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J Appl Physiol 102: 26-36, 2007. First published September 21, 2006; doi:10.1152/japplphysiol.00066.2006
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Exercise affects the gene expression profiles of human white blood cells

Petra Büttner,1 Sandy Mosig,1 Anja Lechtermann,2 Harald Funke,1 and Frank C. Mooren3

1Institute of Vascular Biology and Medicine, Friedrich-Schiller-University Jena, Jena; 2Institute of Sports Medicine, University Hospital Münster, Münster; and 3Department of Sports Medicine, Institute of Sports Sciences, Justus-Liebig-University, Giessen, Germany

Submitted 19 January 2006 ; accepted in final form 5 September 2006


   ABSTRACT
TOP
 ABSTRACT
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
White blood cells (WBCs) express tens of thousands of genes, whose expression levels are modified by genetic and external factors. The purpose of the present study was to investigate the effects of acute exercise on gene expression profiles (GEPs) of WBCs and to identify suitable genes that may serve as surrogate markers for monitoring exercise and training load. Five male participants performed an exhaustive treadmill test (ET) at 80% of their maximal O2 uptake (VO2 max) and a moderate treadmill test (MT) at 60% VO2 max for exactly the same time ~2 wk later. WBCs were isolated by the erythrocyte lysis method. GEPs were measured using the Affymetrix GeneChip technology. After scaling, normalization, and filtering, groupwise comparisons of gene expression intensities were performed, and several measurements were validated by real-time PCR. We found 450 genes upregulated and 150 downregulated (>1.5-fold change; ANOVA with Benjamini-Hochberg correction, P < 0.05) after ET that were closely associated with the gene ontology lists "response to stress" and "inflammatory response". Analysis of mean expression levels after MT showed that the extent of up- and downregulation was workload dependent. The genes for the stress (heat shock) proteins HSPA1A and HSPH1 and for the matrix metalloproteinase MMP-9 showed the most prominent increases, whereas the YES1 oncogene (YES1) and CD160 (BY55) were most strongly reduced. Despite different methodological approaches used, the consistency of our results with the expression data of another study (Connolly PH, Caiozzo VJ, Zaldivar F, Nemet D, Larson J, Hung SP, Heck JD, Hatfield GW, Cooper DM. J Appl Physiol 97: 1461–1469, 2004) suggests that expression fingerprints are useful tools for monitoring exercise and training loads and thereby help to avoid training-associated health risks.

inflammation; stress response; microarrays; surrogate marker

REGULAR EXERCISE is of great importance for public health. It has been shown to be effective in the prevention and therapy of many widespread diseases in Western civilizations (2, 3, 34), including hypertension, coronary heart disease, and diabetes.

Many aspects of how the prophylactic and therapeutic effects of exercise on chronic diseases are mediated remain unclear (34, 36). One important way to improve our understanding of these beneficial effects is the investigation of the cellular and molecular responses to exercise. The use of the microarray technology thereby enables us to investigate the complete gene response to an external factor like exercise (4, 8). Since exercise has been shown to be an important regulator of immune cells and their functions, we have chosen white blood cells (WBCs) as target cells (39). There is evidence that the exercise stress evokes an inflammation-like reaction of the immune system with the activation of both proinflammatory and anti-inflammatory pathways (31). The balance of both is supposed to be dependent on exercise intensity and duration. Therefore, acute exhaustive exercise is expected to transiently decrease the individuals' immune competence, while moderate exercise has an anti-inflammatory effect with improved anti-infectious capabilities (29). Moreover, there is growing evidence that the immune system may serve as an important physiological indicator for a person's individual ability to recover from workload stresses. This has led to the hypothesis that the overtraining syndrome, a condition of long-term decrement in performance capacity despite continuous training loads, is based on a derangement of cellular immune regulation (18).

Besides the study of underlying signaling mechanisms, the future use of microarray technology in exercise physiology might be of special value for monitoring the athletes' training process. One prerequisite for this intention would be the identification of robust exercise-induced gene expression profiles or fingerprints that can be related to exercise intensity or type, etc. However, the available data in the literature show very heterogeneous results most likely due to different analytical platforms and experimental procedures (4, 8, 14, 45, 46).

