Gene expression signature predicts rate of type 1 diabetes progression

BACKGROUND

Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes.

METHODS

Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations.

FINDINGS

We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression.

INTERPRETATION

There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes.

FUNDING

A full list of funding bodies can be found under Acknowledgments.

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes

Type 2 diabetes (T2D) is a complex chronic disease characterized by considerable phenotypic heterogeneity. In this study, we applied a reverse graph embedding method to routinely collected data from 23,137 Scottish patients with newly diagnosed diabetes to visualize this heterogeneity and used partitioned diabetes polygenic risk scores to gain insight into the underlying biological processes. Overlaying risk of progression to outcomes of insulin requirement, chronic kidney disease, referable diabetic retinopathy and major adverse cardiovascular events, we show how these risks differ by patient phenotype. For example, patients at risk of retinopathy are phenotypically different from those at risk of cardiovascular events. We replicated our findings in the UK Biobank and the ADOPT clinical trial, also showing that the pattern of diabetes drug monotherapy response differs for different drugs. Overall, our analysis highlights how, in a European population, underlying phenotypic variation drives T2D onset and affects subsequent diabetes outcomes and drug response, demonstrating the need to incorporate these factors into personalized treatment approaches for the management of T2D.

A reduced level of consciousness affects non-conscious processes

Being conscious is a profound aspect of human existence, and understanding its function and its inception is considered one of the truly grand scientific challenges. However, the nature of consciousness remains enigmatic, to a large part because "being conscious" can refer to both the content (phenomenology) and the level (arousal) of consciousness, and how these different aspects are related remains unclear. To empirically assess the relation between level and content of consciousness, we manipulated these two aspects by presenting stimuli consciously or non-consciously and by using Propofol sedation, while brain activity was measured using fMRI. We observed that sedation affected both conscious and non-conscious processes but at different hierarchical levels; while conscious processing was altered in higher-order regions (the intraparietal sulcus) and spared sensory areas, the opposite effect was observed for non-conscious processing. The observation that Propofol affected non-conscious processing calls for a reconsideration of what kind of information one can gain on "consciousness" from recording neural responses to sedation without considering both (content) conscious and (content) non-conscious processing.

Development of an ICU discharge instrument predicting psychological morbidity: a multinational study

Purpose

To develop an instrument for use at ICU discharge for prediction of psychological problems in ICU survivors.

Methods

Multinational, prospective cohort study in ten general ICUs in secondary and tertiary care hospitals in Sweden, Denmark and the Netherlands. Adult patients with an ICU stay ≥ 12 h were eligible for inclusion. Patients in need of neurointensive care, with documented cognitive impairment, unable to communicate in the local language, without a home address or with more than one limitation of therapy were excluded. Primary outcome was psychological morbidity 3 months after ICU discharge, defined as Hospital Anxiety and Depression Scale (HADS) subscale score ≥ 11 or Post-traumatic Stress Symptoms Checklist-14 (PTSS-14) part B score > 45.

Results

A total of 572 patients were included and 78% of patients alive at follow-up responded to questionnaires. Twenty percent were classified as having psychological problems post-ICU. Of 18 potential risk factors, four were included in the final prediction model after multivariable logistic regression analysis: symptoms of depression [odds ratio (OR) 1.29, 95% confidence interval (CI) 1.10–1.50], traumatic memories (OR 1.44, 95% CI 1.13–1.82), lack of social support (OR 3.28, 95% CI 1.47–7.32) and age (age-dependent OR, peak risk at age 49–65 years). The area under the receiver operating characteristics curve (AUC) for the instrument was 0.76 (95% CI 0.70–0.81).

Conclusions

We developed an instrument to predict individual patients’ risk for psychological problems 3 months post-ICU, http://www.imm.ki.se/biostatistics/calculators/psychmorb/. The instrument can be used for triage of patients for psychological ICU follow-up.

Trial registration

The study was registered at clinicaltrials.gov, NCT02679157.

Electronic supplementary material

The online version of this article (10.1007/s00134-018-5467-3) contains supplementary material, which is available to authorized users.

