A decade of experience with genetically tailored pig models for diabetes and metabolic research

Anim Reprod. 2020 Aug 26;17(3):e20200064. doi: 10.1590/1984-3143-AR2020-0064.

Abstract

The global prevalence of diabetes mellitus and other metabolic diseases is rapidly increasing. Animal models play pivotal roles in unravelling disease mechanisms and developing and testing therapeutic strategies. Rodents are the most widely used animal models but may have limitations in their resemblance to human disease mechanisms and phenotypes. Findings in rodent models are consequently often difficult to extrapolate to human clinical trials. To overcome this 'translational gap', we and other groups are developing porcine disease models. Pigs share many anatomical and physiological traits with humans and thus hold great promise as translational animal models. Importantly, the toolbox for genetic engineering of pigs is rapidly expanding. Human disease mechanisms and targets can therefore be reproduced in pigs on a molecular level, resulting in precise and predictive porcine (PPP) models. In this short review, we summarize our work on the development of genetically (pre)diabetic pig models and how they have been used to study disease mechanisms and test therapeutic strategies. This includes the generation of reporter pigs for studying beta-cell maturation and physiology. Furthermore, genetically engineered pigs are promising donors of pancreatic islets for xenotransplantation. In summary, genetically tailored pig models have become an important link in the chain of translational diabetes and metabolic research.

Keywords: biobank; diabetes; pig model; xenotransplantation.

Publication types

  • Review

Grants and funding

Financial support: Our projects were funded by the German Center for Diabetes Research (DZD), the Bavarian Research Network on Molecular Biosystems (BioSysNet), the German Research Foundation (DFG; TRR 127, HI 2206/2-1) and the European Union's Horizon 2020 research and innovation programme under grant agreement No 760986 (iNanoBIT).