Together, this prompted us to hypothesize that the impact of the stress factor exercise may be mirrored in the gene expression profiles of leukocytes. We were interested to see whether expression fingerprints in these cells reflect exercise intensity and whether a set of exercise-regulated genes could be identified. To address these questions, we examined the transcriptional response of the same individuals after moderate and exhaustive treadmill tests. Eventually, results from such studies could help to improve the design of individualized exercise programs.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Subjects and experimental design.   Five male subjects of the University of Münster's student population were informed about the nature, purpose, and potential risks of the study and signed an informed consent statement approved by the University of Muenster Ethics Committee. The anthropometric data of the group were as follows (means and SD): height 180.0 cm (SD 4.6); weight 71.8 kg (SD 4.0); age 25.4 yr (SD 3.5). Moreover, the subjects participated predominantly in leisure time sports such as football and running with a mean training time of ~6.0 h/wk (SD 2.6). After a general medical checkup the subjects were first tested for their maximal oxygen uptake (VO2 max) during a continuous, progressive exercise test on a treadmill ergometer (Ergo XELG90 Spezial, Woodway, Weil am Rhein, Germany). The initial velocity was 8 km/h, increasing every 3 min by 2 km/h. Respiration parameters were analyzed using Quark b2 (Cosmed, Rome, Italy). The mean VO2 max (SD) of the subjects was 60.6 ml·min–1·kg–1 (SD 5.3); their mean peak power output was calculated in accordance to the Magaria formula to be 349.0 W (SD 34.4).

About 2 wk later, the participants performed a strenuous treadmill exercise test (ET) at an intensity corresponding to 80% of their individual VO2 max until exhaustion [mean velocity 12.7 km/h (SD 1.7)]. Another 1–2 wk later the subjects performed a second exercise test at an intensity corresponding to 60% of VO2 max (moderate treadmill exercise, MT) for a time identical to that of their strenuous exercise test [mean velocity 9.4 km/h (SD 1.2)]. For both tests blood samples were taken before, after, and 1 h after exercise. Blood samples for gene expression measurements were prepared immediately following blood withdrawal. RNA isolation and microarray measurements were performed only on preexercise samples and the samples taken 1 h after exercise.

Leukocyte counts and lactate concentration.   Blood cell counts, hemoglobin, and hematocrit determinations were performed using a semi-automated hematology analyzer (F-820, Sysmex, Norderstedt, Germany).

Lactate concentration in capillary blood was measured by a photometric method using a commercially available kit (EKF Diagnostic, Magdeburg, Germany).

RNA, cDNA, and cRNA preparation.   WBCs were isolated from 9 ml EDTA-blood using the erythrocyte lysis buffer of the QIAamp RNA Blood Mini Kit (Qiagen, Hilden, Germany) followed by two washing steps in PBS containing 2 mmol/l EDTA. Buffers were used at 4°C; tubes were placed on ice. The resulting cell pellet was resuspended in 20 µl ice-cold PBS-EDTA and transferred into 900 µl Trizol (Invitrogen, Karlsruhe, Germany) at room temperature where cells were homogenized by repeated pipetting. RNA samples can be stored in Trizol for weeks (–20°C) without losses in quality or quantity. In this study the samples were prepared within 2 days after lysis of the WBCs. RNA was extracted using the phase-lock gel tube method (Phase Lock Gel Heavy 1.5 ml; Eppendorf, Hamburg, Germany). Subsequent cleaning was done with the RNeasy Mini Kit (Qiagen), including the optional DNase step. RNA concentrations were determined with the Nanodrop photometer (NanoDrop, Wilmington, DE); RNA quality was checked using the Agilent Bioanalyzer 2100 System (Agilent Technologies, Palo Alto, CA).

cDNA was prepared using the SuperScript double-strand cDNA synthesis kit (Invitrogen) and a T7-oligo(dT) primer (Affymetrix, Santa Clara, CA) starting with 3 µg total RNA as template. In vitro transcription to generate biotinylated cRNA was done with the ENZO Kit (Enzo Life Sciences).

Microarray hybridization.   Resulting cRNA was cleaned up with the RNeasy Mini Kit. RNA fragmentation, hybridization, washing, staining, and scanning were done following the recommendations laid down in the Affymetrix GeneChip Expression Analysis Technical Manual. The wash protocol for the Fluidic Station was Midi_Euk2. Samples were hybridized to Affymetrix U133A 2.0 GeneChips. These arrays represent 18,400 transcripts and variants, including 14,500 well-characterized human genes. The arrays were scanned with the GeneChip Scanner 3000; data were recorded with the GCOS 1.1. program (Affymetrix).

Microarray analysis and statistics.   GCOS 1.1. was used for background/noise correction and scaling of all signals to a mean signal intensity of 500. Additional scaling, normalization, and filtering steps were done using GeneSpring 6.1. (Silicon Genetics, Redwood City, CA). For better comparability, median expression values of all 20 chips were normalized to 1; expression values of each gene on these chips were normalized to the median.

Additional data reduction was done using several filter criteria. All genes not showing expression values of 50 (raw signal) or higher in at least five chips were excluded from further analysis. The same was done with genes that did not show "present" flags on at least five chips. These criteria were chosen to allow a change in gene expression from absent to present or from present to absent as a consequence of the training procedure. The thus-obtained list of genes was used to identify changes in gene expression induced by exercise.

