Background and Aim

Children chronically infected with hepatitis B virus (HBV) are at high risk of progressive liver disease. However, no treatment is available that is consistently effective in curing chronic hepatitis B (CHB) in children. Improved understanding of the natural course of disease is warranted. Identification of specific microRNA (miRNA) profiles in children chronically infected with HBV may provide insight into the pathogenesis of CHB and lead to advances in the management of children with CHB.

Patients and Methods

MiRNA PCR panels were employed to screen plasma levels of 739 miRNAs in pooled samples from HBeAg positive, HBeAg negative, and healthy children. The three groups’ plasma miRNA profiles were compared, and aberrantly expressed miRNAs were identified. The identified miRNAs were then validated. Individual RT-qPCRs were performed on plasma from 34 HBeAg positive, 26 HBeAg negative, and 60 healthy children.

Results

A panel of 16 plasma miRNAs were identified as aberrantly expressed in HBeAg positive and HBeAg negative children (p<0.001). Levels of all of the miRNAs were upregulated in HBeAg positive children compared with in HBeAg negative children. A positive correlation was furthermore found between plasma levels of the identified miRNAs and HBV DNA (p<0.001).

Conclusion

We are the first to investigate the plasma miRNA profile of children chronically infected with HBV. Our data indicates the existence of a relationship between abundance of circulating miRNAs and immunological stages in the natural course of disease. Certain miRNAs may contribute to the establishment and maintenance of CHB in children. Further studies are warranted to advance understanding of miRNAs in the pathogenesis of CHB, hopefully leading to the identification of future therapeutic targets.

This study aims to identify key miRNAs in circulation, which predict ongoing beta-cell destruction and regeneration in children with newly diagnosed Type 1 Diabetes (T1D). We compared expression level of sera miRNAs from new onset T1D children and age-matched healthy controls and related the miRNAs expression levels to beta-cell function and glycaemic control. Global miRNA sequencing analyses were performed on sera pools from two T1D cohorts (n = 275 and 129, resp.) and one control group (n = 151). We identified twelve upregulated human miRNAs in T1D patients (miR-152, miR-30a-5p, miR-181a, miR-24, miR-148a, miR-210, miR-27a, miR-29a, miR-26a, miR-27b, miR-25, miR-200a); several of these miRNAs were linked to apoptosis and beta-cell networks. Furthermore, we identified miR-25 as negatively associated with residual beta-cell function (est.: −0.12, P = 0.0037), and positively associated with glycaemic control (HbA1c) (est.: 0.11, P = 0.0035) 3 months after onset. In conclusion this study demonstrates that miR-25 might be a “tissue-specific” miRNA for glycaemic control 3 months after diagnosis in new onset T1D children and therefore supports the role of circulating miRNAs as predictive biomarkers for tissue physiopathology and potential intervention targets.

Background

We have previously performed a genome-wide linkage study in Scandinavian Type 1 diabetes (T1D) families. In the Swedish families, we detected suggestive linkage (LOD≤2.2) to the chromosome 5p13-q13 region. The aim of our study was to investigate the linked region in search for possible T1D susceptibility genes.

Methodology/Principal Findings

Microsatellites were genotyped in the Scandinavian families to fine-map the previously linked region. Further, SNPs were genotyped in Swedish and Danish families as well as Swedish sporadic cases. In the Swedish families we detected genome-wide significant linkage to the 5-hydroxytryptamine receptor 1A (HTR1A) gene (LOD 3.98, p<9.8×10−6). Markers tagging two separate genes; the ring finger protein 180 (RNF180) and HTR1A showed association to T1D in the Swedish and Danish families (p<0.002, p<0.001 respectively). The association was not confirmed in sporadic cases. Conditional analysis indicates that the primary association was to HTR1A. Quantitative PCR show that transcripts of both HTR1A and RNF180 are present in human islets of Langerhans. Moreover, immunohistochemical analysis confirmed the presence of the 5-HTR1A protein in isolated human islets of Langerhans as well as in sections of human pancreas.

