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The Journal of Immunology, 2008, 180, 1071 -1079
Copyright © 2008 by The American Association of Immunologists, Inc.
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Genome-Wide Microarray Expression Analysis of CD4+ T Cells from Nonobese Diabetic Congenic Mice Identifies Cd55 (Daf1) and Acadl as Candidate Genes for Type 1 Diabetes1

Junichiro Irie*, Brian Reck{dagger}, Yuehong Wu*, Linda S. Wicker{ddagger}, Sarah Howlett{ddagger}, Daniel Rainbow{ddagger}, Eleanor Feingold{dagger} and William M. Ridgway2,*

* Division of Rheumatology and Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261; {dagger} Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261; and {ddagger} Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Disclosures
 References
 
NOD.Idd3/5 congenic mice have insulin-dependent diabetes (Idd) regions on chromosomes 1 (Idd5) and 3 (Idd3) derived from the nondiabetic strains B10 and B6, respectively. NOD.Idd3/5 mice are almost completely protected from type 1 diabetes (T1D) but the genes within Idd3 and Idd5 responsible for the disease-altering phenotype have been only partially characterized. To test the hypothesis that candidate Idd genes can be identified by differential gene expression between activated CD4+ T cells from the diabetes-susceptible NOD strain and the diabetes-resistant NOD.Idd3/5 congenic strain, genome-wide microarray expression analysis was performed using an empirical Bayes method. Remarkably, 16 of the 20 most differentially expressed genes were located in the introgressed regions on chromosomes 1 and 3, validating our initial hypothesis. The two genes with the greatest differential RNA expression on chromosome 1 were those encoding decay-accelerating factor (DAF, also known as CD55) and acyl-coenzyme A dehydrogenase, long chain, which are located in the Idd5.4 and Idd5.3 regions, respectively. Neither gene has been implicated previously in the pathogenesis of T1D. In the case of DAF, differential expression of mRNA was extended to the protein level; NOD CD4+ T cells expressed higher levels of cell surface DAF compared with NOD.Idd3/5 CD4+ T cells following activation with anti-CD3 and -CD28. DAF up-regulation was IL-4 dependent and blocked under Th1 conditions. These results validate the approach of using congenic mice together with genome-wide analysis of tissue-specific gene expression to identify novel candidate genes in T1D.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Disclosures
 References
 
In complex genetic diseases such as type 1 diabetes (T1D),3dozens of allelic variants interact to promote or discourage disease manifestation (1, 2, 3). Similar findings have been made in animal models of complex genetic disease such as the NOD mouse, which spontaneously develops T1D (4, 5). Positional cloning to discover genes causing T1D in NOD mice has involved the generation of congenic mice and this approach has been very successful in excluding candidate genes and narrowing the list of possible candidate genes (5, 6, 7, 8, 9, 10, 11, 12, 13, 14).

There is a practical limit, however, to fine-mapping disease genes using congenic strains of mice since recombination in the genome is not random and often the smallest congenic interval that can be isolated can contain up to 50 genes (4). Expression analysis of genes within the interval can contribute evidence of genetic variation and thereby greatly assist candidate gene prioritization in a manner unbiased by previous biological knowledge of any of the genes. The use of genome-wide microarray expression analysis (15, 16) not only allows simultaneous assessment of all of the genes within the interval (given microarray chips with sufficient gene substrate to recognize splice variants), but also highlights any genes controlled in trans by a candidate causal gene.

The combination of microarray analysis with congenic strain fine-mapping has had variable success in genetic mouse models of autoimmunity. In two different lupus mouse models, microarray analysis identified strong genetic candidates (17, 18). In T1D, however, an earlier attempt at this analysis was not successful when applied to expression in whole, naive spleen (19). The authors concluded that analyzing expression in a noninduced whole organ was not informative, implying that selecting specific immune cell subsets may be more productive. Therefore in this report, we have chosen to analyze differential gene expression in purified, activated CD4+ T cells because a large amount of literature supports a pathogenic role for this cell type in NOD mice (20, 21, 22), suggesting that some genes causing T1D, known as Idd (for insulin dependent diabetes) genes, are expressed in the CD4+ T cell subset.

