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* Department of Basic Medical Sciences, University of Missouri-Kansas City;
Genetics Institute, Cambridge, Massachusetts; and
Department of Microbiology and Immunology, Uniformed Services University of the Health Sciences, Bethesda, Maryland
Correspondence: Jian Jun Gao, Ph.D., Department of Basic Medical Science, UMKC School of Medicine, 2411 Holmes Street, MC-CO3, Kansas City, MO 64108. E-mail: gaoj{at}umkc.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Key Words: cellular activation • gene regulation • inflammation • microarray
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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CpG-DNA has also been shown in some studies to exert detrimental effects on the immune system. In vivo studies by Sparwasser et al. [14
] indicate that b-DNA can trigger shock in D-galactosamine-sensitized mice, consistent with findings that b-DNA can synergize with lipopolysaccharides (LPS) or interferon-
(IFN-) to trigger production of tumor necrosis factor 
(TNF-) in vivo [15
]. Our own studies indicate that b-DNA synergizes with LPS to induce mouse macrophages to enhance production of TNF- and nitric oxide (NO) in vitro [16
, 17
]. These effects of b-DNA on the immune system are mimicked by synthetic oligodeoxynucleotides (ODNs) containing CpG dinucleotides but not by non-CpG-containing ODNs or methylated CpG-containing ODNs [5
, 14
, 16
17
18
].
Recent studies of CpG-DNA-mediated immune activation have focused on clarifying molecular signaling pathway(s) responsible for its activity. Recent data suggest that CpG-DNA functions through Toll-like receptor 9 (TLR9) to deliver signals intracellularly, culminating in activation of mitogen-activated protein kinases (MAPKs) and stress kinases [e.g., c-jun NH2-terminal kinase (JNK) 1, JNK1/2, and p38], as well as activation of transcription factors, nuclear factor (NF)-
B and activated protein-1 (AP-1) [19
20
21
22
]. Transcription factor activation leads to enhanced expression of a number of genes previously implicated in control of various immune cells [23
]. Despite these successes in identifying upstream pathways for b-DNA signaling, the inflammatory consequences of this signaling cascade are not fully understood.
In contrast to CpG-DNA, Gram-negative, enterobacterial LPS, also a potent activator of mouse macrophages, has been reported to signal through TLR4, a transmembrane protein that shares a high degree of homology with TLR9 [24
]. Signaling by CpG-DNA and LPS through their respective TLRs requires participation of the adaptor protein MyD88 [25
26
27
] and results in activation of common transcription factors NF-B and AP-1 (reviewed in ref. [28
]). These common features in CpG-DNA and LPS signaling might suggest that these two agents use similar intracellular pathways to effect their shared actions, including adjuvanticity and toxicity. Although previous reports suggest that LPS and CpG-DNA may trigger different signaling pathways (reviewed in ref. [28
]), those studies were based on assays of the expression of only a limited number of genes. To understand further whether CpG-DNA and LPS trigger distinct signaling pathways to activate macrophages, we examined the gene expression profiles using high-density oligonucleotide microarray technology in LPS versus CpG-DNA-stimulated murine macrophages. Our data indicate that CpG-DNA induces a relatively large number of genes. Of the 11,000 titled sequences representing known genes and expressed sequence tags (ESTs), 69 genes were induced more than twofold by CpG-DNA and include genes that encode cytokines, chemokines, cell-surface receptors, enzymes, intracellular signaling proteins, transcription factors, and proteins related to cell proliferation and differentiation. CpG-DNA also repressed expression of a limited number of genes. Of importance, LPS induced and repressed a significantly greater number of genes in macrophages than did CpG-DNA, yet all genes induced or repressed by CpG-DNA were also modulated by LPS. Collectively, our data offer a more complete view of the downstream events of the CpG-DNA signaling pathway and suggest that TLR9-initiated activation signals represent a subset of those induced by activation of TLR4.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Culture of macrophages
The murine macrophage-like cell line RAW 264.7 (TIB-71; American Type Culture Collection, Manassas, VA) was used in all of the experiments described in this manuscript. Macrophages were cultured in RPMI-1640 medium (Life Technologies, Grand Island, NY), supplemented with 100 U/ml penicillin, 100 µg/ml streptomycin, and 10% heat-inactivated fetal bovine serum (endotoxin content of <0.06 ng/ml; Sigma-Genosys) at 37°C in a humidified, 5% CO2 environment. For each experiment, macrophages (5x106 cells/plate) were seeded into 100 mm tissue-culture plates and cultured overnight until they reached confluence.
