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Telethon Institute for Child Health Research, and Centre for Child Health Research, Faculty of Medicine and Dentistry, University of Western Australia, Perth, Western Australia
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionDisclosuresReferences |
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionDisclosuresReferences |
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The likelihood that additional atopy-associated genes remain to be identified can be inferred from recent experiences reported in the immunological literature following the introduction of microarray technology. Collectively, these studies indicate that individual immune responses typically involve up-regulation of multiple hundreds of genes (7, 8, 9). This technology has also been applied in the allergy field to study Th2 responses and related signaling pathways in freshly isolated T cells (10), polyclonally stimulated T cells (11, 12), allergen-specific T cell clones and lines (13, 14), polarized Th1/Th2 cell lines (15, 16, 17, 18, 19, 20), and in animal models (21). A consistent finding in the studies was the substantial number of differentially expressed genes identified, however, the lack of consistency in the gene lists reported by each study is striking.
In the current study, a different approach was undertaken. We reasoned that while the pattern of T cell gene expression induced in vivo at sites such as the airway mucosa is ultimately controlled by local tissue microenvironmental factors, significant elements of the potential gene response "program" of allergen-specific T memory cells (exemplified by the IL-4/IL-5-dominant cytokine profile of T cell clones from atopics) can be accurately revealed by low-intensity in vitro stimulation of recirculating memory cells harvested from peripheral blood. In adopting this approach, we have minimized in vitro manipulations and avoided the use of strong activation stimuli, which have the potential to distort patterns of gene expression in T cells (22, 23), and report for the first time the results of microarray analysis of PBMC-derived T cell responses to the house dust mite (HDM)3 allergen in short-term primary culture. The fidelity of the experimental system was evident by the parallel detection of Th2 cytokine hyperexpression in atopics as pooled samples by microarray, and individually by ELISA. In addition, several novel atopy-associated genes were identified, and the preferential expression of these genes in atopics was validated in HDM-stimulated CD4+ T cells from an additional panel of atopic patients and nonatopic controls, both as pooled samples by microarray and individually by quantitative RT-PCR. We additionally demonstrate differential expression of these novel genes in an unrelated T cell response system, notably activation which is driven by the bacterial superantigen staphylococcal enterotoxin B (SEB).
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionDisclosuresReferences |
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Subjects were volunteers aged 11–58 years. Atopic status to HDM was determined by skin prick test (wheal 
5 mm) and/or positive serum HDM-specific IgE (0.35 kU/L). The study was approved by our institutional human ethics committee.
Cell preparation and culture methodologies
PBMC were cultured as detailed (24) with medium alone or containing optimal stimulatory concentrations of 10 µg/ml HDM (Dermatophagoides pteronyssinus; CSL) or 10 µg/ml purified protein derivative (PPD; Mycobacterium tuberculosis; CSL), 200 ng/ml SEB (Sigma-Aldrich), 1 µg/ml PHA (Murex Biotech), or soluble anti-CD3 and 20 U/ml rIL-2 (Cetus). Optimal concentrations of in vitro stimuli were established in forerunner dose response experiments using panels of PBMC from healthy donors and sung lymphoproliferation as a readout. Where specified, CD8+ T cells followed by CD4+ T cells were isolated from Ag/allergen-stimulated PBMC by positive selection using Dynabeads (Dynal Biotech) to 92 and 99% purity, respectively. Selection of dividing cells in HDM-stimulated cultures using the dye CFSE was based on methodology in Turcanu et al. (25). Analysis of cytokine protein secretion in parallel 48 h cultures was routinely performed as an internal control to confirm the Th2 polarity of atopic responses, before RNA extraction for microarray analyses. Forerunner studies (24) within the batch of HDM used in this study demonstrated that the in vitro stimulatory effects of this allergen on PBMC cytokine production were not influenced significantly by covert LPS contamination.
Affymetrix methodologies
Total RNA was extracted using TRIzol (Invitrogen Life Technologies) followed by RNeasy (Qiagen). RNA integrity was assessed on the Bioanalyser (Agilent Technologies). For gene expression studies of unfractionated PBMC, cultures from each individual were set-up with the same initial cell number to ensure equivalent amounts of RNA from each subject were added to the pool. For gene expression studies of T cell subsets, RNA samples were quantitated by spectrophotometry and equal amounts of RNA from each individual were added to the RNA pool. Pooled RNA samples (
2 µg) were labeled using the one cycle labeling kit (Affymetrix) according to manufacturer’s instructions except: 1) 20 U RNase inhibitor (Geneworks) was added to first-strand cDNA synthesis; 2) reactions were heat denatured (70°C, 10 min) after first-strand cDNA synthesis; 3) samples were incubated for 10 min with T4 DNA ligase after second-strand cDNA synthesis. Fragmentation of cRNA, hybridization to Affymetrix microarrays, washing, staining, and scanning was performed according to manufacturer’s instructions. In the experiment in Fig. 5, total RNA was amplified using the small sample labeling protocol, version 2, from Affymetrix before hybridization.
