Dissecting Interferon-Induced Transcriptional Programs in Human Peripheral Blood Cells

Interferons are key modulators of the immune system, and are central to the control of many diseases. The response of immune cells to stimuli in complex populations is the product of direct and indirect effects, and of homotypic and heterotypic cell interactions. Dissecting the global transcriptional profiles of immune cell populations may provide insights into this regulatory interplay. The host transcriptional response may also be useful in discriminating between disease states, and in understanding pathophysiology. The transcriptional programs of cell populations in health therefore provide a paradigm for deconvoluting disease-associated gene expression profiles. We used human cDNA microarrays to (1) compare the gene expression programs in human peripheral blood mononuclear cells (PBMCs) elicited by 6 major mediators of the immune response: interferons α, β, ω and γ, IL12 and TNFα; and (2) characterize the transcriptional responses of purified immune cell populations (CD4+ and CD8+ T cells, B cells, NK cells and monocytes) to IFNγ stimulation. We defined a highly stereotyped response to type I interferons, while responses to IFNγ and IL12 were largely restricted to a subset of type I interferon-inducible genes. TNFα stimulation resulted in a distinct pattern of gene expression. Cell type-specific transcriptional programs were identified, highlighting the pronounced response of monocytes to IFNγ, and emergent properties associated with IFN-mediated activation of mixed cell populations. This information provides a detailed view of cellular activation by immune mediators, and contributes an interpretive framework for the definition of host immune responses in a variety of disease settings.


Introduction
Interferons are a class of cytokines first identified in 1957 as having a protective effect against viral infection [1]. Interferons can be divided into three groups; type I (IFNa/b/e/k/v) that engage the IFNAR1/2 receptor, type II (IFNc, the sole member) that signal through the IFNGR1/2 receptor [2], and type III (IFNl) that utilize IFN-lR1 and IL-10R2 receptors [3,4].
The type I interferons, IFNa (of which there are 13 subtypes), IFNb and IFNv are secreted by most cell types in response to viral infection [5]. Mice lacking intact interferon receptors are highly susceptible to viral infection [6]. Type I IFN stimulation induces a number of different systems involved in the activation of the immune response, cell growth and the control of apoptosis, in addition to the PKR (dsRNA-dependent protein kinase), 2-5A synthetase and Mx antiviral systems [7,8]. Type I interferon subtypes have also been reported to have distinct activities [9,10]; these IFN subtype-specific effects are influenced by factors such as receptor binding efficiencies [11], constitutive levels of IFN expression [12], and the specific viral-target cell combination [13]. By contrast type II interferon (IFNc), secreted by activated NK cells and T lymphocytes, has been implicated primarily in the activation of macrophages and has been demonstrated to be important for the protection of the host against intracellular pathogens such as Leishmania, Toxoplasma and Mycobacterium species [14]. Mutations in the IFNc receptor have been associated with increased susceptibility to mycobacterial infection [15]. Interferons are involved in a wide range of clinically important phenomena, ranging from activation of immune responses to infection [14] to cancer suppression [16] to depression [17]. Recombinant interferon therapy has been approved for a spectrum of conditions such as hepatitis B and C infections, Kaposi's sarcoma, multiple sclerosis and chronic granulomatous disease [5].
DNA microarray analysis of gene expression has enabled the description and discrimination of disease states [18][19][20][21]; and presents an opportunity for both diagnostic and prognostic marker discovery [22][23][24]. IFN signatures have been identified as prominent aspects of many transcriptional profiles [25][26][27][28]. However, to interpret these gene expression patterns further, a basic understanding of the response of complex cell populations to various stimuli is required. Microarray analyses have been used previously to investigate the global effects of interferon stimulation in human non-immune cells after 6 h incubation in a fibrosarcoma cell line [29], in murine fibroblast cells [30], in primary endothelial cells after 18 h treatment [31], and in epithelial cells using ChIPchip technology to investigate STAT1/STAT2 binding events [32]. An increased understanding of the temporal and cell-specific nature of gene expression in response to cytokine stimulation may reveal insights into the activation and interactions of different cell types during infection.
In this study we used human cDNA microarrays (1) to compare the responses of a mixed population of immune cells (human peripheral blood mononuclear cells) to stimulation with 6 major mediators of immune activation -the human type I interferons (IFNa, b and v), type II interferon (IFNc), and two factors involved in cell-mediated immunity (IL12 and TNFa); and (2) to contrast the transcriptional reorganization of purified immune cell populations (CD4 + and CD8 + T cells, B cells, NK cells and monocytes) to treatment with IFNc. This gene expression analysis of both mixed and purified immune cell populations sampled over time allowed both cytokine-specific and cell-specific transcriptional patterns to be identified. This information may assist in the recognition of host immune responses in disease settings, help to reveal novel immuno-modulatory actions of pathogens during infection, and offers a global view of cellular activation after exposure to a variety of cytokines.

