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Keywords:

  • CD4+ T cells;
  • Cytokine expression;
  • IFNs;
  • Memory cells;
  • Th cells

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Materials and methods
  7. Acknowledgements
  8. Conflict of interest
  9. References
  10. Supporting Information

Human type I interferons (IFNs) include IFN-β and 12 subtypes of IFN-α. During viral infection, infiltrating memory CD4+ T cells are exposed to IFNs, but their impact on memory T-cell function is poorly understood. To address this, we pretreated PBMCs with different IFNs for 16 h before stimulation with Staphylococcus aureus enterotoxin B and measured cytokine expression by flow cytometry. IFN-α8 and -α10 most potently enhanced expression of IFN-γ, IL-2, and IL-4. Potency among the subtypes differed most at doses between 10 and 100 U/mL. While enhancement of IL-2 and IL-4 correlated with the time of preincubation with type I IFN, IFN-γ production was enhanced best when IFN-α was added immediately preceding or simultaneously with T-cell stimulation. Comparison of T-cell responses to multiple doses of Staphylococcus aureus enterotoxin B and to peptide libraries from RSV or CMV demonstrated that IFN-α best enhanced cytokine expression when CD4+ T cells were suboptimally stimulated. We conclude that type I IFNs enhance Th1 and Th2 function with dose dependency and subtype specificity, and best when T-cell stimulation is suboptimal. While type I IFNs may beneficially enhance CD4+ T-cell memory responses to vaccines or viral pathogens, they may also enhance the function of resident Th2 cells and exacerbate allergic inflammation.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Materials and methods
  7. Acknowledgements
  8. Conflict of interest
  9. References
  10. Supporting Information

Memory CD4+ T cells play a critical role in coordination of the adaptive immune response by production of cytokines and chemokines, which expand or activate effector cells, and recruit key cell populations. Of the multiple memory CD4+ T-cell subsets described, the best characterized are Th1 and Th2 [1, 2]. Th1 cells produce interferon-gamma (IFN-γ), which is involved in the cell-mediated immune response and important for clearance of viral infection [3]. Th2 cells produce IL-4, IL-5, IL-9, and IL-13, which are not only important in clearance of parasites, but are also associated with allergic disease. Both IL-4 and IL-13 participate in regulation of B-cell class-switching and promotion of IgE production [4]. In addition, IL-5 promotes activation and recruitment of eosinophils [5, 6], and IL-13 increases mucus production by epithelial cells, a hallmark of asthma [7].

IFN was first described as an antiviral factor in chick chorioallantoic membrane in 1957 [8]. Twenty years later, IFN-α1 was purified [9]. It became apparent that in humans, IFN-α is a family of 17 homologous genes [10-12] that produces 12 distinct gene products; there are four non-functional pseudogenes and mature IFN-α1 and -α13 are identical. In addition to type I IFNs (which also include IFN-β and others), IFN-γ is the lone type II IFN, and there are three type III IFNs: IFN-λ1, -λ2, and -λ3 [13].

Each type I IFN binds with different affinity to each of the two IFN receptor subunits, coligation of which may differentially activate JAK-STAT and other signaling pathways [14, 15]. Most human cells express type I IFN receptor and express IFN in response to viral infection [11]. Cell types differ, however, in the levels and subtypes of expressed IFNs. For example, plasmacytoid dendritic cells (pDCs) express high levels of IFN-β and all IFN-α subtypes after ligation of TLRs, while mDCs express a more limited panel of IFNs in response to ligation of different pattern recognition receptors [16].

Many studies that address the effects of type I IFNs on CD4+ T cells focus on stimulation and polarization of naïve cells [17, 18]. While type I IFNs alone cannot polarize human naïve CD4+ T cells [19], they can enhance Th1 polarization by increasing the activation of STAT4 in response to IL-12 [20]. Type I IFNs also suppress expression of GATA3 [21], which is necessary for Th2 polarization.