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RNA was reverse-transcribed with Omniscript (Qiagen) according to manufacturer’s instructions. Primer sequences were obtained from a database (
http://pga.mgh.harvard.edu/primerbank/
; Ref.26), designed in-house using Primer Express software (Applied Biosystems), or purchased from Qiagen. Reverse-transcribed RNA samples were quantitated using QuantiTect SYBR Green (Qiagen) on the ABI Prism 7900HT (Applied Biosystems). Relative standard curves were prepared from serially diluted RT-PCR products or plasmid standards and data were normalized to the stably expressed gene EEF1A1 (27). Data were expressed as expression level above background (level in stimulated cells minus unstimulated cells) and multiplied by a scaling factor to obtain whole numbers.
Study design and statistical analyses
Extensive physiological variation in gene expression has been reported in multiple species (28, 29, 30, 31), making it difficult to discriminate between donor-specific changes in gene expression and changes associated with the phenotype of interest (32). Therefore, a large number of individuals were included in the study and a pooling strategy was adopted for the initial microarray screens. The rationale for this approach is the fact that variability due to biological variation is much greater than that due to technical variation (33, 34); this was accordingly emphasized in the study design by creating multiple independent RNA pools and hybridizing them once rather than performing multiple hybridizations of a single pool. Hybridization of multiple independent RNA pools minimizes the chance of an outlying individual biasing the results, and more importantly separate RNA samples were reserved from each culture to evaluate subject-to-subject variation in gene expression patterns and to determine the consistency of expression of the novel Th2-associated genes detected by microarray screening, using more precise qRT-PCR methodology.
Microarray data were analyzed in the open-source statistical software R (www.r-project.org/), using several additional add-on packages from the Bioconductor Project (www.bioconductor.org/) (35) including affy, affyQCReport, affyPLM, annotate, hgu133a, hgu133plus2, limma, q value, and time course. The probe-level model algorithm (Refs.34 and 36 , http://stat-www.berkeley.edu/users/bolstad/Dissertation/Bolstad_2004_Disseration.pdf), which is based on the robust multi-array average algorithm (37), was used for background subtraction, normalization, and summarization of probe set intensities. All microarrays within each experiment were of comparable quality as assessed using the affyQCReport package. Microarray scan images were checked for spatial defects/artifacts using diagnostic plots from the affyPLM package. RNA integrity was confirmed by the Affymetrix RNA degradation controls (GAPDH and actin, 3'–5' ratios <3).
To identify differentially expressed genes, gene expression intensity was compared in HDM-stimulated cells with unstimulated cells (each time point was analyzed independently where applicable) using a moderated t test (38) using an empirical Bayes approach which is more powerful than standard t tests when the number of replicates is low (39). To account for multiple testing, the false discovery rate (proportion of false positives among the tests called significant) was estimated from p values derived from the moderated t test statistics (40) using the method of Storey and Tibshirani (41). A false discovery rate of 0.05 (i.e., q value <0.05) was selected as the significance level for differential expression. A total number of 1482 genes were up-regulated in atopics and nonatopics, and these data were then analyzed by hierarchical clustering (Pearson correlation, average linkage; Ref.42) and quality threshold (QT) clustering (Pearson correlation, maximum cluster diameter 0.5; Ref.43).
To rank differentially expressed genes according to their nonconsistency of expression in atopics and nonatopics over time, a multivariate empirical Bayes (MB statistic) (Ref.44 , www.stat.berkeley.edu/users/yuchuan/preprints/667.pdf) analysis was used. The MB statistic is based on the univariate models proposed in Refs.38 and 45) which use moderated/Bayesian estimates of the variance to overcome the lack of precision due to the low number of replicates. In addition, the MB statistics also take into account any temporal correlation structure typical of time-course experiments. The MB statistics were plotted against their null distribution (quantile-quantile plot) to select an appropriate cut-off to allow discrimination of genes which had the same expression pattern/level in atopics and nonatopics from those which were different. The null distribution was generated in a permutation manner, whereby for each gene at each time point, the atopic status labels were randomly permuted among the experimental groups. The MB statistics were then recalculated for this permuted data and the process was reiterated 100 times. If no genes varied in expression between atopics and nonatopics over the time course then a qq plot of the real data and this randomly permuted data would fall upon a straight line whereas deviations from such a line identify genes varying in expression between atopics and nonatopics over time.
