Treatment of cell lines with type I IFNs activates the formation of IFN-stimulated gene factor 3 (STAT1/STAT2/IFN regulatory factor-9), which induces the expression of many genes. To study this response in primary cells, we treated fresh human blood with IFN-β and used flow cytometry to analyze phosphorylated STAT1, STAT3, and STAT5 in CD4+ and CD8+ T cells, B cells, and monocytes. The activation of STAT1 was remarkably different among these leukocyte subsets. In contrast to monocytes and CD4+ and CD8+ T cells, few B cells activated STAT1 in response to IFN-β, a finding that could not be explained by decreased levels of IFNAR2 or STAT1 or enhanced levels of suppressor of cytokine signaling 1 or relevant protein tyrosine phosphatases in B cells. Microarray and real-time PCR analyses revealed the induction of STAT1-dependent proapoptotic mRNAs in monocytes but not in B cells. These data show that IFN-stimulated gene factor 3 or STAT1 homodimers are not the main activators of gene expression in primary B cells of healthy humans. Notably, in B cells and, especially in CD4+ T cells, IFN-β activated STAT5 in addition to STAT3, with biological effects often opposite from those driven by activated STAT1. These data help to explain why IFN-β increases the survival of primary human B cells and CD4+ T cells but enhances the apoptosis of monocytes, as well as to understand how leukocyte subsets are differentially affected by endogenous type I IFNs during viral or bacterial infections and by type I IFN treatment of patients with multiple sclerosis, hepatitis, or cancer.
Interferons are pleiotropic cytokines that play important roles in infection and inflammation. Three classes of IFNs are known: type I IFNs includes IFN-α, -β, -ω, -τ, -δ, -κ, and -ε (1); type II is IFN-γ; and type III is IFN-λ. IFN-α and -β use the IFNAR1 and IFNAR2c receptor subunits to signal (2), each of which binds constitutively to a single member of the JAK family of kinases: IFNAR1 to tyrosine kinase 2 and IFNAR2 to JAK1. Ligand binding induces the phosphorylation of JAK1, tyrosine kinase 2, intracellular tyrosine residues of each receptor subunit, and STATs. Activated STATs dimerize, dissociate from the receptor, and translocate to the nucleus to induce the expression of IFN-stimulated genes [ISGs (3)]. Current data suggest that IFN-stimulated gene factor 3 (ISGF3) is the major transcription factor activated in response to IFN-α/β (3, 4). ISGF3, a complex of phosphorylated STAT1, STAT2, and unphosphorylated IFN regulatory factor (IRF)-9, binds to the IFN-stimulated response element (ISRE) present in the promoters of many ISGs. In response to type I IFNs, activated STAT1 can also form homodimers that bind to gamma-activated sequence (GAS) elements in some ISG promoters (3, 5).
It is becoming clear that, in addition to ISGF3 and STAT1 homodimers, other transcription factors play important roles as cytoplasmic messengers between the receptor and the nucleus (4), helping to explain why type I IFNs, which were discovered on the basis of their potent antiviral activities, are now known to act much more broadly, as pleiotropic cytokines that regulate many different cellular functions. For example, STAT3 is activated in response to type I IFNs in most cell lines, forming STAT3 homodimers or heterodimers with activated STAT1 (4). In contrast, activation of STAT4 and STAT5 by IFN-α/β is found mostly in NK and T cells (6–8). Interestingly, the activation of STAT6 induced by type I IFNs has only been described in B cell lines (9). STAT homo- and heterodimers bind to GAS elements in the promoters of ISGs, but it is clear that different STAT dimers have different preferences for specific GAS elements (5). The differential activation of STAT4, STAT5, and STAT6 in different cell lines suggests the possibility of cell type-specific activation of STATs by IFN-α/β in vivo. Of note, evidence for a cell type-specific response to IFN-α was described with respect to differential ISG induction in human T cells and dendritic cells (10).
In this study, we investigated how primary human leukocytes signal in response to IFN-β. Undiluted freshly drawn human whole blood was stimulated with IFN-β in vitro to mimic the situation in vivo as closely as possible. Because the activation of STATs occurs only transiently, the isolation of many different leukocyte subsets after stimulation of whole blood in combination with Western blot analysis is not feasible because, by the time the subsets could be isolated, the optimal time point for activation of STATs would have passed. Therefore, we used a flow cytometry-based technique that enables the detection of intracellular phosphotyrosine-STAT (PY-STAT)1, STAT3, and STAT5 at the single-cell level, allowing cells to be fixed at the optimal time for STAT activation. IFN-β–induced activation of STAT1, STAT3, and STAT5 was chosen because these three transcription factors regulate cell survival in opposite directions (11–13). Furthermore, this approach allowed us to address whether differential activation of these STATs might explain how IFN-β enhances the survival of mature B cells and T cells (14–19) while increasing apoptosis in monocytes and many cancer cell lines (20–23). Notably, we found that IFN-β induced significant differences in the activation of STAT1 and STAT5 in different leukocyte subsets and that these differences are related to the induction of pro- and antiapoptotic genes, respectively. Our results provide important insights into the differential effects that type I IFNs may have on leukocyte subsets during infection and upon treatment of multiple sclerosis, hepatitis, and some cancers with type I IFNs.
Materials and Methods
Cell culture and IFN-β stimulation
The HT cell line was acquired from the American Type Culture Collection (Manassas, VA) (CRL-2260, human B lymphoblast derived from a patient with diffuse mixed lymphoma) and cultured in RPMI 1640 medium supplemented with 2 mM l-glutamine, 1 mM sodium pyruvate, 4500 mg/l glucose, and 10% FBS. This cell line was maintained in 10- or 15-cm culture dishes and was always stimulated at a concentration of 1 × 106 cells/ml with IFN-β1a (Avonex, 30 μg/0.5 ml Prefilled Syringe, 12 × 106 IU/ml; Biogen Idec, Cambridge, MA). Stimulated cells were subsequently fixed for flow-cytometry analysis or lysed for Western blot analysis.
Heparinized whole blood was obtained from healthy donors according to an Institutional Review Board-approved protocol (Cleveland Clinic). Within 15 min after venipuncture, undiluted whole blood was stimulated in 6- or 10-cm culture dishes or 50-ml canonical tubes (BD Biosciences, San Jose, CA) in vitro with recombinant human IFN-β1a (Biogen Idec), IFN-α1 (3 × 103 IU/μl), IFN-α2 (Intron A, 10 × 103 IU/μl; Schering-Plough, Kenilworth, NJ) or IFN-γ (10 × 103
Western blot analysis of HT cells and isolated leukocyte subsets
After stimulation of HT cells with IFN-β1a or after leukocyte subsets were purified from unstimulated whole blood, the cells were washed once with PBS, and the cell pellets were lysed for 30 min at 4°C in 250 μl (per 5 × 106 cells) lysis buffer containing 50 mM HEPES (pH 7.9), 250 mM potassium chloride, 0.1% Nonidet P-40, 10% glycerol, 0.1 mM EDTA, 10 mM sodium fluoride, 5 mM sodium orthovanadate, 1 mM phenylmethane-sulfonyl fluoride, 20 μg/ml aprotinin, 20 μg/ml pepstatin, and 20 μg/ml leupeptin. Cellular debris was pelleted by centrifugation at 13,000 × g at 4°C for 10 min. Cell extracts were fractionated by electrophoresis in 10 or 12% SDS-PAGE and transferred to polyvinylidene difluoride membranes (Millipore, Bedford, MA). The following Abs were used: rabbit polyclonal anti-SOCS1 and rabbit polyclonal anti–SH-PTP1 (clones H-93 and C-19, respectively; Santa Cruz Biotechnology, Santa Cruz, CA), mouse monoclonal anti-T cell protein tyrosine phosphatase of 45 kDa (TCP45) (clone CF4-1D; EMD Chemicals, Gibbstown, NJ), mouse monoclonal anti–N-terminal STAT1 (clone 42, BD Biosciences), rabbit polyclonal anti–PY701-STAT1, rabbit polyclonal anti–PY705-STAT3, rabbit polyclonal anti-STAT3, rabbit polyclonal anti–PY694-STAT5, rabbit polyclonal anti-STAT5 (Cell Signaling Technology, Beverly, MA), rabbit polyclonal anti–PY689-STAT2 and rabbit polyclonal anti-STAT2 (Upstate Biotechnology, Lake Placid, NY), and mouse monoclonal anti–β-actin (clone AC-74; Sigma-Aldrich, St. Louis, MO). HRP-coupled goat anti-rabbit or goat anti-mouse IgG (Rockland Immunochemicals, Gilbertsville, PA) was used for visualization, using the ECL (ECL Plus) Western blot analysis detection system (PerkinElmer, Waltham, MA).