PBMC cytokine activation profiles
To investigate the temporal program of gene expression in a physiologically relevant mixed cell population specific to activators of innate immunity, we treated PBMCs with recombinant IFNa2b, IFNb1a, IFNv, IFNc, IL12 and TNFa and sampled at intervals of 0.5, 1, 4, 8, 12, and 24 h post stimulation. Differentially expressed genes were identified using the Significance Analysis of Microarrays (SAM) algorithm to compare the temporal response of each treatment with a mock time course (treatment with 0.1% BSA/PBS), as well as to all other treatments. The temporal expression pattern of 1857 genes found to be significantly differentially expressed (FDR,1%, minimum of 2 fold change) are shown in Figure 1. Tables 1 and 2 detail the numbers of genes differentially expressed by each treatment relative to the mock time series. The magnitude of differences in transcript abundance, and the correlations between responses to each pair of stimuli are detailed further in Tables S1 and S2. No significant changes were detected by flow cytometry in the proportion of major cellular subsets in PBMCs over the 24 h time period, confirming that differences in gene transcript abundance were due to changes in transcript abundance and not to shifting demographies of PBMC sub-populations.
The human immune cells were treated with cytokines at a standardized concentration of 0.6 pM, in order to facilitate comparisons among the cytokine activation profiles; the use of units (U) was undesirable in this setting as these values are calculated with biological assays, and are based on different parameters for each cytokine. However, the influence of cytokine concentration on the pattern of transcription was addressed in an additional experiment, in which PBMCs were treated with 0.006, 0.6 and 60 pM of recombinant IFNc (corresponding to 1, 100 and 10,000 U IFNc, respectively) and sampled at 0.5, 1, 2, 4, 8 and 12 h post stimulation ( Figure S1). The qualitative transcriptional responses were similar for the three IFNc concentrations, but the amplitude of the responses increased with IFNc dose. The subset of genes differentially induced by type I, as opposed to type II IFN, was not induced even at the highest tested concentrations of IFNc; suggesting that the differential responses of PBMCs to type I and type II interferons cannot be explained by a difference in effective dose.

Type I vs. type II IFN programs
IFNc signals through a different specific receptor and has biological effects distinct from those of the type I interferons in vitro and in vivo [34]. The transcriptional response of PBMCs to IFNc stimulation in the initial 24 h appears largely to be restricted to a subset of type I IFN-inducible genes ( Figure 1). This statement must be qualified with the observation that transcripts of IFNc itself were induced within the first 4 h of type I IFN treatment (Figure 2), which may result in the initiation of an IFNc transcriptional program in type I IFN-treated cells. IFNa has also been demonstrated to promote the proliferation of IFNc-secreting T cells [35].
We identified 114 genes that were significantly more highly induced by all type I IFNs than by IFNc treatment, and 10 genes that were more highly induced by IFNc stimulation (in a direct comparison between type I and II IFN responses, Table S4). As expected, genes associated with the anti-viral PKR, 2-5A synthetase and Mx pathways were more highly induced by type I IFNs, correlating with the greater antiviral potency of type I compared to type II IFNs [5]. The interferon regulatory factors IRF2 and IRF7, and the transcription factors ATF3, ELF1, IFI16, KLF6, OLIG2, SP100, SP110, and SPIB were also consistently more highly expressed by type I than by type II IFNs. The 10 genes more highly induced by IFNc than by type I IFN treatment included FCGR1A which encodes a high affinity Fc receptor, the IL6 receptor (IL6R), the chemokine CXCL9, the macrophage lectin (CLECSF14), ubiquitin D (UBD), the transcriptional enhancer C/EBPa and the MHC class II regulator MHC2TA (CIITA). MHC2TA has previously been noted to be induced to a greater degree by IFNc treatment than by type I IFN treatment [14]. and TNFa from 30 minutes to 24 h after treatment. 1857 genes were identified to be significantly differentially expressed in response to one or more stimuli compared to mock-treated PBMC (0.1% BSA/PBS). The expression profiles are ordered by hierarchical clustering; the genes are displayed as rows, time points/conditions as columns, in temporal order (see yellow key). Red coloring signifies the up-regulation of expression; blue coloring denotes down-regulation. Samples were taken at time intervals of 0.5, 1, 4, 8, 12 and 24 h. The column marked SAM indicates (in red) which genes were identified to be significantly differentially expressed in response to each cytokine. doi:10.1371/journal.pone.0009753.g001

Parallels between transcriptional responses to IFNc and IL12
Stimulation with IFNc resulted in the induction of 111 genes, including the chemokines CCL8, CXCL9, CXCL10 and CXCL11 and the interleukins IL1A, IL7 and IL15, together with the cell surface receptors CCR5, CD38, CSF2RB, FCGR1A, ICAM, IL15RA, MSR1, NKTR, P2RY13, SLAMF1. IFNc stimulation also induced several factors involved in the complement system (BF, C1QB, C4A and SERPING1). Additionally, genes implicated in the metabolism of tryptophan (INDO, WARS) and MHC class I processing (PSMB9, TAP1/2) were highly expressed after treatment with both type I and type II IFNs. The transcriptional response of PBMCs to IL12 stimulation was similar to the IFNc activation program, as evidenced by the high correlation score and small number of differentially expressed genes that distinguished these two treatments (Table S1). Interestingly the transcriptional response to IL12 appeared to be delayed by approximately 4 h when compared to the response to IFNc (Figure 1). IFNc-induced genes were significantly enriched in the subset of genes up-regulated by IL12 (hypergeometric p value = 5.6610 24 ). Transcripts of IFNc were significantly induced by IL12 treatment (Figure 2), as were IFNc levels in the culture medium (data not shown). The delayed IFNc-like response following IL12 treatment may therefore be due to the transcriptional induction and subsequent release of IFNc from IL12activated T cells in the mixed cellular population of PBMCs. The similarity in PBMC gene expression patterns resulting from IFNc or IL12 stimulation may also reflect the induction of analogous pathways involved in macrophage activation, and explain why polymorphisms in IFNc and IL12-related genes often result in similar pathological conditions [15,36].