Volcano plots (46) (used in Fig. 3) were derived as follows. Stimulation ratios (SR; the expression level in HDM-stimulated cells divided by that in unstimulated cells) were calculated for atopics and nonatopics. SR in atopics and nonatopics were then compared using the moderated t test, and the t test statistics were plotted on the vertical axis. The ratio of the SR in atopics and nonatopics was calculated and plotted on the horizontal axis.
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Statistical analyses by Mann-Whitney U test were performed in SPSS software. The paired unequal variance t test, moderated t test, MB statistics, and hierarchical clustering were performed in R software. QT clustering was performed in MeV software (47).
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionDisclosuresReferences |
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Recirculating T memory cells are present in peripheral blood at frequencies usually below 1 in 1000 PBMC (48). Accordingly, published studies on microarray analysis of gene expression in allergen-specific T memory cells have used a variety of tissue culture techniques to enrich for the cells of interest, before extraction of RNA. Prolonged in vitro manipulation is known to alter gene expression profiles of T cells, and in this study we sought to minimize such potential artifact by studying gene expression in freshly activated memory cells in short-term primary cultures of PBMC.
The results of differential gene expression studies on HDM-stimulated unfractionated PBMC from two groups of subjects, half of each group being nonatopic controls and half atopic and skin prick test-positive to HDM, are illustrated in Fig. 1. PBMC from all these individuals (total n = 40) were cultured in the presence or absence of optimal stimulatory levels of HDM for 12, 24, and 48 h. These time points were selected because they bracket the period during which expression of T cell activation markers is maximal following in vitro stimulation (49). Total RNA was extracted from PBMC at the termination of these cultures. At each time point for each treatment (unstimulated or HDM-stimulated), aliquots from the RNA samples were combined into six different pools (three atopic: AT1 and AT2 from study group 1, AT3 from study group 2; and their nonatopic counterparts NA1 and NA2 from study group 1 and NA3 from study group 2) as detailed in Fig. 1, and hybridized to Affymetrix series U133 plus 2.0 microarrays. The pooling strategy was adopted to reduce the variance (50, 51) due to normal physiological subject-to-subject variation in gene expression (see Materials and Methods); separate individual RNA samples were reserved for follow-up studies by qRT-PCR (see below).
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Hyperexpression of Th2 cytokines has consistently been associated with asthma and allergy in vitro and in vivo, and genes which are coexpressed with these Th2 cytokine genes over the time course are thus potential candidates for an effector role in disease pathogenesis. To identify such coexpressed genes, we used QT cluster analysis, which divides the data into groups of genes with similar expression patterns (see Materials and Methods). The algorithm identified 29 clusters, 12 of which contained 16 genes. The expression pattern of these 12 major clusters in atopics and nonatopics is illustrated in Fig. 1. The QT algorithm identified two separate Th2-associated gene clusters demonstrating peak expression early vs late in the response. The early cluster comprised a set of genes peaking in expression at 12–24 h in conjunction with the principal Th2 growth factor IL-4 and its receptor IL-4R, together with a series of novel genes including DACT1, MAL, NDFIP2, GNG8, RAB27B, DPP4, and NSMCE1 which were selected for further study (early Th2 cluster in Fig. 1A). Expression of the other principal Th2 effector cytokines IL-5, IL-9, and IL-13 peaked later at 48 h and clustered with additional genes of interest notably IL-17RB, CISH, and CCL17 (TARC) (late Th2 cluster in Fig. 1A; gene nomenclature detailed in Table I). It is noteworthy that these same clusters were also identified by hierarchical clustering (data not shown). Previous microarray studies in model systems such as Saccharomyces cerevisiae and Caenorhabditis elegans have indicated that clusters of genes are generally involved in the same biological function/pathway (Ref.52 ; reviewed in Ref.53). It was therefore not surprising that a distinct functional bias was also observed within the Th2-associated clusters. The early Th2 cluster was enriched for genes known to play a role in signal transduction, whereas the late Th2 gene cluster was enriched for genes mainly involved in immune effector function (functions detailed in Table I).
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Expression of the "novel" Th2-associated genes in these clusters has not been reported previously in the context of specific allergen-triggered T cell memory responses in atopics. Some have been observed previously in experiments broadly related to Th2 immunity, notably 1) CISH was induced by IL-4 in mouse T cells (21) and was also detected in human Th2 cells (55); 2) IL-17RB was reported to be anti-CD3/CD28 up-regulated in restimulated Rye-specific T cells (14) and in Th2 cell lines (19); 3) MAL was reported in a differential display screen of resting peripheral blood T cells from atopics with dermatitis and asthma, but follow-up PCR experiments failed to confirm these observations (56); 4) DPP4 was detected in bronchial biopsies of allergic asthmatics (57) and in Th1 and Th2 cells (58). To our knowledge, this is the first report of any associations of DACT1, NDFIP2, GNG8, NSMCE1, and RAB27B with atopy or Th2 responses.
