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Originally published online as doi:10.1189/jlb.0105029 on June 16, 2005

Published online before print June 16, 2005
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(Journal of Leukocyte Biology. 2005;78:794-803.)
© 2005 by Society for Leukocyte Biology

Profiling dendritic cell maturation with dedicated microarrays

Dorian McIlroy*, Séverine Tanguy-Royer*, Nolwenn Le Meur{dagger}, Isabelle Guisle{dagger}, Pierre-Joseph Royer*, Jean Léger{dagger}, Khaled Meflah* and Marc Grégoire*,1

* INSERM 601 and
{dagger} Ouest Génopole, Nantes, France

1Correspondence: INSERM U601, Institut de Biologie, 9 quai Moncousu, 44000, Nantes, France. E-mail: marc.gregoire{at}nantes.inserm.fr


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ABSTRACT
 
Dendritic cell (DC) maturation is the process by which immature DC in the periphery differentiate into fully competent antigen-presenting cells that initiate the T cell response. However, DC respond to many distinct maturation stimuli, and different types of mature DC induce qualitatively different T cell responses. As DC maturation involves the coordinated regulation of hundreds of genes, comprehensive assessment of DC maturation status would ideally involve monitoring the expression of all of these transcripts. However, whole-genome microarrays are not well-suited for routine phenotyping of DC, as the vast majority of genes represented on such chips are not relevant to DC biology, and their cost limits their use for most laboratories. We therefore developed a DC-dedicated microarray, or "DC Chip", incorporating probes for 121 genes up-regulated during DC maturation, 93 genes down-regulated during maturation, 14 DC-specific genes, and 90 other genes with known or probable immune functions. These microarrays were used to study the kinetics of DC maturation and the differences in maturation profiles among five healthy donors after stimulation with tumor necrosis factor-{alpha} + polyI:C. Results obtained with the DC Chip were consistent with flow cytometry, enzyme-linked immunosorbent assay, and real-time polymerase chain reaction, as well as previously published data. Furthermore, the coordinated regulation of a cluster of genes (indoleamine dioxygenase, kynureninase, kynurenine monoxygenase, tryptophanyl tRNA synthetase, and 3-hydroxyanthranilate 3,4-dioxygenase) involved in tryptophan metabolism was observed. These data demonstrate the use of the DC Chip for monitoring the molecular processes involved in the orientation of the immune response by DC.

Key Words: phagocyte • Toll-like receptor • human


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INTRODUCTION
 
Dendritic cells (DC) are professional antigen-presenting cells, which are essential for the initiation and regulation of T cell responses. Immature DC, resident at peripheral sites, such as the skin and mucosal surfaces, are specialized in antigen capture and continually sample soluble and particulate antigens in their local environment [1 , 2 ]. They also express receptors for proinflammatory cytokines and numerous pattern recognition receptors [3 ], which allow them to sense local inflammation or the presence of pathogens. These stimuli provoke a differentiation program known as DC maturation, which results in the secretion of multiple cytokines and chemokines, inhibition of antigen uptake, migration of DC to secondary lymphoid organs, and presentation of antigen to T cells [2 ]. The magnitude and the nature of the T cell response that is induced depend, to a large extent, on the presence of costimulatory molecules, such as CD80, CD86 [4 ], and signaling lymphocytic activation molecule [5 ], on the DC surface and local production of cytokines, including interleukin (IL)-12 [6 ], IL-18 [7 ], and IL-10 [8 ], by DC.

However, this simple, binary view of DC maturation has been considerably complicated by two important observations. First, several different DC subtypes with distinct functional capacities have been identified. In human blood, for example, there are three distinct populations of circulating DC, characterized by expression of CD11c, CD1c, CD123, and blood dendritic cell antigen-3 [9 ], and according to some investigators, this list should be supplemented by a fourth CD16+ subset [10 ]. Work from several groups has shown that these subtypes of DC are not functionally equivalent [10 11 12 13 ]. Overall, it is becoming clear that mature DC from different DC lineages have distinct but overlapping functions with respect to activation of T cells and the orientation of the T cell response. Second, even within a single DC lineage, different maturation stimuli provoke distinct maturation pathways. Hence, depending on the nature and the timing of the maturation stimulus, human monocyte-derived DC can preferentially induce T helper cell type 1 (Th1) or Th2 responses [14 15 16 ].

In parallel with these advances in our understanding of the functional complexity of DC maturation, microarray experiments have demonstrated the complexity of DC maturation at a molecular level [17 18 19 20 21 22 23 24 ]. In effect, DC maturation is an extensive differentiation program that involves the coordinated regulation of hundreds of genes. Ideally, an assessment of DC maturation should take into account the expression of all of these genes. However, DC maturation is often routinely measured using 5 to 10 surface markers [for example, CD83, DC-lysosome-associated membrane protein, CD86, CD80, CD40, and CC chemokine receptor 7 (CCR7)] and two or three secreted molecules such as IL-12 p70 and IL-10. Consequently, more than 95% of the DC maturation program is effectively ignored. Furthermore, as important roles in DC function are uncovered for more and more molecules, it will become increasingly necessary to develop more comprehensive techniques for the phenotyping of DC.

Microarray technology has made it possible to study the expression level of thousands of genes in parallel. However, commercially available whole genome microarrays are not particularly well-suited for routine phenotyping of DC, as the vast majority of genes represented on such chips are not relevant to DC biology, and their relatively high cost limits their use for most laboratories. We therefore sought to develop DC-dedicated DNA chips as a tool for DC phenotyping, which would exploit the advantages of microarray technology and limit the genes analyzed to 200–300 targets relevant for DC biology. These DC-dedicated microarrays or "DC Chips" were then used to study the kinetics of DC maturation and the differences in maturation profiles between healthy blood donors.


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MATERIALS AND METHODS
 
DC culture
Human peripheral blood mononuclear cells (PBMC) were obtained from platelet donors at the Nantes regional blood transfusion center. PBMC-enriched blood (100–150 ml) was recovered from the disposable plastic tubing from the COBE cell separator used for platelet enrichment. Monocytes were purified by elutriation using a Beckman Avanti J20 centrifuge equipped with a JE5.0 rotor and an 8-ml elutriation chamber or by using the Monocyte Rosette-Sep kit (Stem Cell Technologies, Canada). Monocyte purity was assessed by flow cytometry using scatter-gating to define monocyte and lymphocyte populations. Monocytes enriched by Rosette-Sep were 72–85% pure (median 77.5%, n=4), and elutriated monocytes were 84–94% pure (median 89%, n=7). Monocytes were cultured at 2–2.5 x 106 cells/ml in X-vivo 15, supplemented with 500 U/ml recombinant human (rh) granulocyte macrophage-colony stimulating factor (Schering-Plough, Kenilworth, NJ) and 40 ng/ml rhIL-4 (specific activity ≥5 U/ng supplied by AbCys, Paris, France) in hydrophobic culture bags (Baxter S.A.S., Maurepas, France). Cytokines were renewed on day 3 of culture, and immature DC were harvested at days 6–7. DC maturation was induced by addition of 10 ng/ml tumor necrosis factor (TNF)-{alpha} (AbCys) or 50 µg/ml polyI:C (Sigma, St. Quentin Fallavier, France) or the combination of these two agents.