Transcriptional signature of TNFa
The response to TNFa was strikingly different from all of the IFN response programs ( Figure 1). The 130 genes induced by TNFa treatment of PBMCs encode cytokines and chemokines implicated in the orchestration of the inflammatory response (CCL15/20/23, CCL3L1, CXCL1/2/3, IL16, IL18, IL1A, IL1B, IL6, IL8, SLAMF1, TNF), and a number of genes involved in NFKB activation and its regulation (IKBKG, NFKB1, NFKB2, NFKBIA, NFKBIZ, RELB, TNFAIP2/3, TNFRSF11A, TRAF1). The gene ontology terms associated with this group were significantly enriched for functional categories such as cytokine activity, response to external stimulus, and NFKB signaling. In addition, functional groups involved in chemotaxis, and the regulation of cell proliferation or apoptosis were also associated with the genes induced by TNFa treatment. The activation of the NFKB pathway is a well-established consequence of TNFa ligation [37,38], and was not associated with the gene ontology terms linked to IFN stimulation.

Cell-type associated gene expression
To determine which cell types present in PBMCs contributed to the IFNc signature described above, and to investigate whether discrete subpopulations of immune cells respond in the same way to stimuli as do mixed populations, CD4+ T cells, CD8+ T cells, B cells, NK cells and monocytes were each isolated from human PBMCs and then stimulated with IFNc. The reliability of this cell separation and microarray analysis strategy was confirmed by comparing the gene expression profiles of the unstimulated cell subsets (time zero and 0.1% BSA/PBS treated time points). Genes were defined as cell type-associated if they were identified (by multiple SAM two class pair wise comparisons, FDR,1%, minimum of 2 fold change) as significantly more highly expressed in a single cell type when compared to all other cell subsets ( Figure 3a). Through this procedure, 179 genes were determined to be B cell associated, 210 genes were T cell associated (with 17 CD4+ and 25 CD8+ associated genes); 161 genes were NK cell associated and 1042 were monocyte associated (Table S5). The expression of known cell type-specific genes (such as CD4, 8, 14, 19 and HLA molecules) was restricted to the appropriate purified cell subset. In addition, the match between the B cell and T cell specific genes found here and in a previously published study was highly significant (hypergeometric probability 5.68610-149, and 5.87610-74 respectively) [39].

Cell-type specific IFNc responses
To investigate the immune cell specificity of activation programs induced by a major component of cell-mediated  Expression of class II MHC has been previously demonstrated in non-professional antigen-presenting cells after IFNc stimulation, and in activated T cells [40]. The B cell response to IFNc included the induction of genes encoding CD38 and CD69, both of which have been implicated in B cell activation and maturation, and a number of genes involved in MHC class I presentation such as the proteasome subunits (PSMB8 and B9, PSME1 and 2) and TAP1 and 2 transporters. Interestingly, neither STAT1 nor IRF1 were significantly induced as part of the limited transcriptional response of purified NK cells to IFNc. As expected, monocytes had the greatest response to IFNc stimulation; activation of macrophages is a well-defined role of IFNc. Of the 294 genes that were induced by IFNc (61 of which were identified to be monocyte-associated), over 40 coded for cell Genes were defined as cell type-associated if they were identified as significantly more highly expressed in a single cell type compared to all other cell types. The expression profiles are ordered by hierarchical clustering; the genes are displayed as rows, cell type/time points as columns. Red coloring signifies high expression; blue coloring denotes low expression. Cell typeassociated gene clusters are marked. B (right). The cell type-specific nature of IFNc transcriptional responses. Purified subsets of B cells, CD4 + T cells, CD8 + T cells, NK cells and monocytes were stimulated with 0.6 pM IFNc and sampled at 0.5, 1, 4, 8, 12 and 24 h. 807 significantly induced genes were identified, and ordered by cell type and hierarchical clustering. The genes are displayed as rows, time points/cell subsets as columns. Red coloring signifies up-regulation, and blue coloring signifies down-regulation of expression after IFNc exposure relative to the mock-treated discrete cell population. The column marked SAM indicates (in red) which genes were significantly differentially expressed by IFNc in each cell type. Genes of interest are marked as annotated in Source [73]. doi:10.1371/journal.pone.0009753.g003 surface molecules such as the chemokine/cytokine receptors (CCRL2, CRLF1, CSF2RB, HM74, IL15RA, IL2RA, IL6R,  IL8RB), the cell activation markers (CD36, CD38, CD69, CD97), and a number of proteins involved in cellular adhesion (CD226,  EVA1, ICAM1, ITGAL, ITGA4, ITGB7, LGALS3BP, MUC1, SIAT1). The up-regulation of numerous genes involved in MHC class I and II expression (TAP1 and 2, TAPBP, MHC2TA, RFX5, HLA-DMA/B, HLA-DNA, HLA-DPB1), proteasome formation (PSMA2, 4, and 5, PSMB8, 9, and 10, PSME1 and 2) and protein turnover (specifically ubiquitination -UBD, UBE2E2, UBE2L6, UBE3A, USP25, LOC51619) underscores the role of IFNc in promoting antigen processing and presentation in monocytes. The major mediators of IFNc-induced signaling, STAT1, JAK2, and IRF1, were induced together with several factors that may regulate STAT and JAK function (CISH, NMI, PTPRC, PTPRO, SOCS1) [34]. IFNc stimulation also resulted in the increased expression of an additional 32 proteins predicted to regulate transcription, including C/EBPa, EGR2, HLX1, IFI16, IRF8, KLF2 and 4, STAT2, SP110 and the MHC II regulatory elements MHC2TA and RFX5. In a complementary approach, putative C/EBP, EGR, HOX, IRF, ISRE, MEF and STAT transcription factor binding sites were amongst 104 motifs identified after searching 4000 nt either side of the start sites of the 294 genes induced by IFNc treatment in the purified monocyte population. The integration of temporal patterns of gene expression with transcription factor motif mining tools promises to reveal novel regulatory networks affecting immune cell function [41].