Memory CD4+ T cells exclusively migrate to local sites of viral infection where type I IFNs are first expressed [22, 23]. To investigate effects of type I IFNs on memory CD4+ T cells, we used a simplified model in which human PBMCs were pretreated with IFN-β or each of the 12 IFN-α subtypes at different doses, followed by activation with either Staphylococcus aureus enterotoxin B (SEB) or peptide libraries from one of two viral pathogens, cytomegalovirus (CMV) or respiratory syncytial virus (RSV). We demonstrate that type I IFNs enhance both Th1 and Th2 function with dose and subtype specificity, and most importantly, are most potent in the context of suboptimal T-cell stimulation. These data contribute toward understanding the role of type I IFNs in viral and vaccine-induced immunity, and the pathogenesis of autoimmunity and allergic diseases.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Materials and methods
  7. Acknowledgements
  8. Conflict of interest
  9. References
  10. Supporting Information

Type I IFNs differentially enhance expression of IL-2, IL-4, and IFN-γ

To first determine whether the subtypes of IFN-α differentially affect cytokine expression, PBMCs were incubated overnight (∼16 h) with 2500 U/mL of an IFN-α subtype or IFN-β, stimulated with SEB for 6 h, and then harvested and stained for cytokine expression by polychromatic flow cytometry. We defined SEB-activated CD4+ T cells as CD3+, CD8, and CD40L+ (Supporting Information Fig. 1). There were minimal contributions to cytokine expression by the CD3+ cells negative for both CD4+ and CD8+ (11.2–11.8% of CD3+ cells, n = 2, data not shown) since only 2–5% of CD3+, CD4, and CD8 cells expressed CD40L or any cytokine (n = 2, data not shown). The expression of CD40L on T cells is a sensitive and robust method to identify antigen-stimulated cells [24, 25]. While type I IFNs alone did not induce cytokine expression, and preincubation with type I IFNs did not affect the frequency of CD40L+ cells induced by SEB (not shown), type I IFNs did enhance the frequency of IFN-γ, IL-2, and IL-4 expression by activated (CD40L+) CD4+ T cells. Of these three cytokines, the frequency of IFN-γ+ cells was least affected and was offset by an increase in frequency of IL-2-expressing IFN-γ+ CD4+ T cells (Fig. 1A) and enhanced median fluorescence intensity (MFI) of IFN-γ οn CD40L+, IFN-γ+ cells (Fig. 1B, bottom row).

image

Figure 1. Type I IFNs enhance cytokine production by SEB-stimulated CD4+ T cells. PBMCs were treated overnight with 2500 U/mL of each IFN-α subtype, and then stimulated with SEB for 6 h, harvested, and stained for polychromatic flow cytometry. (A) Representative data from one donor showing frequency of expression of IFN-γ, IL-2, and IL-4 by activated CD4+ (CD3+CD8CD40L+) T cells in response to IFN-α1 and IFN-α8. (B) Cumulative data from six donors showing the frequency of activated CD4+ (CD3+CD8CD40L+) T cells that express each of these three cytokines (top), and the median fluorescence intensity (MFI, bottom). The data are normalized to the response to SEB alone, each shape represents a different donor.

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We then demonstrated that lower (and biologically more meaningful) doses of IFN-β and each IFN-α subtype differentially enhanced the frequency of expression of IFN-γ, IL-2, and IL-4 by activated CD4+ T cells in a dose-dependent manner (Fig. 2A). These differences were most pronounced at doses of 25 U/mL of type I IFNs. IFN-α8 and -α10 were the most potent subtypes while IFN-α1 and -α17 were among the least potent subtypes (Fig. 2B).

image

Figure 2. IFN-α subtypes enhance cytokine expression by SEB-stimulated CD4+ T cells in a dose-dependent manner. PBMCs were treated overnight with type I IFN subtypes (0.25, 2.5, 25, 250, or 2500 U/mL), and then stimulated with SEB for 6 h, harvested, and stained for flow cytometry. The frequency of cytokine expression by activated CD4+ (CD3+CD8CD40L+) T cells was quantified. (A) Dose-dependent responses to IFN-β and each subtype of IFN-α from one of two donors. (B) Cytokine expression, from the same experiment as in (A), ordered from highest to lowest at 25 and 2.5 U/mL IFN. IFN-α8 and -α10 are among the most potent subtypes at inducing T-cell cytokine expression, while IFN-α1 and -α17 are among the least. Data shown is from one of two independent experiments, each performed with a different donor with similar results.

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To further explore these effects, we focused on IFN-α8 and -α1 as representative of the most and least potent IFN subtypes, respectively, and demonstrated among multiple donors that the EC50 of IFN-α8 is approximately six- to tenfold greater than IFN-α1 (Fig. 3A). Once again, these differences are greatest at doses <100 U/mL.

image

Figure 3. IFN-α8 more potently enhances cytokine expression by SEB stimulated activated CD4+ T cells than IFN-α1. PBMCs were treated overnight with either IFN-α1 or IFN-α8 (0.25, 2.5, 25, 250, or 2500 U/mL), and then stimulated with SEB for 6 h, harvested, and stained for flow cytometry. The frequency of cytokine expression by activated CD4+ (CD3+CD8CD40L+) T cells was quantified. (A) Compiled dose responses to two subtypes of IFN-α from 6 (0.25 U/mL), 10 (2.5, 250 U/mL), or 12 (25, 2500 U/mL) independent experiments each performed with different donors. *p ≤ 0.01 Wilcoxon signed-rank test. (B) Polyfunctionality of activated CD4+ T cells in response to IFN-α1 or IFN-α8. Hierarchical testing using paired t-tests with correction for multiple comparisons was used to determine statistical significance of the polyfunctional populations compared to SEB alone. *p < 0.05 by paired t-test after correction. Data shown are compiled from ten independent experiments each with a different donor. Each point represents a single donor.