Intracellular detection of PY-STATs using flow cytometry
The published method of Chow et al. (24), which was developed to measure intracellular phospho-ERK in whole blood cells, was adapted slightly to measure the induction of phospho-STATs in human whole blood. A number of commercially available anti-human CD3, CD4, CD8, CD19, and CD14 Abs were screened. The best anti-CD3 and anti-CD14 clones were those that performed optimally after fixation and erythrocyte lysis but before methanol incubation. In contrast, the best anti-CD8, anti-CD4, and anti-CD19 Abs performed best after methanol incubation. As previously published (25), IFN-β–induced phospho-STATs were optimally detected in leukocytes that were permeabilized with 90% methanol (data not shown).
After stimulation with IFN-β1a in vitro or leaving cells untreated for the same time period, whole blood or cell lines were fixed in 4 or 2% formaldehyde, respectively, by adding 10% prewarmed methanol-free formaldehyde (Polysciences, Warrington, PA), followed by incubation at 37°C for 10 min. After fixation, cell lines were washed twice with 50 ml ice-cold wash buffer (Dulbecco’s PBS [D-PBS], 5% FBS, 0.09% NaN3). Erythrocytes were lysed by adding 0.12% Triton X-100 (0.1% Triton X-100 final concentration; Pierce, Rockford, IL) dissolved in 1× D-PBS and incubating for 30 min at room temperature with rocking. Lysed erythrocytes were removed by washing three times with 50 ml ice-cold wash buffer, followed by spinning at 300 × g for 10 min at 4°C. One hundred microliters of fixed cells was subsequently added to each 5-ml Falcon FACS tube (equivalent to 100 μl HT or 130 μl whole blood per tube), containing FITC- or Pacific blue-conjugated anti-CD3 (clone UCHT1; BD Biosciences), anti–CD14-FITC (clone RM052; Beckman Coulter, Miami, FL), or anti–CD14-AF700 (clone TÜK4, Invitrogen, Carlsbad, CA) Abs in the amounts recommended by the manufacturers. Cells were incubated for 30 min at room temperature in the dark and washed twice: once with 2 ml ice-cold wash buffer per tube and once with 2 ml ice-cold 1× D-PBS. While vortexing at high speed, 1 ml 90% methanol in 1× D-PBS was added per tube, and the mixture was incubated at −20°C overnight. The next day, the contents of each tube were washed twice with 2 ml wash buffer (with spinning at 300 × g, 4°C). For the blocking step, the cell pellets were resuspended in 50 μl wash buffer and incubated for 10 min at room temperature in the dark. A combination of Abs directed against human CD8 (clone B9.11, PE-Cy5 conjugated; Beckman Coulter), CD19 (clone J4.119, PE-Cy5 or PE-Cy7 conjugated; Beckman Coulter), or CD4 (clone 13B8.20, PE-Cy5 conjugated; Beckman Coulter) and the following Alexa Fluor 647- or PE-conjugated Abs against PY(701)-STAT1, PY(705)-STAT3, or PY(694)-STAT5 (clones 4A, 4, and 47, respectively, BD Biosciences) were added in amounts advised by the manufacturer, followed by incubation at room temperature in the dark for 1 h. Anti-CD8 or anti-CD4 Abs were used; the alternative T cell subset was identified by selecting the CD8−CD3+ or CD4−CD3+ population, respectively. To detect total STAT1, cells were incubated with anti–STAT1-PE (N terminus of STAT1, clone 1/Stat1; BD Biosciences) after permeabilization with 90% methanol. To detect the activation of STAT2, rabbit polyclonal anti-PY(689)-STAT2 (Upstate Biotechnology) was added at 6.5 μg/ml. Experiments demonstrating the specificity of this anti–PY-STAT2 Ab are shown in Supplemental Fig. 1A and 1B. For the fluorochrome-conjugated Abs, the last wash step was performed with 3 ml wash buffer per tube (300 × g; 10 min; 4°C). To detect PY-STAT2, cells were incubated with 5 μl of a 1:10 dilution of goat anti-rabbit IgG-PE (Jackson ImmunoResearch Laboratories, West Grove, PA) at room temperature for 30 min. After the final washing step, each cell pellet was resuspended in 350 μl wash buffer and measured on an LSRI or LSRII (both from BD Biosciences) flow cytometer. Samples stained with a single color were used for compensation. Intact cells were gated on forward and side scatter, and 50,000 cells were measured. Flow data were analyzed with WinList (Verity Software House, Topsham, ME). The percentage of IFN-β–induced phospho-STAT+ cells was determined by subtracting the percentage of positive cells in unstimulated cells, which was set at <2%. An example of an analysis is shown in Fig. 2.
Detection of IFNAR2 and caspase 3 activation by flow cytometry
To detect induction of apoptosis, whole blood that was diluted 1:3 with plain RPMI 1640 was not stimulated or was stimulated with 2000 IU/ml IFN-β for different time periods up to 48 h at 37°C or was kept at 50°C for 1 h (positive control). Cells were then washed one time with 25 ml 1× D-PBS, and whole blood was divided (150 μl/tube) and incubated with the same anti-CD Abs as mentioned above for 30 min at 4°C. Whole blood cells were subsequently fixed, and erythrocytes were lysed as mentioned above; after the washing steps, each pellet was eventually resuspended in 100 μl Permeabilization Medium B (Invitrogen) and incubated with 20 μl anti-activated caspase 3-PE Ab (0.25 μg) for 30 min at room temperature. Finally, after each tube was washed with 3 ml stain buffer, the cell pellet was resuspended in 350 μl stain buffer and measured on a BD Biosciences LSR II flow cytometer the same day. Induction of activated caspase 3 in leukocyte subsets by IFN-β was determined by subtracting the percentage of caspase 3+ cells in unstimulated cells from those in IFN-β–stimulated cells.
