Genotype-Based Housing as a Potential Confounder in Studies Using Transgenic Mouse Models—Insight from the A53T Mouse Model of Parkinson’s Disease

<b>Background/Objectives</b>: Environmental factors, including the differences in genotype-based housing (GbH), can act as confounding variables in studies using transgenic mouse models, potentially influencing experimental outcomes and limiting their reproducibility and translational value. Despite the widespread use of transgenic models in preclinical studies, the extent to which housing conditions can affect the behavioral and molecular parameters of interest remains poorly understood. This study aims to investigate how different GbH conditions influence visuo-spatial memory and gene expression in the A53T mouse model (JAX006823) of Parkinson's disease (PD) during the pre-motor phase. <b>Methods</b>: A53T+ transgenic male mice and their non-transgenic littermates (A53T-) were housed in either mixed-genotype (MGH) or single-genotype (SGH) environments from postnatal day (PND) 30, with C57BL/6J mice serving as the controls. A behavioral assessment using the Novel Object Recognition and Object Location Tests was conducted at PND 180, followed by a qPCR analysis of <i>Iba1</i>, <i>Gfapα</i>, <i>Bdnf</i>, <i>Tnfα</i>, <i>Il-1β</i>, and <i>Il-6</i> expression in the medial prefrontal cortex and the hippocampus. <b>Results</b>: The variations in GbH influenced behavior and mRNA expression differently in the A53T+ and A53T- animals. Specifically, the A53T- mice in SGH environments displayed behavioral and molecular profiles similar to the C57BL/6J controls, while the same was not evident in the MGH environments. In the A53T+ mice, the mRNA expression of <i>Iba1, Gfapα, Bdnf,</i> and <i>Tnfα</i> was sensitive to variations in GbH, while memory impairment was not. <b>Conclusions</b>: This study highlights the importance of considering environmental factors in studies using transgenic animal models. The obtained data suggests that GbH can influence the parameters of interest in preclinical research, implicating the need for the optimization of future study designs.