Microarrays &Proteomics in MZ Twins Discordant for CFS

University Of North Carolina Chapel Hill, Chapel Hill NC

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Abstract

DESCRIPTION: Despite considerable study, chronic fatigue syndrome (CFS) continues to be both idiopathic and controversial. CFS is associated with considerable morbidity, impairment, and chronicity. Multiple lines of investigation have not yielded widely-accepted and empirically-based hypotheses about its etiology. The goal of this application is to identify biomarkers for CFS, which can also be used to generate hypotheses about the etiology of CFS. The current plan is to correlate microarray and proteomic techniques to biological samples from monozygotic twins who are rigorously discordant for CFS. Discordant monozygotic twins represent an excellent case-control design given their high degree of genetic matching and similarity for many environmental variables and exposure. 28.089 twins from the population-based Swedish Twin Registry have already screened for the symptoms of CFS (funded by NS-41483) and 156 pairs of monozygotic twins, preliminarily discordant for CFS-like illness, have been documented. The application requests funds for clinical evaluation in order to recruit and classify 50 monozygotic twin pairs as rigorously discordant for CFS Zygosity will be proven by genotyping 30 microsatellite markers. Under standardized conditions with careful sample handling, three biological samples can be obtained from consenting twins: total RNA extracted from peripheral blood lymphocytes, peripheral serum, and cerebrospinal fluid. The RNA sample will be analyzed with Affymetrix HG-U133 microarrays that interrogate approximately 45,000 mRNA targets from approximately 33,000 validated human genes. Following removal of high abundance proteins, serum and cerebrospinal fluid samples will be subjected to two-dimensional gel electrophoresis and identification of protein spots of interest via MALDI (aka "proteomics").False discovery rate calculations to show that this design is capable of identifying biomarkers for CFS under many plausible scenarios. Sophisticated statistical, data mining, and bioinformatic techniques will be applied to these data to understand the high dimensionality data generated in these experiments. The team of investigators and consultants assembled for this project has the proven capacity to perform all aspects of this project. This project has the potential to identify biomarkers for CFS and to derive falsifiable hypotheses about its etiology. If successful, this work could lead to profound changes in the understanding of CFS and resolution of some attendant controversies.