1 Section on Immunology and Immunogenetics, Joslin Diabetes Center, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, Massachusetts
2 Division of Pediatric Endocrinology, Childrens Hospital, Harvard Medical School, Boston, Massachusetts
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ABSTRACT |
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The nonobese diabetic (NOD) mouse, discovered in the 1970s, has been a commonly used model of human type 1 diabetes. As in humans, disease progresses in these mice in at least two definable stages. In a first stage, infiltration of autoreactive cells into the pancreatic islets begins after 4 weeks of age and becomes progressively more extensive, but spares a proportion of insulin-producing ß-cells. This pre-diabetic state can persist for months (years in patients), the autoimmune attack remaining controlled and relatively nondestructive (although our perspective on the exact nature of the pre-diabetic lesion is far less precise for human patients than for mice). In a second stage, typically between 15 and 25 weeks of age in NOD mice, unknown events provoke the innocuous insulitis to progress to active ß-cell destruction. Although the NOD mouse provides a performant diabetes model, disease is still very complex in these animals, with intricate genetic determinism. Disease pathogenesis likely involves a defect in central tolerance induction as well as faulty immunoregulatory cells or molecules. Evidence for the involvement of multiple antigens, lymphocyte populations, and final effector mechanisms has lead researchers to investigate simpler models.
Transgenic mouse lines have been exploited to elucidate this complexity. In several such lines, the expression of a transgene-encoded, prearranged T-cell receptor (TCR) gene confers on a majority of T-cells reactivity to an islet ß-cell antigen presented by either a major histocompatibility complex (MHC) class I or class II molecule (14). These lines bypass the early steps of breakdown of tolerance and amplification of the autoimmune repertoire and allow the analysis of peripheral effector and regulatory mechanisms. Among these transgenic models is the BDC2.5 TCR transgenic line, derived from a diabetogenic CD4+ T-cell clone (3,5). The dominant CD4+ T-cells are restricted by the MHC class II Ag7 molecule and are specific for an unidentified antigen derived from ß-cell granules. BDC2.5 mice develop insulitis between 2 and 3 weeks of age, with very extensive infiltration of essentially all islets by 4 weeks. However, progression to overt diabetes is under tight control; on the NOD background, BDC2.5 animals develop diabetes only 515% of the time.
The state of balanced and benign insulitis can be disrupted by a number of perturbations, e.g., blockade of costimulatory molecules in the initial phase of autoreactive T-cell activation (68), triggering of toll-like receptors by lipopolysaccharide administration (9), apoptosis of pancreatic cells due to viral infection (10). All of these perturbations provoke an aggressive form of insulitis in BDC2.5/NOD mice that leads to destruction of ß-cells and diabetes within days. Similarly, the cytotoxic drug cyclophosphamide (CY) induces diabetes within 57 days after injection into young adult BDC2.5/NOD mice (11). CY has been known for quite some time to accelerate diabetes in NOD mice as well, but less efficiently and over a more protracted course (1214). In BDC2.5/NOD mice, no changes are histologically visible in the first 2 days after CY treatment; by 3 days, the fairly innocuous appearance of the insulitic lesion begins to be perturbed, and by 5 days, the lesion has become "explosive," with extensive dilacerations of the endocrine tissue and cell apoptosis. These studies demonstrated no striking changes in the proportions of the different cell populations infiltrating the islets, arguing rather for the activation of preexisting infiltrating cells.
How CY achieves destabilization of the local immunoregulatory balance is not known. The literature repeatedly invokes an effect of CY on suppressor cells, but primary data supporting this notion are hard to find. It is also not clear which cell or cells are the targets of the CY induction process. Does this cytotoxic drug primarily affect the islet ß-cell, releasing intracellular contents and increasing the local autoantigen load? Or does it activate myeloid or lymphoid cells in the infiltrate?
Because of its very rapid and synchronous nature, the induction of diabetes in BDC2.5/NOD mice affords the possibility of investigating, in a highly controlled manner, the molecular and cellular events that accompany conversion to an aggressive insulitis and the accompanying diabetogenic decompensation. In earlier studies (11), we focused on cellular and histological changes that take place. Here, we have studied the unfolding of events during CY-induced diabetes by monitoring the time course of gene expression changes via microarray analysis. The aim was to determine whether, in a broad analysis of gene expression changes, one might be able to discern a signature of the programmatic changes taking place during the early phases of diabetes induction.
