Obstructive sleep apnea in a mouse model is associated with tissue-specific transcriptomic changes in circadian rhythmicity and mean 24-hour gene expression.

Intermittent hypoxia (IH) is a major clinical feature of obstructive sleep apnea (OSA). The mechanisms that become dysregulated after periods of exposure to IH are unclear, particularly in the early stages of disease. The circadian clock governs a wide array of biological functions and is intimately associated with stabilization of hypoxia-inducible factors (HIFs) under hypoxic conditions. In patients, IH occurs during the sleep phase of the 24-hour sleep-wake cycle, potentially affecting their circadian rhythms. Alterations in the circadian clock have the potential to accelerate pathological processes, including other comorbid conditions that can be associated with chronic, untreated OSA. We hypothesized that changes in the circadian clock would manifest differently in those organs and systems known to be impacted by OSA. Using an IH model to represent OSA, we evaluated circadian rhythmicity and mean 24-hour expression of the transcriptome in 6 different mouse tissues, including the liver, lung, kidney, muscle, heart, and cerebellum, after a 7-day exposure to IH. We found that transcriptomic changes within cardiopulmonary tissues were more affected by IH than other tissues. Also, IH exposure resulted in an overall increase in core body temperature. Our findings demonstrate a relationship between early exposure to IH and changes in specific physiological outcomes. This study provides insight into the early pathophysiological mechanisms associated with IH.

An in silico genome-wide screen for circadian clock strength in human samples.

MOTIVATION: Years of time-series gene expression studies have built a strong understanding of clock-controlled pathways across species. However, comparatively little is known about how 'non-clock' pathways influence clock function. We need a strong understanding of clock-coupled pathways in human tissues to better appreciate the links between disease and clock function. RESULTS: We developed a new computational approach to explore candidate pathways coupled to the clock in human tissues. This method, termed LTM, is an in silico screen to infer genetic influences on circadian clock function. LTM uses natural variation in gene expression in human data and directly links gene expression variation to clock strength independent of longitudinal data. We applied LTM to three human skin and one melanoma datasets and found that the cell cycle is the top candidate clock-coupled pathway in healthy skin. In addition, we applied LTM to thousands of tumor samples from 11 cancer types in the TCGA database and found that extracellular matrix organization-related pathways are tightly associated with the clock strength in humans. Further analysis shows that clock strength in tumor samples is correlated with the proportion of cancer-associated fibroblasts and endothelial cells. Therefore, we show both the power of LTM in predicting clock-coupled pathways and classify factors associated with clock strength in human tissues. AVAILABILITY AND IMPLEMENTATION: LTM is available on GitHub (https://github.com/gangwug/LTMR) and figshare (https://figshare.com/articles/software/LTMR/21217604) to facilitate its use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Normalized coefficient of variation (nCV): a method to evaluate circadian clock robustness in population scale data.

SUMMARY: Robust oscillation of clock genes is a core feature of the circadian system. Relative amplitude (rAMP) measures the robustness of clock gene oscillations but only works for longitudinal samples. We lack a method for estimating robust oscillations from human samples without labeled time. We show that the normalized coefficient of variation (nCV) of 10 clock genes is linearly correlated with their normalized rAMP, independent of time labels. We found that the mean nCV of clock genes are consistently decreased in tumors compared to nontumors, suggesting a new therapeutic target in cancer treatment by enhancing clock robustness. nCV can provide a simple measure of the clock robustness in population-level datasets. AVAILABILITY AND IMPLEMENTATION: The nCV package (https://github.com/gangwug/nCV) and web application (https://github.com/gangwug/nCVapp) are available on the GitHub repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A large-scale study reveals 24-h operational rhythms in hospital treatment.

Hospitals operate 24 h a day, and it is assumed that important clinical decisions occur continuously around the clock. However, many aspects of hospital operation occur at specific times of day, including medical team rounding and shift changes. It is unclear whether this impacts patient care, as no studies have addressed this. We analyzed the daily distribution of ∼500,000 doses of 12 separate drugs in 1,546 inpatients at a major children's hospital in the United States from 2010 to 2017. We tracked both order time (when a care provider places an electronic request for a drug) and dosing time (when the patient receives the drug). Order times were time-of-day-dependent, marked by distinct morning-time surges and overnight lulls. Nearly one-third of all 103,847 orders for treatment were placed between 8:00 AM and 12:00 PM. First doses from each order were also rhythmic but shifted by 2 h. These 24-h rhythms in orders and first doses were remarkably consistent across drugs, diagnosis, and hospital units. This rhythm in hospital medicine coincided with medical team rounding time, not necessarily immediate medical need. Lastly, we show that the clinical response to hydralazine, an acute antihypertensive, is dosing time-dependent and greatest at night, when the fewest doses were administered. The prevailing dogma is that hospital treatment is administered as needed regardless of time of day. Our findings challenge this notion and reveal a potential operational barrier to best clinical care.

Population-level rhythms in human skin with implications for circadian medicine.

Skin is the largest organ in the body and serves important barrier, regulatory, and sensory functions. The epidermal layer shows rhythmic physiological responses to daily environmental variation (e.g., DNA repair). We investigated the role of the circadian clock in the transcriptional regulation of epidermis using a hybrid experimental design, in which a limited set of human subjects (n = 20) were sampled throughout the 24-h cycle and a larger population (n = 219) were sampled once. We found a robust circadian oscillator in human epidermis at the population level using pairwise correlations of clock and clock-associated genes in 298 epidermis samples. We then used CYCLOPS to reconstruct the temporal order of all samples, and identified hundreds of rhythmically expressed genes at the population level in human epidermis. We compared these results with published time-series skin data from mice and found a strong concordance in circadian phase across species for both transcripts and pathways. Furthermore, like blood, epidermis is readily accessible and a potential source of biomarkers. Using ZeitZeiger, we identified a biomarker set for human epidermis that is capable of reporting circadian phase to within 3 hours from a single sample. In summary, we show rhythms in human epidermis that persist at the population scale and describe a path to develop robust single-sample circadian biomarkers.

Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges.

A wealth of data is available from electronic health records (EHR) that are collected as part of routine clinical care in hospitals worldwide. These rich, longitudinal data offer an attractive object of study for the field of circadian medicine, which aims to translate knowledge of circadian rhythms to improve patient health. This narrative review aims to discuss opportunities for EHR in studies of circadian medicine, highlight the methodological challenges, and provide recommendations for using these data to advance the field. In the existing literature, we find that data collected in real-world clinical settings have the potential to shed light on key questions in circadian medicine, including how 24-hour rhythms in clinical features are associated with-or even predictive of-health outcomes, whether the effect of medication or other clinical activities depend on time of day, and how circadian rhythms in physiology may influence clinical reference ranges or sampling protocols. However, optimal use of EHR to advance circadian medicine requires careful consideration of the limitations and sources of bias that are inherent to these data sources. In particular, time of day influences almost every interaction between a patient and the healthcare system, creating operational 24-hour patterns in the data that have little or nothing to do with biology. Addressing these challenges could help to expand the evidence base for the use of EHR in the field of circadian medicine.

Chronobiological Spatial Clusters of Cortical Regions in the Human Brain.

PURPOSE: We demonstrate that different regions of the cerebral cortex have different diurnal rhythms of spontaneously occurring high-frequency oscillations (HFOs). METHODS: High-frequency oscillations were assessed with standard-of-care stereotactic electroencephalography in patients with drug-resistant epilepsy. To ensure generalizability of our findings beyond patients with drug-resistant epilepsy, we excluded stereotactic electroencephalography electrode contacts lying within seizure-onset zones, epileptogenic lesions, having frequent epileptiform activity, and excessive artifact. For each patient, we evaluated twenty-four 5-minute stereotactic electroencephalography epochs, sampled hourly throughout the day, and obtained the HFO rate (number of HFOs/minute) in every stereotactic electroencephalography channel. We analyzed diurnal rhythms of the HFO rates with the cosinor model and clustered neuroanatomic parcels in a standard brain space based on similarity of their cosinor parameters. Finally, we compared overlap among resting-state networks, described in the neuroimaging literature, and chronobiological spatial clusters discovered by us. RESULTS: We found five clusters that localized predominantly or exclusively to the left perisylvian, left perirolandic and left temporal, right perisylvian and right parietal, right frontal, and right insular-opercular cortices, respectively. These clusters were characterized by similarity of the HFO rates according to the time of the day. Also, these chronobiological spatial clusters preferentially overlapped with specific resting-state networks, particularly default mode network (clusters 1 and 3), frontoparietal network (cluster 1), visual network (cluster 1), and mesial temporal network (cluster 2). CONCLUSIONS: This is probably the first human study to report clusters of cortical regions with similar diurnal rhythms of electrographic activity. Overlap with resting-state networks attests to their functional significance and has implications for understanding cognitive functions and epilepsy-related mortality.

Integration of genome-scale data identifies candidate sleep regulators.

STUDY OBJECTIVES: Genetics impacts sleep, yet, the molecular mechanisms underlying sleep regulation remain elusive. In this study, we built machine learning models to predict sleep genes based on their similarity to genes that are known to regulate sleep. METHODS: We trained a prediction model on thousands of published datasets, representing circadian, immune, sleep deprivation, and many other processes, using a manually curated list of 109 sleep genes. RESULTS: Our predictions fit with prior knowledge of sleep regulation and identified key genes and pathways to pursue in follow-up studies. As an example, we focused on the NF-κB pathway and showed that chronic activation of NF-κB in a genetic mouse model impacted the sleep-wake patterns. CONCLUSION: Our study highlights the power of machine learning in integrating prior knowledge and genome-wide data to study genetic regulation of complex behaviors such as sleep.

Intermittent Hypoxia Alters the Circadian Expression of Clock Genes in Mouse Brain and Liver.

At least one-third of adults in the United States experience intermittent hypoxia (IH) due to health or living conditions. The majority of these adults suffer with sleep breathing conditions and associated circadian rhythm disorders. The impact of IH on the circadian clock is not well characterized. In the current study, we used an IH mouse model to understand the impact of IH on the circadian gene expression of the canonical clock genes in the central (the brain) and peripheral (the liver) tissues. Gene expression was measured using a Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR). CircaCompare was used to evaluate the differential rhythmicity between normoxia and IH. Our observations suggested that the circadian clock in the liver was less sensitive to IH compared to the circadian clock in the brain.

Short-term exposure to intermittent hypoxia leads to changes in gene expression seen in chronic pulmonary disease.

Obstructive sleep apnea (OSA) results from episodes of airway collapse and intermittent hypoxia (IH) and is associated with a host of health complications. Although the lung is the first organ to sense changes in oxygen levels, little is known about the consequences of IH to the lung hypoxia-inducible factor-responsive pathways. We hypothesized that exposure to IH would lead to cell-specific up- and downregulation of diverse expression pathways. We identified changes in circadian and immune pathways in lungs from mice exposed to IH. Among all cell types, endothelial cells showed the most prominent transcriptional changes. Upregulated genes in myofibroblast cells were enriched for genes associated with pulmonary hypertension and included targets of several drugs currently used to treat chronic pulmonary diseases. A better understanding of the pathophysiologic mechanisms underlying diseases associated with OSA could improve our therapeutic approaches, directing therapies to the most relevant cells and molecular pathways.