Data were grouped according to time point (pre- and postexercise) and extent of physical activity (ET or MT). Each of the four groups contained data from the same five participants. Groups were compared using ANOVA with and without correction for multiple testing (using Benjamini-Hochberg or Bonferroni algorithms). To discover the genes with the most prominent postexercise changes in gene expression, we used two different strategies. In one strategy, the differences in gene expression from before and after exercise were determined within each individual; in the other strategy we compared mean expression levels for each gene calculated from all participants before and after exercise. We used a 1.5-fold change as well as a 2-fold change as the cutoffs. Next the within-individual data gene lists were matched for intersections between these genes (intersection lists). Gene lists emanating from the groupwise comparisons (mean change lists) contained genes whose expression was significantly changed (ANOVA with Benjamini-Hochberg correction; P < 0.05).

Hierarchical clustering (using the standard correlation setting) and principal component analysis (PCA) were done using GeneSpring 6.1. PCA is a decomposition technique that produces a set of expression patterns known as principal components. Linear combinations of these patterns can be assembled to represent the behavior of all of the genes in a given data set. PCA is not a clustering technique, but it is a tool to characterize the most abundant themes or building blocks that reoccur in many genes of the experiment (GeneSpring technical manual). The ANOVA was based on the prefilter list and was carried out without multiple testing corrections. The list, containing 2,777 genes related to exercise, was used for a PCA on conditions [before treadmill test (pre-T), MT, ET].

Finally, we matched the lists comprising genes with a 1.5-fold change and significance levels of P < 0.05 (ANOVA with Benjamini Hochberg multiple testing correction) with lists supplied by the gene ontology (GO) consortium to sort our genes into functional groups. The gene list belonging to Term GO 0006954 ("inflammatory response") was imported to GeneSpring because it caught our interest but was not a part of the standard GO SLIM used in GeneSpring by default. All samples were transmitted to the NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and stored in the database as GSM82670 [NCBI GEO] to GSM82689 [NCBI GEO] . The associated series can be found under the accession number GSE3606.

Quantitative real-time PCR.   To validate the relative gene expression data of microarray analysis, we performed quantitative real-time PCR. RNA samples were reversely transcribed using the Omniscript RT Polymerase kit (Qiagen, Hilden, Germany); 1.2 µg RNA and 82.5 pmol poly(dT)15-primer (Roche, Mannheim, Germany) were mixed in 11.3 µl of RNase-free water (Roth, Karlsruhe, Germany) and denatured at 72°C for 4 min. The RT reaction was carried out in 25 µl RNase-free water containing 500 µmol/l deoxynucleoside triphosphates (dNTPs) (Qiagen), 3.3 µmol/l poly(dT)15-primer, 0.25 U/µl Omniscript polymerase, 0.5 U/µl RNase inhibitor (Roche), the before-mentioned amounts of RNA and primers, 0.01% (wt/vol) BSA (Sigma, Taufkirchen, Germany), and 1x Omniscript buffer at 37°C for 1 h. Two microliters of cDNA was then used for RT-PCR.

Primers were designed using the Affymetrix U133A 2.0 oligonucleotide target sequence and the PRIMER3 program (http://frodo.wi.mit.edu/). They were checked for specificity through blast/in silico PCR using the PUNS (Primer-UniGene Selectivity) program (http://okeylabimac.med.utoronto.ca/cgi-bin/PUNS/). Primer sequences for GAPDH (NM_002046 [GenBank] ) were 5'-tcggagtcaacggatttggtcgta-3' and 5'-atggactgtggtcatgagtccttc-3'; the annealing temperature was 66°C. MMP-9 (NM_004994 [GenBank] ) amplification was done using 5'-aaagcctatttctgccaggac-3' and 5'-gtggggatttacatggcact-3' as primers. Primers for S100P (NM_005980 [GenBank] ) amplification were 5'-taccaggcttcctgcagagt-3' and 5'-ctccagggcatcatttgagt-3'; in both reactions an annealing temperature of 60°C was used. Quantitative RT-PCR was done by comparison of cycle threshold values for specific genes measured in total cDNA with those of standards of known copy numbers using the SybrGreen method on a RotorGene 2000 (Corbett Research, Mortlake, Australia) cycler. Reaction conditions were 0.5 µmol/l for each primer, 200 µmol/l dNTPs, 1.8 mmol/l MgCl2, 1.5 U Taq polymerase (Platinum Taq; Invitrogen), 0.01% (wt/vol) BSA, 1x reaction buffer (Invitrogen), and 0.16x SybrGreen (Roche) in a final volume of 25 µl. The PCR program started with a denaturation step at 96°C. Forty cycles of denaturation (96°C/30 s), annealing (temperature as indicated by the specific primer used/30 s), and extension (72°C/25 s) were then carried out. Finally, the product melting curves ranging from 75°C to 99°C were measured to exclude the presence of unspecific by-products.


