| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
III. Department of Internal Medicine, University of Mainz, Germany;
* Genomic and Information Sciences, Hoffmann-La Roche Inc., Nutley, New Jersey, USA;
Department of Dermatology, University Hospital Zürich, Switzerland; and
Affymetrix, Santa Clara, California, USA
1Correspondence: III. Medizinische Klinik und Poliklinik, Johannes Gutenberg Universität Mainz, Obere Zahlbacherstr. 63, 55131 Mainz, Germany, E-mail: Tureci{at}mail.uni-mainz.de
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
Key Words: DC • B cells • apoptotic death
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
, 2)
. This maturation is irreversible and ends with apoptotic death (3)
.
DCs loaded with tumor antigens have become the centerpiece of clinical trials testing active immunotherapy strategies. Promising pilot studies have induced specific anticancer responses, including some clinical responses (4
, 5)
. Current clinical trials are still in phase I, with many differences in study design and execution. Important variables include the source of DCs, the choice of antigens, the method of antigen loading and the route and timing of administration (6)
. The requirement for and the method of DC maturation are receiving particular attention. This is due to observations from in vitro studies and animal models demonstrating that mature DCs induce more potent antigen-specific T cell responses than immature DCs. Furthermore, preliminary observations in human studies suggest that immature DCs might actually down-regulate antigen-specific T cell responses but mature DCs augment them (7)
.
There is still much debate on how to define DC maturation, how to maintain the maturational status, and how to harmonize the route of administration with the state of the DC preparation.
The prerequisite for answering such questions is the dissection of DC maturation at the molecular level. In fact, identification of DC-associated gene products and maturation-induced changes has paved the way toward a better molecular understanding of dendritic cell immunobiology (8)
. However, how DCs undergo such dramatic and well-coordinated changes in phenotype and function is still not fully understood. Traditional experimental designs have been unable to sufficiently capture the biology of DC, given the rapid morphing of this cell type and the functional switches made during DC maturation.
Oligonucleotide microarray technology is a powerful means for systematically and extensively assessing entire transcriptomes (9)
. The present study was designed to explore transcriptional changes accompanying maturation of dendritic cells in response to CD40L-triggering (10)
using high-density oligonucleotide arrays. To capture the sequentially changing properties of DCs during the maturation process, kinetic studies by quantitative real-time PCR were performed to identify different coregulated functional classes along the time course. Our observations provide further understanding of the biological function of DCs, allow dissection of the temporo-spatial topography of immune responses, and thus teach crucial lessons concerning design of clinical studies.
|
MATERIALS AND METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
Flow cytometry
Analysis of cell surface antigens was performed by flow cytometry (FACScan, Becton Dickinson). Cells were washed, resuspended in PBS, added to each fluorescently labeled antibody diluted to the optimal concentration, mixed, and incubated for 20 min on ice in the dark. Labeled cells were then washed, fixed in 1% paraformaldehyde, and analyzed for fluorescence. Data analysis was based on examination of 10,000 cells/sample. Staining was performed with the following fluorescein isothiocyanate- (FITC) or phycoerythrin- (PtdEtn) labeled monoclonal antibodies: anti-CD83, anti-CD86, anti-CD14, and anti-HLA-DR (all from Becton Dickinson, Rutherford, NJ, USA). For intracellular staining with polyclonal affinity-purified rabbit-antibody against IAP-B and IAP-C (R&D Systems, Abingdon, U K), fixation was performed under permeabilizing conditions with 2% paraformaldehyde/0.1% saponin; 0.1% saponin was included in all buffers.
Immunofluorescence
Immature and mature DCs were generated as described but in chamber slides (Sigma, Munich, Germany). Before staining, cells were fixed with PBS/1% paraformaldehyde and permeabilized in 0.1% saponin/PBS. Staining of fixed cells was performed with monoclonal mouse anti-human clusterin antibody (Alexis) diluted 1:100 in PBS/2%FCS and subsequently with Cy3-conjugated anti-mouse IgG Fab fragment. For immunofluorescence analysis, cells were counterstained with Hoechst stain. Coverslips were mounted on slides in Slow-Fade (Molecular Probes, Eugene, OR, USA).
