mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations

Yim, Annie; Koti, Prasanna; Bonnard, Adrien; Marchiano, Fabio; Duerrbaum, Milena; Garcia-Perez, Cecilia; Villaveces, Jose; Gamal, Salma; Cardone, Giovanni; Perocchi, Fabiana; Storchova, Zuzana und Habermann, Bianca H. (2020): mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations. In: Nucleic Acids Research, Bd. 48, Nr. 2: S. 605-632

Abstract

Mitochondria participate in metabolism and signaling. They adapt to the requirements of various cell types. Publicly available expression data permit to study expression dynamics of genes with mitochondrial function (mito-genes) in various cell types, conditions and organisms. Yet, we lack an easy way of extracting these data for mito-genes. Here, we introduce the visual data mining platform mitoXplorer, which integrates expression and mutation data of mito-genes with a manually curated mitochondrial interactome containing similar to 1200 genes grouped in 38 mitochondrial processes. User-friendly analysis and visualization tools allow to mine mitochondrial expression dynamics and mutations across various datasets from four model species including human. To test the predictive power of mitoXplorer, we quantify mito-gene expression dynamics in trisomy 21 cells, as mitochondrial defects are frequent in trisomy 21. We uncover remarkable differences in the regulation of the mitochondrial transcriptome and proteome in one of the trisomy 21 cell lines, caused by dysregulation of the mitochondrial ribosome and resulting in severe defects in oxidative phosphorylation. With the newly developed Fiji plugin mitoMorph, we identify mild changes in mitochondrial morphology in trisomy 21. Taken together, mitoXplorer (http://mitoxplorer.ibdm.univ-mrs.fr) is a user-friendly, web-based and freely accessible software, aiding experimental scientists to quantify mitochondrial expression dynamics.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Chemie und Pharmazie > Department Biochemie ; Medizin > Munich Cluster for Systems Neurology (SyNergy)
• Themengebiete: 500 Naturwissenschaften und Mathematik > 540 Chemie
• ISSN: 0305-1048
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198