The NOD.Idd3/5 congenic mouse, with the B6-derived Idd3 interval on chromosome 3, and the B10-derived Idd5 interval on chromosome 1, is almost completely protected from diabetes (1–2% incidence at 7 mo of age for NOD.Idd3/5 females compared with 80% for NOD females) (23). The genetic basis of T1D protection from diabetes in NOD.Idd3/5 mice has been partially characterized. The Idd3 region is a 650-kb interval containing five known genes (Tenr, Il2, Il21, Centrin4, and Fgf2), two predicted genes of unknown function (KIAA1109 and KIAA1371), and three pseudogenes. The prime candidate gene encodes IL-2 and the causative single nucleotide polymorphisms are hypothesized to subtly alter its mRNA expression levels (24). As detailed in the accompanying article (25), there are four subregions within the larger Idd5 region: Idd5.1 (2.0 Mb), Idd5.2 (1.5 Mb), Idd5.3 (3.5 Mb), and Idd5.4 (78 Mb). The genes accounting for Idd5.1 and Idd5.2 are most likely Ctla4 (6, 7, 8) and Nramp1 (4, 8), respectively. The molecular basis of these two candidate genes has been attributed to single nucleotide polymorphisms in the coding regions which alter splicing in case of Ctla4 and the primary amino acid sequence for Nramp1.

To further characterize the genetic basis of T1D resistance in NOD.Idd3/5 mice, we formulated the hypothesis that the genetically controlled resistance to diabetes would be correlated to altered gene expression patterns in candidate genes on chromosomes 1 and 3 of activated NOD.Idd3/5 CD4+ T cells. Thus, activated CD4+ T cells from NOD and NOD.Idd3/5 should show differential expression primarily in the congenic intervals on chromosomes 1 and 3; conversely, the common NOD genome outside the congenic region on these two chromosomes should be equivalently expressed. Exceptions to this hypothesis, i.e., differential expression of genes from NOD and NOD.Idd3/5 CD4+ T cells outside the congenic intervals, could result from downstream or trans effects of genes in the congenic intervals. As a control, we also compared gene expression between CD4+ T cells purified from NOD and B6.G7 mice, two strains differing throughout the genome except that they share the NOD MHC region on chromosome 17.

Our results demonstrate the potential of this multifaceted approach to identifying candidate genes in congenic mice. Of over 22,000 probe sets analyzed on the chip, 16 of the 20 genes most differentially expressed between both NOD and NOD.Idd3/5 and NOD and B6.G7 CD4+ T cells were located within the boundaries of the NOD.Idd3/5 congenic intervals on chromosomes 1 and 3. The two most differentially expressed genes in the Idd5 region, Cd55 (formerly Daf1) and Acadl, have not been implicated in T1D pathogenesis and are novel candidate genes for Idd5.4 and Idd5.3, respectively. The identification of Cd55 and Acadl as candidate genes illustrates that an unbiased genetic approach to gene identification using congenic mouse strains, relevant cell populations, and genome-wide microarray analysis can identify unexpected candidate genes and provide valuable insights into the biological processes underlying T1D.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Disclosures
 References
 
Mice

NOD.B6 Idd3 B10 Idd5 mice (Ref. 23 , hereafter referred to as NOD.Idd3/5 mice), NOD, and B6.H2g7 (hereafter called B6.G7) mice were bred and housed under specific pathogen-free conditions and all procedures were conducted according to approved protocols of the University of Pittsburgh School of Medicine Animal Care and Use Committee. All mice used were female, aged 8–11 wk. Idd5 congenic strains 974, 1092, 1595, 2574, 1094, and 2193 were obtained from the Taconic Emerging Models program (Taconic Farms). To confirm the purity of the genetic background of the congenic strains, DNA from lines 974, 1092, 1595, and 1094 (from which 2574 and 2193 were derived) was tested by genotyping using a 5 K mouse single nucleotide polymorphism (SNP) chip, performed by ParAllele Biosciences. For all of the strains tested, no non-NOD SNPs were identified outside of the defined congenic regions. The 5 K mouse SNP chip was also used to determine that the B10 and B6 strains are identical by descent (26) within the B10-derived introgressed region of chromosome 1 in NOD.Idd3/5 mice.