DC characterization by flow cytometry and enzyme-linked immunosorbent assay (ELISA)
The surface phenotype of DC was determined using the following antibodies: anti-CD40 phycoerythrin (PE; mab-89, Beckman-Coulter, Fullerton, CA; diluted 1:25), anti-CD83 PE (HB15a, Beckman-Coulter, diluted 1:12.5), and anti-CD86 fluorescein isothiocyanate (FITC; BU63, Caltag, S. San Francisco, CA; 3.3 µg/ml). The percentage of positive cells was determined relative to the staining observed with isotype controls [mouse immunoglobulin G1 (IgG1) FITC and IgG2b PE (Caltag), 3.3 µg/ml]. Cells were incubated with antibodies for 30 min at 4°C, washed once in phosphate-buffered saline, and then analyzed using a FACSCalibur flow cytometer (Becton Dickinson, San Jose, CA) piloted by CellQuest Pro software.

To measure IL-12 production, DC were suspended at 1 x 106 cells/ml in X-vivo 15, supplemented with cytokines and maturation stimuli. Supernatants were harvested at 2, 4, 12, 24, and 48 h maturation, and IL-12 p70 was dosed using a commercial ELISA kit (BD PharMingen, San Diego, CA). Rates of IL-12 production were then calculated, assuming constant production over the time-period analyzed.

RNA isolation, labeling, and array hybridization
Total RNA was extracted from frozen cell pellets using Qiagen RNeasy kits according to the manufacturer’s protocol. RNA quantification and quality control was performed using RNA 6000 nano-chips and an Agilent 2100 bioanalyzer (Agilent Technologies, Palo Alto, CA). For hybridization on MWG Pan Human 30kA microarrays, total RNA from DC of three donors was pooled (20 µg RNA from each donor), reverse-transcribed, and labeled using the CyScribe post-labeling kit (Amersham-Pharmacia, Little Chalfont, UK). Cy3-labeled cDNA from immature DC and Cy-5-labeled cDNA from DC matured under different conditions were resuspended in 35 µl hybridization buffer containing 50% formamide (MWG Biotech, Germany) and then hybridized overnight at 42°C in Telechem hybridization chambers (Proteigene, Saint-Marcel, France). Slides were washed according to the manufacturer’s instructions and then dried by centrifugation.

For hybridization on custom microarrays, 10 µg total RNA was reverse-transcribed and labeled. Cy3- and Cy5-labeled cDNAs were resuspended in 10 µl hybridization buffer containing 50% formamide (MWG Biotech) and then hybridized as described above. For each sample pair, one slide was hybridized for experiments with Donors 4 and 6, and two slides were hybridized in a dye-swap for experiments with Donor 5. For Donors 7 and 8, two slides were hybridized for each time-point using cDNAs prepared from duplicate cultures. Slides were scanned on a GSI Lumonics ScanArray 400XL (Agilent Technologies).

Quantitative reverse transcriptase-polymerase chain reaction (QT-RT-PCR)
For each gene analyzed, full-length cDNAs were amplified and then subcloned into the TA-cloning vector (Invitrogen, Carlsbad, CA). The resulting plasmids were then used to prepare a tenfold dilution series of amplification standards from 107 to 101 copies per µl.

For QT-RT-PCR, 1 µg total RNA was reverse-transcribed using the First Strand cDNA synthesis kit from Roche (Nutley, NJ). cDNA, equivalent to 5 or 10 ng RNA, was used as a template for real-time PCR with the following reaction conditions: 0.2 mM dNTP, 100 nM primers, 1 U Taq polymerase, SYBR Green diluted 1/30,000 in a final volume of 25 µl. Rox (67 nM) was included in each reaction as an internal standard. Annealing temperature and magnesium concentration were optimized for each primer pair, as listed in Table 1 . Standards and samples were amplified in duplicate using an Mx4000 cycler (Stratagene, La Jolla, CA), and product specificity was verified by melting curve analysis. For the amplification standards, cycle threshold (Ct) was plotted against Log10 copy number to obtain the standard curve used to calculate cDNA copy numbers from the Ct observed for test samples. mRNA transcript copy numbers were calculated assuming a 39% conversion from RNA to cDNA, as indicated in the technical data supplied by the manufacturer. Finally, mRNA copy numbers were calibrated to HPRT expression, assuming constant expression of this control gene under our experimental conditions.


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  		Table 1. Primers and PCR Conditions for Real-Time RT-PCR

Array production and data analysis
C6-amine-linked 50-mer oligonucleotide probes (MWG Biotech) were resuspended in spotting buffer A (MWG Biotech) at a concentration of 50 µM and then spotted onto epoxy silane-coated slides (MWG Biotech) using a Eurogentec robot with eight or 16 pins. Probes were arranged in two 384-well plates so that each probe was spotted in duplicate by two different pins. For experiments with cDNA from Donors 4, 6, 7, and 8, each probe was spotted in sextuplicate on each slide, whereas for analysis of Donor 5 cDNA, each probe was spotted in quadruplicate.

Scan data were acquired using ScanArray software, and the resulting TIFF files were analyzed using Genepix Pro 4.0 to extract median fluorescence intensities (MFI). Spots with obvious experimental artifacts were manually flagged, and then data were analyzed using in-house software (MADSCAN [25 ], http://www.madtools.org). First, MADSCAN physically validates each spot on a chip and filters out flawed data. Second, a Loess fitness algorithm is applied locally to minimize signal-dependent, nonlinear biases between the two fluorescence channels [26 ]. This normalization curve is constructed using a set of invariant genes selected a posteriori by the rank invariant method [27 ]. Mean values of the Log2 fluorescence ratio (M) were then calculated for each gene on the chip. Of the 60 positive control genes represented on DC-dedicated arrays, 49 were found to show invariant expression during the course of DC maturation and were therefore designated as the invariant control or "cont" genes. For each hybridization, the mean (Mcont) and standard deviation (SDcont) of the mean M values of these 49 genes were used to define the limits of differential expression for the other genes on the chip. A given gene, x, was considered to be significantly induced if the mean Mx were greater than Mcont + 2.576 SDcont and significantly repressed if Mx were less than Mcont – 2.576 SDcont, corresponding to a P< 0.01, assuming a normal distribution. This analysis takes into account differences in the data quality between chips and defines significant differential gene expression with respect to a predefined set of control genes rather than a theoretical value of M = 0. Threshold values of Mcont + 2.576 SDcont varied from 0.65 to 1.94, corresponding to a 1.6- to 3.8-fold induction.


