Temperature-driven coordination of circadian transcriptional regulation.

The circadian clock is an evolutionarily-conserved molecular oscillator that enables species to anticipate rhythmic changes in their environment. At a molecular level, the core clock genes induce circadian oscillations in thousands of genes in a tissue-specific manner, orchestrating myriad biological processes. While previous studies have investigated how the core clock circuit responds to environmental perturbations such as temperature, the downstream effects of such perturbations on circadian regulation remain poorly understood. By analyzing bulk-RNA sequencing of Drosophila fat bodies harvested from flies subjected to different environmental conditions, we demonstrate a highly condition-specific circadian transcriptome: genes are cycling in a temperature-specific manner, and the distributions of their phases also differ between the two conditions. Further employing a reference-based gene regulatory network (Reactome), we find evidence of increased gene-gene coordination at low temperatures and synchronization of rhythmic genes that are network neighbors. We report that the phase differences between cycling genes increase as a function of geodesic distance in the low temperature condition, suggesting increased coordination of cycling on the gene regulatory network. Our results suggest a potential mechanism whereby the circadian clock mediates the fly's response to seasonal changes in temperature.

Platform-independent estimation of human physiological time from single blood samples.

Abundant epidemiological evidence links circadian rhythms to human health, from heart disease to neurodegeneration. Accurate determination of an individual's circadian phase is critical for precision diagnostics and personalized timing of therapeutic interventions. To date, however, we still lack an assay for physiological time that is accurate, minimally burdensome to the patient, and readily generalizable to new data. Here, we present TimeMachine, an algorithm to predict the human circadian phase using gene expression in peripheral blood mononuclear cells from a single blood draw. Once trained on data from a single study, we validated the trained predictor against four independent datasets with distinct experimental protocols and assay platforms, demonstrating that it can be applied generalizably. Importantly, TimeMachine predicted circadian time with a median absolute error ranging from 1.65 to 2.7 h, regardless of systematic differences in experimental protocol and assay platform, without renormalizing the data or retraining the predictor. This feature enables it to be flexibly applied to both new samples and existing data without limitations on the transcriptomic profiling technology (microarray, RNAseq). We benchmark TimeMachine against competing approaches and identify the algorithmic features that contribute to its performance.

Detecting Rhythmic Gene Expression in Single Cell Transcriptomics.

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

Temperature-driven coordination of circadian transcriptome regulation.

The circadian rhythm is an evolutionarily-conserved molecular oscillator that enables species to anticipate rhythmic changes in their environment. At a molecular level, the core clock genes induce a circadian oscillation in thousands of genes in a tissue-specific manner, orchestrating myriad biological processes. While studies have investigated how the core clock circuit responds to environmental perturbations such as temperature, the downstream effects of such perturbations on circadian regulation remain poorly understood. By analyzing bulk-RNA sequencing of Drosophila fat bodies harvested from flies subjected to different environmental conditions, we demonstrate a highly condition-specific circadian transcriptome. Further employing a reference-based gene regulatory network (Reactome), we find evidence of increased gene-gene coordination at low temperatures and synchronization of rhythmic genes that are network neighbors. Our results point to the mechanisms by which the circadian clock mediates the fly's response to seasonal changes in temperature.

A minimal model of peripheral clocks reveals differential circadian re-entrainment in aging.

The mammalian circadian system comprises a network of endogenous oscillators, spanning from the central clock in the brain to peripheral clocks in other organs. These clocks are tightly coordinated to orchestrate rhythmic physiological and behavioral functions. Dysregulation of these rhythms is a hallmark of aging, yet it remains unclear how age-related changes lead to more easily disrupted circadian rhythms. Using a two-population model of coupled oscillators that integrates the central clock and the peripheral clocks, we derive simple mean-field equations that can capture many aspects of the rich behavior found in the mammalian circadian system. We focus on three age-associated effects that have been posited to contribute to circadian misalignment: attenuated input from the sympathetic pathway, reduced responsiveness to light, and a decline in the expression of neurotransmitters. We find that the first two factors can significantly impede re-entrainment of the clocks following perturbation, while a weaker coupling within the central clock does not affect the recovery rate. Moreover, using our minimal model, we demonstrate the potential of using the feed-fast cycle as an effective intervention to accelerate circadian re-entrainment. These results highlight the importance of peripheral clocks in regulating the circadian rhythm and provide fresh insights into the complex interplay between aging and the resilience of the circadian system.

