Department of Cellular Biology and Anatomy, Louisiana State University Health Sciences Center, Shreveport, Louisiana 71130
1 To whom correspondence should be addressed. Fax: (318) 675-5889. E-mail: spruet{at}lsuhsc.edu.
Received August 11, 2004; accepted October 19, 2004
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ABSTRACT |
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Key Words: modeling; stress; biomarker; ethanol; atrazine; propanil.
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INTRODUCTION |
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It has been established that stress can affect immune function through the activation of the hypothalamic pituitary adrenal axis resulting in the production of a number of neuroendocrine mediators (Riley, 1981; Zwilling et al., 1993
). Some of these mediators, such as corticosterone (in rodents) or cortisol (in humans), have been shown to be immunosuppressive in both rodents and humans (Dhabhar et al., 1994
). Plasma corticosterone levels have been used for many years as an indicator of stress in mice. The effect that a stress response has on immunological parameters can be quantified by relating immunosuppression to the quantity and duration of the stress response represented by the area under the corticosterone concentration vs. time curve (AUC) (Pruett et al., 1999
). Linear regression analysis can then be used to indicate correlations between AUC and immunosuppression of each immunological parameter examined. Quantitatively consistent results showing similar effects on several parameters by both chemical and physical stressors, at comparable corticosterone AUC values have been demonstrated in a series of studies using spleen and thymus (Pruett et al., 1999
, 2000a
,b
, 2003
). In the present study, similar procedures were used to determine if an immunological biomarker for stress can be identified using blood samples instead of spleen or thymus. Finding such a biomarker would improve the accuracy of risk assessment in humans because blood is the only tissue routinely available for such comparative studies.
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MATERIALS AND METHODS |
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General dosing. Five cages of five mice were used for each chemical or restraint stressor. Each cage comprised one of five dosage groups. One of these cages of mice was used as a control group that was either naive (untreated) or treated with the vehicle for the chemical that was administered. All doses were administered at 21302200 h to allow for corticosterone circadian rhythms to be similar with those used to calculate AUC values (Pruett et al., 1999, 2000a
,b
, 2003
).
Restraint. The treated groups were placed in 50 ml conical tubes for 2, 4, 6, or 8 h. These conical tubes were ventilated by a longitudinal slit used to carefully pull each mouse into the tube. After placing each mouse in its tube, the tubes were placed back in the cage for the duration of the restraint period. The mice in the groups receiving the two highest dosages of restraint (6 and 8 h) were allowed access to food and water for 10 min at the 4-h time point.
Corticosterone dosing. A corticosterone (Sigma, St. Louis, MO) suspension was made in a vehicle of phosphate buffered saline (Sigma, St. Louis, MO) containing 2% ß-cyclodextrin (Sigma, St. Louis, MO). Corticosterone doses were administered via sc injections at concentrations of 9 mg/kg, one dose at 18 mg/kg, two doses at 18 mg/kg (2 h apart), or three doses at 18 mg/kg (2 h apart).
Propanil dosing. A propanil (Chem Service, West Chester, PA) suspension was made by mixing the chemical in corn oil (Mazola). Propanil was given by ip injection at dosages of 50, 75, 100, or 150 mg/kg (in 0.2 ml).
Atrazine dosing. Atrazine (Chem Service, West Chester, PA) was mixed in corn oil (Mazola) and administered by ip injection at dosages of 75, 150, 225, or 300 mg/kg (in 0.2 ml).
Ethanol dosing. Ethanol (AAPER Alcohol Chemical Co., Shelbyville, KY) used for dosing was diluted in sterile tissue culture-grade water (Sigma, St. Louis, MO) to 32% by volume. Ethanol was given by po gavage at dosages of 4, 5, 6, or 7 g/kg.
Blood cell harvesting. Mice were placed under halothane anesthesia. Their blood was collected in heparin coated tubes (Becton Dickinson and Company, Franklin Lakes, NJ) by bleeding from the retrorbital plexus 12 h after dosing.
