms, and brain function, highlighting cilia crucial role in the brain's response to environmental changes and regulation of time-dependent physiological processes.

Circadian transcriptome processing and analysis: a workflow for muscle stem cells

Circadian rhythms coordinate biological processes with Earth's 24-h daily light/dark cycle. In the last years, efforts in the field of chronobiology have sought to understand the ways in which the circadian clock controls transcription across tissues and cells. This has been supported by the development of different bioinformatic approaches that allow the identification of 24-h oscillating transcripts. This workflow aims to describe how to isolate muscle stem cells for RNA sequencing analysis from a typical circadian experiment and introduces bioinformatic tools suitable for the analysis of circadian transcriptomes.

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.

Metabolomic Analysis of Plasma in Huntington's Disease Transgenic Sheep (Ovis aries) Reveals Progressive Circadian Rhythm Dysregulation

BACKGROUND

Metabolic abnormalities have long been predicted in Huntington's disease (HD) but remain poorly characterized. Chronobiological dysregulation has been described in HD and may include abnormalities in circadian-driven metabolism.

OBJECTIVE

Here we investigated metabolite profiles in the transgenic sheep model of HD (OVT73) at presymptomatic ages. Our goal was to understand changes to the metabolome as well as potential metabolite rhythm changes associated with HD.

METHODS

We used targeted liquid chromatography mass spectrometry (LC-MS) metabolomics to analyze metabolites in plasma samples taken from female HD transgenic and normal (control) sheep aged 5 and 7 years. Samples were taken hourly across a 27-h period. The resulting dataset was investigated by machine learning and chronobiological analysis.

RESULTS

The metabolic profiles of HD and control sheep were separable by machine learning at both ages. We found both absolute and rhythmic differences in metabolites in HD compared to control sheep at 5 years of age. An increase in both the number of disturbed metabolites and the magnitude of change of acrophase (the time at which the rhythms peak) was seen in samples from 7-year-old HD compared to control sheep. There were striking similarities between the dysregulated metabolites identified in HD sheep and human patients (notably of phosphatidylcholines, amino acids, urea, and threonine).

CONCLUSION

This work provides the first integrated analysis of changes in metabolism and circadian rhythmicity of metabolites in a large animal model of presymptomatic HD.

Tryptophan metabolism is a physiological integrator regulating circadian rhythms

OBJECTIVE

The circadian clock aligns physiology with the 24-hour rotation of Earth. Light and food are the main environmental cues (zeitgebers) regulating circadian rhythms in mammals. Yet, little is known about the interaction between specific dietary components and light in coordinating circadian homeostasis. Herein, we focused on the role of essential amino acids.

METHODS

Mice were fed diets depleted of specific essential amino acids and their behavioral rhythms were monitored and tryptophan was selected for downstream analyses. The role of tryptophan metabolism in modulating circadian homeostasis was studied using isotope tracing as well as transcriptomic- and metabolomic- analyses.

RESULTS

Dietary tryptophan depletion alters behavioral rhythms in mice. Furthermore, tryptophan metabolism was shown to be regulated in a time- and light- dependent manner. A multi-omics approach and combinatory diet/light interventions demonstrated that tryptophan metabolism modulates temporal regulation of metabolism and transcription programs by buffering photic cues. Specifically, tryptophan metabolites regulate central circadian functions of the suprachiasmatic nucleus and the core clock machinery in the liver.

CONCLUSIONS

Tryptophan metabolism is a modulator of circadian homeostasis by integrating environmental cues. Our findings propose tryptophan metabolism as a potential point for pharmacologic intervention to modulate phenotypes associated with disrupted circadian rhythms.