Gene expression profiling of inhalant allergen driven gene expression in CD4+ and CD8+ T cells purified from HDM-stimulated PBMC
A second series of experiments was performed to identify the cellular source(s) of HDM-induced gene expression within PBMC. Of particular interest was the potential contribution of CD4+ vs CD8+ T cells, as these subsets within PBMC are known to have distinct gene expression profiles (59).
As part of the experiments involving generation of the RNA samples used in Fig. 1, an additional series of replicate PBMC cultures was set up in parallel from the individuals (the 10 atopics and 10 nonatopics from the subgroups AT3 and NA3 of study group 2) at each time point, and CD4+ and CD8+ T cells were purified from HDM-stimulated and unstimulated PBMC at the end of the cultures. Total RNA was extracted, pooled into two groups (AT3, pool of 10 atopics, NA3 pool of 10 nonatopics) and analyzed on Affymetrix microarrays (Fig. 2). For logistical reasons, it was necessary to use U133a microarrays for this part of the study (as opposed to U133 plus 2.0 series in Fig. 1), and the U133a series did not contain probe sets for NDFIP2, GNG8, NSMCE1, or RAB27B. Expression of the Th2 index cytokines IL-4, IL-5, IL-9, and IL-13 was clearly evident in both T cell subsets in the atopics (Fig. 2), clustering into early and late groups as in Fig. 1A. Expression of these genes appeared more consistent and of higher intensity within the CD4+ compartment (see also study group 3 below). The results in Fig. 2 also demonstrate elevated expression of DACT1 and MAL in both the CD4+ and CD8+ T cell compartments of atopics relative to nonatopics. In addition, the kinetics of gene expression was similar to that in unfractionated PBMC viz DACT1 and MAL expression peaked early in the time course, whereas the main Th2 effector cytokines peaked late. Several additional genes of interest were identified in the experiments of Fig. 2, presumably unmasked as a result of the improvement in signal: noise ratio on the microarrays resulting from enrichment of responder cells by subset purification. PECAM1 and PLXDC1 genes were reciprocally expressed in atopics and nonatopics, and substantial GZMB expression was also detected in the CD4+ compartment in both groups. Up-regulation of PECAM1 has been previously reported in anti-CD3/CD28-activated CD4+ T cells from atopic asthmatics (11), and also in a microarray screen of Th2-like CD8 T cells in the mouse (16). GZMB expression has also been previously reported in Th2 cells (17, 60).
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To confirm the preferential expression of the novel genes identified in Figs. 1 and 2 in allergen-stimulated CD4+ and CD8+ T cells from atopics, PBMC from an additional independent panel of (study group 3) subjects comprising 10 atopic and 10 nonatopic individuals were cultured in the presence or absence of HDM for 24 h. T cell subsets were purified from these cultures, and total RNA was extracted and pooled into four separate groups of subjects (two groups of five atopics, two groups of five nonatopics), and hybridized to Affymetrix U133 Plus 2.0 microarrays. Separate RNA samples were reserved for follow-up studies by qRT-PCR. To identify differences in the expression patterns of atopics and nonatopics, the data was visualized on a volcano plot (Fig. 3). The plot displays differential gene expression according to magnitude along the horizontal axis, and statistical significance along the vertical axis (see Materials and Methods). In the plot, genes which are located in the top right quadrant are hyperexpressed in atopics relative to nonatopics. Strikingly, the analysis revealed that all of the novel Th2-associated genes (except for GNG8) which were identified in the experiments of Figs. 1 and 2 were also hyperexpressed in the responses of CD4+ T cells in the atopics but not the nonatopics from this independent panel of subjects. The novel genes appear as extreme outliers on the plot indicating their importance relative to all other genes in the genome. Analysis of these purified CD4+ cells allowed the identification of additional atopy-associated genes including CAMK2D and SYTL3 which were not detected in unfractionated PBMC.
