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Published ahead of print on January 10, 2003, doi:10.1165/rcmb.2002-0241OC
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American Journal of Respiratory Cell and Molecular Biology. Vol. 29, pp. 106-116, 2003
© 2003 American Thoracic Society
DOI: 10.1165/rcmb.2002-0241OC

Cytokine–Chemokine Networks in Experimental Mycobacterial and Schistosomal Pulmonary Granuloma Formation

Bo-Chin Chiu, Christine M. Freeman, Valerie R. Stolberg, Eric Komuniecki, Pamela M. Lincoln, Steven L. Kunkel and Stephen W. Chensue

Department of Pathology, University of Michigan Medical School; and the Department of Pathology and Laboratory Medicine, Veterans Affairs Healthcare System, Ann Arbor, Michigan

Address correspondence to: Stephen W. Chensue, M.D., Ph.D., Pathology and Laboratory Medicine 113, Veterans Affairs Medical Center, 2215 Fuller Road, Ann Arbor, MI 48105. E-mail: schensue{at}med.umich.edu


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Type-1 and type-2 lung granulomas, respectively, elicited by bead immobilized Mycobacteria bovis and Schistosoma mansoni egg antigens (Ags) display different patterns of chemokine expression. This study tested the hypothesis that chemokine expression patterns were related to upstream cytokine signaling. Using quantitative transcript analysis, we defined expression profiles for 16 chemokines and then examined the in vivo effects of neutralizing antibodies against interferon-{gamma} (IFN-{gamma}), interleukin (IL)-4, IL-10, IL-12, and IL-13. Transcripts for CXCL2, -5, -9, -10, and -11 and the CCL chemokine, CCL3, and lymphotactin (XCL1), were largely enhanced by Th1-related cytokines, IFN-{gamma} or IL-12. Transcripts for CCL11, CCL22, CCL17, and CCL1 were enhanced largely by Th2-related cytokines, IL-4, IL-10, or IL-13. Transcripts for CCL4, CCL2, CCL8, CCL7, and CCL12 were potentially induced by either Th1- or Th2-related cytokines, although some of these showed biased expression. IFN-{gamma} and IL-4 enhanced the greatest complement of transcripts, and their neutralization had the greatest anti-inflammatory effect on type-1 and type-2 granulomas, respectively. Th1/Th2 cross-regulation was evident because endogenous Th2 cytokines inhibited type-1, whereas Th1 cytokines inhibited type-2 biased chemokines. These findings reveal a complex cytokine–chemokine regulatory network that dictates profiles of local chemokine expression during T cell–mediated granuloma formation.

Abbreviations: antigens, Ags • CC chemokine ligand, CCL • complete Freund's adjuvant, CFA • CXC chemokine ligand, CXCL • enzyme-linked immunosorbent assay, ELISA • interferon-{gamma}, IFN-{gamma} • interleukin, IL • monocyte chemotactic protein, MCP • macrophage inflammatory protein, MIP • phosphate-buffered saline, PBS • purified protein derivative of mycobacteria, PPD • schistosomal soluble egg antigen, SEA • tumor necrosis factor, TNF


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
The lung is the primary site of organ involvement in a number of granulomatous conditions that can be caused by a wide range of agents including infectious microorganisms, allergens and metals (1). The term "granuloma" fails to confer a sense of the rich histologic heterogeneity observed among these lesions, which can vary from simple mononuclear cell aggregates to florid necrotizing and even eosinophil-rich inflammations. We and others have demonstrated that hypersensitive-type granulomas are mediated by T cell–mediated immune mechanisms and T cell–derived cytokines appear to dictate the severity and histologic character of the lesions (2–4). Moreover, cytokine analyses indicate that granulomas can display polarized profiles consistent with predominant Th1 or Th2 cell involvement (5).

In addition to cytokines, granulomas are associated with a variety of chemokines (6, 7), which represent a family of molecules whose presumed function is to direct cellular movement. Chemokines participate during innate recognition stages of immunity and may help direct Th1 and Th2 cytokine-producing cells during the generation of adaptive immunity (8, 9). Furthermore, there is also considerable in vitro evidence that immune-related cytokines further capitalize on these effector molecules by regulating their expression and secretion. Chemokine expression by a variety of cultured cell types has been demonstrated to display positive and negative regulatory responses to cytokine stimulation (10–15). It is unknown how such regulatory networks might operate in vivo during granuloma formation, and it is unclear how the current paradigm of Th1/Th2 biased immunity might influence such networks. In a previous report, we demonstrated different patterns of chemokine expression in well-defined models of Th1 and Th2 cytokine–mediated experimental lung granulomas, respectively, induced by mycobacterial and schistosomal Ag-coated beads (7). The different patterns of chemokine expression in these models were likely shaping the cellular composition and function of the lesions.

