A research group including Graduate Student Naoaki Sakamoto and Associate Professor Takahisa Murata of the Graduate School of Agricultural and Life Sciences at the University of Tokyo, and Contract Service Center Director Takamasa Numano and General Manager Taichi Yamamoto at the Central Institute for Experimental Medicine and Life Science, has established an objective, quantitative method for analyzing the behavior of a metabolic dysfunction-associated steatohepatitis (MASH) mouse model over 24 hours based on artificial intelligence (AI). The results were published in Scientific Reports.
MASH is accompanied by symptoms such as liver inflammation and fibrosis, as well as "extrahepatic symptoms" including fatigue, itching, anxiety/depression and loss of appetite. However, it has been difficult to evaluate these symptoms in experimental animals with high reproducibility.
The research group has previously developed an AI-based technology for analyzing mouse and rat behaviors and conducted non-invasive, long-term quantification of behaviors of animal disease models based on recorded videos. In this study, the AI behavioral analysis technology was applied to a MASH model to examine how the overall daily behavior of the animals changes along with the progression of liver pathology.
The MASH model was generated by allowing C57BL/6J mice to feed ad libitum on a CDAHFD or control diet starting at 6 weeks of age. At 14 weeks of age, the MASH group showed elevated AST/ALT levels, increased liver weight, steatosis, inflammatory cell infiltration, mild fibrosis and hepatocyte ballooning, indicating the establishment of MASH pathology.
In the experiment, 24-hour videos including light and dark phases were recorded with 60 frames per second at 8, 10, 12 and 14 weeks of age. Immobility, eating, drinking, rearing, grooming and scratching behaviors were automatically classified using an AI-based behavioral analysis system, and the duration and frequency of each behavior and changes during the light and dark phases were calculated. In the MASH group, mice exhibited decreased activity levels during the dark phase at 10 weeks of age and thereafter. A reduction in activity was also observed during the light phase at 14 weeks of age. This behavioral change may serve as an index of fatigue, a typical symptom observed in MASH patients.
In the MASH group, eating behavior progressively decreased, while drinking behavior increased. In particular, solitary drinking behavior, not accompanied by eating behavior, was found to be increased, which possibly reflects an alteration in central metabolic regulation of drinking behavior along with the progression of liver pathology. Moreover, grooming behavior was increased in the MASH group, and scratching behavior during the light period was also increased at 14 weeks of age. These behavioral changes may partially reflect an anxiety-like state, abdominal discomfort or itching that is suffered by human MASH patients.
In addition, some of the behavioral changes showed effect sizes comparable to those of AST/ALT and liver weight. Meanwhile, liver injury biomarkers and behavioral changes were not necessarily strongly correlated in the MASH group, suggesting that the AI-based behavioral analysis may have captured the aspects of systemic symptoms different from liver injury.
The study presented a new evaluation platform for viewing MASH not only as a "liver-only disease" but also as a systemic disease that affects the behavior, sensation and emotion. By using the AI-based 24-hour behavioral analysis, systemic conditions associated with diseased states can be continuously measured in a non-invasive manner, without relying on researcher's subjectivity or placing burdens on animals.
Further verification using different MASH models and obesity/diabetes models, combined with evaluation of reversibility of behavioral changes through drug administration or dietary change and measurements of bile acids, cytokines and molecules related to the liver-brain axis, is expected to promote the elucidation of mechanisms of fatigue, itching and anxiety-like symptoms associated with MASH. Furthermore, using the behavioral data as digital biomarkers potentially contributes to efficacy evaluation in drug discovery research and bridging between animal and human symptoms.
Journal Information
Publication: Scientific Reports
Title: Deep behavioral phenotyping reveals novel features in a mouse model of metabolic dysfunction-associated steatohepatitis
DOI: 10.1038/s41598-026-60820-3
This article has been translated by JST with permission from The Science News Ltd. (https://sci-news.co.jp/). Unauthorized reproduction of the article and photographs is prohibited.