- Open Access
Impairment of organ-specific T cell negative selection by diabetes susceptibility genes: genomic analysis by mRNA profiling
© Liston et al.; licensee BioMed Central Ltd. 2007
- Received: 29 August 2006
- Accepted: 21 January 2007
- Published: 21 January 2007
T cells in the thymus undergo opposing positive and negative selection processes so that the only T cells entering circulation are those bearing a T cell receptor (TCR) with a low affinity for self. The mechanism differentiating negative from positive selection is poorly understood, despite the fact that inherited defects in negative selection underlie organ-specific autoimmune disease in AIRE-deficient people and the non-obese diabetic (NOD) mouse strain
Here we use homogeneous populations of T cells undergoing either positive or negative selection in vivo together with genome-wide transcription profiling on microarrays to identify the gene expression differences underlying negative selection to an Aire-dependent organ-specific antigen, including the upregulation of a genomic cluster in the cytogenetic band 2F. Analysis of defective negative selection in the autoimmune-prone NOD strain demonstrates a global impairment in the induction of the negative selection response gene set, but little difference in positive selection response genes. Combining expression differences with genetic linkage data, we identify differentially expressed candidate genes, including Bim, Bnip3, Smox, Pdrg1, Id1, Pdcd1, Ly6c, Pdia3, Trim30 and Trim12.
The data provide a molecular map of the negative selection response in vivo and, by analysis of deviations from this pathway in the autoimmune susceptible NOD strain, suggest that susceptibility arises from small expression differences in genes acting at multiple points in the pathway between the TCR and cell death.
- Negative Selection
- Additional Data File
- Double Positive Cell
- Double Positive
- Single Positive
Immunological self-tolerance depends upon negative selection in the thymus, whereby T cells bearing T cell receptors (TCRs) with high avidity for self peptide-major histocompatibility complex (MHC) complexes are purged from the developing repertoire before they become functionally active in the periphery . Negative selection occurs by TCR-induced apoptosis during the transition of immature CD4+8+ double positive (DP) cells into mature CD4+ or CD8+ single positive (SP) cells. Nevertheless, a measure of TCR signaling is required for thymocytes to mature from DP into SP cells, an opposite process requiring a weak avidity for self peptide-MHC in order to initiate the changes of cell survival and maturation referred to as positive selection. It is thought that these two opposite processes, of cell survival or death, initiated by binding of the same receptor to its ligand, are controlled by quantitative differences in TCR affinity for self peptide-MHC that are translated into qualitatively opposite cellular responses. However, the molecular basis by which the two processes are differentially controlled and how the cellular responses initiated are achieved are unclear .
Recent studies have found that inherited defects in negative selection underlie autoimmune disease. In the human disorder autoimmune polyendocrinopathy syndrome type 1, defects in the AIRE gene reduce transcription of organ-specific genes in the thymus so that organ-reactive T cells are not negatively selected [3–5]. The non-obese diabetic (NOD) mouse strain is an intensely studied model for human autoimmune diabetes, as well as being susceptible to other autoimmune disorders , and displays a striking cellular deficiency in MHC class II- [7, 8] and class I-restricted negative selection , compared to non-autoimmune-prone strains. Elucidating the molecular basis for defective negative selection in NOD mice may shed light on the process of differentiating negative from positive selection and the pathogenesis of human autoimmunity.
The NOD thymic deletion defect is T cell autonomous [7, 8] and represents a quantitative (approximately ten-fold) decrease in negative selection efficiency to membrane-bound or soluble proteins, regardless of low or high thymic expression controlled by organ-specific or systemic promoters . Genetic linkage studies between the C57BL10.H2k (B10k) and NOD strains identified four NOD-derived recessive loci that contribute to defective negative selection in vivo, by tracing CD4 T cell deletion triggered in the thymus of transgenic mice by expression of an Aire-dependent antigen (insHEL) that mirrors the expression of the insulin gene itself . As would be expected, the four defective deletion loci correspond to four NOD loci known to contribute to diabetes susceptibility, linked to the markers D7mit101, D15mit229, D2mit490/Idd13 and D1mit181/Idd5. Parallel analyses in vitro, in a thymic organ culture system with exogenously added antigen, found NOD loci that acted dominantly to interfere with apoptosis linked to two recessive diabetes susceptibility loci, Idd5 and Idd3 .
Gene expression profiling on microarrays provides an opportunity to visualize the molecular differentiation of negative from positive selection and defects in negative selection at a global level. Several key questions can, in principle, be addressed. First, does negative selection involve induction or repression of a unique set of genes, or simply quantitatively exaggerated changes in the same set of genes as positive selection? Does the negative selection defect in NOD mice interfere with all or only a small number of negative selection genes, or does it cause a new profile of counter-regulatory genes to be triggered, and does it equally affect positive selection gene responses? Several studies have begun to explore this approach, although they have been limited by complications, such as premature negative selection at the double negative (DN) to DP transition, pooling of developmental subsets, TCR heterogeneity, and the peripheral cytokine storm that is produced after cognate antigen injection [12–15].