We hypothesized that differences in chemokine expression observed during adaptive immune pulmonary inflammation were the result of differential regulation by upstream Th1- and Th2-related cytokines. To test this hypothesis, we undertook a comprehensive analysis using in vivo neutralization of Th1 (interferon [IFN]-{gamma}, interleukin [IL]-12)- and Th2 (IL-4, IL-10, IL-13)-related cytokines combined with quantitative transcript analysis of 16 chemokines induced in lungs during type-1 and type-2 Ag-bead granuloma formation. Our findings reveal a complex network in which type-1– and type-2–associated chemokine profiles are the result of cytokine-mediated amplification and inhibitory crossregulation by Th1- and Th2-related cytokines. Awareness of such networks will help design appropriate cytokine- and chemokine-directed therapies to help modulate granulomatous inflammatory conditions.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Animals
Female, CBA/J mice were obtained from Jackson Laboratories, Bar Harbor, ME. All mice were maintained under specific-pathogen free conditions and provided food and water ad libitum.

In Vivo Generation Type-1 and Type-2 Granulomas
Type-1 and type-2 secondary Ag-bead pulmonary granulomatous responses were generated in CBA mice as previously described (16). Briefly, mice were sensitized by subcutaneous injection of 20 µg Mycobacteria bovis purified protein derivative (PPD; Department of Agriculture, Veterinary Division, Ames, IA) incorporated in to 0.25 ml complete Freund's adjuvant (CFA, product number F-5881; Sigma, St. Louis, MO) or 3,000 Schistosoma mansoni eggs suspended in 0.5 ml phosphate-buffered saline (PBS). Fourteen to sixteen days later, PPD- and schistosome egg–sensitized mice were respectively challenged by tail vein with 6,000 Sepharose 4B beads (in 0.5 ml PBS) covalently coupled to PPD or to S. mansoni–soluble schistosome egg antigens (SEA) obtained from the World Health Organization, Geneva, Switzerland.

At designated intervals following challenge, lungs were removed after perfusion with cold isotonic buffer, then placed into RNAase inhibitor solution (RNAlater; Ambion, Austin, TX) before RNA extraction.

Assessment of Granuloma Cytokine Production
Granulomas were collected from bead-challenged lungs and cultured as previously described (5). Supernates were collected at 48 h and assayed for cytokine levels. Murine IFN-{gamma} and IL-4, -10, -12, and -13 were measured by standard enzyme-linked immunosorbent assay (ELISA) using commercially available reagents and standards (R&D Systems, Minneapolis, MN; and PharMingen, San Diego, CA); sensitivities ranged from 15–50 pg/ml. In addition, aqueous lung extracts were prepared and endogenous cytokines measured by ELISA, then normalized to lung protein as previously described (7).

Antibodies and Treatment Regimen
Polyclonal anti-murine IFN-{gamma} IL-10, IL-12, and IL-13 antisera were prepared by monthly immunizations of rabbits with the respective recombinant cytokines emulsified with CFA. Direct ELISA was used to determine antibody titers and specificity. Antibodies were used when titers reached 1:1,000,000 in direct ELISA, and if found specific when tested against a battery of recombinant murine cytokines that included tumor necrosis factor (TNF)-{alpha}, IL-1-{alpha}/ß, IL-2, IL-4, IL-5, IL-10, IL-12, IL-13, monocyte chemotactic protein (MCP)-1, and macrophage inflammatory protein (MIP)-1{alpha}. In vitro neutralizing activity of anti–IFN-{gamma} was determined by its capacity to block IFN-{gamma}–mediated induction of macrophage IA antigen expression. Neutralizing activity of anti–IL-10 was determined by its capacity of to block IL-10–mediated suppression of macrophage IL-6 production. Anti–IL-12 neutralizing activity was assessed by inhibition of IL-12 stimulation of IFN-{gamma} production by phytohemagglutinin-stimulated spleen cells. Anti–IL-13 neutralizing activity was confirmed by reversal of IL-13–mediated inhibition of macrophage nitric oxide production in vitro. Polyclonal IgG preparations were purified from antisera using protein A columns, dissolved in PBS, and then frozen at –50°C before use. Nonimmune rabbit IgG served as a control. Monoclonal rat anti-murine IL-4 was prepared from the 11B11 hybridoma line, grown in serum-free culture medium, and purified by immunoaffinity chromatography. This preparation showed no cross-reactivity with IL-13. Rat IgG served as a control. Neutralizing activity of anti–IL-4 was determined by inhibition of the IL-4–driven growth of CT.4S cells.

Groups of mice were administered Abs intraperitoneally (10 mg polyclonal IgG or 2 mg monoclonal IgG) or corresponding control IgG 30 min before challenge with Ag-coated beads, and then lungs were analyzed for chemokine transcript expression 2–4 d later.

