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Mol Syst Biol. 2019 Feb 19;15(2):e8636. doi: 10.15252/msb.20188636.

Unsupervised identification of disease states from high-dimensional physiological and histopathological profiles.

Author information

1
Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA, USA kenichi_shimada@hms.harvard.edu.
2
Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Abstract

The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic-induced injury. Xenobiotic-induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine-learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin-induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole-body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.

KEYWORDS:

automated diagnosis; body weight loss; data mining; ferroptosis; toxicogenomics

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