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Combinatorial analysis of each subset demonstrates that IFN-α enhanced the percentage of IL-2 “single-positive” cells, IL-2+IL-4+IFN-γ “double-positive” subsets, and IL-2+IL-4+IFN-γ+ “triple-positive” subsets (Fig. 3B and Supporting Information Fig. 2). Consistent with the dose response curves, IFN-α8 significantly enhanced polyfunctionality at lower doses than IFN-α1. Enhancement of the frequency of expression of each pair of these three cytokines correlated well at almost all type I IFN doses (Supporting Information Fig. 3), and best at 25 U/mL (Fig. 4).

image

Figure 4. Correlation of enhanced cytokine expression by IFN-α pretreated CD4+ T cells. Correlation of frequency of expression of each cytokine pair of IFN-γ, IL-2, and IL-4 by activated CD4+ (CD3+CD8CD40L+) T cells after pretreatment with 25 U/mL type I IFN. Supporting Information Figure 3 shows correlations at other doses. Data from one of two independent experiments is shown, each performed with a different donor with similar results.

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Parallel experiments in which brefeldin A was excluded confirmed that IFN-α1 and -α8 enhanced expression of the IL-2, IL-4, and IFN-γ genes by qRT-PCR, and enhanced their secretion into supernatants by ELISA (data not shown). In addition, IFN-α1 and -α8, at doses as low as 25 U/mL, enhanced gene expression of IL10 by approximately twofold of SEB alone (not shown). The effects of type I IFNs on IL-13 appear to be biphasic-attenuated at low doses and enhanced at 2500 U/mL (Supporting Information Fig. 4); however, the frequency of IL-13+ cells is low (<1.5%) and varies among donors (not shown).

Type I IFN enhances cytokine expression by both naïve and memory CD4+ T cells

To determine whether memory CD4+ T cells are selectively affected by type I IFN, we FACS purified naïve (CD27+, CD45RO), central memory (CD27+, CD45RO+), and effector memory (CD27, CD45RO+) CD4+ T cells (Supporting Information Fig. 5). Each purified subset was cultured with autologous monocytes (T cells:monocytes, 3:1). The mixed culture was preincubated with IFN-α8 (250 U/mL), stimulated with SEB, and harvested to measure IFN-γ and IL-2 expression. As shown in Table 1, preincubation with IFN-α8 enhanced IL-2 expression by the naïve subset, and IFN-γ and IL-2 expression by both memory subsets. CD4+ T cells expressed cytokines at lower levels in the absence of monocytes, but those levels were enhanced by IFN-α8 as well, demonstrating that these type I IFN effects are, at least in part, direct and not mediated by another cell type in the PBMCs.

Table 1. Effect of IFN-α8 on cytokines produced and CD40L expressed by sorted naïve (CD27+, CD45RO), central memory (CD27+, CD45RO+), and effector memory (CD27, CD45RO+) CD4+ T-cell subsets cultured with autologous monocytesa
   Frequency ofFrequency ofFrequency of 
   total IFN-γ+IFN-γ+ IL-2+total IL-2+CD40L MFI
CD4+ subsetMonocytesDonor12121212
  1. a

    Percent change in expression (indicated in bold) by CD40L+ subset calculated compared to the same subset stimulated with SEB and no IFN-α8.

