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S100A1-deficient male mice exhibit increased exploratory activity and reduced anxiety-related responses.

Gabriele E Ackermann | Ingo Marenholz | David P Wolfer | Wood Yee Chan | Beat Schäfer | Paul Erne | Claus W Heizmann
Biochimica et biophysica acta | 2006

S100 proteins comprise a family of Ca(2+) binding proteins of at least 21 members. They are distinctly expressed in a variety of cell types and tissues and are thought to play unique roles, although they share a high degree of sequence homology and expression overlap. S100A1 is prominently expressed in the heart, where it takes part in Ca(2+)-cycling. Its role in the central nervous system (CNS) is largely unknown. We have generated S100A1-deficient mice by gene trap mutagenesis to study the involvement of S100A1 in the cytoarchitecture of the brain, in learning and memory, and in avoidance-approach behavior. S100A1 knock out (KO) mice develop well and their brains present with normal morphology. In wild type (Wt) mice, S100A1 protein was found in the hippocampus, cerebral cortex and amygdala, and partially co-localized with the astrocyte marker glial fibrillary acidic protein (GFAP) in the stratum radiatum of the hippocampus. Astrocytes and neurons of S100A1KO mice did not differ from those of Wt mice regarding shape, distribution and density. In the water maze, S100A1KO mice performed equally well as Wt, implying that S100A1 is not involved in spatial learning and memory. In avoidance-approach tests, predominantly male S100A1KO mice showed reduced anxiety-like responses and enhanced explorative activities. We conclude that S100A1 plays a role in modulating innate fear and exploration of novel stimuli.

Pubmed ID: 17045663

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Commercial organization that develops tools and systems for measurement and analysis of animal and human behavior. They develop instrumented home cage systems, in which rodent behavior and ultrasonic vocalizations can be measured in a non-intrusive manner. Furthermore, they develop computer vision and pattern recognition technology for automated behavior detection. They believe that this approach has significant potential in research on animal models for Autism Spectrum Disorder.

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