Real Time RT-PCR Analysis of mRNA
Poly (A) pure mRNA was isolated from lung tissue using Poly(A)Pure mRNA isolation kits (Ambion). The samples were each tested for contaminating genomic DNA by PCR analysis. Any contaminated sample was treated with DNAase until determined to be pure. Each mRNA sample of ~ 1 µg was reverse-transcribed in a 20-µl reaction in a PCR reaction-tube using Reverse Transcription System kits (Promega, Madison, WI). Four separate reactions were conducted to minimize variability between tubes. The products from each reaction tube were pooled to make one cDNA sample. ABI PRISM 7000 Sequence Detection System (Applied Biosystems, Foster City, CA) was used for real-time PCR analysis. For the current study, comparative CT method recommended by the manufacturer (Applied Biosystems) was adopted with glyceraldehyde phosphate dehydrogenase (GAPDH) as endogenous reference transcript, and primer-probed sets were designed with manufacturer's software. Primer and probe sequences for the 16 target chemokines and GAPDH are presented in Table 1. In all cases, the thermal cycling condition was programmed according to the manufacturer's instructions. Transcript levels were expressed as arbitrary units (AU) and were calculated as previously described (17). Briefly, AU were calculated from the fluorescence amplication factor as measured by the real-time PCR fluorescent detection unit. The original gene copy number (Co) is related to fluorescence of the generated signal as follows: Co = F x E-1 x I x 2-n, where F is an arbitrary conversion constant, E-1 is amplification efficiency constant (approximately =1 for manufacturer's real-time primers sets), I is the fluorescent intensity reading, and n is the amplification cycle number. Hence, F x E-1 x I x 2-n constitutes an arbitrary measure of original copy number that is directly related to the fluorescent product and inversely related to cycle number. Because E is roughly equivalent for the various primer sets, the expression levels among genes are comparable at orders of magnitude.


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  		TABLE 1 Chemokine probe and primer sequences

 
Statistics
The Student's t test was used for comparison of control and treated groups. Values of P greater than or equal to 0.05 were considered to indicate lack of significance. In depletion studies, statistical analysis was performed for every chemokine transcript analysis. Statistically significant changes were generally observed when transcript levels were enhanced by 2-fold or more or decreased by more than 35%.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Temporal Chemokine Transcript Profiles during Type-1 (Mycobacterial) and Type-2 (Schistosomal) Antigen-Bead Pulmonary Granuloma Formation
We previously reported that synchronous type-1 (mycobacterial) and type-2 (schistosomal) Ag bead granulomas displayed characteristic chemokine expression profiles (7). That study used a semiquantitative method for transcript analysis, which had a limited capacity to discern differences among high copy number transcripts. As part of the present study, we analyzed the expression of 16 chemokines in individual lungs using quantitative real-time, reverse transcriptase PCR, which is a more precise indicator of transcript levels. As shown in Figure 1, distinct patterns of chemokine transcript expression were identified using this approach. The CXCL chemokines, CXCL5 (LIX), CXCL2 (MIP-2), CXCL11 (I-TAC), CXCL10 (IP-10), and CXCL9 (MIG), as well as the CCL chemokines CCL3 (MIP-1{alpha}) and CCL4 (MIP-1ß), were biased to the type-1 response, and the greatest expression occurred during the rapid growth phase of the lesion (1–3 d). Transcripts for CCL7 (MCP-3), CCL12 (MCP-5), CCL11 (eotaxin), and CCL17 (TARC) showed biased expression in the type-2 response. Interestingly, the type-2 bias of TARC expression was not discerned in our previous analysis, likely because this represents a high copy number transcript in both types of responses. CCL2 (MCP-1), CCL8 (MCP-2), and CCL1 (TCA-3) also showed trends to a type-2 bias, but overall these were expressed in both types of responses. In contrast to our previous study, we did not observe a clear type-1 bias of XCL1 (lymphotactin), but we did confirm that this chemokine was associated with the mid and late stages of lesion development. In terms of highest levels of expression (i.e., 2,000 AU or more), MIP-2, I-TAC, IP-10, MIG, MIP-1{alpha}, MIP-1ß, MCP-1, and TARC dominated on Days 1–2 in the type-1 response. By Day 3, only MIP-1ß, MIG, MDC, and TARC were expressed in this range, and by Day 8 only MDC persisted as a high copy number transcript. In the type-2 (SEA) response, MIP-1{alpha}, MCP-1, MCP-3, eotaxin, and TARC were at high copy number on Days 1–2. On Days 3–4, MIP-1ß, MCP-2, MDC, and TARC were the dominant transcripts; and similar to the type-1 response, MDC persisted in this range by Day 8. Of the chemokine transcripts examined, the strongest signals were obtained for MDC, TARC, and MIG in the type-1 response and MDC, TARC, and MCP-3 in the type-2 response. Thus, MDC and TARC, ligands for CC-chemokine receptor 4 (CCR4), were expressed in both responses, and one or both were present at nearly all stages of the response. Figure 2 shows the temporal expression of these dominant transcripts plotted on a single scale.