Here we extend a preliminary published analysis  using an approach that provides a unique opportunity to visualize the global expression changes under physiological in vivo conditions of negative and positive selection. The model employs TCR transgenic mice to trace selection of T cells with a homogeneous TCR recognizing a well-characterized high affinity peptide-MHC agonist, hen egg lysozyme (HEL) 46-61/I-Ak. This TCR is expressed at low levels on DP cells, and promotes positive selection into TCRhi CD4+ SP cells following the physiological pathway and induction of markers such as CD69 and CCR7 . When the TCR transgenic is crossed to insHEL transgenic mice on the non-autoimmune B10k strain background, the insulin-promoter activates Aire-dependent transcription to produce low levels of HEL antigen in thymic medullary epithelial cells, triggering negative selection at the physiological stage during the transition from cortical DP cells to medullary SP cells. Unlike thymocyte apoptosis initiated through the intravenous injection of antigen or anti-TCR antibodies, deletion of HEL-reactive thymocytes by endogenous presentation of HEL activates a physiological apoptotic process that is T cell intrinsic, with efficient deletion of HEL-reactive thymocytes at various dilutions of precursor frequency and no apoptosis of neighboring non-HEL-specific thymocytes (data not shown). This system of negative selection also has the advantage of faithfully replicating the expression pattern of a key autoantigen in human disease, insulin. By comparing global gene expression in homogeneous subsets of sorted cells from these mice, either pre-selection or undergoing positive or negative selection, we present here a detailed analysis of the gene expression differences distinguishing negative from positive selection in a non-autoimmune prone strain, and analyze the underlying defect in negative selection in the autoimmune NOD strain.
Global gene expression changes differentiating positive and negative thymocyte selection in the B10kstrain
To characterize the gene expression changes occurring during positive and negative selection in a non-autoimmune strain, gene expression profiles were measured in three relatively homogeneous thymocyte populations from the B10k strain . Pre-selection thymocytes ('PreS') that had not yet received positive or negative selection signals were identified as CD4+8+ DP cells that are negative for CD69 and 1G12 TCR clonotype and sorted from TCR transgenic and TCR insHEL double transgenic mice. Early single positive cells that were beginning to undergo positive selection (S+) were sorted from TCR transgenic mice as CD4+CD8lowCD69+1G12+ cells. Early single positive cells with an identical cell surface phenotype but beginning to undergo negative selection (S-) were sorted from TCR insHEL double transgenic animals. Three independent pools of mRNA from sorted cells were analyzed by labeling and hybridization to Affymetrix 430A microarrays and normalized using MAS 5.0. MAS 5.0 was used as the normalization method in preference to more precise project-based model fitting systems, such as RMA, GCRMA and PLM, due to advantages in simple reduction of the false discovery rate. Use of the MAS 5.0 normalization method produces 'present' and 'absent' calls and, when used as a filtering device, this has been demonstrated to reduce the false discovery rate with little true cost to the true positive rate . Furthermore, by taking the mismatch probe into consideration, MAS 5.0 reduces the level of false positives based on cross-hybridization . It should be noted that all normalization methods involve a trade-off of accuracy or precision at various levels and, while MAS 5.0 performs strongly for background correction and medium and high intensity signals , it is less accurate than some alternatives for probesets in the low intensity range [18, 19]. The complete dataset, with raw values and statistical analysis is given in Additional data file 1 and has been deposited in the NCBI Gene Expression Omnibus (GEO)  accessible through GEO Series accession number GSE3997.
This measure of global expression 'closeness' between conditions gave a result consistent with the Euclidean analysis, where the pattern categories with the largest number of probesets were those (patterns A and G in Figure 2) displaying an expression difference between pre-selection and selection (both positive and negative selection) but no difference between positive and negative selection. These are likely to represent genes that are developmentally regulated as part of the differentiation of immature DP cells into more mature early SP cells. Some may be induced or repressed by TCR signaling mechanisms that are unable to distinguish between TCR engagement by weak positively selecting agonists and the strong negatively selecting agonist. Pattern A comprised 531 probesets that were induced during maturation/selection, and pattern G comprised 692 probesets that were repressed. The induced genes included well established markers and mediators of maturity, Tcr, Ccr7, S1P1 and IL7R, and genes that are known to be targets of positive and negative selection TCR signals, such as the calcineurin-response genes Ian1, Egr2 and CD52 and ERK-response genes Nab2 and Zfp36l1. The repressed genes included Rag1, preTαβ, CD8α and CD8β, known to be involved in thymocyte maturation changes, and cell cycle genes Cdc7, Cdk2, Cdk2ap1, and Cdk4, which are consistent with the exit from cell cycle that accompanies maturation of early DP cells, which are cycling, into later DP and SP cells, which are non-cycling. The identification of these expected expression changes acts as an internal validation of the dataset.