Rhythmic transcription of Bmal1 stabilizes the circadian timekeeping system in mammals

In mammals, the circadian clock consists of transcriptional and translational feedback loops through DNA cis-elements such as E-box and RRE. The E-box-mediated core feedback loop is interlocked with the RRE-mediated feedback loop, but biological significance of the RRE-mediated loop has been elusive. In this study, we established mutant cells and mice deficient for rhythmic transcription of Bmal1 gene by deleting its upstream RRE elements and hence disrupted the RRE-mediated feedback loop. We observed apparently normal circadian rhythms in the mutant cells and mice, but a combination of mathematical modeling and experiments revealed that the circadian period and amplitude of the mutants were more susceptible to disturbance of CRY1 protein rhythm. Our findings demonstrate that the RRE-mediated feedback regulation of Bmal1 underpins the E-box-mediated rhythm in cooperation with CRY1-dependent posttranslational regulation of BMAL1 protein, thereby conferring the perturbation-resistant oscillation and chronologically-organized output of the circadian clock.

The central clock suffices to drive the majority of circulatory metabolic rhythms

Life on Earth anticipates recurring 24-hour environmental cycles via genetically encoded molecular clocks active in all mammalian organs. Communication between these clocks controls circadian homeostasis. Intertissue communication is mediated, in part, by temporal coordination of metabolism. Here, we characterize the extent to which clocks in different organs control systemic metabolic rhythms, an area that remains largely unexplored. We analyzed the metabolome of serum from mice with tissue-specific expression of the clock gene Bmal1. Having functional hepatic and muscle clocks can only drive a minority (13%) of systemic metabolic rhythms. Conversely, limiting Bmal1 expression to the central pacemaker in the brain restores rhythms to 57% of circulatory metabolites. Rhythmic feeding imposed on clockless mice resulted in a similar rescue, indicating that the central clock mainly regulates metabolic rhythms via behavior. These findings explicate the circadian communication between tissues and highlight the importance of the central clock in governing those signals.

RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts

The physiology and behavior of living organisms are featured by time-related variations driven by molecular clockworks that arose during evolution stochastically and heterogeneously. Over the years, several high-throughput experiments were performed to evaluate time-dependent gene expression in different cell types across several species and experimental conditions. Here, these were retrieved, manually curated, and analyzed by two software packages, BioCycle and MetaCycle, to infer circadian or ultradian transcripts across different species. These transcripts were stored in RhythmicDB and made publically available.

Early rhythmicity in the fetal suprachiasmatic nuclei in response to maternal signals detected by omics approach

The suprachiasmatic nuclei (SCN) of the hypothalamus harbor the central clock of the circadian system, which gradually matures during the perinatal period. In this study, time-resolved transcriptomic and proteomic approaches were used to describe fetal SCN tissue-level rhythms before rhythms in clock gene expression develop. Pregnant rats were maintained in constant darkness and had intact SCN, or their SCN were lesioned and behavioral rhythm was imposed by temporal restriction of food availability. Model-selecting tools dryR and CompareRhythms identified sets of genes in the fetal SCN that were rhythmic in the absence of the fetal canonical clock. Subsets of rhythmically expressed genes were assigned to groups of fetuses from mothers with either intact or lesioned SCN, or both groups. Enrichment analysis for GO terms and signaling pathways revealed that neurodevelopment and cell-to-cell signaling were significantly enriched within the subsets of genes that were rhythmic in response to distinct maternal signals. The findings discovered a previously unexpected breadth of rhythmicity in the fetal SCN at a developmental stage when the canonical clock has not yet developed at the tissue level and thus likely represents responses to rhythmic maternal signals.

Atlas of exercise metabolism reveals time-dependent signatures of metabolic homeostasis

Tissue sensitivity and response to exercise vary according to the time of day and alignment of circadian clocks, but the optimal exercise time to elicit a desired metabolic outcome is not fully defined. To understand how tissues independently and collectively respond to timed exercise, we applied a systems biology approach. We mapped and compared global metabolite responses of seven different mouse tissues and serum after an acute exercise bout performed at different times of the day. Comparative analyses of intra- and inter-tissue metabolite dynamics, including temporal profiling and blood sampling across liver and hindlimb muscles, uncovered an unbiased view of local and systemic metabolic responses to exercise unique to time of day. This comprehensive atlas of exercise metabolism provides clarity and physiological context regarding the production and distribution of canonical and novel time-dependent exerkine metabolites, such as 2-hydroxybutyrate (2-HB), and reveals insight into the health-promoting benefits of exercise on metabolism.