GraphPad InStat 3 was used (GraphPad Software, La Jolla, CA). The Friedman test, which is a nonparametric repeated-measures ANOVA for paired samples test, was used to test whether the four blood cell subsets (monocytes, B cells, and CD8+ and CD4+ T cells) differed with respect to activation of STAT1, STAT3, and STAT5. When the Friedman test showed a significant difference (p < 0.05), post hoc analysis was subsequently performed using the Dunn test to detect which blood subsets differed significantly from each other. When calculating the p values, the Dunn test takes into account the number of comparisons one is making (Bonferroni adjustment). The Pearson correlation test was used to determine whether the percentages of PY-STAT+ leukocyte subsets that were induced after stimulation with IFN-β for 45 min correlated with the percentage of activated caspase 3+ subsets after longer periods of stimulation with IFN-β. The coefficient of determination (R2) and the two-tailed p values are shown (if significantly correlated).
Blood cell subset isolation, gene-array analysis, and real-time PCR
Twenty-two milliliters of undiluted whole blood from each of two healthy donors was stimulated with 2000 IU/ml IFN-β1a (Avonex, Biogen Idec) for 3 h, and 22 ml of blood was left unstimulated for 3 h. An aliquot of blood was taken out after 45 min of stimulation with IFN-β to determine the activation of STAT1, STAT3, and STAT5 by flow cytometry. Immediately following stimulation for 3 h, 10 ml whole blood was incubated with 500 μl whole blood anti-CD14 or anti-CD19 microbeads (Miltenyi Biotec, Auburn, CA) for 15 min at 4°C to isolate monocytes and B cells, respectively. After washing with cold running buffer (PBS, 2 mM EDTA, 0.5% BSA, 0.09% sodium azide; Miltenyi Biotec) to remove unbound microbeads and after bringing the volume of the whole blood back to the starting volume by adding cold running buffer, the cells of interest were positively selected using the AutoMACS Pro Separator (Miltenyi Biotec) and program posselWB. During the entire isolation procedure, the blood cells were kept cold. The procedure is very fast (maximally 20 min), which helps to preserve the quality of the RNA. The purity (90–99%) of the positively selected fraction and the yield were excellent, because the negative sorted fraction was totally depleted of each subset of interest. Total RNA was isolated from the isolated unstimulated (control) or IFN-β–stimulated blood cell subset by dissolving the cells in TRIzol (Invitrogen; 1–10 × 106 cells in 1 ml), following the protocol of the manufacturer. One microgram of total RNA (100 ng is minimally needed) was sent to the Cleveland Clinic Genomics Core. A single round of in vitro transcription amplification was carried out using the Illumina RNA Amplification Kit (Ambion, Austin, TX) to amplify mRNA and, thus, to obtain ample amounts of cRNA to perform the whole human genome gene-expression assay using the humanRef-8 v2 expression bead chips microarray (Illumina, San Diego, CA), which has 22,184 transcript probes, representing 18,189 genes in total. The microarray data discussed in this publication have been deposited in the National Center for Biotechnology Information's Gene Expression Omnibus and are accessible through GEO Series accession number GSE23307. Expression data normalization and differential expression analysis were handled through the Illumina BeadStudio Gene Expression module V3.2. The data were first normalized by using the Illumina background normalization algorithm and then the differential-expression analyses were performed using Illumina’s custom model. Downstream data processing and reporting were handled in R packages (http://www.r-project.org). Genes for downstream analyses were filtered to include only those with both differential-expression analysis p values < 0.001 and fold changes >2 compared with the unstimulated (control) condition. Genes in this filtered list were further grouped into those that were changed in all B cells and monocytes, those that were changed in monocytes only, and those that were changed in B cells only (healthy individual [HI] #1 and HI #2). The Gene Ontology Enrichment Analysis Software Toolkit (available at http://omicslab.genetics.ac.cn/GOEAST/) was used to sort these groups of genes according to gene ontology, particularly apoptosis, proliferation, and cell-cycle regulation.
Real-time PCR (rtPCR) was used to confirm changes in gene expression obtained by microarray analysis. rtPCR was done with RNA isolated from B cells and monocytes (present in whole blood of six healthy individuals, and purification occurred after stimulation of whole blood, as mentioned above), which were left untreated or were stimulated with 2000 IU/ml IFN-β for 3 h. Thus, rtPCR was performed with 24 samples to detect changes in seven mRNAs. The following seven TaqMan Gene Expression Assays from Applied Biosystems (Foster City, CA) were used (gene, assay ID number): BAK1, Hs00832876_g1; CASP3, Hs00263337_m1; CDKN1A, Hs00355782_m1; BCL2L13, Hs00209789_m1; STK3, Hs00169491_m1; IL2RA, Hs00907779_m1; and NAMPT, Hs00237184_m1. Two candidate genes were chosen for endogenous control determination based on studies about rtPCR performed with RNA from B cells and monocytes: eukaryotic 18S rRNA and HPRT1. An Applied Biosystems ABI 7900HT unit with automation attachment was used for rtPCR. This unit is capable of collecting spectral data at multiple points during a PCR run. To execute the first step and make archive cDNA, 150 ng total RNA was reverse transcribed in a 25-μl reaction using Applied Biosystems enzymes and reagents, in accordance with the manufacturer’s protocols. RNA samples were accurately quantitated using an ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE). The cDNA reaction from above was diluted by a factor of 10. For the PCR step, 9 μl this diluted cDNA was used for each of three replicate 15-μl reactions carried out in a 384-well plate. Standard PCR conditions were used for the Applied Biosystems assays: 50°C for 2 min, 95°C for 10 min, followed by 40 cycles of 95°C for 15 s alternating with 60°C for 1 min each. The comparative cycle threshold method was used for relative quantitation. 18S rRNA had very little variation in expression across the sample sets; therefore, it was chosen as the endogenous control. For rtPCR data analysis, RNA abundance was normalized for each gene with respect to the endogenous control in that sample (18S), and mean values for fold changes were calculated for each gene (IFN-β stimulated over control treated). PCR confirmation of gene-expression array data required that the direction of the change in expression had to be the same with rtPCR as with gene-expression arrays and be increased ≥2-fold.
Exposure to low doses of IFN-β causes differential activation of STAT1, STAT3, and STAT5 in primary human blood cell subsets
To verify Ab specificity, we compared our flow cytometry method to Western blot analysis, using the human leukemic cell line HT, which was stimulated with 1000 IU/ml of IFN-β (Fig. 1). The two methods yielded the same overall pattern. Of note, the highest percentage of PY-STAT+ HT cells was found 30 min after stimulation with IFN-β, as usually found in human cell lines. The advantage of flow cytometry is that it reveals the percentage of each cell subset in which a certain STAT is activated (Figs. 1, 2). To begin to investigate how primary human monocytes, B cells, and CD4+ and CD8+ T cells respond to IFN-β, we stimulated undiluted whole blood samples from nine healthy individuals with 500 IU/ml of IFN-β for 25 min (Fig. 3A). We observed significant differences in the fractions of leukocyte subsets in which STAT1 (p ≤ 0.0001), STAT3 (p = 0.003), and STAT5 (p = 0.006) were activated. Unexpectedly, remarkably few B cells and CD4+ T cells showed activation of STAT1 in comparison with monocytes. The differences in activation of STAT3 were very similar to those seen for the activation of STAT1; much fewer B cells and CD4+ T cells displayed activation of STAT3 compared with monocytes. Thus, of the blood cell subsets investigated, the highest percentage of PY-STAT1+ and PY-STAT3+ cells were found among monocytes, whereas the percentages of CD8+ T cells positive for PY-STAT1 and PY-STAT3 were intermediate (Fig. 3A). In contrast, many more CD4+ T cells than B cells showed activation of STAT5 when whole blood was stimulated with 500 IU/ml IFN-β for 25 min.