Flow cytometric analysis. Blood from each mouse (0.15 ml) was labeled with anti-MHC II FITC (BD Pharmingen, San Diego, CA) and anti-CD45R/B220 (BD Pharmingen, San Diego, CA) by adding 5 µl of each antibody diluted 1/10 in FACS buffer (phosphate buffered saline with 0.1% bovine serum albumin and 0.1% sodium azide pH 7.4). Another blood sample from each mouse was labeled with 5 µl anti-CD4 PE (BD Pharmingen, San Diego, CA) and 5 µl anti-CD8 Cychrome (BD Pharmingen, San Diego, CA) diluted 1/10 in FACS buffer. These samples were allowed to incubate at 4°C in the dark for 30 min. After incubation, RBCs were lysed by adding 8 ml of ammonium chloride buffer (4.13 g NH4Cl, 0.5 g NaHCO3, 0.03 g EDTA per 500 ml water, pH 7.0) warmed to 37°C. The samples were allowed to incubate at 37°C for 10 min. They were then washed with FACS buffer. Following one wash the samples were resuspended in 1% paraformaldehyde (in PBS) and incubated in the dark for 10 min at room temperature. The samples were washed twice using FACS buffer, resuspended, and stored at 4°C in FACS buffer until analyzed by flow cytometry (FAC Calibor BD, Franklin Lakes, NJ) no more than five days after fixation.
Spleen cell counts. Each spleen was removed and placed in 3 ml of RPMI (Invitrogen, Carlsbad, CA), which was kept on ice. Frosted slides were then used to press the spleens yielding single cell suspensions in RPMI. The cells were centrifuged at 300 x g for 7 min, resuspended in 3 ml of RPMI 1640, and 20 µl of each cell suspension was added to 10 ml of isoton (Beckman Coulter, Miami, FL) in a counting vial. Three drops of Manual Lyse reagent (J&S Medial Associates Inc., Framingham, MA) were added, and the samples were then counted using a Coulter Z1 counter (Coulter Corporation, Miami, FL).
White blood cell counts. White blood cells were counted by placing 20 µl of whole blood in 10 ml of isoton (Beckman Coulter, Miami, FL). Manual Lyse Reagent (J&S Medial Associates Inc., Framingham, MA) was then added to lyse erythrocytes. The samples were counted using a Coulter Z1 counter (Coulter Corporation, Miami, FL).
NK assay. Spleen cells were diluted to 1.0 x 107 cells/ml and were plated in a 96 well v-bottom plate using triplicate samples for at least three effector/target ratios. YAC-1 target cells were labeled with 51Cr (ICN Biomedicals, Irvine, CA), then diluted to 1 x 105 cells/ml and plated in a 96 well plate, and incubated for 4 h at 37°C. A gamma counter (Perkin-Elmer, Wellesley, MA) was then used to measure release of 51CR into culture supernatants, as an indication of NK cell activity. The assay and calculation of lytic units were carried out as described previously (Pruett et al., 1999).
Differential counts. Manual differential counts were made using blood smears for each animal. These slide were then stained using a Diff-Quik three step staining kit (Dade Behring Inc., Newark, DE). Cells were then counted by observation under the microscope. At least 100 cells were counted for each sample.
Statistical analysis. Statistical analysis was carried out by using Microsoft Excel to first normalize data to control values to facilitate comparison of results from different experiments. Data were then transferred to Prism Graph Pad 4.0 (San Diego, CA) where linear regression models were generated by comparing the change in each immune parameter to the area under the corticosterone AUC value corresponding to the dose that caused the change. These AUC values were obtained in our previously published studies (Pruett et al., 1999, 2000a
,b
, 2003
), which indicate that the values are quite reproducible (Pruett et al., 1999
, 2000a
,b
, 2003
). Each regression generated was analyzed using the "runs test" to determine if there was a significant nonlinear component in the regression models. None was detected for any of the data shown. The linear regression models were then compared by evaluating overlap in their 83.7% confidence intervals. These confidence intervals were calculated using StatView software (v4.5 for Macintosh). Overlap of the 83.7% confidence intervals for linear regression models indicate that the values are not significantly different at the p = 0.05 level: lack of overlap indicates significance at the 0.05 level (Barr, 1969
; Nelson, 1989
).