Untargeted and Targeted Circadian Metabolomics Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) and Flow Injection-Electrospray Ionization-Tandem Mass Spectrometry (FIA-ESI-MS/MS)

A diverse array of 24-h oscillating hormones and metabolites direct and reflect circadian clock function. Circadian metabolomics uses advanced high-throughput analytical chemistry techniques to comprehensively profile these small molecules (<1.5 kDa) across 24 h in cells, media, body fluids, breath, tissues, and subcellular compartments. The goals of circadian metabolomics experiments are often multifaceted. These include identifying and tracking rhythmic metabolic inputs and outputs of central and peripheral circadian clocks, quantifying endogenous free-running period, monitoring relative phase alignment between clocks, and mapping pathophysiological consequences of clock disruption or misalignment. Depending on the particular experimental question, samples are collected under free-running or entrained conditions. Here we describe both untargeted and targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) and flow injection-electrospray ionization-tandem mass spectrometry (FIA-ESI-MS/MS) based assays we have used for circadian metabolomics studies. We discuss tissue homogenization, chemical derivatization, measurement, and tips for data processing, normalization, scaling, how to handle outliers, and imputation of missing values.

Bioinformatics and Systems Biology of Circadian Rhythms: BIO_CYCLE and CircadiOmics

Circadian rhythms are fundamental to biology and medicine and today these can be studied at the molecular level in high-throughput fashion using various omic technologies. We briefly present two resources for the study of circadian omic (e.g. transcriptomic, metabolomic, proteomic) time series. First, BIO_CYCLE is a deep-learning-based program and web server that can analyze omic time series and statistically assess their periodic nature and, when periodic, accurately infer the corresponding period, amplitude, and phase. Second, CircadiOmics is the larges annotated repository of circadian omic time series, containing over 260 experiments and 90 million individual measurements, across multiple organs and tissues, and across 9 different species. In combination, these tools enable powerful bioinformatics and systems biology analyses. The are currently being deployed in a host of different projects where they are enabling significant discoveries: both tools are publicly available over the web at: http://circadiomics.ics.uci.edu/ .

Large-scale analysis reveals spatiotemporal circadian patterns of cilia transcriptomes in the primate brain

Cilia are dynamic subcellular systems, with core structural and functional components operating in a highly coordinated manner. Since many environmental stimuli sensed by cilia are circadian in nature, it is reasonable to speculate that genes encoding cilia structural and functional components follow rhythmic circadian patterns of expression. Using computational methods and the largest spatiotemporal gene expression atlas of primates, we identified and analyzed the circadian rhythmic expression of cilia genes across 22 primate brain areas. We found that around 73% of cilia transcripts exhibited circadian rhythmicity across at least one of 22 brain regions. In 12 brain regions, cilia transcriptomes were significantly enriched with circadian oscillating transcripts, as compared to the rest of the transcriptome. The phase of the cilia circadian transcripts deviated from the phase of the majority of the background circadian transcripts, and transcripts coding for cilia basal body components accounted for the majority of cilia circadian transcripts. In addition, adjacent or functionally connected brain nuclei had large overlapping complements of circadian cilia genes. Most remarkably, cilia circadian transcripts shared across the basal ganglia nuclei and the prefrontal cortex peaked in these structures in sequential fashion that is similar to the sequential order of activation of the basal ganglia-cortical circuitry in connection with movement coordination, albeit on completely different timescales. These findings support a role for the circadian spatiotemporal orchestration of cilia gene expression in the normal physiology of the basal ganglia-cortical circuit and motor control. Studying orchestrated cilia rhythmicity in the basal ganglia-cortical circuits and other brain circuits may help develop better functional models, and shed light on the causal effects cilia functions have on these circuits and on the regulation of movement and other behaviors.

