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1Behavioral Pharmacology, PsychoGenics, Inc., Tarrytown, NY, USA;
2Department of Pharmacology and Toxicology, Department of Neurology, Department of Geriatrics, The Center for Translational Neuroscience, The University of Arkansas for Medical Sciences, Little Rock, AR, 72223, USA

The present study was aimed at identifying behavioral tests that are most sensitive to the emergence of behavioral/neurological deficits in SOD1-G93A (SOD1) mice in a cross comparison with the newly proposed hPFN1G118V (PFN1) (Fil et al., 2016) mouse model of motor neuron disorder.

The test battery consisted of commonly used metrics such as rotarod, open field and more complex proprietary algorithm-based behavioral platforms such as NeuroCube® and SmartCube®. In addition to behavioral assessment, histological evaluation of neuroinflammation in the spinal cord will also be presented.

The behavioral findings demonstrate similarities in progressive muscle weakness and a decline in motor coordination in both models of motor neuron disease with a later onset and slower progression noted with PFN1 animals. Additionally, sophisticated algorithm-based systems determined a strong phenotype effect in both the PFN1 and SOD1 mice at very early ages. Our NeuroCube® analyses identified early changes (8-12 weeks) related to gait geometry/ dynamics in both SOD1 and PFN1 mice, which progressed over time. Similarly, using SmartCube® technology we identified distinct behavioral changes as early as 6-7 weeks of age in SOD1 mice and 12-14 weeks for PFN1 mice.

Histological assessment demonstrated reactive gliosis of both astro- and microglia in the spinal cord of end-stage disease SOD1 and PFN1 mice. Quantification of GFAP and Iba1 immunoreactivity revealed parallels in both mouse models in addition to distinct patterns of microglial activation.

In summary, we demonstrate similar neurological/motor function deficits as well as glial cell activation in SOD1 and PFN1 mice, both exhibiting clinically-relevant attributes of Amyotrophic Lateral Sclerosis (ALS), with more advanced computer vision systems identifying distinctive behavioral patterns and discriminating the phenotype at very early disease stages in both models. This earlier period of disease identification presents a valuable model for early intervention and improved assessment of potential therapeutic approaches for ALS.