RNA extraction and labeling
Total RNA was extracted from snap-frozen human cells using "Ultraspec RNA isolation kits" (Biotecx, Houston, TX, USA) and purified using "RNeasy mini kits" (Qiagen, Valencia, CA). Five to 20 µg of total RNA was converted into double-stranded cDNA by reverse transcription (GIBCO BRL Life Technologies, Grand Island, NY, USA) using T7-T24 primer (5'-GGC CAG TGA ATT GTA ATA CGA CTC ACT ATA GGG AGG CGG (dT24)). The double-strand cDNA product was cleaned up by phenol/chloroform/isoamyl extraction using phase lock gel (5 Prime-3 Prime, Inc., Boulder, CO, USA). Double-stranded cDNA was then converted into cRNA using an in vitro transcription (IVT) MEGAscriptTM T7 kit (Ambion, Austin, TX, USA) and biotinylated nucleotides, as described. The IVT product was purified using RNeasy mini kits and fragmented.
Hybridization, washing and staining
Hybridization of fragmented IVT product to Affymetrix GeneChip® arrays was performed as suggested by the manufacturer (Affymetrix, Santa Clara, CA, USA). Hybridized arrays were washed with nonstringent buffer (6xSSPE, 0.01% Tween 20, 0.005% antifoam), then Stringent buffer (100 mM MES, 0.1 M Na+, 0.01% Tween 20). The arrays were stained with R-phycoerythrin streptavidin (SAPE, Molecular Probes, P/N S-866), the signals were amplified with goat biotinylated anti-streptavidin antibody (Vector, P/N BA-0500), and the arrays were further stained with SAPE. The commercially available Affymetrix HuGeneFL (6800 human full-length genes) array was used.
Image analysis
Each hybridized Affymetrix GeneChip® array was scanned with an argon-ion laser scanner at 570 nm (Agilent/ Affymetrix, (GeneChip® version 3.1.) The initial absolute and comparison analysis were performed from images obtained from the scanned array using Affymetrix custom image analysis software.
Real-time quantitative PCR
Master 96-well plates were generated containing 5 ng/µL double-strand cDNA derived from total RNA using method described in "RNA isolation and labeling" section. Daughter plates were produced (final cDNA concentration: 40 pg/µL [200pg/well]) either manually or via robotics. Duplex real-time PCR (target gene and GAPDH as reference gene) on 96-well optical plates was performed using TaqMan® technology and analyzed on an ABI PRISM® PE7700 Sequence Detection System [Perkin-Elmer Applied Biosystems (PtdEtn), Lincoln, CA, USA], which uses the 5' nuclease activity of Taq DNA polymerase to generate a real-time quantitative DNA analysis assay. PCR mix per well (25 µL) consisted of commercially available, premixed GAPDH TaqMan® primers/probe (PtdEtn), 900 nM each of 5' and 3' primers, and 200 nM TaqMan® probe from each target gene, 
200pg cDNA and TaqMan® Universal PCR Master Mix (PtdEtn). The following PCR conditions were used: 50°C for 2 min, then 95°C for 10 min, followed by 40 cycles at 95°C/15 s and 62°C/1 min. The expression level of target gene was normalized to internal GAPDH and represented as relative Expression E = 1/2-(-