Conclusions

We have identified and confirmed the association of both HTR1A and RFN180, two genes in high linkage disequilibrium (LD) to T1D in two separate family materials. As both HTR1A and RFN180 were expressed at the mRNA level and HTR1A as protein in human islets of Langerhans, we suggest that HTR1A may affect T1D susceptibility by modulating the initial autoimmune attack or either islet regeneration, insulin release, or both.

OBJECTIVE

Interactions between genetic and environmental factors lead to immune dysregulation causing type 1 diabetes and other autoimmune disorders. Recently, many common genetic variants have been associated with type 1 diabetes risk, but each has modest individual effects. Familial clustering of type 1 diabetes has not been explained fully and could arise from many factors, including undetected genetic variation and gene interactions.

RESEARCH DESIGN AND METHODS

To address this issue, the Type 1 Diabetes Genetics Consortium recruited 3,892 families, including 4,422 affected sib-pairs. After genotyping 6,090 markers, linkage analyses of these families were performed, using a novel method and taking into account factors such as genotype at known susceptibility loci.

RESULTS

Evidence for linkage was robust at the HLA and INS loci, with logarithm of odds (LOD) scores of 398.6 and 5.5, respectively. There was suggestive support for five other loci. Stratification by other risk factors (including HLA and age at diagnosis) identified one convincing region on chromosome 6q14 showing linkage in male subjects (corrected LOD = 4.49; replication P = 0.0002), a locus on chromosome 19q in HLA identical siblings (replication P = 0.006), and four other suggestive loci.

CONCLUSIONS

This is the largest linkage study reported for any disease. Our data indicate there are no major type 1 diabetes subtypes definable by linkage analyses; susceptibility is caused by actions of HLA and an apparently random selection from a large number of modest-effect loci; and apart from HLA and INS, there is no important susceptibility factor discoverable by linkage methods.

Interferon induced with helicase C domain 1 (IFIH1) senses and initiates antiviral activity against enteroviruses. Genetic variants of IFIH1, one common and four rare SNPs have been associated with lower risk for type 1 diabetes. Our aim was to test whether these type 1 diabetes-associated IFIH1 polymorphisms are associated with the occurrence of enterovirus infection in the gut of healthy children, or influence the lack of association between gut enterovirus infection and islet autoimmunity.

After testing of 46,939 Norwegian newborns, 421 children carrying the high risk genotype for type 1 diabetes (HLA-DR4-DQ8/DR3-DQ2) as well as 375 children without this genotype were included for monthly fecal collections from 3 to 35 months of age, and genotyped for the IFIH1 polymorphisms. A total of 7,793 fecal samples were tested for presence of enterovirus RNA using real time reverse transcriptase PCR.

We found no association with frequency of enterovirus in the gut for the common IFIH1 polymorphism rs1990760, or either of the rare variants of rs35744605, rs35667974, rs35337543, while the enterovirus prevalence marginally differed in samples from the 8 carriers of a rare allele of rs35732034 (26.1%, 18/69 samples) as compared to wild-type homozygotes (12.4%, 955/7724 samples); odds ratio 2.5, p = 0.06. The association was stronger when infections were restricted to those with high viral loads (odds ratio 3.3, 95% CI 1.3–8.4, p = 0.01). The lack of association between enterovirus frequency and islet autoimmunity reported in our previous study was not materially influenced by the IFIH1 SNPs.

We conclude that the type 1 diabetes-associated IFIH1 polymorphisms have no, or only minor influence on the occurrence, quantity or duration of enterovirus infection in the gut. Its effect on the risk of diabetes is likely to lie elsewhere in the pathogenic process than in the modification of gut infection.

