. The long term goal of the proposed project is to define the development of signaling networks that induce differentiation of cells into mature salivary serous acinar cells to allow gene therapy approaches to regenerating or replacing salivary tissue in patients. Millions of patients suffer loss of salivary gland function due to Sjogren's syndrome or radiation therapy. Understanding the differentiation of salivary cells is a necessary step to enable the restoration of diseased or destroyed parotid salivary tissue. Previous work has described terminal differentiation of acinar cells histologically, and by characterizing the expression of markers of differentiation, but has not used genomics-level approaches, or mathematical models, to define regulatory pathways. The primary goal of the current application is to develop formal mathematical and statistical models that will identify networks which cause terminal differentiation of parotid acinar cells. The dynamical mathematical models will serve to generate hypotheses which will be tested, and the model will be repeatedly refined by the incorporation of new data. This proposal is responsive to the RFA "A Systems Approach to Salivary Gland Biology." Our overall hypothesis for these studies is that a mathematical model can identify key regulatory pathways that control parotid acinar cell differentiation.
Specific Aim #1 will use Laser Capture Microdissection (LCM) to obtain RNA from embryonic and newborn rat parotid acinar cells for microarray analysis of the patterns of gene expression across the period of differentiation. A coupled Ordinary Differential Equation (ODE) model will be created to describe the hypothetical interactions that direct the process of differentiation. The hypotheses will be tested, and the ODE model refined, by a combination of RT-PCR, IHC, and western blots. Since microRNAs are important regulators of development, Specific Aim #2 will define the expression of microRNAs in acinar cells, and the pattern of changes during differentiation. There are currently no publications describing microRNAs in the parotid. The results will be used to revise the mathematical model of differentiation.
Specific Aim #3 will create a statistical algorithm to validate and revise the ODE model by defining the sources of bias and variation as well as by assessing the model's predictive power overall, and in its various sub-modules. This will allow confidence intervals to be associated with different pathways within the ODE model.
Specific Aim #4 will use the ODE model to make hypotheses about specific pathways regulating gene expression in the parotid acinar cells. These hypotheses will be tested by transfection and transduction experiments, and the results shall be used to refine and validate the mathematical model. This systems biology approach should identify molecular pathways that drive cytodifferentiation of parotid acinar cells. Project Narrative. The overall goal of this research is to define the molecular mechanisms which control differentiation of cells into secretory salivary acinar cells. This addresses the needs of millions of Americans who suffer from salivary gland dysfunction due to Sj?gren's Syndrome, radiation therapy, or xerostomia due to essential medications. This research is a necessary foundation for developing new technologies such as gene transfer therapy and biologics for treating or alleviating the oral symptoms of xerostomia.

National Institute of Health (NIH)
National Institute of Dental & Craniofacial Research (NIDCR)
Research Project (R01)
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Special Emphasis Panel (ZDE1-RW (17))
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Burgoon, Penny W
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University of Louisville
Schools of Dentistry
United States
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Rempala, Grzegorz A; Yang, Yuhong (2013) On Permutation Procedures for Strong Control in Multiple Testing with Gene Expression Data. Stat Interface 6:
Kim, Jaejik; Li, Jiaxu; Venkatesh, Srirangapatnam G et al. (2013) Model discrimination in dynamic molecular systems: application to parotid de-differentiation network. J Comput Biol 20:524-39
Wang, Minghu; Li, Jiaxu; Lim, Gareth E et al. (2013) Is dynamic autocrine insulin signaling possible? A mathematical model predicts picomolar concentrations of extracellular monomeric insulin within human pancreatic islets. PLoS One 8:e64860
Craciun, Gheorghe; Kim, Jaejik; Pantea, Casian et al. (2013) Statistical Model for Biochemical Network Inference. Commun Stat Simul Comput 42:121-137
Li, Jiaxu; Wang, Minghu; De Gaetano, Andrea et al. (2012) The range of time delay and the global stability of the equilibrium for an IVGTT model. Math Biosci 235:128-37
Choi, Boseung; Rempala, Grzegorz A (2012) Inference for discretely observed stochastic kinetic networks with applications to epidemic modeling. Biostatistics 13:153-65
Venkatesh, S G; Goyal, D; Carenbauer, A L et al. (2011) Parotid secretory protein binds phosphatidylinositol (3,4) bisphosphate. J Dent Res 90:1085-90
Pietrzak, Maciej; Rempala, Grzegorz; Nelson, Peter T et al. (2011) Epigenetic silencing of nucleolar rRNA genes in Alzheimer's disease. PLoS One 6:e22585
Kuczma, Michal; Kopij, Magdalena; Pawlikowska, Iwona et al. (2010) Intratumoral convergence of the TCR repertoires of effector and Foxp3+ CD4+ T cells. PLoS One 5:e13623
Hetman, Michal; Vashishta, Aruna; Rempala, Grzegorz (2010) Neurotoxic mechanisms of DNA damage: focus on transcriptional inhibition. J Neurochem 114:1537-49

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