           
Total CD4+- 23.312.314.96.820.825.811813319
+ IFN-α8- 30.018.620.710.026.729.710441620
Percent change  +29+52+39+46+28+15−12−51
Total CD4++ 36.518.233.812.647.939.032322772
+ IFN-α8+ 39.919.537.913.253.142.723332084
Percent change  +10+7+12+5+11+10−28−25
Central memory+ 31.714.522.511.144.244.141743578
+ IFN-α8+ 33.115.824.812.449.048.829522121
Percent change  +4+8+10+12+11+11−29−41
Effector memory+ 45.545.842.519.453.338.731522213
+ IFN-α8+ 50.649.148.322.059.744.022161592
Percent change  +11+7+14+13+12+14−30−28
Naive+ ----16.222.321432321
+ IFN-α8+ ----22.136.815551729
Percent change      +36+65−27−26

We had observed with our studies of PBMCs that while the frequency of CD40L+ cells is not affected by type I IFNs, the MFI of CD40L expression on CD40L+ cells is decreased (not shown). Similar to enhanced cytokine expression, IFN-α8 pretreatment decreased the MFI of CD40L on CD40L+ naïve, central memory, and effector memory CD4+ T cells, and is independent of the presence of monocytes.

Enhancement of cytokines is dependent on the time of preincubation with type I IFN

We chose 16 h as the length of the type I IFN preincubation to simulate the exposure of CD4+ T cells to type I IFNs while migrating toward an infection site. To test whether 16 h was an optimal time point, we compared it to preincubation with type I IFN for 4 and 1 h before, simultaneous to SEB, and 1 h afterward. The extent by which expression of IL-2 and IL-4 was enhanced on CD40L+ CD4+ T cells increased with length of time of preincubation. By contrast, treatment with type I IFN immediately proximal to SEB best enhanced both the frequency (Fig. 5A) and MFI of IFN-γ expression. As shown in Figure 5A, the kinetics of the decrease in MFI of CD40L on CD40L+ cells was similar to the enhancement of IFN-γ.

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Figure 5. The kinetics of type I IFN treatment, rather than SEB stimulation, determines enhanced cytokine expression. (A) PBMCs were treated with 25 or 250 U/mL of IFN-α1, IFN-α8, or IFN-β for 16, 4, or 1 h before, simultaneously with, or 1 h after stimulation with SEB. The cells were stimulated with SEB for a total of 6 h, harvested, and stained for flow cytometry. Data shown are from one representative experiment of four, each performed with a different donor with similar results. (B) PBMCs were treated with 250 U/mL IFN-α8 for 16 h before, or 0.5 h after stimulation with SEB for 4, 6, or 8 h before harvesting and staining for flow cytometry. Data shown are compiled from four independent experiments each with a different donor. Each symbol represents a different donor. The cytokine expression and CD40L MFI by activated CD4+ (CD3+CD8CD40L+) T cells (A and B) and the frequency of CD40L expression by total CD4+ (CD3+CD8) T cells (B) were quantified.

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To determine whether type I IFNs alter the kinetics of the response to SEB rather than the profile of cytokine production, we pretreated PBMCs with IFN-α8 for either 16 h prior to or 0.5 h after stimulation with SEB for 4, 6, and 8 h. Figure 5B demonstrates that, as expected, the percentage of activated (CD40L+) cells increases over time of incubation with SEB, but that the frequency of IL-2-, IL-4-, and IFN-γ-expressing cells (of CD40L+ CD4+ T cells) remains constant. Similarly, the MFI of CD40L on these cells is a function of the time of IFN-α pretreatment rather than duration of SEB stimulation.

Type I IFNs best enhance cytokine expression by suboptimally stimulated T cells

We then asked whether the intensity of T-cell stimulation factors into the enhancement of cytokine expression by type I IFNs by pretreating PBMCs with IFN-α1 or -α8 at ascending doses of SEB. As expected, the frequency of CD40L+ CD4+ T cells, the MFI of CD40L on those cells, and their expression of IFN-γ, IL-2, and IL-4 rose with the SEB dose (Fig. 6A and top row of B). Of interest, the percentage of activated cells that expresses IL-2, IFN-γ, and probably IL-4 peaks or plateaus at an SEB dose of 10 to100 pg/mL (Fig. 6B, second row). Above these SEB doses, increases in expression of these three cytokines best correlate with an increase in the percentage of activated (CD40L+) T cells (Fig. 6A, top row insets). Consistent with previous observations, IFN-α8 was more potent than IFN-α1. Most significantly, as SEB doses rise above 100 pg/mL, the impact of IFN-α on cytokine expression decreases (Fig. 6B and C), demonstrating that type I IFNs best enhance cytokine expression when antigen stimulation is suboptimal (i.e. submaximal).

image

Figure 6. Enhancement of T-cell cytokine expression by type I IFN is greatest at low levels of TcR stimulation. PBMCs were stimulated overnight with either IFN-α1 or -α8 followed by increasing doses of SEB for 6 h before harvesting for flow cytometry. (A) Frequency of total CD4+ T cells that express CD40L (left) and the MFI of CD40L on CD40L+ CD4+ T cells (right) in response to increasing doses of SEB with IFN-α pretreatment. (B) Frequency of cytokine expression by total (CD3+CD8, top) and activated (CD3+CD8CD40L+, bottom) CD4+ T cells in response to increasing doses of SEB (insets, top row). Correlation of cytokine expression with frequency of CD40L expression by total (CD3+CD8) CD4+ T cells. (C) Frequency of cytokine expression by total (CD3+CD8) and activated (CD3+CD8CD40L+) CD4+ T cells (top and bottom, respectively) was normalized to SEB alone to show the effect of IFN-α at low doses of SEB. Data shown are representative of one of five independent experiments, each performed with a different donor with similar results.

