Portail de transparence
Information sur la collecte des données patientLes patients ayant bénéficié d'un diagnostic génétique au CHU d'Angers ENTRE 2016 ET 2021 dans le cadre d'une recherche de maladies rares mitochondriale sont intégrés dans l'entrepôt de données du projet MITOMICS. Seuls des données nécessaires au projet sont collectés, c'est à dire: l'âge, les variants du génome mitochondrial, les informations concernant les techniques utilisés pour l'extraction, le séquençage et l'analyse du génome mitochondrial, ainsi que les données cliniques relatives aux maladies mitochondriales.
Information sur le devenir des données patient
Projets de recherche en cours
Description du projet MITOMICS
Mitochondrial diseases are rare, clinically and genetically extremely heterogeneous, caused by a
deficit of energy production via the mitochondria. Mitochondria are dependent on 2 genomes
mitochondrial DNA (mtDNA) and nuclear DNA, and many pathogenic variants carried by these 2
genomes are responsible for mitochondrial diseases. There is a cross talk and regulatory
mechanisms between both genomes, which are still poorly understood, involved in the control and
maintenance of mitochondrial biogenesis. All of these mechanisms play an important role in the
clinical and genetic heterogeneities presented by patients suffering from mitochondrial disease
and are difficult to identify by "classical" high-throughput mtDNA, whole exome (WES) or genome
(WGS) sequencing approaches.
Mitomatcher is the first French database collecting genetic and clinical data for patients with
mitochondrial diseases, implemented by the national mitochondrial laboratory network MitoDiag in
conjunction with the reference centres (CARAMMEL and CALISSON) and Filnemus the rare
disease network. Mitomatcher comprises 3 different modules: i) HPO related
phenotypic data module, ii) genetic module currently containing mtDNA variants from more than 3000
patients with mitochondrial disease and iii) query and cross-reference module for
the clinical-biological data.
The MITOMICS project aims to better understand the molecular mechanisms responsible for the
clinical-genetic heterogeneity of mitochondrial diseases. The integration of multi-Omics data
(transcriptomics/proteomics/metabolomics) combined with clinical and genomic data (WES,
WGS) in the Mitomatcher database should help to unravel the complexity of these diseases.
Societal implications (ethics, juridics) of genomics research will be investigated and guidelines will
be defined. The cross analysis of these data requires the development of in silico tools. Different
approaches will be developed to (i) identify co-occurrences of mtDNA and/or nuclear DNA
variants responsible for mitochondrial diseases in order to reveal new genotype/phenotype
correlations, (ii) characterise the mitochondrial and nuclear crosstalk, and (iii) identify OMICS
signatures specific to mitochondrial dysfunction.
The study will be divided into 4 WPs starting with data collection and mitomatcher implementation
(WP1) and from WP2-4 with the development of algorithms and data integration with increasing
complexity with sequential data implementation starting with mtDNA variants alone, then
combined with nuclear variants and finally multi-OMICs. Innovative machine learning, neural
networks and artificial intelligence approaches will be developed such as Ruche a multi-layeR
mUlti-omics maCHine learning intEgration tool or ABEILLE (ABerrant Expression Identification
empLoying machine LEarning), an autoencoder-based method for the identification of aberrant
gene expression from RNA-seq which will be applied to other OMICs.
The identification of combinations of variants or affected signaling pathways from homogeneous
groups of patients will be further verified by laboratory experiments. This project will also allow
further development of in silico tools for the analysis of mtDNA variants such as Eklipse to detect
mtDNA rearrangements. Mitomatcher database will be accessible, already following international
standards (Fast Healthcare Interoperability Resources, HPO), interoperable with other national or
international databases and reusable for the development of ancillary studies for these disorders.
In the long term, the results obtained will allow the identification of new genotype/phenotype
correlations and a better understanding of the pathophysiological mechanisms of mitochondrial
diseases. The identification of specific signatures via an integrated multi-OMICs approach through
Mitomics should also target specific pathways, thus enabling the development of new therapeutic
strategies for mitochondrial diseases.