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RESULTS
 
Design of DC-dedicated microarrays
To select the genes for incorporation into DC-dedicated microarrays, changes in the expression profiles of DC during maturation were first studied using the generalist Pan Human 30kA array from MWG Biotech, which incorporates 50-mer oligonucleotide probes specific for 9850 human genes. Results from our group had previously shown that maturation can be induced efficiently in monocyte-derived DC cultured in serum-free conditions by a combination of TNF-{alpha} plus polyI:C [28 ]. Monocyte-derived DC were therefore prepared from three unrelated donors, and maturation was induced by the addition of TNF-{alpha}, polyI:C, or the combination of both of these agents. Phenotypic maturation, as shown by increased surface expression of CD86, CD83, and CD40, was obtained in all three donors, although to a lesser extent in DC from Donor 2 (Fig. 1A 1B 1C ).



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  		Figure 1. Surface phenotype of DC after different maturation stimuli. Monocyte-derived DC were prepared from three unrelated donors, and at days 6–7, maturation was induced by the addition of 10 ng/ml TNF-{alpha} (TNF), 50 µg/ml polyI:C (pIC), or the combination of both of these agents (TNF+pIC). Immature DC (Im) underwent continued culture without addition of maturation agents. After 48 h, the surface phenotype of DC was determined by flow cytometry. Data are expressed as MFI for CD86 (A) and CD40 (C) staining, as 100% of DC were positive for these markers in all culture conditions. CD83 expression (B) is shown as the percentage of viable DC positive for CD83.

Total RNA was isolated from immature DC and DC after 14–16 h maturation. This time-point was chosen, as it represents the best compromise for the simultaneous detection of genes with early, intermediate, and late induction kinetics [20 ]. RNAs from the three individuals were pooled to reduce the effects of donor-specific variation, reverse-transcribed, and then labeled with Cy3 or Cy5. Three arrays were hybridized, directly comparing immature DC with DC matured with TNF-{alpha}, immature DC versus DC matured with polyI:C, and immature DC versus DC matured with TNF-{alpha} plus polyI:C. For each gene, induction or repression was measured by the M value, which is equivalent to the Log2 fold induction. Positive M values indicate up-regulation during DC maturation, and negative values indicate down-regulation. Two distinct expression profiles, with significant overlap, were observed, comparing maturation induced by TNF-{alpha} and polyI:C (Fig. 2 ). Consistent with our flow cytometry and previously published microarray data [19 ], TNF-{alpha} was clearly the weaker stimulus, inducing far fewer changes in gene expression than polyI:C (Table 2 ), and many genes, including IDO and IL-6, were induced exclusively by polyI:C but not TNF-{alpha} (Fig. 2) . Combining the two maturation stimuli appeared to produce additive but not synergistic effects, as the great majority of the genes induced by TNF-{alpha} and polyI:C, in combination, was induced to some extent by one or both of the two stimuli acting alone.



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  		Figure 2. Comparison of DC maturation induced by polyI:C versus TNF-{alpha} by hybridization on commercial microarrays. Log2 fluorescence ratios (M) are plotted for DC stimulated with 10 ng/ml TNF-{alpha} (x-axis) or 50 µg/ml polyI:C (y-axis). Each point refers to the fluorescence ratio of a single spot on the slide. Positive values of M indicate genes up-regulated after 14–16 h in the presence of the maturation stimulus, and negative values indicate down-regulated genes. A sample of the genes showing preferential induction by polyI:C (R1), common induction by polyI:C and TNF-{alpha} (R2), and preferential induction by TNF-{alpha} (R3) are listed, as are a number of genes down-regulated only by polyI:C (R4) or by both stimuli (R5).


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  		Table 2. Summary of Data from MWG 30kA Arrays

After excluding sequences without well-characterized protein products (annotated as KIAA or hypothetical proteins in GenBank), genes that showed differential expression (M>1.5 or M<–1.5) during maturation in at least two hybridizations were selected for incorporation into DC-dedicated microarrays. Genes with known differential expression in DC, such as CD83, CD33, and IL-12a, were incorporated even if these criteria were not met, or if the gene was not represented on the MWG30kA array. In addition, genes reported in the literature as specific for particular DC subsets were selected, as were genes with known or suspected immunological functions that had not shown differential expression under our experimental conditions. Finally, five negative control and 60 positive control sequences were selected to give a total 383 genes represented on the DC Chip. Positive controls were preferentially drawn from genes involved in basic metabolic processes, such as protein synthesis and energy metabolism, and were selected over a range of fluorescence intensities to ensure accurate normalization over the whole detection range of the scanner. The full list of genes incorporated into the DC-dedicated microarray is shown in Supplementary Table 1 and summarized in Table 3 .


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  		Table 3. Composition of DC-Dedicated Microarrays

DC-dedicated arrays were initially tested using the same pools of RNAs hybridized on the MWG 30kA array in the previous experiments. Overall, there was good concordance between the two types of arrays. However, the differential expression of 33 genes observed using the commercial array was not confirmed with the dedicated array (data not shown). Twenty-eight of these 33 genes did not have functions related to DC biology and were therefore likely to be artifacts generated by the use of a single probe per gene in the commercial array. Dye-swap experiments demonstrated the reproducibility of results obtained with DC-dedicated arrays but also identified two genes that gave consistently lower signals when labeled with Cy5 as a result of a bias in fluorochrome incorporation. Overall, 30 genes were found to have given unreliable results in experiments with commercial microarrays, and these genes are denoted as artifacts in Supplementary Table 1.

Use of dedicated arrays to study kinetics of DC maturation
Dedicated microarrays were then used to study the kinetics of DC maturation in three unrelated donors. Immature DC from Donors 4 and 5 were stimulated with TNF-{alpha} plus polyI:C, and cells were harvested at different time-points from 2 to 48 h after the addition of the maturation stimulus. DC from Donor 6 were harvested at 8 and 48 h after the start of maturation. Microarrays were hybridized using each donor’s immature DC as a reference sample. The full results of this experiment are given in Supplementary Table 1. Overall, DC from Donors 5 and 6 up-regulated ~100 genes, whereas those from Donor 4 only up-regulated 72 genes (Table 4 ). Donors 4 and 5 down-regulated ~70 genes, and only 29 genes were significantly down-regulated in DC from Donor 6.