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Type I IFNs enhance stimulation by peptides from RSV but not CMV

Finally, we compared the effects of IFN-α on cytokine expression by CD4+ T cells in response to MHC II presented peptides from CMV and RSV. CMV is a chronic persistent infection controlled by CMV-specific CD4+ and CD8+ T cells, which are greatly expanded [26] and have a mature memory phenotype [27, 28] suggesting that they are activated in response to CMV and play an active role in controlling CMV replication. In contrast, RSV infects the apical surface of lung epithelium [29] and antigen is taken up by lung DCs; however, the RSV peptides involved in T-cell stimulation are less defined and RSV-stimulated DCs are also less functionally mature than those stimulated with other viruses [30-32] and therefore RSV-specific T cells are probably less mature. As shown in Figure 7, IFN-α8 enhanced frequencies of IL-2+ and “triple-positive” polyfunctional subsets of the RSV fusion (F) and attachment (G) protein specific CD4+ T cells, but not those specific for CMV pp65 peptides.

image

Figure 7. Effects of IFN-α subtypes on CD4+ T cells are antigen dependent. PBMCs were treated for 16 h with either IFN-α1 or -α8 at the indicated doses, stimulated with peptides from CMV pp65, RSV F and G proteins, or SEB for 6 h. The frequency of cytokine expression by activated CD4+ (CD3+CD8CD40L+) T cells was quantified. Data shown are compiled from five independent experiments each with a different donor. Each symbol represents a different donor. Differences were tested using the Friedman test, total IL-2 – RSV, p = 0.007; SEB, p = 0.0009; triple positive – SEB, p = 0.002. Significance of different stimulation conditions was tested compared to antigen alone using Dunn's post hoc test, *p < 0.05.

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Materials and methods
  7. Acknowledgements
  8. Conflict of interest
  9. References
  10. Supporting Information

The family of type I IFNs in humans includes IFN-β and 12 subtypes of IFN-α. While patterns of expression of these IFNs vary dependent on cell type and stimulus [16, 33], the reason for multiple, highly similar IFNs that share the same heterodimeric receptor is unknown. It is known that while the affinities of the type I IFNs for IFNAR1 and IFNAR2 differ [15], it is the stability of the ternary complex that critically affects the IFN response [14]. This includes not only the magnitude of “classical” activation of the STAT1/STAT2 complex, but also whether and to what degree STAT3, STAT4, and STAT5 are activated [34]. For example, IFN-α1, -α2, and -α21 differentially activate STAT1–5 in CD4+ T cells and IFN-α1 is 10- to 100-fold less potent than the other two [35]. Similar to our findings, however, these differences among the subtypes are minimal when used at high, nonphysiologic concentrations [35].

Our study is not the first to address the effects of type I IFN on human memory CD4+ T cells. Most previous studies, however, did not compare the effects of different IFN-α subtypes, used purified T cells, treated them with anti-CD3, or mitogens such as PHA or PMA/ionomycin, added IFN at the same time point as the stimulus, or used a single very high dose of one subtype of IFN. These experimental differences probably account for the reported differences of the effect of type I IFN on expression of IFN-γ [36, 37], IL-2 [38, 37], and IL-4 [39, 40]. These studies did consistently demonstrate that type I IFN decreased IL-13 expression [38, 40, 41], and increased IL-10 expression [38, 39, 42, 43].

Here, we used PBMCs because they include B cells and monocytes that express MHC class II, a 16-h preincubation time to mimic the exposure of T cells to type I IFN as they migrate toward a site of infection or inflammation, and stimulated with SEB because it reasonably simulates antigen presentation by APC. In addition, with SEB, the level of stimulation is more easily varied, and SEB-unresponsive CD4+ T cells serve as an internal control. We measured the CD4+ T-cell responses by flow cytometry because both frequency and intensity of responses are quantified, as is polyfunctionality.