Temporal response to IFNc
The temporal response of the monocyte population to IFNc treatment was analyzed by partitioning the differentially expressed genes into 7 significantly represented expression profiles using Short Time-series Expression Miner (STEM) [42] (Figure 4a, detailed in Table S7). Correlating the temporal pattern of monocyte gene expression with the enriched functional categories of genes may help to elucidate the timing of events following monocyte activation. For example, the functional category for the proteasome complex was highlighted in profiles of induced genes with peak transcript abundance at 4 h and 8 h after IFNc treatment (Figure 4a, profiles D and E); perhaps reflecting a time-delayed transcriptional response after signaling cascades were triggered by IFNc receptor cross-linking. The GO ontology terms for chemotaxis (and taxis) were significantly enriched in the subset of genes repressed after IFNc stimulation (Figure 4a, profile G); which may be an indicator of monocyte activation and differentiation.
Most interestingly, the functional category associated with negative regulators of cellular or biological processes was significantly enriched among genes induced within 1 h of IFNc treatment (Figure 4a, profiles A-C). 15 genes shared this gene ontology term, including BCL6 (a zinc finger transcriptional repressor) [43], and two suppressors of cytokine signaling, SOCS1 a pseudosubstrate inhibitor of JAK-STAT signaling [44], and CISH (CIS/SOCS) an inhibitor of growth hormone signaling through STAT5b [45]. The up-regulation of CISH together with the significant enrichment of genes containing STAT5 binding motifs induced within 1 h of IFNc stimulation (Figure 4a, profiles A-C) predicts that monocyte activation is likely to be partially mediated through STAT5 (even though STAT5 itself was not identified in this analysis as significantly differentially expressed). RHOH, a repressor of Rac GTPase-mediated signaling (specifically RAC1) [46], was also induced immediately after IFNc treatment; conversely, RAC1 was repressed with time ( Figure 4a, profile G). RHOH has recently been demonstrated to downregulate leukotriene production in neutrophils [47], and may therefore fulfill a similar negative feedback function during monocyte activation. Tristetraprolin (TTP, ZFP36), induced within 1 h by IFNc (Figure 4a, profile A), has been implicated in the rapid degradation of IFNc and IL2 mRNA [48] and may therefore play an important role in constraining the monocytic pro-inflammatory response. Other interactions that may temper the activation state of monocytes after IFNc stimulation include the repression of pro-inflammatory ligand receptors, IFNc receptor 1 (IFNGR1, although this was not identified by the significance testing algorithm), the IL1 receptor 1 (IL1R1) [49], and chemokine receptor (CXCR4). The induction of CISH and RHOH and the repression of IFNGR1 in monocytes after IFNc exposure were confirmed by quantitative RT-PCR, as was the modest induction of STAT5A ( Figure S2).
By modeling the monocyte response over time, possible signaling cascades could be recognized; for example, the generation of MHC class II molecules is dependent on the expression of the class II transactivator CIITA (MHC2TA) that is in turn induced by IRF-1 [50]. This pathway, resulting in MHC class II molecule expression after IFNc stimulation, can be followed through the time series with the induction of IRF-1 within minutes of IFNc treatment (Figure 4a Figure 4a, profile F). In an example of negative regulation, the subset of genes repressed after IFNc treatment (Figure 4a, profile G) was significantly enriched for AP-1 binding sites. This (together with the down-regulation of FOS itself) may reflect the action of BCL6, a transcriptional repressor that has been demonstrated to block AP-1 activity [43], which is induced with a peak at 1 h after IFNc stimulation (Figure 4a, profile B). The complexity of potential crosstalk following monocyte stimulation by IFNc is summarized in Figure 4b; where the previously characterized interactions (identified in the ResNet 6 Mammalian database) downstream of IFNc are mapped onto the genes identified in this study to be differentially expressed by IFNc treatment. Proteins whose expression is altered by IFNc, that may also have a regulatory effect on IFNc expression itself, are highlighted in yellow.