Several genes including DACT1, MAL, CISH, IL-9, IL-4R, GNG8, and DPP4 were also hyperexpressed in the CD8+ compartment of atopics. Other genes including NSMCE1, IL-5, PECAM1, PLXDC1, and NDFIP2 were not prominent in the CD8+ responses of the atopics, however, it is not clear from these experiments whether this is due to relative insensitivity of the microarrays or to the lack of expression within the CD8+ compartment. This and related issues were accordingly addressed using more sensitive qRT-PCR methodology (see below).
qRT-PCR validation of microarray findings
The microarray experiments reported above were performed on pooled RNA samples and therefore do not provide information on subject-to-subject variation in gene expression patterns. To obtain more detailed information in this regard, and to formally demonstrate statistically significant differences in gene expression patterns between atopics and nonatopics, reserved individual RNA samples were assayed by qRT-PCR. The results of these studies are summarized in Table II. In the table, study group 1 refers to the individual RNA samples from the subjects pooled into the groups AT1, AT2, NA1, NA2 (from Fig. 1). Study group 2 refers to the individuals from RNA pools AT3, and NA3 used in the experiments from Figs. 1 and 2. Study group 3 refers to the subjects used in the experiments in Fig. 3.
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Expression of the Th2 signaling cluster depends on the nature of the stimulus received by the T cell
The consistent correlations between the panel of Th2 reference genes and the novel genes identified above strongly suggest that the latter are associated selectively with activation of Th2 memory cells. However, these genes have escaped detection in previous microarray studies which have typically used methodology radically different to the present study (14, 15, 16, 17, 18, 19, 20), including initial enrichment of putative Th2 cells via initial culture under conditions which favor survival of the cells of interest, followed by reactivation with mitogenic stimuli. One such approach which is standard in this area involves flow cytometric positive selection of cells which have been driven through repeated cycles of division by culture with specific allergen (thus revealing their allergen-specific memory phenotype), and subsequently comparing gene expression patterns in these selected cells at rest vs following reactivation by brief exposure to the potent mitogen PMA/ionomycin. We reproduced this experimental system in the study in Fig. 5, which illustrates gene expression ratio (stimulated/unstimulated) in atopics, plotted against average signal strength. Consistent with earlier reports (e.g., Ref.25), clear differential up-regulation of the Th2 reference genes (IL-4, IL-5, IL-9, and IL-13) can be seen in the atopics together with a range of additional pan-specific T cell activation markers (IL-2, IL-3, IL-17, IFNG, TNF, CSF2, and IL-2RA). However, the novel Th2-associated genes which were prominent in our primary T cell cultures stimulated only with allergen, most notably the highly expressed early genes DACT1, MAL, DPP4, IL-17RB, and PLXDC1, were not up-regulated in the CFSE-selected T cell lines (Fig. 5).
We hypothesized that the explanation for this discrepancy could be that the more physiological methods for allergen-induced T cell activation used in the present study used TCR-associated activation pathway(s) that are bypassed in the cell line approaches, in particular, those encompassing the use of high-intensity activation stimuli which have a lower requirement for (or are often independent of) help from APCs. To investigate this issue further, we analyzed expression of the novel Th2-associated signaling genes in primary T cells after stimulation with a panel of alternative activating agents comprising the polyclonal mitogens soluble anti-CD3/IL2 and PHA, and the oligoclonal T cell stimulant bacterial superantigen (SEB) which activates T cells expressing a defined range of TCRV
molecules (63). PBMC from six patients with severe atopic dermatitis were cultured for 12 h in the presence or absence of the selected stimuli.
The T cells responded to the mitogen panel with a mixed Th1/Th2 profile as demonstrated by IL-4, IL-5, IL-13, and IFNG production (Fig. 6). Strikingly, DACT1, MAL, DPP4, and NDFIP2 were all up-regulated after SEB stimulation, but only NDFIP2 was up-regulated in response to PHA. Furthermore, MAL was down-regulated after PHA stimulation, and not significantly up-regulated after anti-CD3 stimulation (p = 0.11). Additionally, consistent with earlier findings with CFSE-selected cell lines, a follow-up study on a panel of primary PBMC T cells stimulated with PMA/ionomycin also demonstrated up-regulation of Th2 effector genes in the absence of accompanying activation of these novel genes (data not shown).