Exposure to low doses of IFN-β causes differential activation of STAT1 and STAT5 in primary human blood cell subsets at the optimal time for PY-STAT induction
Although 25 min of stimulation with IFN-β is optimal for STAT activation in many cell lines, it is possible that this is not optimal in CD4+ T cells and B cells in whole blood. Therefore, blood samples from three healthy individuals were stimulated with 500 IU/ml of IFN-β for various times, up to 75 min (Fig. 4). The greatest activation of STAT3 and STAT5 was detected in all blood cell subsets after 45 min of exposure to IFN-β; it declined gradually thereafter. Optimal activation of STAT1 in CD8+ T cells and monocytes was also found after 45 min. Interestingly, although the IFN-β–induced activation of STAT3 in CD4+ T cells and B cells was very low after 25 min (≤15%), it increased by ≥ 1.8-fold (between 22 and 32% on average) after 45 min. In contrast, the activation of STAT1 in CD4+ T cells, as well as B cells, remained very low for the entire period tested (<10% positive cells).
Because the optimal time point for activation of STATs in whole blood cells was 45 min, we tested whether the same significant differences in PY-STAT1, PY-STAT3, and PY-STAT5 induction could be found as after 25 min of stimulation with IFN-β. To this end, whole blood from seven healthy subjects was stimulated with 500 IU/ml IFN-β for 45 min (Fig. 3B). There were significant differences in the fractions of blood cell subsets that showed activation of STAT1 (p = 0.0005) and STAT5 (p = 0.025). Thus, even after longer stimulation with IFN-β, much fewer B cells and CD4+ T cells showed induction of PY-STAT1 than did monocytes, whereas more CD4+ T cells than CD8+ T cells showed activation of STAT5 (Fig. 3B). Although monocytes still showed the highest percentage of PY-STAT3+ cells, the difference, compared with B cells and CD4+ T cells, was no longer significant. Therefore, the activation of STAT3 by IFN-β in CD4+ T cells and B cells is delayed compared with activation of STAT3 in CD8+ T cells and monocytes, in particular after 25 min (Fig. 3A), but it eventually catches up after 45 min (Figs. 3A, 3B, 4).
Exposure to high-dose IFN-β also causes differential activation of STAT1, STAT3, and STAT5 in primary human blood cell subsets
Because the observed induction of PY-STATs at the optimal times occurred in ≤50% of the cells after stimulation with 500 IU/ml IFN-β, we explored whether stimulation with higher concentrations of IFN-β would yield a higher percentage of cells positive for PY-STATs. We stimulated whole blood of three healthy individuals with increasing doses of IFN-β (up to 10,000 IU/ml) for 45 min (Fig. 5). At a concentration of 200 IU/ml, virtually no activation of STATs was detected in any of the blood cell subsets tested. All four subsets responded with activation of STAT3 and STAT5 at 500 IU/ml, and more cells became positive at the higher concentrations. Furthermore, the activation of STAT1 by IFN-β was also dose responsive in CD8+ T cells and monocytes. Remarkably, it was still observed that very few B cells and CD4+ T cells (≤10%) showed activation of STAT1, even at the higher concentrations of IFN-β (2,000–10,000 IU/ml).
To test whether the same differences in STAT1 and STAT5 activation could be observed with 2,000 IU/ml as with 500 IU/ml IFN-β, whole blood from 20 healthy subjects was stimulated for 45 min with the higher concentration (Fig. 3C); 2,000 IU/ml was used instead of 10,000 IU/ml because the activation of STAT1 in monocytes and B cells was lower with the latter concentration (Fig. 5). At 2000 IU/ml of IFN-β, there were significant differences in the fractions of blood cell subsets that showed activation of STAT1 (p < 0.0001), STAT3 (p = 0.0026), and STAT5 (p < 0.0001). Remarkably, in contrast to the lower dose, using this high dose of IFN-β to stimulate whole blood of many donors also revealed high numbers of PY-STAT1+ CD4+ T cells (Fig. 3C). However, even at such a high concentration of IFN-β, much fewer B cells than monocytes and CD4+ and CD8+ T cells activated STAT1. Also, fewer B cells than monocytes activated STAT3 and STAT5 (Fig. 3C). Of interest, the highest fraction of PY-STAT5+ cells was still found among the CD4+ T cell subset (Fig. 3C).
In summary, irrespective of IFN-β concentration or stimulation time, peripheral blood B cells do not show appreciable activation of STAT1 in response to IFN-β, indicating that ISGF3 or STAT1 homodimers are not the main transcription factors driving ISG induction in the majority of primary human B cells or in CD4+ T cells at lower IFN-β concentrations. In contrast, these subsets show activation of STAT3 and STAT5, and the highest activation of STAT5 occurs in CD4+ T cells.
Differential activation of STAT1, STAT3, and STAT5 in leukocyte subsets by IFN-β is associated with differences in apoptosis induction
Activation of STAT1 usually leads to induction of cell-cycle arrest and apoptosis, whereas enhanced survival and proliferation result from PY-STAT3 and PY-STAT5 induction. Therefore, we explored whether the differential activation of these STATs could be responsible for previously unexplained differences in induction of apoptosis in the various primary human leukocyte subsets by IFN-β. To this end, whole blood from four healthy subjects was stimulated with IFN-β for 4, 6, 8, 10, 12, 24, or 48 h; apoptosis induction in the various subsets was determined by the activation of caspase 3 (Fig. 6A). Stimulation with IFN-β induced the greatest apoptosis in monocytes, much less in CD8+ T cells, and the least in B cells and CD4+ T cells (Fig. 6A), in agreement with previous reports. Using the blood of the same donors, the induction of PY-STAT1, PY-STAT3, and PY-STAT5 was also determined after stimulation with IFN-β for 45 min in vitro. Interestingly, in none of the leukocyte subsets did we observe a correlation between the total percentages of PY-STAT1+ cells after 45 min and the percentages of activated caspase 3+ cells at any of the later time points (data not shown). This result might be explained by the fact that many of the PY-STAT1+ cells are actually doubly positive for PY-STAT3 and PY-STAT5, resulting in opposing effects on apoptosis induction at an individual cell level. Therefore, a double staining was also performed with anti–PY-STAT1/PY-STAT3 or anti–PY-STAT1/PY-STAT5 Abs to detect doubly positive cells after IFN-β stimulation. Fig. 6B shows that the induction of PY-STAT1+/PY-STAT3−, PY-STAT1+/PY-STAT3+, PY-STAT1−/PY-STAT3+, PY-STAT1+/PY-STAT5−, PY-STAT1+/PY-STAT5+, or PY-STAT1−/PY-STAT5+ positive cells among the four leukocyte subsets is strikingly different. For instance, the percentage of PY-STAT1+/PY-STAT3− cells is highest in CD4+ T cells, followed by CD8+ T cells, and it is lowest in B cells and monocytes (Fig. 6B). In contrast, the percentage of PY-STAT1+/PY-STAT5− cells is highest in CD8+ T cells, followed by CD4+ T cells and monocytes, but again is lowest in B cells. Remarkably, the generation of PY-STAT1−/PY-STAT3+ and PY-STAT1−/PY-STAT5+ positive cells