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RESEARCH DESIGN AND METHODS |
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Islet isolation.
Islets were isolated by the Islet Preparation Core of the Juvenile Diabetes Research Foundation (JDRF) Center for Islet Transplantation at Harvard using a standard technique (15). Briefly, ice-cold collagenase (Liberase; Roche) was infused into the common bile duct of an anesthetized mouse. The pancreas was then removed and incubated at 37°C for 20 min with gentle agitation. Further separation was accomplished with a Histopaque density gradient (Histopaque-1077; Sigma). The islets were handpicked under a stereomicroscope to ensure a pure islet preparation. Total RNA was isolated from these islets using Trizol followed by ethanol precipitation.
Conventional gene expression analysis.
Before amplification of islet mRNA or to confirm the DNA chip analysis, transcripts of some genes were quantitated with quantitative real-time PCR. Amplification of specific genes was monitored during the PCR utilizing an internal fluorescent probe on an ABI Prism 7700 Sequence Detection System (Applied Biosystems). For each gene of interest, a TaqMan primer/probe set was designed such that both the amplified fragment and the probe spanned an intron to avoid spurious amplification from genomic DNA. For gene expression analysis, cDNA was made from 100 ng of total pancreatic islet RNA in a 20-µl reverse transcriptase reaction. Six microliters of this reverse transcription product were used for triplicate 20-µl TaqMan reactions. Transcripts of the genes of interest (interleukin [IL]-18, IL-12 p40, interferon [IFN]-, and Cxcl9) were normalized relative to transcripts of the ubiquitous housekeeping gene glycerol phosphate dehydrogenase (GPDH). For each experiment, a standard curve was generated for every gene based on
-Ct (the number of cycles required to generate
50% of the maximum amount of amplified product, set during the linear phase of amplification). Normalization of the relative amounts of a gene of interest (X) from RNA from a CY-treated (cy) versus a PBS-treated (ctl) mouse were calculated as: Xcy/Xctl (normalized) = (Xcy/Xctl) x (GPDHctl/GPDHcy).
Microarray gene expression analysis.
Islet RNA was amplified using a standardized protocol (MessageAmp aRNA kit; Ambion). Briefly, cDNA was synthesized from 1 µg total RNA template by reverse transcription primed by a hybrid oligonucleotide containing oligo-T and T7 RNA polymerase promoter sequences. Double-stranded DNA was then synthesized by incubation with RNase A followed by extension with polymerase I. Multiple copies of antisense RNA were then produced with T7 RNA polymerase (100-fold amplification). This RNA was then primed with random hexamers, and the entire amplification process was repeated, producing biotinylated RNA with 10,000-fold amplification of the original mRNA. Biotinylated cRNA was then prepared with biotinylated ribonucleotides in another T7 RNA polymerase reaction (Enzo BioArray HighYield RNA Transcript Labeling Kit; Affymetrix).
Biotinylated RNA was fragmented in a proprietary Affymetrix buffer optimized to break down full-length cRNA by metal-induced hydrolysis (https://www.affymetrix.com/download/manuals/expression_print_manual.pdf). The samples were hybridized to Affymetrix MU74v2A microarray chips in an Affymetrix Fluidics Station 400 hybridizer/analyzer, with streptavidin-phycoerythrin to detect biotinylated probe. Fluorescence on the chip was quantified on an Affymetrix Fluidics Station 400 hybridizer/analyzer and digitized using an Affymetrix GeneChip Scanner with Affymetrix GeneChip operating software. The initial reads were processed through the robust multiarray average (RMA) algorithm (implemented on the Array analyzer; Insightful) for probe-level normalization. These primary values were averaged (with outlier elimination) and an approximate measure of significance calculated (Welchs approximation t test). For analysis of significance, control datasets approximating the values and variances of the real data were generated by random reshuffling of the data (random draws within rows) or as detailed in RESULTS. The raw datasets have been deposited on the GEO databank.