   RESULTS
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
The mean running time in the treadmill exercise tests was 39.0 min (SD 14.8). Heart rate was 182 beats/min (SD 3) at the end of ET, while after MT it was 142 beats/min (SD 10). After both tests we observed typical exercise intensity-related changes in metabolic parameters and in WBC counts. At the end of the ET, lactate concentrations were significantly enhanced [3.4 (SD 0.7) vs. 1.1 mmol/l (SD 0.2); P < 0.05] while they remained unchanged after the MT [1.2 (SD 0.3) vs. 1.3 mmol/l (SD 0.2)]. Similarly, the magnitude of the exercise-induced leukocyte change was dependent on exercise intensity, whereas after both tests an exercise-induced lymphopenia was observed (Table 1).


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  		Table 1. Exercise-induced changes in leukocyte count

 
We analyzed whether exercise induces changes in gene expression profiles (GEPs) of WBCs and if exercise intensity is likewise mirrored. Individual gene chips hybridized with RNA from our probands' WBCs showed positive expression signals (flagged "present") for 11,807 to 13,140 transcripts. A total of 12,911 transcripts passed the prefilter conditions.

To get an idea of intraindividual differences in gene expression, we first compared the gene expression data under resting conditions before the ET test with the data before the MT test. For comparison the mean signal intensities for the pre-ET data were plotted against those for the pre-MT data (Fig. 1A). A high degree of reproducibility was found for the data from the two groups. None of the expressed genes was significantly (ANOVA without correction for multiple tests; data not shown) different between the two data sets. This prompted us to pool all preexercise data for further data analysis. The other scatterplots in Fig. 1 show a comparison of the pooled preexercise gene expression data with data either after the MT (Fig. 1B) or after the ET (Fig. 1C). Already this very basic tool of raw data visualization clearly indicated that regulation (> or < 2-fold change) was more pronounced after ET than after MT.


Figure 1
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  		Fig. 1. Scatterplots visualizing mean raw gene expression data. A: the variation between 2 measurements of the same 5 individuals [before moderate treadmill test (pre-MT) vs. before exhaustive treadmill test (pre-ET)]. B: comparison of the mean gene expression levels of all before-exercise data (pre-T) (n = 2 x 5) with those after moderate exercise (post-MT; n = 5). C: pre-T vs. after ET (post-ET); blue lines indicate a 2-fold change.

 
To test if exercise tests of different intensities leave gene expression fingerprints (46), an experiment-specific pattern of up- or downregulated genes, we performed a hierarchical cluster analysis. Individual preexercise data and post-ET data were sorted to different clusters (resting vs. exercise) while the post-MT data were inconsistently distributed. Two post-MT gene expression profiles, from probands 2 and 3, were grouped together with the resting group, while the others were grouped to the ET group (data not shown). Similar results were found using PCA. We identified two components, which are visualized in Fig. 2, which clearly separate between the before-exercise GEPs and those obtained after ET. Figure 2 also confirmed the intermediate state of the MT data between preexercise and ET.


Figure 2
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  		Fig. 2. A: visualization of the behavior of all samples in the context of exercise via hierarchical clustering using standard correlation based on ANOVA without multiple testing corrections (2,777 genes) The first cluster includes all preexercise data together with the data of participants 2 and 3 after the MT, while all other postexercise data were sorted into the second cluster. Color codes: red, high expression; yellow, medium expression; green, low expression. B: diagram shows the 2 main principal components calculated from 2,777 genes (ANOVA, P < 0.05). {circ}, pre-T; {square}, MT; {triangleup}, ET.

 
To detect genes that might work as indicators of the exercise workload, we used a two-stage approach. First we generated individual gene lists for all five probands containing all genes changed more than 1.5-fold within a specific proband. The five lists were used to create an intersection list (list 1). This list contained 40 genes that were upregulated and 10 genes that were downregulated. A second gene list (list 2) was created containing genes whose mean expression levels were significantly changed (ANOVA with Benjamini-Hochberg correction; P < 0.05) and showed an increase or decrease in expression levels of at least 1.5-fold after ET. A total of 450 and 150 genes were differentially up- and downregulated, respectively (see Supplementary Tables 1 and 2, contained in the online version of this article). The intersection of gene lists 1 and 2 resulted in a final list of potential exercise marker genes, which contained 39 upregulated and 7 downregulated transcripts (summarized in Tables 2 and 3, respectively).


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  		Table 2. Exercise marker genes (upregulated)

 

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  		Table 3. Exercise marker genes (downregulated)

 
On the U133A 2.0 chip many genes are represented by several sets of oligonucleotides. Therefore, for all genes that were part of the final exercise marker gene list, we analyzed the expression change for all transcripts present on the chip (Tables 2 and 3). For the vast majority of the analyzed genes, we obtained concordant results. When we used the same approach with a 2.0-fold change as a filter instead of a 1.5-fold change, only one transcript [for heat shock protein 105 kDa (HSPH1)] passed the filter.