could only be observed in the B cells subset (Fig. 6B), because in this subset IFN-β induced the lowest percentage of PY-STAT1+ cells. The monocyte subset showed the highest amount of apoptosis induction by IFN-β; Fig. 6A illustrates that after 8 h of stimulation, significant apoptosis induction could be observed for the first time in all four donors tested. Strikingly, the generation of PY-STAT1+/PY-STAT3− monocytes after 45 min correlated very significantly with the fraction of activated caspase 3+ monocytes after 8 h of IFN-β stimulation (p = 0.0008; Fig. 6C). Of note, after 10 or 12 h of IFN-β stimulation, the number of activated caspase 3+ monocytes doubled or tripled, compared with 8 h of stimulation (Fig. 6A), suggesting that apoptosis was induced eventually, even in PY-STAT1+/PY-STAT3+ or PY-STAT1+/PY-STAT5+ monocytes. None of the other subsets showed a correlation between induction of PY-STAT1+/PY-STAT3− or PY-STAT1+/PY-STAT5− after 45 min with apoptosis induction after 8 h (data not shown), but this could be because individual donors showed variation in the B cell or CD4+ or CD8+ T cell subsets with respect to significant activation of caspase 3. Therefore, the highest observed activation of caspase 3 within the T and B cell subsets of four healthy subjects (based on Fig. 6A, CD4+ T cells: at 6 h, 6 h, 10 h, and 12 h; CD8+ T cells: at 4 h, 6 h, 10 h, and 10 h; and B cells: at 10 h, 10 h, 12 h, and 12 h) was plotted against the percentage of PY-STAT1+/PY-STAT3− cells after 45 min, revealing a significant correlation only within the CD8+ T cell subset (Fig. 6C; p = 0.0357). Because this subset has the highest percentage of PY-STAT1+/PY-STAT5− cells, many of the PY-STAT1+/PY-STAT3− CD8+ T cells are likely to be PY-STAT1+/PY-STAT3−/PY-STAT5− and prone to apoptosis induction. In contrast, CD4+ T cells and B cells have the lowest percentages of PY-STAT1+/PY-STAT5− cells and, consequently, possibly the lowest amount of apoptosis induction due to the antiapoptotic effects in PY-STAT1+/PY-STAT5+ cells.
Differential activation of STAT1 in primary human monocytes and B cells is connected to differential induction of apoptosis-promoting mRNAs by IFN-β
We investigated whether the observed differential activation of STAT1 in B cells and monocytes would result in differences in mRNA induction and, in particular, in the induction of proapoptotic mRNAs. To this end, undiluted whole blood from two healthy individuals was stimulated with 2000 IU/ml of IFN-β for 3 h, and pure B cells and monocytes were isolated by using magnetically labeled Abs. An aliquot of blood was taken out after 45 min of stimulation with IFN-β to detect the activation of STAT1, STAT3, and STAT5 by flow cytometry. Supplemental Fig. 2A shows the expected differential activation of STATs in B cells and monocytes from two healthy individuals. Remarkably, mRNA induction by IFN-β was very different in primary human B cells and monocytes. Three hours of stimulation with IFN-β caused 1462 mRNAs to be up- or downregulated by ≥2-fold in monocytes or B cells (836 upregulated, 626 downregulated). Fig. 7A shows the Venn diagram of the 836 mRNAs that were increased by ≥2-fold in monocytes and B cells in response to IFN-β. Remarkably, of the mRNAs that changed in the monocytes of HI #1, 337 of 596 (57%) were increased in monocytes only, whereas 233 of 596 (39%) were shared between monocytes and B cells of HI #1, and 229 of 596 (38%) were shared with B cells of HI #2. In contrast, the responses in B cells of HI #1 and HI #2 were very similar, because 316 mRNAs of 405 (HI #1) or 410 (HI #2) were increased by 2-fold (78 and 77%, respectively).
The mRNAs that increased by ≥2-fold in monocytes only were sorted according to their ontology, using the Gene Ontology Enrichment Analysis Software Toolkit program. The following mRNAs, increased in monocytes only, are classified as inducers of programmed cell death: CDKN1A, BAK1, BCL2L13, CASP3, and STK3 (Table I); they are all known to be very potent apoptosis-inducing proteins (26–29). The mRNA of CDKN1A (p21 or Cip1) was increased 3-fold in monocytes, whereas the mRNA expression in B cells of both healthy individuals remained unchanged. The induction of p21 is dependent on binding of activated STAT1 to the GAS element in the p21 promoter after IFN-γ stimulation (27). Likewise, increased expression of CASP3 is dependent on STAT1 activation (28). Although it has not been conclusively shown that expression of BAK1, BCL2L13 (BCL-RAMBO), and STK3 are dependent on the activation of STAT1 homodimers, this conclusion is very likely to be correct because all of these mRNAs are induced after IFN-γ stimulation (29) (http://www.interferome.org).
After IFN-β stimulation, some mRNAs were induced ≥2-fold in B cells and monocytes (TNFSF13B, IRF1, TNFSF10, FAS; Table I). TNFS10 (TRAIL) and FAS cause apoptosis in cancer cells but not necessarily in normal immune cells (30, 31). Remarkably, after sorting the mRNAs that were increased by ≥2-fold in B cells only according to their ontology, we did not find any mRNA to be related to apoptosis induction or cell-cycle arrest. In contrast, IL2RA and PBEF1 are two mRNAs that were increased in primary human B cells only (Table I), and TNFSF13B (BAFF) was increased 3.5-fold more in B cells compared with monocytes (Table I). These mRNAs are all related to increased survival and proliferation (32–36). Of note, IL2RA and PBEF1 are induced by type I IFNs only and not by IFN-γ (http://www.interferome.org). In response to IL-2 or IL-6, the mRNAs of IL2RA and PBEF1 are known to be increased after binding of activated STAT5 or STAT3 (32, 35), respectively, to the promoters. Because IL-10 increases the expression of TNFSF13B mRNA, the enhanced transcription is likely dependent on PY-STAT3 binding to the TNFSF13B promoter (36). Because only B cells show the formation of PY-STAT1−/PY-STAT3+ and PY-STAT1−/PY-STAT5+ cells after stimulation with IFN-β (Fig. 6B), it is likely that activated STAT5 and STAT3 cause the increases in IL-2RA, PBEF1, and BAFF in B cells only.
To confirm these microarray data from two healthy individuals, the induction of specific mRNAs in monocytes and B cells after 3 h of stimulation with IFN-β was replicated in six healthy individuals. After 45 min of stimulation, the activation of STAT1, STAT3, and STAT5 showed the typical pattern (Supplemental Fig. 2B). Induction of BCL2L13 and IL2RA mRNA was not replicated by rtPCR, but we confirmed increased BAK1, CASP3, CDKN1A, and STK3 mRNAs (in monocytes only) and an increase in PBEF1 mRNA (in B cells only) after IFN-β stimulation (Fig. 7B). In summary, the differential activation of STAT1 by IFN-β in primary human monocytes and B cells is associated with very significant differences in mRNA induction. High activation of STAT1 in monocytes is related to an increase in the expression of potent inducers of apoptosis, whereas poor STAT1 activation in B cells could explain why the activation of STAT3 and STAT5 leads to increased induction of certain proliferation-stimulating genes in B cells only.