Ct), where Ct is the difference of threshold of cycle number between GAPDH and the target gene. Specific PCR primer pairs (5', 3') and fluorogenic probes (P) respectively were used for the following genes of interest: MIP-1a (sense GAG ACG AGC AGC CAG TGC TC, antisense GCA CAG ACC TGC CGG C, probe CCG TGT CAT CTT CCT AAC CAA GCG A), MIP-1b (sense TCT CAG CACC AAT GGG CTC, antisense GCT TCC TCG CGG TGT AAG AA, probe CCC TCC CAC CGC CTG CTG CT), MIP-2a (sense AAG GTG AAG TCC CCC GGA C, antisense GCC CATT CTT GAG TGT GGC TA, probe CCA CTG CGC CCA AAC CGA AGT C), MIP-2b (sense TGA ATG TAA GGT CCC CCG G, antisense TTC CCA TTC TTG AGT GTG GCT, probe CCC ACT GCG CCC AAA CCG AAG T), IL-8 (antisense CGT GGC TCT CTT GG CAG C, antisense TTA GCA CTC CTT GGC AAA ACT G, probe TCC TGA TTT CTG CAG CTC TGT GTG AAG GT), MGSA (sense TGA GGA GCC TGC AAC ATG C, antisense TCA TTG GCC ATT TGC TTG G, probe TCC GCC AGC CTC TAT CAC AGT GGC t), MIP-1d (sense CCA CTG GGT TTG GCA CAG A, antisense GAG TGC TCC AAG CCC AGG T, probe TGC CGC CCC TTC TTG GTG AGG), TARC (sense TCT CTG CAG CAC ATC CAC G, antisense GGG AAT GGC TCC CTT GAA G, probe ATG TGG GCC GGG AGT GCT GC), RANTES (sense GAC ACC ACA CCC TGC TGC T, antisense ATA CTC CTT GAT GTG GGC ACG, probe TGC CTA CAT TGC CCG CCC ACT G), BRUNOL-2 (sense CAA ATG CTC TCA GGT ATG GCG, antisense TGG TGC CAG CCG TGC, probe TGG CGC CAC AGG CTT GAC GAA T), IAP-B (sense GGT TGC AAG AAG AAC GAA CTT GT, antisense CAG TAC CCT TGA TTA TAC CCC TGC, probe ATC TGG TAG TAT GCC AGG AAT GTG CCC CT), IAP-C (sense GGA CAG GAG TTC ATC CGT CAA, antisense TCT CCT GGG CTG TCT GAT GTG, probe AGC CAG TTA CCC TCA TCT ACT TGA ACA GCT GC), clusterin (sense ACT ATC GCG GGT CAC CAC G, antisense ACC ACC TCA GTG ACA CCG GA, probe TTC CCA CAC TTC TGA CTC GGA CGT TCC), IRF-1 (sense CAT GGC TGG GAC ATC AAC AAG, antisense GCT TTG TAT CGG CCT GTG TGA, probe ATG CCT GTT TGT TCC GGA GCT GGG), IRF-4 (sense CAA CGC CTT ACC CTT CGC T, antisense GGG ACG TAG TCC CTC CAG C, probe AGC CCA GGT TCA CAA CTA CAT GAT GCC AC), DAPP-1 (sense CTT GAA CCC GGG AGG TGG, sense TGA CTC TGT CAC CCA GGC TAG A, probe CAC CTT CTC TGG GCA CCA AAG AAG GTT AC).
Kinesin-2 (sense AAA TCC TTT GCG TGC ATG C, antisense AGGAAAAGGTGAGCACAGCTG, probe TCA GTG ATT GTA CAT ACC TTG CCC ACT CCT AGA), MT-2 (sense CGC CGC CGG TGA CTC, antisense TGC AGC CTT GGG CAC ACT, probe CTG CTG CCC TGT GGG CTG TGC).
Computational analysis of data
Primary analysis of array data was performed using the Affymetrix GeneChip® software, resulting in parameters that were fed into our expression database and used for querying (see manufacturer for a detailed description of parameters): ‘Absolute Call’ (absent, present); ‘Difference Call’ (increased, decreased); ‘Average Difference’ (intensity); ‘Fold Change’; and ‘Sort Score’.
Cells of donors were not pooled for array analysis but processed individually.
