Abstract:
This thesis describes two investigations into the disease Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), specifically its genetic aetiology and immune system alterations.
The first study investigated the genetic basis of ME/CFS using Genome-wide Association Studies (GWAS) by attempting to replicate and extend results previously found using UK Biobank cohort data. GWAS attempt to identify associations between DNA variants and phenotypes. T his GWAS was novel, conducted on new phenotypes constructed by combining those in the most up-to-date UK Biobank data release. A new, previously unseen, genome-wide significant association was found on chromosome 6 for males with ME/CFS within the gene PDE10A. Further results were not genome-wide significant, but many were suggestive and hence independent replication may justify further research.
A previous analysis on the UK Biobank cohort had identified an indicative association in females between variants around the SLC25A15 gene at genome-wide significance. I adopted a hypothesis that the dietary protein intake of people with the CFS risk variants would be lower than those with the alternative alleles, due to potentially reduced production of mitochondrial ornithine transporter 1 (ORNT1). However, this association with dietary protein intake was not supported by UK Biobank data.
Additionally, I investigated associations between the human leukocyte antigen (HLA) alleles and the ME/CFS phenotype using UK Biobank data. Associations between alleles within the HLA-C and -DQB1 genes had previously been found in a cohort of Norwegian people with ME/CFS, and my goal was to seek replication of these results in a larger dataset. None of the associations found in the UK Biobank proved to be genome-wide significant.
In my second study I investigated the use of T-cell clonal diversity as a potential biomarker for ME/CFS. This project used cells from CureME Biobank samples in collaboration with Systems Biology Laboratory (SBL). I developed a data analysis pipeline to analyse T-cell receptor (TCR) genomic DNA data based on the best practices currently used in the fields of immunology and mathematical biology. This approach used a mathematical notion of entropy as a measure for the diversity of TCR repertoires, in this way combining all of the most commonly used metrics in mathematical biology. When combined, these measures form a profile for each repertoire, a set of which can be sorted using a machine learning algorithm to partition the repertoires into subgroups.
My hypothesis was that the T-cell clonal expansion of people with ME/CFS would be greater than for healthy controls, and comparable to disease (multiple sclerosis) controls. Although this method was able to effectively classify TCR chains using simulated data, results from experimentally-derived data did not support the hypothesis, with the most effective classifications for both CD4+ and CD8+ cells failing to pass corrections for multiple hypothesis significance testing.
Lay summary
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disease that affects millions of people around the world. Very little is understood about the cause or progression of the disease, and there is no known cure. At present, there is also no reliable clinical test to determine whether a person has ME/CFS.
This thesis explores the potential for a genetic or immunological basis for ME/CFS, with the goal to eventually find a biomarker that could be used in diagnosis.
The first part of this thesis investigates whether genetic variants are more (or less) common among those with ME/CFS than in the general population. In particular, the region of the genome that encodes immune system proteins was of interest, as previous studies have shown associations between this region and the disease.
Using strict statistical thresholds, none of the previously found associations were replicated. However, one new association was found, with the gene PDE10A, which is implicated in central nervous system diseases, such as Parkinsons and Huntingtons disease. This association has never been seen before, and would require replication in a new cohort before its role in ME/CFS could be confirmed. However, it represents a promising avenue for new research.
The second part of this thesis investigates T-cells. These are highly specialised immune cells in the blood, each of which targets an antigen (foreign substance) such as from a virus. When a T-cell recognises this antigen, it clones itself repeatedly. This clonal expansion is measurable, and can serve as evidence of immune system activation.
My hypothesis was that this immune signature could be used to distinguish people with ME/CFS from healthy controls and others diagnosed with another disease.
I used a mathematical measure of diversity and a machine learning method to sort their immune profiles into groups. However, the pattern of immune activation was not sufficiently clear to provide consistent classification. Hence, the role of the immune system in ME/CFS is still unclear, and the utility of this method as a diagnostic biomarker is not proved.
Source: Joshua James Dibble. Investigating the Genetic and Immunological Aetiology of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. PhD Thesis [University of Edinburgh] https://era.ed.ac.uk/bitstream/handle/1842/39763/DibbleJJ_2022.pdf?sequence=1&isAllowed=y (Full text)