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RESULTS AND DISCUSSION |
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In order to ensure the validity of the microarray analysis, three or four independent experiments were performed at each time point (Table 1). Each experiment also included an untreated group (hereafter referred to as "day 0"). From previous experiments (11), we knew that IL-12 expression should be induced in samples from mice progressing to diabetes. We used RT-PCR to show that IL-12 p40 transcripts were indeed induced in the samples used for microarray analysis (data not shown). From all samples, RNA was prepared, with an average yield of 80 ng, and labeled probes for microarray analysis were synthesized by using biotinylated ribonucleotides in the final RNA polymerase reaction. These probes were then hybridized to Affymetrix Mu74Av2 oligonucleotide microarrays, which represent 8,063 unique genes, of which 1,935 are expressed sequence tags.
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Two main strategies were used to assess the significance of the observations. First, since each experiment included a control pool, the study design generated more samples in the control day 0 group than for other time points. In validation analyses, this allowed us to divide the day 0 group into two individual subgroups (four samples each), providing an internal estimation of the intragroup experimental variability. As will be detailed below, a number of changes were observed at day 3 after CY treatment. Figure 1A shows that the differences between the day 3 and day 0 conditions were far more numerous than those between the replicate day 0 groups. This first analysis indicated that most of the variations described below were likely to be true and not merely a consequence of experimental fluctuation. Second, randomized data groups were generated by permutation between the 18 datasets of the expression values for each gene (row-wise permutation) and were then processed following the same methods as those for the true datasets. This generated matrices of fold change and P values for the null hypothesis (no time-dependent expression changes). This randomized dataset was used in unimodal comparisons of fold-change distributions (Table 2, see below). We also took advantage of the kinetic nature of the data. While the limited number of time points precluded the utilization of sophisticated time series techniques, an apparent false-positive rate (FPR) was estimated for the genes of interest by combining the probabilities of coordinated variation in successive time points. For each gene in the real dataset, we counted the number of genes in the random dataset presenting a similar fold-change pattern (day 2 and day 3 only). For the simulation shown in Fig. 1B, close to 100,000 random "gene patterns" were thus queried. The vast majority of the patterns were observed quite frequently (104 times or more), but a few were very rare or absent from the randomly generated data. Not surprisingly, these corresponded to genes showing the greatest fold change at day 3 relative to day 0 (Fig. 1C). From these computations, we derived an estimate of the FPR for each gene that showed a time-dependent variation.
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We then analyzed in more detail the kinetics of expression of those genes in which transcripts were clearly altered by day 3 of CY treatment (filtered as described below). Figure 2 displays the distribution of expression at each day after CY treatment, plotted in reference to the day 0 values. Genes that eventually became induced by day 3 are shown in red, and those repressed are in blue. At day 1 (Fig. 2A), the majority of genes in the induced set were not yet affected, still clustering around the diagonal, except for a few repressed genes already showing a clear change. At day 2 (Fig. 2B), most genes in the set began to show clear differences from the day 0 values, albeit still less than the full induction seen at day 3. For the repressed gene set, a few were already underexpressed at day 1, with an amplification of the trend at day 2.
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IFN- and the genes it controls dominated the response in a striking manner.
The color-coding of the "gene symbol" column of Fig. 3A, based on text searches through the cMu74Av2 chip annotation fields and PubMed databases, highlights those genes in which expression is controlled to some extent by IFN-. Twenty-five of the 88 genes in the induced set belong to this class (more may also be IFN-
responsive, but not yet recognized as such). To some extent, the earlier descriptions of inflammatory changes in CY-induced diabetes did foretell the upregulation of a range of inflammatory cytokines and downstream transcriptional programs (11,14,26,27). Yet, the current data highlight the fact that the effects were very much focused on IFN-
. Much less prevalent were other inflammatory cytokines that one might have expected to find, such as tumor necrosis factor (TNF)-
or other members of the TNF family. There have been conflicting reports on the importance of IFN-
in the development of diabetes in the NOD mouse, with different gene ablations of the cytokine or its receptor having contrasting effects in backcrossed animals (2831). IFN-
blockade by antibody treatment also had a limited effect on disease (11). The overwhelming impact of IFN-
uncovered here argues for a reevaluation of this question.