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  		Table 4. Comparison of the Maturation Response of DC from Three Donors

Analyzing the kinetics of the changes in gene expression (Fig. 3 ) uncovered two types of maturation response. At 2–4 h after the induction of maturation, DC from Donor 4 had relatively few up-regulated genes but showed rapid and sustained down-regulation of many genes. Conversely, in DC from Donor 5, many up-regulated genes were rapidly induced, and few genes were down-regulated up to 24 h after the induction of maturation (Fig. 3) . Although fewer time-points were analyzed, the response of DC from Donor 6 closely resembled that of Donor 5. After 8 h maturation, up-regulated genes vastly outnumbered down-regulated genes, and the number of down-regulated genes remained low throughout the experiment.



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  		Figure 3. Kinetics of maturation response in DC from three donors. The number of significantly up- and down-regulated genes at different time-points after maturation induced by 10 ng/ml TNF-{alpha} plus 50 µg/ml polyI:C is shown for Donors 4–6. Significant differential expression was defined relative to the 49 invariant control genes. Probes giving artifactual results were excluded from this analysis.

Comparison of DC Chip data with flow cytometry and ELISA
It is somewhat surprising that microarrays did not detect up-regulation of standard DC markers in all three of these donors. To determine whether these microarray results reflected real phenotypic differences between the donors, microarray data were compared with the DC phenotypes determined by flow cytometry and ELISA.

Surface expression of CD83 was clearly detected in mature DC from Donors 5 and 6, whereas less than 10% of DC from Donor 4 were CD83+ (Fig. 4A ). Microarray data showed early induction of CD83 mRNA in Donors 5 and 6 but not in Donor 4, which is consistent with the flow cytometry data (Fig. 4B) . Similarly, mature DC from Donor 5 showed strong surface expression of CD86 and significant induction of CD86 mRNA. In Donor 4, surface expression of CD86 was weakly up-regulated, but this slight change was not detected on microarrays, and in Donor 6, no induction of CD86 was observed by flow cytometry or by microarray hybridization (Fig. 4C and 4D) .



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  		Figure 4. Validation of microarray results by flow cytometry and ELISA. (A, B) Expression of CD83 in DC from Donors 4 (solid bars), 5 (open bars), and 6 (hatched bars) during maturation. (A) Percentage of CD83+ cells by flow cytometry. Im, Immature DC; TNF+pIC, DC after 48 h maturation induced by 10 ng/ml TNF-{alpha} plus 50 µg/ml polyI:C. (B) Microarray results for CD83 shown as Log2 fluorescence ratios (M) comparing immature DC with DC matured at early (T=12 h Donors 4 and 5, T=8 h Donor 6) and late (T=48 h) time-points. *, Significant (P<0.01) differential expression with respect to invariant control genes. (C, D) Expression of CD86 in DC from Donors 4 (solid bars), 5 (open bars), and 6 (hatched bars) during maturation. (C) MFI of CD86 staining by flow cytometry. (D) Microarray results for CD86 shown as Log2 fluorescence ratios (M) comparing immature DC with DC matured at early (T=12 h Donors 4 and 5, T=8 h Donor 6) and late (T=48 h) time-points. *, Significant (P<0.01) differential expression with respect to invariant control genes. (E, F) Correlation of microarrays results for the expression of IL-12a mRNA with secretion of IL-12 p70 measured by ELISA in Donors 4 (E) and 5 (F). Microarray results are shown as Log2 fluorescence ratios (M) comparing immature DC with DC matured at different time-points. ELISA results are expressed as the rate of production of the cytokine (pg/ml/h) per million DC. For example, DC from Donor 4 produced 6.7 pg/ml/h IL-12 p70 over the period from 12 to 24 h after addition of the maturation stimulus.

In addition, the expression of IL-12a mRNA (which codes for the p35 subunit of IL-12), detected by the DC Chip, was confirmed by IL-12 p70 ELISA (Fig. 4E and 4F) . In Donor 4, induction of the IL-12a gene peaked after 12 h of maturation. Indeed, this was the only time-point at which IL-12a expression was up-regulated significantly compared with immature DC. This peak of mRNA expression preceded IL-12 p70 secretion, which reached its maximum from 12 to 24 h after maturation (Fig. 4E) . In contrast, microarray results from Donor 5 never showed significant up-regulation of IL-12a expression, and indeed, IL-12 p70 secretion was barely detected (Fig. 4F) . In Donor 6, significant induction of IL-12a mRNA was detected by microarrays at 8 h but not at 48 h after maturation (Supplementary Table 1). IL-12 p70 secretion was also confirmed by ELISA for this donor (1 million DC produced 486 pg/ml IL-12 p70 over 48 h after stimulation with TNF-{alpha}+poly I:C, and no IL-12 p70 was produced by unstimulated DC over 48 h). For CD83, CD86, and IL-12 p70, the DC Chip therefore gave results that were consistent with standard techniques.

Coordinated regulation of genes involved in tryptophan metabolism during DC maturation
Despite these differences in the maturation response, many genes showed significant differential expression in all three donors, including a cluster of enzymes involved in tryptophan metabolism. It has recently become clear that IDO expression by DC is involved in the induction of tolerance in vitro and in mouse models in vivo [29 30 31 ]. Results obtained with DC-dedicated microarrays indicated that in addition to IDO, WARS, KYNU, and KMO were also strongly up-regulated during DC maturation induced by TNF-{alpha} + polyI:C. QT-RT-PCR confirmed these results (Fig. 5 ) and showed that microarray data gave a good, qualitative description of the regulation of these genes during DC maturation. IDO was clearly the most strongly induced gene, and WARS, KYNU, and KMO were all induced to the same extent (six- to 20-fold by QT-RT-PCR). RT-PCR and microarray data showed maximal induction of KMO after only 2 h, and IDO, KYNU, and WARS showed slower induction, peaking at 12 h. In contrast to the strong up-regulation of these genes during DC maturation, HAAO was not induced, and although microarray data did not reveal statistically significant regulation of this gene at any time-point, RT-PCR indicated that HAAO was weakly down-regulated 24 to 48 h after DC maturation. Overall, there was good qualitative agreement between microarray and RT-PCR data, and together, these results demonstrate the coordinated transcriptional regulation of enzymes involved in tryptophan metabolism during DC maturation.