The exercise marker gene list contained genes that are involved in several different physiological processes. The most prominent changes were found for stress proteins such as HSPH1 and heat shock 70-kDa protein 1A (HSPA1A). Moreover, genes related to inflammatory mediators, transcriptional regulators, membrane transporters/channels, and molecules involved in the breakdown of the extracellular matrix were among the potential marker genes. To validate the microarray experiments, the expression level changes of three genes, HSPA1A, S100P (part of the mean gene expression list), and MMP-9, were also determined by quantitative real-time PCR (Fig. 3). Data were normalized to the housekeeping protein GAPDH. The PCR results confirmed an upregulation of these particular genes by exercise (Fig. 3).


Figure 3
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  		Fig. 3. Comparison of microarray data with quantitative real-time PCR (RT-PCR) data (all samples normalized to GAPDH). A: S100P. B: matrix metalloproteinase-9 (MMP). C: heat shock 70-kDa protein 1A (HSPA1A). {blacktriangleup}, RT-PCR; bullet, array data.

 
The upregulated genes (list 2) were highly significantly associated with the gene ontology lists stress response (14 genes/18 transcripts), heat-shock response (7 genes/11 transcripts), and inflammatory response (14 genes/16 transcripts) (Table 4). Exemplarily we imported the intersectional list between list 2 and "inflammatory response," which contained the largest number of genes, into PathwayAssist 3.0. Using all available information and considering one regulatory element between the genes the program calculated direct interactions for 8 of 14 genes, which have been shown to exist at the protein level (Fig. 4).


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  		Table 4. Association of exercise-induced genes with gene ontology lists

 

Figure 4
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  		Fig. 4. Pathway model of genes that were found significantly (ANOVA Benjamini-Hochberg) upregulated more than 1.5-fold by ET and that were included in the gene ontology (GO) term "inflammatory response" (provided by GO consortium); genes with gray shading are part of the mentioned lists; genes without gray shading are not differentially regulated through exercise [transforming growth factor-beta1 (TGF- beta1)] or flagged absent (IL-13). Pathways were calculated using Stratagene PathwayAssist 3.0. IL8RA, IL-8 receptor-{alpha}; IL1R1, IL-1 receptor type I; FOS, v-fos FBJ murine osteosarcoma viral oncogene homolog; IL1RN, IL-1 receptor antagonist; FPRL1, formyl peptide receptor-like 1; ALOX5, arachidonate 5-lipoxygenase; CXCL1, chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity-{alpha}); IL1A, IL-1{alpha}.

 
When we compared the expression level changes of the marker genes for exhaustive exercise with those after MT, we found that after MT the expression level change was much more moderate for both up- and downregulated genes (Fig. 5). This effect of exercise intensity on the expression level was also confirmed by quantitative PCR for MMP9 and HSPA1A (Fig. 3).


Figure 5
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  		Fig. 5. Normalized expression signals of marker genes before exercise (Pre), after moderate exercise (MT), and after excessive exercise (ET). A: the genes upregulated after exercise (39 transcripts representing 35 genes). B: the downregulated genes (7 genes).

 
In their recently published study, Connolly and coworkers (4) identified 78 genes that were significantly upregulated by a factor of ~1.3 or larger following exercise and a 1-h rest. Interestingly, 75 of these genes (96%) were found to be upregulated in our study, too, of which 43 (56%) showed a significant (ANOVA; P < 0.05) expression increase by a factor of 1.3 or more. None of the three genes from the list of Connolly et al. (4) whose expression was not increased in our study showed a significant decrease. Moreover, 53 genes were reported to be significantly downregulated in the study of Connolly et al. Of these, 50 (94%) were also found downregulated in our study. Ten of them (19%) reached significance (factor ≥1.3; ANOVA P < 0.05). Only one gene that was found to be downregulated by Connolly and coworkers (4), IL-18 receptor accessory protein (IL18RAP), showed an opposite behavior in our study, where it was significantly increased (Fig. 6; for details, see Supplementary Tables 3 and 4, contained in the online version of this article).


Figure 6
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  		Fig. 6. Between-laboratory comparison of exercise-induced gene expression changes. Similar test conditions and identical microarrays but different preanalytical procedures were used in the 2 studies. The graph shows how genes that have been reported by Connolly et al. to be significantly different in pre- and postexercise samples 1 h past exhaustive exercise (most of them showed an at least 1.3-fold gene expression change) behave in the study presented here. Black boxes indicate genes that were found significantly (ANOVA + Benjamini-Hochberg) regulated in our data set. A: upregulated genes; B: downregulated genes. Dashed lines indicate a 1.3-fold change.

 

   DISCUSSION
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
The present study aimed at the detection in WBCs of specific patterns of gene expression in response to different exercise loads. To apply different exercise loads on the subjects two exercise protocols of different exercise intensities were used. Treadmill running at velocities corresponding to either 80% or 60% VO2 max has been used by us recently (25). These protocols have been shown to be effective in achieving different metabolic and inflammatory responses (25). Likewise, in the present study, significantly different values for leukocytes and lactate were obtained.

