Elevated inflammatory cytokines are associated with increased risks of developing cardiovascular disease (3), osteoporosis (25), diabetes mellitus (7), and geriatric cachexia (2) in later life. The onset of disease in older persons may accentuate the increase in tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) that is typically associated with normal aging (5,6,27). Regular exercise may exert “anti-inflammatory” effects (11,27) that counter the age-associated increase in inflammatory cytokines, reducing the risk of developing diseases of physical inactivity and decreasing the rate of morbidity and mortality (11,21).
Strenuous exercise appears to provide the greatest stimulus for release of inflammatory cytokines. Rhind et al. (24) reported that 4 h of combined strenuous exercise (4.9-km run, 3 × 800-m run, resistance exercise on four arm exercises at 70% of the 1-repetition maximum (1RM) to failure, and four 20-min exercise bouts at 65% of; V̇O2peak) increased both lipopolysaccharide (LPS)-stimulated intracellular and serum IL-6, interleukin-1β (IL-1β), and TNF-α. Similar changes in inflammatory cytokines have been reported following a 5-km run (20), 80 min of cycling (22), and a single resistance exercise session (9,14,23).
With respect to chronic training, Smith et al. (27) reported that 24 wk of combined resistance and endurance training reduced mitogen-stimulated inflammatory cytokine production at rest. In a recent study from our lab (23), we found that 10 wk of resistance exercise training caused a significant reduction in LPS-stimulated inflammatory cytokine production. Collectively, previous findings from our lab and others suggest that regular exercise training may produce anti-inflammatory effects (9,11,27).
Whereas the mechanism by which regular exercise modulates inflammatory cytokines is not fully understood, we have hypothesized a potential role of Toll-like receptors (TLR). TLR, similar to Toll receptors originally discovered in Drosophila, detect pathogen associated molecule patterns (12). Kalis et al. (15) reported a linear relationship between cell-surface TLR4 and inflammatory cytokine production after LPS stimulation, suggesting that TLR4 is an essential requirement for response to LPS.
We recently reported that resistance-exercise–trained older women (65–85 yr) have lower mRNA for the “LPS receptors” TLR4 and CD14 than untrained, older women (9). We also found that “high” TLR4 mRNA expressers produced significantly more LPS-stimulated IL-6, TNF-α, and IL-1β than “low” TLR4 expressers (9), which was consistent with what others have reported (15,28). To our knowledge, this was the first study to present TLR4 as a possible mechanism to explain resistance exercise training induced declines in LPS-stimulated inflammatory cytokine production.
The effect of acute exercise on cell-surface TLR4 is undocumented. Individuals of advanced age tend to have a greater degree of chronic inflammation than younger individuals and chronic resistance exercise training has been reported to offset aging-induced changes. Therefore, we hypothesized that resistance-exercise–trained older women would have significantly less chronic inflammation than untrained older women, measured by LPS-stimulated inflammatory cytokine production and C-reactive protein. To explain the potential difference in inflammation, we hypothesized that the “LPS receptors” (TLR4 and CD14) would be significantly lower in trained compared with untrained women before and after an acute bout of resistance exercise. The purpose of this study was to examine the affect of both regular physical activity and acute exercise on TLR4 and inflammatory cytokines in older, postmenopausal women.
MATERIALS AND METHODS
Approach to the problem and experimental design.
We used a two-group cross-sectional design with repeated measurements. The present investigation was completed to extend our previous findings (9), that 10 wk of resistance training significantly lowered LPS-stimulated cytokine production, and that mRNA for TLR4 was lower in exercise-trained older women than controls. The independent variables were training status (trained vs untrained) and acute resistance exercise. The dependent variables measured were glucose, high-sensitivity C-reactive protein (hsCRP), cholesterol, blood urea nitrogen (BUN), total leukocyte count, flow cytometry cell-surface evaluation (TLR4 and CD14), LPS-stimulated inflammatory cytokine production (IL-1β, IL-6, and TNF-α), plasma inflammatory cytokines (IL-6 and TNF-α), and gene expression (TLR4, CD14, IL-6, IL-1β, and TNF-α).