Preparation, purification, and stimulation of splenocytes

The spleen from each mouse was removed aseptically and minced. After lysing RBC, the cells were washed three times with PBS. To purify CD4-positive splenocytes, splenocytes were prepared by magnetic separation using a MiniMACS system (Miltenyi Biotec) according to the manufacturer’s instructions. Purified CD4-positive splenocytes were suspended in RPMI 1640 medium (Invitrogen Life Technologies) supplemented with 10% heat-inactivated FBS (Invitrogen Life Technologies) and 1 mM L-alanyl-glutamine (Invitrogen Life Technologies), 100 U/ml penicillin, 100 µg/ml streptomycin (Invitrogen Life Technologies), 1 mM sodium pyruvate (Invitrogen Life Technologies), and 50 µM 2-ME. The CD4-positive splenocytes (1 x 106) were transferred to each well of a 24-well plate precoated with anti-CD3 Ab (10 µg/ml) and anti-CD28 Ab was added (1 µg/ml final concentration). The cells were cultured for the indicated period and harvested. Th2 conditions were: recombinant mouse IL-4 (10 ng/ml) and/or anti-mouse IFN-{gamma} Abs (10 µg/ml), whereas Th1 conditions were: recombinant mouse IL-12 (5 ng/ml) and/or anti-mouse IL-4 Abs. In some studies, anti-mouse IL-4R Abs alone were added (BD Pharmingen).

Flow cytometry

After culture, cells were incubated with Fc blocker (BD Pharmingen) and stained with labeled Abs for 20 min at 4°C. Samples were analyzed on a FACSCalibur (BD Biosciences). Anti-CD4 and -CD55 Abs were purchased from BD Pharmingen.

RNA extraction

Total RNA was extracted from cultured cells using the RNeasy mini kit (Qiagen). The RNA was redissolved in RNase-free water and yield was estimated by spectrophotometry; equal quantities of RNA were used for analysis. Samples were hybridized to the Affymetrix mouse chips (see below) at the Genomics and Proteomics Core Laboratory at the University of Pittsburgh.

Real-time RT-PCR analysis of decay-accelerating factor (DAF) mRNA expression

CD4+ T cell RNA was reverse-transcribed using an oligo-dT primer and reverse transcription system (Promega) according to the manufacturer’s instructions. Real-time PCR was conducted for CD55 and GAPDH (internal control) in an ABI Prism 7300 sequence detector (Applied Biosystems). All reactions were performed using TaqMan Universal MasterMix; primer/probe sets were purchased from Applied Biosystems (CD55 primer/probe set = Mm00438377_m1; Applied Biosystems). The obtained mRNA level was expressed relative to the GAPDH PCR product amplified from the same sample; DAF value = 2{wedge}((Ct of GAPDH) – (Ct of DAF)), where Ct is the cycle threshold.

Real-time RT-PCR analysis of acyl-coenzyme A dehydrogenase, long chain (ACADL) mRNA expression

RNA was extracted from purified CD4+ T cells in TRIzol (Invitrogen Life Technologies) and 1000 ng of total RNA was used in a cDNA synthesis reaction with Superscript II reverse transcriptase (Invitrogen Life Technologies). cDNA was used as template in a TaqMan PCR (prepared with TaqMan Universal PCR Master Mix; Applied Biosystems) with the following primers and probes designed to detect ACADL mRNA: forward GATTTATCAAGGGCCGGAAG, reverse GAAATCGCCAACTCAGCAAT and probe FAM-TGTCCGATTGCCAGCTAATGCC-TAMRA. β2-Microglobulin was used to normalize expression levels as described previously (6).

Microarray techniques

MOE430A Affymetrix high-density oligonucleotide array chips containing 506,944 oligonucleotide probes for 22,690 probe sets were used in the analysis. Total RNA was converted to double-stranded cDNA according to standard methods and purified using an Affymetrix cDNA clean-up column. An aliquot of the double-stranded cDNA equivalent to 5–7 µg of starting RNA was added as template to an in vitro transcription reaction as per the ENZO BioArray High-Efficiency RNA Transcript Labeling kit, and the resulting biotinylated cRNA was purified using an Affymetrix RNA clean-up column. After elution, the cRNA was quantified by spectrophotometry and 20 µg of cRNA was incubated at 94°C in fragmentation buffer (40 mM Tris-acetate (pH 8.1), 100 mM KOAc, 30 mM MgOAc) for 35 min. A 1-µl aliquot of the sample was run on an Agilent Bioanalyzer to verify that fragmentation resulted in RNA of the desired size distribution.

Fifteen micrograms of the fragmented RNA was added to a final volume of 300 µl of hybridization mixture, applied to the GeneChip, and incubated overnight at 45°C with rotation. Following hybridization, the sample was removed and the GeneChip cassette was filled with nonstringent wash buffer. The chip was loaded onto an Affymetrix Fluidics station for wash and stain. The GeneChip was then stained for 10 min in streptavidin-PE (SAPE) solution (1x MES stain buffer, 2 mg/ml acetylated BSA, 10 µg/ml SAPE; 1x MES stain buffer contains 100 mM MES, 1M [Na+], 0.05% Tween 20). Nonstringent buffer was used to wash off the first stain solution. Signal amplification was achieved by 10-min incubation with biotinylated anti-streptavidin (1x MES stain buffer, 2 mg/ml acetylated BSA, 0.1 mg/ml normal goat IgG, 3 µg/ml biotinylated anti-streptavidin) followed by a second 10-min incubation with SAPE. The chip was washed and filled with nonstringent wash buffer before being removed from the fluidics station and scanned using the GeneArray scanner.

