ArticleFrontiers in neurology
A multi-dimensional phenotyping framework reveals coordinated neuromuscular, immune, and imaging-derived phenotypes in experimental autoimmune myasthenia gravis.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Experimental autoimmune myasthenia gravis (EAMG) is widely used to investigate the pathogenesis and treatment of myasthenia gravis. However, current evaluation strategies rely predominantly on isolated endpoints and do not fully capture the relationship between neuromuscular dysfunction, immune alterations, and whole-animal phenotypic changes. Methods: We established EAMG in C57BL/6J mice and implemented a two-tier analytical framework consisting of full-cohort core phenotyping and phenotype-enriched imaging analyses. Core assessments included longitudinal body weight, clinical scoring, electrophysiological testing, behavioral assays, serum anti-AChR levels, and peripheral blood Th17/Treg ratio. In a selectively assessed imaging subset, cardiac ultrasound, micro-CT, and 18F-FDG PET/CT were performed to explore selected imaging-derived cardiac, metabolic, and postural phenotypes. Associations between phenotypic readouts and disease severity were assessed using correlation analyses. Results: EAMG mice developed consistent neuromuscular and immunological abnormalities, including significant electrophysiological decrement, reduced grip strength and endurance, elevated anti-AChR levels, and increased Th17/Treg ratio. Among these readouts, electrophysiological impairment showed the strongest association with disease severity (rho = -0.79, Conclusions: EAMG was associated with coordinated electrophysiological, functional, and immunological abnormalities in the full cohort. Exploratory imaging analyses in a phenotype-enriched subset further identified selected cardiac, metabolic, and postural phenotypes in animals with overt disease manifestations. This multi-dimensional framework may support more comprehensive assessment of disease progression in preclinical myasthenia gravis studies, while the imaging-derived findings require validation in unselected cohorts.
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