Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.533202
Title: Functional study of beta cell mass regulation in vivo
Author: Zhou, Luxian
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2010
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Abstract:
Those key factors, supporting re-expansion of beta cell numbers after injury in various model systems, are largely unknown. Insulin-like growth factor II (IGF-II), an important member from the IGF family, plays a critical role in supporting cell division and differentiation during ontogeny, but its role in the adult is not known. In this study we investigated the effect of IGF-II in beta cell regeneration. An in vivo model of „switchable‟ c-Myc-induced beta cell ablation, pIns-c-MycERTAM (Pelengaris, Khan et al. 2002), which exhibits beta cell regeneration once Myc is deactivated, is employed in this study of the IGF-II function. Here we show for the first time that IGF-II is re-expressed in the adult pancreas following beta cell injury. Moreover, whereas a 90% beta cell ablation was induced in both pIns-cMycERTAM/IGF-II WT (MIG) and pIns-cMycERTAM/IGF-II KO (MIGKO) mice, a recovery up to 3 months was performed. By investigating the beta cell mass and numbers our results demonstrate that re-expression of IGF-II is important in supporting the beta cell regeneration in adult mice. Moreover this study supports the utility of using such ablation-recovery models for identifying other potential factors critical for underpinning successful beta cell regeneration in vivo. Both Myc and PML contribute to the regulation of apoptosis. Recent studies suggest that Myc and PML may interact at several levels in control of cell fate. Here we examined whether loss of the PML protein, which has been shown to regulate apoptosis via the p53 pathway, can prevent or affect c-Myc-induced beta cell apoptosis in pIns-c-MycERTAM transgenic mice. Together with the Applied Neuroinformatics Group at the University of Bielefeld in Germany, we have been developing and validating a machine learning based system to analyze beta and alpha cell numbers (Herold, Zhou et al. 2009). Comparative results between traditional techniques and this new method are presented here.
Supervisor: Not available Sponsor: University of Warwick
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.533202  DOI: Not available
Keywords: QP Physiology
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