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  		Figure 5. Expression of genes involved in tryptophan metabolism during DC maturation. Diagram of the major pathways of tryptophan catabolism in DC. (B–E) Expression of mRNAs for WARS (•), IDO ({diamondsuit}), KMO ({blacksquare}), KYNU ({blacktriangleup}), and HAAO ({circ}) in DC from Donor 4 (B, C) and Donor 5 (D, E). mRNAs were quantified by real-time PCR (B, D) and shown as Log2 fold-induction values, corrected relative to HPRT mRNA for direct comparison with the relative changes in mRNA expression determined by hybridization on dedicated microarrays (C, E).

Reproducibility of DC Chip data
For CD83, CD86, and IL-12, differences in the maturation response between donors that were observed by microarray hybridization corresponded to real phenotypic differences in DC derived from Donors 4–6. However, it is impractical to validate microarray data by independent techniques for all of the genes represented on the DC Chip. It was therefore important to test the reproducibility of results obtained with the DC Chip to determine to what extent apparent differences in microarray data for any given gene reflect underlying biological differences between samples and not variability from one chip to another. To address this question, DC from two more donors were prepared. Immature DC from each donor were distributed in four separate cultures, and maturation was induced by the addition of TNF-{alpha} + poly I:C. Two wells were harvested after 8 h and two wells after 34 h maturation. RNA extraction, cDNA synthesis and labeling, and hybridization on DC Chips were performed separately for each well. RNA from immature DC from both donors was pooled and used as a common reference sample for all hybridizations. The results of this experiment are given in full in Supplementary Table 2 and are summarized in Table 5 .


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  		Table 5. DC Chip Reproducibility and Comparison between Two Donors

Reproducibility between duplicate chips was assessed in two ways. First, mean M values from duplicate chips were plotted (Fig. 6A 6B 6C ) and found to be highly correlated (mean r2=0.90). Correlation coefficients from duplicate data were significantly higher than those observed comparing data from different donors (mean r2=0.40, P<0.005 by Student’s t-test). For example, as shown in Figure 6D , the mean M values observed in DC from Donors 7 and 8 after 34 h maturation are only weakly correlated, and indeed, a group of genes that are down-regulated in DC from Donor 7 but not in DC from Donor 8 is clearly visible (Fig. 6D , circled region). Second, qualitative agreement between duplicate chips was tested after designating each gene as up-regulated, down-regulated, or showing no change in expression and then comparing the resulting categorical data with the weighted {kappa} statistic. Duplicate chips were found to give good agreement (0.65<{kappa}<0.80), and results were rather more divergent at 34 h ({kappa}=0.66 for Donor 7, 0.69 for Donor 8) than at 8 h ({kappa}=0.80 for Donor 7, 0.77 for Donor 8). In contrast, different permutations of data from two different donors at the same time-point gave moderate agreement at 8 h maturation (0.42<{kappa}<0.57) and poor-to-fair agreement at 34 h maturation (0.16<{kappa}<0.32). Hence, differences between donors were greater than differences between duplicate cultures set up from immature DC derived from the same donor.



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  		Figure 6. Correlation between microarray results from duplicate cultures. Log2 fluorescence ratios (M) are plotted for DC after 8 h (A) and 34 h (B–D) maturation induced by TNF-{alpha} plus polyI:C. Each point represents the mean M of sextuplicate probes for a single gene. Positive values of M indicate up-regulated genes, and negative values indicate down-regulated genes. Data from duplicate hybridizations are shown in (A) Donor 7 DC, 8 h maturation; (B) Donor 7 DC, 34 h maturation; and (C) Donor 8 DC, 34 h maturation. (D) Comparison of Donor 7 DC with Donor 8 DC at 34 h maturation. The circled region indicates genes that were down-regulated in Donor 7 DC but not Donor 8 DC.

Bearing this in mind, it is clear that the expression profiles of the two different donors diverged over time. At 8 h, a similar number of genes were up-regulated in Donors 7 and 8, and most of these were up-regulated in both donors (Table 5) , which showed few down-regulated genes at 8 h. In contrast, more extensive differences were observed after 34 h maturation. Although the number of genes up-regulated at this time-point was again similar in both donors, a smaller proportion of these genes was up-regulated in both donors. This is likely to reflect differences in the kinetics of maturation rather than radically different maturation programs, as nine of the 11 genes up-regulated at 34 h in Donor 7 but not in Donor 8 were up-regulated at 8 h in Donor 8, and similarly, seven of the 10 genes up-regulated at 34 h in Donor 8 but not in Donor 7 were up-regulated at 8 h in Donor 7. However, a more extensive difference was observed with respect to down-regulated genes, as 44 genes were down-regulated at 34 h in DC from Donor 7 but not in DC from Donor 8. Again, this may reflect differences in maturation kinetics, as down-regulation of gene expression during DC maturation occurs later than up-regulation. Down-regulation of these genes may have been retarded rather than absent in Donor 8.


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DISCUSSION
 
Design of DC Chips
Several clinical and research applications would benefit from the availability of routine transcriptional profiling of DC. In particular, it would significantly increase the power of comparisons of different protocols for the preparation of DC destined for tumor immunotherapy and comparisons of different subsets of circulating DC in healthy individuals and patients suffering from immune pathologies. Currently available commercial microarrays are not well-suited to these types of application, as the vast majority of probes represented on these arrays have no relevance for DC biology. The main objective of this study was therefore to develop and validate a new molecular tool for the study of DC that would allow us to measure the expression of 200–300 genes of interest in DC biology on a routine basis.

The choice of genes to incorporate on DC-dedicated chips was guided by the results obtained using commercial microarrays and by referring to published data. We used 50-mer oligonucleotide arrays produced by MWG Biotech for the initial experiments so that results from the commercial arrays and our in-house microarrays could be compared directly, as the oligonucleotide probes and hybridization conditions were identical for the two types of array. This dual approach allowed us to incorporate several genes that had not previously been identified as showing differential expression during DC maturation, such as BZRP, TLN1, CLEC1, LIR6, LSP1, and RAC2, and a number of genes known to be involved in DC biology, but which were not represented on the commercial microarray that we used (DC-associated lectin-1, DC-specific transmembrane protein, B7-h2) or did not show strong differential expression at the time-point analyzed with commercial microarrays [IL-12a, CD83, CC chemokine ligand 18 (CCL18), TNF-{alpha}].

Validation and limitations of DC Chips
The validity of the microarray data we obtained was assessed in three ways. First, results obtained with the DC Chip were compared with the literature and found to be coherent with previously published microarray studies. For example, in agreement with Huang et al. [20 ] and Tureci et al [23 ], we observed rapid up-regulation of a number of proinflammatory cytokines and chemokines, including TNF-{alpha}, IL-6, IL-1ß, IL-8, CCL3 [macrophage-inflammatory protein-1{alpha} (MIP-1{alpha})], and CCL4 (MIP-1ß), and slower induction kinetics were observed for genes such as CCR7, CXC chemokine receptor 4, CD80, CD86, PA28a, and PA28b. Second, for a limited number of genes, microarray data were found to be consistent with results obtained by flow cytometry, ELISA, and QT-RT-PCR. Our DC-dedicated chips did therefore give reliable results. The sensitivity of the microarrays was also satisfactory. For example, quite low levels of IL-12p70 secretion by DC from Donor 4 were detected by microarray hybridization.

