A Novel Chronic Psoriasis Mouse Model via Optimized Imiquimod Dosing and Machine Learning Evaluation.

Area of Science:

• Dermatology
• Immunology
• Translational Research

Background:

• Imiquimod (IMQ)-induced psoriasis-like mouse models are crucial for research.
• Existing models struggle to maintain disease manifestation over time.
• There is a need for more clinically relevant and sustained psoriasis models.

Purpose of the Study:

• To investigate the impact of altered IMQ dosing frequencies on psoriasis-like mouse models.
• To assess the long-term maintenance of psoriasis symptoms and associated molecular changes.
• To develop a more robust and clinically relevant model for chronic psoriasis.

Main Methods:

• Comparison of a general IMQ model with alternate-day (D-D) and 3 days on/1 day off (3D-D) dosing regimens over 28 days.
• Assessment of pathological features, immune cell accumulation, and inflammatory factors.
• Transcriptomic analysis to evaluate gene expression, particularly IL-17 and IL-23 signaling.
• Machine learning and cross-species analysis for biomarker identification.

Main Results:

• Both D-D and 3D-D models sustained psoriasis symptoms, including immune cell infiltration and inflammatory markers.
• Increased expression of IL-17A, IL-17F, and S100A9 genes was observed in D-D and 3D-D models.
• Metabolic profiling revealed alterations in TRP channels and 2-oxocarboxylic acid metabolism in D-D mice.
• Identification of a conserved psoriasis biomarker signature.

Conclusions:

• Adjusted IMQ dosing frequencies (D-D, 3D-D) effectively maintain long-term psoriasis symptoms in mouse models.
• These modified models enhance IL-17/IL-23 signaling, offering greater clinical relevance for chronic psoriasis.
• The study provides a more robust model for psoriasis research, though it does not fully replicate clinical disease cyclicity.