Research projects

This section introduces all current projects of the Bioinformatics Core group.

Improving knowledge management and data sustainability 

ELIXIR Luxembourg Node, a data hub for Translational Medicine (ELIXIR-LU)

ELIXIR, the European infrastructure for life science information, aims to provide long-term access to bioinformatics tools and biological data. ELIXIR-LU, the national ELIXIR node, focuses on long-term sustainability of tools and data for translational medicine, the combination between the clinical and experimental environment. On the national level, support in standardising and electronic capture of clinical data is provided together with hosting and analysis pipelines. Internationally, translational medicine data is hosted by the Bioinformatics Core and support is given in the curation and standardisation of data sets to improve the reusability and value of the data for the research community.

Coordinator: Head of Node: Reinhard Schneider, Luxembourg Centre for Systems Biomedicine, University of Luxembourg

Official website: 

European Translational Information and Knowlegde Management Services (eTRIKS)

eTRIKS is an Innovative Medicines Initiative (IMI) project addressing the knowledge management (KM) needs of the other IMI projects. IMI is Europe’s largest public-private partnership that is focused on accelerating the development of better and safer medicines for patients. Many of the IMI projects are targeting data intensive translational research requiring a KM environment that allows storing data and facilitates combined analysis of different data types. The main goal of eTRIKS is to develop a sustainable IMI translational research/KM platform and to implement a sustainable KM service for the IMI data.
The eTRIKS KM platform “… is an integrative tool that assures synergies with management and exploitation of research results by bringing data together in an open and consistent format that is suitable for overall data analysis. The creation of such a platform can lead to new biopharmaceutical insight through extensive data sharing.” (
This KM environment (Figure 1) relies on three major components:
1. clinical and pre-clinical data capture and standardization by implementing controlled terminology into existing electronic data capture (EDC) systems;
2. a data repository for raw and processed data;
3. a software platform, which is able to handle a variety of data types including clinical and omics data

tranSMART was chosen as the software basis for the development of an open eTRIKS KM platform. In addition to the software platform, we provide guidelines and data curation services as well as selection of appropriate ontologies and terminologies. This allows standardizing the data in a homogenous way and and thus guarantees better cross-study comparability. One of the goals of eTRIKS is to improve the operational efficiency in translational research by providing an adapted software and data storage environment as well as data standards.
eTRIKS is a collaborative project which aims to increase the productivity of translational research by:
▪ enabling cross study analyses;
▪ allowing non-statisticians to perform exploratory analyses;
▪ reducing the costs of knowledge management in translational research;
▪ selecting existing and defining novel data standards to facilitate data curation.
eTRIKS is a collaboration between 16 different partners bringing expertise in different domains including scientific data curation, analysis and modeling, software engineering and biomedical standards respectively The consortium includes 8 academic and 8 European Federation of Pharmaceutical Industries and Associations members. Our group is involved in data curation and development of analytics.

Funding body: Innovative Medicines Initiative (IMI-JU)

Coordinator: Yi-Ke Guo, Imperial College, London, United Kingdom

Official website:

FAIRification of IMI and EFPIA data (FAIRplus)

Wide sharing of knowledge and data drives the progression of science. Shared data allows other researchers to reproduce  findings and benchmark quality of experiments. Sharing data so that other researchers can Find, Access and Interoperate – i.e. integrate the data with the outcomes of their own experiments - allows Reuse and an opportunity to build the large aggregated cohorts we need to detect rare signals and manage the many confounding factors in translational research. This project will develop the guidelines and tools needed to make data FAIR. Through worked examples using IMI and EFPIA data and application and extension of existing methods we will improve the level of discovery, accessibility, interoperability and reusability of selected IMI and EFPIA data. In addition, through disseminated guidelines and tailored training for data handlers in academia, SMEs and pharmaceuticals, data management culture will change and be sustained and datasets will be reused by pharmaceutical companies, academia and SMEs. Our FAIR SME & Innovation programme will enable wide data reuse and foster an innovation ecosystem around these data that power future re-use, knowledge generation, and societal benefit. We call this approach ‘FAIRplus’.