Transcriptional plasticity of immune cells to stimulation
This transcriptional analysis of immune cell subtype responses to IFNc treatment allowed us to investigate the effect of immune stimulation on the expression of cell-associated signatures. The changing RNA abundance profiles of these cell type-associated genes were revealed by comparing the cell type-associated gene expression signatures (Figure 3a, Table S5) with the cell subset responses to IFNc stimulation (Figure 3b, Table S6). 79 genes, identified as cell type-associated in unstimulated discrete populations, were recognized to be induced by IFNc treatment in a different cell subtype population ( Figure 5). After IFNc stimulation, these genes were no longer expressed in a cell type-specific manner; moreover, these genes were not necessarily IFNcinducible in their associated cell type. The term transcriptional plasticity has been adopted in this setting to describe the regulation of cell type-associated genes in other cell subsets after stimulation. For example, MHC Class II (HLA-DMB/DNA/DQA1, TAP2) and immunoglobulin genes (CD79B, IGHM) associated with B cell gene expression were induced after stimulation in other cell types; of these genes, only TAP2 was induced by IFNc treatment in the discrete B cell population. The transcriptional flexibility of immune cells following stimulation, with the differential expression of cell type-associated genes in distinct isolated cell populations, suggests a plasticity of the cellular immune response that deserves further attention.

IFNc-induced transcripts unique to mixed-cell populations
The overlapping transcriptional signatures of cells stimulated with IFNs is both cell subset-and IFN-specific; for example, STAT2, an important mediator of type I interferon signaling, was induced by IFNc treatment only in the purified monocyte cell population, as were an additional 22 genes induced by type I IFNs, compared to IFNc stimulation in PBMCs (Table S4). We did not detect changes in relative abundance of transcripts in the IFNctreated PBMCs for many of the genes whose transcripts were detected as induced by IFNc in the purified monocyte population, which was possibly a reflection of the dilution of monocyte-specific transcripts in the total PBMC cellular RNA population. More interestingly, 40 genes (including CCR5, CD83/86, GOS2, INHBA, IRF4, OAS1/3, CCL2/3/4/7, SELPLG and TNFAIP6, Table S8) that were significantly induced by IFNc in the heterogeneous population comprising PBMCs were not detected as induced to a significant degree in any of the purified cell subsets. 15 of these genes were identified to be part of the TNFa activation program ( Figure 1) and 22 genes were monocyte-associated (Figure 3a), suggesting that monocytes may respond to additional interactions within the heterogeneous PBMC population. This rudimentary analysis highlights the emergent physiology of a tissue, in this case, peripheral blood, comprising a mixture of communicating cells.