Data analysis

Activated CD4+ T cell gene expression data was obtained from NOD (four samples), B6.G7 (four samples), and NOD.Idd3/5 (three samples) mice. Preprocessing of the data consisted of robust multichip average background correction, quantile normalization, and robust multichip average expression summarization as described by Irizarry et al. (27). Preprocessing was implemented using the affy library of the bioconductor package of R (28). The advantages of this preprocessing procedure over other methods, for instance, the stock Affymetrix MAS5.0, are described in Bolstad et al. (29). Correlations, box plots, and variability vs mean plots were used to verify that the preprocessing successfully reduced the variability of the expression measures between chips.

After normalization of the microarray data, we created ranked lists of the most differentially expressed genes—one list for each pair of strains. The ranked lists were created using an empirical Bayes method (30), which is effectively a t test that is "smoothed" to remove the erratic effects of genes with unusually low-variance estimates. This is very similar to the method used in the SAM software (31). After application of the empirical Bayes method to remove the low-variance genes from the ranked lists, we assessed the statistical significance of the most differentially expressed genes using a standard two-sample t test. For statistical analysis of protein expression, the Mann-Whitney and {chi}2 tests were performed in GraphPad Prism and JMP-IN software. Physical location of genes was established using Ensembl version 46.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Disclosures
 References
 
Empirical Bayes analysis of normalized microarray expression data sets from NOD, NOD.Idd3/5, and B6.G7 purified, activated CD4+ T cells to identify candidate genes in the Idd3/5 congenic regions

To generate candidate genes in the NOD.Idd3/5 congenic intervals, we purified CD4+ T cells from NOD, NOD.Idd3/5, and B6.G7 spleens, activated them with anti-CD3 and CD28, and subjected the mRNA to genome-wide analysis with Affymetrix microarray chips. Using an empirical Bayes approach (see Materials and Methods), an estimated posterior logarithm of odds of differential expression for each probe set was generated to create ranked lists of differentially expressed probe sets. The log odds score was determined for each Affymetrix probe set for ranking expression in CD4+ T cells between two pairs of strains: NOD vs NOD.Idd3/5, and NOD vs B6.G7. The top 7% most differentially expressed probe sets were then selected from each list (the 7% cutoff being chosen arbitrarily; 7% represents 1588 probe sets). After removing probe sets that were lacking identifiable genes or chromosomal locations in Ensembl, there were 208 probe sets that were in the top 7% in both the NOD vs NOD.Idd3/5 and the NOD vs B6.G7 comparisons. We considered that a subset of these 208 probe sets represented potential candidate genes (Fig. 1; the complete list of probe sets is shown in supplemental table I).4Fig. 1 represents the expression by log odds of all the probe sets on the chip (each circle represents ~10 probe sets); the vertical and horizontal lines represent the cutoffs for our top 7% lists. The upper right quadrant of Fig. 1 shows the probe sets resulting after intersecting the top 7% lists from the NOD vs NOD.Idd3/5 and NOD vs B6.G7 comparisons. In addition to using the log odds to rank all the probe sets, we assessed the statistical significance of differential expression between two strains at any single probe set by using the usual t statistic to generate p values, and then eliminated probe sets with a p value >0.05. This resulted in 43 unique genes, all significantly differentially expressed between both NOD vs NOD.Idd3/5 and NOD vs B6.G7, as shown in Table I. Remarkably, 16 of the 20 most differentially expressed genes are located in the NOD.Idd3/5 congenic intervals on chromosomes 1 and 3 (Table I, column eight). Fifteen of 43, or 35%, of all the significantly differentially expressed genes were from chromosome 1; in contrast, all the other chromosomes averaged only 1.45 genes per chromosome. This represents a significant enrichment (p < 0.0001) of chromosome 1 genes in the sample, as assessed by {chi}2 analysis comparing the number of genes found in the ~90 Mb chromosome 1 segment (15/90) vs the number (28 genes) found in the ~2571 Mb of chromosome outside the chromosome 1 segment (golden path length, based on Ensembl version 46). Using the Idd5 subregion boundaries as defined by the T1D frequencies of Idd5 congenic strains (Fig. 2 and Ref. 25), 14 of the 15 differentially expressed genes in the NOD.Idd3/5 chromosome 1 congenic interval are still included within the boundaries of two of the four known Idd5 subregions: Acadl in Idd5.3 and 14 genes, including Cd55, in Idd5.4 (Table I). Four of the top 20 genes, (including Suclg2, which is the most differentially expressed gene between NOD and both NOD.Idd3/5 and B6.G7), and all of the remaining 23 genes in Table I, were located outside of the congenic intervals, suggesting that these expression differences represent downstream effects of differential gene expression within the introgressed regions (see Discussion).


