The mouse age phenome knowledgebase and disease-specific inter-species age mapping

Area of Science:

• Biomedical Research
• Translational Science
• Comparative Medicine

Background:

• Mouse models are widely used in preclinical research but species differences, particularly in aging, can limit their predictivity for human diseases.
• Age is a critical factor in disease manifestation and progression, yet it's often inadequately considered in mouse models.
• Existing mouse models may not accurately reflect human aging-related disease patterns due to disparate lifespans and developmental timelines.

Purpose of the Study:

• To develop a comprehensive knowledgebase of age-related phenotypic patterns in mice.
• To establish a robust method for mapping ages between mice and humans across various diseases.
• To enhance the reliability and translational accuracy of mouse models in preclinical research.

Main Methods:

• Developed the mouse-Age Phenotype Knowledgebase (mouse-APK) using text mining of scientific literature.
• Extensively populated the knowledgebase with literature-derived data on age-related phenotypes in mice.
• Compared age distribution patterns for 887 diseases in mice and humans to create age-mapping correlations.

Main Results:

• The mouse-APK contains over 9800 instances with high accuracy (estimated precision >86%).
• Clustering diseases by age patterns in mice revealed biologically relevant groupings.
• Successfully mapped ages between mice and humans for 223 diseases, showing significant variations (e.g., human 30 years maps to mouse 120 days for Leukemia vs. 295 days for Anemia).
• A publicly available mice-to-human age map was generated.

Conclusions:

• The mouse-Age Phenotype Knowledgebase (mouse-APK) provides a valuable resource for understanding age-related disease patterns.
• Mapping ages between mice and humans for 223 diseases demonstrates significant species-specific differences.
• This work represents a significant advancement in bridging the translational gap between mouse models and human diseases in biomedical research.