Introduction

Interferon-alpha (IFN-α) is used as an adjuvant therapy in patients with malignant melanoma following surgical resection of high-risk lesions (lymph node metastases or primary tumor thickness >4 mm). However, the precise molecular targets of exogenously administered IFN-α are unknown. IFN-α exerts direct anti-proliferative, pro-apoptotic, and anti-angiogenic effects on melanoma cells in culture, and has potent immunologic actions when administered in vivo [2, 3, 14, 28]. The binding of IFN-α to its heterodimeric receptor (IFNAR) activates Janus kinase 1 (Jak1) and Tyrosine kinase 2 (Tyk2) that in turn phosphorylate tyrosine residues on the cytoplasmic region of the receptor. These phosphotyrosine residues provide docking sites for the signal transducer and activator of transcription (STAT) family of proteins that are phosphorylated on tyrosine and serine residues by the activated Janus kinases [19]. The prototypical IFN-α signaling reaction results in the formation of a DNA binding complex known as the interferon-stimulated gene factor 3 (ISGF3) that consists of tyrosine phosphorylated STAT1 (STAT1α or STAT1β), tyrosine phosphorylated STAT2, and a p48 binding protein, known as interferon regulatory factor 9 (IRF9) [9]. This complex then translocates to the cell nucleus and activates the transcription of IFN-responsive genes [25].

Our group has previously demonstrated that the anti-tumor effects of IFN-α are critically dependent on STAT1-mediated signal transduction within host immune cells [1, 23]. In these reports, tumoral expression of STAT1 had no bearing on the ability of IFN-α to prolong the survival of tumor-bearing mice. In distinct contrast, STAT1−/− mice could not utilize exogenous IFN-α to inhibit the growth of STAT1+/+ melanoma cells. Thus, STAT1-mediated gene regulation within the host was most important to the anti-tumor effects of IFN-α in this experimental system. There is also compelling evidence from other groups to suggest that the immunostimulatory effects of IFN-α are a critical component of its anti-tumor activity [3-5, 11, 17, 18]. In fact, recent data have shown that the occurrence of autoimmune sequelae and the presence of tumor-infiltrating lymphocytes correlate with clinical response in patients receiving IFN-α [22, 30]. Together, these data suggest that the immunomodulatory actions are critical to the anti-tumor actions of this cytokine. However, a comprehensive analysis of gene regulation within immune effector cells following IFN-α treatment has not been reported. We have previously shown that activation of STAT1 within host immune cells is critical to the anti-tumor activity of IFN-α. With this in mind, we wished to identify the interferon-stimulated genes whose transcription in immune cells was dependent on STAT1 activity. We theorized that elucidation of these genes might provide insight into the mechanisms whereby IFN-α exerts its anti-tumor activity.

In the present study, microarray analysis was utilized to examine the differential gene expression within splenocytes from wild type (WT) and STAT1-deficient mice following in vitro treatment with IFN-α. These studies provide a transcriptional profile of IFN-α-stimulated murine immune effector cells and demonstrate that STAT1 is involved in both the induction and repression of multiple genes. This data may aid in understanding the mechanism by which IFN-α exerts its anti-tumor activity.

Materials and methods

Reagents

Universal Type I IFN (IFN-A/D, specific activity of 1.1 × 108 U/mg) was purchased from R&D Systems Inc., Minneapolis, MN, and used in murine experiments.

Animals

C57BL/6 mice were purchased from Taconic Farms Inc., Germantown, NY. STAT1−/− mice (C57BL/6 background) provided by the Dr. Durbin laboratory were generated by homologous recombination as previously described and housed in a pathogen-free environment [10].

Murine in vitro studies

All experiments were performed in compliance with the guidelines of the Institutional Laboratory Animal Care and Use Committee of The Ohio State University. Female mice (5-6 weeks of age) were used in all experiments. Spleens from C57BL/6 and STAT1−/− mice were removed aseptically and dispersed through 70 μM cell strainers. Splenocytes were washed with PBS, pelleted by centrifugation, and resuspended in RPMI-1640 supplemented with 10% FBS. Previous studies conducted in our laboratory indicated that IFN-α-induced signal transduction in immune cells was maximal following stimulation with 104 U/ml [24]. In addition, expression of well-characterized IFN-α-responsive genes (IFIT2 and ISG15) were greater following stimulation with IFN-α for 12 h vs. 6 h (data not shown). Thus, the in vitro studies employed a 12-h stimulation. Purified splenocytes were stimulated with either 104 U/ml IFN-A/D or PBS (negative control). Cells were harvested, lysed with TRIzol reagent (Invitrogen, Carlsbad, CA) and then processed for RNA extraction.

