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  • 标题:Quantifying small molecule phenotypic effects using mitochondrial morpho-functional fingerprinting and machine learning
  • 本地全文:下载
  • 作者:Lionel Blanchet ; Jan A. M. Smeitink ; Sjenet E. van Emst - de Vries
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2015
  • 卷号:5
  • 期号:1
  • DOI:10.1038/srep08035
  • 语种:English
  • 出版社:Springer Nature
  • 摘要:In primary fibroblasts from Leigh Syndrome (LS) patients, isolated mitochondrial complex I deficiency is associated with increased reactive oxygen species levels and mitochondrial morpho-functional changes. Empirical evidence suggests these aberrations constitute linked therapeutic targets for small chemical molecules. However, the latter generally induce multiple subtle effects, meaning that in vitro potency analysis or single-parameter high-throughput cell screening are of limited use to identify these molecules. We combine automated image quantification and artificial intelligence to discriminate between primary fibroblasts of a healthy individual and a LS patient based upon their mitochondrial morpho-functional phenotype. We then evaluate the effects of newly developed Trolox variants 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.
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