Figure 1
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  		FIGURE 1. Dot plot representation of the whole gene chip data set from NOD-, NOD.Idd3/5-, and B6.G7-activated CD4+ T cells. Shown is the distribution of probe sets representing the entire data set from the NOD, NOD.Idd3/5, and B6.G7 gene chips, analyzed by intersecting probe sets from different strains represented on the x and y axes (see Results). Each circle represents ~10 probe sets. Probe sets in the top 7% of differential expression between NOD- and NOD.Idd3/5-activated CD4+ T cells (vertical line) intersected with probe sets in top 7% of differential expression between NOD- and B6.G7-activated CD4+ T cells (horizontal line). The upper right quadrant shows the probe sets that were in the top 7% in both the NOD vs NOD.Idd3/5 and the NOD vs B6.G7 comparisons.; the full list of these genes is found in supplemental table 1.

 

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  		Table I. Gene expression ranking by log odds ratio obtained by intersecting the top 7% of probe set expression between NOD and NOD.Idd3/5 with the top 7% of probe set expression between NOD and B6.G7a

 

Figure 2
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  		FIGURE 2. Genetic maps of NOD Idd5 congenic strains used in this study. Vertical arrows indicate the Idd5 subregions as identified in the accompanying article (25 ).

 
Confirming differential expression of candidate genes Cd55 and Acadl

The 43 genes shown in Table I represent ~0.2% of all probe sets on the gene chip. Nonetheless, in experimental terms, investigating this number of genes represents an enormous effort. We therefore decided to initially focus our investigation on the most differentially expressed candidate genes in the Idd5.4 and Idd5.3 regions, Cd55 and Acadl. Cd55 encodes the protein DAF, also known as CD55. Cd55 is located in the distal segment of the B10-derived Idd5 region present in the NOD.Idd3/5 mouse. First, we evaluated DAF protein expression on purified CD4+ T cells (cultured under the same conditions as those used for the gene chip analysis) from NOD, NOD.Idd3/5, and B6.G7 mice. As shown in Fig. 3a, DAF was significantly up-regulated on the cell surface of NOD CD4+ T cells compared with NOD.Idd3/5 or B6.G7 CD4+ T cells, thereby confirming the gene chip results at the protein level. Reverse transcription of RNA obtained from the CD4+ T cells followed by quantitative PCR (TaqMan methodology) analyses also confirmed the results obtained in the microarray experiments; there was increased expression of DAF RNA in activated NOD CD4+ T cells compared with similarly activated cells from NOD.Idd3/5 and B6.G7 mice (Fig. 3b). The differential expression of the DAF protein and its mRNA is consistent with the B6 and NOD Cd55 alleles having different haplotypes that could alter gene regulation (Fig. 4a).


Figure 3
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  		FIGURE 3. DAF protein and mRNA expression analyses on NOD, NOD.Idd3/5, and B6.G7 CD4+ T cells. CD4+ T cells were purified and stimulated with anti-CD3/CD28 for 3 days and were then analyzed by flow cytometry for cell surface expression of the DAF protein (a) and quantitative PCR following reverse transcription for mRNA expression, analyzed with the Mann-Whitney U test (b).

 

Figure 4
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  		FIGURE 4. Chromosome one SNP maps generated from the T1D base web site (http://dil.t1dbase.org/page/ PerlegenSNPs) showing (a) Cd55, (b) the Idd5.3 region, and (c) Acadl. SNP data generated for the NOD strain by Perlegen (http://mouse.perlegen.com/mouse/index.html) was compared with the B6 sequence present on mouse Ensembl (http://www.ensembl.org/Mus_ musculus/index.html). SNPs that are not polymorphic between NOD and B6 are shown as blue vertical lines and polymorphic SNPs between NOD and B6 are shown as red lines. Yellow vertical lines indicate a known SNP that has not been defined in the NOD strain. Regions lacking SNPs often indicate that comparative sequences were not obtained due to an abundance of repetitive sequence in the region or to other technical issues.