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RESULTS |
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It is commonly assumed that if the 95% confidence intervals for points on different lines do not overlap, this means that they are significantly different at the p < 0.05 level. In reality, comparing 95% confidence intervals in this way is more stringent than the p < 0.05 level. Independent investigators have determined by mathematical analysis that the critical confidence interval that can be used to precisely identify differences where p < 0.05 is the 83.7% confidence interval (Barr, 1969; Nelson, 1989
). Thus, if the 83.7% confidence intervals of points on two lines do not overlap, then these points are significantly different at the p < 0.05 level.
The predictive ability of both the exogenous corticosterone and restraint models using the AUC value at which suppression of MHC II/B cell is 50% is shown in Table 1. The results in Table 1 demonstrate that only one of the parameters, WBC, can be accurately predicted for all three chemical stressors using the corticosterone AUC values with 50% MHC II/B-cell expression as a reference point. Implementing the corticosterone regression in this predictive model proved to be more accurate in predicting the lymphocyte and neutrophil subpopulations reactions to stress than did the restraint based model. The slopes of the regression lines generated for the MHC II/B-cell parameter are reasonably similar, and the actual effect of stressors on MHC II/B cell was predicted accurately for all of the stressors when either the corticosterone or restraint regression equation was used. This is consistent with the results shown in Figure 6, which indicate generally similar but not identical effects of all treatments on MHCII/B cell expression in the blood.
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Quality Control
To examine consistency in the amount of stress induced by the treatment used in this study as compared to previous studies, NK cell activity in the spleen was examined using a chromium 51-release assay. This allowed for a comparison of responses seen in these studies to those seen in previous studies of chemical stressor effects on immune parameters in the spleen (Pruett et al., 1999, 2000a
,b
, 2003
). Linear regressions for NK cell activity generated in previous experimentation were compared to those generated in this study. Comparison of NK cell activity linear regressions generated in the present study and in previous studies indicated that there was no significant difference (p > 0.05) between the slopes of the linear regressions generated for each individual stressor. No significant difference in the slopes of the linear regressions indicates that the effects of these stressors are sufficiently consistent to suggest that prediction of stressor effects on immune parameters is feasible.
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DISCUSSION |
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The expression of a suggested biomarker for stress-induced immunosuppression would also need to be unaffected by immunotoxicants that do not cause dramatic increases in plasma corticosterone levels. A review of literature indicates that there are no reports of MHC class II suppression by immunotoxicants that are not stressors (indicated by a Medline search using the terms immunotoxic and MHC class II). This was further investigated in this study by using a well-known broad-spectrum immunotoxicant, cyclophosphamide, which had no effect on MHC II expression. Even though MHC II expression seems to correlate well with stress-induced immunosuppression, the present study did reveal limitations to predictive models based on the blood parameters.
Previous studies showed that chemical stressors affected immune parameters in spleen and thymus more like restraint stress than exogenous corticosterone (Pruett et al., 1999, 2000a
,b
, 2003
). Differences seen in these studies indicate that the same endpoints in blood are affected differently by the stressors than in the spleen. As compared to restraint stress, exogenous corticosterone and chemical stressors caused more dramatic decreases and increases in the percentages of lymphocytes and neutrophils, respectively. It is conjectured that increases in neutrophil number may act to counter balance some of the immunosuppressive effects of corticosterone (Dhabhar, 2000
). Increases in neutrophil number have been associated with an increase in the amount of glucocorticoid in circulation (Miller et al., 1994
). It has also been suggested that an increase in glucocorticoid, such as corticosterone, increases both the longevity and rate of production of neutrophils (Fauci and Dale, 1974
; Friedman et al., 1995
; Mishler, 1977
). Since chemical stressors and exogenous corticosterone seem to affect the leukocyte population to a greater extent and plasma corticosterone levels are similar in restrained mice, it can be suggested that restraint stress elicits either the production of other stress mediators or different amounts of these mediators, which may counteract the effects of corticosterone (Ader and Cohen, 1993
).