Background

Filaggrin proteins are located in the skin and prevent epidermal water loss and impede the entry of micro-organisms, allergens and chemicals. Filaggrin null mutations are strongly associated with ichthyosis vulgaris and atopic dermatitis.

Objective

The authors aimed to investigate the association between filaggrin null mutations, atopic dermatitis and diabetes.

Design

A random sample of 3335 adults from the general population in Denmark was filaggrin-genotyped for R501X and 2282del4 null-mutations and questioned about atopic dermatitis and diabetes. Furthermore, two independent study populations of patients with type 1 (n=104) or 2 (n=774) diabetes were genotyped.

Results

In a crude data analysis, a positive association was detected between the filaggrin null genotype and, respectively, subjects from the general population who reported diabetes (p=0.04) and patients with established type 2 diabetes (p=0.073). Adjustment for age and gender resulted in significant associations for patients with type 2 diabetes (p=0.048) and subjects with self-reported diabetes (p=0.032).

Conclusions

Adult Danes with a filaggrin null genotype had a significantly increased prevalence of self-reported diabetes. This finding was replicated when an independent sample of Danish patients with established type 2 diabetes was compared with control subjects from the general population.

Article summary

Article focus

A few studies have suggested the existence of an inverse association between atopic dermatitis and type 1 diabetes.

The existence of a specific endotype of asthma that is not driven by sensitisation but rather driven by skin barrier dysfunction was recently suggested.

It is unknown whether a putative impairment of the skin barrier may increase the propensity to low-grade inflammation in other organs as well.

Key messages

Data from a general population study suggested that the prevalence of filaggrin null mutations was higher in adult Danes who reported diabetes than in non-diabetics.

This finding was replicated when an independent sample of Danish patients with type 2 diabetes was compared with participants from the general population who did not report diabetes and who had normal fasting plasma-glucose and glycated haemoglobin (HbA1c) levels.

Strengths and limitations of this study

Two independent samples were investigated and showed similar results.

The question on self-reported diabetes was not validated in the general population allowing for misclassification.

Background

Several approaches have been developed for miRNA target prediction, including methods that incorporate expression profiling. However the methods are still in need of improvements due to a high false discovery rate. So far, none of the methods have used independent component analysis (ICA). Here, we developed a novel target prediction method based on ICA that incorporates both seed matching and expression profiling of miRNA and mRNA expressions. The method was applied on a cellular model of type 1 diabetes.

Results

Microrray profiling identified eight miRNAs (miR-124/128/192/194/204/375/672/708) with differential expression. Applying ICA on the mRNA profiling data revealed five significant independent components (ICs) correlating to the experimental conditions. The five ICs also captured the miRNA expressions by explaining >97% of their variance. By using ICA, seven of the eight miRNAs showed significant enrichment of sequence predicted targets, compared to only four miRNAs when using simple negative correlation. The ICs were enriched for miRNA targets that function in diabetes-relevant pathways e.g. type 1 and type 2 diabetes and maturity onset diabetes of the young (MODY).

Conclusions

In this study, ICA was applied as an attempt to separate the various factors that influence the mRNA expression in order to identify miRNA targets. The results suggest that ICA is better at identifying miRNA targets than negative correlation. Additionally, combining ICA and pathway analysis constitutes a means for prioritizing between the predicted miRNA targets. Applying the method on a model of type 1 diabetes resulted in identification of eight miRNAs that appear to affect pathways of relevance to disease mechanisms in diabetes.

Objective

A large, population-based case–control cohort was used to test the hypothesis that glutamic acid decarboxylase-65 (GAD65) and islet antigen-2 autoantibodies (IA-2A) at birth predict type 1 diabetes.

Design and methods

The design was an individually matched case–control study of all Danish type 1 diabetes patients born between 1981 and 2002 and diagnosed before May 1 2004 (median age at diagnosis was 8.8 years). Dried blood spot samples collected 5 days after birth in the 1981–2002 birth cohorts and stored at −25 °C were identified from 2023 patients and from two matched controls (n=4042). Birth data and information on parental age and diabetes were obtained from Danish registers. GAD65A and IA-2A were determined in a radiobinding assay. HLA-DQB1 alleles were analyzed by PCR using time-resolved fluorescence.