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  		Figure 1. Expression of chemokine receptor transcripts during synchronized type-1 (PPD) and type-2 (SEA) T cell–mediated granuloma formation as determined by real-time RT-PCR. Bars are mean arbitrary units (AU) ± SE derived from five to six individual lungs per point.

 


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  		Figure 2. Time course of expression of dominant chemokine transcripts during type-1 (PPD) and type-2 (SEA) T cell–mediated granuloma formation. Bars represent the maximum level of expression of dominant chemokine transcripts during the indicated time windows. Striped bars, 1–2 d; solid bars, 3–4 d; hatched bars, 8 d. Values are plotted on a common scale to show relative levels of expression.

 
Cytokine Depletions Exert Different Effects on Chemokine Transcript Expression during Type-1 (Mycobacterial) and Type-2 (Schistosomal) Antigen-Bead Pulmonary Granuloma Formation
We next tested the potential influence of cytokines in dictating the spectrum of chemokines produced during type-1 and type-2 granuloma formation. The targeted cytokines, IFN-{gamma}, IL-4, IL-10, IL-12, and IL-13, were chosen based upon their known expression in these models and their potential regulatory function (5, 18–20). Figure 3 shows the temporal expression of these cytokines in whole lung extracts as well as in isolated and cultured granulomas during an 8-d time course. In whole lung extracts, background expression of cytokines was detectable even in naive lungs (dashed lines) and bead challenge increased levels in both responses except for IL-4, which increased by 2-fold only at 4 d in the type-2 response. The lung extracts suggested some degree of biased expression, but this was more clearly revealed when granulomas were isolated and cultured. As we have previously reported, IFN-{gamma} was biased to the type-1 (PPD), whereas IL-4 and IL-13 were biased to the type-2 (SEA) response. Surprisingly, IL-10 was produced by both lesion types, as was IL-12 (p70 heterodimer), though there was a trend to earlier production in the type-1 response. Both extract and culture results indicated that the type-1 response began active cytokine production on Days 1 and 2, whereas the type-2 response was more pronounced on Days 2 and 4.



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  		Figure 3. Cytokine levels in whole lung extracts and cultured type-1 (PPD) and type-2 (SEA) Ag-bead lung granulomas. Aqueous lung extracts were prepared from 5–10 mice at each point, then assayed by ELISA for the indicated cytokines. Levels were normalized to total extracted lung protein. Dashed lines indicate levels in naive unchallenged lungs. In the right, cultured granulomas were isolated from 3–4 mice per day point and cultured (1,000/ml) for 48 h in the presence of either PPD or SEA antigens (5 µg/ml). Supernates were collected and assayed by ELISA in triplicate for the indicated cytokines. Data were derived from three separate experiments.

 
To explore the relationship of chemokine expression to the above cytokines, mice were treated with neutralizing anti-cytokine antibodies as described in MATERIALS AND METHODS, and then lungs were analyzed for chemokine transcript expression. Analysis was performed on Days 2–3 for the type-1 model and on Days 3–4 for the type-2 because cytokine production generally peaked later in the latter. For cytokine depletion analysis, anticytokine antibody preparations included anti–IFN-{gamma}, anti–IL-12, anti–IL-4, anti–IL-10, and anti–IL-13; the in vivo efficacy of each of these preparations has been previously reported (18–20).

Effect of IFN-{gamma} depletion. Figure 4 shows the effect of in vivo IFN-{gamma} depletion on the levels of chemokine transcripts produced during the type-1 (PPD) and type-2 (SEA) responses. To consolidate and simplify the presentation, the data are normalized to show positive (fold-increases) or negative (percent decreases) changes from control IgG-treated animals. Parametric statistical analysis of all raw data indicated that significant changes were generally those with > 2-fold increase or > 35% decrease. Asterisks indicate significant changes and dashed lines are included only as a reference points. Insets show the effect of the treatment on granuloma inflammatory area on Day 4. Figure 4A demonstrates that in the type-1 PPD response, anti–IFN-{gamma} treatment significantly reduced transcript levels for 10 of 16 chemokines. The Day 2 transcript reductions were associated with a 41% decrease in average granuloma inflammatory area by Day 4. Thus, it appeared that IFN-{gamma} was supporting the expression of a number of type-1–biased chemokines, including LIX, MIP-2, I-TAC, IP-10 MIG and MIP-1ß. The IFN-{gamma}–dependence of I-TAC, IP-10, and MIG is fully consistent with their reported in vitro characterization (12, 21). In addition, lymphotactin and MCP-2, -3, and -5 were reduced by anti–IFN-{gamma} treatment.



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  		Figure 4. Effect of anti–IFN-{gamma} treatment on chemokine expression during type-1 (PPD) and type-2 (SEA) granuloma formation. A, type-1 (PPD) response; B, type-2 (SEA) response. Upper sections of charts indicate fold increase over control, and lower sections percent decrease from control as calculated from measured AU. Statistical analysis was performed on measured values (five separate lungs per control and treated groups). Asterisks indicate P < 0.05. Dashed lines demarcate zones of no statistically significant change. Inset shows average granuloma cross-sectional area on Day 4; indicated changes were significant at P < 0.05 unless marked NS.