All testing procedures were approved by the institutional review board at Purdue University (approval no. 02-129). Before testing, the risks of the study were explained to the subjects, and they were asked to sign a written informed consent form. Twenty older women (Table 1), at least 10 yr postmenopausal, were recruited into two groups based on training status: trained (TR, N = 10, a minimum of 72 exercise sessions over the previous 6 months) or untrained (UT, N = 10, physically active but not involved in any organized exercise program). Subjects taking selective estrogen receptor modulators, bis-phosphates, beta-blockers, or antidepressant drugs were excluded from participation. Subjects who smoked, used smokeless tobacco, supplementary nicotine, or had more than a moderate intake of alcohol were also excluded. A preexercise physical screening included a written medical history questionnaire, resting electrocardiogram, heart rate, blood pressure, a “Get-up-and-Go” test of mobility, and written approval from the subject’s personal physician.
Acclimation to resistance exercise.
Approximately 2 wk after the initial screening, subjects were instructed in proper weightlifting technique using a minimal resistance (4 kg, 1 set of 8 repetitions) on each of the following nine exercises: seated chest press, chest fly, lat pull down, hip abduction, hip adduction, seated row, knee extension, knee flexion, and seated leg press (Cybex, Medway, MA). Before the acclimation sessions, the subjects were allowed a brief warm-up (10 min walking on a treadmill) followed by a short period of stretching. The second acclimation session was completed 48 h after the first and was the same except that the resistance was increased slightly (8 kg, 2 sets of 8 repetitions). Forty-eight hours after the second acclimation session, muscular strength was determined on all nine exercises using a 1RM test (9). During 1RM testing, subjects were allowed at least 2 min for recovery between attempts. Strength values for selected upper- and lower-body exercises are presented in Table 1.
All subjects were instructed to consume their “typical” diet on the day before, day of, and day after the experimental exercise trial. Dietary records were completed on these 3 d and analyzed for energy, carbohydrate, protein, fat, and dietary fiber intake using a computer-based program (Nutrionist Pro 1.3.36, First DataBank Inc., San Bruno, CA). Review and analysis of diet records was supervised by a registered dietitian. Subjects were asked to refrain from consumption of alcohol, caffeine, and over the counter medications 24 h before and after the experimental trial. On the experimental trial day, subjects were provided a standardized meal (500 kcal) after the 2-h blood sample, and instructed to consume nothing else but water until after the final blood sample of the day (6 h).
Seven days after the 1RM strength testing, subjects reported to the laboratory following an overnight fast (8 h). Venous blood samples were collected from a peripheral arm vein by venipuncture before exercise (PRE), immediately postexercise (POST), and 2 h (2H), 6 h (6H), and 24 h (24H) after exercise. With the exception of POST, blood samples were taken after 30 min of seated rest. Blood was collected into evacuated tubes treated with either sodium heparin or EDTA (Becton-Dickinson, U.S.). Whole-blood aliquots (EDTA) were frozen at −80°C within 5 min of collection for later analysis by reverse transcription-polymerase chain reaction (RT-PCR). Plasma was separated from EDTA-treated blood within 30 min of collection and frozen (−80°C) until later analyses. Sodium heparinized blood was placed on a rocker and used within 3 h of collection for determination of LPS-stimulated cytokine production, and cell-surface TLR4 and CD14.
Determination of blood analytes.
Plasma concentrations of glucose, high-sensitivity C-reactive protein (hsCRP), cholesterol, and blood urea nitrogen (BUN) were determined using a Cobas Mira Plus automated clinical analyzer (Roche Diagnostics, Basel, Switzerland). Commercially available controls were included to monitor quality of the analysis. Intra- and interassay coefficients of variations were less than 5% for all analyses.
Total leukocyte count.
Whole-blood aliquots (20 μL) were combined with phosphate-buffered saline (PBS) (20 mL) and five drops of manual lysing solution in a standard counting cuvet (Fisher-Scientific, U.S.). All samples were counted in triplicate using a particle counter (Z2, Beckman-Coulter, Miami, FL). Upper and lower thresholds were set at 6 and 4 μm, respectively.