To determine gene products with a significant increase of expression, we interrogated our data sets for an increase in ‘Average Difference’ (intensity) of at least threefold in both duplicates of at least 2 donors after 2 h and 40 h of maturation, respectively.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
). Immature DCs were characterized by lack of expression of CD14 and low expression of CD80, CD83, and HLA-DR. Maturation induced high levels of CD83, CD86, and HLA-DR.
|
Global characteristics of transcriptional alterations in dendritic cells
Choosing stringent criteria, we interrogated our microarray data sets for genes with an increase in expression level of at least threefold after 2 or 40 h of maturation.
The Change Fold was obtained by comparing the Average Differences of the group mentioned first vs. the second. Average Difference (intensity) is the GeneChip® parameter indicating the gene expression level. It represents here the average of the Average Difference values for the genes from the three donor replicates for each time point. We found 175 genes increased early at 2 h and 107 genes increased after 40 h. These data were further curated and edited. First, all ESTs found to be differentially expressed were blasted against the GenBank database (http://www.ncbi.nlm.nih.gov/Entrez). For most of them, identity with annotated full-length sequences could be unraveled. Second, the sequence databases were accessed to evaluate all annotations for hits derived from the full-length gene as well as the EST arrays to determine redundant appearance of gene products. Eventually, 112 hits up-regulated after 2 h and 101 hits up-regulated after 40 h remained. No overlap in up-regulated genes in either time point was observed (for complete data, see supplementary Tables 1
and 2
).
|
|
Categorization of transcripts induced significantly upon DC maturation
The majority of genes were tentatively grouped into classes based on either pathways or functional groups (see a categorized selection in Table 1
, Table 2
).
The most prominent increases in transcript levels were observed in the first 2 h of maturation—for example, for IL1-ß (183 x), A20 protein (97x), TNF-inducible factor TSG-6 (89 x), IL-6 (56 x), IL-1 ß (56 x), MIP1-delta (46x). After the first 2 h the average fold change in transcript abundance was not as high, with diubiquitin (61x), cIAP2 (30x), clusterin (24x), and metallothionein MT1G (26x) being the most prominent ones at 40 h. Entire sets of chemokines and their receptors were found to be increased. Cytokines (IL-1ß, TNF-
, IL-6, etc.), growth factors (endothelial cell growth factor, etc.), and their receptors (IL-7R, IL-13R, GM-CSF-R, etc.) as well as accessory molecules involved in their regulation were induced. Genes related to survival, antigen processing/presentation (UBH1, TAP1, and ß2-microglobulin) and cell structure (actin bundling protein, kinesin-2, tubulin) displayed maturation-induced transcriptional increase. Other transcripts induced were clusters of differentiation such as CD83, CD150, and SLAM ligand CDw155. As expected, molecules involved in CD40 signaling were strongly induced (ERK3, MAP3K4, etc.). Transcripts such as GRSF-1, AUH, and ATF-3, which are involved in general transcriptional regulation/repression, increased along with transcripts participating in G-protein signaling (RGS1, RGS2, ras p21, etc.).
Regulated switches in expression levels of chemokines and their receptors
Since chemokines are well established as mediators of the different functional states of DCs, we sought a complete image of their time-ordered activities.
Receptors CCR7 and CXCR4 were barely detectable in immature dendritic cells but strongly up-regulated after 40 h stimulation with CD40L (Table 2)
.
Regarding chemokine ligands, two distinct classes of regulation were found. One class comprised chemokines with immediate induction, their expression peaking transiently after 2 h but returning to baseline or even lower levels after 40 h of maturation. These were MIP-1a, MIP-1b, MIP-2a, MIP-2b, IL-8, IP10, MIP-1d, and MGSA, most of which are known as proinflammatory chemokines (Table 1)
.