RNA isolation
RAW 264.7 macrophages were treated for 6 h with medium, LPS (100 ng/ml), CpG-DNA (30 µg/ml), or non-CpG-DNA (30 µg/ml) as described in Results. For reverse transcriptase-polymerase chain reaction (RT-PCR) and microarray analyses, total RNA from triplicate sets of macrophages of two independent experiments was isolated using the RNeasy Mini kit from Qiagen (Valencia, CA) according to the manufacturer’s instructions. Briefly, 1 x 107 cells were lysed using the lysis buffer provided with the kit. The cell lysate was then loaded on a QIAshredder column (Qiagen) for homogenization. After homogenization, the homogenate was transferred to an RNeasy column and washed three times with the washing buffer. RNA was finally eluted with RNase-free water.
High-density oligonucleotide microarray and data analysis
First-strand cDNA synthesis (from 10 µg total RNA), in vitro antisense RNA amplification, biotin-labeling, as well as second-strand cDNA synthesis followed protocols exactly as described earlier by Byrne et al. [29
]. To prevent potential mispriming from rRNA, first-strand synthesis was performed at 50°C. Synthesized cDNA was purified using BioMag carboxyterminated beads (Polysciences, Warrington, PA) according to the manufacturer’s instructions and was then eluted in 10 mM sodium acetate (pH 7.8). In vitro T7 polymerase-driven transcription reactions for synthesis and biotin-labeling of antisense cRNA as well as subsequent purification and fragmentation of cRNA were performed also as described by Byrne et al. [29
]. GeneChip® hybridization mixtures contained 10 µg fragmented cRNA, 0.5 mg/ml acetylated bovine serum albumin, and 0.1 mg/ml herring sperm DNA in 1 x 2-(N-morpholino)ethanesulfonic acid (MES) buffer (100 mM MES, pH 6.5–6.7) in a total volume of 200 µl as per the manufacturer’s instructions. Reaction mixtures were hybridized for 18 h at 45°C to Affymetrix Mu11KsubA and Mu11KsubB oligonucleotide arrays (Santa Clara, CA). The hybridization mixtures were then removed, and the arrays were washed and stained with Streptavidin R-phycoerythrin (Molecular Probes, Junction City, OR) using the GeneChip® Fluidics Station 400. GeneChip® hybrids were scanned with a Hewlett-Packard GeneArray scanner following the manufacturer’s instructions (Palo Alto, CA). Fluorescent data were collected and converted to gene-specific difference averages using MicroArray Suite 4.0 software (Affymetrix).
RT-PCR analysis
A total of 1.0 µg RNA from each sample was used for RT-PCR using the One-step RT-PCR kit from Qiagen according to the manufacturer’s protocols. The conditions for PCR amplification of the genes in this manuscript were the same as previously reported: chemokine genes [30
], DNA methyltransferase (DNMT) [31
], and ß-actin [16
]. PCR products were analyzed using agarose gel electrophoresis, stained with ethidium bromide, and photographed. The photographs were scanned using Adobe PhotoShop software (Adobe Systems Inc., San Jose, CA).
Statistical analysis
A standard curve was obtained by spiking 11 gene fragments derived from cloned bacterial and bacteriophage sequences into each hybridization mixture. Standard curve RNA concentrations range from 0.5 pM to 150 pM, representing RNA frequencies of 
3.3–1000 ppm, assuming an average transcript size of 2 kb. The biotinylated standard curve fragments were synthesized by T7-polymerase-driven in vitro translation reactions from plasmid-based templates [29
]. The common standard curve fragments introduced into each sample thus serve a threefold function. First, they provide an internal standard to assess individual gene-chip sensitivity. Second, they serve as a standard curve to convert measured fluorescent difference averages into RNA frequencies. Finally, they provide a way for internal normalization, allowing comparison between individual chips and different experiments [32
]. In addition, a second set of algorithms based primarily on the fraction of individual positive- or negative-responding probe pairs was used to assess the absolute presence or absence of the gene product in single-file comparisons and increases or decreases in two-file comparisons [33
]. The sensitivity of the individual oligonucleotide microarray is predetermined as one-half the minimum concentration at which two of any three adjacent standard curve values are determined to be positive. The standard curve linear regression is set to require a fit through the zero point of the abscissa/ordinate, and the minimum-reported gene frequency is set to the sensitivity of the individual GeneChip®.