Members of chemokine gene families were among the most strongly induced transcripts.
These included Ccl2 and -7 and Cxcl9, -5, and -1. These chemokine transcripts showed parallel profiles of induction, i.e., slightly elevated at day 2, more fully induced at day 3 (Fig. 4A), but with a more robust induction for Ccl1 and -5 than for the other chemokine genes. Some of these genes are known to be IFN- responsive (but this is not true of all), and their kinetics of induction (contemporary with those of IFN-
) suggest that other factors might have been responsible for their induction. Interestingly, these induced chemokine genes are clustered in murine genomes, particularly in two regions located on Chr5 and Chr11. Examination of the regions (Fig. 4B) shows that the transcriptional activation did not involve the entire cluster, i.e., only some members were induced, whereas others remained silent or unchanged (Figs. 3 and 4B and online appendix). Thus, the activation of gene expression did not represent a wholesale activation of chromosomal regions, but rather quite specific effects on particular genes. The disposition of the induced chemokines follows an interesting pattern: in both cases, the most 5' members of the cluster were those most significantly activated during CY-induced progression to diabetes, whereas there was much less effect on the more distal members of the locus. Is this disposition purely coincidental? It may reflect shared enhancer/response elements, although 130 kb separate Cxcl1 from Cxcl5, which is only 15 kb from the uninduced Cxcl2. It may also reflect the evolutionary history of the loci, inducibility being unequally conferred to duplicated members.
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Repressed genes.
The 84 genes in which transcripts decreased in the 3 days following CY treatment are listed in Fig. 3B. They are very different in class and composition from the induced gene set.
A major proportion of the decreased transcripts were genes expressed in B-cells (highlighted in dark green).
Twenty-three of the repressed transcripts issued from immunoglobulin loci (heavy or light chains, variable or constant regions) and were among those that showed the most profound reduction, 8 of 10 of the transcripts showing the greatest reduction were from immunoglobulin genes. The reduction was not limited to immunoglobulin genes, a number of other B-cell transcripts were also affected, such as genes encoding molecules involved in antigen presentation (MHC class II, invariant chain), cell surface receptors (CD52, CD79, and CD83), and signal transduction molecules (CD5, Blk, and Dgk). More than likely, this broad set of decreases reflects a drop in the proportion of B-cells in the insulitic lesion. Insulitis in BDC2.5/NOD mice (and in NOD animals as well) includes a very sizeable proportion of B-cells, which can account for up to 30% of the infiltrate area in immunohistochemical analyses (M.M., R.P., D.M., C.B., unpublished data). CY is known to be particularly toxic to B-cells; treatment with this agent provokes the death of B-cells within 2448 h after administration (32). Thus, a wave of B-cell death within the infiltrate is likely to be the root of this strong reduction in B-cellspecific transcripts.
As was the case for the induced gene set, only a minority of the "reduced set" transcripts originated from the islet cell compartment.
None of those corresponded to the primordial function of pancreatic endocrine cells; in particular, insulin transcripts were only reduced from 6,760 to 6,300 and 5,240 to 4,390 units for Ins1 and Ins2, respectively. This finding indicates that a generalized loss of ß-cell function had not yet occurred by 3 days after CY treatment. The transcriptional changes that affected islet cells were predominantly centered on secretory or protease control functions (secretogranin, ChromograninA, Kexin2, and Spi family members), perhaps indicative of early changes in secretory pathways.
Genomic clustering of the CY-induced response.
Given the suggestive genomic clustering of the induced chemokine genes, we analyzed more extensively the genomic distribution of those genes in which expression was affected by CY, clustering them according to their chromosomal position (information from the Affymetrix website [NatAffx.com], complemented by BLAST [basic local alignment search tool] searches on the Ensembl genome browser). Genes were considered to potentially belong to a cluster when they mapped <500 kb apart and were either induced or repressed after CY administration. Several interesting clusters were highlighted by this search (Fig. 5). Some were expected, such as the chemokine cluster described above or the immunoglobulin heavy and light chain clusters. The Reg genes also all map within a 140-kb stretch. Others were somewhat of a surprise and may underscore hitherto unrecognized relationships; for example, the "prostate tumor overexpressed gene 1" and "kidney-derived aspartic protease-like protein" were coordinately repressed and map close to each other on Chr7. Conversely, the genes encoding Pbef and Gdap10 (preB-cell colony-enhancing factor and ganglioside-induced differentiation associated protein 10, respectively) map within 20 kb of each other on Chr12 and were induced in concert. Only in one instance did genes in the same geographical cluster respond discordantly, underscoring the significance of the observations, given that random clustering would be expected to generate equal numbers of concordant and discordant clusters.