As well as these previously identified markers of positive and negative selection, several important T cell regulatory genes were also found here to be upregulated in selection: those encoding nucleic acid binding proteins, such as Bcl3 , Dicer1, Fosb , Hivep1 , Irf1 , Irf4 , Irf7 , JunB , Klf2 , Nfat5 , Rora , Stat1 , and Stat6 ; signaling-associated genes Ccnd2 , Evl , Hspa5 , Ly9 , Mcl1 , Ndfip1 , Psen2 , Rassf5 , Upf2  and Zfpn1a ; and those encoding receptors, such as Crry , Gpr83 , 2-K1 , Ms4a6b , Sema4a , Slamf1 , Tlr1 , Tnfsf11 , and Tslpr .
Previously unassociated genes identified as part of this analysis are, therefore, excellent candidates for novel maturation markers, and include those encoding nucleic acid binding proteins, such as 1810007M14Rik, AA408868, Aptx, Arts1, D11Lgp2e, D1Ertd161e, Ddef1, Ddx19b (2810457M08Rik), Dedd2, Dnmt3a, Elk3, Hnrpa1, Hrb, Ifih1, Isgf3g, Mxd4, Nab1, Rab8b, Rbms2, Rnaset2, Rpl12, Rpo1-1, Skil (9130011J04Rik), Sp100, Tcf3, Tef, Trim21, Trim30, Wasl, Zbp1, Zcchc7, Zfp260, Zfp313, and Zfp445 (AW610627); signaling-associated genes Bin1, Dlgh1, Myd88, Nedd9, Pacsin1, Pscdbp, Sdc3, Shkbp1, Sytl2, Traf1, Vps28, Xpo6; and those encoding receptors, such as 1810011E08Rik, Brd8, Cd9, Folr4, Ptger2, Ptgir, Sorl1, and Tlr2. The complete set of genes identified in this category is listed in Additional data file 2.
The next largest pattern categories comprised probesets that were unique to positive selection, consistent with this condition being the most differentiated based on the Euclidean analysis. These categories (Figure 2) included genes that were induced (patterns B and C) or repressed (patterns H and I) during positive selection, but were either not altered at all during negative selection (C and I) or underwent a change of lesser magnitude but in the same direction (B and H). Genes in these categories are candidates for translating TCR engagement by weak agonists into survival and maturation rather than negative selection. However, this category will also include genes that are developmentally regulated at later stages of SP cell maturation, after CD69 induction and increased TCR surface expression, since negative selection will remove SP cells before reaching this stage. Patterns B and C comprised 278 probesets that were preferentially induced during positive selection, including the well established functional genes CD3ζ and the calcineurin-response gene Itgb7. Patterns H and I comprised 394 probesets that were preferentially repressed during positive selection, including developmental markers Cxcr4, CD8α, CD24a, CD25, and cell cycle genes Cdc2, Cdc6, Cdc20, Cdc25b, Cdka2c and Myb.
As well as these previously identified markers of positive selection, a number of important T cell regulatory genes were also found here to be upregulated in positive selection: those encoding nucleic acid binding proteins, such as Dbp ,Foxo1  and Zfp67 ; the signaling-associated gene Stam2 ; and those encoding receptors, such as Il6ra  and Itgb2 .
Previously unassociated genes identified as part of this analysis are, therefore, excellent candidates for novel markers of positive selection, and include those encoding nucleic acid binding proteins, such as Bhc80, Ddb2, Ezh1, Gata1, Pcbp4, Rbms1, Smarca2, and Trim26, Ddit3, Foxp1, Oas2, Tgif2, and Zfp467; signaling-associated genes Arrb1, Bcap31, Emid1, L1cam, Numb, Pea15, Rabip4, Rcbtb1, Rsn, Selpl, and Sh3gl1; and those encoding receptors, such as AA691260, D7Ertd458e, Frag1, Gpr97 Grina, Il6st, Paqr7, Robo3 and Sh2d3c. The complete list (Additional data file 2) dramatically expands the set of candidate mediators and markers for understanding positive selection and SP cell maturation.
A smaller transcriptome of 246 probesets comprised genes that were preferentially or selectively induced or repressed during negative selection (patterns D, E, F, J, K, L in Figure 2), consistent with negative selection being closer to pre-selection in the global analysis. Genes in these categories are candidate mediators or markers of negative selection and thymocyte apoptosis. Pattern categories E and K were selectively induced or repressed during negative selection, showing no change in expression between pre-selection cells and positive selection. Pattern E comprised 87 probesets, including the TCR-induced pro-apoptotic gene Bim (Bcl2l11), which has previously been shown to be induced selectively during negative selection in this system and is an essential mediator of negative selection, and activation markers such as Ccr6.
