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<p>Patients with metabolic dysfunction-associated steatohepatitis (MASH) often suffer from a broad range of extrahepatic symptoms including fatigue and pruritus. However, evaluating behavioral abnormalities in preclinical mouse models remains challenging due to the limitations of conventional short-duration behavioral tests. This study aimed to comprehensively profile 24-hour behaviors of a MASH mouse model using a high-resolution, artificial intelligence (AI)-based approach. C57BL/6J mice were fed a choline-deficient, L-amino acid-defined high-fat diet from 6 weeks of age. We recorded videos across the dark/light phases at 8, 10, 12, and 14 weeks of age and analyzed behaviors using a novel AI-based behavioral analysis system. Twenty-four-hour behavioral analysis revealed that this model exhibited decreased locomotor activity alongside increased grooming and scratching behaviors as steatohepatitis progressed. These mice also exhibited altered eating-drinking rhythms and rearing patterns. Notably, these behavioral changes may reflect symptoms observed in patients with MASH; for instance, the reduction in locomotor activity is indicative of a fatigue-related phenotype. This comprehensive profiling highlights that AI-based behavioral analysis can identify novel behavioral phenotypes, effectively bridging the gap between mouse models and humans.</p>

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Deep behavioral phenotyping reveals novel features in a mouse model of metabolic dysfunction-associated steatohepatitis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Naoaki Sakamoto,
  • Takamasa Numano,
  • Yui Kobayashi,
  • Masahiro Fukuda,
  • Maria Osaki,
  • Keisuke Omori,
  • Taichi Yamamoto,
  • Takahisa Murata

摘要

Patients with metabolic dysfunction-associated steatohepatitis (MASH) often suffer from a broad range of extrahepatic symptoms including fatigue and pruritus. However, evaluating behavioral abnormalities in preclinical mouse models remains challenging due to the limitations of conventional short-duration behavioral tests. This study aimed to comprehensively profile 24-hour behaviors of a MASH mouse model using a high-resolution, artificial intelligence (AI)-based approach. C57BL/6J mice were fed a choline-deficient, L-amino acid-defined high-fat diet from 6 weeks of age. We recorded videos across the dark/light phases at 8, 10, 12, and 14 weeks of age and analyzed behaviors using a novel AI-based behavioral analysis system. Twenty-four-hour behavioral analysis revealed that this model exhibited decreased locomotor activity alongside increased grooming and scratching behaviors as steatohepatitis progressed. These mice also exhibited altered eating-drinking rhythms and rearing patterns. Notably, these behavioral changes may reflect symptoms observed in patients with MASH; for instance, the reduction in locomotor activity is indicative of a fatigue-related phenotype. This comprehensive profiling highlights that AI-based behavioral analysis can identify novel behavioral phenotypes, effectively bridging the gap between mouse models and humans.