Funding body: Innovative Medicines Initiative (IMI-JU)

Coordinator: Serena Scollen, ELIXIR-Europe, United Kingdom

Official website:

Citizen-centred EU-eHR exchange for personalised health (Smart4Health)

Smart4Health will enable the citizen-centred EU EHR exchange for personalised health. This will pave the way for the full
deployment of citizen-centred solutions and services in a digital single market for wellbeing and healthcare. It will provide for
interoperability, complementarity and cooperativity with profiles that are currently used e.g. by Member States and regions.
Smart4Health will enable the bridging between the diverse EU EHR data and citizen-generated health data. It will connect
citizens to science and personalised health services.
The European Commission 2017 review of the Digital Single Market lists three priorities: 1) Citizens´ secure access to electronic health records and the possibility to share it across borders, 2) Supporting data infrastructure, to advance
research, disease prevention and personalised health and care 3) Facilitating feedback and interaction between patients and
healthcare providers, to support prevention and citizen empowerment as well as quality and patient-centred care.
Smart4Health addresses these priorities with an outstanding consortium that develops, tests and validates a platform
prototype for the Smart4Health citizen-centred health record EU-EHR exchange. Smart4Health provides an easy-to-use,
secure, constantly accessible and portable health data and services prototype, thus advancing citizen health and wellbeing,
and digital health innovation.
Smart4Health builds on the strength of the European infrastructures CEF, ELIXIR, the EIT Health, BBMRI-ERIC, the
European Virtual Laboratory for Enterprise Interoperability (I-VLab), the experience and knowledge gained and generated,
the EU/US collaboration in eHealth, e.g. the Icahn School of Medicine at Mount Sinai, the momentum of initiatives in the
Member States, e.g. 'Medizininformatik Initiative' of the BMBF in Germany. Smart4Health enables citizens to manage and
bridge their own health data throughout the EU and beyond, advancing own and societal health and wellbeing.

Funding body: Horizon 2020

Coordinator: Ricardo Goncalves, UNINOVA, Portugal

Official website: 

Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research (CHARME)

Biotechnology is an enabling technology that alone, or in combination with cognate technologies, provides the capacity to spur huge leaps in the performance and capabilities of numerous sectors, such as healthcare and medicine, agricultural production, and industrial production. In this context, a prerequisite for modern R&D is a high quality of the research data. By enabling re-use of research assets, research is made considerably more efficient and economical.
This can only be achieved reliably and efficiently if these data are generated according to standards and Standard Operating Procedures (SOPs). Standardisation and quality management are thus important drivers in the life sciences and biotechnology, as only data generated with minimum quality assurance can be easily implemented into industrial applications. Furthermore, standards assure and ensure that data become easily accessible, shareable and comparable along the value chain. The use of common standards may hence result in improved efficiency and competitiveness of European life-science research. Moreover, standardisation strategies are required or have gained in importance in the assessment of proposals in the new H2020 framework programme. It was logical then that measures were taken by several initiatives and institutions to develop and implement standards in the life sciences: one of these was set up by the International Organisation for Standardisation (ISO), which is seeking comprehensive agreement on standards in the life sciences, particularly in biotechnology and related fields. Under the auspices of the German Institute for Standardisation (DIN), an international committee (ISO/TC 276 Biotechnology) has been created that will endorse necessary standards and - if necessary - encourage the development of new norms and standards in a top-down approach. Unfortunately, current and new efforts remain fragmented and largely disconnected from each other.
The COST Action CHARME aims to bridge and combine the fragmented areas to achieve a breakthrough in standardisation efforts. CHARME will identify needs and gaps, teaming up with other initiatives and organisations to avoid duplication and overlap of standardisation activities. Only through a common, coordinated, long-term strategy, by active involvement of all stakeholders (from research, industry and policy), can standards be succesfully assimilated into the daily work-flow and thus increase efficiency and competitiveness of European life-science research.