Results

GAD65A and IA-2A were found in 70/2023 (3.5%) patients compared to 21/4042 (0.5%) controls resulting in a hazard ratio (HR) of 7.49 (P<0.0001). The HR decreased to 4.55 but remained significant (P<0.0003) after controlling for parental diabetes and HLA-DQB1 alleles. Conditional logistic regression analysis showed a HR of 2.55 (P<0.0001) for every tenfold increase in the levels of GAD65A and IA-2A. This HR decreased to 1.93 but remained significant (P<0.001) after controlling for parental diabetes and HLA-DQB1 alleles.

Conclusion

These data suggest that GAD65A and IA-2A positivity at birth are associated with an increased risk of developing type 1 diabetes in Danish children diagnosed between 1981 and 2004.

Background

High doses of anti-inflammatory drugs, such as aspirin and salicylates, improve glucose metabolism in insulin resistant and type 2 diabetic patients. It has also been shown that the glucose lowering effect is related to the unspecific ability of these drugs to inhibit inhibitor kinaseβ (IKKβ). In this study we have investigated the effect of a selective IKKβ-inhibitor on beta cell survival and the prevention of diet induced type 2 diabetes in the gerbil Psammomys obesus (P. obesus).

Methodology/Principal Findings

P. obesus were fed a diabetes inducing high energy diet for one month in the absence or presence of the IKKβ-inhibitor. Body mass, blood glucose, HbA1C, insulin production and pancreatic insulin stores were measured. The effects on beta cell survival were also studied in INS-1 cells and primary islets. The cells were exposed to IL-1β and subsequently reactive oxygen species, insulin release and cell death were measured in the absence or presence of the IKKβ-inhibitor. In primary islets and beta cells, IL-1β induced the production of reactive oxygen species, reduced insulin production and increased beta cell death, which were all reversed by pre-treatment with the IKKβ-inhibitor. In P. obesus the IKKβ-inhibitor prevented the development of hyperglycaemia and hyperinsulinaemia, and maintained pancreatic insulin stores with no effect on body weight.

Conclusions/Significance

Inhibition of IKKβ activity prevents diet-induced diabetes in P. obesus and inhibits IL-1β induced reactive oxygen species, loss of insulin production and beta cell death in vitro.

Type 1 diabetes (T1D) is a common autoimmune disorder that arises from the action of multiple genetic and environmental risk factors. We report the findings of a new genome-wide association study of T1D, combined in a meta-analysis with two previously published studies. The total sample set included 7,514 cases and 9,045 reference samples. Forty-one distinct genomic locations provided evidence for association to T1D in the meta-analysis (P < 10-6). After excluding previously reported associations, 27 regions were further tested in an independent set of 4,267 cases, 4,463 controls and 2,319 affected sib-pair (ASP) families. Of these, 18 regions were replicated (P < 0.01; overall P < 5 × 10-8) and four additional regions provided nominal evidence of replication (P < 0.05). The many new candidate genes suggested by these results include IL10, IL19, IL20, GLIS3, CD69 and IL27.

Introduction

As part of a clinical proteomics programme focused on diabetes and its complications, it was our goal to investigate the proteome of plasma in order to find improved candidate biomarkers to predict diabetic nephropathy.

Methods

Proteins derived from plasma from a cross-sectional cohort of 123 type 1 diabetic patients previously diagnosed as normoalbuminuric, microalbuminuric or macroalbuminuric were enriched with hexapeptide library beads and subsequently pooled within three groups. Proteins from the three groups were compared by online liquid chromatography and tandem mass spectrometry in three identical repetitions using isobaric mass tags (iTRAQ). The results were further analysed with ingenuity pathway analysis. Levels of apolipoprotein A1, A2, B, C3, E and J were analysed and validated by a multiplex immunoassay in 20 type 1 diabetic patients with macroalbuminuria and 10 with normoalbuminuria.