 
Figure 4B shows that type-2 (SEA) inflammation was exacerbated by anti–IFN-{gamma} treatment as others and we have previously reported (19, 22). This exacerbation was associated with augmented expression of the higher copy number transcripts, MCP-2, MCP-3, MDC, and TARC. Thus, these transcripts were subject to negative regulation by endogenous IFN-{gamma}, suggesting that Th2-related cytokines were mainly responsible for their induction. This apparent inhibitory influence of IFN-{gamma} on dominant chemokines expressed during the type-2 response provides further explanation for the reported crossregulatory effect of this cytokine in the granulomatous response to schistosome egg Ags (22, 23). Interestingly, even the weakly expressed I-TAC, IP-10, and MIG transcripts were positively regulated by IFN-{gamma} in the type-2 response, indicating their strong IFN-{gamma} dependence. There was also a trend for IFN-{gamma} depletion to reduce lymphotactin transcripts, but this just fell short of significance.

Effect of IL-12 depletion. Anti–IL-12 treatment abrogated 6 of 16 chemokine transcripts in the type-1 PPD response (Figure 5A). These included several of those that were also reduced by IFN-{gamma} depletion, including I-TAC, IP-10, MIG, and MCP-2, supporting the known link between IL-12 and IFN-{gamma}. Unlike IFN-{gamma} depletion, IL-12 depletion reduced MIP-1{alpha} and MCP-1 transcript levels, but had no effect on LIX, MIP-2, MIP-1ß, or lymphotactin. IL-12 depletion caused only a 14% decrease in the inflammatory area, suggesting that the affected chemokines were not essential to ultimately establish a lesion; however, shifts in rate of granuloma formation or cell composition cannot be ruled out.



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  		Figure 5. Effect of anti–IL-12 treatment on chemokine expression during type-1 (PPD) and type-2 (SEA) granuloma formation. A, type-1 (PPD) response; B, type-2 (SEA) response. Upper sections of charts indicate fold increase over control, and lower sections percent decrease from control as calculated from measured AU. Statistical analysis was performed on measured values (five separate lungs per control and treated groups). Asterisks indicate P < 0.05. Dashed lines demarcate zones of no statistically significant change. Inset shows average granuloma cross-sectional area on Day 4; indicated changes were significant at P < 0.05 unless marked NS.

 
IL-12 depletion had an opposite effect on the type-2 response, enhancing 9 of 16 chemokine transcripts (Figure 5B). Unexpectedly, this was not associated with augmented granuloma formation as observed with IFN-{gamma} depletion. Unlike with IFN-{gamma} depletion, TARC was not included among the enhanced group. In addition, MIP-1{alpha} and MIP-1ß were among the enhanced group; chemokines generally more biased to the type-1 response. Thus, the lack of exacerbated inflammation may be related to differences in the spectrum of enhanced chemokine transcripts as discussed later.

Effect of IL-10 depletion. Anti–IL-10 treatment had no significant effect on overall granuloma inflammatory area and marginally affected only 4 of 16 chemokines in the type-1 response (Figure 6A). Augmentations of MIP-1{alpha} and MIP-1ß were observed, suggesting that IL-10 was a negative regulator and opposed the effect of IFN-{gamma} and IL-12. In contrast, MCP-2 and TARC declined, suggesting that IL-10 supported their expression. In the type-2 response, IL-10 depletion caused a significant reduction in transcripts for only MCP-1, and this effect was associated with no significant change in granuloma sizes (Figure 6B). Thus overall, IL-10 had relatively limited regulatory effect on local chemokine transcript expression.



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  		Figure 6. Effect of anti–IL-10 treatment on chemokine expression during type-1 (PPD) and type-2 (SEA) granuloma formation. A, type-1 (PPD) response; B, type-2 (SEA) response. Upper sections of charts indicate fold increase over control, and lower sections percent decrease from control as calculated from measured AU. Statistical analysis was performed on measured values (five separate lungs per control and treated groups). Asterisks indicate P < 0.05. Dashed lines demarcate zones of no statistically significant change. Inset shows average granuloma cross-sectional area on Day 4; indicated changes were significant at P < 0.05 unless marked NS.

 
Effect of IL-13 depletion. Figure 7 demonstrates the effect of IL-13 depletion on chemokine transcripts. In the type-1 response, the treatment augmented I-TAC, IP-10, and MIG, thus IL-13 was a negative regulator of these chemokines and opposed IFN-{gamma} and IL-12 (Figure 7A). However like IFN-{gamma}, it seemed to support MCP-2 and MCP-3, although marginally. The latter finding conflicts with our previous study, likely because at that time optimal sampling times were unknown and quantitative transcript analysis was not used (20). In addition, it was identified as a significant contributor to Eotaxin, MDC, and TARC expression. Despite these changes in chemokine transcript expression, lesion sizes were unchanged, possibly because negative and positive regulatory effects neutralized each other or ultimate effects on protein expression were negligible.