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In the case of CY-induced diabetes, one might speculate that the disappearance of B-cells is at the root of the process, either because B-cell depletion results in the release of large amounts of apoptotic cell fragments, thus activating the myeloid cells that abound in the lesion and altering their capacity to interact with/control T-cells, or because B-cells exert a regulatory influence in and of themselves, as was proposed by Turk (34) in the mid-1970s and more recently in a murine model of arthritis (35). Disappearance of B-cells would unleash, then, aggressive insulitis. Although not usually thought as central to the pathogenesis of type 1 diabetes, B-cells do play an important role, as shown by the quasi-absence of autoimmune attacks in B-celldeficient NOD mice (3638), although this point is more controversial in the human milieu (39). This is usually interpreted as a perturbation of antigen presentation, which prevents the initial priming of autoreactive T-cells. The present data lead one to speculate that B-cells may also be involved in later stages of the process, when regulatory balances condition the outcome of the autoimmune lesion.
On the other hand, our data do not support the oft-evoked notion that CY exerts its influence by depleting suppressor T-cells. The aggressiveness of the insulitic lesion in BDC2.5 mice is strongly conditioned by regulatory T-cell populations, which are under the control of costimulatory family genes such as ICOS or CTLA-4 (6,40) (A. Herman, C.B., D.M., unpublished observations). In the latter study, diabetes induced by ICOS blockade correlated with clear changes in the "Treg signature," a set of 150 genes differentially expressed in CD25+CD4+ regulatory cells (41). In the present data, such changes were conspicuously absent. In addition, we have not observed significant reductions in pancreatic CD25+ T regulatory cells after CY treatment (A. Herman, D.M., C.B., unpublished data). Thus, and unless CY affects a very minor subpopulation with regulatory potential, changes in T regulatory populations do not seem prevalent in CY-induced diabetes.
In conclusion, this analysis reveals a landscape dominated, to a striking degree, by IFN- and the genes it controls. The model used here certainly represents an extreme form of ß-cell destruction, and it will be important to assess whether IFN-
occupies such a central position in other instances of autoimmune ß-cell destruction, and whether the molecular and cellular pathways uncovered in this mouse system play a role in human patients.
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ACKNOWLEDGMENTS |
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We thank members of the Benoist-Mathis lab and John Rogus for discussion; Jack ONeill, Vaja Tchipashvili, and Gordon Weir of the JDRF Center for Islet Transplantation at Harvards Islet Preparation Core for provision of islets; Robert Saccone of the DERC Microarray Core for processing and hybridization of the probes; and Judy George and Ella Hyatt for maintaining the BDC2.5 colony.
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FOOTNOTES |
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Address correspondence and reprint requests to Diane Mathis and Christophe Benoist, Section on Immunology and Immunogenetics, Joslin Diabetes Center, 1 Joslin Place, Boston, MA 02215. E-mail: cbdm{at}joslin.harvard.edu
Address correspondence and reprint requests to Diane Mathis and Christophe Benoist, Section on Immunology and Immunogenetics, Joslin Diabetes Center, 1 Joslin Place, Boston, MA 02215. E-mail: cbdm{at}joslin.harvard.edu
Received for publication March 11, 2004 and accepted in revised form May 26, 2004
CY, cyclophosphamide; FPR, false-positive rate; GPDH, glycerol phosphate dehydrogenase; IFN, interferon; IL, interleukin; JDRF, Juvenile Diabetes Research Foundation; MHC, major histocompatibility complex; NK, natural killer; RMA, robust multiarray average; TCR, T-cell receptor; TNF, tumor necrosis factor
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REFERENCES |
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