Genetic influences on motor learning and performance and superperforming mutants revealed by random mutational survey of the mouse genome

Abstract: Evolution depends upon genetic variations that influence physiology. As defined in a genetic screen, phenotypic performance may be enhanced or degraded by such mutations. We set out to detect mutations that influence motor function, including motor learning. Thus, we tested the motor effects of 36,444 non-synonymous coding/splicing mutations induced in the germline of C57BL/6J mice with N-ethyl–N-nitrosourea by measuring changes in the performance of repetitive rotarod trials while blinded to genotype. Automated meiotic mapping was used to implicate individual mutations in causation. In total, 32,726 mice bearing all the variant alleles were screened. This was complemented with the simultaneous testing of 1408 normal mice for reference. In total, 16.3% of autosomal genes were thus rendered detectably hypomorphic or nullified by mutations in homozygosity and motor tested in at least three mice. This approach allowed us to identify superperformance mutations in Rif1, Tk1, Fan1 and Mn1. These genes are primarily related, among other less well-characterized functions, to nucleic acid biology. We also associated distinct motor learning patterns with groups of functionally related genes. These functional sets included, preferentially, histone H3 methyltransferase activity for mice that learnt at an accelerated rate relative to the remaining mutant mice. The results allow for an estimation of the fraction of mutations that can modify a behaviour influential for evolution such as locomotion. They may also enable, once the loci are further validated and the mechanisms elucidated, the harnessing of the activity of the newly identified genes to enhance motor ability or to counterbalance disability or disease. (Figure presented.). Key points: We studied the effect of chemically induced random mutations on mouse motor performance. An array of mutations influenced the rate of motor learning. DNA regulation genes predominated among these mutant loci. Several mutations in unsuspected genes led to superperformance. Assuming little-biased mutagenicity, the results allow for an estimation of the probability for any spontaneous mutation to influence a behaviour such as motor learning and ultimate performance.