Exogenous corticosterone and chemical stressors also cause a more dramatic change in CD4+ cells and CD 8+ cells as compared to restraint stress. Decreases in both CD4+ and CD8+ T-cell subpopulations with increasing plasma glucocorticoid levels did correspond to previously observed decreases in thymus weight and cellularity (Pruett et al., 1999, 2000a
,b
, 2003
). However, the relationship between these decreases and the corticosterone AUC values were more variable among the three chemicals than in previous studies in which these cells were evaluated in the thymus. The data presented here suggest that the blood parameters are more sensitive to exogenous corticosterone and chemical stressors than to restraint stress. The chemicals themselves may also have some direct effect on the cell types examined in the blood.
Testing the suitability of MHC II expression on blood leukocytes as a predictive parameter was accomplished by implementing the MHC II/B220 parameter in a predictive model. Comparison of the predictive ability of the blood models showed that using the particular dosage of the stressor at which 50% suppression of MHC II/B220 was observed was more accurate in predicting experimental values for each parameter measured than the corticosterone AUC based model, but it was not quite as effective as models based on decreased MHC II expression in the spleen (Pruett et al., 1999, 2000a
,b
, 2003
).
No data yet collected completely explains the differences in predictive value of spleen and thymus parameters as compared to the same parameters in blood. However, results from other studies suggest some of the factors that may be involved. Blood seems to show a greater sensitivity to the differences in stressors. One explanation of difference in the sensitivity of blood, as compared to spleen, may be the extremely dynamic nature of change in blood leukocyte populations in response to stress. Previous studies have shown that both catecholamines and glucocorticoids are involved in these changes (Dhabhar, 2002; Shephard, 2003
). The relative amounts of norepinephrine and glucocorticoids may vary following treatment with different chemicals (Pacák et al., 1998
), causing substantial changes in leukocyte trafficking (Richter et al., 1996
) which may not be reflected in the spleen or thymus.
Although it is now clear that stressors can significantly decrease resistance to infection (Cohen et al., 1998, 1999
; Kiecolt-Glaser et al., 1996
; Vedhara et al., 1999
; Zhang et al., 1998
), quantitative estimations are not yet possible. To obtain such quantitative predictions of the effect of stressors on host resistance to infection in humans one could employ the parallelogram approach (Loveren et al., 1998
). The parallelogram approach is based upon the idea that if the effects of a chemical on immune functions and host resistance are known for an animal model and its effects are known for the same immune functions in humans, then using these three corners of the parallelogram it is possible to extrapolate the fourth, host resistance to infection in humans. However, it is not usually possible to obtain immune function data from humans exposed to chemicals under controlled conditions. If the chemical is immunosuppressive primarily because it induces a stress response, it should be possible to model at least some of the immunotoxic effects of the chemical in humans by administering the major immunosuppressive stress hormone, cortisol, to attain stress inducible cortisol levels (Blazar et al., 1986
; Davis et al., 1991
; Tonnesen et al., 1987
; Vedhara et al., 1999
). This would allow extrapolation of an endpoint that would otherwise be unattainable without a predictive model, the effect of chemical-induced stress on resistance to infection.
Although the differences between blood, spleen, and thymus are distinct in their reactions to stress, they still share one parameter that is effected similarly, MHC II. Using MHC II expression coupled with observing for a pattern of change in other blood parameters affected by stress (e.g., >the ratio of neutrophils to lymphocytes and the number of WBC) could indicate a stress effect of a chemical. Predicting the stressor's effect on other parameters would be less reliable than hoped (Tables 1 and 2). Nevertheless, identifying MHC II as a reliable biomarker for stress induced immunosuppression in mouse blood has brought the first corner of the parallelogram to completion.
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