cRNA preparation and array hybridization

Mouse Genome U74Av2 Set GeneChips (Affymetrix, Santa Clara, CA), which query ∼6,000 murine genes, were used for these analyses. The cRNA was synthesized as suggested by Affymetrix. Briefly, total RNA from cells was prepared in TRIzol (Invitrogen) followed by RNeasy purification (Qiagen, Valencia, CA). Double stranded cDNA was generated from 8 μg of total RNA using the Superscript Choice System kit according to the manufacturer’s instructions (Invitrogen). Biotinylated cRNA was generated by in vitro transcription using the Bio Array High Yield RNA Transcript Labeling System (Enzo Life Sciences Inc., Farmingdale, NY). The cRNA was purified using the RNeasy RNA purification kit (Qiagen). cRNA was fragmented according to the Affymetrix protocol and the biotinylated cRNA was hybridized to U133A or U74va2 microarrays [29]. The arrays were then scanned (Affymetrix GMS418) and analyzed (GenePix Pro 4.0) according to Affymetrix protocols.

Data analysis

Raw data were collected with a confocal laser scanner (Hewlett Packard, Palo Alto, CA) and probe level data was analyzed using dChip Version 1.3 [26]. Array normalization was performed using the invariant set procedure. Then, model-based expression indices (MBEI) were computed using the perfect match only model. Probe-set level data that was identified as an “array outlier” by dChip was omitted and considered to be missing data in subsequent analyses. Array quality characteristics (including percent array outliers, percent present calls and median intensity) were examined. After MBEI computation and log-transformation of the values, data were imported into BRB-ArrayTools Version 3.22 for subsequent statistical analysis. Probe sets receiving an Affymetrix “Absent” call for more than 50% of the specimens were omitted. Univariate paired t-tests were used to make comparisons between saline and IFN-α treatment conditions. A nominal significance level of 0.001 was employed. To determine whether specific genes were differentially regulated in the two groups of mice, the random variance t- and F-tests were utilized. The F-test assumes that different genes have different variances, but that these variances can be regarded statistically as independent samples from the same distribution. Using this assumption, we performed a comparison of the gene expression profiles of the STAT1-deficient and WT mice using a 0.001 level of significance.

Real time PCR

Gene expression estimates from the microarray experiments were validated by Real Time PCR for select genes. Following TRIzol extraction and RNeasy purification for microarray analyses, 2 μg of total RNA was reverse transcribed and the resulting cDNA was used as a template to measure gene expression by Real Time PCR using pre-designed primer/probe sets (Assays On Demand; Applied Biosystems, Foster City, CA) and 2X Taqman Universal PCR Master Mix (Applied Biosystems) according to manufacturer’s recommendations as previously described [8]. Pre-designed primer/probe sets for human β-actin were used as an internal control in each reaction well (Applied Biosystems). Real Time PCR reactions were performed in triplicate in a capped 96-well optical plate. Real Time PCR data was analyzed using the ABI PRISM® 7900 Sequence Detection System (Applied Biosystems).

Results

STAT1-mediated gene regulation in murine splenocytes

To identify candidate genes within host immune cells involved in mediating the STAT1-dependent immunomodulary effects of IFN-α, the gene expression profile of splenocytes from individual WT and STAT1−/− mice (n = 3) was examined following 12-h treatment with IFN-α (104 U/ml) or PBS (negative control; 6 mice and 12 oligonucleotide arrays were utilized). Microarray analysis of gene expression indicated that STAT1-deficiency within the host resulted in the impaired or altered expression of many genes involved in immune function and the response to viral pathogens. From these studies, three categories of genes were identified based on the importance of STAT1 in controlling their expression. Of note, the splenocytes used in this experiment were not pooled but were analyzed individually following treatment with either PBS or IFN-α. The profile of gene expression of individual STAT1−/− mice was quite similar (i.e., not significantly different) as determined by the paired t-test. Thus, the gene profiles were specific to the STAT1 genotype and were not unduly influenced by inter-individual variation.

STAT1-enhanced genes

Thirty genes were significantly up-regulated to a greater degree in response to IFN-α in WT mice as compared to STAT1−/− mice such that the ratio of expression in WT mice versus knockout (KO) mice was >2.0 (P < 0.001; Table 1). Included in this category were genes involved in the regulation of T-cell adhesion (Ly6c), natural killer (NK) cell and T-cell cytotoxicity (Gzmb), chemotaxis (Ccl3 and Ccrl2), and several genes involved in regulating the immune response (Ifit2, Isg20, and Irf7).