Funding body: COST

Coordinator: Susanne Hollmann, Institute of Biochemistry and Biology, University of Potsdam, Germany

Official website:

Redefinition of diseases on the molecular level 

Organising Knowledge about Neurodegenerative Disease Mechanisms for the Improvement of Drug Development and Therapy (AETIONOMY)

In January 2014 the AETIONOMY consortium started a project aiming to develop a new way to classify Alzheimer’s and Parkinson’s disease. The 5-year-project is funded by the Innovative Medicines Initiative (IMI), a joint undertaking between the European Union and the pharmaceutical industry association EFPIA. The new classification will be generated using data derived from a wide range of new biological approaches and will be based on the underlying causes of the disease. Currently, Alzheimer’s disease and Parkinson’s disease are classified by their symptoms and severity but it is clear that this does not represent the many different causes of these diseases. It has been widely recognised that within these broad disease groups there are sub-groups where the different causes result in the symptoms of memory loss or movement disorder.
The AETIONOMY project will involve the collection of all available data including clinical data, imaging and genetic data and will create a new way to combine all the data together to look for patterns which could identify sub-groups of patients with similar causes of their disease. The project will run for the next 5 years and will include a Clinical Study, which aims at a validation of the “mechanism-based taxonomy” generated in the course of the first years.
AETIONOMY is a collaboration of 17 partners across 11 countries. It includes 4 EFPIA Pharmaceutical companies (UCB, Novartis, Sanofi-Aventis and Boehringer Ingelheim), 2 SMEs, 9 Academic institutions and 2 patient advocacy groups. The collaboration is funded as part of the IMI Taxonomy Call (Call 8), which aims at improving the way we classify diseases to ensure patients get the right drugs and to improve how we find new drugs.

Funding body: Innovative Medicines Initiative (IMI-JU)

Coordinator: Duncan McHale, UCB Pharma, Belgium



Data integration across disease and disciplines 

PerMedCoE: HPC/Exascale Centre of Excellence in Personalised Medicine

This HPC centre of excellence optimises codes for cell-level simulations in HPC/Exascale and bridges the gap between organ and molecular simulations, thus contributing to the European Personalised Medicine Roadmap.

The centre will become the entry point to Exascale-ready cell-level simulation software, able to transform personal omics data into actionable mechanistic models of medical relevance, supporting developers and end-users with know-how and best practices. It will connect simulation software developers with HPC, HTC and HPDA experts at centres of excellence such as POP and HiDALGO. PerMedCoE will also work with other biomedical consortia such as ELIXIR and LifeTime, connecting pre-exascale infrastructures hosted by supercomputing centres such as the Barcelona Supercomputing Center and CSC–IT Center for Science.

The LCSB is one of the 12 partners from across Europe participating in this project. The Bioinformatics Core is in charge of developing and optimising a pre exascale cell level simulation software and leads the development of guidelines for data protection and privacy preservation in an exascale HPC environment.

Funding body: European Commission - Horizon 2020 research and innovation programme

Coordinator: Barcelona Supercomputing Center (BSC), Spain

Official website:

Biomarker Development for Postoperative Cognitive Impairment in the Elderly (BioCOG)

Postoperative delirium (POD) is characterized by the progressive deterioration of sensory/cognitive function after surgery with incidences of up to 30-80%. It is frequently followed by postoperative cognitive dysfunction (POCD) which tends to persist over time. In elderly patients, POCD resembles chronic dementia and appears to accelerate the cognitive decline in Alzheimer dementia. POD is strongly associated with subsequent dementia after 3.2 and 5.0 years of follow-up: odds ratio = 12.52 [95% CI, 1.86-84.21] corrected for baseline dementia, severity of illness, age. In an aging society like the EU, the socioeconomic implications of POD/POCD are therefore profound. At present no treatment exists and there are no established molecular or imaging biomarkers that allow risk and clinical outcome prediction. We will establish valid biomarkers panels for risk and clinical outcome prediction of POD/POCD in N=1200 surgical patients according to the regulatory requirements of the European Medicines Agency. Thus, a valuable database will be created not yet existing worldwide.
Neuroimaging investigations, which directly provide information on brain structure/function, will include structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), arterial spin labelling (ASL), functional magnetic resonance imaging with simultaneous electrophysiology (fMRI/EEG) and glutamate MR-spectroscopy (MRS). These investigations will be complemented by genetic/gene expression analyses (sequencing of cholinergic candidate genes/corresponding transcripts) and analyses of plasma and cerebrospinal fluid markers (inflammatory/metabolic). Supported by bioinformatics approaches, integration of neuroimaging data with knowledge from molecular biomarkers (multivariate expert system) is expected to allow patient stratification. This will greatly support decision-making before surgical intervention (balancing benefits and risks) as well as the development of novel therapies in POD/POCD.