 
Having confirmed the gene chip results for Cd55, we examined additional Idd5 congenic strains of mice with progressively smaller B10-derived chromosome 1 regions, some with NOD alleles at Cd55 and some with B10 alleles, for DAF protein expression (Fig. 2, genetic map, and Fig. 5, expression studies). In each case, CD4+ T cells from mice with B10 alleles at Cd55 (lines 1092 and 974) had decreased DAF expression as compared with CD4+ T cells from mice with the NOD allele (lines 1595 and 2574) (Fig. 5). These results confirm Cd55 as a candidate gene for Idd5.4, although further dissection of this region by making additional Idd5.4 congenic strains is required to substantiate this candidacy because the B10-derived genetic interval comprising Idd5.4 is large, ~70 Mb (Fig. 2). Thus, hundreds of genes are located in Idd5.4 including Cd55 and several other differentially expressed genes listed in Table I.


Figure 5
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  		FIGURE 5. Increased DAF expression is observed only on cells having the NOD DAF allele. CD4+ T cells were purified from NOD.Idd5 congenic strains and stimulated with anti-CD3/CD28. Cell surface analysis of DAF expression was performed as in Fig. 3. One representative of three experiments is shown.

 
In contrast to the very large Idd5.4 region, Acadl is located in the relatively small 3.553 Mb Idd5.3 interval (25), which is highly polymorphic between NOD and B6 for much of the region (Fig. 4b). Notably, of the Idd5.3 genes interrogated for differential expression, only Acadl was highly differentially expressed by log odds ranking (Table II). The observation that Acadl is differentially expressed is again consistent with B6 and NOD having different haplotypes throughout much of the Idd5.3 region (Fig. 4b) including Acadl (Fig. 4c).


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  		Table II. Differential expression of the known genes in the Idd5.3 interval by microarray log odds rankinga

 
We confirmed and extended the gene chip results of increased expression of the B10 ACADL allele by assessing ACADL mRNA expression by quantitative PCR in activated CD4+ T cells from congenic mice having smaller B10-derived intervals in the Idd5 region than the NOD.Idd3/5 congenic strain (Figs. 2 and 6). Line 1094 CD4+ T cells, which have the B10 ACADL allele, had increased expression of ACADL mRNA compared with cells from line 2193 mice having the NOD allele (Fig. 6). These observations highlight Acadl as a major candidate gene for mediating the disease-causing phenotype localized to the Idd5.3 region (25).


Figure 6
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  		FIGURE 6. Differential expression of Acadl. Expression of Acadl was assessed in RNA isolated from CD4+ T cells stimulated in vitro with anti-CD3/CD28 for 0, 5, and 20 h. Two strains were compared for Acadl expression, line 1094 ({blacksquare}) and line 2193 (•). Line 1094 has B10 alleles at Idd5.3 whereas line 2193 has NOD alleles at Idd5.3. Data from two experiments of six performed are shown (a and b). All six experiments had similar results. Comparisons of the {Delta} Ct values of Acadl mRNA obtained from CD4 T cells from lines 1094 and 2193 demonstrated significant differences at the 5 and 20 h time points (p = 0.08, 0.0003, and 0.005 using the Mann-Whitney U test at 0, 5, and 20 h, respectively).

 
DAF up-regulation on CD4+ T cells is promoted by IL-4 and suppressed in Th1 conditions

Our discovery of a variation in DAF expression that localizes to a chromosome region that includes Cd55 is of interest because the knockout of the DAF/CD55 gene increases T cell activity and susceptibility to experimental autoimmune encephalomyelitis (see Discussion). We thus hypothesized that the differential expression of DAF by the B10 and NOD alleles would have functional consequences on the immune response, and initiated studies to investigate the regulation of DAF expressed at the cell surface of CD4+ T cells in different cytokine environments (Fig. 7 and Table III). As noted above, NOD CD4+ T cells up-regulate DAF under neutral conditions. However, as shown in Fig. 7, Th1 conditions were not associated with DAF up-regulation on NOD CD4+ T cells whereas Th2 conditions strongly enhanced DAF up-regulation. Th2 conditions did not, however, increase DAF expression on NOD.Idd3/5 CD4+ T cells (data not shown). Next, we asked which components of the Th2 culture conditions were sufficient for up-regulation of DAF. IL-4 alone was sufficient for strong up-regulation of DAF on NOD CD4+ T cells. Up-regulation of DAF was significantly different between IL-4 alone vs Th1 conditions (p = 0.009) and IL-4 alone vs anti-IL-4R Ab (p = 0.02), while expression under Th2 conditions did not differ statistically from IL-4 alone (p = 0.66) (Fig. 7, Table III). Moreover, anti-IL-4R Abs alone added to culture with anti-CD3/-CD28 stimulation completely prevented DAF up-regulation; the expression levels of DAF with anti-IL-4R Ab were significantly different from neutral conditions (p = 0.01), whereas expression under Th1 conditions was not significantly different from anti-IL-4R Ab alone.