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  		Figure 7. Effect of anti–IL-13 treatment on chemokine expression during type-1 (PPD) and type-2 (SEA) granuloma formation. A, type-1 (PPD) response; B, type-2 (SEA) response. Upper sections of charts indicate fold increase over control, and lower sections percent decrease from control as calculated from measured AU. Statistical analysis was performed on measured values (five separate lungs per control and treated groups). Asterisks indicate P < 0.05. Dashed lines demarcate zones of no statistically significant change. Inset shows average granuloma cross-sectional area on Day 4; indicated changes were significant at P < 0.05 unless marked NS.

 
In contrast to the PPD response, IL-13 is produced in greater amounts in the type-2 response, and depletion caused significant reductions in inflammation as well as in 7 of 16 chemokine transcripts (Figure 7B). Decreases were observed for MIP-1ß, MCP-1, MCP-2, MCP-3, eotaxin, TARC, and CCL1 (TCA-3), suggesting that at least some of these chemokines were contributing to the type-2 response.

Effect of IL-4 depletion. Interleukin-4 is largely produced during the type-2 SEA response, and we have previously demonstrated this Th2-related cytokine to positively regulate MCP-1 and MCP-3 in vivo (20, 24). Figure 8 shows that anti–IL-4 treatment caused nearly a 40% decrease in granuloma size, which was associated with significant declines in 9 of 16 chemokine transcripts. Similar to IL-13, IL-4 supported MIP-1ß, MCP-1, MCP-2, MCP-3, eotaxin, TARC, and CCL1 (TCA-3). In addition, it supported MCP-5 and MDC.



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  		Figure 8. Effect of anti–IL-4 treatment on chemokine expression during type-2 (SEA) granuloma formation. Upper section indicates fold increase over control, and lower section percent decrease from control as calculated from measured AU. Statistical analysis was performed on measured values (five separate lungs per control and treated groups). Asterisks indicate P < 0.05. Dashed lines demarcate zones of no statistically significant change. Inset shows average granuloma cross-sectional area on Day 4; indicated changes were significant at P < 0.05 unless marked NS.

 
Interleukin-4 is produced negligibly in the type-1 (PPD) response, and its depletion did not significantly alter the gross degree of inflammation. Despite this, endogenous IL-4 was found to negatively influence LIX, MIP-2, and MIP-1{alpha}, and to positively regulate a similar complement of chemokine transcripts as it did in the type-2 response, including MCP-2, -3, and -5 as well as MDC and TARC (data not shown). Thus, this latter group of chemokines displayed a significant positive correlation to the Th2-related cytokines

Table 2 summarizes the regulatory influence of the studied cytokines in the two models. Taken together, the findings demonstrate that endogenous cytokines have both positive and negative regulatory effects on chemokine expression during the course of mycobacterial (type-1) and schistosomal (type-2) granuloma formation. The ultimate pattern of chemokines expressed is likely dependent on the net effect of these regulatory signals.


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  		TABLE 2 Summary of in vivo cytokine regulatory effects on chemokine transcript expression during type-1 (PPD) and type-2 (SEA) Ag-bead granuloma formation

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
The present study was undertaken to test the hypothesis that differences in chemokine expression observed during adaptive immune pulmonary inflammation were the result of differential regulation by upstream Th1- and Th2-related cytokines. Because our study used real-time quantitative transcript analysis, we initially wished to determine if our previously reported observations regarding type-1 and type-2 chemokine profiles could be substantiated and extended using this new method. Indeed, the majority of our previous observations were confirmed. In addition, we were able to better discern temporal expression and relative differences in copy numbers as well as reveal previously undetected greater expression of CCL17 (TARC) transcripts during type-2 (SEA) granuloma formation. We believe transcript monitoring is a reasonable approach to study chemokine dynamics because our previous study showed good correlation of transcript and protein expression (7). This approach proved highly effective in revealing intriguing and subtle relationships of chemokine and cytokine expression. The targeted cytokines, IFN-{gamma}, IL-4, IL-10, IL-12, and IL-13, were chosen on the basis of their demonstrable local production and their established roles in Th1 and Th2 immune responses. As discussed below, all participated to a greater or lesser degree in shaping local chemokine expression.

