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In Vivo Modeling of the Morbid Human Genome using Danio rerio

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Revisiting Minamata disease through computational phenotypic similarity analysis.

Edoardo Marchi^1,2, Paolo Boldi^2,3, Elena Casiraghi³

¹Department of Environmental Science and Policy, Università degli Studi di Milano, Milan, Italy.

|February 26, 2026

Summary

Minamata disease, caused by methylmercury, shares phenotypic similarities with movement and neurodegenerative disorders. This network analysis reveals environmental exposures can trigger complex, systemic neurological responses.

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Area of Science:

Environmental Health
Computational Biology
Neuroscience

Background:

Minamata disease, a severe neurological disorder from methylmercury exposure, has significant historical environmental health implications.
Its complex phenotype and potential overlap with other neurological conditions lack systematic computational assessment.

Purpose of the Study:

To computationally analyze Minamata disease's phenotypic profile using network approaches.
To identify and quantify similarities between Minamata disease and other diseases in a broader landscape.

Main Methods:

Mapped clinical symptoms of 269 Minamata patients to Human Phenotype Ontology (HPO) terms.
Utilized network-based similarity measures (Jaccard Index, Resnik, GraphIC) and TF-IDF with query expansion.
Compared Minamata's phenotypic profile against over 12,000 diseases.

Main Results:

Consistently identified strong phenotypic links between Minamata disease and movement/neurodegenerative disorders like cyanide-induced parkinsonism and progressive supranuclear palsy.
A weighted rank aggregation revealed a consensus network of diseases with overlapping symptoms.
Highlighted systemic pathophysiological responses to environmental exposures.

Conclusions:

Demonstrates the utility of integrating historical data with modern network tools for disease analysis.
Reveals novel associations between environmental triggers and complex neurological disorders.
Provides a framework for exploring environmentally triggered disease mechanisms and network-based disease understanding.