Discussion
We characterized the differential effects of 6 cytokines on gene expression in a mixed population of human immune cells; defining for the first time the conserved transcriptional response to IFNv, together with IFNa and b, and revealing major similarities in IFNc and IL12 transcriptional programs. We and many others have examined gene expression profiles in peripheral blood in vivo and ex vivo in order to identify both gene expression signatures and specific regulatory pathways activated in health-associated homeostasis and in disease. Data derived from carefully controlled experiments establishing the transcriptional response to specific stimuli in relevant cell populations have become an important part of the framework used to interpret patterns of gene expression observed in these complex studies. The response to interferons has gained a prominent central role in the study of many immune processes. We have observed an interferon-response gene expression signature associated with shock in dengue infection [26], with early pre-clinical responses to Ebola [51], with the response of leukocytes to Neisseria meningitidis [52], and with a pattern of gene expression that discriminates Kawasaki Disease and acute adenovirus infection [53]. The detailed description of the shared and unique features of the transcriptional programs of the cytokines used in this study, and of the contribution of individual cell types to the gene expression patterns observed in a mixed cell population constitutes a valuable resource that has facilitated our analysis of these previously-published data sets, and will enable researchers to better delineate regulatory networks, elucidate pathways that are disrupted during disease, and deconvolute gene expression patterns in cell populations exposed to multiple complex stimuli.
Other groups have examined the response to interferons in nonimmune cell populations. Many of the observations of Der et al. [29] who investigated the expression profiles of HT1080 cells (a human fibrosarcoma cell line) after 6 h stimulation with interferons IFNa, b, and c by microarray analysis, were confirmed in this study. A core set of established IFN-regulated genes were identified by both analyses; however, there are subsets of genes that appear to be induced differentially in these two studies. This may be due to technical differences such as concentration of interferons, microarray platform and gene annotation, but may also reflect cell-specific (professional immune vs non-immune) responses to IFN stimulation [54]. It is also possible that differences reflect donor-to-donor variation in interferon-mediated responses. However, the overall similarity in findings, in addition to the results from studies investigating inter-individual variation in gene expression patterns, indicate that the stimulus rather than a donor-specific background is likely to be the primary feature driving transcriptional responses of peripheral blood immune cell populations [22,[55][56][57]. Similarly, comparison of our results with those from studies of IFNc stimulation of primary endothelial cells and HT1080 cells [31,58] revealed broad concordance, with a few exceptions. A recent study by Indraccolo et al. [59] identified 41 genes to be more highly induced by IFNa compared to IFNc in human umbilical vein endothelial cells; as in our study, the majority of these genes were predicted to be involved in antiviral responses. Six genes were induced to a greater degree by IFNc than by IFNa; of these, CXCL9 (MIG) and ubiquitin D (UBD or FAT10), both of which are involved in targeting proteins for proteasomal degradation [60], were also identified in the present study. Therefore, the chemokine, CXCL9, and UBD, which is a marker of immune activation in hepatocellular carcinoma and colon carcinoma [61], are preferentially induced by type II interferon in both professional immune and non-immune cells.
A parallel analysis of IFNc responses of isolated subsets of the cells comprising the PBMC pool allowed us to begin dissecting the contribution of specific cells in this complex tissue. The transcriptional response to IFNc in T cell, B cell and NK cellular populations was limited in comparison to the large number of genes induced in purified monocytes, where RHOH and CISH are predicted to have novel regulatory roles in controlling monocyte activation. These transcript abundance datasets may be utilized in combination to examine which particular cell types are responding to specific stimuli in dynamic scenarios. For  A (top). The temporal transcriptional responses of monocytes to IFNc stimulation. Of 363 genes that were significantly differentially expressed following treatment with 0.6 pM IFNc, 264 genes were assigned to 7 significantly represented expression profiles, A-G. The temporal response (measured in mean fold change) of the genes assigned to each cluster is plotted at 0.5, 1, 4, 8, 12 and 24 h post treatment. Clusters A (square), B (triangle) and C (diamond) colored red; D (triangle) and E (square) in green; F (square) in purple; and G (square) in blue. B (bottom). A network of predicted crosstalk downstream of IFNc exposure. Previously characterized interactions between IFNc and the 363 genes identified as differentially expressed after IFNc treatment in monocytes were mapped. The expression of 109 genes were predicted to be directly affected by IFNc. Secondary regulatory events were characterized by the expression of 65 additional genes (identified to be differentially expressed by IFNc treatment) whose expression was modified by those 109 genes but not by IFNc directly. This network illustrates the complexity of possible interplay downstream of IFNc stimulation. In addition, 61 proteins reported to have an effect on IFNc expression itself are highlighted in yellow, and may represent positive and negative feedback loops mediating cell activation. Gene identifiers are colored by temporal expression pattern after IFNc treatment as detailed in Figure 4A (dark red (profiles A-C), lighter red (D and E), pink (F), blue (G), grey (unassigned)). Gene identifiers are also shaped by putative function, transcription factors (ellipse), kinase/phosphatases (triangle), ligands (rhombus), receptors (cross). The nature of the interactions are indicated with connecting lines either reflecting a positive (green), negative (blue), or undefined (grey) impact on downstream gene or protein expression. doi:10.1371/journal.pone.0009753.g004 example, the transcription factors C/EBPa, involved in myeloid cell differentiation [62] and associated with acute myeloid leukemias [63], and MHC2TA (CIITA), the major regulator controlling expression of MHC class II molecules [50], were both preferentially induced by type II IFN (in PBMCs compared to type I IFNs and TNFa). These genes were also up-regulated with time in the monocyte population alone (in response to IFNc treatment), suggesting that the expression of these major mediators of cell activation in a complex model of infection may be due primarily to the response of monocytes to IFNc. This study also highlighted the plasticity of the cellular transcriptional response to stimulation, with the induction of ostensibly cell type-specific genes in alternative cell types after treatment with IFNc. Comparison of the expression patterns derived from mixed and purified immune cell populations after IFNc treatment illuminated differences in the way in which cells responded to a stimulus in isolation versus a complex environment, with a number of genes only induced by IFNc stimulation in complex cellular populations. The immune response to pathogens requires the coordinated interaction of multiple cells, and the presence of different cell populations; thus, the cross-talk between populations may be more representative of in vivo functioning than are the findings from isolated cell populations. Further investigation into these interactions may provide novel insights into cellular activation and complex responses measured from mixed populations.
The focus of this study has been on the defined actions of cytokines in the activation of the immune system. While infection is one setting in which these actions may be relevant, these data may also help to elucidate additional functional roles of interferons, such as in tumor suppression. Exploration of the transcriptional programs initiated by interferons in heterogenous and homogenous cell populations, extends our understanding of the global actions of these major mediators of inflammation, improving our comprehension of disease states, immune cell signaling cascades, the immunomodulatory mechanisms of infectious agents and potential recombinant therapies.

PBMC extraction and cell subset isolation
Human primary peripheral blood mononuclear cells (PBMCs) were purified from whole blood of healthy donors using Ficoll-Paque PLUS (GE Healthcare) according to manufacturer's instructions. To control for genetic variation [64], PBMCs were extracted from a single healthy donor for the cytokine comparison and dose response experiments. For the cell subset isolations where larger quantities of PBMCs were required, PBMCs were purified from buffy coat extractions from three healthy donors supplied by the Stanford University Blood Center. PBMC cell subsets (CD4 + and CD8 + T cells, B cells, NK cells and monocytes) were purified by negative selection using antibody-coated magnetic bead separation (Invitrogen) following the manufacturer's instructions. The cell subsets were isolated by negative rather than positive selection, as the consequences of cross-linking cell surface selection markers used in positive selection (CD2, CD3, CD4, CD8, CD19, CD14) on the activation state of the purified cell populations before stimulation are unknown. Cell subset purity was assessed by flow cytometry, and determined to be 67-98%.

Cell stimulation
PBMCs and individual cell subsets were incubated at 1.5-2.0610 6 cells/well in RPMI 1640 medium supplemented with 10% heat-inactivated fetal calf serum and 2 mM L-glutamine (Invitrogen) at 37uC, 5% CO 2 for 24 h before stimulation. Cells were treated with 0.6 pM recombinant IFNa2b, IFNb1a, IFNc, IFNv, IL12, or TNFa (R&D Systems), and sampled at time intervals from 0.5 h to 24 h after stimulation. Additionally, cells were treated with 0.1% BSA/PBS alone and used for untreated (mock) control time courses. As reference points for these time series, multiple replicates of untreated cells were sampled at time 0 (5 PBMC time zeros for the cytokine comparison, 4 PBMC time zeros for the dose response study, and 3 time zeros for each purified cell subset).