Three independent replicas for each of the treatment or control experimental conditions were measured, and the expression data were then subjected to routine statistical analysis to eliminate false positives. Frequency values and increase/decrease estimations determined from individual two-file comparisons were initially plotted using Spotfire® software. Subsequently, frequency values were compared, and the fold-change for each of the nine possible comparisons between experimental and control measurements was calculated. Values reported (see Tables 2 and 3 ) represent the average of the fold-change values and calculated standard deviations for each mRNA. Two-tailed Student’s t-tests were calculated using unequal covariance with raw frequency values. In this work, only those genes that vary in average fold-change, more than twofold coupled to a Student’s t-test with P < 0.05 in at least one of the experimental conditions, have been reported. The gene sets established by the dual average fold-change, more than twofold and t-test P < 0.5 criteria, were subsequently edited to remove genes identified as absent in the majority of test files and to remove redundancy as a result of genes that were named multiple times on the Mu11KsubA and -subB oligonucleotide arrays.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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and NO from RAW 264.6 macrophages [16
, 17
], suggesting CpG-DNA and LPS use different signaling pathways to activate gene expression. To further analyze the global consequences of CpG-DNA and LPS signaling, we performed studies to determine gene expression profiles in RAW 264.7 macrophages in response to CpG-DNA or LPS using oligonucleotide microarray technology. To monitor CpG-DNA-induced changes in gene expression profiles in RAW 264.7 macrophages, biotinylated cRNA probes were prepared using total RNA isolated from RAW 264.7 macrophages treated for 6 h with culture medium alone, medium containing 30 µg/ml CpG-containing ODN T3, or non-CpG-containing ODN C3. The cRNA probes were then hybridized to the murine genome Mu11K microarrays that contain more than 11,000 full-length genes and EST sequences on two individual chips. After hybridization, washing, and scanning of the arrays, fluorescent data were collected and converted to gene frequencies (expression levels) as described in Materials and Methods. Detected transcripts, called "present", were displayed in two-dimensional scatter plots with typical two-file comparisons of treatment and control samples. As shown in Figure 1A
, the control, non-CpG-ODN C3 up-regulated (blue squares) or down-regulated (red squares) expression of only a very small number [26
] of genes, and the majority of mRNA species "present" (5184 genes in triplicate samples) remains unchanged (green squares). After statistical analysis, no genes fulfilled the dual-selection criteria of more than twofold, and P < 0.05 (Table 1
). In contrast, the CpG-containing ODN T3 (Fig. 1B)
significantly modified expression of a relatively larger number of genes. Of an average 5112 genes "present" in triplicate samples, 107 genes were identified as being induced more than twofold, and after editing, a total of 106 genes fulfilled the dual-selection criteria of more than twofold, and P < 0.05 (Table 1)
. Among these, 79 genes were up-regulated. These included 69 known genes (Table 2
, highlighted in pink) that encode cytokines, chemokines, cell-surface receptors, transcription factors, intracellular signaling proteins, cell proliferation/differentiation-related proteins, enzymes, and others, as well as 10 ESTs whose full-length sequences could not be identified by BLAST analysis (data not shown). In addition to genes up-regulated by CpG-DNA, three genes (Table 3
, highlighted in pink) and one EST (not shown) were identified as being down-regulated by CpG-DNA by more than twofold. These data provide a global overview of the changes in gene-expression patterns downstream of the CpG-DNA (TLR9) signaling pathway. Our data also identify for the first time a number of new genes that are significantly repressed in macrophages following stimulation with CpG-DNA.
