Mitochondrial disorders: Phenotypic analysis of patient cells and treatment strategies using artificial intelligence.
In primary fibroblasts from patients with the rare metabolic disorder Leigh Syndrome (LS), isolated deficiency of the first complex of the respiratory chain (complex I) is associated with increased reactive oxygen species (ROS) levels and alterations in mitochondrial morphology and function. Empirical evidence suggests these aberrations constitute linked therapeutic targets for small chemical molecules, including the widely-used antioxidant Trolox. However, small chemical molecules generally induce multiple subtle effects, meaning that in vitro potency analysis or single-parameter high-throughput cell screening are of limited use to identify and/or functionally characterize these molecules. Here we combined automated image quantification and artificial intelligence techniques to discriminate between primary fibroblasts of a healthy individual and a LS patient based upon their mitochondrial morpho-functional phenotype. Using this strategy we then evaluated the effects of novel small molecules in LS patient cells. This revealed that Trolox ornithylamide hydrochloride best counterbalanced mitochondrial morpho-functional aberrations, effectively scavenged ROS and increased the maximal activity of mitochondrial complexes I, IV and citrate synthase. Our results suggest that Trolox-derived antioxidants are promising candidates in therapy development for human mitochondrial disorders.
The above study was carried out within the Centre for Systems Biology and Bioenergetics (CSBB) in a collaborative effort between the SME Khondrion and the Departments of Biochemistry (286), Analytical Chemistry and Pediatrics of the Radboud University Medical Center and Radboud University.
The paper was published in Scientific Reports,the open access journal of the Nature group.
Lionel Blanchet (photo below), Jan A.M. Smeitink, Sjenet E. van Emst - de Vries, Caroline Vogels, Mina Pellegrini, An I. Jonckheere, Richard J. T. Rodenburg, Lutgarde M.C. Buydens, Julien Beyrath, Peter H.G.M. Willems and Werner J.H. Koopman (photo above) (2015). Quantifying small molecule phenotypic effects using mitochondrial morpho-functional fingerprinting and machine learning.Sci. Rep. 5:8035.
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