Differential effects of exercise on gene expression.   Basal gene expression data before the MT and the ET tests were quite similar, which supports the specificity and the reproducibility of our method. Performing exercise results in an enhanced variation of gene expression. Visual inspection of the scattergrams in Fig. 1 shows that the extent of gene expression changes is related to the intensity of exercise. The stress effects of acute exercise depend on enormous loads of free radicals, electrolyte, and acid-base imbalances (23, 24). Such exercise-induced deviation from homeostatic equilibrium is related to exercise intensity, too. The body's acute response to exercise is characterized by systemic efforts to compensate for these challenges and to gain homeostatic recovery (22). This is achieved on different regulatory levels. On the transcriptional level it results in the activation of predominantly acute-phase and stress-related genes. Indeed, in our study acute exercise addressed gene ontology lists related to inflammation, cell stress, and apoptosis.

The pathway model for upregulated genes involved in inflammatory response also confirms an activation of complex inflammatory processes through exhaustive exercise. Eight of 14 genes have been found to directly interact at the protein level. The changes we observed are supposed to be more than just coincidence and therefore suggest the operation of these regulatory networks in the WBCs response to exercise. Thereby the global gene response showed remarkable similarities within the individuals, which suggests that exercise is able to leave characteristic fingerprint-like gene expression profiles in leukocytes. This is suggested by both the hierarchical cluster analysis and the PCA. Both analytical procedures sorted preexercise and post-ET data into clearly different groups while the post-MT data were scattered across both groups. This suggests that exercise is a major inductor of specific gene expression patterns. One reason for the scattered distribution of the MT samples may be differences in the individual training status of the participants. This goes along with changes in the relative workload (26). Obviously, for two subjects the load of moderate exercise was therefore not effective to induce an exercise gene pattern.

Potential marker genes for acute exercise.   Acute exercise has an effect on a large number of genes in WBCs. When we used the most rigid conditions for the composition of the exercise marker gene list (the effect had to be present in every individual tested and, in a groupwise comparison, these differences had to be statistically significant after correction for multiple testing), our analysis resulted in a final list of 35 upregulated genes. Four of them had two significant transcripts. The genes from this restricted list could be related to the following functional classes: stress proteins/stress signaling, extracellular matrix, electrolyte and substrate transport, cytokines, and transcription factors.

Among the genes with the highest significant fold changes were HSPH1 and HSPA1A, which code for stress proteins belonging to the HSP105/110 and HSP70 family, respectively (13, 41). These proteins play important roles as molecular chaperones, thereby preventing the stress-induced irreversible aggregation of denatured proteins; they assist in folding, assembly, and translocation of cellular proteins across the membrane (10, 13, 40, 44). In leukocytes exercise-induced regulation of HSPs on both the transcriptional and the translational level has been found (6). Interestingly, the cytoplasmic expression of HSP27 and HSP70 was found to be weaker in trained subjects than in untrained, which is in good agreement with our data on mRNA levels (7).

A novel finding in circulating WBCs was the exercise-induced upregulation of MMP-9, which breaks down native type IV collagen as well as other extracellular matrix molecules. There are reports about an increase of intramuscular MMP-9 synthesis and its collagen-degrading activity, as well as about a release of MMP-9 into muscle interstitial fluid and serum, especially after muscle-damaging eccentric exercise (15–17). Therefore, MMP-9 is supposed to be of importance for tissue remodeling after exercise-induced muscle damage. In contrast, the role of MMP-9 in WBCs during exercise is less clearly defined. An attractive hypothesis about the role of MMP-9 for the hematopoetic system during exercise involves the mobilization of hematopoetic progenitor cells (38). Recently it could be shown that exercise is an effective stimulus for the mobilization of hematopoetic progenitor cells (27). Moreover, studies in rhesus monkeys suggest an involvement of MMP-9 in IL-8-induced mobilization of hematopoietic progenitor cells via cleavage of those matrix molecules the stem cells adhere to (19). In agreement with these findings, we found the IL-8 receptor-{alpha} (IL8RA) to be upregulated significantly (ANOVA; Benjamini-Hochberg). However, it remains to be shown whether these mechanisms are operative during exercise, too.

Next, we found genes upregulated that are involved in energy metabolism and potassium homeostasis, such as CGI-58 protein (CGI-58), solute carrier family 2 (facilitated glucose transporter) member 3 (SLC2A3), and the potassium inwardly-rectifying channel, subfamily J, member 15 (KCNJ15) (20, 42). The upregulation of these genes can be regarded as a response to changes in energy requirements and alterations in electrolyte equilibrium (39). Depending on the type and intensity of exercise, marked increases in plasma potassium concentration occur. Therefore, exercise challenges cellular potassium homeostasis, especially in muscle cells although also in other cell types, which results in well-known alterations of potassium-regulating membrane proteins (24). At a first glance it seems surprising that genes are upregulated in lymphocytes that are not doing the actual work. However, a recent study by Zeibig et al. (43) confirmed that the training-induced upregulation of muscle genes involved in oxidative energy metabolism is mirrored in lymphocytes, too.