Sodium-heparin–treated whole blood aliquots (100 μL) were placed into two 12- by 75-mm polystyrene tubes (Sarstead, St. Louis, MO). Monoclonal antibodies for CD14-FITC (fluorescein isothiocyanate, Beckman-Coulter) and TLR4-PE (phycoerythrin, E-bioscience, San Diego, CA) were added to the first tube. Isotype control antibodies (IgG1-FITC and IgG2a-PE) were added to the second tube as a negative control to set “positive gates.” After addition, the antibody-blood mixture was incubated for 30 min in the dark and processed for flow cytometry using an automated system (Immunoprep, Beckman-Coulter). Quality-control checks were performed each day by analyzing standard-sized polystyrene beads (Beckman-Coulter) to ensure that photomultiplier tube (PMT) settings on the cytometer had not changed since the previous data analysis session. Primary gates were established for monocytes based on forward and side scatter light. Secondary gates were set to identify CD14+ cells within the monocyte band. Mean fluorescence intensity (MFI) for TLR4 was then determined within the CD14+ cell band. We also used a commercially available antibody binding capacity (ABC) standard composed of four standard-sized polystyrene beads, each with a different ABC (Bangs, Indianapolis, IN) to calculate ABC for TLR4, based on MFI. All analyses were completed using an XL-MCL (Beckman-Coulter) equipped with a 488-nm air-cooled argon laser. Representative flow panels from three TR and three UT are presented in Figure 1.
Determination of LPS-stimulated cytokine production.
Diluted, heparinized blood (1:20 in RPMI 1640) was dispensed into separate wells of a 24-well flat-bottom tissue culture plate (Corning Costar, U.S.). Cultures were stimulated by the addition of lipopolysaccharide from Salmonella enteriditis (LPS, 25 μg·mL−1 final concentration; Sigma-Aldrich). Optimal LPS concentration was determined by a lot-specific titer completed before the study (data not shown). After a 24-h incubation (37°C, 5% CO2), culture plates were centrifuged (1500 × g for 10 min), supernatant harvested using aseptic technique, recentrifuged (1500 × g for 10 min), and stored at −80°C.
Culture supernatant was diluted 1:40 (IL-1β), 1:80 (IL-6), and 1:10 (TNF-α) with assay diluent (10% FBS in PBS) to determine LPS-stimulated cytokine concentrations using separate enzyme linked immunosorbent assay (ELISA) sets (BD Pharmingen, U.S.) as previously described (10). Optimal dilution of supernatant was determined during a previous study from our laboratory (9). Intra- and interassay coefficients of variations were less than 5% for all analyses. The detection range for the ELISA used was 1000–15.6 (IL-1β), 300–4.7 (IL-6), and 500–9.8 (TNF-α) pg·mL−1.
Determination of plasma cytokines.
Plasma concentrations of IL-6 and TNF-α were determined using EDTA treated plasma and separate “high-sensitivity” ELISA kits (E-bioscience, San Diego, CA) that employ a quantitative method. Intra- and interassay coefficient of variations were less than 5% for all analyses. The detection range was 25–0.2 and 62.5–0.5 pg·mL−1 for IL-6 and TNF-α, respectively.
Total cellular RNA was isolated from aliquots of EDTA treated whole blood (200 μL) using a phenol/chloroform extraction method (TRIReagent BD; Sigma-Aldrich). Purified total cellular RNA was analyzed for integrity of the 18S and 28S ribosomal bands on a 1% agarose/TAE gel (VWR Scientific). After confirmation of total RNA integrity, mRNA expression was determined as previously described (9). Briefly, mRNA expression was determined using a semiquantitative reverse transcription-polymerase chain reaction (RT-PCR) method. The PCR phase was completed using gene (mRNA) specific primers for: TLR4, CD14, IL-6, IL-1β, TNF-α, and GAPDH (loading control) (Ransom Hill Biosciences, Ramona, CA) and reverse-transcribed cDNA (600 ηg). Positive and negative controls were included with all PCR analyses to confirm observed results. Ethidium bromide stained-agarose gels were illuminated by UV light, photographed (Polaroid DS-34 camera), and scanned into a computer. PCR band intensity was determined using Scion Image Analysis Software (version Beta 4.0.2, NIH, U.S.).
Comparison of high and low TLR4 expressers.
To facilitate the comparison of our present data to our previous study (9) and others (15,28), we sorted our subjects according to cell-surface TLR4 expression, irrespective of training status, and compared LPS-stimulated, plasma, and mRNA expression of inflammatory cytokines. This arbitrary grouping allowed for comparison of the relationship between TLR4 and inflammatory cytokines without respect exercise training status.