The type-1 (PPD) response displayed biased expression of all CXCL chemokines examined, including CXCL2 (MIP-2), 5 (LIX), 9 (MIG), 10 (IP-10), and 11 (I-TAC) and the CCL chemokines, CCL3 (MIP-1{alpha}) and CCL4 (MIP-1ß). Other than LIX, all were among the "high copy number" transcript group. This pattern fits the emerging concept of a "type-1" cluster of cytokines/chemokines coexpressed with IFN-{gamma} and associated with NK cells, CD8+ T cells, and Th1 cells. Indeed, our study indicated strong dependence and temporal correlation of all of these chemokine transcripts with the type-1–associated cytokines IFN-{gamma} and IL-12. Interestingly, only IFN-{gamma} depletion reduced inflammation and correspondingly affected a broader scope of chemokines, also causing reductions in CCL8 (MCP-2), CCL7 (MCP-3), CCL5 (MCP-5), and (XCL1) lymphotactin. In our previous study using semiquantitative RNA analysis we were unable to demonstrate the broad influence of IFN-{gamma} likely due to the lower sensitivity of that method (7). Although it is tempting to infer that the additionally reduced chemokines were critical for the inflammation, this might be simplistic because IFN-{gamma} likely affects other proinflammatory elements such as cellular adhesion and oxygen metabolites. Our findings also indicated that IL-12 and IFN-{gamma} have some independent functions promoting different chemokines, and that IFN-{gamma} production was not wholly dependent on IL-12, likely due to other inducing factors such as IL-18 (25).

In the type-1 response, endogenous IL-10, IL-13, and IL-4 had both positive and negative regulatory effects. In general, all of the above-mentioned type-1–biased chemokines were negatively regulated by these cytokines, and surprisingly, each affected different chemokines. IL-10 mostly tempered CCL3 (MIP-1{alpha}) and CCL4 (MIP-1ß), whereas IL-13 limited CXCL9 (MIG), 10 (IP-10), and 11 (I-TAC), and IL-4 reduced CXCL2 (MIP-2) and CXCL5 (LIX) transcript expression. Differential regulation of CCL3 (MIP-1{alpha}) and CCL4 (MIP-1ß) by IFN-{gamma}, IL-4, and IL-10 was reported for endotoxin-stimulated macrophages (10). We now provide evidence for differential cytokine crossregulation in vivo. Although the individual treatments did not significantly affect the degree of inflammation, we previously demonstrated that combined IL-4 and IL-13 depletion significantly exacerbates type-1 granuloma formation (20).

Although IL-10, IL-13, and IL-4 downregulated the type-1–biased chemokine transcripts, they had positive effects on unbiased and normally type-2–biased chemokines. For example, MCP-2 (CCL8) was the only transcript that was enhanced by all of the cytokines examined, but IFN-{gamma} and IL-12 were clearly the dominant positive stimulants during the type-1 response. Similarly, IFN-{gamma} and IL-4 both promoted MCP-3 and MCP-5, further indicating that not all chemokines are subject to opposing influences. In contrast, eotaxin, MDC, and TARC were promoted only by IL-13, IL-4, or IL-10. The latter indicated that despite their low levels of expression in the type-1 response, the type-2–related cytokines, IL-13 and IL-4, were influencing the chemokine milieu, and IL-10, although not strictly a type-2 cytokine, was likewise promoting "type 2" chemokines. It should be noted that although our study did not address potential synergistic effects, such interactions are likely. The apparent biased expression of some chemokines may be in part due to contributing synergizing cytokines such as TNF-{alpha} (19, 22). We have previously demonstrated that TNF-{alpha} appears to promote several chemokines in vivo (7).

Our cytokine depletion analysis of the type-2 (SEA) response further revealed the existence of a dynamic cytokine–chemokine crossregulatory network. As we and others have previously reported, IFN-{gamma} depletion exacerbated type-2 granulomatous inflammation (19, 22). This was associated with significant increases in CCL8 (MCP-2), CCL7 (MCP-3), CCL22 (MDC), and CCL17 (TARC). Further attesting to their IFN-{gamma} dependence, the minor component of "type-1" chemokine transcripts, CXCL9 (MIG), 10 (IP-10), and 11 (I-TAC), were virtually eliminated. Thus, whereas IFN-{gamma} was a major proinflammatory mediator in the type-1 granuloma, it served as a background counter-regulatory agent in the type-2 response. Of significant interest was the role of IL-12 in the type-2 lesion. Like IFN-{gamma}, it had a counter-regulatory role, and its depletion augmented a broad spectrum of chemokine transcripts, including CCL3 (MIP-1{alpha}), CCL4 (MIP-1ß), CCL2 (MCP-1), CCL8 (MCP-2), CCL7 (MCP-3), CCL12 (MCP-5), CCL11 (eotaxin), CCL22 (MDC), and CCL1 (TCA-3). Paradoxically, and unlike IFN-{gamma} depletion, anti–IL-12 treatment did not cause a demonstrable increase in granuloma size, which may relate to the different spectrum of affected chemokines. Unlike IFN-{gamma} neutralization, anti–IL-12 treatment concurrently increased transcripts for type-1–biased CCR1 ligands, CCL3 (MIP-1{alpha}), and CCL4 (MIP-1ß), but did not enhance the type-2–biased chemokine, TARC (CCL17). The latter, being the dominantly expressed type-2 chemokine, may be a major proinflammatory signal. In addition, the type-1–biased ligands may act as crossregulatory receptor antagonists (26, 27). We specifically demonstrated that CCR1 ligation inhibited type-2 granuloma formation (28). Thus, any proinflammatory effects of the CCR2 and CCR3 ligands might be neutralized by concomittant augmentation of CCR1 ligands, resulting in no net change in type-2 inflammation. These different effects of IFN-{gamma} and IL-12 blockade might have therapeutic implications. For example, attempts to neutralize IFN-{gamma} to control Th1-domininant inflammatory conditions could have possible immunocompromising effects such as tuberculosis reactivation and potential exacerbation of allergic inflammation. However, targeting of IL-12 may provide anti-inflammatory benefit without as serious counter-regulatory effects.

