Metabolic Blood Biomarkers for Personalised Disease Monitoring Royal College of Surgeons in Ireland

Mitochondrial dysfunction and alterations in energy and lipid metabolism are hallmarks of Parkinson's Disease (PD). The aim of this project is to perform a metabolic analysis of blood-based expression profiles to derive clinically accessible, peripheral biomarker signatures based on metabolic dysfunction for personalised PD disease monitoring and targeting. Specific objectives: 1) Identify metabolic signatures in the blood of PD patients and their relation to diagnosis, symptoms and disease progression; 2) Predict altered metabolic fluxes/pathways in individual patients by incorporation of gene expression profiles and network-wide analysis; 3) Identify metabolic reactions/nodes as putative therapeutic targets; 4) Incorporate clinical markers to derive enhanced signatures of metabolic dysfunction for disease monitoring. The student will first determine differentially expressed metabolites and will perform correlation analyses on metabolomics datasets derived from peripheral blood mononuclear cells (PBMCs) from LuxPARK to describe the heterogeneity of metabolic signatures in PD patients. Signatures/biomarkers will be correlated with diagnosis (IPD, PDD, DLB etc), symptoms (motor, non-motor) and disease stage. Next, at RCSI, the student will be trained in the application of systems biology tools such as genome-scale metabolic models (GSMM) and will generate GSMMs of PBMC/blood gene and/or protein expression profiles to derive functional insight into the distinct metabolic signatures, and predict individual, specifically altered metabolic fluxes/pathways and their relation to diagnosis, symptoms and disease progression. Blood- and brain-based models will be aligned to determine shared and distinct patterns of metabolic dysfunction. Both publicly available datasets as well as brain transcriptomic, proteomic, and metabolomic data generated by the IMI2 PDMitoQUANT project will be available. Knockin/ knock-out and perturbation analyses in silico will predict critical metabolic reactions/nodes and putative therapeutic targets. At University of Luxembourg (UL) student will apply AI/ML and XAI techniques to combine clinical features as well as genomic mutations to derive enhanced signatures of metabolic dysfunction to further dissect specific pathways for therapeutic monitoring. Signatures/biomarkers will be validated using PPMI. Together, derived metabolic biomarkers will enable a refined diagnosis of PD patients and allow for longitudinal, personalised monitoring of symptoms.

Supervisors:

• Dr Niamh Connolly ( https://www.rcsi.com/people/profile/niamhmconnolly )
• Dr Patricia Martins Conde (jointly with Prof. Jochen Klucken), University of Luxembourg ( https://www.uni.lu/lcsb-en/people/jochen-klucken/ )
• Prof Jochen Prehn ( https://www.rcsi.com/people/profile/JPrehn )

Mandatory Specifications:

We are seeking a highly skilled and motivated Research Scientist with a demonstrable background in Data Science (M.Sc. with excellent grades in Computer Science, Bioinformatics, Computational/Systems Biology, Mathematics or a similar subject). The successful candidate should have a passion for research on computational methods in medicine.

Desirable Specifications:

• Clear understanding of statistical and mathematical concepts underlying Data Science.
• Knowledge of Parkinson’s biology, biomarker research or energy metabolism is a plus.
• Knowledge of constraint-based metabolic modelling is also an advantage.
• Strong background in AI/ML.
• Experience with biomedical data is highly desirable.
• Demonstrable proficiency in Matlab, Python and/or R programming.
• Ability to work independently.
• Very good English speaking and writing skills.
• Very good communication skills.

Applications:

To apply, please access the following link and complete the application form in full: https://euraxess.ec.europa.eu/jobs/290650

Unfortunately, we are unable to provide individual feedback to applicants.

Shortlisted candidates will be invited for interview.

At this stage only successful candidates will be contacted to submit CV, transcripts and other relevant documentation.

For successful candidates, referees will also be contacted at this stage for a reference.

Applications must include:

1. a completed application form
2. English language requirements – see https://www.rcsi.com/dublin/postgraduate/policies-and-guidelines/english-language-requirements .

All applications must be made online by 28th February 2025

It is the candidate’s responsibility to ensure the application form is completed in full and on time. Late and/or incomplete applications will not normally be assessed.

Duration: 36 months (3 years)

Application Deadline: 28th February 2025

Shortlisting: March 2025

Interviews: March-April 2025 (virtual interviews available)

Start Date: October 2025

Further information on the Royal College of Surgeons in Ireland can be found here: https://www.rcsi.com/