Integration of feeding behavior by the liver circadian clock reveals network dependency of metabolic rhythms

The mammalian circadian clock, expressed throughout the brain and body, controls daily metabolic homeostasis. Clock function in peripheral tissues is required, but not sufficient, for this task. Because of the lack of specialized animal models, it is unclear how tissue clocks interact with extrinsic signals to drive molecular oscillations. Here, we isolated the interaction between feeding and the liver clock by reconstituting Bmal1 exclusively in hepatocytes (Liver-RE), in otherwise clock-less mice, and controlling timing of food intake. We found that the cooperative action of BMAL1 and the transcription factor CEBPB regulates daily liver metabolic transcriptional programs. Functionally, the liver clock and feeding rhythm are sufficient to drive temporal carbohydrate homeostasis. By contrast, liver rhythms tied to redox and lipid metabolism required communication with the skeletal muscle clock, demonstrating peripheral clock cross-talk. Our results highlight how the inner workings of the clock system rely on communicating signals to maintain daily metabolism.

MOSAIC: a joint modeling methodology for combined circadian and non-circadian analysis of multi-omics data

MOTIVATION

Circadian rhythms are approximately 24-h endogenous cycles that control many biological functions. To identify these rhythms, biological samples are taken over circadian time and analyzed using a single omics type, such as transcriptomics or proteomics. By comparing data from these single omics approaches, it has been shown that transcriptional rhythms are not necessarily conserved at the protein level, implying extensive circadian post-transcriptional regulation. However, as proteomics methods are known to be noisier than transcriptomic methods, this suggests that previously identified arrhythmic proteins with rhythmic transcripts could have been missed due to noise and may not be due to post-transcriptional regulation.

RESULTS

To determine if one can use information from less-noisy transcriptomic data to inform rhythms in more-noisy proteomic data, and thus more accurately identify rhythms in the proteome, we have created the Multi-Omics Selection with Amplitude Independent Criteria (MOSAIC) application. MOSAIC combines model selection and joint modeling of multiple omics types to recover significant circadian and non-circadian trends. Using both synthetic data and proteomic data from Neurospora crassa, we showed that MOSAIC accurately recovers circadian rhythms at higher rates in not only the proteome but the transcriptome as well, outperforming existing methods for rhythm identification. In addition, by quantifying non-circadian trends in addition to circadian trends in data, our methodology allowed for the recognition of the diversity of circadian regulation as compared to non-circadian regulation.

AVAILABILITY AND IMPLEMENTATION

MOSAIC's full interface is available at https://github.com/delosh653/MOSAIC. An R package for this functionality, mosaic.find, can be downloaded at https://CRAN.R-project.org/package=mosaic.find.

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.

Collecting mouse livers for transcriptome analysis of daily rhythms

Molecular daily rhythms can be captured by precisely timed tissue harvests from groups of animals. This protocol will allow the investigator to identify transcriptional rhythms in the mouse liver while also providing a template for similar analyses in other whole metabolic organs. We describe steps for mouse entrainment, liver dissection, and rhythmicity analysis from total RNA sequencing data. The resulting rhythmic transcriptome will provide the user with a starting point for defining specific biological processes that undergo daily rhythms.

For complete details on the use and execution of this protocol, please refer to Koronowski et al. (2019). A similar protocol for interfollicular epidermal cells is demonstrated in Welz et al. (2019).