RNA extraction and amplification
Total RNA was extracted in TRIzol LS (Invitrogen) followed by standard chloroform purification and isopropanol precipitation. RNA was re-suspended in RNase-free water, quantitated with a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies) and stored at 280uC. 500 ng total RNA was amplified using the MessageAmp modified Eberwine linear amplification procedure (Applied Biosystems). All samples to be compared were extracted and amplified together.
Microarray analysis and data processing 4 mg amplified RNA was labeled with Cy5-dUTP (GE Healthcare) and combined with 3 mg of Cy3-labelled reference cDNA derived from a pool of RNA from a panel of 11 human cell lines (Stratagene Universal Human Reference RNA). The samples were washed and concentrated using MinElute columns (Qiagen) and competitively hybridized once to custom printed cDNA microarrays containing 37,632 elements from cDNA clones representing approximately 18,000 unique human genes (as previously described [18]). The hybridized slides were scanned using a GenePix 4000A microarray scanner (Axon Instruments). Comparative spot intensities were calculated from the images, and areas of poor quality excluded from further analysis using GenePix Pro 6.0 (Axon Instruments). The datasets discussed in this publication have been deposited in the Stanford Microarray Database [65], and NCBI's Gene Expression Omnibus [66] and are accessible through GEO Series accession number GSE17762 [67]. Analysis was restricted to cDNA elements with a regression correlation of .0.6, fluorescence intensities of .2.5 fold signal/ background in Cy3 or Cy5 channels and a minimum signal intensity of .100 in both channels for at least 80% of the arrays. The expression ratios were normalized for array variation, and the Figure 5. The transcriptional plasticity of immune cellular responses to IFNc stimulation. 79 genes associated with a single isolated immune cell subtype (without stimulation) were induced after IFNc treatment in other cell types. Genes defined as cell type-associated (by comparing unstimulated time series, as detailed in Figure 3A) are marked on the left. The top panel detailing B cell associated genes, followed by T cell, NK cell, and monocyte associated genes illustrated in the bottom panel. The differential regulation of these genes after IFNc treatment in each cell type (as in Figure 3B) is described on the right. The expression profiles are ordered by hierarchical clustering; the genes are displayed as rows, cell type/time points as columns. Red coloring in the cell-type associated panels (on left) signifies highly expressed in a single cell type compared to all other cell types; blue coloring denotes low expression. Red coloring in the IFNc responsive panels (on right) signifies up-regulation, and blue coloring signifies down-regulation of expression after IFNc exposure relative to the mock-treated discrete cell population. B (B cells); T4 (CD4 + T cells); T8 (CD8 + T cells); NK (NK cells); M (monocytes). The column marked SAM details (in red) which genes were significantly differentially expressed by IFNc in each cell type. Genes are marked as annotated in Source [73]. doi:10.1371/journal.pone.0009753.g005 data zero-transformed using a custom-designed Microsoft Excel macro (C. Liu, Stanford University) to the study-specific time zeros (average of 5 biological replicates for the cytokine comparison, 4 for the dose response study, and 3 for each purified cell subset). Time zero replicate expression profiles were highly similar with average r 2 values .0.95. The statistical package SAM (Significance Analysis of Microarrays, version 1.15) was used to identify genes significantly differentially expressed in the normalized data sets by pair wise comparison with a minimum 2 fold cutoff at a false discovery rate of ,1% of the median [68]. The transformed datasets were then hierarchically clustered using Cluster 2.11 and the results displayed using Treeview 1.60 [69]. The hypergeometric function was used to determine the significance of overlapping gene lists. Short Time-series Expression Miner (STEM) [42] was used to identify significantly represented temporal expression profiles (p,0.05 after Bonferroni multiple testing correction). The Database for Annotation, Visualization and Integrated Discovery (DAVID) [70,71] allowed significantly enriched functional classifications of genes to be recognized. Transcription factor binding motif searching was performed using TFSearch after sequences were prepared using EZRetrieve and RepeatMasker [72]. The monocyte temporal response to IFNc treatment was explored using Ariadne Pathway Studio 6.2 (ResNet 6 Q1 2009 Mammalian database, derived in part through text mining) mapping downstream expression and promoter binding interactions. Significantly differentially expressed genes are presented in Tables S1, S2, S3, S4, S5, S6, S7, S8.
Quantitative RT-PCR cDNA was synthesized from total RNA using an anchored oligo(dT)20 primer (Invitrogen) and Superscript III RT (Invitrogen), in accordance with the manufacturer's instructions. PCRs were prepared using the TaqMan Universal PCR Master Mix (Applied Biosystems), and cDNA derived from 10 ng total RNA. Relative abundance of the target transcripts was calculated by comparison to a standard curve, and normalized to the expression level of ribosomal protein L5 (RPL5). Applied Biosystems (ABI) assay IDs are as follows: CISH Hs01003603_m1; IFNGR1 Hs00988304_ m1; RHOH Hs00180265_m1; RPL5 Hs00851991_u1; STAT5A Hs00559643_m1.