Figure 7
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  		FIGURE 7. DAF expression on NOD CD4+ T cells is up-regulated by both Th2 conditions and IL-4, and down-regulated by both Th1 conditions and anti-IL-4R Ab. NOD CD4+ T cells were purified and stimulated with anti-CD3/CD28 as in Fig. 3, under neutral, Th1, and Th2 conditions (see Materials and Methods) or with only IL-4 or anti-IL-4R Ab. A, One representative of seven experiments is shown. B, The mean channel fluorescence from all experiments plus SEM is shown for each condition. The complete data set is shown in Table III. Neutral condition DAF expression was significantly different from DAF expression using Th1, Th2, and anti-IL-4R conditions (p = 0.005, 0.048, and 0.01, respectively), and Th1 DAF expression was significantly different from that under Th2 conditions (p = 0.008). Th1 condition DAF expression was not significantly different from that using the anti-IL4R condition nor was Th2 condition DAF expression significantly different from that using the IL-4 condition. Cells cultured under IL-4 conditions showed significantly different expression than Th1 (p = 0.009) and anti-IL-4R conditions (p = 0.02). All significance testing was performed using the Mann-Whitney U test.

 

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  		Table III. Expression of CD55 on activated NOD CD4+ T cellsa

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Disclosures
 References
 
Identification of candidate genes in complex autoimmune diseases, even when they are confined to a small, well-defined genetic interval, remains a challenging scientific problem. Hypothesis-driven approaches (e.g., investigating immunological mediators in T1D, or investigating possible autoantigens) will overlook genes in novel and unexpected pathways whereas a "hypothesis-free" whole genome approach can miss candidate genes by investigating too many variables simultaneously. Indeed, a prior published attempt to discover causative genes in NOD congenic mice analyzed expression in whole, unstimulated spleen preparations and did not detect compelling candidate genes in the Idd intervals (19). In the current study, we have approached the search for candidate loci by focusing on global expression analysis of activated CD4+ T cells from both NOD and NOD congenic mice to define a subset of differentially expressed genes located in the introgressed congenic intervals.

As summarized in Table I, this approach was highly successful; 16 of the top 20 differentially expressed genes identified were localized to introgressed non-NOD DNA present in the congenic mice, and 14 of the 16 genes were located within the boundaries of Idd5.3 or Idd5.4 on chromosome 1 and therefore represent candidate genes. Two of the most differentially expressed genes, Daf1 and Acadl, were confirmed using quantitative PCR and are located within the boundaries of the Idd5.4 and Idd5.3 regions, respectively, thereby establishing them as strong candidate genes.

Several of the candidate genes in Table I were not located in the introgressed regions on chromosomes 1 and 3 present in NOD.Idd3/5 mice. Suclg2, for example, emerges as the most differentially expressed gene overall, but it is located on chromosome 6. A reasonable assumption is that the differential expression of Suclg2 is a downstream effect of one or more of the non-NOD-derived genes in the introgressed Idd3/5 intervals. The identification of Acadl as a candidate gene highlights its potential to mediate such downstream effects. Acadl encodes an enzyme, acyl-coenzyme A dehydrogenase, which controls the first step in fatty acid β-oxidation (32). Studies from the Thompson laboratory (33, 34) have highlighted the unique bioenergetic challenges facing T cells upon activation: they require enormous energy expenditures, but also face significant anabolic challenges in synthesizing sufficient materials for cell division, including fatty acids needed for new cell membrane construction. The outcome of these energetic demands is that T cell activation directly suppresses fatty acid β-oxidation via the PI3K/Akt pathway (33, 34). We have shown that the protective B10 allele of Acadl has higher expression compared with the NOD allele, presumably leading to increased fatty acid use; moreover, Suclg2 mRNA is also higher in Idd3/5 compared with NOD-activated CD4+ T cells. Suclg2 encodes succinyl coenzyme A ligase, which is a component of the citric acid cycle. Higher levels of two enzymes involved in energy production, Acadl and Suclg2 in T cells with a B10-derived protective Idd5.3 region, suggest a change in T cell bioenergetics that could alter the function and survival of T cells. The highlighting of such downstream expression differences by microarray analysis should provide significant insights into disease pathogenesis because it is possible that protective alleles at distinct Idd genes would cause partially overlapping downstream patterns. Because the combined activity of protective alleles at Idd3 and Idd5 provide more disease protection than would be expected if the alleles were acting in a multiplicative fashion (35), the discovery of gene expression differences that occur only when a particular combination of protective alleles is present would also illuminate key pathways causing autoimmunity.