Funding body: The European Union Seventh Framework Programme (FP7)

Coordinator: Georg Winterer, Charité Berlin, Germany

Official website:

A Systems medicine approach to chronic inflammatory disease (SYSCID)

The SYSCID consortium aims to develop a systems medicine approach for disease prediction in CID. We will focus on three major CID indications with distinct characteristics, yet a large overlap of their molecular risk map: inflammatory bowel disease, systemic lupus erythematodes and rheumatoid arthritis. We have joined 15 partners from major cohorts and initiatives in Europe (e.g.IHEC, ICGC, TwinsUK and Meta-HIT) to investigate human data sets on three major levels of resolution: whole blood signatures, signatures from purified immune cell types (with a focus on CD14 and CD4/CD8) and selected single cell level analyses. Principle data layers will comprise SNP variome, methylome, transcriptome and gut microbiome. SYSCID employs a dedicated data management infrastructure, strong algorithmic development groups (including an SME for exploitation of innovative software tools for data deconvolution) and will validate results in independent retrospective and prospective clinical cohorts. Using this setup we will focus on three fundamental aims : (i) the identification of shared and unique "core disease signatures” which are associated with the disease state and independent of temporal variation, (ii) the generation of "predictive models of disease outcome"- builds on previous work that pathways/biomarkers for disease outcome are distinct from initial disease risk and may be shared across diseases to guide therapy decisions on an individual patient basis, (iii) "reprogramming disease"- will identify and target temporally stable epigenetic alterations in macrophages and lymphocytes in epigenome editing approaches as biological validation and potential novel therapeutic tool. Thus, SYSCID will foster the development of solid biomarkers and models as stratification in future long-term systems medicine clinical trials but also investigate new causative therapies by editing the epigenome code in specific immune cells, e.g. to alleviate macrophage polarization defects.

Funding body: Horizon 2020

Coordinator: Philip Rosenstiel, Christian-Albrechts-Universität zu Kiel, Germany

Official website:

Biomarkers in Atopic Dermatitis and Psoriasis (BIOMAP)

Our objective is to provide a taxonomic and predictive systems medicine model of Atopic Dermatitis and Psoriasis based on
clinical and molecular profiling to (i) identify determinants of clinically relevant outcomes (disease manifestation, progression,
comorbidity development and treatment response) (ii) improve understanding on shared and distinct disease mechanism(s)
and associated signatures, and their relative importance in patient subpopulations and (iii) deliver biomarkers that identify
disease trajectories and treatment response for use in drug development and clinical practice. BIOMAP will create a
biospecimen and data resource of unprecedented scale and depth accessible via a central, open-source data and analysis
portal, harmonizing diverse, high quality, multi-dimensional datasets on skin and blood (whole and single cell), large scale
population-based and trial data alongside clinical research infrastructure delivering supplementary material flexible to the
needs of the consortium. This resource will be systematically analyzed using state-of-the-art methodologies in epidemiology,
molecular profiling, skin biology and mathematical modelling to define disease and drug endotypes and how these interact
with lifestyle and environmental factors. Selected, highly discriminatory, associated biomarkers will pass through a
diagnostics pipeline (novel in-silico trial methods and assay development), ready for immediate translation. BIOMAP is
expected to drive drug discovery to target causal mechanisms, shorten drug development pathways, and fundamentally
change the diagnosis and management paradigm, from re-active to pro-active strategies that encompass disease biology
and life-time trajectory, matching the intervention (prevention, modification of risk factors, therapeutics) with endotypes.
Clinically annotated endotypes and associated biomarkers will identify when, in whom and how to intervene to minimize
disease impact and improve outcomes.

