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It is generally accepted that exhausting endurance exercise exhibits strong effects on the immune system. Such effects have been attributed to changes in the cellular composition of peripheral blood as well as to changes in the expression of plausible candidate genes. The list of candidate genes is far from being complete, since this issue has not yet been investigated in a systematic way. In this study, we used a custom-made cDNA microarray focused on inflammation as a screening approach to study gene expression in eight one-half marathon runners before, immediately after, and 24 h after exercise. Significant differential gene expression was verified by quantitative real-time PCR. Linear regression analysis showed that microarray expression analysis of cell type-specific surface molecules reflects the observed individual cellular shifts in peripheral blood cells with high statistical significance. In line with the results of former studies, we observed an upregulation of mitogen-activated protein kinase-activated protein kinase-2 (MAPKAP-K2), L-selectin, and IL-1 receptor antagonist (IL-1ra) after exhaustive exercise. The main results of this study report, for the first time, the downregulation of CD81; the upregulation of thioredoxin, which may play an important part in anti-oxidative defense; and, surprisingly, the downregulation of the anti-carcinogenic gene glutathione-S-transferase-3 (GSTM3) in peripheral blood. The study shows cDNA microarray expression analysis as a reliable systematic instrument to complete the list of candidate genes that may play a role in exhaustive exercise-induced modulation of the immune response.

Exhausting endurance exercise has strong effects on the immune system. Changes have been shown in the cellular composition of peripheral blood and in gene expression within those cells. In this study, custom-made cDNA microarrays focused on inflammation were used to analyze gene expression blood cells obtained from eight half-marathon runners before (t0), immediately after (t1) and 24 hours after exercise (t2). The microarrays that were used contained 384 different cDNAs spotted in triplicate. Differentially-regulated gene expression was analyzed using a simple rule-based clustering. Comparing t1 vs. t0, and t2 vs. t0, 36 and 21 sequences respectively, showed a consistent pattern of changes in all eight athletes. Taken together, the pattern of these modified genes can be viewed as a "gene expression fingerprint" for each time point in response to a half marathon. The known and novel genes identified here represent targets for further molecular characterization of the complex reaction of the body to an exhaustive challenge. These data suggest that gene expression fingerprints can serve as a powerful research tool to design novel strategies for diagnosis and treatment of exercise related injury and stress.

Microarray analysis offers a set of analytical platforms that provide rapid, affordable and substantial information at the DNA, RNA or protein level. It enables the analysis of thousands of genes simultaneously in a parallel manner across samples derived from various biological sources and treatment regimens. This development became possible as a result of the combination of three technological advances achieved at the beginning of the 1990's, namely parallelism, miniaturization and automation. In regular physical checkups the microarray technology could be used to supplement the current spectrum of tests and therefore enhance the quality of the data obtained. Arrays for analyses of RNA expression will allow gene expression profiling also in exercise physiology. DNA chips may also be used for genetic screening and diagnostics to analyze polymorphisms and mutations which may underlie genetic diseases or interindividual variations between subjects. Using microarrays in exercise physiology will provide new insights into the complex molecular mechanisms of the exercise-induced stress response, adaptation to training and modulation of immune function. Gene expression profiling and genetic screening will probably help to characterize and predict the individually variable response to and efficiency of training.