Furthermore, some genes were found upregulated that all are part of the anti-inflammatory response associated with exercise. This included the release of receptor molecules for inflammatory cytokines such as IL-1 receptor (found significantly altered), which has been shown at the protein level in a number of studies (4, 37). This included also the membrane metalloendopeptidase (MME), which codes for a glycosylated zinc-dependent membrane-bound metalloproteinase (neutral endopeptidase; NEP) known to control the bioavailability of inflammatory neuropeptides such as substance P, which can be released from sensory nerves, skin, and immune cells (11, 28, 35).

The list of genes that were downregulated by exercise contained only seven transcripts. Two of these, the v-yes-1 Yamaguchi sarcoma viral oncogene homolog 1 (YES) and the natural killer cell receptor BY55/CD160, should be discussed in more detail. YES is a member of the Src family of tyrosine protein kinases that function as regulators of the actin-cytoskeletal structure by acting on the anchoring structures, namely focal adhesions and focal contacts (9, 21). A recent study by Hansen et al. pointed to an exciting relation of YES to heat shock proteins and MMP-9 (12). The overexpression of HSP27 induced an upregulation of MMP-9 expression and activity and at the same time downregulated YES expression. Such a connection seems to exist after exercise stress, too, as suggested by our microarray data.

Finally, we found a downregulating effect of acute exercise on the natural killer cell receptor (BY55; CD160) (1). Nikolova et al. (30) demonstrated that CD160 behaves as an important costimulant of the T-cell receptor, especially in CD28-negative cells with consequences on T-cell expansion and cytotoxicity. In contrast, a decrease in CD160 expression should result in an impaired functional response of at least some lymphocyte subpopulations, which might account partially for the well-known immunosuppressive effects of exhaustive exercise (29).

Gene expression-based training control.   Our results show that the acute exercise response measured in WBC addresses only a rather limited number of genes significantly and that the gene response is related to exercise intensity. This opens the perspective for the future to design an "exercise chip" that may serve as an innovative tool for monitoring and controlling training processes. Knowing the individual response to exercise could improve our ability to optimize exercise dosage and recovery time. One cornerstone down this alley is the identification of suitable genes that are differentially affected by exercise and that show good reproducibility in other microarray studies. Connolly et al. (4) used Affymetrix arrays to examine the impacts of a very similar exercise protocol on gene expression, the same that were used in this study. This allows a sufficient comparison of both data sets, which results in many similarities. However, our results are more than just a confirmation of previous data sets because both studies showed substantial methodological differences in sample handling and RNA preparation. This suggests the applicability of this approach for exercise-related purposes as long as the same analytical platform is used. Moreover, it is a strong argument for the existence of specific exercise-induced gene expression patterns and therefore a prerequisite for the design of exercise chips. Using similar cutoffs and statistical approaches (1.3-fold expression changes; ANOVA with Benjamini-Hochberg correction), both studies revealed 42 genes to be significantly upregulated and 5 genes downregulated. This common gene list contained some of the genes that were already discussed above such as HSPA1A, HSPH1, IL1R2, FOSL2, NFE2, PTGDR, and BY55. Additional genes (expression data in supplementary list) were related to environmental stress like hypoxia-inducible factor-1{alpha} subunit (HIF1A), heat shock protein-1{alpha} 90 kDa (SPCA), stress-induced-phosphoprotein 1 (STIP1), and vanin1/2 (VNN1/2), to substrate transport-like solute carrier family 16 (monocarboxylic acid transporters) member 3 (SLC16A3), to apoptosis regulation such as BCL2-related protein A1 (BCL2A1), and to surface receptors such as chemokine (C-C motif) receptor 2 (CCR2), chemokine (C-X3-C motif) receptor 1 (CX3CR1), and CD14 antigen (CD14).

Two other studies investigated the effects of exercise on leukocyte gene expression using custom-made gene chips. Hilberg et al. (14) used chips with transcripts of 5,000 stress and inflammation relevant genes, while Zieker et al. (45) used spotted cDNA microarrays containing 277 different genes focused on inflammation. Since both groups utilized different methods in sample processing and used different oligonucleotide probes for their research, a comparison of their results with our data is difficult. In addition, Hilberg et al. (14) used a different time scale, with sampling points 2 and 6 h postexercise. A comparison of up- and downregulated genes at either of these points with our data revealed a common list of 9 and 2 genes, respectively. The list contained genes coding for arachidonate 5-lipoxygenase (ALOX5) and ALOX5-activating protein, which are related to arachidonic acid metabolism; and for Casp8 and FADD-like apoptosis regulator (CFLAR), which like the above-mentioned BCL2A plays an antiapoptotic role. Other genes were the soluble IL-6 receptor, phosphoinositide 3-kinase (PIK3CG), CD16b (FCGR3B), CD14, colony-stimulating factor 3 receptor (CSF3R), and leukocyte immunoglobulin (Ig)-like receptor subfamily A member 2 (LILRA2). In the study by Zieker et al. (45), a group of highly trained endurance athletes was investigated, who often show a diminished response of inflammatory parameters to exercise. Moreover, exercise duration (~104 min) and sampling points (before and after exercise) were different compared with our protocol. These might be just a few reasons why we could find only a limited number of genes demonstrating a similar regulatory pattern as in our study, e.g., CD1c, CD81, CD244, selectin L, intercellular adhesion molecule 2 (ICAM2), mitogen-activated protein kinase activating protein kinase 2 (Mapkap K2), and IL-1 receptor antagonist. Another likely reason is the different microarray technology used.