Funding body: Innovative Medicines Initiative (IMI-JU)

Coordinator: Stephan Weidinger, University Kiel, Germany


Scalable Machine Learning And Reservoir Computing Platform for Analyzing Temporal Data Sets in the Context of Parkinson's Disease and Biomedicine

A myriad of computational and biomedical data flow through time with strong dependencies on the previous states. In Parkinson's research, well-established longitudinal studies generate sufficient snapshots of patients' phenotypes, omics, and imaging data so they could be treated as time series. More profoundly, phone applications and wearables capturing patient's gait and tremor, daily or as a continuous stream, make a strong case for performant and general-purpose temporal analysis. To predict disease progression and to obtain an immediate feedback on the therapeutic efficiency would allow to closely follow individual patient's trajectories and quickly adjust their treatments.
The existing computational methods for temporal data are insufficient. Commonly, they either discard time entirely, i.e., they treat visits independently form each other, or use naive linear prediction methods, such as ARMA. In case of longer, sensor-captured time series, a standard approach is to first extract features, thus, replace a time series with some course-grained statistics, and then apply static, non-temporal classifiers, e.g., support vector machines, on these features. As a result, a tremendous amount of information expressed in time series is lost. Our response to these problems is to apply advanced temporal machine learning on PD data sets designed either for short clinical visits, molecular readouts, and imaging or long time series kinetic (phone) and shimmer data, with the aim to decipher the etiology of Parkinson's disease, discover new biomarkers, and identify and prioritize the targets. This endeavor will cover all objectives defined in the grant call, i.e., 1) propose novel applications of analytical methodology for biomedicine, and 2) develop enterprise software allowing users to quickly exploit these routines within or outside our platform.

Funding body: Michael J Fox Foundation


Exploiting GLIOblastoma intractability to address European research TRAINing needs in translational brain tumour research, cancer systems medicine and integrative multi-omics (GLIOTRAIN)

Glioblastoma (GBM) is the most frequent, aggressive and lethal of all brain tumours. It has a universally fatal prognosis with
85% of patients dying within two years. New treatment options and effective precision medicine therapies are urgently
required. This can only be achieved by focused multi-sectoral industry-academia collaborations in newly emerging,
innovative research disciplines. GLIOTRAIN will exploit the intractability of GBM to address European applied biomedical
research training needs. The ETN, which comprises 9 beneficiaries and 14 partner organisations from 8 countries, will train
15 innovative, creative and entrepreneurial ESRs. The research objective of GLIOTRAIN is to identify novel therapeutic
strategies for application in GBM, while implementing state of the art next generation sequencing, systems medicine and
integrative multi-omics to unravel disease resistance mechanisms. Research activities incorporate applied systems medicine, integrative multi-omics leveraging state of the art platform technologies, and translational cancer biology
implementing the latest clinically relevant models. The consortium brings together leading European and international
academics, clinicians, private sector and not-for-profit partners across GBM fields of tumour biology, multi-omics, drug
development, clinical research, bioinformatics, computational modelling and systems biology. Thus, GLIOTRAIN will address currently unmet translational research and clinical needs in the GBM field by interrogating innovative therapeutic strategies
and improving the mechanistic understanding of disease resistance. The GLIOTRAIN ETN addresses current needs in
academia and the private sector for researchers that have been trained in an environment that spans translational research,
medicine and computational biology, and that can navigate confidently between clinical, academic and private sector
environments to progress applied research findings towards improved patient outcomes.

Funding body: Marie Sklodowska Curie Actions Innovative Training Networks

Coordinator: Annette Byrne, Royal College of Surgeons in Ireland, Ireland



Text and data mining

PD Map

Please find the PD map here.                                                                                                                                                                                                          


BioKB platform, a pipeline which, by exploiting text mining and semantic technologies, helps researchers easily access semantic content of thousands of abstracts and full text articles. The text mining component analyzes the articles content and extracts relations between a wide variety of concepts, extending the scope from proteins, chemicals and pathologies to biological processes and molecular functions. Extracted knowledge is stored in a knowledge base publicly available for both, human and machine access, via this web application and SPARQL endpoint.


