Loss of MeCP2 in the rat models regression, impaired sociability and transcriptional deficits of Rett syndrome

大鼠中 MeCP2 的缺失导致雷特综合征的退化、社交能力受损和转录缺陷

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作者:Surabi Veeraragavan, Ying-Wooi Wan, Daniel R Connolly, Shannon M Hamilton, Christopher S Ward, Sirena Soriano, Meagan R Pitcher, Christopher M McGraw, Sharon G Huang, Jennie R Green, Lisa A Yuva, Agnes J Liang, Jeffrey L Neul, Dag H Yasui, Janine M LaSalle, Zhandong Liu, Richard Paylor, Rodney C Sam

Abstract

Mouse models of the transcriptional modulator Methyl-CpG-Binding Protein 2 (MeCP2) have advanced our understanding of Rett syndrome (RTT). RTT is a 'prototypical' neurodevelopmental disorder with many clinical features overlapping with other intellectual and developmental disabilities (IDD). Therapeutic interventions for RTT may therefore have broader applications. However, the reliance on the laboratory mouse to identify viable therapies for the human condition may present challenges in translating findings from the bench to the clinic. In addition, the need to identify outcome measures in well-chosen animal models is critical for preclinical trials. Here, we report that a novel Mecp2 rat model displays high face validity for modelling psychomotor regression of a learned skill, a deficit that has not been shown in Mecp2 mice. Juvenile play, a behavioural feature that is uniquely present in rats and not mice, is also impaired in female Mecp2 rats. Finally, we demonstrate that evaluating the molecular consequences of the loss of MeCP2 in both mouse and rat may result in higher predictive validity with respect to transcriptional changes in the human RTT brain. These data underscore the similarities and differences caused by the loss of MeCP2 among divergent rodent species which may have important implications for the treatment of individuals with disease-causing MECP2 mutations. Taken together, these findings demonstrate that the Mecp2 rat model is a complementary tool with unique features for the study of RTT and highlight the potential benefit of cross-species analyses in identifying potential disease-relevant preclinical outcome measures.

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