We identified Cd55 as one of the most differentially expressed genes between NOD and NOD.Idd3/5 T cells. Cd55 is well-known as a complement regulatory gene, but recent research has also identified it as a T cell costimulatory molecule that interacts with its ligand, CD97 (36). Splice variants of CD97 produce isoforms having variable numbers of epidermal growth factor domains that engage DAF with variable affinity (37). DAF likely binds to the first CD97 epidermal growth factor domain but also requires domains 2 and 5; the alternately spliced variant of CD97 that expresses only domains 1, 2, and 5 binds DAF with the highest affinity (38).

Cd55 knockout mice have been studied in several models of autoimmunity and show worsened experimental glomerulonephritis, experimental myasthenia gravis, and experimental autoimmune encephalomyelitis (39, 40, 41, 42). Moreover, Cd55–/– mice demonstrated significantly enhanced T cell responses with hypersecretion of IFN-{gamma} (42). However, it is unclear whether proinflammatory or anti-inflammatory biological effects will be caused in vivo by high vs low CD55 expression levels in the presence of IL-4. If the lack of CD55 expression renders T cells more responsive, as suggested by the knockout studies, the higher CD55 levels in the presence of IL-4 could prevent the T cells from responding as efficiently to other IL-4-mediated differentiation signals, thereby causing the T cells to remain more Th1-like. We have previously shown that NOD T cells are biased to Th1 expression (43, 44), consistent with many other publications suggesting a Th1 bias in NOD mice (45, 46, 47, 48, 49, 50).

It is also possible that CD55 expression caused by IL-4 is anti-inflammatory and that the genetic program generating Th1 conditions in NOD mice in vivo negates the potentially protective effect of DAF by preventing its up-regulation because of a paucity of IL-4. Indeed, many therapeutic interventions associated with the induction of Th2-related phenotypes have prevented T1D (51, 52, 53). Our genetic mapping studies indicate that the NOD allele of Idd5.4 acts as a T1D-resistance allele whereas the B10 allele increases T1D susceptibility (25); however, it is likely that the ~70 Mb Idd5.4 interval will have more than one gene affecting T1D, a hypothesis we are currently testing. In particular, a NOD congenic strain having a small interval encompassing the B10 allele of Cd55 will be tested to determine whether this isolated region contributes resistance or susceptibility to T1D.


   Acknowledgments
 
We acknowledge Deborah Hollingshead and the University of Pittsburgh Genomics and Proteomics Core Laboratories for their assistance with microarrays.


   Disclosures
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Disclosures
References
 
The authors have no financial conflict of interest.


   Footnotes
 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 W.M.R. was supported by National Institutes of Health (NIH) National Institute of Diabetes and Digestive and Kidney Diseases 60714 and NIH RFA A102-006. L.S.W. is a Juvenile Diabetes Research Foundation (JDRF)/Wellcome Trust Principal Research Fellow and the research in the laboratory of L.S.W. for this study was also supported by NIH P01 AI039671. The availability of NOD congenic mice through the Taconic Farms Emerging Models Program has been supported by grants from the Merck Genome Research Institute, National Institute of Allergy and Infectious Diseases, and the JDRF. Back

2 Address correspondence and reprint requests to Dr. William M. Ridgway, Division of Rheumatology and Immunology, School of Medicine, University of Pittsburgh, S725 Biomedical Science Tower, 200 Lothrop Street, Pittsburgh, PA 15261. E-mail address: ridgway2{at}pitt.edu Back

3 Abbreviations used in this paper: T1D, type 1 diabetes; Idd, insulin-dependent diabetes; SNP, single nucleotide polymorphism; DAF, decay-accelerating factor; Ct, cycle threshold; ACADL, acyl-coenzyme A dehydrogenase, long chain; SAPE, streptavidin-PE. Back

4 The online version of this article contains supplemental material. Back

Received for publication September 13, 2006. Accepted for publication October 31, 2007.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Disclosures
 References
 

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