Before statistical analyses, data were examined to ensure that all appropriate assumptions of normality (quantile-quantile plot) and constant variance (residual-predicted plot) were met. If transformations were required, they are noted in the results section. Subject characteristics and muscular strength data from TR and UT groups were compared using separate student t-tests. Dietary intakes were analyzed using separate 2 × 3 factor ANOVA, with repeated measures on the second factor. The first factor had two levels of group (trained and untrained), and the second factor had three levels of time (day prior, day of, and day after acute exercise session).
All blood measurements were analyzed using a 2 × 5 ANOVA, with repeated measures on the second factor. The first factor had two levels of group (trained and untrained) and the second factor had five levels of time (PRE, POST, 2H, 6H, and 24H). Subjects were also grouped into high (HI, N = 10) and low (LO, N = 10) TLR4 cell-surface expression groups. After sorting, LPS-stimulated and mRNA expression of IL-6, TNF-α, and IL-1β was compared in HI and LO.
All significant alpha values (P < 0.05) were adjusted using the Huynh-Feldt method to account for repeated measurements. Location of significance was determined using separate student t-tests with a Bonferroni correction for multiple comparisons. A Pearson stepwise correlation analysis was used to determine relationships between blood measurements. Subject characteristics and dietary analysis were reported as the mean ± SD. All blood measurements were reported as the mean ± SE.
Total energy, carbohydrate, protein, fat, and dietary fiber intakes were not different between the TR and UT groups on any of the 3 d. Therefore, the data were averaged and presented as a mean of the 3 d (Table 1).
No significant group, time, or interaction effects were found for plasma levels of glucose, hsCRP, cholesterol, and BUN (Table 1).
No significant main effects for group or group × time interactions were found for total leukocyte or CD14+ cell counts. A significant main effect for time was found for both total leukocyte (F = 6.535, P < 0.001, β = 0.017, φ2 = 0.266) and CD14+ (F = 6.158, P = 0.001, β = 0.044, φ2 = 0.255) counts. For total leukocyte count, PRE was 16% less than 6H, and POST was 31% greater than both PRE and 24H. For CD14+ count, POST was 28% greater than 2H, and 6H was 43%, and 46% greater than 2H and 24H, respectively.
No time or group × time interactions were found for cell-surface TLR4. However, a significant main effect for group (F = 5.983, P = 0.025, β = 0.362, φ2 = 0.249) was found for cell-surface mean fluorescence intensity (MIF) of TLR4 (Fig. 2A). UT had 124% more TLR4 (MFI) on CD14+ cells than TR (comparison of group averages). A similar group effect (F = 5.979, P = 0.025, β = 0.362, φ2 = 0.249) was found when cell-surface antibody binding concentration (ABC) was examined for TLR4 (Fig. 2B). UT had 287% more TLR4 (ABC) on CD14+ cells than TR.
LPS-stimulated cytokine production.
No significant main effect for group or group × time interaction was found for LPS-stimulated production of IL-6, IL-1β, or TNF-α; however, a significant time effect was found for IL-6 (F = 13.533, P < 0.001, β = 0.001, φ2 = 0.429), IL-1β (F = 31.149, P < 0.001, β<0.001, φ2 = 0.634), and TNF-α (F = 11.106, P < 0.001, β<0.001, φ2 = 0.382). For IL-6 (Fig. 3A), PRE (−52%), POST (−38%), 2H (−69%), and 24H (−46%) were all significantly lower than 6H. Also for IL-6, POST was 23% greater than 2H, PRE was 72% lower than POST, and both PRE (−113%) and 2H (−94%) were significantly lower than 24H. For TNF-α (Fig. 3B), PRE (−81%), POST (−49%), 2H (−41%), and 24H (−42%) were significantly lower than 6H. For IL-1β (Fig. 3C), PRE (−315%), POST (−142%), 2H (−277%), and 24H (−95%) were significantly lower than 6H.
No significant main or interaction effects were found for plasma IL-6 (2.58 ± 0.22 pg·mL−1) or TNF-α (1.16 ± 0.08 pg·mL−1). Averages represent the grand mean ± SE. There were also no significant effects when plasma cytokine values were expressed as a percent change from resting values.
No significant main or interaction effects were found for CD14, IL-6, IL-1β, or TNF-α mRNA. The group effect for TLR4 mRNA approached significance (P = 0.063, β = 0.533, φ2 = 0.179). Therefore, we completed a post hoc sample size analysis, which revealed that addition of two to three subjects per group would have resulted in a significant group effect for TLR4 mRNA, where untrained subjects would be significantly greater than trained subjects.