Using this model, we demonstrate that the type I IFNs differentially enhance expression of IFN-γ, IL-2, and IL-4, as reflected not only by the percentage of cells expressing each of these cytokines, but also by the increase in polyfunctional cells that express two or all three of these cytokines. The greatest differences in effect between the IFNs were between 10 and 100 U/mL, a range that is considered biologically relevant [44, 45]. We further defined the differences between the type I IFNs by demonstrating that IFN-α8 is six to ten times more potent than IFN-α1. By varying the time of preincubation of the type I IFN, we showed two different kinetic response patterns, one shared by IL-2 and IL-4, and another shared by IFN-γ and CD40L. By varying the dose of SEB, we demonstrated that these effects of type I IFNs are greatest when the T cells are suboptimally stimulated, which we verified by comparing the response to peptide libraries from CMV and RSV. By varying the duration of SEB stimulation, we showed that this effect is not due to changes in the kinetic response to antigen stimulation.

While the mechanisms of naïve to memory CD4+ T-cell polarization have been intensively studied, surprisingly little is known about what controls the quality of memory CD4+ T-cell cytokine expression. Intensity of T-cell receptor engagement (i.e. concentration of SEB, Fig. 6) is one factor. Suppression of regulatory T-cell (Treg) function is another factor that we and others ruled out by modest enhancement by type I IFN of IL-10 expression (not shown and [38, 39, 42, 43]). More likely relevant to enhancement of IFN-γ expression is activation of STAT4 by type I IFN [46]. While the shared kinetics between expression of IFN-γ and CD40L (Fig. 5) suggests the potential for a shared mechanism by which type I IFN modulates expression of these two proteins, regulation of CD40L by STAT4 has not been reported. Shared kinetics between IL-2 and IL-4 (Fig. 5) also suggests a shared mechanism by which type I IFN enhances their expression, which may possibly be STAT5 activation. Ectopic expression of STAT5 activates GATA3-dependent Th2 polarization [47], and c-maf [48] that selectively activates the Il4 gene [49].

These two kinetic profiles suggest two potential consequences of type I IFN combined with TcR activation in vivo. First, because the level of enhancement of IL-2 and IL-4 increases with time of preincubation, tissue levels of type I IFNs may be more important than their source. Cells that reside in or that migrate through inflamed tissues will, upon antigenic stimulation, produce more of these two cytokines. For IFN-γ, however, exposure to type I IFN immediately prior to, or simultaneous with, TcR engagement appears to be critical. Therefore, presentation by APC, such as pDCs, that express high levels of type I IFNs may have more impact on IFN-γ expression than, for example, mDCs or macrophages.

Similarly, the increased expression per cell of CD40L with type I IFN exposure more proximal to TcR engagement may be an additional, indirect mechanism for enhanced IFN-γ expression, particularly when TcR engagement is weak (Fig. 6). This mechanism may explain the reported greater enhancement of IFN-γ by IFN-α in response to tuberculin-purified protein derivative when compared to tetanus toxoid, to which multiple immunizations practically insure a robust T-cell response [50].

Taken together, these data help explain the complex and often confusing contextual effects of type I IFNs on adaptive immunity. In the course of viral infection or vaccination, type I IFNs may enhance protective immunity to primary challenge by enhancing Th1 polarization, and to secondary challenge by enhancing CD4+ T-cell polyfunctionality [51, 52], particularly when the antigenic peptides have a lower affinity for TcR or are in limited quantity. Since the effect of type I IFN is subtype specific, analysis of the type I IFN signature profile elicited by natural infection or vaccination may be useful when disease persists or protection is inadequate. Furthermore, a more potent IFN subtype such as IFN-α8 may be therapeutic or serve as an effective adjuvant. Conversely, in the context of IFN-γ-mediated autoimmune diseases, such as type I diabetes, type I IFNs may enhance or prolong inflammation [53] particularly to weak auto-antigens that cross-react with viral peptides. Here, analysis of the type I IFN signature may suggest that inhibition of potent subtypes with synthetic antagonists will be beneficial.