Genomic Analysis

National Centre of Excellence in Research: Early diagnosis and stratification of Parkinson’s Disease (NCER-PD)

NCER-PD represents a joint effort between 4 partners in Luxembourg that unite their expertise in Parkinson’s disease. In order to answer the urgent questions surrounding the occurance of Parkinson’s disease, researchers need to analyse clinical data and samples from hundreds of patients and healthy control persons. Our group provides NCER-PD with our competences and technology for the integration, curation and analysis of multidimensional data. To this end, the Data and Computation platform will establish secure and anonymized data ows among other NCER-PD platforms. Well-grounded machine learning and computational modeling approaches will enable data analysis and interpretation. 

Funding body: Fonds National de la Recherche

Coordinator: Rejko Krüger, University of Luxembourg

Official website:

Using Whole Genome Sequencing data from LRRK2 families to identify novel rare variants of LRRK2 associated Parkinson's disease

The age of onset and penetrance (likelihood of disease) of individuals with the LRRK2 G2019S mutation varies considerably, the latter ranging in some families from as high as 100 percent to as low as 22 percent. This variation suggests that genetic modifiers contribute to LRRK2 pathogenesis in Parkinson’s disease (PD). The objective of this project is to collect and sequence the genomes of multiple LRRK2 families and use innovative technology and computational approaches to identify and validate novel genetic modifiers of LRRK2-mediated neurodegeneration. The overarching goal is to identify genetic modifiers of LRRK2 G2019S–induced neurodegeneration in PD. To do this, researchers propose a four phase plan:
1. identification and collection of samples from LRRK2 families
2. integrating analysis of existing genetic data on PD patients with LRRK2 G2019S mutations to confirm the identity of candidate genetic modifiers
3. whole genome sequencing data on LRRK2 G2019S families to identify novel genetic modifiers
4. funneled into a validation scheme to directly test potential genetic modifiers for modifying LRRK2 G2019S-induced neurodegeneration in induced pluripotent stem cell (iPSC)-derived neurons from patients with the LRRK2 G2019S mutation.
This project hopes to identify genes that are important for the progression of LRRK2 associated PD. By sequencing the genomes of individuals from many families harboring LRRK2 mutations, investigators will use computational approaches to pinpoint specific genetic mutations that either enhance or lessen the onset and/or progression of LRRK2-associated PD. They will then use neurons, derived from patients with LRRK2 mutations, to validate and understand the role of these genetic mutations within cells. This work would not only increase understanding of what goes wrong in the cells of LRRK2 patients, but also help with genetic testing and in identifying potential therapeutic targets.

Funding body: Michael J Fox Foundation

Coordinator: Rudi Balling, University of Luxembourg


Epi25 Collaborative for Large-Scale Whole Genome Sequencing in Epilepsy (Epi25 Collaborative)  

Epi25  is a collaborative of more than 200 partners from 40 research cohorts from around the world. More than 14,000 exomes have been sequenced as part of this collaborative effort. We expect to find evidence that accurate and detailed phenotypic data reduces genetic heterogeneity, allows for identification of a well-matched replication cohort, and clarifies the phenotypic spectrum associated with a gene.  This approach will help us address fundamental questions about the importance of rare variants, common variants, or de novo changes as the basis for specific forms of epilepsy. 

Funding body: NHGRI


Epileptogenesis of genetic epilepsies (FOR 2715)

Epilepsy is a common, severe, and disabling condition with a significant disease burden worldwide. Despite
many available treatment options, the seizures are not well controlled in one third of all patients with epilepsy.
Gene discovery and first functional analyses of genetic defects have been major drivers to unravel disease
mechanisms in the last 20 years and have brought about the first personalized treatment options. However,
most of the genetic alterations underlying epilepsy remain to be elucidated and the mechanisms driving a
healthy into an epileptic brain are not well understood. A common feature of genetic epilepsies is the typical
age dependency the origin of which is largely unknown and which differs between syndromes. Therefore,
developmental factors are likely to play a pivotal role for epileptogenesis of genetic epilepsies. In this Research
Unit (RU), we aim to investigate if and how genetic mutations induce a cascade of multidimensional
epileptogenic processes, such as transcriptional, cellular (morphological, neurophysiological), and network
changes, and how these interact with developmental processes which likely contribute to the age‐dependent
manifestation of seizure and behavioral phenotypes in genetic epilepsies.

