Machine characterization of rat toxin responses identifies disease states, tolerance mechanisms and organ to whole-body communication

Abstract

BackgroundLiving organisms are constantly exposed to toxic xenobiotics and have therefore evolved protective responses. In mammals, the liver and kidney play central roles in protecting the organism from xenobiotics, and are at high risk of xenobiotic-induced injury. Liver and kidney damage by drugs and industrial toxins have been extensively studied from both classical histopathologic and biochemical perspectives.\n\nMethods and FindingsWe introduce a machine learning approach for the analysis of toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin-induced disease states. Transcriptome analysis showed that some of the machine-identified disease states correspond to known pathology, and to known effects of certain toxin classes, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly in the direction of decreased pathology, which implies induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and novel decreased ferroptosis sensitivity biomarkers. These data reinforce emerging evidence that ferroptosis drives organ pathology, and suggest that its downreagulation may promote tolerance and recovery. Lastly, mechanism of body weight decrease, a known primary marker for toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ disease states promote cachexia by whole-body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.\n\nConclusionsApplication of machine learning to systematic data collection of physiology, histopathology, transcriptome reveals multiple disease states, tolerance mechanisms and organ to whole-body communication.