Genes in patterns D and J were induced or repressed more strongly in negative than positive selection. Probesets in these categories are likely to include genes that report the quantitative differences in TCR signaling thought to differentiate strong TCR engagement by negative selecting agonists from weak engagement by positively selecting agonists. Pattern D comprised 48 probesets, including the gene encoding the thymocyte apoptosis-inducing transcription factor, Nur77, markers of activated T cells or regulatory T cells, Gitrd, Ox40 and 41bb, and the ERK-response gene Fos. Category F and L comprised a small number of probesets that exhibited a change in expression during negative selection that was in the opposite direction to that induced during positive selection. Pattern L includes genes such as CD25, CD24a and Annexin A4.
Overall, the negative selection transcriptome is quite small: 144 induced probesets, and 102 repressed probesets. By contrast, positive selection of early DP thymocytes into early SP thymocytes induced 809 probesets and repressed 1,086 probesets - a transcriptional program that is eight-fold larger. A complete list of the positive and negative selection transcriptomes, all the genes falling into patterns A to L, in the B10k strain is given in Additional data file 2.
Genomic localization of gene expression changes induced by selection on the B10kbackground
Recent studies have found that sets of genes induced by similar stimuli can co-localize in genomic clusters . To determine if positive or negative selection likewise work by the activation or suppression of broad clusters of genes we queried the data to identify cytogenetic bands with a significantly increased proportion of induced or repressed genes, normalized to their gene density.
Genomic regions with enriched gene expression changes
NOM p val‡
FDR q val§
Regions enriched for gene expression changes during positive selection (B10k)
Decreased in positive selection
Regions enriched for gene expression changes during negative selection (B10k)
Increased in negative selection
Decreased in negative selection
Regions enriched for expression differences between strains in pre-selection thymocytes
Increased in B10k
Decreased in B10k
Regions with enriched strain expression differences during negative selection
Increased in B10k
Global gene expression changes induced upon positive and negative thymocyte selection in the NODkstrain
In parallel with the above analyses of negative and positive selection in B10k mice, the same cell subset markers, sorting methods, and mRNA labeling and hybridization to microarrays were applied to pre-selection (PreS), early positive selection (S+) and early negative selection (S-) thymocytes from TCR and TCR insHEL animals on the NOD.H2k strain background. This allowed developmentally matched, homogeneous populations of T cells to be traced during positive and negative selection using the same TCR and self peptide-MHC ligands, but carrying all the NOD genomic differences from B10 with the exception of the congenically matched H2k haplotype.
The global gene expression differences between pre-selection DP cells and early SP cells undergoing positive or negative selection were first used to compare these states by Euclidian distance (Figure 1). The difference between pre-selection and positive selection thymocytes was similar on the NODk background (23.4 units) to that observed on the B10k background (26.7). On the NOD background, however, there was much less difference between positive and negative selection, with the Euclidean distance between these states decreased from 13.9 in B10k to 8.9 in NOD.
Individual gene expression differences between pre-selection, positive selection and negative selection on the NOD background were categorized into the same 12 patterns, as conducted above for the equivalent cells from B10 strain animals (Figure 2).
Focusing first on patterns A and G, representing probesets that were induced or repressed equivalently during positive or negative selection, these categories contain the largest number of genes that were induced (787 probesets) or repressed (994 probesets), which are comparable to the numbers observed for these categories in the B10 strain (Figure 2). Again, category A includes genes that are developmentally increased during maturation from DP to SP cells, such as IL7R, and genes that are induced by TCR signals during positive and negative selection, such as calcineurin-response genes Ian1, Egr2 and CD52 and ERK-response genes Nab2 and Zfp36l1. In total, 240 of the probesets assigned to category A in NOD were also assigned to this category in B10. The stringent cut-offs used to assign probesets to pattern categories underestimated the similarity of gene expression during DP to SP maturation on the two strain backgrounds, because only 39 of the 531 pattern A probesets in B10 thymocytes have significantly different values from NOD for the corresponding cell types. Likewise, genes that were decreased during maturation (pattern G) included expected developmentally regulated genes such as Rag1, Cd8α, PreTα and cell cycle genes. Of the 692 B10 pattern G probesets, only 56 have significantly different values to NOD for both positive and negative selection. Thus, the NOD background had little effect on gene expression changes associated with early SP maturation from pre-selection DP cells.
By contrast, the NOD background has markedly reduced numbers of genes with expression patterns that differentiate negative from positive selection (patterns B to F and H to L), consistent with the smaller Euclidean distance between these two states in the global analysis. Thus, of the patterns with increased expression in both positive and negative selection (A, B, D), 79% show the same degree of regulation during positive and negative selection (assigned to pattern A) in B10k mice, but 94% show the same degree of regulation in NODk mice (with NODk pattern A containing many probesets assigned to patterns B or C in B10k mice). Likewise, of the patterns with decreased expression in both positive and negative selection (G, H, J), 72% show the same degree of regulation (assigned to pattern G) in B10k mice, but 95% show the same degree of regulation in NODk mice. By this assessment, positive and negative selection are less distinct in NODk mice than in B10k mice.