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Comparison of gene expression profiles in macrophages treated with CpG-DNA and LPS
As CpG-DNA and LPS have been reported to use cell-surface receptors of the innate-immune system (TLR9 and TLR4, respectively) that share a high degree of homology [24
], we next compared the gene expression profiles in CpG-DNA- and LPS-treated RAW 264.7 macrophages with the expectation of delineating the extent to which CpG-DNA and LPS might use similar or distinct signaling pathways that lead to alterations in gene expression. All the genes that were modulated more than twofold in CpG-DNA- and LPS-treated macrophages are listed and aligned in Tables 2
and 3
. As shown in Table 2
, all of the genes induced in RAW 264.7 macrophages by CpG-DNA were also induced to a similar or greater extent by LPS (highlighted in pink). Moreover, all of the genes repressed by CpG-DNA treatment were also down-regulated to a similar degree by LPS treatment (Table 3
, highlighted in pink). To illustrate these findings further, we randomly selected the genes within the "Chemokine and cytokine" and "Immune response" functional groups (Table 2)
and plotted the average expression levels of these genes in medium-, non-CpG-DNA-, CpG-DNA-, and LPS-treated macrophages in Figure 2A
and 2B
. All the genes induced by CpG-DNA were also induced to a similar or greater extent by LPS (see the genes in the left portions of Fig. 2A
and 2B ). These data indicate that compared with LPS, CpG-DNA does not induce any unique set of genes. CpG-DNA-inducible genes comprise only a subset of LPS-stimulated genes, suggesting that CpG-DNA signaling engages only a subset of LPS-induced signaling pathways.
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Verification of the microarray results with RT-PCR
Although our previous studies have shown that high-density oligonucleotide microarray analysis is a powerful, sensitive, and accurate technique by which to determine expression of genes regulated by signaling molecules [33
], we performed additional experiments to confirm the expression of some of the detected genes using semiquantitative RT-PCR. All of the RT-PCR results (data not shown) were found to be consistent with the microarray results. Data in Figure 3
show some of these representative RT-PCR results. Genes identified as being up- or down-regulated by CpG-DNA and LPS in microarray studies were also similarly regulated in RT-PCR analyses (cf. genes in Fig. 3
to the same genes in Tables 2
and 3
).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Classic immunology textbooks underscore the key interplay of the innate and acquired immune systems in concerted actions and counteractions of different types of immune cells (e.g., macrophages, NK cells, T cells, and B cells) under the control of a collection of factors generated from these immune cells in response to foreign agents. These factors include different cell-surface receptors, intracellular signaling proteins, enzymes, transcription factors, as well as soluble factors involved in intercellular communication such as cytokines and chemokines. The microarray data presented here relative to CpG-DNA and LPS-stimulated macrophages are consistent with this concept. In the presence of microbe-derived CpG-DNA or LPS, macrophages respond rapidly by modulating expression of genes that encode the aforementioned factors that are collectively responsible for controlling the activities of the innate and acquired-immune systems (Tables 2 and 3) . These data provide us with a systematic and comprehensive comparison of the immune-activating and proinflammatory activities of CpG-DNA and LPS.
CpG-DNA has been reported to signal through TLR9, a receptor with a high degree of structural homology with the enterobacterial LPS receptor, TLR4 [24
]. CpG-DNA- and LPS-induced signaling through their respective TLRs results in recruitment of a common adaptor protein, MyD88, which binds to and controls the transmission of the TLR signal [25
26
27
]. Activation of both signaling pathways results in activation of transcription factors NF-B and AP-1 [28
], which in turn, enhance expression of inflammatory mediator genes such as TNF- [28
]. These findings support the notion that CpG-DNA and LPS use similar intracellular signaling pathways. However, a few previous studies suggest that CpG-DNA and LPS may use different signaling pathways (reviewed in ref. [28
]), but these studies were based primarily on assays of the expression of only a very limited number of genes. In the study presented here, we compared the expression of 11,000 genes (1/3 of the mouse genome) in RAW 264.7 macrophages in response to CpG-DNA and LPS using the high-density microarray technology. This information provides us with an extensive database from which to compare the expression patterns of genes in response to CpG-DNA and LPS.