Single cell transcriptomics reveals reduced stress response in stem cells manipulated using localized electric fields.

Membrane disruption using Bulk Electroporation (BEP) is a widely used non-viral method for delivering biomolecules into cells. Recently, its microfluidic counterpart, Localized Electroporation (LEP), has been successfully used for several applications ranging from reprogramming and engineering cells for therapeutic purposes to non-destructive sampling from live cells for temporal analysis. However, the side effects of these processes on gene expression, that can affect the physiology of sensitive stem cells are not well understood. Here, we use single cell RNA sequencing (scRNA-seq) to investigate the effects of BEP and LEP on murine neural stem cell (NSC) gene expression. Our results indicate that unlike BEP, LEP does not lead to extensive cell death or activation of cell stress response pathways that may affect their long-term physiology. Additionally, our demonstrations show that LEP is suitable for multi-day delivery protocols as it enables better preservation of cell viability and integrity as compared to BEP.

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.

Reconstruction of 3-dimensional tissue organization at the single-cell resolution.

Recent advances in spatial transcriptomics (ST) have allowed for the mapping of tissue heterogeneity, but this technique lacks the resolution to investigate gene expression patterns, cell-cell communications and tissue organization at the single-cell resolution. ST data contains a mixed transcriptome from multiple heterogeneous cells, and current methods predict two-dimensional (2D) coordinates for individual cells within a predetermined space, making it difficult to reconstruct and study three-dimensional (3D) tissue organization. Here we present a new computational method called scHolography that uses deep learning to map single-cell transcriptome data to 3D space. Unlike existing methods, which generate a projection between transcriptome data and 2D spatial coordinates, scHolography uses neural networks to create a high-dimensional transcriptome-to-space map that preserves the distance information between cells, allowing for the construction of a cell-cell proximity matrix beyond the 2D ST scaffold. Furthermore, the neighboring cell profile of a given cell type can be extracted to study spatial cell heterogeneity. We apply scHolography to human skin, human skin cancer and mouse brain datasets, providing new insights into gene expression patterns, cell-cell interactions and spatial microenvironment. Together, scHolography offers a computational solution for digitizing transcriptome and spatial information into high-dimensional data for neural network-based mapping and the reconstruction of 3D tissue organization at the single-cell resolution.

Light at night in older age is associated with obesity, diabetes, and hypertension.

Light at night (LAN) has been associated with negative health consequences and metabolic risk factors. Little is known about the prevalence of LAN in older adults in the United States and its association with CVD risk factors. We tested the hypothesis that LAN in older age is associated with higher prevalence of individual CVD risk factors. Five hundred and fifty-two community-dwelling adults aged 63-84 years underwent an examination of CVD risk factor profiles and 7-day actigraphy recording for activity and light measures. Associations between actigraphy-measured LAN, defined as no light vs. light within the 5-hour nadir (L5), and CVD risk factors, including obesity, diabetes, hypertension, and hypercholesterolemia, were examined, after adjusting for age, sex, race, season of recording, and sleep variables. LAN exposure was associated with a higher prevalence of obesity (multivariable-adjusted odds ratio [OR] 1.82 [95% CI 1.26-2.65]), diabetes (OR 2.00 [1.19-3.43]), and hypertension (OR 1.74 [1.21-2.52]) but not with hypercholesterolemia. LAN was also associated with (1) later timing of lowest light exposure (L5-light) and lowest activity (L5-activity), (2) lower inter-daily stability and amplitude of light exposure and activity, and (3) higher wake after sleep onset. Habitual LAN in older age is associated with concurrent obesity, diabetes, and hypertension. Further research is needed to understand long-term effects of LAN on cardiometabolic risks.

Quantitative analysis of transcriptome dynamics provides novel insights into developmental state transitions.