To confirm these data, quantitative real-time PCR (qRT-PCR) was performed for most of these transcripts using GAPDH as reference (Fig. 2
). In addition to the RNA samples hybridized to the Affymetrix GeneChip® arrays for generation of the original data sets, at least five healthy blood bank donors were screened with qRT-PCR (Fig. 2B
). For some chemokines, inter-individual differences were observed in terms of baseline and peak expression levels (see data for TARC in Fig. 3
as an example), but kinetics were comparable between all individuals; data from one donor are shown (Fig. 2B
).
|
|
To dissect the kinetics of induction more accurately, additional time points were included (Fig. 4
). For generation of this series of samples, sCD40L was used. For all transcripts investigated, the induction kinetics observed by hybridization to the array could be confirmed by qRT-PCR and for additional donors. There were no major differences between induction by cell membrane-bound CD40L vs. recombinant soluble CD40L. For MIP-1a, MIP-2a, MIP-2b, and IL-8, peak transcript levels were reached in the first 45 min and for MIP-1b and MGSA after 
2 h. However, MIP-1d showed different kinetics, reaching its maximal transcript level after 16 h.
|
In contrast, TARC, RANTES, MDC, and ENA78 expression levels increased slowly in a sustained way (Fig. 2A
). qRT-PCR studies confirmed these data, revealing that transcript levels of TARC and RANTES have their peaks 16 h after initiation of maturation.
Since chemokines were found to be most prominently regulated, we went back to the entire data set to review expression levels of all the chemokines or chemokine receptors represented on the array (Table 3
). MCP-3, MCP-4, MPIF, and CCR1 were found to be expressed constitutively in DCs and were down-regulated strongly and rapidly upon maturation. MDC and ELC were induced, but because they do not reach high expression levels, they had escaped our initial search strategy.
|
Up-regulation of a set of survival proteins in late maturation
Several molecules described as inhibiting apoptosis or protecting cells from apoptotic death were found to be induced by maturation, among them clusterin, IAP-B, IAP-B, FLIP, Brunol-3, bcl2-related protein, and several members of the metallothionein family (Tables 1
, 2)
. For clusterin, IAPs, and Brunol-2, qRT-PCR was performed with samples from several donors, confirming the array data (Fig. 2B
). Studies of kinetics by qRT-PCR revealed that the expression of these genes was induced late (Fig. 4B
). In contrast to the chemokines, which had uniform kinetic patterns, different kinetics were observed for survival proteins. Brunol-2 has a steep and transient peak at 16 h whereas IAP-C is immediately but slowly increasing, reaching a plateau after 8 h. Clusterin shows a delayed induction after 4 h.
We screened the entire data set for molecules annotated as being involved in apoptosis or protection from it. We found that transcript levels of apoptosis inducing molecules such as Fas, Fas ligand, bak, bik, bax, and RAIDD were largely unaffected by maturation-induced processes (Table 3)
.
Though for chemokines and their receptors it is well established that regulation is centered on mRNA stability (11), this is not necessarily the case for other protein families. To confirm up-regulation of clusterin, IAP-B, and IAP-C on a protein level, we used specific antibodies. Mature dendritic cells were found to express higher amounts of both inhibitors of apoptosis compared with immature dendritic cells as revealed by flow cytometric analysis upon intracellular staining (Fig. 5
). However, the extent of maturation-induced increase on the protein level was moderate compared with what was expected from RNA abundance, suggesting that protein turnover might be higher in mDCs. Clusterin expression as assessed by immunofluorescence microscopy was strong in a variable proportion of mDC but not detectable in iDC (Fig. 6
). Cells show predominantly staining of the cell membrane. Occasionally, intracellular dots appeared positive.
|
|
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
The exploitation of high-density oligonucleotide array technology combined with analysis of different time points opens a new dimension of understanding. Similar to viewing the entire movie in one setting instead of successively examining a few pixels at a time from each frame, this technology allows one to unravel complex inter-relationships between protagonist proteins and the theme of DC maturation (11
12
13)
. Indeed, our data reveal that entire sets of functionally related transcripts are up-regulated rather than individual transcripts only.