The results presented in this study clearly show that LPS induced and repressed a substantially greater number of genes than CpG-DNA and that the measured repertoire of CpG-DNA-induced genes is a subset of the genes altered by LPS-mediated signaling. Several of the CpG-DNA-induced transcripts reached levels similar to LPS induction including C10-like, monocyte chemoattractant protein-1 (MCP-1), macrophage-inflammatory protein-1 (MIP-1)/ß, and TNF (Fig. 2
and Table 1
). Other genes induced in common to both treatments demonstrate significantly greater induction by LPS [e.g., IL-1ß, IFN-stimulated gene (ISG)15, and immunoresponsive gene (IRG)1; Fig. 2
and Table 1
], whereas an additional 109 induced and 67 repressed genes were completely specific to LPS signaling. Although a full dose-response curve of the two immune stimulators was not investigated, the observed similar and differential range of gene activation argues that a CpG-DNA-induced gene expression profile represents only a subset of that initiated by LPS. Compared with CpG-DNA, LPS is significantly more potent in terms of the range and magnitude of induced and repressed gene transcription, thus providing a molecular rationale for why LPS is a much more toxic agent than CpG-DNA.
Among the genes repressed by LPS treatment, many are involved in regulation of cell growth and proliferation or enzymes that are essential for DNA and RNA synthesis during cell growth and proliferation (Table 3) . These data are consistent with the previous findings that LPS is able to inhibit DNA synthesis and cause cell-cycle arrest in macrophages [37 , 38 ]. Most studies analyze only genes that are up-regulated by microbial stimuli, and repressed genes generally go unreported. It is very important to consider the cumulative effects of inducible and repressible genes when comparing various agents. Further study of the interplay among these genes and the effect of their repression on those genes found to be strongly inducible will likely provide more insights for elucidating the mechanisms by which LPS exerts its potent proinflammatory but antiproliferative effects.
To date, little is known about differential signaling initiated by TLR4 versus TLR9. However, a recent report by Horng et al. [39 ] found that TLR4 interacts with a novel adaptor protein distinct from MyD88, called TIRAP, which interacts with and activates RNA-dependent protein kinase (PKR) as one of its downstream targets. CpG-DNA failed to induce TIRAP activity, and inhibitors of TIRAP blocked TLR4-mediated but not TLR9-mediated signaling. These findings are consistent with our data that TLR4-dependent LPS signaling and TLR9-dependent signaling result in different arrays of gene expression and suggest that the evolution of various TLRs may reflect the adaptation to different microbial insults that require additional signaling pathways for their elimination.
It is also worth mentioning that although CpG-DNA has been shown to activate a significant number of genes (including those identified in this report), to date, only a limited number of transcription factors (e.g., NF-B and AP-1; ref. [28
]) are known to be responsible for the molecular actions of CpG-DNA. It is therefore difficult to comprehend how such a small number of transcription factors are sufficient to modulate expression of a large number of genes in different immune cells. Our studies presented here have identified that CpG-DNA up-regulated at least six new genes that encode transcription factors (Table 2)
. In addition to these transcription factor genes, CpG-DNA also induced 14 genes that encoded cell surface receptors and 9 genes involved in cellular signal transduction. Targeted studies of the regulation of these genes and interactions of their products will allow us to understand further the molecular mechanisms by which CpG-DNA modulates gene expression in the immune system. Ongoing studies in our laboratory have focused on the roles of different transcription factors [e.g., C/EBP 
and signal transducer and activator of transcription (STAT)3] in mediating the immune-stimulating activities of CpG-DNA.
It should be noted that the results reported here represent data at only one time-point (6 h of stimulation). Our studies therefore have likely failed to detect some of the early- and late-response genes. Although this does not compromise the conclusions reached in this report, studies of the gene expression profiles in macrophages at more time-points will undoubtedly provide a more complete understanding of the mechanisms by which CpG-DNA and LPS activate macrophages. In addition, studies of the complete gene expression profiles in other types of immune cells (e.g., NK cells, dendritic cells, T cells, and B cells) in response to CpG-DNA will facilitate understanding of the mechanisms by which CpG-DNA activates the innate- and acquired-immune systems. Understanding the mechanisms that underlie CpG-DNA activity may then allow investigators to avoid the detrimental effects of CpG-DNA, still being able to harness the beneficial effects of CpG-DNA in designing therapeutic strategies against disorders such as infection, cancer, and allergy.
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ACKNOWLEDGEMENTS |
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Received March 28, 2002; revised September 10, 2002; accepted September 26, 2002.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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