Third, the reproducibility of DC Chip data was assessed in experiments hybridizing two arrays with cDNA prepared from duplicate cultures. Overall, there was good agreement between duplicates, but differences were not negligible. For example, genes observed to be up-regulated on one chip were only found to be up-regulated on the duplicate chip 80% of the time. The major factor contributing to these differences appears to be statistical noise. In many cases where results were qualitatively discordant between duplicates (for example, significantly up-regulated expression found on Chip A but no significant change found on Chip B), inspection of M values showed that the same tendency was often present in the duplicate array. This indicates that the technique used to determine significantly up- and down-regulated genes was not sufficiently powerful, and it is reasonable to expect that application of more sophisticated statistical techniques, such as Significance Analysis of Microarrays [32 ], will increase the concordance between duplicates.

Two general limitations of microarray experiments also apply to our data. First, it is clear that the fold-induction values obtained from microarrays are not quantitatively accurate. Comparison of DNA chip results with QT-RT-PCR reinforced this point and indicated that the principal reason for this discrepancy may be the limited dynamic range of the microarray technology that we used—neither low frequency transcripts nor highly expressed transcripts can be quantified accurately—and this results in underestimation of fold induction for certain genes. This may also be the reason why the down-regulation of HAAO expression was not reliably detected by our microarrays. HAAO mRNA was reduced from 790 copies/ng RNA in immature DC to 200 copies/ng in Donor 4 and from 1350 to 720 copies/ng in Donor 5. As 106 DC yielded ~10 µg total mRNA, these values represent two to 13 mRNA copies per cell, which is at the lower limit of our detection range where sensitivity is low, especially for down-regulation. Overall, our microarray results must be considered as qualitative indications of up- or down-regulation and not quantitative measures of gene induction. Second, as mRNA profiling gives a snapshot of genes expressed at a given time, at least three time-points (early, intermediate, late) need to be studied for each condition; otherwise, slight differences in DC maturation kinetics may be misinterpreted as major differences in the profile of DC responses.

Original observations from DC chip experiments
Although the DC Chip did not give an exhaustive description of all the transcriptional events occurring in DC, it did allow us to make several new observations concerning DC maturation. First, our results confirm those of a recent publication showing down-regulation of CCL18 expression during DC maturation [33 ]. However, CCL18 was not the only chemokine to show this pattern of expression, as CX3 chemokine ligand 1 (CX3CL1) expression was also strongly diminished in DC from Donors 4, 5, and 7. Recent publications show that there are two populations of monocytes that differ by their expression of CX3 chemokine receptor 1 (CX3CR1) and show different patterns of tissue migration [34 , 35 ]. Strong expression of CX3CL1 by immature DC could play a role in recruiting the CX3CR1+ monocyte subset in the steady state, and down-regulation of CX3CL1 during maturation could be important in the exclusion of CX3CR1+ monocytes from sites of inflammation. Second, microarray results revealed maturation-dependent expression of some recently described DC-specific molecules, such as the lectin CLEC1 [36 ], which was up-regulated rapidly during maturation of DC from four out of five donors, and the CD20-like molecule MS4A6A [21 ], which was down-regulated in DC from four out of five donors.

Furthermore, we observed coordinated regulation of enzymes involved in tryptophan metabolism during DC maturation. It has recently become clear that IDO expression by DC plays an important role in the maintenance and induction of T cell tolerance. However, the precise mechanism involved remains controversial. According to Munn and colleagues [37 ], T cell activation in the absence of tryptophan induces anergy, whereas other groups have proposed that IDO acts by the accumulation of toxic tryptophan metabolites that induce T cell apoptosis [38 , 39 ]. In the present work, microarray and RT-PCR data pointed to a coordinated up-regulation of enzymes involved in the degradation of tryptophan to anthranilate, 3-hydroxy kynurenine and 3-hydroxy-anthranilate, and HAAO, which converted 3-hydroxy-anthranilate to quinolinate, had a low level of baseline expression, and was not induced during DC maturation. Although it is difficult to extrapolate from mRNA expression to levels of enzyme activity, the most likely effect of these changes would be an accumulation of 3-hydroxy-kynurenine and 3-hydroxy-anthranilate, as well as kynurenine. It is interesting that these three tryptophan metabolites are all toxic for T cells, whereas quinolinate is not [38 , 39 ]. Our results are consistent with the view that the tolerogenic effects of IDO act via toxic tryptophan metabolites and imply that although IDO is the rate-limiting enzyme for the degradation of tryptophan, its role in immune regulation may depend, to some extent, on the coordinated regulation of KMO, KYNU, and HAAO.

Why mature DC should express genes that are involved in the induction of tolerance is not clear. However, IDO also has an important function in the innate immune response to bacteria, parasites [40 ], and viruses [41 ]. Its up-regulation during DC maturation in response to polyI:C may therefore be more relevant to the inhibition of viral replication at the site of infection rather than the subsequent regulation of the T cell response. The molecular mechanism responsible for the spectacular up-regulation of IDO expression during DC maturation is also unclear. In most cell types, induction of IDO is dependent on interferon (IFN)-{gamma}, suggesting that autocrine IFN-{gamma} production by DC could be involved, possibly in synergy with the TNF-{alpha} [42 ], which was a component of the DC maturation stimulus. Although we did not measure IFN-{gamma} production by DC, we consider such an indirect mechanism to be unlikely for two reasons. First, IFN-{gamma} production has never been reported in human monocyte-derived DC. Second, IDO was rapidly induced during DC maturation, showing significant up-regulation after 4 h by microarray analysis and QT-RT-PCR, which is more consistent with a direct effect of the maturation stimulus.


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CONCLUSION
 
DC-dedicated microarrays are a new molecular tool for DC phenotyping, which give results consistent with RT-PCR, flow cytometry, and ELISA techniques. One million DC is required per hybridization, although duplication is necessary to ensure the reliability of results. The availability of these DC-dedicated microarrays will make it possible to couple microarray data with functional tests of antigen presentation, T cell costimulation, and tolerance induction to define the functional consequences of different DC maturation profiles. In addition, the DC Chip described herein could be used to monitor the maturation status of DC destined for clinical use in cancer immunotherapy. Clinical trials of DC therapy often involve multiple injections of 107 DC per injection, so hybridization of two duplicate DC Chips using only 2 x 106 DC could easily be integrated into existing protocols.


