Transcriptome analysis of patients with chronic fatigue syndrome

Abstract

Fatigue is a central component of many diseases and illnesses. Fatigue of unknown etiology and pathophysiology lasting more than six months, together with at least four out of eight specified symptoms, is termed chronic fatigue syndrome (CFS). Several causes have been suggested for the illness, including immune dysfunction, stress, sleep disturbances and infectious agents. CFS diagnosis is currently based on self-reported symptoms. The lack of physical abnormalities and laboratory tests makes the diagnosis harder. Identification of biological illness markers would contribute to increase insight into the pathophysiology of the illness and facilitate diagnosis. Powerful methods for transcript analysis have been developed during the past decade. Microarray technology and realtime PCR are two methods commonly used to identify genes involved in disease. The identified genes are disease markers, which may be used for diagnostic purposes.

Researchers involved with microarray experiments need standardization to facilitate comparisons between studies and laboratories. We show here that different RNA extraction methods can yield comparable results. Even so, only one method should be used in any one study and the ambition should be to use identical conditions for each and every experiment. CFS is not characterized by any diseased tissue, and this raises the question of what is a representative sample. The hypothesis has been raised that peripheral blood cells function as indicators for different biological processes going on throughout the human body. We show here that genes involved in psycho-neuroendocrineimmune (PNI) communication can be studied using peripheral blood mononuclear cells (PBMCs). The PBMC sample can be used to study diseases, such as CFS, with unknown pathophysiology and etiology.

The individual transcript expression variability in PBMCs is small and differences in gene activity due to abnormalities caused by illness or disease are larger. We expected to find only small gene expression differences, if any, between CFS patients and healthy controls. In our transcript expression studies we observed only a few differentially expressed genes. We found reduced levels of estrogen receptor beta (ERbeta) in CFS patients compared to healthy controls using real-time PCR. Three genes were identified using microarray technology with significant expression differences: CD83, NRK1 and BOLA1. The differences were only found between a subgroup of CFS patients, female patients with no previous infection and gradual illness onset, compared with healthy female controls. We verified the results with real-time PCR. The results indicate the need for subgrouping of the heterogeneous group of patients with fatiguing illness in search for pathogenic mechanisms.

In conclusion, the difference in gene expression could contribute to some of the symptoms observed in CFS. Further studies to investigate the protein levels and cellular effects will be required to determine whether any of these genes are involved in CFS pathology. The differences in transcript expression levels could also simply be a marker for changed functions of other cellular components that are involved in CFS. In this case, the altered levels could contribute to diagnostic criteria, they may form a surrogate marker, or they may provide an entry point to identifying potential diseasecausing candidate molecules for further study.