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We finally sought to characterize the T cell response to HDM in nonatopic individuals in more detail. This response is currently assumed to be Th1-like (64, 65), and as noted above a significant number of Th1 marker genes including GZMB, LTA, and IFNG were detected in HDM-stimulated PBMC from the nonatopics in the microarray experiments in Figs. 1 and 2. Similar findings have been reported by many groups, however, the lack of evidence for expression of Th1-associated delayed-type hypersensitivity (DTH) to this or other allergens among nonatopics, despite lifetime allergen exposure, has led investigators to question the applicability of the murine Th1/Th2 paradigm in this context (66). We speculated that this apparent paradox may be explicable by covert quantitative or possibly qualitative differences between the allergen-specific Th1-like HDM responses of nonatopics, and classical Th1 immunity associated with DTH expression exemplified by the Mantoux response to mycobacterial Ag. To test this possibility, we compared gene expression profiles in nonatopic HDM responses with those in Th1-polarized responses to mycobacterial PPD. Pooled RNA from PPD-stimulated CD4+ T cells from a group of eight healthy adults with a previous bacillus Calmette-Guérin vaccination history were screened by microarray as per Fig. 2, and their expression profiles compared with those of HDM-stimulated CD4+ T cells from the nonatopics used in Fig. 2. A broad range of differentially expressed Th1-associated genes that were common to both responses were identified as per Fig. 1 (data not shown). An initial comparison of signal strength on the microarray was made on a Th1 gene-by-gene basis between the two responses, and this analysis revealed that almost all of the relevant genes appeared 3- to 10-fold more highly expressed in the PPD response relative to the response to HDM in nonatopics. The striking exception was IFNG which appeared >100-fold higher for PPD, and to a lesser extent CCL8 and GZMB. In the experiments in Fig. 7, a panel of the most highly expressed Th1-associated genes were selected for qRT-PCR validation, using individual RNA samples from the pools, and this confirmed selective enrichment of the IFNG and CCL8 components within the PPD response.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionDisclosuresReferences |
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In the current study, microarray technology was used to obtain more detailed information on patterns of allergen-induced gene expression in T memory responses in atopics and nonatopics, using short-term primary cultures. Despite the low frequency of responder cells within unfractionated PBMC, we have demonstrated consistent hyperexpression of classical Th2 reference genes in allergen-stimulated PBMC from atopics, in particular IL-4 which is expressed at only low levels. This suggests that the microarray technology used here has sufficient sensitivity to yield cogent information from mixed PBMC samples in this experimental setting. Microarray analyses also revealed that an additional Th1-like gene expression signature which includes LTA, IFNG, and a number of IFNG-induced genes which was qualitatively similar to that observed in responses to mycobacterial PPD, was present in the HDM responses (data not shown), regardless of the atopic status of the PBMC donors. This is consistent with findings from a range of laboratories (reviewed in Ref.67) which indicate that the cytokine phenotype of Th-memory responses to inhalant allergens in atopics is typically of the mixed Th1/Th2 or Th0 phenotype. Recent evidence from our laboratory (24) and others (68) (reviewed in Ref.69) suggests that Th1-like mechanisms superimposed on Th2 responses in atopics may exacerbate allergic disease expression, and further microarray based studies targeted specifically at this class of genes have potential to shed further light on this issue.
The substantial overlap between the PPD and nonatopic HDM responses was noteworthy. However, unlike PPD, the Th1-like response against HDM is not associated with potential for DTH, which suggests that some form of covert internal regulation attenuates the overall intensity of the nonatopic HDM response. It is feasible that this may be due (at least in part) to a comparatively lower frequency of HDM responsive T memory cells in vivo compared with those participating in PPD responses, and consistent with this possibility, we report a generalized 3- to 10-fold increase in Th1 gene expression levels in the latter. However, if variations in responder cell frequencies accounted entirely for the differences in the intensity of in vivo reactivity, then there should be broad consistency in relative expression levels across the entire spectrum of inflammation-associated genes, comparing the two in vitro T memory responses. Instead, as illustrated in Fig. 7, a small subset of proinflammatory genes (most notably IFNG and CCL8) are expressed on a log-fold higher scale in T memory responses to the classical DTH stimulant PPD. This suggests that these potent proinflammatory genes are preferentially attenuated in the HDM responses of the nonatopics. A precedent for this form of selective regulation is the "modified Th2 response" to cat allergen recently described by Platts-Mills et al. (70), in which a proportion of nonatopics exhibit ongoing production of IL4-dependent IgG4 Ab against cats in the absence of parallel IgE production or skin prick reactivity to the same allergen. Both these response patterns to environmental allergens may reflect forms of split tolerance (71) resulting from chronic exposure, and this possibility will be addressed in follow-up studies.
More detailed analysis of the HDM response in atopics using QT clustering identified several genes that exhibited expression patterns similar to the Th2 reference genes. Two major waves of synchronous gene expression were identified in the atopics, an early group peaking in activity between 12 and 24 h, which was enriched for genes involved in signaling, and a late cluster peaking at 48 h enriched for genes associated with Th2 effector function. Quantitative RT-PCR validation experiments confirmed the association of these genes with atopy in independent panels of subjects, and cell separation experiments demonstrated that all of the genes (except for GNG8) were induced in purified CD4+ and CD8+ T cells, although gene expression levels in the latter were generally lower. Elevated expression of the GNG8 gene was also associated with atopy, however it was restricted to the CD8+ T cell subset. Additionally, we have shown large-scale and consistent activation of several of these novel signaling-associated genes in a different experimental setting, notably in oligoclonal and polyclonal Th2-like responses initiated via stimulants which mediate T cell activation by APC-dependent interactions with the TCR (anti-CD3 and SEB). However, these same genes appear redundant in T cell responses initiated by more potent mitogenic stimuli such as PHA and PMA/ionomycin, indicating that alternative intracellular-signaling pathways mediate T cell activation under these less physiological conditions of stimulation.