BACKGROUND: During embryogenesis, the developmental potential of initially pluripotent cells becomes progressively restricted as they transit to lineage restricted states. The pluripotent cells of Xenopus blastula-stage embryos are an ideal system in which to study cell state transitions during developmental decision-making, as gene expression dynamics can be followed at high temporal resolution. RESULTS: Here we use transcriptomics to interrogate the process by which pluripotent cells transit to four different lineage-restricted states: neural progenitors, epidermis, endoderm and ventral mesoderm, providing quantitative insights into the dynamics of Waddington's landscape. Our findings provide novel insights into why the neural progenitor state is the default lineage state for pluripotent cells and uncover novel components of lineage-specific gene regulation. These data reveal an unexpected overlap in the transcriptional responses to BMP4/7 and Activin signaling and provide mechanistic insight into how the timing of signaling inputs such as BMP are temporally controlled to ensure correct lineage decisions. CONCLUSIONS: Together these analyses provide quantitative insights into the logic and dynamics of developmental decision making in early embryos. They also provide valuable lineage-specific time series data following the acquisition of specific lineage states during development.

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. AVAILABILITYAND IMPLEMENTATION: 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.

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.

Rest-activity rhythm disturbance in liver cirrhosis and association with cognitive impairment.

Cognitive impairment and disturbed sleep-wake rhythms are disabling complications of liver cirrhosis, yet there is limited understanding of how they are related. We tested the hypothesis that alterations of sleep, rest-activity, and light exposure patterns are associated with worse cognition in cirrhosis. A total of 54 ambulatory adult patients with cirrhosis and 41 age-/gender-matched healthy controls wore wrist actigraphy for rest-activity and light measurements and completed Patient-Reported Outcomes Measurement Information System sleep instruments for self-reported sleep quality. We used standard nonparametric descriptors to characterize rest-activity and light patterns, and wake after sleep onset and sleep efficiency to assess objective sleep quality. The NIH Toolbox cognition battery was used for objective cognitive evaluation using T-scores from a demographically adjusted population reference. Spearman's correlation and multivariable models were used to explore associations between measures of cognition, sleep, rest-activity, and light. Cognition was significantly impaired in cirrhosis patients. Sleep quality was worse in cirrhosis patients by subjective and objective measures compared with controls. Cirrhosis patients exhibited fragmented and dampened rest-activity rhythms, lower daytime and higher nighttime light exposure compared with controls. Worse working memory and processing speed was associated with lower daytime activity level, higher rest-activity fragmentation, lower day-to-day stability, and greater nocturnal light exposure. No association was found between cognition and sleep quality. Rest-activity fragmentation and abnormal light exposure patterns are common in patients with liver disease and are associated with the severity of cognitive impairment. Further research is needed to investigate the effects of timed bright light and exercise intervention on cognitive function in patients with liver disease.

Autonomic dysregulation and sleep homeostasis in insomnia.

STUDY OBJECTIVES: Insomnia is common in older adults, and is associated with poor health, including cognitive impairment and cardio-metabolic disease. Although the mechanisms linking insomnia with these comorbidities remain unclear, age-related changes in sleep and autonomic nervous system (ANS) regulation might represent a shared mechanistic pathway. In this study, we assessed the relationship between ANS activity with indices of objective and subjective sleep quality in older adults with insomnia. METHODS: Forty-three adults with chronic insomnia and 16 age-matched healthy sleeper controls were studied. Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), objective sleep quality by electroencephalogram spectral components derived from polysomnography, and ANS activity by measuring 24-h plasma cortisol and norepinephrine (NE). RESULTS: Sleep cycle analysis displayed lower slow oscillatory (SO: 0.5-1.25 Hz) activity in the first cycle in insomnia compared to controls. In insomnia, 24-h cortisol levels were higher and 24-h NE levels were lower than controls. In controls, but not in insomnia, there was a significant interaction between NE level during wake and SO activity levels across the sleep cycles, such that in controls but not in insomnia, NE level during wake was positively associated with the amount of SO activity in the first cycle. In insomnia, lower 24-h NE level and SO activity in the first sleep cycle were associated with poorer subjective sleep quality. CONCLUSION: Dysregulation of autonomic activity may be an underlying mechanism that links objective and subjective measures of sleep quality in older adults with insomnia, and potentially contribute to adverse health outcomes.

Circadian Gene Expression Rhythms During Critical Illness.

