henhaobio
Sign in

bioRxivPhysiology

Expression of a tetracycline-controlled transactivator (Tet-On/Off system) in beta cells reduces insulin expression and secretion in mice

N. Jouvet, K. Bouyakdan, C. Baldwin, J. Marcinkiewicz, T. Alquier, J. L. Estall

PDFDOIPreprint

Abstract

Controllable genetic manipulation is an indispensable tool in research, greatly advancing our understanding of cell biology and physiology. However, in beta cells, transgene silencing, low inducibility, ectopic expression and off-targets effects on cell function and glucose homeostasis are a persistent challenge. In this study, we investigated whether an inducible, Tet-Off system with beta-cell specific MIP-itTA driven expression of TetO-CreJaw/J could circumvent previous issues of specificity, efficacy and toxicity. Following assessment of tissue-specific gene recombination; beta cell architecture; in vitro and in vivo glucose-stimulated insulin secretion (GSIS); and whole-body glucose homeostasis, we discovered that expression of any tetracycline-controlled transactivator (e.g. itTA, rtTA or tTA) in beta cells significantly reduced Insulin gene expression and decreased insulin content. This translated into lower pancreatic insulin levels and reduced insulin secretion in mice carrying a MIP-itTA transgene, independent of Cre-recombinase expression or doxycycline treatment. These results raise significant concern regarding the use of Tet-On or Tet-Off systems for genome editing in beta cells and emphasize the need to control for effects of transactivator expression. Our study echoes ongoing challenges faced by fundamental researchers focused on beta cells and highlights the need for consistent and careful control of experiments using these research tools.

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISSkovso '20Tellez '19Hu '26Ahn '19Mattis '22Rui '20
64% semantic matchPhysiology
S. Skovso, E. Panzhinskiy, J. Kolic+32 more·bioRxiv·Oct 15, 2020