IFNAR2 and STAT1 levels are similarly expressed in primary human leukocytes, and STAT2 is activated normally in B cells
To begin to understand the mechanism that causes few B cells to activate STAT1 in response to IFN-β, we tested surface expression of IFNAR2. IFNAR1 is crucial for ligand binding, but IFNAR2, with its long cytoplasmic tail, possesses two conserved tyrosine residues that are crucial for activation of STAT1, STAT2, and STAT3 (1–4). IFNAR2 expression on leukocyte subsets present in whole blood of six healthy individuals was studied by flow cytometry. Fig. 8A shows that 100% of monocytes, B cells, and CD4+ and CD8+ T cells expressed IFNAR2; therefore, lack of its expression cannot be the reason why few B cells activated STAT1. Although IFNAR2 expression was equal, the functionality of this receptor chain might be different in B cells. We previously found that the activation of STAT2 preceded the activation of STAT1 in fibrosarcoma cells and that STAT2-null cells are severely hampered in their ability to activate STAT1 (37). Fig. 8B shows the percentage of cells positive for PY-STAT2 after stimulating whole blood of six healthy individuals with 2000 IU/ml IFN-β for 45 min. Monocytes, B cells, and CD4+ and CD8+ T cells demonstrated activation of STAT2 in response to IFN-β (Fig. 8B); therefore, failure to activate STAT2 is not the cause of low STAT1 activation in B cells. Another possible explanation for the very low activation of STAT1 in B cells is that many fewer B cells express STAT1 protein. We used flow cytometry to compare the four different leukocyte subsets for the percentages of cells that express total STAT1 (Fig. 8C), finding that all subsets were positive for STAT1. Although the percentages of total STAT1+ B cells were slightly lower compared with the other subsets, this difference cannot explain why usually <15% of the B cells showed activation of STAT1 in response to IFN-β. Finally, Western blot analysis of total STAT1 expression in isolated monocytes, B cells, and CD4+ or CD8+ T cells from two healthy individuals revealed similar levels of STAT1 expression in all subsets (data not shown).
Types I and II IFNs induce similar amounts of PY-STAT1+ B cells
To test whether other type I IFNs and type II IFN also stimulate few B cells to activate STAT1, undiluted whole blood of six healthy subjects was stimulated with 2000 IU/ml IFN-γ for 30 min (the optimal time for activation of STATs by IFN-γ; data not shown), and 2000 IU/ml IFN-α1, IFN-α2b, or IFN-β for 45 min. Fig. 9 shows that within all four leukocyte subsets tested (B cells, monocytes, CD4+ or CD8+ T cells), all type I IFNs induced similar percentages of cells to activate STAT1. In B cells and monocytes, type II IFN stimulated similar percentages of PY-STAT1+ cells as did type I IFNs (Fig. 9). Because T cells that produce IFN-γ, such as Th1 and Tc1 cells, are unresponsive to IFN-γ due to changed IFN-γR expression levels (38), CD4+ and CD8+ T cells generated lower amounts of PY-STAT1+ cells in response to IFN-γ than in response to type I IFNs (Fig. 9). Remarkably, also on an individual donor level, IFN-α1, -α2b, -β, and -γ activated equal percentages of PY-STAT1+ B cells (Fig. 9). We found that only 7% of healthy subjects (3 of 41) showed activation of STAT1 in >60% of their B cells in response to IFN-β. As shown in Fig. 9, two individuals had >60% of their B cells positive for PY-STAT1 in response to IFN-α/β and IFN-γ. The four individuals who showed lower activation of STAT1 in B cells in response to IFN-β (i.e., ∼10–20% of the cells positive for PY-STAT1) showed a similar response after stimulation with IFN-α1, -α2b, and -γ. Therefore, it can be concluded that the same mechanism that influences the activation of STAT1 in B cells after ligation of the type I IFNAR also influences its activation by the IFN-γR.
Suppressor of cytokine signaling 1 (SOCS1), which diminishes activation of STAT1 by the types I and II IFNRs by influencing the activation of the JAKs (39), and Src homology region 2 domain-containing phosphatase 1 (SHP1) and TCP45, two protein tyrosine phosphatases known to decrease tyrosine phosphorylation of STAT1 (40, 41), are all candidates to explain the lack of PY-STAT1 induction in B cells by types I and II IFNs. We tested whether the expression of SOCS1, SHP1, and TCP45 was enhanced in B cells, compared with other leukocyte subsets, in isolated monocytes, B cells, and CD4+ and CD8+ T cells of one healthy individual, as well as isolated monocytes and B cells from two other healthy subjects. However, Western blot analysis showed no SOCS1 expression in any of the unstimulated subsets, whereas it did in an IFN-γ–stimulated monocytic cell line, and SHP1 and TCP45 were expressed at similar levels in all subsets (data not shown).
Mechanistic aspects of differential STAT induction in response to IFN-β
The aims of this study were to investigate how certain primary human blood cells signal in response to IFN-β and to explore how such signals might be related to apoptosis or cell survival. Nonimmune cells and cell lines have primarily been used to study type I IFN signaling and to elucidate the mechanisms by which these IFNs regulate transcription. We developed a flow cytometry-based assay to detect, at the single-cell level, the activation of specific STATs in primary human leukocytes. IFN-α/β–induced activation of STAT1, which results mainly in the formation of the ISGF3 complex, but also leads to the formation of STAT1 homodimers in adherent and nonadherent cell lines, is a hallmark of type I IFN signaling. The human leukemic B cell line HT and the leukemic CD4+ T cell line Jurkat form abundant amounts of PY-STAT1 in response to IFN-β (Fig. 1; A.H.H. van Boxel-Dezaire and G.R. Stark, unpublished data). Unexpectedly, we found that only a small fraction of primary human B cells activated STAT1 in response to IFN-β and that this activation was independent of the concentration of IFN used (500–10,000 IU/ml) or the time of stimulation (10–75 min; Fig. 4). We tested IFN-β–induced signaling in 41 individuals. Although most of the time we saw that a maximum of 25% of the B cells responded by activating STAT1, in 7% of these individuals, we detected >65% of the B cells positive for PY-STAT1 induction (3 of 41). It may be that these persons have an underlying disease that has not been diagnosed or that this response is normal during a subclinical virus infection. Nevertheless, despite individual variations, we found that many fewer B cells showed activation of STAT1 compared with monocytes and CD4+ and CD8+ T cells after stimulation with 2000 IU/ml IFN-β (Fig. 3C). To begin to understand the mechanism, we studied IFNAR expression and found that 100% of primary human B cells, monocytes, and CD4+ and CD8+ T cells expressed the IFNAR2 chain (Fig. 8A), which has a long cytoplasmic tail containing two conserved tyrosine residues that are crucial for activation of STAT1, STAT2, and STAT3 (1–4). Although IFNAR2 expression was found to be equal, the functionality of this receptor chain could still be different in B cells. An explanation for the low activation of STAT1 could be decreased activation of STAT2, because activation of STAT1 depends on the activation of STAT2 in fibrosarcoma cells and primary fibroblasts (37, 42). However, STAT2-deficient peritoneal macrophages retained the ability to activate STAT1, highlighting intriguing differences in the ability of the IFNAR to activate STAT1 in fibroblasts and monocytic cells (42). Nothing is known about this function of the IFNAR in other leukocyte subsets, but we found low activation of STAT1 in primary human B cells at every IFN-β concentration and in CD4+ T cells at lower IFN-β concentrations, despite normal activation of STAT2. At the optimal time point for IFN-β–induced STAT activation, we found the highest activation of STAT5 in primary human CD4+ T cells and we also found activation of STAT5 and STAT3 in primary human B cells, suggesting that the type I IFNR is functional in B cells.
