We have previously in a large T1D linkage data set demonstrated statistical evidence for gene-gene interactions [7]. The data set comprised data from 1,321 affected sib pairs genotyped for 298 microsatellite markers [7], [8]. By an integrative approach combining genetic data and high-confidence (human) protein interaction networks, we identified four protein interaction networks significantly enriched in proteins from the predicted genetic interactions. This supported interaction in biological pathways. For each of these networks the identified protein or proteins were viewed in a biological context [7].

In a model of T1D, expression levels were evaluated in human islet preparations, representing the target organ, as well as in human lymphocytes representing the effector cells in T1D. Expression profiling in human islets was performed by comparing the constitutive expression versus cytokine-stimulated expression levels. Gene expression levels in lymphocytes were compared among controls and T1D individuals.

Additional support for individual genes and genetic interactions in the networks comes from evidence for genetic association. The Wellcome Trust Case Control Consortium (WTCCC) has made the results of their large genome-wide association study of T1D and other diseases publicly available (www.wtccc.org) [9]. In this data set we searched for T1D associated SNPs in the 30 candidate genes located in the four interaction networks. To test for combined evidence for T1D association of the protein networks we measured the over-representation (enrichment) of significant SNPs associated with T1D in the four interaction networks, compared to randomly generated networks with similar properties. For each network we tested the enrichment of SNPs in the best 0.1 percentile, 1 percentile and 5 percentile of the WTCCC data for T1D. A nominal P-value and an adjusted P-value was determined for enrichment at each of those thresholds by comparing to 1,000 randomly generated networks with an equal number of proteins and proteins encoded by genes of similar size to the actual test genes.

Interaction network A, table 1 and figure 1, represents genetic interactions between the HLA region on chromosome 6 and a region on chromosome 13, a region on chromosome 4, as well as regions on chromosome 16 and 2. Based on validated protein-protein interactions the proteins/genes responsible for these interactions in network A are the four HLA region genes, BAT1 (Spliceosome RNA helicase, HLA-B associated transcript-I), ITPR3 (Inositol 1,4,5-triphosphate receptor type 3), RPS18 (40S ribosomal protein S18) and TUBB (Tubulin beta-2 chain) interacting with the LMO7 (LIM domain only protein 7) gene on chromosome 13, the WDR1 (WD repeat domain I) gene on chromosome 4, the RPS15A (40S ribosomal protein S15a) on chromosome 16 and the HNRPLL (Heterogeneous nuclear ribonucleoprotein L-like, stromal RNA-regulating factor) on chromosome 2, as well as two genes directly interacting from other chromosomal regions, DNAJC14 (Nuclear protein Hcc-1, proliferation associated cytokine-inducible protein CIP29) and ELF5 (ETS-related transcription factor Elf-5). Network A is significantly enriched, after correction for multiple testing, for SNPs in the 0.1 percentile and the 1 percentile, and borderline significant for SNPs in the best 5 percentile of the WTCCC study, table 2, indicating that the interaction network as a whole is a risk factor in T1D and further supporting the genetic interactions observed in previous work. For genes in network A no significant differences in expression levels between lymphocytes from eight newly diagnosed T1D patients and nine control individuals was identified. For the nine human islet preparations we found that four of the genes demonstrated significant down-regulation upon cytokine-stimulation. These were BAT1, RPS18 and TUBB from the HLA-region and the WDR1 gene, on the short arm of chromosome 4, a gene involved in actin binding, table 1 and figure 1. This gives further support to these four genes as functionally relevant in this model of T1D (human islets stimulated with cytokines). Neither the RPS18 nor the TUBB gene has known functional roles in relation to T1D. The BAT1 gene has been designated HLA-B associated transcript and BAT1 is a negative regulator of inflammation [10], probably affecting production of TNFα, IL-1β and IL-6. WD-repeats are involved in protein-protein interactions and have been shown to be involved in regulation of transcription, mRNA modification and transmembrane signaling, and mutations in the Wdr1 gene has in a mouse model been shown to be related to auto-inflammation [11], [12]. Such effects may be relevant in T1D.

Interaction network B, table 1 and figure 2, consists of two HLA region genes, RDBP (RD RNA-binding protein, MHC complex gene RD) and GTF2H4 (General transcription factor II H) interacting with two genes from a region on chromosome 16, RRN3 (RNA polymerase I-specific transcription initiation factor) and ERCC4 (DNA excision repair protein, DNA repair endonuclease) and the TAF1A (TATA box binding protein (TBP)-associated factor, RNA polymerase I) gene on chromosome 1. The ERCC4 gene furthermore directly interacts with the TYW3 (tRNA–yW synthesizing protein 3 homolog) and the GUF1 (GTP-binding protein GUF1 homolog, GTPase of unknown function) genes from other regions. Comparing the constitutive expression level of these genes in human pancreatic islets with the level after cytokine-exposure of the islets did not identify significant differences. When comparing expression levels between newly diagnosed T1D patients and controls, we demonstrated a significantly higher expression level of the TAF1A gene in the T1D patients, table 1 and figure 2. The TAF1A gene on chromosome 1 encodes a transcription factor involved in RNA synthesis. It has not been implicated in T1D before and the effect of different expression levels in relation to disease state in human lymphocytes is not clear, neither is the putative functional effect on the other network B genes by this difference. No enrichment of T1D associated SNPs in this interaction network could be demonstrated, providing no further genetic support to this network.

Interaction network C contains only three genes and originates from an identified gene-gene interaction between the HLA-region and a region on chromosome 11. Genes predicted to be responsible for this interaction is the MOG (Myelin-oligodendrocyte glycoprotein precursor) gene on chromosome 6 and the APLP2 (Amyloid-like protein 2 precursor (APPH)) and NTRI (Neurotrimin precursor (hNT)) genes on chromosome 11, table 1. No significant differences in either of the tissues/model systems were demonstrated for these genes, not providing any functional support from expression studies for the network C genes, also no enrichment of T1D associated SNPs was observed in this network.

Interaction network D, table 1 and figure 3, originates from predicted genetic interactions among a region on chromosome 17, and markers on chromosomes 5, 1 and 2. Candidate genes presumed to be responsible for the interactions are DDX52 (Probable ATP-dependent RNA helicase DDX52, DEAD box protein 52) and RPL23A (60S ribosomal protein L23a) on chromosome 17, NPM1 (Nucleophosmin, nucleolar phosphoprotein B23) and RPL26L1 (60S ribosomal protein L26-like I) on chromosome 5, PRDX1 (Natural killer cell-enhancing factor A, Peroxiredoxin-I) on chromosome 1 and RPS7 (40S ribosomal protein S7) on chromosome 2. The PRDX1 gene additionally interacts with the SESN2 (Sestrin 2, hypoxia induced gene 95) and SESN1 (Sestrin 1, p53-regulated protein PA26) genes, of which the latter further interacts with the FLOT1 (Flotillin I, integral membrane component of caveolae) gene from the HLA region. Interaction network D is significantly enriched for SNPs in the best 0.1 percentile, table 2, indicating that genetic variations in this network could also contribute to T1D susceptibility. No differences in the lymphocyte expression studies were demonstrated. Comparing human islets expression profiles, we demonstrated that NPM1, a gene involved in ribosomal protein assembly and transport was significantly down-regulated upon stimulation of islets with a mixture of cytokines and the PRDX1 gene, encoding a natural killer cell enhancing factor, to be significantly up-regulated after such stimulation, when compared to constitutive expression, table 1 and figure 3. Natural killer cell enhancing factors have been described as important for different cells in their defense against oxidants/oxidative stress [13], [14]. An up-regulation of the PRDX1 encoded protein could be part of a defense mechanism in the beta cell against oxidative stress. The NPM1 gene has been shown to be important in certain cancer forms, and it has been described that inhibition of the expression of this gene may cause other proteins, e.g. STAT5A to act as a tumor suppressor [15]. This network, build on genetic interactions between chromosome 17 and chromosomes 5, 1 and 2, may be interesting in individuals with a low HLA risk, even though a second order interaction with a gene from the HLA region was found in the protein interactions. The network could point at underlying mechanisms and putatively important combinations of genes responsible for T1D in such individuals. A stronger effect of non-HLA genes could be expected in individuals carrying a lower HLA risk, as indicated by a recent T1D genome-wide association scan and meta-analysis [16].

In three out of four interaction networks our attempts to functionally characterize the candidate genes by expressional profiling have identified novel genes for further analysis. The third interaction network, network C, did not reveal any differentially expressed genes. Genetic support by evaluation of whether networks were enriched in T1D associated SNPs was obtained for network A and to a lesser degree for network D, highlighting these two interaction networks as the most important.

A recently published study evaluated changes at the proteome level after cytokine stimulation of INS-1E cells (a rat tumor beta-cell line serving as an in vitro model for T1D) [17]. Among the proteins that changed expression levels after 4 hours of stimulation with IL-1β and IFN-γ were the rat gene products of the WDR1 and NPM1 genes and after 24 hours the PRDX1 protein was highly up-regulated. In that study a large protein interaction network containing many of the differentially expressed proteins including WDR1, NPM1 and PRDX1 was identified [17]. Despite use of different species and model systems and unknown dynamic differences in the transcriptome and proteome we find it of interest that these three genes were pin-pointed as functionally relevant in the current study as well as in the study by D'Hertog [17]. The likelihood of an overlap between three genes in these two studies has a P-value of<0.05, as calculated using hypergeometric statistics.

To understand biological systems integration of genetic and functional information is necessary. This includes studies of gene-gene and protein-protein interactions and transcriptional or proteome profiling. In a previous study we identified genetic interactions observed in T1D and explained their functionality by using an approach for integrating protein-protein interactions generating protein interaction networks. In this work we validated the discovered networks and analyzed their functionality by expressional profiling in relevant target tissue and by using SNP association data. Protein interaction data generally are noisy and databases probably contain many false positives. The system used in the current study is, however, rigorously quality controlled to only include interactions that have been replicated in independent screens [18].

GO (gene ontology) terms (www.geneontology.org) for molecular function and biological processes of interaction networks A and D and the differentially expressed genes in particular support that oxidative stress and regulation of transcription and apoptosis are of relevance for beta-cell destruction in T1D pathogenesis, and points directly at these pathways as the most important. Despite the overall impression from recent genome-wide association studies [9], [19], [20] that genes of importance in T1D are mainly immune system genes and not beta-cell genes, it seems that by integrative genomics also genes involved in the genetic disposition to e.g. cytokine induced beta-cell death by apoptosis can be identified.

Only few studies have addressed genetic interaction in T1D and focused on interactions between classical disease loci [21], [22], [23], and in the case of high risk HLA class II genotypes and PTPN22 a less than multiplicative association has been demonstrated in T1D and rheumatoid arthritis [16], [24], [25]. The general impression is that interactions may exist, even though they have been difficult to identify. Attempts to identify gene-gene interactions in T1D in previous studies, e.g. in the recent T1D genome wide association studies [9], [19], [26] have not been fruitful, however, stratifying for known T1D loci while searching for dependent effects at other known or unknown loci may not be the best method. Studies using simulated data have shown that the power to detect risk variants can be increased when allowing for epistasis in addition to single marker effects in e.g. genome-wide association studies [27]. Novel methods taking multiple loci at a time into account may offer possibilities of detecting interactions not detectable by classical methods. Evaluation of suggested interactions is necessary to support novel methods, and by no doubt replication of genetic interactions is important, even though it is currently not obvious how this should be done.