The question remains whether leukocyte gene expression patterns can be used as surrogate markers that mirror exercise and training-induced responses of other tissues such as muscle tissue. An example for such a relation is the exercise-induced mRNA expression of heat shock proteins in muscle, which has been shown to occur depending on type and intensity of exercise (5, 33). Mahoney et al. (22) found that exercise differentially affected the expression of genes involved in metabolism and mitochondrial biogenesis, cell growth, and transcriptional activation, as well as apoptosis, cell stress, and proteolysis (22). For a number of genes among the three latter groups, we found identical regulatory patterns in leukocytes after exercise, e.g., genes involved in cell stress management [e.g., DnaJ (Hsp40)], proteolysis (e.g., MMPs), and apoptosis (e.g., BCL2-related anti-apoptotic proteins). Another example is the study by Zeibig et al. (43), which described the nearly identical regulation of genes related to oxidative energy metabolism in both working muscle cells and nonworking lymphocytes following a 6-mo training period of cross-country skiers.

Methodological limitations.   The present study presented a number of results that fit very well with our current understanding of the organism's responses and adaptations towards acute exercise. Nonetheless some limitations of our approach should be addressed. First, the present study suffers from the reduced number of participants as is the case in many microarray studies. The high costs of this technology often prevent the inclusion of larger cohorts. When working with small cohorts the danger of detecting false positives is high. We tried to reduce the number of false positives by using multiple testing corrections, which, however, increase the number of false negatives. False-positive results in microarray analyses mostly stem from the high technical variance of the methodology. When such data are entered into a group classification procedure like the GO class assignment, false positives are likely to be distributed among the classes at random. The critical number of class assignments necessary for a statistically significant result is usually only reached by genes that are involved in the same biological process and not by genes whose expression is modified by technical insufficiencies.

The fact that most genes that changed expression in our study did this after moderate as well as after exhaustive exercise speaks against a chance finding. The possibility that the expression changes do not stem from exercise but rather represent changes induced during the workup of the leukocytes is also not very likely because this would equally affect samples from before and after exercise and is thus not likely to reach significance. In addition, we have observed a stepwise change in gene expression related to the intensity of exercise.

Second, it might be possible that many of the observed expression changes relate to changes in the composition of the WBCs. However, most of the genes in our final positive list (after correction for multiple testing) show expression changes that are bigger than the changes in leukocyte subpopulations. While there is certainly an important effect of cell population shifts, it is likely that gene expression is also affected. This is further supported by the fact that we and Connolly et al. (4) found similar gene expression patterns despite working on different cell populations. Connolly et al. (4) worked on peripheral blood mononuclear cells isolated by a Ficoll gradient while we worked on the total leukocyte fraction. We chose this method to guarantee short intervals from blood sampling until RNA stabilization. Indeed the harmony of the results suggests a stable reproducibility and applicability of the microarray technology for investigating exercise-induced gene expression changes. To avoid difficulties in data interpretation and to elucidate the contribution of each subpopulation, further studies are required to utilize subpopulations isolated by cell sorting or by immunoprecipitation techniques.

In summary, our results indicate that exercise is able to affect leukocyte gene expression in a dose-dependent fashion and to induce specific gene expression profiles that can be detected successfully by microarray analysis. Moreover, we found evidence that adaptational training responses known, e.g., from muscle tissues are reflected partially in leukocytes. Further studies are required to verify these findings and to investigate these relationships with respect to different types of exercise, training regimens, etc. Finally, a number of acute exercise marker genes could be either newly established or confirmed. They represent potential tools for monitoring exercise and training processes in the future.


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This work was supported by Bundesinstitut für Sportwissenschaft BISP VF0407/01/5/2003.


   FOOTNOTES
 

Address for reprint requests and other correspondence: F. C. Mooren, Dept. of Sports Medicine, Institute of Sports Sciences, Justus-Liebig-Univ., Kugelberg 62, 35394 Giessen, Germany (e-mail: frank-christoph.mooren{at}sport.uni-giessen.de)

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


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