Comparison of high and low TLR4 expressers.
We grouped the subjects, irrespective of training status, according to their cell-surface TLR4 expression, into HI (N = 10) and LO (N = 10) expressers. Six of the subjects in HI were trained, and six of the subjects in LO were untrained. This grouping resulted in cell-surface expression of TLR4 (MFI) 429% greater in HI compared with LO (F = 27.443, P < 0.001, β = 0.001, φ2 = 0.604). When TLR4 was expressed as ABC, HI was 706% greater than LO (F = 27.431, P < 0.001, β = 0.001, φ2 = 0.604). No significant differences between HI and LO were found for plasma (IL-6 and TNF-α) or mRNA expression of cytokines (IL-6, IL-1β, and TNF-α). However, when LPS-stimulated cytokines (Fig. 4B) were compared, HI produced 167% more IL-6 than LO (F = 9.727, P = 0.006, β = 0.161, φ2 = 0.351). HI also produced 302% more IL-1β (F = 11.594, P = 0.003, β = 0.104, φ2 = 0.392) and 209% more TNF-α (F = 15.856, P = 0.001, β = 0.036, φ2 = 0.468). Significant group × time interactions were found for both LPS-stimulated IL-1β (F = 3.999, P = 0.027, β = 0.318, φ2 = 0.182) and TNF-α (F = 2.724, P = 0.036, β = 0.274, φ2 = 0.131).
Several significant correlations were identified when all variables measured were compared. Cell-surface TLR4 was significantly correlated to LPS-stimulated IL-6 production at PRE (r = 0.52, P < 0.05), 2H (r = 0.71, P < 0.01), 6H (r = 0.52, P < 0.05), and 24H (r = 0.86, P < 0.01). Also, cell-surface TLR4 and LPS-stimulated inflammatory cytokines were strongly correlated to themselves at the various time points. Cell-surface TLR4 was correlated with mRNA for IL-6, IL-1β, and TNF-α, such that early samples correlated more strongly with later samples (that is, TLR4-POST to TNF-α-2H; 0.85, P < 0.01), TNF-α-6H (0.84, P < 0.01), and TNF-α-24H (0.86, P < 0.01)). The mRNA expression of IL-6, IL-1β, TNF-α, TLR4, and CD14 were strongly correlated within variables at most of the time points (r = 0.64–0.99).
This study was undertaken to examine the influence of both acute exercise and training status on cell-surface expression of TLR4 and potential relationships between TLR4 and inflammatory cytokines. Acute resistance exercise in older women did not influence cell-surface expression of TLR4, plasma inflammatory cytokines (IL-6 and TNF-α), or whole-blood mRNA expression of inflammatory cytokines (IL-6, IL-1β, and TNF-α); however, there were potential TLR4-associated changes in LPS-stimulated inflammatory cytokine production. Because no group × time interactions were found for any of our measures, it appeared that trained and untrained women responded similarly to the acute resistance exercise session. This finding suggests that previous history of regular exercise training did not affect the inflammatory cytokine response to acute resistance exercise in older women. Despite there being no difference in LPS-stimulated or plasma cytokines between groups, trained subjects expressed less cell-surface TLR4 on CD14+ cells than untrained subjects.
Wang et al. (28) reported that human fibroblasts with high cell-surface TLR4 produced more inflammatory cytokines when stimulated with LPS than fibroblasts with low TLR4. Kalis et al. (15) reported that mRNA expression of TLR4 was significantly correlated to LPS-stimulated IL-6 production in mice. We previously reported (9) that women with high mRNA for TLR4 produced more LPS-stimulated IL-6, TNF-α, and IL-1β than women with low mRNA for TLR4. Therefore, similar to what has been described previously (9,15,28), HI cell-surface TLR4 expressers produced more LPS-stimulated IL-6, TNF-α, and IL-1β than LO TLR4 expressers. Correlational analysis in the present study also revealed significant relationships between both mRNA and cell-surface expression of TLR4 and LPS-stimulated inflammatory cytokine production (IL-6, TNF-α, and IL-1β), supporting what has been reported previously (9,15).