It will be important to investigate whether low STAT1 activation is an intrinsic property of mature B cells and CD4+ T cells or whether it is a result of other factors present in whole blood. Notably, our results comparing IFN-α1, IFN-α2b, and IFN-γ with IFN-β showed very similar low activation of STAT1 in B cells (Fig. 9), suggesting that the same mechanism is involved. By influencing activation of the JAKs, SOCS1 can diminish the activation of STAT1 by the types I and II IFNRs (39). SHP1 and TCP45 are protein tyrosine phosphatases that decrease tyrosine phosphorylation of STAT1 (40, 41). Although SOCS1, SHP1, and TCP45 are excellent candidates to explain the lack of PY-STAT1 induction in B cells by types I and II IFNs, we could not find evidence of their enhanced protein expression in B cells only. Based on our experiments, we propose that physical properties of STAT1 protein itself are altered or that a selective negative regulator of STAT1 tyrosine phosphorylation is present in the majority of B cells. B cells are a heterogeneous population, and it will be important to characterize the minor fractions that show activation of STAT1 by studying the expression of CD markers, chemokine receptors, and adhesion molecules. Perhaps the STAT1-activating cells are immature, because type I IFNs inhibit B and T cell lymphopoiesis (43). In contrast, IFN-α induces STAT1-dependent proliferation in dormant hematopoietic stem cells (44), suggesting that the response to type I IFNs may change during the maturation of leukocyte subsets. Future experiments could address this issue by analyzing subsets separated on the basis of lineage and differentiation markers expressed on their surfaces.
Biological consequences of differential STAT activation
Type I IFNs cause apoptosis in many cancer cell lines and are used to treat several types of tumors (22, 23). Similarly, type I IFNs induce apoptosis in primary monocytes (20, 21) but, in contrast, increase the survival and proliferation of primary B cells and T cells (14–19). In agreement, we found the highest activation of caspase 3, a hallmark of apoptosis induction, in monocytes, followed by CD8+ T cells, and the least amount in CD4+ T cells and B cells after stimulation with IFN-β. Interestingly, during virus infection of mice, only CD8+ T cells with low STAT1 activation proliferate in response to type I IFNs, as a result of lower STAT1 protein expression (45). The apoptosis-inducing capacity of type I IFNs is largely attributed to the activation of STAT1 (11, 46), whereas the activation of STAT3 and STAT5 by type I IFNs is related to survival and proliferation (12, 13, 47). By performing double staining of IFN-β–stimulated cells with anti–PY-STAT1/PY-STAT3 or anti–PY-STAT1/PY-STAT5 Abs, we were able to detect the activation of STAT1/STAT3 and STAT1/STAT5 together, in individual cells. Notably, the percentage of PY-STAT1+/PY-STAT3– monocytes found in response to IFN-β after 45 min correlated significantly with the percentage of activated caspase 3+ monocytes after 8 h (Fig. 6C). Enhanced STAT3 protein levels in monocytes have been demonstrated to suppress DNA binding of STAT1 homodimers by sequestering STAT1 into STAT1/STAT3 heterodimers (48). Therefore, it is to be expected that the PY-STAT1+/PY-STAT3− monocytes would become apoptotic first and that PY-STAT1+/PY-STAT3+ monocytes are protected from apoptosis induction only if PY-STAT3 levels are high enough to sequester activated STAT1. However, after longer stimulation with IFN-β (>10 h), the percentage of apoptotic cells is doubled or tripled compared with the percentage at 8 h, indicating that even PY-STAT1+/PY-STAT3+ monocytes eventually die. Very few B cells are PY-STAT1+/PY-STAT3− and, because STAT1 activation is so low in these cells, it is more likely that enhanced PY-STAT3 levels could suppress DNA binding of STAT1 homodimers by sequestering STAT1 into STAT1/STAT3 heterodimers in PY-STAT1+/PY-STAT3+ B cells than in monocytes (48). It is interesting that CD4+ T cells that show the highest percentage of PY-STAT1+/PY-STAT3− cells in response to IFN-β after 45 min display the lowest apoptosis induction, along with B cells (Fig. 6A, 6B). Both leukocyte subsets have very low numbers of PY-STAT1+/PY-STAT5− cells and significant numbers of PY-STAT1+/PY-STAT5+ cells, suggesting that the activation of STAT5 in the majority of CD4+ T cells and B cells protects against apoptosis induction. Indeed, the antiapoptotic and mitogenic properties of type I IFNs in mouse T cells are dependent on the activation of STAT3 and STAT5 (47). Strangely, despite the fact that monocytes also generate very high numbers of PY-STAT1+/PY-STAT5+ cells in response to IFN-β, they are the most sensitive to apoptosis induction. This disparity between monocytes, B cells, and CD4+ T cells might be explained by the fact that our anti–PY-STAT5 Ab recognizes activated STAT5A and STAT5B, and human monocytic cells activate only STAT5A (49), in contrast to human T cells, which activate STAT5A and STAT5B in response to type I IFNs (8).
In accordance with the above-mentioned data, when monocytes and B cells were compared for proapoptotic mRNA induction by IFN-β, we only found enhancement of CDKN1A, BAK1, CASP3, and STK3 mRNA in monocytes (Fig. 7B, Table I). Evidence that the induction of these mRNAs depends upon phosphorylated STAT1 homodimers is as follows. First, IFN-γ does not induce CDKN1A (or p21) in STAT1-deficient U3A fibrosarcoma cells, but it does enhance p21 expression in U3A cells in which STAT1 has been reintroduced (27), and enhancement of CASP3 expression is dependent on PY-STAT1 formation (28). Second, because BAK1 expression is induced directly in HT-29 cells by IFN-γ (29), its induction probably depends on the formation of STAT1 homodimers. Finally, because types I and II IFNs enhance STK3 expression (http://www.interferome.org), it is likely that the induction of this mRNA occurs through activation of STAT1. p21, BAK1, CASP3, and STK3 are known to be involved at different stages of the intrinsic apoptotic pathway. For instance, increased p21 leads to cell-cycle arrest in the G1 phase of fibrosarcoma and Burkitt’s lymphoma cells, and the induction of G1 arrest in Burkitt’s B cell lymphoma by type I IFNs is followed by induction of apoptosis (26). BAK1 is a member of the BCL-2 family of proapoptotic proteins that, upon activation by IFN-α, forms oligomers or heterodimers that interact with the mitochondria, leading to the release of cytochrome c and apoptosis induction (50, 51). Notably, it was shown that apoptosis induction through activation of STAT1 is mediated by activation of the effector caspase 3, among others (46). Interestingly, STK3 is a direct substrate of caspase 3 and, following cleavage, translocates to the nucleus and induces chromatin condensation, followed by internucleosomal DNA fragmentation (52, 53). However, increased levels of STK3 can also accelerate apoptosis induction through the activation of caspase 3 (52).
