The microarray or differential display approach has been used to examine the CFS-specific gene expression in peripheral blood mononuclear cells (2
). Two sources are commonly used for preparation of RNA, whole blood, or its leukocyte populations (15
). Because of advantages and disadvantages associated with both systems (16
), at present there is no consensus regarding the optimal technique for isolation of RNA from peripheral blood. A whole-blood RNA collection system is appealing, particularly in clinical settings, since the RNA isolation method is easy to use and reduces operator time and sample volume. In addition, this system reduces the risk of exposure of laboratory personnel to biohazards relative to the risk involved in isolation of leukocyte populations.
Using RNA from whole blood, we show here that both microarray analysis and real-time PCR identify nine genes whose mRNA expression are significantly different in 11 patients with CFS, compared with age- and sex-matched healthy controls. Although the individual genes identified as CFS-related genes did not overlap with those identified in other studies (2
), most them could be categorized into distinct clusters, including host defense, energy metabolism, or small G protein-dependent signal transduction (2
). The significance of our study can be considered from three different perspectives.
First, the identified genes are informative in considering the pathophysiology of CFS. The upregulated GZMA
encodes a T cell- and natural killer cell-specific serine protease that functions as a common component necessary for lysis of target cells by these cytotoxic cells. The proteasome subunits PSMA3
also were upregulated. The proteasome is the central proteolytic system that also plays an important role in the major histocompatibility complex-class I antigen processing. Previous studies identified genes involved in T cell activation (2
). Our findings also suggest that patients with CFS may have altered immunity, such as that involved in anti-viral defense. As reported in other studies (3
), we also have identified genes encoding molecules catalyzing oxidative phosphorylation in mitochondria (COX5B
encode a cytochrome c
oxidase subunit and a subunit of the mitochondrial proton channel, respectively. Although we were unable to measure the mRNA level of another cytochrome c
oxidase subunit (COX7C
) by real time PCR, these nuclear-encoded subunits (COX5B
) function in the regulation and assembly of the cytochrome c
oxidase complex and mitochondrial ATPase. In addition, our CFS patients had significantly increased DBI
mRNA levels. The diazepam binding inhibitor (DBI) is known as a GABA receptor modulator or acyl-coenzyme A (acyl-CoA) binding protein (ACBP). ACBP binds thiol esters of long fatty acids and coenzyme A in a one-to-one binding mode with high specificity and affinity. This molecule is suggested to act as an intracellular acyl-CoA transporter and to form a pool of ACBP-acyl-CoA complex that is an important intermediate in lipid synthesis and fatty acid degradation that participates in regulating intermediary metabolism and gene expression. The increased mRNA expression of DBI
, and ATP5J2
strongly suggests abnormalities in energy metabolism in our CFS patients.
We also found that the STAT5A
mRNA level was decreased significantly in CFS patients. The protein encoded by STAT5A
is a member of the STAT family of transcription factors. STAT-5 mediates the signal transduction triggered by various cell ligands, such as IL2, IL4, colony-stimulating factor 1, and growth hormones. Adult growth hormone deficiency (AGHD) is a CFS-like disorder characterized by fatigue, tiredness, and myalgia; replacement therapy with human growth hormone improves these symptoms (21
). Growth hormone activates STAT1, 3, 5A, and 5B in different cell systems (22
). Webb et al
. reported that STAT-5 isoform, but not STAT-1 or STAT3, were increased markedly in skeletal muscles in patients with AGHD and suggested that the STAT5 signal transduction pathway in skeletal muscle might be abnormal in AGHD (21
). The decreased expression of STAT5A
mRNA in peripheral blood cells from CFS patients suggests that the abnormality in STAT5 signaling might be associated with symptoms of CFS.
In the Wichita study directed by CDC (6
), fatigue-associated gene expression patterns in isolated blood mononuclear cells were identified by several groups sharing the same data sets. Most of the groups in that study did not divide subjects into CFS and non-CFS cases by CDC classification but focused instead on fatigue itself and accompanying symptoms for elucidation of fatigue-associated genes. It was confirmed that 9 of 16 genes reported by Kaushik et al
. as differentially expressed genes in CFS (5
) also were included among fatigue-associated genes measured by quantitative trait analysis (QTA) in the Wichita study (23
). Our study also revealed that two genes, STAT5A
, were categorized in the same pathways as STAT5B
, which were identified as fatigue-associated genes according to QTA. STAT5A
belong to the Jak-STAT signaling pathway and oxidative phosphorylation pathway, respectively. Furthermore, Fang et al
. in the Wichita study succeeded in separating CFS from non-CFS patients based on expression profiles of 24 genes that were differentially expressed in subjects between those who received high scores and low scores in both multidimensional fatigue inventory scores and the Zung depression scale (24
). The reported 24 genes included ACBD6
that encodes one of the acyl-CoA-binding proteins. Another homologue of the acyl-CoA-binding proteins, DBI
), was up-regulated in our CFS cases. The Wichita study was a population-based study, while our data are based on a clinical cohort. Despite the fact that we used a different RNA preparation method and a different microarray platform, there was a significant overlap between our results and those of the Wichita study.
Second, most of the non-CFS patients in our study were psychiatric disorders, which usually presents a challenge for the clinicians to differentiate. Thus, the present study may provide a potential tool to clinicians who see chronically fatigued patients in daily practice with no objective marker for CFS.
Third, the expression pattern of the nine marker genes did not distinguish all of our clinically diagnosed CFS cases, since they include heterogeneous populations. However, identification of a group of CFS patients having this unique expression pattern will be useful for future treatment studies.
With regard to the limitations of our study, although we could not detect any common abnormality in the CBC and leukocyte subpopulations of our CFS cases, the use of whole blood RNA could not rule out heterogeneity of the cell population and the potential diversity of the cell-specific responses. The second concern is that microarrays carrying different gene probes may pick up different groups of genes. Although our microarray carries cDNA probes for 1,467 genes that have been confirmed to be actually detectable by reverse transcription PCR, more genome-wide examinations in larger numbers of cases may reveal additional marker genes for CFS. However, we also found that the expression pattern of nine genes measured by the microarray could classify 79% (11/14) of CFS and 85% (17/20) of non-CFS patients. Finally, real-time PCR measurement of the nine mRNA levels in another group of subjects (18 CFS and 12 non-CFS patients) classified 94% (17/18) of CFS and 92% (11/12) of non-CFS patients. A clinical trial of a larger number of CFS and non-CFS patients with long-lasting fatigue is now under way.