These data also suggest that respiratory viral infections may induce a loop that amplifies chronic allergic inflammation. In this context, type I IFNs expressed by virally infected epithelial cells will enhance expression of IL-4 by resident allergen-specific T cells, which induces expression of FcεR1 by pDCs [54-56]. Upon cross-linking of FcεR1 expressed by pDCs by inhaled (IgE-bound) allergen, their expression of type I IFNs in response to viral stimulation is decreased, which in turn adversely affects virus-specific expression of IFN-γ by memory CD4+ T cells [54-56]. The consequent delay in viral clearance may paradoxically prolong type I IFN expression by the infected respiratory epithelium and macrophages, which then enhances expression of IL-4 by resident allergen-specific T cells, completing a loop of suppressed viral-specific immunity and increased IL-4 expression, which contributes to mast cell and eosinophil activation, and enhanced local IgE production [4, 57, 58]. Our model, therefore, provides a potential mechanism for the association of viral respiratory infections with exacerbation of allergic asthma.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Materials and methods
  7. Acknowledgements
  8. Conflict of interest
  9. References
  10. Supporting Information

Cells

Buffy coats, and elutriated monocytes and lymphocytes from healthy donors, were obtained from the NIH Clinical Center Department for Transfusion Medicine. PBMCs were isolated from buffy coats, and the lymphocytes were further purified by Ficoll-Hypaque (Sigma, St. Louis, MO, USA) density centrifugation. For the peptide studies, healthy adult DRB1*0401 positive donors were selected since they present epitopes from RSV F and G proteins [59]. The Institutional Review Boards of the NIH and the U.S. Food and Drug Administration approved the study.

Reagents and antibodies

RPMI 1640 containing L-glutamine was obtained from Life Technologies (Grand Island, NY, USA), and was supplemented with 10% FBS (HyClone, Logan, UT, USA) and gentamicin (20 μg/mL, Life technologies). Low-endotoxin Staphylococcus aureus enterotoxin B (SEB) was purchased from Toxin Technologies (Sarasota, FL, USA) and, unless otherwise stated, was used at a concentration of 1 μg/mL. The 15-mer overlapping peptide library from CMV pp65 [28] was the kind gift of Joseph Casazza. Peptide 15-mers overlapping by 11 amino acids with >70% purity were designed, which span the entire RSV A2 F0 and G protein sequence (New England Peptide, Gardner, MA, USA). Peptide pools were then made for the RSV F0 and G proteins. Each peptide library was used at a final concentration of 2 μg/mL. Type I IFNs were purchased from PBL InterferonSource (Piscataway, NJ, USA), and type III IFNs were purchased from R&D systems (Minneapolis, MN, USA). The activity of each type I IFN was measured by the manufacturer by IFN inhibition of vesicular stomatitis virus on MDCK cells as described in Rubenstein et al. [60].

Cell culture

PBMCs were cultured at a concentration of 2.5 × 106 cells/mL and incubated for 16 h with type I or III IFN followed by the addition of SEB for 6 h prior to harvest, and Brefeldin A (10 μg/mL, Sigma) for the final 4 h. Prior to harvesting, the PBMCs were treated with DNase I (3 mg/mL, Calbiochem, San Diego, CA, USA).

Flow cytometry and cell sorting

The panel of mAb and fluorescent reagents used for flow cytometry is shown in Supporting Information Table 1A. All mAbs were titered for use at saturation. Cells were stained for intracellular cytokine expression according to previously published methods [61]. Briefly, cells were stained for CD8, CD14, CD16, and CD20 and with live dead fixable blue stain (Invitrogen), fixed with Cytofix/Cytoperm (BD Biosciences, Franklin Lakes, NJ, USA), blocked and permeabilized overnight with PBS/0.1% saponin/5% nonfat dry milk, and then stained with all other mAbs in the PBS/saponin/milk. After collection of data (BD Biosciences LSR II flow cytometer), the files were analyzed with FlowJo (Treestar Software, Ashland, OR, USA). In some experiments, pie-charts were generated by combinatorial analyses of Boolean gating (Flowjo) with SPICE software [62].

For FACS purification of CD4+ T cells from elutriated lymphocytes, they were stained with mAb (Supporting Information Table 1B) and dead cells were excluded with 7-amino-actinomycin D (Sigma). Supporting Information Figure 5 shows the gating strategy and sample sort purities. CD45RO+CD27+ and CD45ROCD27+ were all 95–99% pure, while CD45RO+CD27 was 93.5 and 86.7% positive, with low expression of CD27 on a further ∼5% of the CD45RO+ cells. Elutriated monocytes were further purified with anti-CD14 magnetic beads (Miltenyi Biotech, Auburn, CA, USA).