Similarly, genes that were selectively decreased in negative selection (patterns J, K, F) accounted for only 46 probesets in the NOD strain compared to 102 in B10. Of the 102 probesets decreased upon negative selection in B10 mice, 100 remained at higher levels during negative selection in NODk mice, 84 by more than 20% and 44 significantly so (Figure 4a). Combining both transcriptional increases and decreases, of the 246 probesets specifically changed by negative selection in the B10k mouse, 51% were significantly less changed in the NODk mouse. By contrast, of the 531 pattern A probesets that were increased equivalently during positive and negative selection in the B10k mouse, only 7% had significantly different expression during negative selection in NODk compared to B10k mice, and the majority showed similar expression (Figure 4b).
The NOD background also caused a large decrease in probesets assigned to categories B, C, H, and I, comprising genes that are preferentially or selectively altered during positive selection (Figure 2). This result has two non-exclusive explanations. First, there may be a less efficient positive selection response in NOD. Alternatively, many of the genes in this category may normally be developmentally regulated to appear at later stages of SP cell maturation, before CD69 is lost but at a stage when negative selection would have removed most such cells.
Constitutive differences in thymocyte gene expression caused by the NOD background
In addition to the altered negative and positive selection response above, the NOD background also had altered pre-selection gene expression in TCRlowCD69- DP cells, which may set the stage for altered responses when the cells encounter negative selecting antigens. Six independent pools of pre-selection DP cells were analyzed on both B10 and NOD backgrounds: three from TCR animals and three from TCR insHEL animals. There were few differences between TCR and TCR insHEL pre-selection pools within a strain background, consistent with sorting for antigen-nasïve thymocytes that had yet to display TCRs for HEL and induce CD69. Comparing pre-selection cells between the strains at a global level first (Euclidian distance, Figure 1), the difference between these states (14.2) was approximately half that of the difference between pre-selection DP and early positive selection SP cells (26.7), with a total of 1,484 probesets significantly different between NOD PreS and B10 PreS (1,484 probesets). It is unknown if this degree of pre-selection divergence is specific to the NODk strain, or if it is observed across multiple strains based on comparative divergence.
In terms of genomic location, these changes are particularly concentrated in 20 cytogenetic regions (Table 1). Twelve regions show increased expression in B10k pre-selection thymocytes, eleven of which meet stringent false discovery rates: 8E, 7E, 18E, 12F, 6C, 1B, 15D, 5F, 10C, 19A and 4E. Likewise eight regions show increased activity in NODk pre-selection thymocytes, all of which meet stringent false discovery rates: XF, 3E, XD, 3H, 5C, 12C, 1A and XA. With regard to the phenomenon of defective negative selection in the NODk strain, it may be of relevance that two of these regions co-localize with genomic loci that contribute to defective negative selection , 7E and 15D.
Gene expression differences between NODk and B10k strains induced upon negative selection were also analyzed for cytogenetic clustering. Only four cytogenetic bands show enrichment, after eliminating regions changed in the basal (that is, pre-selection thymocytes) state. Two regions, 2F and 3A, were broadly suppressed in the NODk strain, and two regions were broadly activated in the NODk strain, neither of which meet stringent false discovery rates (Table 1). The region 2F is of particular interest for several reasons. Firstly, this is the only region that was broadly activated upon negative selection in the B10k strain (Figure 3b). Secondly, it is one of only two regions that show broad strain differences in regulation upon induction of negative selection, with lower activity in the NODk strain (Figure 3c). Thirdly, this region overlaps one of the six identified loci with a causative effect in defective negative selection in the NODk strain . A comparative analysis of the gene expression changes in B10k and B10k-NODk negative selection demonstrated that this locus comprises the same cluster of genes that are upregulated upon negative selection in the B10k strain and show poor induction of gene expression in the NODk strain (Figure 3e). These data indicate that the NODk strain has a genetic defect preventing the efficient induction of the negative selection 2F cluster, including Bim, preventing initiation of apoptosis.
Gene expression variants representing causal candidates for defective thymic deletion in NOD
Combining the global transcription profiles for NODk and B10k pre-, positive and negative selection with linkage data for efficiency of negative selection in the same in vivo conditions  provides a way to identify candidate genes responsible for the NOD trait of defective negative selection. While expression differences are promising candidates for quantitative traits, we recognize that this approach is unable to detect allelic variants arising from differential mRNA splicing, such as the Idd5 allele of Ctla4 , or from amino acid substitutions, such as the Idd13 polymorphism in β2 m .