Results

A total of 112 proteins were identified in at least two out of three replicates. The global protein ratios were further evaluated by ingenuity pathway analysis, resulting in the recognition of apolipoprotein A2, B, C3, D and E as key nodes in the top-rated network. The multiplex immunoassay confirmed the overall protein expression patterns observed by the iTRAQ analysis.

Conclusion

The candidate biomarkers discovered in this cross-sectional cohort may turn out to be progression biomarkers and might have several clinical applications in the treatment and monitoring of diabetic nephropathy; however, they need to be confirmed in a longitudinal cohort.

Electronic supplementary material

The online version of this article (doi:10.1007/s12014-010-9053-0) contains supplementary material, which is available to authorized users.

OBJECTIVE

Type 1 diabetes arises from the actions of multiple genetic and environmental risk factors. Considerable success at identifying common genetic variants that contribute to type 1 diabetes risk has come from genetic association (primarily case-control) studies. However, such studies have limited power to detect genes containing multiple rare variants that contribute significantly to disease risk.

RESEARCH DESIGN AND METHODS

The Type 1 Diabetes Genetics Consortium (T1DGC) has assembled a collection of 2,496 multiplex type 1 diabetic families from nine geographical regions containing 2,658 affected sib-pairs (ASPs). We describe the results of a genome-wide scan for linkage to type 1 diabetes in the T1DGC family collection.

RESULTS

Significant evidence of linkage to type 1 diabetes was confirmed at the HLA region on chromosome 6p21.3 (logarithm of odds [LOD] = 213.2). There was further evidence of linkage to type 1 diabetes on 6q that could not be accounted for by the major linkage signal at the HLA class II loci on chromosome 6p21. Suggestive evidence of linkage (LOD ≥2.2) was observed near CTLA4 on chromosome 2q32.3 (LOD = 3.28) and near INS (LOD = 3.16) on chromosome 11p15.5. Some evidence for linkage was also detected at two regions on chromosome 19 (LOD = 2.84 and 2.54).

CONCLUSIONS

Five non–HLA chromosome regions showed some evidence of linkage to type 1 diabetes. A number of previously proposed type 1 diabetes susceptibility loci, based on smaller ASP numbers, showed limited or no evidence of linkage to disease. Low-frequency susceptibility variants or clusters of loci with common alleles could contribute to the linkage signals observed.

Background

Insulin gene (INS) mutations have recently been described as a common cause of permanent neonatal diabetes (PNDM) and a rare cause of diabetes diagnosed in childhood or adulthood.

Methods

INS was sequenced in 116 maturity-onset diabetes of the young (MODYX) patients (n = 48 Danish and n = 68 Czech), 83 patients with gestational diabetes mellitus (GDM), 34 type 1 diabetic patients screened negative for glutamic acid decarboxylase (GAD), and 96 glucose tolerant individuals. The control group was randomly selected from the population-based sampled Inter99 study.

Results

One novel heterozygous mutation c.17G>A, R6H, was identified in the pre-proinsulin gene (INS) in a Danish MODYX family. The proband was diagnosed at 20 years of age with mild diabetes and treated with diet and oral hypoglycaemic agent. Two other family members who carried the INS R6H were diagnosed with diabetes when 51 years old and with GDM when 27 years old, respectively. A fourth mutation carrier had normal glucose tolerance when 20 years old. Two carriers of INS R6H were also examined twice with an oral glucose tolerance test (OGTT) with 5 years interval. They both had a ~30% reduction in beta-cell function measured as insulinogenic index. In a Czech MODYX family a previously described R46Q mutation was found. The proband was diagnosed at 13 years of age and had been treated with insulin since onset of diabetes. Her mother and grandmother were diagnosed at 14 and 35 years of age, respectively, and were treated with oral hypoglycaemic agents and/or insulin.