This latter finding has important practical implications as it demonstrates how the validity of T cell-related drug target identification by microarray screening can be potentially compromised by the choice of stimulants applied to the cells being screened. It is additionally of interest to note in this context that SEB has been suggested to play an important ancillary role in driving atopic disease pathogenesis in both the skin (72) and the upper respiratory tract (73), and the potency of this molecule as an oligoclonal T cell stimulant capable of mimicking the effects of specific allergen on atopic Th2 memory cells, may account for this facet of its biological activity.
Further studies are required to determine the importance and function of the novel Th2-associated genes in allergic responses, however some hints as to their potential roles can be deduced from what is known in other areas of the literature:
DACT1 has not previously been implicated in T cell regulation. However, studies in mesenchymal cells have demonstrated that overexpression of DACT1 blocks disheveled-mediated activation of the JNK and -catenin-signaling pathways (74) and by inference, DACT1 may also regulate these pathways in T cells. -catenin is required for normal T cell development, and -catenin-deficient T cells show defective proliferation in response to anti-CD3 mAb (75). Moreover, increased expression of the -catenin target genes LEF-1 and TCF-1 has also been reported in peripheral T cell lymphomas expressing cell surface markers characteristic of Th1 cells, but is absent in lymphomas expressing Th2-associated markers (76). Regulation of the JNK-signaling pathway by DACT1 is a plausible possibility because JNK regulates NFAT activity, which in turn regulates expression of multiple target genes including the Th2 cytokines (reviewed in Ref.77). JNK1-deficient T cells also preferentially differentiate into Th2 cells (78).
MAL localizes to lipid raft domains in T cells in association with several TCR-signaling molecules including Lck, suggesting a role in T cell activation (Ref.79 , reviewed in Ref.80). It is also noteworthy that MAL is induced by the ICOS-signaling pathway (81), which is required for optimal Th2 effector function (82). MAL has been shown to play a role in raft-dependent protein trafficking (Ref.83 , reviewed in Ref.84), a process which is central to T cell activation (85). By inference, MAL may thus play a role in establishment of the immunological synapse (86) during the activation process.
DPP4 localizes to lipid raft domains in T cells, and interaction with adenosine deaminase anchored to dendritic cells provides a costimulatory signal augmenting T cell activation (87, 88). It is of interest to note that in cell lines MAL has been demonstrated to mediate intracellular trafficking of DPP4 (83), and interactions between these molecules may thus serve to modulate T cell activation threshold. In the direct context of atopy, DPP4 has also been reported to augment allergen-specific IgE production, regulate chemokine activity, and influence T cell recruitment in a rat model of asthma (89, 90).
NDFIP2 interacts with Nedd4, an E3 ubiquitin ligase (91) and promotes NF-
B signaling (92). In anergic T cells, the E3 ubiquitin ligases Itch and Nedd4 translocate to lipid raft domains and flag signaling proteins for degradation, thereby inhibiting T cell activation (93). NDFIP2 regulation of Nedd4 activity (91), may thus influence T cell activation and signaling. NDFIP2 also has been reported to localize with multivesicular bodies and may also play a role in protein trafficking (94). It is noteworthy that the most commonly used family of drugs used for treatment of chronic inhalant allergy, the corticosteroids, target NF-B signaling (95).
RAB27B is highly homologous to RAB27A, and they have similar functions, however, they also have distinct expression patterns and are therefore not redundant (96). Mutations in the latter cause Griscelli syndrome, a disease which is characterized by inter alia attenuated CD8+ T cell (CTL) cytolytic activity resulting from defective secretion of lytic granules containing GZMB (97, 98). The high level of GZMB expression observed in atopic CD4 T cells in the current study was noteworthy. Studies in gene-targeted and mutant mice have demonstrated that CD4 and CD8 T cells mainly use the FAS and granule exocytosis pathways (perforin dependent), respectively, to mediate cytotoxic effector functions (99, 100, 101). However, recent evidence in human systems has demonstrated that CD4 T cells express GZMB and display perforin-dependent cytolytic activity (102, 103). CD4 T cells expressing both GZMB and perforin have also been detected in lesions from atopic dermatitis patients (104). RAB27A has been reported to interact with several synaptotagmin-like proteins including SYTL3, which are required for lysosomal secretion (98, 105).