Flow cytometry
The cellular composition of PBMCs and the purity of isolated PBMC cell subsets were determined by flow cytometry using a BD LSRII flow cytometer (BD Biosciences). A five color assay for CD4 (pacific blue, Molecular Probes), CD8 (FITC), CD56 (PE), CD19 (PerCP-Cy5.5), CD14 (APC-Cy7) (all BD Biosciences unless otherwise stated) was performed on cells extracted and labeled according to manufacturer's recommendations. Results were captured and analyzed using the BD FACSDiva software (BD Biosciences). The induction of apoptosis was estimated at 24 or 48 h after treatment using the Annexin V-FITC/propidium iodide detection assay (BD Biosciences). Figure S1 The transcriptional profile of PBMCs stimulated with one of three concentrations of IFNc. 370 genes were significantly induced by IFNc treatment, as assessed at 0.5, 1, 2, 4, 8 and 12 h after exposure to 0.006 pM, 0.6 pM or 60 pM IFNc (corresponding to 1, 100 or 10,000 U, respectively). The expression profiles are ordered by hierarchical clustering; the genes are displayed as rows, time points/IFNc dose as columns. Red coloring signifies the up-regulation of expression relative to T0. The column marked S indicates (in red) which genes were significantly induced by each concentration of IFNc. Found at: doi:10.1371/journal.pone.0009753.s001 (2.37 MB TIF) Figure S2 Quantitative RT-PCR validation. Confirmation of the differential regulation of CISH, RHOH, IFNGR1 and STAT5A in monocytes after stimulation with IFNc. Fold change is detailed relative to the untreated monocyte profile at 0.5, 1, 4, 8, 12 and 24 h. Relative abundance of the target transcripts was calculated by comparison to a standard curve, and normalized to the expression level of ribosomal protein L5 (RPL5). Standard deviations, calculated from triplicate samples, are marked with error bars. The corresponding transcriptional patterns of these genes derived from microarray analysis are displayed in Figure 4. Found at: doi:10.1371/journal.pone.0009753.s002 (0.55 MB TIF) Table S1 A matrix describing the differential gene expression of PBMCs stimulated with each cytokine. The top half of the table lists the number of genes identified by SAM as significantly more highly expressed (numerator) or under-expressed (denominator) in each comparison (y axis vs. x axis) after exposure to 0.6 pM IFNa, b, v, and c, IL12 and TNFa. The bottom half of the matrix provides the mean correlation score (from 6 time points sampled) of the PBMC responses to each treatment compared to all other stimuli; standard deviations are marked in italics. Found at: doi:10.1371/journal.pone.0009753.s003 (0.03 MB DOC) Table S2 PBMC cytokine activation profiles. The PBMC responses to stimulation with 0.6 pM IFNa, b, v, and c, IL12 and TNFa from 30 minutes to 24 h after treatment. Genes identified by SAM analysis (minimum 2 fold cutoff at a false discovery rate of ,1% of the median) are identified for each cytokine compared to mock (0.1% BSA/PBS) treated PBMCs. The genes are partitioned into induced/repressed lists for each cytokine, together with mean fold expression ratios, and are ordered alphabetically using gene annotation from Source [70]. Data are summarized in Figure 1 and Table 1.  Table S3 Common response to type I interferon. 201 genes were induced after 0.6 pM treatment with IFNa, b and IFNv compared to mock (0.1% BSA/PBS) treated PBMCs. Genes identified by SAM analysis (minimum 2 fold cutoff at a false discovery rate of ,1% of the median). Gene fold induction is detailed for each IFN treatment. Data are summarized in Figure 1 and Table 1.  Table S5 Cell-type associated genes. Genes were defined as cell type-associated if they were identified (by multiple SAM two class pairwise comparisons, FDR,1%, minimum of 2 fold change) as significantly more highly expressed in a single (unstimulated) cell type compared to all other (unstimulated) cell type gene expression profiles. Data are summarized in Figure 3a. Found at: doi:10.1371/journal.pone.0009753.s007 (0.20 MB XLS)

Table S6
The cell type-specific nature of IFNc transcriptional programs. Purified subsets of CD4 + T cells, CD8 + T cells, B cells, NK cells and monocytes were stimulated with 0.6 pM IFNc and sampled at 0.5, 1, 4, 8, 12 and 24 h. Genes significantly induced (determined using the SAM algorithm, minimum 2 fold cutoff at a false discovery rate of ,1% of the median) by IFNc are indicated for each purified cell subset together with mean fold expression ratios. The genes are ordered alphabetically, using gene annotation from Source [70]. Data are depicted in Figure 3b. Found at: doi:10.1371/journal.pone.0009753.s008 (0.08 MB XLS)

Table S7
The temporal transcriptional response of monocytes to 0.6 pM IFNc. The significantly differentially expressed genes (determined using the SAM algorithm, minimum 2 fold cutoff at a false discovery rate of ,1% of the median), and sampled at 0.5, 1, 4, 8, 12 and 24 h post IFNc treatment, were separated into 7 significantly represented profiles using STEM. The table is ordered by STEM profile (as illustrated in Figure 4a), then alphabetically using gene annotation from Source [70]. Found at: doi:10.1371/journal.pone.0009753.s009 (0.07 MB XLS) Table S8 IFNc-induced transcripts unique to mixed-cell populations. 40 genes significantly induced by IFNc in the heteroge-neous PBMC population that were not significantly induced after IFNc treatment in any of the purified cell subsets. The genes are ordered alphabetically using gene annotation from Source [70]. Found at: doi:10.1371/journal.pone.0009753.s010 (0.02 MB XLS)