Table 1 STAT1-enhanced genes: augmented up-regulation (>2-fold; P < 0.001) in WT splenocytes following IFN-α
Full size table

STAT1-suppressed genes

Twenty genes were significantly up-regulated to a greater degree in response to IFN-α in STAT1−/− mice as compared to WT mice such that the ratio of expression in WT mice versus KO mice was <0.5 (P < 0.001; Table 2). Included were genes that encoded negative regulators of Jak-STAT signal transduction (Socs3), genes involved in the suppression of alloreactive T-cell function (Arg-1), and genes contributing to chemotaxis (Ccl2, Ccl7, and Ccr5).

Table 2 STAT1-suppressed genes: augmented up-regulation (>2-fold; P < 0.001) in STAT1 KO splenocytes following IFN-α
Full size table

STAT1-independent genes

Nineteen genes were up-regulated to a similar degree in response to IFN-α in WT mice and STAT1−/− mice (such that the ratio of expression in WT mice versus KO mice was <2.0 and >0.5; Table 3). Included in this category were genes involved in transcriptional regulation (Ifi204), class I MHC antigen processing (Psmb9 and Tapbp), and genes involved in the ubiquitination of proteins (Ube2l6 and Zubr1). Of note, the majority of the genes in this group were expressed to a somewhat greater degree in WT mice.

Table 3 STAT1-independent genes: up-regulation (>2-fold; P < 0.001) in WT and/or STAT1 KO splenocytes following IFN-α
Full size table

The expression of representative IFN-α-induced genes from each category was validated in WT and STAT1−/− splenocytes via Real Time PCR. The following genes were evaluated: Gzmb, Ifit2, Irf7, Ly6c, Sh3bp2 (STAT1-enhanced genes), Ccr5, Gadd45g, Ifi30, Nfil3, Socs3 (STAT1-suppressed genes), CD86, Ifi204, Igtp, Ly6a, and Tapbp (STAT1-independent genes). In response to IFN-α, the expressions of Gzmb, Ifit2, Irf7, Ly6c, and Sh3bp2 were less in STAT1−/− mice as compared to WT mice. Conversely, Ccr5, Gadd45g, Ifi30, Nfil3, and Socs3 were induced to a greater degree by IFN-α in STAT1−/− mice. Finally, CD86, Ifi204, Igtp, Ly6a, and Tapbp were induced to a similar degree in WT and STAT1−/− mice (Fig. 1a–c).

Fig. 1
figure 1

Real time PCR validation of murine microarray data. Real Time PCR analysis was used to validate the expression of A Gzmb, Ifit2, Irf7, Ly6c, and Sh3bp2 (STAT1-enhanced genes), B Ccr5, Gadd45g, Ifi30, Nfil3, and Socs3 (STAT1-suppressed genes), and C CD86, Ifi204, Igtp, Ly6a, and Tapbp (STAT1-independent genes) in WT and STAT1−/− splenocytes following treatment with IFN-α. Data were expressed as the mean fold increase relative to baseline levels (PBS treatment). All real time PCR data were normalized to the level of β-actin mRNA (housekeeping gene). Error bars denote the standard deviations of triplicate experiments

Full size image

Discussion

The gene expression profile elicited by IFN-α in murine splenocytes was investigated using microarray analysis. These studies were conducted in an effort to identify genes that might be instrumental in mediating the STAT1-dependent anti-tumor effects of IFN-α [23]. Murine studies identified a panel of genes whose expression was enhanced by (or suppressed by) STAT1 signal transduction. In addition, a number of genes were found to be up-regulated independently of STAT1, that is, they were significantly induced in murine splenocytes regardless of the presence or absence of STAT1.

Mice with genetic deficiencies are an important tool for analyzing the role of specific transcription factors and signaling pathways in the response of immune effectors to cytokine stimulation [15, 33]. Previous studies from our laboratory have demonstrated that elimination of STAT1 signal transduction within mouse immune effector cells completely abrogated the anti-tumor effects of IFN-α in a murine model of malignant melanoma [23]. Thus, it is likely that the interferon-stimulated genes identified in this study as being significantly induced or repressed by STAT1 play a role in the elimination of tumor cells by activated immune effectors. Both NK cells and T cells have been implicated as mediators of the anti-tumor effects of IFN-α [6, 7, 32]. Thus in order to further characterize the immunostimulatory effects of IFN-α and the ability of this cytokine to promote the elimination of malignant cells, it will be important in future studies to examine the IFN-α-induced gene expression profile of specific immune compartments over time in both murine tumor models and patients with cancer. Importantly, our studies of IFN-α gene expression in human cancer patients indicate significant overlap with the present study [37]. Of the 50 murine genes whose regulation was dependent on STAT1 (30 STAT1-enhanced and 20 STAT1-suppressed), 19 genes have been identified in the PBMCs of patients receiving IFN-α [36, 37]. These included human homologues of Csprs, G1p2, Gzmb, Ifi27, Ifi30, Ifi204, Ifit2, Igtp, Isg20, Lgals3bp, Lgals9, Irf1, Irf7, Nfil3, Pml, Socs3, Tgtp, Trim21, and Wars.