For example, recent studies have already implicated the involvement of chemokines and chemokine receptors in migration of DCs and their interactions with T cells (14
15
16
17)
. Their appearance among the significantly increased transcripts in our data not only serves as a positive control for our system, but also further refines some of the existing concepts envisioned for chemokines in the context of DC maturation.
The initial down-regulation of MCP-3, MCP-4, and MIP-3 followed by the production of inflammatory chemokines at early time points and of constitutive chemokines later on, in concert with the respective receptors, has crucial implications for DC physiology. Inflammatory chemokines such as MIP-1a, MIP-1b, MIP-2a, MIP-2b, IL-8, MCP-1, MCP-2, MGSA, and IP-10 are expressed rapidly and at high levels (Table 1)
. Their expression is transient and confined to the time when DCs are supposed to still be in peripheral tissues. These inflammatory chemokines may contribute to the recruitment of immature DCs and their precursors as well as of effector cells. This is consistent with the finding that immature DCs express the inflammatory chemokine receptor CCR1 (Table 3)
. RANTES and MIP-1d are up-regulated with a delay and in a more sustained fashion, consistent with their role to attract T cells. Maturing DCs with up-regulated CCR7 are attracted, together with naive T and B cells, into the T cell zone by constitutively expressed chemokines such as SLC and ELC (12)
. Thus, rare antigen-specific T cells can be primed by Ag-loaded DCs. In a second phase, other newly arriving, Ag-bearing DCs producing chemokines such as MDC and TARC attract more efficiently activated dividing and memory T cells expressing more CCR4 than the surrounding naive T cells. Up-regulation of SDF-1a receptor CXCR4 in maturing DC may be an additional mechanism to trap these cells in the lymph node.
Similarly, even though the role of cytokines, growth factors, and their receptors is well established in maturing DCs, our array data elucidate additional aspects by revealing sequential induction of other groups of molecules interconnected with cytokines as well as with CD40 signal transduction. A significant number of gene products increasing by at least threefold in the first 2 h are annotated as being cytokine-inducible (by either INF-
, TNF-, or GM-CSF), indicating immediate downstream realization of cytokine effects. It is noteworthy that nearly all of these follow the same pattern in which peaks are transient and transcript levels are back to baseline or below after 40 h. Despite the lack of interferon, elevation of interferon-associated genes occurs since CD40 signal transduction pathway involves JAK/STAT activation. Thus, similar signaling components as with IFN- are recruited and similar molecular activation patterns were observed (18)
. After some delay, counter-regulatory events occur. IRF1 and IRF4, which are involved in regulation of JAK/STAT-mediated effects, increase (19)
. Also showing an increase are a TG-interacting factor that inhibits activation of the retinoic acid responsive element and AUH, which mediates fast degradation of AUUUA-rich RNAs like IL-3, granulocyte/macrophage colony-stimulating factor, c-fos, and c-myc.
DCs are thought to be short-lived (1)
, dying shortly after reaching the draining lymph node, thus ensuring adequate space for the constant influx of fresh DCs loaded with different antigens. However, it is also relevant that sufficient longevity and abundance of antigen-pulsed DCs are critical factors in the magnitude of a T cell response to antigen. Factors controlling DC survival are equally important for antigen-specific stimulation in the induction of immunity. It has already been reported that dendritic cells are resistant to apoptosis through Fas (20)
and that CD40 ligation exerts a survival-promoting effect, presumably by up-regulation of Bcl-2 (21)
. Molecules such as c-FLIP (22)
or T cell-produced factors like TRANCE and TNF (23
24
25)
are reported to confer this resistance, but the molecular basis for this is not clearly understood. We report an entire set of protective molecules increasing. Among them are IAP-C (inhibitor of apoptosis C), as previously reported (26)
, but also IAP-B, FLIP, and clusterin.
