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Utility of the blood for gene expression profiling and biomarker discovery in chronic fatigue syndrome.

Vernon SD, Unger ER, Dimulescu IM, Rajeevan M, Reeves WC.
Utility of the blood for gene expression profiling and biomarker discovery in chronic fatigue syndrome.
Disease Markers 18: 193-199, 2002.

Summary

Gene expression profiling examines the activity (transcription) of genes by analyzing cell's messenger RNA (mRNA). Gene expression is altered by many factors including cell differentiation, metabolic state, and disease status. By comparing expression of many genes simultaneously, gene expression profiles can be compared between persons (and populations) with and without disease and characteristic differences (or associations) can be identified. These differences, known as differential gene expression, can point to markers for diagnosis or altered physiologic pathways. Gene expression analysis is particularly suited to studying CFS because it examines RNA transcripts from thousands of genes at the same time, covering all from immune system proteins, neuroendocrine pathways, stress response proteins, metabolism, apoptosis, cell adhesion receptors, cell cycles and such data is essential for understanding biological pathways and processes. Most gene expression studies have focused on samples derived from cells or tissues with a known lesion, but CFS has no known to sample. We have hypothesized that peripheral blood mononuclear cells (PBMCs) reflect the systemic state, thus allowing for evaluation of multiple pathophysiological pathways. Some of these pathways should be affected by CFS.

This paper is the first use of gene expression profiling as a biomarker to distinguish persons with CFS from healthy controls and for identifying genes that could serve as CFS biomarkers. The manuscript is intended as a "proof of concept" for use of gene expression profiling for an illness without a known lesion. This study measured gene expression profiles in peripheral blood cells from five persons with CFS and 17 non-fatigued controls identified during physician surveillance in Atlanta. Various other publications (Studies of Causes of CFS) also used these subjects. Both cluster analysis and multidimensional scaling analysis grouped and separated CFS from controls. The materials and methods section provides considerable detail as to the assays that CDC uses for measuring gene expression profiles. The materials and methods also provides detail as to analysis strategies.

Abstract

Chronic fatigue syndrome (CFS) is a debilitating illness lacking consistent anatomic lesions and eluding conventional laboratory diagnosis. Demonstration of the utility of the blood for gene expression profiling and biomarker discovery would have implications into the pathophysiology of CFS. The objective of this study was to determine if gene expression profiles of peripheral blood mononuclear cells (PBMCs) could distinguish between subjects with CFS and healthy controls. Total RNA from PBMCs of five CFS cases and seventeen controls was labeled and hybridized to 1764 genes on filter arrays. Gene intensity values were analyzed by various classification algorithms and nonparametric statistical methods. The classification algorithms grouped the majority of the CFS cases together, and distinguished them from the healthy controls. Eight genes were differentially expressed in both an age-matched case-control analysis and when comparing all CFS cases to all controls. Several of the differentially expressed genes are associated with immunologic functions (e.g., CMRF35 antigen, IL-8, HD protein) and implicate immune dysfunction in the pathophysiology of CFS. These results successfully demonstrate the utility of the blood for gene expression profiling to distinguish subjects with CFS from healthy controls and for identifying genes that could serve as CFS biomarkers.

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