Using repeated anti–IL-10 treatments, Boros and coworkers demonstrated a clear regulatory role for IL-10 in the inductive phase of the response to schistosome eggs (29). In our type-2 bead lesion, which represents an elicitation phase response, we observed only modest changes in the chemokine response manifest as increased CCL2 (MCP-1) transcript expression. This was consistent with our observation that IL-10 depletion did not affect inflammation, as well as with our previous report indicating that the major effect of IL-10 during the elicitation phase was detectable in draining lymphoid tissues and not at the site of inflammation (19). In contrast to IL-10, IL-13 and IL-4 had clear proinflammatory roles in type-2 granuloma formation. These cytokines promoted expression of a similar spectrum of chemokine transcripts, although that for IL-4 was broader, providing an explanation for the greater suppressive effect of IL-4 depletion. The broader effect of IL-4 may relate to its capacity to bind both IL-4 and IL-13 receptors, whereas IL-13 can only signal through its own receptor (30). The chemokine transcripts promoted by these cytokines included CCL4 (MIP-1ß), CCL2 (MCP-1), CCL8 (MCP-2), CCL7 (MCP-3), CCL12 (MCP-5), CCL11 (eotaxin), CCL22 (MDC), CCL17 (TARC), and CCL1 (TCA-3). Of these, only CCL4 was classed among "type-1"–biased chemokines based upon our initial survey, and its promotion by both Th1- and Th2-related cytokines indicates that it is not limited to type-1 responses. This finding also provides further evidence for the notion that despite their similarity, CCL3 (MIP-1{alpha}) and CCL4 (MIP-1ß) are independently expressed or regulated (10).

Of the many findings provided by our in vivo study, special mention should be made regarding CCL22 (MDC) and CCL17 (TARC). Both of these chemokines are ligands for the CCR4 receptor (31, 32) and have been associated with Th2-mediated hypersensitivity responses (33, 34). Our study showed significant transcript levels in both types 1 and 2 granuloma models, although CCL17 (TARC) was type-2 biased. We consistently showed that IL-13 enhanced CCL17 (TARC) transcripts and IL-4 enhanced CCL22 (MDC) and CCL17 (TARC) in both granuloma models. This finding is fully consistent with in vitro studies (32, 34), and supports these chemokines as important participants in Th2 cytokine–mediated responses. However, CCL22 (MDC) can be elicited by cytokines such as IL-1 and TNF-{alpha} (35), which are produced during the type-1 (PPD) response, and would explain the unbiased nature of MDC expression. Germane to this is our finding that unlike in vitro–generated Th cell clones, CCR4 was a major chemokine receptor expressed by both IL-4– and IFN-{gamma}–producing CD4+ T cells generated in vivo during Ag-bead granuloma formation (17). Thus, MDC and TARC may not necessarily be recruiting only Th2-restricted cells. Consequently, the net "polarized" state may reflect more on Th cell frequency and crossregulatory signaling.

In summary, although our in vivo study has interpretive limitations regarding actual translated protein levels and specific sites of chemokine synthesis within granulomatous lungs, it provides the first comprehesive analysis of cytokine-mediated regulation of chemokine expression during type-1 and type-2 models of chronic inflammation. The following findings emerge from this study: (i) chemokine expression is subject to crossregulation by Th1- and Th2-related cytokines; (ii) enhancement or decrease in chemokine expression will not necessarily result in changes in inflammation unless critical chemokines or critical thresholds are affected; (iii) chemokines are differentially regulated by cytokines in vivo; (iv) there is profound expression of CCR4 agonists, during granuloma formation, which are clearly augmented by IL-4 and IL-13; and (v) biased patterns of chemokine expression during the adaptive immune response can be related to the cytokine milieu. Such in vivo studies will be relevant to current efforts to target cytokine and chemokines in pulmonary disease.


   Acknowledgments
 
This work was supported by NIH-NIAID grant AI43460 and in part by the Department of Veterans Affairs. Schistosome life stages or materials for this work were supplied through NIH-NIAID contract NO1-AI-55270.

Received in original form November 7, 2002

Received in final form December 31, 2002


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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