Survival-related proteins seem to appear later in maturation, so they may not be detected in the first 6 h, as investigated by Aicher et al. (26)
using Multiprobe RPA. In accord with this, several proteins inducible by different stress factors or damaging agents and with reported general protective functions are also found to be significantly increased early (SOD-2, hypoxia-inducible factor , etc.) as well as late (RAD21, DNA damage-inducible GADD45, etc.) in maturation.
The relevance of our findings to the fate of DCs during natural immune responses in humans is so far unknown. However, monocyte-derived DCs like those in our studies are being used in clinical trials as cancer vaccines (4
, 5
, 27)
. Therefore, it is important to determine the factors promoting their survival by functional studies. There are indications that current trials operate below the adjuvant potential of DCs (28)
, suggesting that application of information for the elongation of DC lifespan is of high interest. Also important for immunotherapy is that treating DCs with maturation signals immediately initiates coordinated cascades of rapid molecular changes. Early on, factors contributing crucially to the successful generation of immune responses are produced. Hence, it may be an advantage to mature DCs briefly for 2 h ex vivo rather than 24–48 h and inject them at early maturation stages. The concept of dendritic cell exhaustion would also favor such a procedure (29)
.
A surprising observation was maturation-induced up-regulation of clusterin in dendritic cells. Clusterin has been implicated in a variety of functions such as lipid transport, reproduction, cell-cell interaction, complement regulation, tissue remodeling, and cell survival (30
, 31)
. Initially it was believed to be proapoptotic because of its accumulation in tissues undergoing apoptosis. The observation of clusterin accumulating in the surviving cells adjacent to the apoptotic led to reassessment of its role in apoptosis (32)
. According to recent studies, clusterin has been implied in cytoprotection of vital cells, presumably by assisting in the clearance of apoptotic vesicles and membrane remnants. Clusterin seems to act as adaptor between apoptotic phosphatidylserine-coated vesicles and its high-affinity receptor gp330/megalin. Moreover, clusterin was assigned potent chaperone-like activities, protecting a wide range of proteins from denaturation (33)
. Recent data suggest that clusterin is a novel type of secreted extracellular heat shock protein (34)
. Recently, clusterin was suggested as a diagnostic marker for anaplastic large cell lymphomas by microarray studies (35)
. Performing immunohistochemistry on primary lymphoid neoplasms, Wellmann et al. (35)
not only observed strong staining of ALCL cells but also of follicular dendritic cells. This confirms that not only dendritic cells generated in vitro from monocytes, but also dendritic cells in situ, express clusterin. Ongoing studies are needed to evaluate clusterin function in the context of mature dendritic cells.
Besides known genes that have not been identified as involved in DC maturation before (Tables 1
and 2)
, yet unclassified genes represented by ESTs and genomic clones have been observed to be differentially expressed. Determination of their identity and analysis of their function may reveal novel aspects.
In summary, we have reported that global transcript analysis by microarrays in combination with kinetics analysis of single transcripts is a powerful new tool that will aid in developing a more comprehensive understanding of dendritic cell maturation. We not only elucidated functional networks of molecules and temporal relationships between classes of transcripts; we were able to identify differentially expressed gene products including clusterin, which we suggest as a new marker for dendritic cell maturation.
|
|
|
ACKNOWLEDGMENTS |
|---|
|
FOOTNOTES |
|---|
Received for publication July 30, 2002. Accepted for publication December 17, 2002.