Measurement of cytokines

Concentrations in supernatants of IFN-γ, IL-2, IL-4, IL-6, IL-10, IL-13, IL-17, TNF-α, and sCD40L were measured using a Milliplex MAP kit (EMD Millipore, Billerica, MA, USA) according to the manufacturer's instructions. Data were acquired using a Luminex 100 fluorescent bead analyzer (Luminex, Austin, TX, USA) and analyzed with Prism software, Version 5 (Graphpad, San Diego, CA, USA).

qRT-PCR

RNA was purified with an RNeasy mini kit, including a step for homogenization (Qiashredder) and on-column DNase digestion according to the manufacturer's directions (Qiagen, Valencia, CA, USA) followed by RT (Superscript III, Life technologies). All primer/probe sets for qRT-PCR were purchased from Applied Biosystems (Foster City, CA, USA) and qRT-PCR was performed on the 7900HT Real-Time PCR System (Applied Biosystems). The ∆∆Ct method was used for calculating fold change of gene expression against the housekeeping genes RPL13A and GAPDH.

Statistics

Data were analyzed for statistical differences using the appropriate tests in Graphpad Prism as mentioned in the legend of each figure.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Materials and methods
  7. Acknowledgements
  8. Conflict of interest
  9. References
  10. Supporting Information

The authors thank Howard Mostowski for performing flow-activated cell sorting, Joanne Yu for antibody conjugation, Rachel Shepard for help with preparing the figures, Joe Casazza for the CMV pp65 peptide library, and Dragana Jankovic, Jack Ragheb, and Calman Prussin for helpful discussion in preparing the manuscript. This project was supported by CBER intramural funds.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Materials and methods
  7. Acknowledgements
  8. Conflict of interest
  9. References
  10. Supporting Information
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Abbreviations
pDC

plasmacytoid dendritic cell

SEB

Staphylococcus aureus enterotoxin B

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Materials and methods
  7. Acknowledgements
  8. Conflict of interest
  9. References
  10. Supporting Information

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Figure S1. Flow cytometry gating strategy. Using the gating strategy depicted here, we first eliminated dead cells, monocytes, NK cells, and B cells, then gated on CD3+ cells. We ensured we were only examining single cells by comparing forward scatter area with forward scatter height and then gated on cells of interest in the forward scatter – side scatter gate. We then used a gate on the CD3+ CD8- cells to indicate the CD4 T cell population and gated on CD40L+ (activated) cells of this population.

Figure S2. IFN-⟨8 more potently enhances polyfunctionality in SEB stimulated activated CD4 T cells than IFN-⟨1. PBMCs were treated overnight with either IFN-⟨1 or IFN-⟨8 (0.25, 2.5, 25, 250, or 2500 U/ mL), and then stimulated with SEB for six hours, harvested, and stained for flow cytometry. The frequency of cytokine expression by activated CD4 (CD3+CD8-CD40L+) T cells was quantified. Boolean analysis was used to determine the relative proportions of cytokine expressing subpopulations depicted according to the colour scheme on the right. Data compiled from ten individual experiments each with one donor (except 0.25 U/mL dose, six donors).

Figure S3. Frequency of cytokine expression best correlated by subtype at the biological dose of 25 U/mL IFN. Representative data from one donor of two demonstrating the positive correlation of frequency of cytokine expression in activated CD4+ T cells by IFN-α subtype for all cytokines examined at different doses (0.25, 2.5, 25, 2500 U/mL). PBMCs were treated overnight with type I IFN subtypes at the indicated concentrations, stimulated with SEB for six hours, and stained for polychromatic flow cytometry. R2 values were calculated using linear regression in graphpad prism.

Figure S4. IL-13 expression is low, but is increased in response to high doses of IFN-⟨. PBMC were treated overnight with type I IFN subtypes (0.25, 2.5, 25, 250, or 2500 U/mL), and then stimulated with SEB for six hours, harvested, and stained for polychromatic flow cytometry. Data are from one representative donor of two. A. Flow cytometry plots showing frequency of expression of IL-13 and IL-4 by activated CD4 (CD3+CD8-CD40L+) T cells in response to IFN-⟨1 and IFN-⟨8. B. Dose dependent responses of IL-13 to IFN-® and each subtype of IFN-⟨. Only the highest (2500 U/mL) dose of IFN-⟨ enhanced IL-13 expression compared to SEB alone. Data depicted is from one representative donor of two.

Figure S5. FACS sorting strategy. Using the gating strategy depicted here, we first eliminated dead cells, then gated on cells in the forward scatter – side scatter gate and ensured we were only examining single cells by comparing forward scatter area with forward scatter width. We then eliminated CD8+ T cells, monocytes, NK cells, and B cells by use of a dump channel, and gated on CD4+ cells. The cells were sorted into CD27+CD45RO-, CD27+CD45RO+ or CD27-CD45RO+ populations.

Table S1A. Flow cytometry antibodies used in this study

Table S1B. Antibodies used for pre-sort staining

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