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ACKNOWLEDGEMENTS
 
This work was supported by the Association pour la Recherche sur le Cancer (ARC). D. M. was the recipient of a post-doctoral fellowship granted by ARC. S. T-R. was supported by a grant from the Ligue Contre le Cancer de Vendée. The authors thank Martine LeCunff for valuable technical advice at several stages of this work.

Received January 18, 2005; revised March 10, 2005; accepted April 17, 2005.


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REFERENCES
 
    1
  1. Banchereau, J., Steinman, R. M. (1998) Dendritic cells and the control of immunity Nature 392,245-252[CrossRef][Medline]
    2. 2
  2. Banchereau, J., Briere, F., Caux, C., Davoust, J., Lebecque, S., Liu, Y. J., Pulendran, B., Palucka, K. (2000) Immunobiology of dendritic cells Annu. Rev. Immunol. 18,767-811[CrossRef][Medline]
    3. 3
  3. Janeway, C. A., Jr, Medzhitov, R. (2002) Innate immune recognition Annu. Rev. Immunol. 20,197-216[CrossRef][Medline]
    4. 4
  4. Salomon, B., Bluestone, J. A. (2001) Complexities of CD28/B7: CTLA-4 costimulatory pathways in autoimmunity and transplantation Annu. Rev. Immunol. 19,225-252[CrossRef][Medline]
    5. 5
  5. Bleharski, J. R., Niazi, K. R., Sieling, P. A., Cheng, G., Modlin, R. L. (2001) Signaling lymphocytic activation molecule is expressed on CD40 ligand-activated dendritic cells and directly augments production of inflammatory cytokines J. Immunol. 167,3174-3181[Abstract/Free Full Text]
    6. 6
  6. Moser, M., Murphy, K. M. (2000) Dendritic cell regulation of TH1-TH2 development Nat. Immunol. 1,199-205[CrossRef][Medline]
    7. 7
  7. Nakanishi, K., Yoshimoto, T., Tsutsui, H., Okamura, H. (2001) Interleukin-18 regulates both Th1 and Th2 responses Annu. Rev. Immunol. 19,423-474[CrossRef][Medline]
    8. 8
  8. Corinti, S., Albanesi, C., la Sala, A., Pastore, S., Girolomoni, G. (2001) Regulatory activity of autocrine IL-10 on dendritic cell functions J. Immunol. 166,4312-4318[Abstract/Free Full Text]
    9. 9
  9. Dzionek, A., Fuchs, A., Schmidt, P., Cremer, S., Zysk, M., Miltenyi, S., Buck, D. W., Schmitz, J. (2000) BDCA-2, BDCA-3, and BDCA-4: three markers for distinct subsets of dendritic cells in human peripheral blood J. Immunol. 165,6037-6046[Abstract/Free Full Text]
    10. 10
  10. MacDonald, K. P., Munster, D. J., Clark, G. J., Dzionek, A., Schmitz, J., Hart, D. N. (2002) Characterization of human blood dendritic cell subsets Blood 100,4512-4520[Abstract/Free Full Text]
    11. 11
  11. Rissoan, M. C., Soumelis, V., Kadowaki, N., Grouard, G., Briere, F., de Waal Malefyt, R., Liu, Y. J. (1999) Reciprocal control of T helper cell and dendritic cell differentiation Science 283,1183-1186[Abstract/Free Full Text]
    12. 12
  12. Ito, T., Amakawa, R., Kaisho, T., Hemmi, H., Tajima, K., Uehira, K., Ozaki, Y., Tomizawa, H., Akira, S., Fukuhara, S. (2002) Interferon-{alpha} and interleukin-12 are induced differentially by Toll-like receptor 7 ligands in human blood dendritic cell subsets J. Exp. Med. 195,1507-1512[Abstract/Free Full Text]
    13. 13
  13. Langenkamp, A., Nagata, K., Murphy, K., Wu, L., Lanzavecchia, A., Sallusto, F. (2003) Kinetics and expression patterns of chemokine receptors in human CD4+ T lymphocytes primed by myeloid or plasmacytoid dendritic cells Eur. J. Immunol. 33,474-482[CrossRef][Medline]
    14. 14
  14. Langenkamp, A., Messi, M., Lanzavecchia, A., Sallusto, F. (2000) Kinetics of dendritic cell activation: impact on priming of TH1, TH2 and nonpolarized T cells Nat. Immunol. 1,311-316[CrossRef][Medline]
    15. 15
  15. de Jong, E. C., Vieira, P. L., Kalinski, P., Schuitemaker, J. H., Tanaka, Y., Wierenga, E. A., Yazdanbakhsh, M., Kapsenberg, M. L. (2002) Microbial compounds selectively induce Th1 cell-promoting or Th2 cell-promoting dendritic cells in vitro with diverse th cell-polarizing signals J. Immunol. 168,1704-1709[Abstract/Free Full Text]
    16. 16
  16. Caron, G., Delneste, Y., Roelandts, E., Duez, C., Bonnefoy, J. Y., Pestel, J., Jeannin, P. (2001) Histamine polarizes human dendritic cells into Th2 cell-promoting effector dendritic cells J. Immunol. 167,3682-3686[Abstract/Free Full Text]
    17. 17
  17. Dietz, A. B., Bulur, P. A., Knutson, G. J., Matasic, R., Vuk-Pavlovic, S. (2000) Maturation of human monocyte-derived dendritic cells studied by microarray hybridization Biochem. Biophys. Res. Commun. 275,731-738[CrossRef][Medline]
    18. 18
  18. Le Naour, F., Hohenkirk, L., Grolleau, A., Misek, D. E., Lescure, P., Geiger, J. D., Hanash, S., Beretta, L. (2001) Profiling changes in gene expression during differentiation and maturation of monocyte-derived dendritic cells using both oligonucleotide microarrays and proteomics J. Biol. Chem. 276,17920-17931[Abstract/Free Full Text]
    19. 19
  19. Granucci, F., Vizzardelli, C., Virzi, E., Rescigno, M., Ricciardi-Castagnoli, P. (2001) Transcriptional reprogramming of dendritic cells by differentiation stimuli Eur. J. Immunol. 31,2539-2546[CrossRef][Medline]
    20. 20
  20. Huang, Q., Liu, D., Majewski, P., Schulte, L. C., Korn, J. M., Young, R. A., Lander, E. S., Hacohen, N. (2001) The plasticity of dendritic cell responses to pathogens and their components Science 294,870-875[Abstract/Free Full Text]
    21. 21
  21. Ahn, J. H., Lee, Y., Jeon, C., Lee, S. J., Lee, B. H., Choi, K. D., Bae, Y. S. (2002) Identification of the genes differentially expressed in human dendritic cell subsets by cDNA subtraction and microarray analysis Blood 100,1742-1754[Abstract/Free Full Text]