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Investigate a gene or pathway in the fourth dimension—time

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Use precisely timed tissue harvest to detect daily transcriptional rhythms

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Full description of mouse entrainment, liver dissection, and rhythmicity analysis

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A reference protocol for investigating other whole metabolic organs

Combined statistical modeling enables accurate mining of circadian transcription

Circadian-regulated genes are essential for tissue homeostasis and organismal function, and are therefore common targets of scrutiny. Detection of rhythmic genes using current analytical tools requires exhaustive sampling, a demand that is costly and raises ethical concerns, making it unfeasible in certain mammalian systems. Several non-parametric methods have been commonly used to analyze short-term (24 h) circadian data, such as JTK_cycle and MetaCycle. However, algorithm performance varies greatly depending on various biological and technical factors. Here, we present CircaN, an ad-hoc implementation of a non-linear mixed model for the identification of circadian genes in all types of omics data. Based on the variable but complementary results obtained through several biological and in silico datasets, we propose a combined approach of CircaN and non-parametric models to dramatically improve the number of circadian genes detected, without affecting accuracy. We also introduce an R package to make this approach available to the community.

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.

Circadian clock effects on cellular proliferation: Insights from theory and experiments

Oscillations of the cellular circadian clock have emerged as an important regulator of many physiological processes, both in health and in disease. One such process, cellular proliferation, is being increasingly recognized to be affected by the circadian clock. Here, we review how a combination of experimental and theoretical work has furthered our understanding of the way circadian clocks couple to the cell cycle and play a role in tissue homeostasis and cancer. Finally, we discuss recently introduced methods for modeling coupling of clocks based on techniques from survival analysis and machine learning and highlight their potential importance for future studies.

Genome-wide circadian regulation: A unique system for computational biology

Circadian rhythms are 24-hour oscillations affecting an organism at multiple levels from gene expression all the way to tissues and organs. They have been observed in organisms across the kingdom of life, spanning from cyanobacteria to humans. In mammals, the master circadian pacemaker is located in the hypothalamic suprachiasmatic nuclei (SCN) in the brain where it synchronizes the peripheral oscillators that exist in other tissues. This system regulates the circadian activity of a large part of the transcriptome and recent findings indicate that almost every cell in the body has this clock at the molecular level. In this review, we briefly summarize the different factors that can influence the circadian transcriptome, including light, temperature, and food intake. We then summarize recently identified general principles governing genome-scale circadian regulation, as well as future lines of research. Genome-scale circadian activity represents a fascinating study model for computational biology. For this purpose, systems biology methods are promising exploratory tools to decode the global regulatory principles of circadian regulation.

Circadian rhythmicity of body temperature and metabolism

This article reviews the literature on the circadian rhythms of body temperature and whole-organism metabolism. The two rhythms are first described separately, each description preceded by a review of research methods. Both rhythms are generated endogenously but can be affected by exogenous factors. The relationship between the two rhythms is discussed next. In endothermic animals, modulation of metabolic activity can affect body temperature, but the rhythm of body temperature is not a mere side effect of the rhythm of metabolic thermogenesis associated with general activity. The circadian system modulates metabolic heat production to generate the body temperature rhythm, which challenges homeothermy but does not abolish it. Individual cells do not regulate their own temperature, but the relationship between circadian rhythms and metabolism at the cellular level is also discussed. Metabolism is both an output of and an input to the circadian clock, meaning that circadian rhythmicity and metabolism are intertwined in the cell.

Defining the Independence of the Liver Circadian Clock

Mammals rely on a network of circadian clocks to control daily systemic metabolism and physiology. The central pacemaker in the suprachiasmatic nucleus (SCN) is considered hierarchically dominant over peripheral clocks, whose degree of independence, or tissue-level autonomy, has never been ascertained in vivo. Using arrhythmic Bmal1-null mice, we generated animals with reconstituted circadian expression of BMAL1 exclusively in the liver (Liver-RE). High-throughput transcriptomics and metabolomics show that the liver has independent circadian functions specific for metabolic processes such as the NAD⁺ salvage pathway and glycogen turnover. However, although BMAL1 occupies chromatin at most genomic targets in Liver-RE mice, circadian expression is restricted to ∼10% of normally rhythmic transcripts. Finally, rhythmic clock gene expression is lost in Liver-RE mice under constant darkness. Hence, full circadian function in the liver depends on signals emanating from other clocks, and light contributes to tissue-autonomous clock function.