In the present study, defects in STAT1 signal transduction led to the decreased transcription of 30 genes pertaining to immune cell function, including genes with well-documented effects on cytolytic T-cell function (Ly6c) and the cytolytic activity of T cells and NK cells (granzyme B). Ly6c is a hemopoietic cell differentiation antigen that is expressed on a subset of peripheral CD8+ T cells. It is involved in cytolytic T-cell elimination of target cells, enhances T-cell receptor-induced production of IL-2 and IFN-γ in CD8+ T cells, and regulates homing of CD8+ T cells in vivo. Jaakkola et al. have shown that cross-linking of Ly6c causes clustering of LFA-1 (CD11a/CD18) on the surface of CD8+ T cells and thereby augments lymphocyte adhesion to endothelium and trafficking to lymph nodes [21]. Granzyme B is essential for the cytolysis of malignant and virally infected cells by T cells and NK cells [31]. The exocytosis of death-inducing granzymes that are stored in the granules of cytotoxic lymphocytes allows the immune system to rapidly eliminate transformed cells. The membrane-disrupting protein perforin permits the entry of granzymes into the target cell, where they induce mitochondrial dysfunction and subsequent apoptosis by cleaving target proteins in the cytoplasm and nucleus. Further studies will be needed to determine whether the reduced expression of other STAT1-dependent genes listed in Table 1 amplifies the immune deficiency that comes with the reduced expression of Ly6c and granzyme B in IFN-stimulated STAT1−/− splenocytes.

We have shown that IFN-α induces several genes involved in the ubiquitin cycle (Ube1l, Ube2l6, Trim21, and Zurbr1), MHC class I expression (Tapbp and Psmb9), and the co-stimulation and clonal expansion of antigen specific T cells (CD86 and Camk2b). The ubiquitination of cellular proteins is a critical first step in the presentation of antigens within the context of MHC class I [16]. Similarly, Psmb9 is a protease that cleaves proteins into peptides of appropriate length for loading onto MHC class I [20]. Tapbp is a key regulator of antigen transport and is associated with MHC class I in the endoplasmic reticulum [34]. Additional co-stimulatory signals are typically required for proper activation of antigen specific T cells. Co-stimulation of T cells via CD86 (B7.2) expressed on antigen-presenting cells can enhance the activation of effector T cells [13]. Upon antigen recognition, Camk2b is involved in T-cell receptor signaling and the autocrine production of IL-2 production for the clonal expansion of activated T cells [27]. IFN-α also activates NK cell cytotoxicity and proliferation. This is likely mediated in part by up-regulation of Sh3bp2, which associates with CD244 and potentiates NK cell cytotoxicity [35].

Interferon-alpha was also able to inhibit the expression of multiple genes in murine splenocytes in a STAT1-dependent fashion, most notably arginase I. L-Arginine plays a central role in several biological systems including the regulation of T-cell function. The release of arginase I by myeloid suppressor cells leads to depletion of L-Arginine from the tumor microenvironment which in turn exerts a suppressive effect on T-cell proliferation and cytokine synthesis [12]. Reduced expression of arginase I following exposure of immune effector cells to IFN-α might therefore be expected to have an overall stimulatory effect on specific immunity.

We observed that several genes were regulated in a STAT1-independent fashion by IFN-α. This has been previously demonstrated for IFN-γ. Gil et al. and Ramana et al. compared the ability of WT and STAT1-null mouse bone-marrow-derived macrophages or embryonic fibroblast cell lines to respond to IFN-γ. They demonstrated that a 1-h treatment with IFN-γ induced the expression of over 51 genes independently of STAT1 [15]. Two of these genes, Gadd45g (regulator of apoptosis) and SOCS3 (an inhibitor of Jak-STAT signal transduction) were also induced by IFN-α in both WT and STAT1-deficient splenocytes [15, 33].

The present study has demonstrated the role of STAT1 signaling in the transcriptional profile of murine immune cells in response to IFN-α. While the transcription of many genes are not affected by STAT1 expression, some genes are induced or suppressed in the absence of STAT1 following IFN-α stimulation. Genes that require STAT1 for optimal response are likely critical for the anti-tumor response of IFN-α. We are currently using murine models to evaluate the role that these species might play in the anti-tumor effects of IFN-α.