Prof Chris Ponting Dr Audrey Ryback Dr Sjoerd Beentjes Dr Ava Khamseh Friday, January 16, 2026 Funded PhD Project (UK Students Only)
EdinburghUnited KingdomBioinformaticsEpidemiologyGenetics
About the Project
Summary: Can we identify biomarkers that correlate with symptom severity or disease activity in people with ME/CFS? Are ME/CFS blood-based diagnostic biomarkers being missed because each person has their own set of haematological setpoints?
About the project: Recently, we discovered a set of 116 molecular or cellular traits that significantly differ, on average, between people with ME/CFS and others, first for females and then separately for males (Beentjes et al. 2025, above). This project seeks to extend these discoveries by investigating whether people with ME/CFS show plasma protein differences over time, in particular differences that correlate with their self-reported symptom severity. If so, then these differences would provide an objective measure of symptom severity that could be used to assist diagnosis, help reveal the mechanisms underlying disease activity, and act as an outcome measure in future clinical trials.
The project was also motivated by findings that a patient’s haematological measurements fluctuate little around a stable value (their setpoint) and that measurements vary more substantially from patient to patient (
https://doi.org/10.1038/s41586-024-08264-5). Because they depend on cell type abundance, we hypothesise that plasma protein abundances similarly have setpoints. Consequently, departure from a setpoint due to disease might be diagnostic, but this would only become evident upon analysing multiple data points longitudinally, rather than merely comparing single timepoint values from cases with controls.
After ethics approval, participants would be recruited via DecodeME: 95% of the 21,620 DecodeME participants consented to being recontacted for new research projects. We would recruit hundreds of DecodeME participants with an ME/CFS diagnosis. Samples would be collected as dried blood spots, provided in participants’ homes, and protein levels would be measured via Olink proximity extension assays. This approach is both feasible (
Fredolini et al.) and desirable given that 25% or more of people with ME/CFS are housebound or completely bedbound (
Prendergrast et al.).
We will initially measure protein levels from 34 selected individuals who have provided blood samples for 5 timepoints taken on days with either high or low symptom burden with matching questionnaire data. Any additional dried blood spot samples would be stored for our and others’ future research.
We are particularly interested in focusing on the 16.3% of people who develop ME/CFS after Glandular Fever. This is because – like in Long Covid – they form a homogeneous cohort, all of whom experienced an Epstein-Barr Virus (EBV) or Cytomegalovirus (CMV) infection. However, results could potentially be relevant to other viral and non-viral triggers. To enhance homogeneity, we will select on females within a narrow age range (e.g., 30-40yo).
Methodologically, we will leverage Longitudinal Targeted Minimum Loss-based Estimation (LTMLE) to correlate protein expression with symptom severity over time, whilst accounting for time-varying confounders (
Van der Laan-Gruber (2012),
LTMLE R package,
Shirakawa et al (2024)). In particular, we will pinpoint if protein measurements fluctuate significantly with symptom severity and extract a minimal and predictive subset across all participants.
The DecodeME study placed people with lived experience at the heart of its science, and this project will do the same. It will use the patient and public involvement pool that will be set up by the PRIME project (lead: Ponting) by May 2026.
The additional funds to undertake Olink analysis of 34 individuals across 5 timepoints are already available for this project as a result of generous philanthropic donations.
Training and skills
Methodologically, the student will develop technical skills in the development and application of rigorous statistical inference (semi-parametric efficiency theory) and machine learning techniques throughout the PhD and by auditing MSc level courses in these areas and beyond. In the application of biomedical data at various scales, on the biomedical front, the student will develop an understanding of high-dimensional and temporal molecular biology via proteomics and disease phenotypes. The student will further develop essential cross-disciplinary and translational communication with access to a supervisory team with diverse expertise ranging across AI/ML, biostatistics and molecular biomedicine.
The student will receive training in patient and public involvement (PPI) and in communicating their research to lay audiences. Optionally, they would have the opportunity to develop surveys for data collection and could gain experience with wet lab work. We could support them with open science research practices such as study pre-registration on the Open Science Foundation.
Recruitment requirements
Experience with programming in R and a basic knowledge of statistics, as well as an interest to expand these skills, is required.
How to apply
Interested candidates must contact the lead supervisor to discuss the project and their application before applying.
For further information about the project, the supervisory team and the application process, please visit the fellowships website:
Future Medicine PhD fellowships
Funding Notes
This 3-year fully-funded project is offered as part of the Future Medicine PhD fellowships at the University of Edinburgh, which seek to explore the contributions of infectious agents to chronic disease.
The project is available to candidates who are UK citizens, or those who have UK-settled status. The fellowships offer a research training support grant of £5-10k per year and £300 per year for travel, in addition to a stipend rate commensurate with the MRC/UKRI.
References
Beentjes et al. Replicated blood-based biomarkers for myalgic encephalomyelitis not explicable by inactivity. EMBO Mol Med (2025) 17: 1868 - 1891
https://doi.org/10.1038/s44321-025-00258-8
DecodeME collaboration. Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome (preprint).
https://institute-genetics-cancer.ed.ac.uk/decodeme-the-worlds-largest-mecfs-study/initial-decodeme-dna-results
Devereux-Cooke et al. DecodeME: community recruitment for a large genetics study of myalgic encephalomyelitis / chronic fatigue syndrome. BMC Neurology volume 22, Article number: 269 (2022)
https://doi.org/10.1186/s12883-022-02763-6
Samms, G.L., Ponting, C.P. Unequal access to diagnosis of myalgic encephalomyelitis in England. BMC Public Health 25, 1417 (2025).
https://doi.org/10.1186/s12889-025-22603-9