    22. 22
  22. Ju, X. S., Hacker, C., Madruga, J., Kurz, S. M., Knespel, S., Blendinger, G., Rose-John, S., Martin, Z. (2003) Towards determining the differentiation program of antigen-presenting dendritic cells by transcriptional profiling Eur. J. Cell Biol. 82,75-86[CrossRef][Medline]
    23. 23
  23. Tureci, O., Bian, H., Nestle, F. O., Raddrizzani, L., Rosinski, J. A., Tassis, A., Hilton, H., Walstead, M., Sahin, U., Hammer, J. (2003) Cascades of transcriptional induction during dendritic cell maturation revealed by genome-wide expression analysis FASEB J. 17,836-847[Abstract/Free Full Text]
    24. 24
  24. Chaussabel, D., Semnani, R. T., McDowell, M. A., Sacks, D., Sher, A., Nutman, T. B. (2003) Unique gene expression profiles of human macrophages and dendritic cells to phylogenetically distinct parasites Blood 102,672-681[Abstract/Free Full Text]
    25. 25
  25. Le Meur, N., Lamirault, G., Bihouee, A., Steenman, M., Bedrine-Ferran, H., Teusan, R., Ramstein, G., Leger, J. J. (2004) A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication Nucleic Acids Res. 32,5349-5358[Abstract/Free Full Text]
    26. 26
  26. Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng, V., Ngai, J., Speed, T. P. (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation Nucleic Acids Res. 30,e15[Abstract/Free Full Text]
    27. 27
  27. Tseng, G. C., Oh, M. K., Rohlin, L., Liao, J. C., Wong, W. H. (2001) Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects Nucleic Acids Res. 29,2549-2557[Abstract/Free Full Text]
    28. 28
  28. Spisek, R., Bretaudeau, L., Barbieux, I., Meflah, K., Gregoire, M. (2001) Standardized generation of fully mature p70 IL-12 secreting monocyte-derived dendritic cells for clinical use Cancer Immunol. Immunother. 50,417-427[CrossRef][Medline]
    29. 29
  29. Grohmann, U., Fallarino, F., Puccetti, P. (2003) Tolerance, DCs and tryptophan: much ado about IDO Trends Immunol. 24,242-248[CrossRef][Medline]
    30. 30
  30. Munn, D. H., Sharma, M. D., Lee, J. R., Jhaver, K. G., Johnson, T. S., Keskin, D. B., Marshall, B., Chandler, P., Antonia, S. J., Burgess, R., Slingluff, C. L., Jr, Mellor, A. L. (2002) Potential regulatory function of human dendritic cells expressing indoleamine 2,3-dioxygenase Science 297,1867-1870[Abstract/Free Full Text]
    31. 31
  31. Munn, D. H., Zhou, M., Attwood, J. T., Bondarev, I., Conway, S. J., Marshall, B., Brown, C., Mellor, A. L. (1998) Prevention of allogeneic fetal rejection by tryptophan catabolism Science 281,1191-1193[Abstract/Free Full Text]
    32. 32
  32. Tusher, V. G., Tibshirani, R., Chu, G. (2001) Significance analysis of microarrays applied to the ionizing radiation response Proc. Natl. Acad. Sci. USA 98,5116-5121[Abstract/Free Full Text]
    33. 33
  33. Vulcano, M., Struyf, S., Scapini, P., Cassatella, M., Bernasconi, S., Bonecchi, R., Calleri, A., Penna, G., Adorini, L., Luini, W., Mantovani, A., Van Damme, J., Sozzani, S. (2003) Unique regulation of CCL18 production by maturing dendritic cells J. Immunol. 170,3843-3849[Abstract/Free Full Text]
    34. 34
  34. Geissmann, F., Jung, S., Littman, D. R. (2003) Blood monocytes consist of two principal subsets with distinct migratory properties Immunity 19,71-82[CrossRef][Medline]
    35. 35
  35. Ancuta, P., Rao, R., Moses, A., Mehle, A., Shaw, S. K., Luscinskas, F. W., Gabuzda, D. (2003) Fractalkine preferentially mediates arrest and migration of CD16+ monocytes J. Exp. Med. 197,1701-1707[Abstract/Free Full Text]
    36. 36
  36. Colonna, M., Samaridis, J., Angman, L. (2000) Molecular characterization of two novel C-type lectin-like receptors, one of which is selectively expressed in human dendritic cells Eur. J. Immunol. 30,697-704[CrossRef][Medline]
    37. 37
  37. Munn, D. H., Shafizadeh, E., Attwood, J. T., Bondarev, I., Pashine, A., Mellor, A. L. (1999) Inhibition of T cell proliferation by macrophage tryptophan catabolism J. Exp. Med. 189,1363-1372[Abstract/Free Full Text]
    38. 38
  38. Frumento, G., Rotondo, R., Tonetti, M., Damonte, G., Benatti, U., Ferrara, G. B. (2002) Tryptophan-derived catabolites are responsible for inhibition of T and natural killer cell proliferation induced by indoleamine 2,3-dioxygenase J. Exp. Med. 196,459-468[Abstract/Free Full Text]
    39. 39
  39. Terness, P., Bauer, T. M., Rose, L., Dufter, C., Watzlik, A., Simon, H., Opelz, G. (2002) Inhibition of allogeneic T cell proliferation by indoleamine 2,3-dioxygenase-expressing dendritic cells: mediation of suppression by tryptophan metabolites J. Exp. Med. 196,447-457[Abstract/Free Full Text]
    40. 40
  40. Daubener, W., MacKenzie, C. R. (1999) IFN-{gamma}-activated indoleamine 2,3-dioxygenase activity in human cells is an antiparasitic and an antibacterial effector mechanism Adv. Exp. Med. Biol. 467,517-524[Medline]
    41. 41
  41. Adams, O., Besken, K., Oberdorfer, C., MacKenzie, C. R., Takikawa, O., Daubener, W. (2004) Role of indoleamine-2,3-dioxygenase in {alpha}/ß and {gamma} interferon-mediated antiviral effects against herpes simplex virus infections J. Virol. 78,2632-2636[Abstract/Free Full Text]
    42. 42
  42. Robinson, C. M., Hale, P. T., Carlin, J. M. (2005) The role of IFN-{gamma} and TNF-{alpha}-responsive regulatory elements in the synergistic induction of indoleamine dioxygenase J. Interferon Cytokine Res. 25,20-30[CrossRef][Medline]



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