Assessment of Circadian Rhythms

Circadian rhythms are observed in most physiologic functions across a variety of species and are controlled by a master pacemaker in the brain called the suprachiasmatic nucleus. The complex nature of the circadian system and the impact of circadian disruption on sleep, health, and well-being support the need to assess internal circadian timing in the clinical setting. The ability to assess circadian rhythms and the degree of circadian disruption can help in categorizing subtypes or even new circadian rhythm disorders and aid in the clinical management of the these disorders.

Time of Exercise Specifies the Impact on Muscle Metabolic Pathways and Systemic Energy Homeostasis

While the timing of food intake is important, it is unclear whether the effects of exercise on energy metabolism are restricted to unique time windows. As circadian regulation is key to controlling metabolism, understanding the impact of exercise performed at different times of the day is relevant for physiology and homeostasis. Using high-throughput transcriptomic and metabolomic approaches, we identify distinct responses of metabolic oscillations that characterize exercise in either the early rest phase or the early active phase in mice. Notably, glycolytic activation is specific to exercise at the active phase. At the molecular level, HIF1α, a central regulator of glycolysis during hypoxia, is selectively activated in a time-dependent manner upon exercise, resulting in carbohydrate exhaustion, usage of alternative energy sources, and adaptation of systemic energy expenditure. Our findings demonstrate that the time of day is a critical factor to amplify the beneficial impact of exercise on both metabolic pathways within skeletal muscle and systemic energy homeostasis.

Atlas of Circadian Metabolism Reveals System-wide Coordination and Communication between Clocks

Metabolic diseases are often characterized by circadian misalignment in different tissues, yet how altered coordination and communication among tissue clocks relate to specific pathogenic mechanisms remains largely unknown. Applying an integrated systems biology approach, we performed 24-hr metabolomics profiling of eight mouse tissues simultaneously. We present a temporal and spatial atlas of circadian metabolism in the context of systemic energy balance and under chronic nutrient stress (high-fat diet [HFD]). Comparative analysis reveals how the repertoires of tissue metabolism are linked and gated to specific temporal windows and how this highly specialized communication and coherence among tissue clocks is rewired by nutrient challenge. Overall, we illustrate how dynamic metabolic relationships can be reconstructed across time and space and how integration of circadian metabolomics data from multiple tissues can improve our understanding of health and disease.

Universal method for robust detection of circadian state from gene expression

Circadian clocks play a key role in regulating a vast array of biological processes, with significant implications for human health. Accurate assessment of physiological time using transcriptional biomarkers found in human blood can significantly improve diagnosis of circadian disorders and optimize the delivery time of therapeutic treatments. To be useful, such a test must be accurate, minimally burdensome to the patient, and readily generalizable to new data. A major obstacle in development of gene expression biomarker tests is the diversity of measurement platforms and the inherent variability of the data, often resulting in predictors that perform well in the original datasets but cannot be universally applied to new samples collected in other settings. Here, we introduce TimeSignature, an algorithm that robustly infers circadian time from gene expression. We demonstrate its application in data from three independent studies using distinct microarrays and further validate it against a new set of samples profiled by RNA-sequencing. Our results show that TimeSignature is more accurate and efficient than competing methods, estimating circadian time to within 2 h for the majority of samples. Importantly, we demonstrate that once trained on data from a single study, the resulting predictor can be universally applied to yield highly accurate results in new data from other studies independent of differences in study population, patient protocol, or assay platform without renormalizing the data or retraining. This feature is unique among expression-based predictors and addresses a major challenge in the development of generalizable, clinically useful tests.

Deep Learning in Biomedical Data Science

Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep learning methods, and application of these methods to biomedical data have led to scientific discoveries and practical solutions. This overview provides technical and historical pointers to the field, and surveys current applications of deep learning to biomedical data organized around five subareas, roughly of increasing spatial scale: chemoinformatics, proteomics, genomics and transcriptomics, biomedical imaging, and health care. The black box problem of deep learning methods is also briefly discussed.