OBJECTIVES: Core clock genes regulate tissue-specific transcriptome oscillations that synchronize physiologic processes throughout the body, held in phase by the central circadian rhythm. The central circadian rhythm rapidly dampens with onset of critical illness, but the effect of critical illness on gene expression oscillations is unknown. The objective of this study was to characterize the rhythmicity and phase coherence of core clock genes and the broader transcriptome after onset of critical illness. DESIGN: Cross-sectional study. SETTING: ICUs and hospital clinical research unit. PATIENTS: Critically ill patients within the first day of presenting from the community and healthy volunteers. INTERVENTIONS: Usual care (critically ill patients) and modified constant routine (healthy volunteers). MEASUREMENTS AND MAIN RESULTS: We studied 15 critically ill patients, including 10 with sepsis and five with intracerebral hemorrhage, and 11 healthy controls. The central circadian rhythm and rest-activity rhythms were profiled by continuous wrist actigraphy, and serum melatonin sampled every 2 hours along with whole blood for RNA isolation over 24 hours. The gene expression transcriptome was obtained by RNA sequencing. Core clock genes were analyzed for rhythmicity by cosinor fit. Significant circadian rhythmicity was identified in five of six core clock genes in healthy controls, but none in critically ill patients. TimeSignature, a validated algorithm based on 41 genes, was applied to assess overall transcriptome phase coherence. Median absolute error of TimeSignature was higher in individual critically ill patients than healthy patients (4.90 vs 1.48 hr) and was correlated with encephalopathy severity by Glasgow Coma Scale in critically ill patients (rho, -0.54; p = 0.036). CONCLUSIONS: Gene expression rhythms rapidly become abnormal during critical illness. The association between disrupted transcriptome rhythms and encephalopathy suggests a path for future work to elucidate the underlying pathophysiology.

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.

NAD+ Controls Circadian Reprogramming through PER2 Nuclear Translocation to Counter Aging.

Disrupted sleep-wake and molecular circadian rhythms are a feature of aging associated with metabolic disease and reduced levels of NAD+, yet whether changes in nucleotide metabolism control circadian behavioral and genomic rhythms remains unknown. Here, we reveal that supplementation with the NAD+ precursor nicotinamide riboside (NR) markedly reprograms metabolic and stress-response pathways that decline with aging through inhibition of the clock repressor PER2. NR enhances BMAL1 chromatin binding genome-wide through PER2K680 deacetylation, which in turn primes PER2 phosphorylation within a domain that controls nuclear transport and stability and that is mutated in human advanced sleep phase syndrome. In old mice, dampened BMAL1 chromatin binding, transcriptional oscillations, mitochondrial respiration rhythms, and late evening activity are restored by NAD+ repletion to youthful levels with NR. These results reveal effects of NAD+ on metabolism and the circadian system with aging through the spatiotemporal control of the molecular clock.

GeneSurrounder: network-based identification of disease genes in expression data.

BACKGROUND: A key challenge of identifying disease-associated genes is analyzing transcriptomic data in the context of regulatory networks that control cellular processes in order to capture multi-gene interactions and yield mechanistically interpretable results. One existing category of analysis techniques identifies groups of related genes using interaction networks, but these gene sets often comprise tens or hundreds of genes, making experimental follow-up challenging. A more recent category of methods identifies precise gene targets while incorporating systems-level information, but these techniques do not determine whether a gene is a driving source of changes in its network, an important characteristic when looking for potential drug targets. RESULTS: We introduce GeneSurrounder, an analysis method that integrates expression data and network information in a novel procedure to detect genes that are sources of dysregulation on the network. The key idea of our method is to score genes based on the evidence that they influence the dysregulation of their neighbors on the network in a manner that impacts cell function. Applying GeneSurrounder to real expression data, we show that our method is able to identify biologically relevant genes, integrate pathway and expression data, and yield more reproducible results across multiple studies of the same phenotype than competing methods. CONCLUSIONS: Together these findings suggest that GeneSurrounder provides a new avenue for identifying individual genes that can be targeted therapeutically. The key innovation of GeneSurrounder is the combination of pathway network information with gene expression data to determine the degree to which a gene is a source of dysregulation on the network. By prioritizing genes in this way, our method provides insights into disease mechanisms and suggests diagnostic and therapeutic targets. Our method can be used to help biologists select among tens or hundreds of genes for further validation. The implementation in R is available at github.com/sahildshah1/gene-surrounder.