The circadian clock regulates many aspects of physiology including metabolism and cardiovascular function. The past decade of research has seen the development of a scaffold model of oscillator function in which the suprachiasmatic nucleus of the hypothalamus harbors a """"""""master clock"""""""" and orchestrates peripheral oscillators present in most major organ systems. These central and peripheral oscillator systems generate a cascade of circadian transcriptional rhythms that ultimately culminate in observed physiological and behavioral oscillations. Genetic disruption of this organization in animal models results in pathophysiological consequences such as glucose intolerance and insulin resistance, components of the metabolic syndrome seen in people at high risk for cardiovascular disease. We present compelling evidence that communication between clocks is more sophisticated and can involve peripheral-to-peripheral and peripheral-to-central clock communication. Here we test the central hypothesis that communication between peripheral and central oscillators is bidirectional and that peripheral oscillators may directly influence each others'function. Using cell type specific conditional mouse models in which Bmal1, a required component of the oscillator, is deleted, we will use physiological and systems approaches to test the hypothesis that oscillator function in endothelial cells regulates vascular smooth muscle function (and vice versa) and influences diurnal variation in blood pressure, thrombogenesis, and locomotor activity (Specific Aim 1). We will also test the hypothesis that oscillator function in adipocytes regulates macrophage function (and vice versa) and influences glucose homeostasis, response to inflammatory stimuli, and feeding rhythms (Specific Aim 2). Furthermore, we propose testing a mechanistic hypothesis that cell type specific disruption of oscillator function results in oscillator abnormalities in nearby cells, and that this disruption propagates to the liver, adrenal, kidneys, and the brain (Specific Aim 3). Finally, using systems approaches we will examine network level changes provoked by genetic disruption of oscillator function in specific cell types and begin to probe network to network conveyance of circadian information as well as identify candidate signaling molecules (Specific Aim 4).
Many physiological and behavioral processes are regulated by the circadian clock, which keeps body time on the 24 hour scale. Timing of feeding, metabolism, and cardiovascular function are critical aspects of maintaining homeostasis, which when disrupted through genetic mutation or behavioral alterations such as shift work, results in diseases such as diabetes and heart disease. This proposal focuses on the idea that communication between metabolic and cardiovascular systems, as well as between these and the brain, is critical in maintaining health.
| Giovannone, Adrian J; Winterstein, Christine; Bhattaram, Pallavi et al. (2018) Soluble syntaxin 3 functions as a transcriptional regulator. J Biol Chem 293:5478-5491 |
| Yang, Guangrui; Chen, Lihong; Grant, Gregory R et al. (2016) Timing of expression of the core clock gene Bmal1 influences its effects on aging and survival. Sci Transl Med 8:324ra16 |
| Fitzgerald, G A; Yang, G; Paschos, G K et al. (2015) Molecular clocks and the human condition: approaching their characterization in human physiology and disease. Diabetes Obes Metab 17 Suppl 1:139-42 |
| McLoughlin, Sarah C; Haines, Philip; FitzGerald, Garret A (2015) Clocks and cardiovascular function. Methods Enzymol 552:211-28 |
| Curtis, Anne M; Fagundes, Caio T; Yang, Guangrui et al. (2015) Circadian control of innate immunity in macrophages by miR-155 targeting Bmal1. Proc Natl Acad Sci U S A 112:7231-6 |
| Zhang, Ray; Lahens, Nicholas F; Ballance, Heather I et al. (2014) A circadian gene expression atlas in mammals: implications for biology and medicine. Proc Natl Acad Sci U S A 111:16219-24 |
| Hughes, Michael E; Peeler, John; Hogenesch, John B et al. (2014) The growth and impact of Alzheimer disease centers as measured by social network analysis. JAMA Neurol 71:412-20 |
| Lahens, Nicholas F; Kavakli, Ibrahim Halil; Zhang, Ray et al. (2014) IVT-seq reveals extreme bias in RNA sequencing. Genome Biol 15:R86 |
| Yang, Guangrui; Paschos, Georgios; Curtis, Anne M et al. (2013) Knitting up the raveled sleave of care. Sci Transl Med 5:212rv3 |
| Olarerin-George, Anthony O; Anton, Lauren; Hwang, Yih-Chii et al. (2013) A functional genomics screen for microRNA regulators of NF-kappaB signaling. BMC Biol 11:19 |
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