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Publication : Cell Type-Specific Genetic Manipulation and Impaired Circadian Rhythms in Vip (tTA) Knock-In Mice.

First Author  Peng Y Year  2022
Journal  Front Physiol Volume  13
Pages  895633 PubMed ID  35592033
Mgi Jnum  J:338875 Mgi Id  MGI:7283060
Doi  10.3389/fphys.2022.895633 Citation  Peng Y, et al. (2022) Cell Type-Specific Genetic Manipulation and Impaired Circadian Rhythms in Vip (tTA) Knock-In Mice. Front Physiol 13:895633
abstractText  The suprachiasmatic nucleus (SCN), the central circadian clock in mammals, is a neural network consisting of various types of GABAergic neurons, which can be differentiated by the co-expression of specific peptides such as vasoactive intestinal peptide (VIP) and arginine vasopressin (AVP). VIP has been considered as a critical factor for the circadian rhythmicity and synchronization of individual SCN neurons. However, the precise mechanisms of how VIP neurons regulate SCN circuits remain incompletely understood. Here, we generated Vip (tTA) knock-in mice that express tetracycline transactivator (tTA) specifically in VIP neurons by inserting tTA sequence at the start codon of Vip gene. The specific and efficient expression of tTA in VIP neurons was verified using EGFP reporter mice. In addition, combined with Avp-Cre mice, Vip (tTA) mice enabled us to simultaneously apply different genetic manipulations to VIP and AVP neurons in the SCN. Immunostaining showed that VIP is expressed at a slightly reduced level in heterozygous Vip (tTA) mice but is completely absent in homozygous mice. Consistently, homozygous Vip (tTA) mice showed impaired circadian behavioral rhythms similar to those of Vip knockout mice, such as attenuated rhythmicity and shortened circadian period. In contrast, heterozygous mice demonstrated normal circadian behavioral rhythms comparable to wild-type mice. These data suggest that Vip (tTA) mice are a valuable genetic tool to express exogenous genes specifically in VIP neurons in both normal and VIP-deficient mice, facilitating the study of VIP neuronal roles in the SCN neural network.
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