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Queisser, G.

Publications and source records attributed to Queisser, G..

2 recordsLinked to original sources

The small GTPase Rem2 modulates sex-dependent spatial learning by regulating CA1 glutamate receptor composition

The small GTPase Rem2 is a key signaling molecule involved in synaptic formation, dendritic complexity, spine formation, and regulation of CaMKII-dependent long-term potentiation (LTP). However, it remains unclear how Rem2 explicitly impacts learning and memory. To address this issue, we deleted Rem2 specifically in dorsal CA1 neurons of male and female mice and assessed spatial learning using a spatial object recognition (SOR) task. We found that males outperform females in this behavioral assay and that deleting Rem2 did not impact spatial learning in males. In contrast, Rem2 deletion significantly improved spatial learning in females enabling them to perform as well as males in the SOR task. Using an automated Western blot system to assess all known AMPA- and NMDA-mediated glutamate receptor (AMPAR and NMDAR) subunits in each mouse, we found that the sex-dependent change in SOR we observed was likely due to Rem2 increasing GluN2D expression in the interneurons of female mice only. Furthermore, using S-statistics to compare the overall AMPAR and NMDAR composition among groups, we found that males and females normally have divergent covariance structures but this divergence is eliminated when Rem2 is deleted from CA1 neurons. These results reveal an unexpected role of Rem2 in maintaining sex differences in glutamate receptor composition and spatial learning abilities. To our knowledge, this is the first demonstration of a signaling molecule that confers sexual dimorphism to excitatory synapses. As such, Rem2 may play a critical role in understanding how sex-dependent symptoms of neurodevelopmental and neurodegenerative disorders arise.

neuroscience↗

Multi-scale Modeling Toolbox for Single Neuron and Subcellular Activity under (repetitive) Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique widely used in research and clinical applications. However, its mechanism of action and the neural response to TMS are still poorly understood. Multi-scale modeling can complement experimental research and provide a framework between the physical input parameters and the subcellular neural effects of TMS. At the macroscopic level, sophisticated numerical models exist to estimate the induced electric fields in whole-brain volume conductor models. However, multi-scale computational modeling approaches to predict TMS cellular and subcellular responses, crucial to understanding TMS plasticity inducing protocols, are not available so far. Here, we develop a multi-scale Neuron Modeling for TMS toolbox (NeMo-TMS) that enables researchers to easily generate accurate neuron models from morphological reconstructions, couple them to the external electric fields induced by TMS, and to simulate the cellular and subcellular responses of the neurons. Both single-pulse and rTMS protocols can be simulated and results visualized in 3D. We openly share our toolbox and provide example scripts and datasets for the user to explore. NeMo-TMS toolbox (https://github.com/OpitzLab/NeMo-TMS) allows researchers a previously not available level of detail and precision in realistically modeling the physical and physiological effects of TMS.

neuroscience↗