Inflammatory cytokine responses to acute aerobic and resistance exercise bouts have been well documented (9,17,19,21,22,24). Some authors (19,22,24) have reported that high-intensity exercise increases inflammatory cytokines, whereas others (9,20) have reported no effect of moderate-intensity exercise. Those reporting changes tended to employ long-duration or severe exercise as the stimulus, suggesting that stress hormone release may play a role in the reported findings (22). Moldoveanu et al. (19) reported that 3 h of combined exercise (cycle and treadmill at 60–65% of; V̇O2peak) in younger men (25 yr) caused significant increases in plasma levels of IL-6, TNF-α, and IL-1β but no change in whole-blood mRNA expression of these cytokines. Our present findings regarding mRNA expression of inflammatory cytokines are similar to those of Moldoveanu et al. (19), but we did not report a change in plasma inflammatory cytokines. Our present findings regarding LPS-stimulated cytokines are supported by a previous study from Rhind et al. (24), who reported that 4 h of strenuous exercise altered LPS-stimulated but not plasma levels of IL-6, IL-1β, and TNF-α. Our present findings also agree with those of Netea et al. (20), who reported that a 5-km run did not affect mRNA or LPS-stimulated production of IL-1β and TNF-α. We speculate that the difference in cytokine response may be related to an age-associated difference in the response to exercise stress. It is also possible that we did not find an effect of exercise because inflammatory cytokine levels changed between POST and 2H. Another possible explanation for the present findings was related to measurement technique and the fact that in vitro measurements do not necessarily reflect in vivo changes.
Our findings are only in partial agreement with what has been reported in younger subjects (19,20,22,24). The differences may be explained by an aging-induced change in the response to exercise. Jozsi et al. (13) compared the skeletal muscle of younger and older men and found that older men had increased expression of “stress” genes (DNA damage) and inhibition of “repair” genes (cell cycle proteins). Based on these findings, Jozsi et al. (14) completed a second study to compare the skeletal muscle gene expression profiles of younger and older men after a single resistance exercise session. Contrary to what has been reported in younger subjects (19,22,24), Jozsi et al. (14) reported that older men did not increase skeletal muscle mRNA and protein levels of inflammatory cytokines in response to an acute bout of resistance exercise, as observed in younger men. Thus, our present findings regarding plasma and mRNA expression of inflammatory cytokines after resistance exercise are consistent with previous findings in skeletal muscle (13,14).
We found no difference in inflammatory cytokines (LPS-stimulated production or mRNA expression) between trained and untrained subjects, despite a significant difference in TLR4 cell-surface expression. This apparent discrepancy from our previous work (9) could be explained by the fact that, unlike our previous study (10), we did not train the subjects but rather selected subjects that met predetermined physical activity criteria. Therefore, it is possible that the training intensity and fitness level of the “trained” subjects in the present study may have been lower than our previous study (9) and the subjects described by Smith et al. (27). The total minutes of exercise reported by the trained group (303 min·wk−1) was substantial. Thus, it is possible that exercise intensity may play a role. Nevertheless, TLR4’s potential importance in LPS-stimulated cytokine production was emphasized when subjects were grouped according to cell-surface expression.
In addition to training status, others have suggested that TLR4 and/or its response to LPS can be regulated by a variety of soluble factors, including heat shock protein (4,8), soluble CD14 (1,26), T-cell distribution (16), and inflammatory cytokines (18). Whereas the present study does not allow us to determine the potential affect of these factors, HI TLR4 expressers may differ from LO TLR4 expressers in their relative concentration of these cell types and/or soluble factors.
In summary, acute resistance exercise did not alter plasma inflammatory cytokines or cell-surface TLR4, but did influence LPS-stimulated inflammatory cytokine production. The present study demonstrated that untrained older women express significantly less cell-surface TLR4 than trained women. Subset analysis demonstrated that cells with HI cell-surface expression of TLR4 produce large quantities of LPS-stimulated inflammatory cytokines. More research is needed to determine the nature of the relationship between exercise stress, inflammatory cytokines, and TLR4. The findings of the present study support what we have reported previously regarding chronic exercise and TLR4 (9), and to our knowledge, the present study is the first to examine the influence of resistance exercise on cell-surface expression of TLR4. It appears that TLR4 expression may play a role in exercise training induced changes in inflammation that have been reported previously (9–11,27), but further work is needed to elucidate potential mechanisms for exercise-augmented changes in TLR4.
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