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
This article has been cited by other articles:
|
|
 |
|
  R. Pluvinet, R. Olivar, J. Krupinski, I. Herrero-Fresneda, A. Luque, J. Torras, J. M. Cruzado, J. M. Grinyo, L. Sumoy, and J. M. Aran CD40: an upstream master switch for endothelial cell activation uncovered by RNAi-coupled transcriptional profiling Blood, November 1, 2008; 112(9): 3624 - 3637. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Emmons, W.H. D. Townley-Tilson, K. M. Deleault, S. J. Skinner, R. H. Gross, M. L. Whitfield, and S. A. Brooks Identification of TTP mRNA targets in human dendritic cells reveals TTP as a critical regulator of dendritic cell maturation RNA, May 1, 2008; 14(5): 888 - 902. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Szameit, K. Vierlinger, L. Farmer, H. Tuschl, and C. Noehammer Microarray-Based In Vitro Test System for the Discrimination of Contact Allergens and Irritants: Identification of Potential Marker Genes Clin. Chem., March 1, 2008; 54(3): 525 - 533. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Kettner, F. Kalthoff, P. Graf, E. Priller, F. Kricek, I. Lindley, and T. Schweighoffer EWI-2/CD316 Is an Inducible Receptor of HSPA8 on Human Dendritic Cells Mol. Cell. Biol., November 1, 2007; 27(21): 7718 - 7726. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Holtkamp, S. Kreiter, A. Selmi, P. Simon, M. Koslowski, C. Huber, O. Tureci, and U. Sahin Modification of antigen-encoding RNA increases stability, translational efficacy, and T-cell stimulatory capacity of dendritic cells Blood, December 15, 2006; 108(13): 4009 - 4017. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. S. Jiang, I-S. Chang, L.-W. Huang, P.-C. Chen, C.-C. Wen, S.-C. Liu, L.-C. Chien, C.-Y. Lin, C. A. Hsiung, and J.-L. Juang Temporal Transcription Program of Recombinant Autographa californica Multiple Nucleopolyhedrosis Virus. J. Virol., September 1, 2006; 80(18): 8989 - 8999. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. McIlroy, S. Tanguy-Royer, N. Le Meur, I. Guisle, P.-J. Royer, J. Leger, K. Meflah, and M. Gregoire Profiling dendritic cell maturation with dedicated microarrays J. Leukoc. Biol., September 1, 2005; 78(3): 794 - 803. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 E. van der Veer, Z. Nong, C. O'Neil, B. Urquhart, D. Freeman, and J. G. Pickering Pre-B-Cell Colony-Enhancing Factor Regulates NAD+-Dependent Protein Deacetylase Activity and Promotes Vascular Smooth Muscle Cell Maturation Circ. Res., July 8, 2005; 97(1): 25 - 34. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. Majumder, M. L. Janket, E. A. Schafer, K. Schaubert, X.-L. Huang, J. Kan-Mitchell, C. R. Rinaldo Jr., and V. Ayyavoo Human Immunodeficiency Virus Type 1 Vpr Impairs Dendritic Cell Maturation and T-Cell Activation: Implications for Viral Immune Escape J. Virol., July 1, 2005; 79(13): 7990 - 8003. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. A. Efron, H. Tsujimoto, F. R. Bahjat, R. Ungaro, J. Debernardis, C. Tannahill, H. V. Baker, C. K. Edwards, and L. L. Moldawer Differential maturation of murine bone-marrow derived dendritic cells with lipopolysaccharide and tumor necrosis factor-{alpha} Innate Immunity, June 1, 2005; 11(3): 145 - 160. [Abstract] [PDF]
|
|
|
|
 |
|
 M. Buettner, C. Meinken, M. Bastian, R. Bhat, E. Stossel, G. Faller, G. Cianciolo, J. Ficker, M. Wagner, M. Rollinghoff, et al. Inverse Correlation of Maturity and Antibacterial Activity in Human Dendritic Cells J. Immunol., April 1, 2005; 174(7): 4203 - 4209. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. Ahmed, K. M. Brown, D. A. Stephan, J. C. Morrison, E. C. Johnson, and S. I. Tomarev Microarray Analysis of Changes in mRNA Levels in the Rat Retina after Experimental Elevation of Intraocular Pressure Invest. Ophthalmol. Vis. Sci., April 1, 2004; 45(4): 1247 - 1258. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||