Summary of candidate genes by loci and expression change group
Ch7, 60 cM
Ch15, 22 cM
Ch2, 65 cM
Ch1, 43 cM
High quality candidates for the D7mit101-linked allele
Vital for CD47-induced apoptosis , activation induced apoptosis , hypoxic apoptosis 
Part of Ig recombination complex , Rac-GEF required for membrane ruffling 
Interacts with Rab6  (Golgi-ER transport)
RING, B-box type 1 and 2, coiled coil domains 
RING, B-box type 1 and 2, coiled coil domains 
Potently activates fibroblast growth factor receptor 
High quality candidates for D15mit229-linked defective thymic deletion
Protection against oxidative damage 
Inhibits signal for secretion of IL2 and proliferation , cross-linking causes clustering of LFA-1 
Structural constituent of ribosomes
Nuclear protein, possibly chromatin binding 
High quality candidates for D2mit490-linked defective thymic deletion
Oxidizes spermine to spermidine , resulting in DNA damage and apoptosis 
Pro-apoptotic initiator for cytokine deprivation  and TCR-stimulation of thymocytes 
Inhibits function of E2A and HEB, increases response to TCR stimulation, increases sensitivity to thymocyte apoptosis 
Upregulated by UV radiation and downregulated by p53 
Disulfide oxidoreductase activity
Component of MHC class I
Oxidation of acetate for acetyl-CoA production (utilized mainly for oxidation) 
Chaperone in the ER lumen, required for mitomycin C-induced cell death 
Interacts with FYN and Grb-2 
Promotes lysosome clustering and fusion in vivo, downstream of rab7 
Isovaleryl coenzyme A dehydrogenase
Neuroendocrine secretory protein 
ER Ca++ binding protein 
High quality candidates for the defective thymic deletion allele linked to D1mit181
Homology to heat shock protein DnaJ (Hsp40)
Putative aspartyl aminopeptidase function
Phenylalanine-tRNA synthetase-like, beta subunit
Involved in the initial step of mitochondrial beta-oxidation of straight-chain fatty acids
Negative regulator of T cell function , reduces positive selection , blockade induces diabetes in NOD mice , KO has autoimmune symptoms [79,80]
Aid in the targeting of the MHC class II complex to endocytic compartments , regulates cell cycle 
DNA primase  involved in primer initiation, elongation, and counting 
The analysis of the negative selection transcriptome here distinguishes among several distinct, but not mutually exclusive, mechanisms accounting for defective negative selection in NOD thymocytes: first, several downstream effectors, such as Bim, are defective; second, the entire negative selection induction process is reduced; third, there is a broad defect in the induction of TCR signaling response genes (both positive and negative selection); and fourth, the NODk strain induces an additional, protective transcriptome during negative selection. By comparison of the NODk strain to the B10k strain at a global transcription level, there is no evidence for the ectopic of an additional, 'protective' gene set, nor for an obvious defect in the TCR signaling-dependent process of positive selection. The second possible mechanism appears correct, as there was a global reduction by approximately 40% in the transcriptional process of negative selection, indicating that a defect in upstream events impacted on multiple downstream mediators. Not exclusive from the upstream defect, several important apoptosis effector genes, including Bim, are almost completely absent from the NOD negative selection response, raising the possibility that these genes are at points where individual quantitative differences summate, with cis-acting promoter defects having an additive effect with the defect in upstream inductive events. In particular, the cluster of poorly induced negative selection genes in cytogenetic band 2F around Bim raises the possibility of a cis-acting allelic variation contributing to poor induction of this locus. Of interest, the defect is not absolute, with partial upregulation seen for the majority of the negative selection gene set, correlating with previous cellular data indicating that NOD thymocytes are capable of strong negative selection when exposed to higher levels of stimuli .
The candidate genes discussed above act at multiple points in the pathway between binding to TCR of negatively selecting peptide-MHC and triggering of thymocyte apoptosis. These data frame a hypothesis that defective negative selection involves the summation of many incremental decreases in the efficiency of this signaling pathway, caused by multiple allelic variants at four chromosomal loci. In NOD thymocytes there is lower surface TCR expression, and lower efficiency of TCR signaling due to increased iCTLA4 and decreased Pdcd1 and Id1. This general decrease in TCR signaling may then be compounded by poor inducibility or low expression of apoptosis inducers Bim, Smox, Bnip3, and Pdia3. By producing a molecular map of negative selection responses in vivo, the results from this study open up pathways to understand the mechanism of negative selection and the basis for its quantitative variation leading to autoimmune disease.
The data discussed in this publication have been deposited in the NCBI Gene Expression Omnibus , as MIAME compliant data, and are accessible through GEO Series accession number GSE3997.