CISH is a member of the suppressor of cytokine signaling protein family and inhibits STAT5 signaling (reviewed in Ref.106). In transgenic mice, CISH overexpression promotes Th2 differentiation (107).
IL-17RB is the receptor for IL-17B and IL-17E (IL-25). Treatment of mice with IL-17E induced the expression of IL-4, IL-5, and IL-13, and the development of allergic symptoms such as eosinophilia and mucous hypersecretion (108).
PECAM1 (CD31) is an adhesion molecule expressed on platelets and leukocytes. PECAM1 contains immunoreceptor tyrosine inhibitory motifs and regulates Ag-induced T cell and B cell activation (reviewed in Ref.109), and PECAM1 knockout mice are prone to develop autoimmunity (110, 111). A study in Jurkat T cells reported that PECAM1 inhibited early calcium mobilization from intracellular stores (112). It is known that transient (as opposed to sustained) calcium flux favors Th2 differentiation and signaling pathways (Ref.113 ; reviewed in Ref.114), and attenuation of calcium mobilization during T cell activation via PECAM1 may thus favor expression of the Th2 response phenotype. PECAM1 also regulates -catenin levels, suggesting a functional link between DACT1 and PECAM1.
PLXDC1 has recently been demonstrated to bind cortactin (115), a protein that activates Arp2/3 complexes (reviewed in Ref.116). These complexes initiate actin polymerization, which is important for many facets of T cell biology, including activation, adhesion, and migration.
CAMKII is a prominent kinase of the CNS which is important in cognitive functions including learning and memory (117). Four closely related genes (
, , 
, and 
) encode distinct isoforms of CAMKII, and alternative splicing of the mRNA transcripts derived from these genes generates a considerable number of variants which assemble into homo- or heteromultimers of 8–12 catalytic subunits (117). A role for CAMKII variants encoded by the and genes has been previously reported in T cells (118, 119), however, the function of CAMK2D which is encoded by the gene is unknown in T cells. Calmodulin kinases are activated by the calcium/calmodulin-signaling pathway and regulate gene expression by regulating the activity/expression of transcription factors and coactivators including CREB, CREB-binding protein, C/EBP, serum response factor, and AP1 (120, 121); it is noteworthy that CAMKII is also activated by disheveled signaling (122), suggesting a functional linkage with DACT1.
NSMCE1 and GNG8 have no known functions in T cells.
Note added in proof.
During the manuscript submission and review process, MAL was reportedly induced by IL-4 during the early polarization of human Th2 cells (Lund, R. H. Ahlfors, E. Kainonen, A. M. Lahesmaa, C. Dixon, and R. Lahesmaa. 2005. Identification of genes involved in the initiation of human Th1 or Th2 cell commitment. Eur. J. Immunol. 35: 3307–3319).
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1 This work was supported by the National Health and Medical Research Council of Australia and Pfizer Pharmaceuticals. 
2 Address correspondence and reprint requests to Prof. Patrick G. Holt, Division of Cell Biology, Telethon Institute for Child Health Research, P.O. Box 855, West Perth, WA 6872, Australia. E-mail address: patrick{at}ichr.uwa.edu.au
3 Abbreviations used in this paper: HDM, house dust mite; SEB, staphylococcal enterotoxin B; PPD, purified protein derivative; qRT-PCR, quantitative RT-PCR; QT, quality threshold; MB, multivariate empirical Bayes; SR, stimulation ratio; DTH, delayed-type hypersensitivity.
Received for publication November 1, 2005. Accepted for publication January 30, 2006.
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during the early polarization of CD4+ lymphocytes. J. Immunol. 171: 5328-5336. or IL-4: characterization of Th cells isolated by FACS based on IFN- and IL-4 secretion. Eur. J. Immunol. 35: 2709-2717. [Medline]: an overview of signals, mechanisms and functions. J. Leukocyte Biol. 75: 163-189. -catenin and JNK signaling, is required for notochord formation. Dev. Cell 2: 449-461. [Medline]-catenin impairs T cell development. Nat. Immunol. 4: 1177-1182. [Medline]-catenin-signaling pathway transcription factors lymphoid enhancer factor-1 (LEF-1) and T cell factor-1 (TCF-1) in a subset of peripheral T cell lymphomas. Am. J. Pathol. 162: 1539-1544. B and MAPK signaling pathways. Oncogene 22: 3307-3318. [Medline]B. Int J. Biochem. Cell Biol. 29: 867-870. [Medline]This article has been cited by other articles:
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, p < 0.01; 
, p < 0.001; 
, p < 0.0001.