Conclusion

Mutations in INS can be a rare cause of MODY and we conclude that screening for mutations in INS should be recommended in MODYX patients.

Background

The individual contribution of genes in the HLA region to the risk of developing type 1 diabetes (T1D) is confounded by the high linkage disequilibrium (LD) in this region. Using a novel approach we have combined genetic association data with information on functional protein-protein interactions to elucidate risk independent of LD and to place the genetic association into a functional context.

Methodology/Principal Findings

Genetic association data from 2300 single nucleotide polymorphisms (SNPs) in the HLA region was analysed in 2200 T1D family trios divided into six risk groups based on HLA-DRB1 genotypes. The best SNP signal in each gene was mapped to proteins in a human protein interaction network and their significance of clustering in functional network modules was evaluated. The significant network modules identified through this approach differed between the six HLA risk groups, which could be divided into two groups based on carrying the DRB1*0301 or the DRB1*0401 allele. Proteins identified in networks specific for DRB1*0301 carriers were involved in stress response and inflammation whereas in DRB1*0401 carriers the proteins were involved in antigen processing and presentation.

Conclusions/Significance

In this study we were able to hypothesise functional differences between individuals with T1D carrying specific DRB1 alleles. The results point at candidate proteins involved in distinct cellular processes that could not only help the understanding of the pathogenesis of T1D, but also the distinction between individuals at different genetic risk for developing T1D.

Background

As part of a clinical proteomics program focused on diabetes and its complications we are looking for new and better protein biomarkers for diabetic nephropathy. The search for new and better biomarkers for diabetic nephropathy has, with a few exceptions, previously focused on either hypothesis-driven studies or urinary based investigations. To date only two studies have investigated the proteome of blood in search for new biomarkers, and these studies were conducted in sera from patients with type 2 diabetes. This is the first reported in depth proteomic study where plasma from type 1 diabetic patients was investigated with the goal of finding improved candidate biomarkers to predict diabetic nephropathy. In order to reach lower concentration proteins in plasma a pre-fractionation step, either hexapeptide bead-based libraries or anion exchange chromatography, was performed prior to surface enhanced laser desorption/ionization time-of-flight mass spectrometry analysis.

Results

Proteomic analysis of plasma from a cross-sectional cohort of 123 type 1 diabetic patients previously diagnosed as normoalbuminuric, microalbuminuric or macroalbuminuric, gave rise to 290 peaks clusters of which 16 were selected as the most promising biomarker candidates based on statistical performance, including independent component analysis. Four of the peaks that were discovered have been identified as transthyretin, apolipoprotein A1, apolipoprotein C1 and cystatin C. Several yet unidentified proteins discovered by this novel approach appear to have more potential as biomarkers for diabetic nephropathy.

Conclusion

These results demonstrate the capacity of proteomic analysis of plasma, by confirming the presence of known biomarkers as well as revealing new biomarkers for diabetic nephropathy in plasma in type 1 diabetic patients.

OBJECTIVE— The Type 1 Diabetes Genetics Consortium (T1DGC) has assembled and genotyped a large collection of multiplex families for the purpose of mapping genomic regions linked to type 1 diabetes. In the current study, we tested for evidence of loci associated with type 1 diabetes utilizing genome-wide linkage scan data and family-based association methods.

RESEARCH DESIGN AND METHODS— A total of 2,496 multiplex families with type 1 diabetes were genotyped with a panel of 6,090 single nucleotide polymorphisms (SNPs). Evidence of association to disease was evaluated by the pedigree disequilibrium test. Significant results were followed up by genotyping and analyses in two independent sets of samples: 2,214 parent-affected child trio families and a panel of 7,721 case and 9,679 control subjects.

