Researchers at the University of Utah have used gene expression patterns to differentiate between patients with fibromyalgia (FMS), chronic fatigue syndrome (CFS) and depression.
In particular, the study looked at gene expression to diagnose CFS and FMS while controlling for co-morbid depression, sex, and age.
Many fibromyalgia and CFS patients suffer from depression as a co-morbid or secondary condition.
The team analyzed leukocyte gene expression from blood samples of 61 healthy controls, 15 fibromyalgia patients, 33 CFS patients, 79 patients with both fibromyalgia and CFS, 42 patients who are resistant to depression medication and 31 who were medication-responsive.
In total, 34 candidate genes were assessed.
They found that the 34 genes could be clustered into four independent groups of biological factors that were categorized by function into: 1) purinergic and cellular modulators, 2) neuronal growth and immune function, 3) nociception and stress mediators, 4) energy and mitochondrial function. Factors 1 and 3 were found to be associated with CFS but not with fibromyalgia, and with a lower depression severity.
The team concluded that CFS and depression were associated with the same two clusters although CFS was linked to increased expression while depression was linked to a decreased expression of these particular genes.
What is gene expression profiling?
Gene expression profiling is a logical next step after sequencing a genome: the sequence tells us what the cell could possibly do, while the expression profile tells us what it is actually doing at a point in time. Genes contain the instructions for making messenger RNA (mRNA), but at any moment each cell makes mRNA from only a fraction of the genes it carries.
If a gene is used to produce mRNA, it is considered “on”, otherwise “off”. Many factors determine whether a gene is on or off, such as the time of day, whether or not the cell is actively dividing, its local environment, and chemical signals from other cells. For instance, skin cells, liver cells and nerve cells turn on (express) somewhat different genes and that is in large part what makes them different. Therefore, an expression profile allows one to deduce a cell’s type, state, environment, and so forth.
Earlier work on gene expression
In 2009 this same team, excepting Donaldson and Iacob, found that after moderate exercise, CFS and CFS-FMS patients showed enhanced gene expression for receptors detecting muscle metabolites – findings that suggested possible new causes, points for intervention, and objective biomarkers for these disorders.
The video below is a summary of that study.
Gene expression factor analysis to differentiate pathways linked to fibromyalgia, chronic fatigue syndrome, and depression in a diverse patient sample.
Iacob E, Light AR, Donaldson GW, Okifuji A, Hughen RW, White AT, Light KC.
To determine if independent candidate genes can be grouped into meaningful biological factors and if these factors are associated with the diagnosis of chronic fatigue syndrome (CFS) and fibromyalgia (FMS) while controlling for co-morbid depression, sex, and age.
We included leukocyte mRNA gene expression from a total of 261 individuals including healthy controls (n=61), patients with FMS only (n=15), CFS only (n=33), co-morbid CFS and FMS (n=79), and medication-resistant (n=42) or medication-responsive (n=31) depression. We used Exploratory Factor Analysis (EFA) on 34 candidate genes to determine factor scores and regression analysis to examine if these factors were associated with specific diagnoses.
EFA resulted in four independent factors with minimal overlap of genes between factors explaining 51% of the variance. We labeled these factors by function as: 1) Purinergic and cellular modulators; 2) Neuronal growth and immune function; 3) Nociception and stress mediators; 4) Energy and mitochondrial function. Regression analysis predicting these biological factors using FMS, CFS, depression severity, age, and sex revealed that greater expression in Factors 1 and 3 was positively associated with CFS and negatively associated with depression severity (QIDS score), but not associated with FMS.
Expression of candidate genes can be grouped into meaningful clusters, and CFS and depression are associated with the same 2 clusters but in opposite directions when controlling for co-morbid FMS. Given high co-morbid disease and interrelationships between biomarkers, EFA may help determine patient subgroups in this population based on gene expression.