The overall aim of this project is to combine experimental studies with mathematical modeling to understand the interactions between the hypothalamus and the pituitary gland that lead to rhythmic secretion of the hormone prolactin. This work will be done in close collaboration between an experimental lab and a mathematical modeling lab; both labs reside at the same university, facilitating daily interactions. The primary goal of this research is to understand how neurosecretory cells within the hypothalamus interact with the pituitary gland to produce daily rhythms of prolactin secretion in the rat during pregnancy. Prolactin is one of the most versatile hormones of mammalian organisms, with over 300 separate biological activities. The prolactin secreted following the mating stimulus has many targets, including other endocrine glands, and is important for maintaining a normal pregnancy in the rat. Prolactin is secreted by pituitary lactotrophs. Secretion from these cells is tightly regulated by the hypothalamus, a region of the brain that transmits time-of-day information to the rest of the body. The interaction between hypothalamic neurons and lactotrophs is complex; the neurons influence each other as well as the lactotrophs, and prolactin from lactotrophs feeds back onto and influences the hypothalamic neurons. Such a complex system is ideal for mathematical modeling, which can provide insight into the influence of the various network interactions, and can be used as a tool for integrating information. In this project, mathematical modeling is combined with experimental studies. The model will be calibrated by experimental data, and will make predictions that will be tested in the laboratory. This joint experimental-computational approach is well suited for understanding the complex hypothalamus-pituitary network. Student training is an important element of this project. It is anticipated that graduate students and postdoctoral fellows will play very active roles in the research described herein. This participation will provide multi-disciplinary training that will be invaluable for an increasingly multi-disciplinary workplace. There are four specific aims in this proposal. First, a mathematical model will be developed for pituitary lactotrophs. This model, based largely on experimental data on cultured lactotrophs from our lab, will provide a mechanistic understanding of the activity patterns of these cells. It will also be used to understand how the activity is modified by hormones such as dopamine and oxytocin. Second, mathematical models will be developed of hypothalamic dopamine- and oxytocin-secreting neurons, using hypothalamus slice data from our lab. These neurosecretory cells regulate prolactin secretion from lactotrophs, and are themselves under the influence of neurons within the suprachiasmatic nucleus (SCN). The third specific aim is to develop a mathematical model of the network interactions among the various hypothalamic neurons and pituitary lactotrophs. This model will be minimal, focusing on the network interactions between cells rather than the detailed biophysical processes that take place within cells (the goal of the first two aims). Fourth, the role of rhythmic clock gene expression in dopamine- and oxytocin-secreting neurons will be investigated. If the expression patterns are shown to be rhythmic, then this suggests that these cells provide circadian input to the pituitary that is separate from, but may be entrained by, neurons within the SCN. These studies will support the mission of NIDA by establishing the way the normal brain functions in the absence of drugs of abuse to support the pituitary gland. It will then lead to studies of effects of drugs of abuse on brain-pituitary function. ? ?
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Sethi, Sumit; Tsutsui, Kazuyoshi; Chaturvedi, Chandra Mohini (2010) Temporal phase relation of circadian neural oscillations alters RFamide-related peptide-3 and testicular function in the mouse. Neuroendocrinology 91:189-99 |
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Tabak, Joel; Mascagni, Michael; Bertram, Richard (2010) Mechanism for the universal pattern of activity in developing neuronal networks. J Neurophysiol 103:2208-21 |
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