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Presentation . 2015
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Generation Of Humanized Models For The Understanding Of Health And Aging

Authors: Marine Kraus;

Generation Of Humanized Models For The Understanding Of Health And Aging

Abstract

In the last decade, great research efforts have been devoted to identify the genetic basis of age related diseases, mostly by genome-wide association studies (GWAS). Despite their highly significant association with the trait of interest, the functional role of these genetic variants is, in most cases, still not elucidated, thus frustrating their potential clinical and pharmaceutical application.To overcome these limitations, we and our collaborators of the European project HUMAN, aim at developing humanized models recapitulating liver and pancreatic human function in various genetic contexts. We are adapting Viacyte's newly established methods (Schulz et al. 2012 PLoS One) to generate macro-encapsulated functional beta cells in vivo from human induced pluripotent stem cells. The iPSC-derived glucose responsive implants not only regulate glucose levels, but also protect mice against STZ-induced hyperglycemia. This technology will be combined with a model of ?humanization? of the liver developed by The Karolinska Institute in which most hepatocytes are human cells deriving from a single human donor (Azuma et al., 2007; Strom et al., 2010). These animals are recapitulating human liver and pancreas function at similar levels observed in human donors, supporting the notion that mice with humanized liver and pancreas will be unique and innovative tools to study human metabolic diseases and human aging.This innovative approach offers the unique possibility of studying the function of genetic risk variants associated with metabolic diseases in an integrated living system (the mouse body), but in human-derived organs, i.e. liver and pancreas. Our ultimate goal is to improve understanding of chronic metabolic disorders such as diabetes with the aim to develop biomarkers of health and disease and science-based nutritional interventions.

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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