As previously described , cell populations were purified from healthy 6-8-week old female mice, stained in 5 μg/ml actinomycin D and 2 μg/ml α-amanitin (Sigma-Aldrich, St Louis, MO) with CD4- fluorescein isothiocyanate (FITC), CD69-phosphatidylethanolamine (PE), CD8- peridinin chlorophyll protein (PerCP) and 1G12 indirectly labeled with Allophycocyanin (APC), then sorted with a Becton Dickinson (Franklin Lakes, NJ) FACVantage. Sorted populations were 'early DP' (CD4+CD8+CD69-1G12-) and 'early SP' (CD4+CD8lowCD69+1G12+). Purified RNA underwent two rounds of in vitro RNA amplification before fragmentation and hybridization to Affymetrix GeneChip 430A arrays (Santa Clara, CA, USA).
Microarray data processing
The Affymetrix 430A chips were scanned using standard Affymetrix protocols. All arrays passed routine quality control assessment for hybridization and data quality. Expression values, referred to as probeset 'signal', were calculated using the Affymetrix GeneChip analysis software MAS 5.0, with a scaling chosen so that each array has a trimmed mean of 150. Following examination, the endogenous control probesets were removed. The MAS 5.0 signal values were then transformed to logarithms (base 2). Signal values of 0 were assigned a value of 0.1 before taking logs. Finally, the arrays were standardized to mean zero and variance one. Initial gene filtering kept only those probesets that were called 'present' by the Affymetrix signal detection algorithm in all replicates for at least one biological group. All statistical analyses were carried out using these transformed signal values. Statistical analysis was carried out in three data sets, one consisting only of the B10k biological groups, one consisting only of the NODk biological groups, and one consisting of both B10k and NODk biological groups.
Assignment of probesets into gene expression patterns
Assignment of probesets to different patterns of gene expression was separately carried out on the B10k and NODk datasets. The patterns are defined in terms of direction of change between means, rather than the extent of change, with assignment of a change having a statistical cut-off rather than a fold-change cut-off. For the separate B10k and NODk datasets, two way analyses of variance (ANOVAs) were performed on each of the selected probesets. For each probeset, if the overall F-test for a test of mean difference was significant (p < 0.005), the probeset was considered significantly changed. Having established that the means were different, t-tests for the contrasts in means were used to determine significantly different means. A significance level of less than 0.05 was used for these tests. Contrasts between 'pre-selection' thymocytes (CD4+CD8+1G12- CD69-) from TCR transgenic and TCR insHEL double transgenic mice showed very few differentially expressed probesets, as is expected for populations that have not been exposed to HEL antigen due to low expression of TCR and anatomical segregation of antigen in the corticomedullary junction. As a consequence, these two groups were merged and the model re-estimated for all the selected probesets. Since there are now three groups, there are twelve distinct patterns of up, down and no difference. Contrasts between the means using the re-estimated models were used to assign probesets to a gene expression pattern, based on a logical set of significant changes between the various conditions. A three-way ANOVA model was used to analyze the combined B10k-NODk dataset for differential expression between strains. The p values are used in this research as indicative of 'evidence' and, except in rare instances, will not be an exact measure of probability. Model assumptions for the use of the different tests were examined for a small number of significant probesets and found to hold. Following analysis, each probeset was annotated for genomic location and gene function using the FACTS (Functional Association/Annotation of cDNA Clones from Text/Sequence Sources) program .
Global gene expression differences
Euclidian distance is the most common measure of metric distance, which is an approximation of the 'distance' between gene expression from replicates in one condition to replicates in other conditions. Euclidean distance is calculated by treating the expression of a group of probesets as a point in n-dimensional space, where the distance from the axis in each dimension represents the expression (the technical mean of transformed signal value for the replicates) of a single probeset. This produces a single point in n dimensions (where n is the number of probesets in the group) that represents one set of conditions, and a single point in the same n-dimensional space that represents the same group of probesets under different conditions. The Euclidean distance between each point (representing each condition) was calculated by using the square root of the sums of squared differences between the points in each dimension, using the formula:
Euclidean distance = √(x1 - y1)2 + (x2 - y2)2+ (x3 - y3)2 + ...
where x is the point representing the probeset group in condition x, with each dimension x1, x2, x3, and so on being the expression of individual probesets within the group, and y is the point representing the probeset group in condition y, with each dimension y1, y2, y3, and so on being the expression of individual probesets within the group .
The straight line distance between the two points thus calculated represents the difference in expression of the included probesets between the two conditions. The probeset group used to calculate the Euclidean distance here included all probesets present in all replicates for at least one condition, thus creating an approximation of the genome-wide similarity between two conditions [21, 83, 84].
Genomic clustering analysis
Gene Set Enrichment Analysis (GSEA) was used to examine the genome for areas of differential expression by cytogenetic band. GSEA R script (script defaults, standard method)  was used with the 'gene label' permutation method. Genes represented by multiple probesets were reduced to a single probeset with the smallest overall p value. A lower cut-off of 20 genes per cytogenetic band was used, which resulted in a total of 113 gene sets being tested for enrichment. A false discovery rate cut-off of 0.4 was initially used, with the stricter criterion of 0.25 for regions listed. Regions with enriched gene expression during positive selection (B10k) refer to cytogenetic bands with an enriched number of genes with expression differences between B10k pre-selection thymocytes and B10k positive selection thymocytes (TCR transgenic early SP cells). Regions with enriched gene expression during negative selection (B10k) refer to cytogenetic bands with an enriched number of genes with expression differences between B10k positive selection thymocytes (TCR transgenic early SP cells) and B10k negative selection thymocytes (insHEL:TCR double transgenic early SP cells). Regions with enriched strain differences in pre-selection thymocytes refer to cytogenetic bands with an enriched number of genes with expression differences between B10k pre-selection thymocytes and NODk pre-selection thymocytes. Regions with enriched strain differences induced during negative selection refer to cytogenetic bands with an enriched number of genes with expression differences between B10k negative selection thymocytes (insHEL:TCR double transgenic early SP cells) and NODk negative selection thymocytes (insHEL:TCR double transgenic early SP cells), with any regions showing enrichment at the pre-selection thymocyte stage removed.
We analyzed 6-10-week old mice (or 6 week post-reconstitution chimeric mice) as described previously [5, 10] using the following antibodies: 1G12 anti-clonotype  (gift of E Unanue and D Peterson, Washington University, St Louis, MO, USA) culture supernatant followed by rat anti-mouse IgG1 allo-phycocyanin; anti-CD8α-PerCP; anti-CD4-FITC or PE; anti-Ly5a-FITC; anti-CD3-PE; and anti-B220-PE (all from BD PharMingen, San Jose, CA, USA).
The following additional data are available with the online version of this paper. Additional data file 1 provides a complete listing of statistically significant gene expression changes. In the 'Statistics and annotation' worksheet, basic annotation data are given for each differentially expressed probeset, with the Affymetrix ID number, the gene symbol, chromosome number and starting/ending location, cytogenetic band, gene ontology/molecular function and the Affymetrix target description. Means and gene expression values are given on the transformed scale. The worksheet also contains information on the significance level of analysis of variance tests. The tests were conducted on the transformed gene expression values for each probeset separately. The p value for significant differences between conditions is listed if the parent p value is less than 0.05. Only genes that showed a significant difference between all the means on this scale, using a threshold of p ≤ 0.005, are included in the worksheet. 'Within B10' tests for differences within the B10k conditions only (using only B10k condition variance data), with subtests 'Within pre' comparing the B10k pre-selection population sorted from insHEL transgenic and non-HEL transgenic hosts, 'PreS vs S-' comparing B10k pre-selection populations to B10k negative selection populations, 'PreS vs S+' comparing B10k pre-selection populations to B10k positive selection populations, and 'S+ vs S-' comparing B10k positive selection populations to B10k negative selection populations. 'Within NOD' tests for differences within the NODk conditions only (using only NODk condition variance data), with subtests performed as per the B10k tests. 'Overall sig' tests for differences between any conditions, with subtests comparing B10k versus NODk populations at the pre-selection stage ('PreS'), during negative selection ('S-') and during positive selection ('S+'). For each probeset the average expression is given as log2 scaled and normalized and arithmetic normalized data, for each condition. Individual data for arithmetic normalized expression are also given for each replicate (PreS 1, 2 and 3 come from non-HEL transgenic hosts, 4, 5 and 6 come from insHEL transgenic hosts). Individual Affymetrix 'Present/Moderate/Absent' calls are given for each replicate. In the 'Raw data' worksheet the individual data for arithmetic normalized expression are given for each probeset, regardless of statistical analysis, along with the Affymetrix target description. Additional data 2 lists the assignment of probesets to expression patterns. The 'B10' worksheet contains information on the expression profiles (patterns) for all probesets that showed a significant difference between the means on the transformed scale, using a threshold of p < 0.05 for the B10k strain. Along with the Affymetrix ID are listed the gene symbol, the pattern to which the probeset is assigned in the B10k strain, the pattern to which the probeset is assigned in the NODk strain, the average arithmetic (unlogged) Affymetrix MAS 5.0 signal values for each condition in the B10k and NODk strains of pre-selection ('PreS'), positive selection ('S+') and negative selection ('S-'), and the annotated molecular function. In the 'NOD' worksheet, all probesets that meet the significant cut-off for assignment to an expression pattern in the NODk strain are listed, in the same manner as the 'B10' worksheet. In the 'B10 vs NOD' worksheet, all probesets that meet the significant cut-off for assignment to a differential expression group between the B10k and NODk strains are listed. Along with Affymetrix ID and gene symbol are listed the group number, the fold-change between the relevant B10k and NODk conditions (dependent on differential expression group), the p values for differential expression between B10k and NODk strains for each condition, the average arithmetic normalized expression for each condition in the B10k and NODk strains, and the annotated molecular function.
We thank S Lesage for advice and D Silva for probeset annotation. This work was supported by grants from the NHMRC and the Juvenile Diabetes Research Foundation.
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