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One major interest of the lab is to understand mitochondrial diversity of structure, metabolism, function and content across different tissues, during development and during disease conditions.
Mitochondria are the power-houses of our cells, producing cellular energy in the form of ATP. They are also involved in a multitude of other cellular and metabolic functions, including metabolism of amino acids, nucleotides or lipids, fructose, nitrogen or pyruvate metabolism, mitochondrial signaling, apoptosis ROS defense and many more. Depending on the cellular demand, mitochondria come in many different shapes and their content varies by cell type. Likewise, in different mito-diseases, mitochondrial function is perturbed, accompanied by different mitochondrial shapes and/or content. Currently, we do not understand how this variety is regulated, nor how mito-gene expression dynamics varies from one tissue to another.
mitoXplorer - a visual data mining platform for mitochondria-centric -omics data analysis
mitoXplorer - a visual data mining platform for mitochondria-centric -omics data analysis
the mitoXplorer visual data mining platform integrates a manually curated mitochondrial interactome with publicly available or user-provided expression and mutation data. A set of highly interactive analysis and visualization methods is provided for data discovery.
Mitochondrial structure and content differs from one cell type to the other. Mitochondrial variability is also at the heart of many disease conditions, often related to the nervous system or the muscle.
We are interested in understanding the dynamics of mitochondria in different species, tissues, disease states and throughout ageing. We look at the content - reflected in systematic transcriptomic and proteomic data - from a variety of different tissues, disease states, ages and in 4 different species to get an idea of what portion of mito-genes are dynamically expressed.
We developed the mitoXplorer platform, an interactive, visual data mining platform to integrate large-scale expression and mutation data with a mitochondrial interactome and mitochondrial processes.
Ay its heart is a manually curated, mitochondrial interactome, which contains all proteins that have mitochondrial function, irrespective of where they are encoded (mtDNA or nuclear genome). We integrate this manually curated and annotated mito-interactome with -omics data from large public repositories, such as TAGC or with data from Gene Expression Omnibus (GEO). mitoXplorer offers a set of powerful, highly interactive analysis and visualization tools to explore the dynamics of mito-associated expression changes and mutations in a variety of bulk, as well as single-cell data sets from different cell types, and conditions. This enables us to rapidly and visually compare a large variety of -omics data-sets with respect to their mitochondrial expression dynamics.
Check out mitoXplorer here!
Download your own version of mitoXplorer here.
Associated publications:
mitoXplorer: Yim, et al., NAR 2020, doi: 10.1093/nar/gkz1128
mitoXplorer2: Marchiano et al., NAR 2022, doi: 10.1093/nar/gkac306
mitoXplorer3: Haering et al., JMB 2025, doi: 10.1016/j.jmb.2025.169004
This project was supported by DFG grant ‘CancerSysDB’ and is currently supported by ANR grant ‘MITO-DYNAMICS’, and 'MitoMetaTis' , as well as the CNRS.
ataxiaXplorer - deep data mining of cerebellar ataxias
ataxiaXplorer - deep data mining of cerebellar ataxias
In an FRM-funded project we hold together with the team of Helene Puccio, we have developed the first sister-platform of mitoXplorer, ataxiaXplorer. AtaxiaXplorer has an ataxia-specific interactome and the same functionalities to mine bulk, and single-cell RNA-seq data. mitoXplorer will soon be online ... keep checking!
mitoMammal to model mitochondrial metabolism using constraint-based modelling
mitoMammal to model mitochondrial metabolism using constraint-based modelling
We also want to take a detailed, systems-level look at the metabolism of mitochondria within the cell. To this end, we use a technique called constraint-based metabolic modelling, which calculates the flux of metabolites through a metabolic network. Toolboxes like CobraPy help to do so.
To focus on mitochondria, we have further improved the mitoCore metabolic model of human mitochondria, and introduced the mitoMammal model. MitoMammal contains the gene-protein-reaction (GPR) rules for both human and mouse, and we have introduced several corrections and additions to the model. We are currenlty using this metabolic network to model the metabolism of human and mouse systems, focusing on muscle ageing, or neuronal development and degeneration. In addition, we are working on a toolbox for comparative analysis of flux data (FBAcomparer).
Check out the mitoMammal repository, which contains a Jupyter Notebook for ease of use.
Check out the mitoMammal paper: Chapman et al., Bioinf Adv 2024, doi: 10.1093/bioadv/vbae172
Check out the FBA_comparer repository.
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