Funding body: DFG Research Unit (FNR co-funded)

Coordinator: Holger Lerche, University of Tübingen, Germany                                                                                                                                                         


Mitochondrial Risk factors in Parkinson's Disease (MiRisk-PD)

Mitochondria play an essential role in neuronal function and survival. Maintaining the functional integrity of mitochondria is important for cell survival. Extensive prior data generated by use of genotyping arrays and/or exome sequencing approaches in monogenetic and sporadic forms of PD has unequivocally implicated mitochondrial dysfunction as one of the central pathophysiological pathways in PD. Nevertheless, there remains an appreciable gap in deciphering the missing heritability in PD. Primarily, this may result from a dominating focus on understanding the impact of common and rare variants encoded by nuclear genes in PD. By contrast, emerging evidence suggests that genetic variability within mitochondrial DNA may explain missing heritability which, hitherto, cannot be deciphered by nuclear encoded genes alone. This hypothesis is supported by various genetic studies which have shown the involvement of mitochondrial “haplogroups” in causing disease susceptibility for PD. However, results have remained inconclusive so far due to inadequate sample sizes.
In the proposed project, MiRisk-PD, we will implement an integrative approach to understand the role and impact of both nuclear encoded mitochondrial genes and the mitochondrial genome in explaining the missing heritability in PD. This new integrative strategy will be based on (i) a large exome repository of clinically well-defined PD cohort (4500 cases and 5500 controls) within Parkinson disease Genomics Sequencing Consortium (PDGSC) to define nuclear encoded “mitochondrial-network map”; (ii) a unique collection of families with autosomal dominant and autosomal recessive PD (for mitochondrial genome sequencing) and (iii) a large cohort of clinically well-defined sporadic PD patients (45,000 cases and 40,000 controls) from the Genetic Epidemiology of Parkinson disease (GEoPD) consortium, covering different populations worldwide for genetic studies that will translate into (iv) functional validation studies in patient-derived cellular models.
The multisystem approach, as outlined in our MiRisk-PD proposal, will identify stratified cohorts based on the genomic profile to identify and explore novel therapeutic targets, paving the way for a personalized medicine program for PD.

Funding body: Fonds National de la Recherche

Coordinator: Rejko Krüger, University of Luxembourg


New Therapies for Neurological Ion Channel and Transporter Disorders (Treat-ION)

Treat-ION represents a network of clinicians and scientists across Germany to advance the knowledge about recognizing and treating rare neurological ion channel and transporter disorders. Those comprise a variety of neuropsychiatric diseases and symptoms including developmental delay, epilepsy, episodic and chronic ataxia, migraine and others, which often occur in combination or are caused by mutations in the same channels. Due to the common fundamental function of channels and transporters to regulate neuronal excitability and ionic homeostasis, pathophysiological and therapeutic principles are shared across diseases. The main goal of this grant application is to translate findings from genetic and pathophysio- logical studies into rational, individualized therapies. We will therefore focus on therapeutic studies in cellular, animal and human models, which will be complemented by in silico searches for new treatments, better predictions for the functional consequences of mutations for therapeutic purposes and cellular drug screens. We will focus our efforts on approved and available ‘repurposed’ drugs. As a proof-of-principle, we successfully have been per- forming n-of-1 trials and established three investigator-initiated trials in specific rare channel disorders with other funding, which will be of great value for the network. The results of our research and the knowledge of experts will be systematically and directly delivered to patients through a structured molecular therapeutic board attached to the German academy of rare neurological diseases (DASNE).

Funding body: BMBF Individualisierte Medizin

Coordinator: Holger Lerche, University of Tübingen, Germany