Induction by IFN-β of CDKN1A, BAK1, CASP3, and STK3 all involved in the intrinsic apoptotic pathway, seems to occur only in monocytes, whereas the induction of certain proapoptotic genes was not found in B cells exclusively. Nevertheless, in monocytes and B cells, TRAIL mRNA was increased by 7–24-fold after IFN-β stimulation (Table I). Although TRAIL expression induces apoptosis in tumor- and virus-infected cells, it exhibits no apparent adverse affect on normal cells (30, 54). Moreover, TRAIL engagement on T cells can even lead to increased proliferation, but it is not known whether this is also true for B cells (55). However, the expression of TRAIL on monocytic cells might still have inhibitory effects, because the rapid maturation of monocytes into short-lived dendritic cells by IFN-β is associated with TRAIL expression (21). TRAIL induction by IFN-β in fibrosarcoma cells is dependent on ISGF3 binding to the ISRE element in the promoter (56). The fact that our data suggest that TRAIL mRNA was increased in B cells by IFN-β, despite low activation of STAT1, could be explained by the binding of STAT2dimer/IRF-9 or STAT2/STAT6/IRF-9 to the ISRE (4, 5), because primary human B cells can activate STAT2 and STAT6 (Fig. 8B). Alternatively, formation of STAT3, STAT5, or STAT6 homodimers (5) in response to IFN-β could be responsible for the observed increase in IRF-1 in primary human B cells (Table I) and IRF1 dimers could subsequently bind to the ISRE in the TRAIL promoter (57). In addition to TRAIL induction, we found that, in monocytes and B cells, the mRNA for the death receptor FAS was increased by 3–4-fold (Table I). Upon FASL binding, the extrinsic apoptosis pathway could be triggered by recruitment of FAS-associated death domain protein, activation of caspase 8, and cleavage of the proapoptotic BCL-2 family member Bid (31, 50). However, we did not observe any simultaneous increase in FASL mRNA expression in B cells or monocytes. Of note, FAS also has nonapoptotic functions (31), because individuals with homozygous caspase-8 reduction-of-function mutations display defects in FAS signaling and impaired proliferation of B, T, and NK cells (58).
Very low percentages of PY-STAT1+/PY-STAT3− and PY-STAT1+/PY-STAT5− were observed in B cells, the only leukocyte subset in which the PY-STAT1−/PY-STAT3+ and PY-STAT1−/PY-STAT5+ combinations were induced upon IFN-β stimulation (Fig. 6B). These findings together are likely to be responsible for decreased induction of apoptosis and increased induction of the PY-STAT3– or PY-STAT5–dependent mRNAs that are responsible for increased survival and proliferation. Notably, the probable PY-STAT3–dependent BAFF (36), which our preliminary data suggest to be increased 3.5-fold more in B cells compared with monocytes (Table I), was shown to overcome any negative effects from FAS signaling and increase the survival of B cells (59). Another activated STAT3-dependent mRNA (35), PBEF1, increased in B cells only (Fig. 7B, Table I) and not in monocytes in response to IFN-β. Notably, PBEF1 inhibits the induction of apoptosis in neutrophils and epithelial cells by reducing the activity of caspases 3 and 8 (34). In addition, PBEF1 synergizes with IL-7 in pre-B cell colony formation (33). We did not observe increased BCL2 or BCL2L1 expression, which was previously suggested to be the antiapoptotic mechanism of IFN-β in B cells (15). The promoters of BCL2 and BCL2L1 are typical targets of activated STAT5 in response to several growth factors, and the STAT5-dependent induction of resistance to apoptosis functions through these proteins (13). Because we studied only early mRNA transcription in response to IFN-β, the induction of BCL2 and BCL2L1 mRNA in B cells might need >3 h of stimulation with IFN-β. We propose that the low activation of STAT1 and much higher activation of STAT3 and STAT5 and, consequently, the absence of induction of genes participating sequentially in the intrinsic apoptosis-induction pathway (as observed in monocytes) are important mechanisms to enable primary human B cells to survive in response to IFN-β. More in-depth studies using Chip assays to determine the residence of specific STATs on specific promoters are necessary to understand in detail how STAT1, STAT3, and STAT5 activated by IFN-α/β exert their proapoptotic and mitogenic effects in specific immune subsets. In addition, it will be vital to unravel the mechanism of low STAT1 activation in the great majority of primary human B cells.
Although to enable survival, few B cells activate STAT1 in response to types I and II IFNs, it is nevertheless important that the antiviral effects of IFNs are preserved. mRNAs derived from virus-responsive genes, such as MX1, OAS, EIF2AK2, ISG15, IFI44, and IFITM3, increased ≥2-fold in B cells and monocytes in response to IFN-β (data not shown). It seems that only the promoter of MX1 harbors a classical ISRE element (57), and increased MX1 transcription in B cells, despite low STAT1 activation by IFN-β, could be the result of activation of similar transcription factor complexes as mentioned above for TRAIL. All of the other typical virus-responsive genes are suggested to belong to a new subtype of ISRE termed “E–twenty-six (ETS)/IRF response elements,” which can bind IRF dimers (similar to classic ISRE) or an ETS/IRF dimer (57). Because B cells express IRF4, IRF8, PU.1, and other ETS family members (57), an IRF/ETS dimer might bind to these promoters in primary B cells after IFN-β stimulation. Therefore, the inability of most B cells to activate STAT1 does not lead to a deficient antiviral response in B cells, but the mechanism has still to be elucidated. These results have important implications for understanding more fully the influence of IFN-α/β on leukocyte subsets during virus infection in humans, as well as the effects of treatment with IFN-α/β on these subsets in patients with multiple sclerosis, hepatitis, or cancer.
We thank Dr. Ian M. Kerr for many helpful suggestions and Dr. Ganes C. Sen for critically reviewing this manuscript. In addition, we thank Mike Sramkoski (Case Comprehensive Cancer Center Flow Cytometry Core, Case Western Reserve University) and Cathy Shemo and Sage O’Bryant (Flow Cytometry Core, Lerner Research Institute of the Cleveland Clinic) for advice and technical assistance. We thank Dr. Pieter Faber (Genomics Core Facility, Cleveland Clinic) for microarray analyses. Moreover, we thank Vai Pathak and Dr. Patrick Leahy (Gene Expression and Genotyping Core Facility, Case Comprehensive Cancer Center, Case Western Reserve University) for assistance with the rtPCR experiments. Finally, we thank Drs. Thomas Hamilton and Ernest Borden (Department of Immunology and Taussig Cancer Institute and Cleveland Clinic, respectively) for their kind gifts of human rIFN-γ, rIFN-α1, and rIFN-α2b.
Disclosures The authors have no financial conflicts of interest.
This work was supported by Pilot Grant PP1086 and Career Transition Fellowship Award TA3032A1/1 (to A.H.H.vB-D.) from the National Multiple Sclerosis Society and by Grants P01 CA06220 (to G.R.S.) and P30 CA43703 (to Gene Expression and Genotyping Facility of the Case Comprehensive Cancer Center) from the National Institutes of Health.
The microarray data presented in this article have been deposited into National Center for Biotechnology Information Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23307) under accession No. GSE23307.
The online version of this article contains supplemental material.
Abbreviations used in this paper:
- Dulbecco’s PBS
- gamma-activated sequence
- healthy individual
- IFN regulatory factor
- IFN-stimulated gene
- IFN-stimulated gene factor 3
- IFN-stimulated response element
- real-time PCR
- Src homology region 2 domain-containing phosphatase 1
- suppressor of cytokine signaling
- T cell protein tyrosine phosphatase of 45 kDa.
- Received July 21, 2009.
- Accepted August 27, 2010.
- Copyright © 2010 by The American Association of Immunologists, Inc.