RESULTS— Three of the SNPs most strongly associated with type 1 diabetes localized to previously identified type 1 diabetes risk loci: INS, IFIH1, and KIAA0350. A fourth strongly associated SNP, rs876498 (P = 1.0 × 10−4), occurred in the sixth intron of the UBASH3A locus at chromosome 21q22.3. Support for this disease association was obtained in two additional independent sample sets: families with type 1 diabetes (odds ratio [OR] 1.06 [95% CI 1.00–1.11]; P = 0.023) and case and control subjects (1.14 [1.09–1.19]; P = 7.5 × 10−8).

CONCLUSIONS— The T1DGC 6K SNP scan and follow-up studies reported here confirm previously reported type 1 diabetes associations at INS, IFIH1, and KIAA0350 and identify an additional disease association on chromosome 21q22.3 in the UBASH3A locus (OR 1.10 [95% CI 1.07–1.13]; P = 4.4 × 10−12). This gene and its flanking regions are now validated targets for further resequencing, genotyping, and functional studies in type 1 diabetes.

Proteins contributing to a complex disease are often members of the same functional pathways. Elucidation of such pathways may provide increased knowledge about functional mechanisms underlying disease. By combining genetic interactions in Type 1 Diabetes (T1D) with protein interaction data we have previously identified sets of genes, likely to represent distinct cellular pathways involved in T1D risk. Here we evaluate the candidate genes involved in these putative interaction networks not only at the single gene level, but also in the context of the networks of which they form an integral part. mRNA expression levels for each gene were evaluated and profiling was performed by measuring and comparing constitutive expression in human islets versus cytokine-stimulated expression levels, and for lymphocytes by comparing expression levels among controls and T1D individuals. We identified differential regulation of several genes. In one of the networks four out of nine genes showed significant down regulation in human pancreatic islets after cytokine exposure supporting our prediction that the interaction network as a whole is a risk factor. In addition, we measured the enrichment of T1D associated SNPs in each of the four interaction networks to evaluate evidence of significant association at network level. This method provided additional support, in an independent data set, that two of the interaction networks could be involved in T1D and highlights the following processes as risk factors: oxidative stress, regulation of transcription and apoptosis. To understand biological systems, integration of genetic and functional information is necessary, and the current study has used this approach to improve understanding of T1D and the underlying biological mechanisms.

Background

β-cells are extremely rich in zinc and zinc homeostasis is regulated by zinc transporter proteins. β-cells are sensitive to cytokines, interleukin-1β (IL-1β) has been associated with β-cell dysfunction and -death in both type 1 and type 2 diabetes. This study explores the regulation of zinc transporters following cytokine exposure.

Methods

The effects of cytokines IL-1β, interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α) on zinc transporter gene expression were measured in INS-1-cells and rat pancreatic islets. Being the more sensitive transporter, we further explored ZnT8 (Slc30A8): the effect of ZnT8 over expression on cytokine induced apoptosis was investigated as well as expression of the insulin gene and two apoptosis associated genes, BAX and BCL2.

Results

Our results showed a dynamic response of genes responsible for β-cell zinc homeostasis to cytokines: IL-1β down regulated a number of zinc-transporters, most strikingly ZnT8 in both islets and INS-1 cells. The effect was even more pronounced when mixing the cytokines. TNF-α had little effect on zinc transporter expression. IFN-γ down regulated a number of zinc transporters. Insulin expression was down regulated by all cytokines. ZnT8 over expressing cells were more sensitive to IL-1β induced apoptosis whereas no differences were observed with IFN-γ, TNF-α, or a mixture of cytokines.

Conclusion

The zinc transporting system in β-cells is influenced by the exposure to cytokines. Particularly ZnT8, which has been associated with the development of diabetes, seems to be cytokine sensitive.

An integrative analysis combining genetic interactions and protein interactions can be used to identify candidate genes/proteins for type 1 diabetes and other complex diseases.

We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases.