Research Projects

This section introduces current projects of the Biomedical Data Science group.

Gender differences in neurodegenerative diseases (GenderADPD project)

In both Parkinson’s (PD) and Alzheimer’s disease (AD) gender differences in the incidence and phenotypic manifestations of the disorder have been observed. Although these differences may result from diverse behaviours and life-styles, previous studies suggest that the underlying causes are more complex and disease-specific, and involve hormonal and genetic influences.

In the GenderADPD project, we aim to investigate whether specific genetic factors contribute to the observed gender differences. For AD, we are currently using cellular and animal models to study a candidate gene-derived from the statistical analysis of multiple large-scale omics datasets from AD case/control studies. The corresponding sex-linked gene, ubiquitin-specific peptidase 9 (USP9), has a gender-biased activity in the human brain and encodes an enzyme reported to interact with MAPT and SIRT1, two proteins with central roles in AD. The Y-chromosomal copy of the gene (USP9Y) was found to be significantly under expressed in AD post mortem brain samples as compared to unaffected controls across multiple datasets.

For this project, a research grant was obtained after successful participation in the “Geoffrey Beene Alzheimer’s Global NeuroDiscovery Challenge”. For PD, a preliminary analysis of sex-linked genes using GWAS and microarray data provided 19 candidate disease genes, including a mitochondrial gene encoding a complex I subunit. These candidates will be further validated and characterised with regard to their possible functional roles in PD using new omics data from collaborating groups. The resulting information on disease-linked gender differences in the brain transcriptome may contribute to the development of more patient-tailored diagnostic and therapeutic approaches for the studied neurodegenerative disorders.

Mitochondrial endophenotypes of Parkinson’s disease (MitoPD project)

The goal of this multi-centre international project, lead by Prof. Thomas Gasser from the University of Tübingen in Germany, is to integrate genomic, transcriptomic and proteomic data to computationally stratify PD patients in terms of disease-related pathway and network alterations. We are particularly interested in a putative subgroup of patients with pronounced mitochondrial dysfunction, suggested by prior evidence from genetic and biochemical studies. We aim to specify this subgroup more precisely by robustly discriminating a postulated “mitochondrial endophenotype” of PD from other cases, using both omics data derived from large, carefully phenotyped patient cohorts as well as corresponding animal and cellular models, within an integrated computational and experimental approach. A first-stage validation will be performed by testing mitochondrial function in patient biomaterials in sub-cohorts of patients with predicted mitochondrial phenotypes. Results of this validation step will be used to improve the machine learning models. The model validity will be confirmed in further cohorts of familial and sporadic patients and genetically-defined at-risk individuals as well as in animal and cellular models of PD. Pathway-specific and MR spectroscopy-based biomarkers will then be developed, and stratified sub-cohorts will be identified for proof-of-concept clinical trials. Within the project consortium, the Biomedical Data Science group is responsible for the pathway- and network-based machine learning analysis of the omics data. We take part in the proposal and design of new validation experiments, and iteratively evaluate and improve our models in collaboration with the experimental groups.