Integrative network analysis highlights biological processes underlying GLP-1 stimulated insulin secretion: A DIRECT study

Glucagon-like peptide 1 (GLP-1) stimulated insulin secretion has a considerable heritable component as estimated from twin studies, yet few genetic variants influencing this phenotype have been identified. We performed the first genome-wide association study (GWAS) of GLP-1 stimulated insulin secretion in non-diabetic individuals from the Netherlands Twin register (n = 126). This GWAS was enhanced using a tissue-specific protein-protein interaction network approach. We identified a beta-cell protein-protein interaction module that was significantly enriched for low gene scores based on the GWAS P-values and found support at the network level in an independent cohort from Tübingen, Germany (n = 100). Additionally, a polygenic risk score based on SNPs prioritized from the network was associated (P < 0.05) with glucose-stimulated insulin secretion phenotypes in up to 5,318 individuals in MAGIC cohorts. The network contains both known and novel genes in the context of insulin secretion and is enriched for members of the focal adhesion, extracellular-matrix receptor interaction, actin cytoskeleton regulation, Rap1 and PI3K-Akt signaling pathways. Adipose tissue is, like the beta-cell, one of the target tissues of GLP-1 and we thus hypothesized that similar networks might be functional in both tissues. In order to verify peripheral effects of GLP-1 stimulation, we compared the transcriptome profiling of ob/ob mice treated with liraglutide, a clinically used GLP-1 receptor agonist, versus baseline controls. Some of the upstream regulators of differentially expressed genes in the white adipose tissue of ob/ob mice were also detected in the human beta-cell network of genes associated with GLP-1 stimulated insulin secretion. The findings provide biological insight into the mechanisms through which the effects of GLP-1 may be modulated and highlight a potential role of the beta-cell expressed genes RYR2, GDI2, KIAA0232, COL4A1 and COL4A2 in GLP-1 stimulated insulin secretion.

Ranking factors involved in diabetes remission after bariatric surgery using machine-learning integrating clinical and genomic biomarkers

As weight-loss surgery is an effective treatment for the glycaemic control of type 2 diabetes in obese patients, yet not all patients benefit, it is valuable to find predictive factors for this diabetic remission. This will help elucidating possible mechanistic insights and form the basis for prioritising obese patients with dysregulated diabetes for surgery where diabetes remission is of interest. In this study, we combine both clinical and genomic factors using heuristic methods, informed by prior biological knowledge in order to rank factors that would have a role in predicting diabetes remission, and indeed in identifying patients who may have low likelihood in responding to bariatric surgery for improved glycaemic control. Genetic variants from the Illumina CardioMetaboChip were prioritised through single-association tests and then seeded a larger selection from protein-protein interaction networks. Artificial neural networks allowing nonlinear correlations were trained to discriminate patients with and without surgery-induced diabetes remission, and the importance of each clinical and genetic parameter was evaluated. The approach highlighted insulin treatment, baseline HbA1c levels, use of insulin-sensitising agents and baseline serum insulin levels, as the most informative variables with a decent internal validation performance (74% accuracy, area under the curve (AUC) 0.81). Adding information for the eight top-ranked single nucleotide polymorphisms (SNPs) significantly boosted classification performance to 84% accuracy (AUC 0.92). The eight SNPs mapped to eight genes - ABCA1, ARHGEF12, CTNNBL1, GLI3, PROK2, RYBP, SMUG1 and STXBP5 - three of which are known to have a role in insulin secretion, insulin sensitivity or obesity, but have not been indicated for diabetes remission after bariatric surgery before.

JNK1 Deficient Insulin-Producing Cells Are Protected against Interleukin-1β-Induced Apoptosis Associated with Abrogated Myc Expression

The relative contributions of the JNK subtypes in inflammatory β-cell failure and apoptosis are unclear. The JNK protein family consists of JNK1, JNK2, and JNK3 subtypes, encompassing many different isoforms. INS-1 cells express JNK1α1, JNK1α2, JNK1β1, JNK1β2, JNK2α1, JNK2α2, JNK3α1, and JNK3α2 mRNA isoform transcripts translating into 46 and 54 kDa isoform JNK proteins. Utilizing Lentiviral mediated expression of shRNAs against JNK1, JNK2, or JNK3 in insulin-producing INS-1 cells, we investigated the role of individual JNK subtypes in IL-1β-induced β-cell apoptosis. JNK1 knockdown prevented IL-1β-induced INS-1 cell apoptosis associated with decreased 46 kDa isoform JNK protein phosphorylation and attenuated Myc expression. Transient knockdown of Myc also prevented IL-1β-induced apoptosis as well as caspase 3 cleavage. JNK2 shRNA potentiated IL-1β-induced apoptosis and caspase 3 cleavage, whereas JNK3 shRNA did not affect IL-1β-induced β-cell death compared to nonsense shRNA expressing INS-1 cells. In conclusion, JNK1 mediates INS-1 cell death associated with increased Myc expression. These findings underline the importance of differentiated targeting of JNK subtypes in the development of inflammatory β-cell failure and destruction.

Polymorphisms in the CTSH gene may influence the progression of diabetic retinopathy: a candidate-gene study in the Danish Cohort of Pediatric Diabetes 1987 (DCPD1987)

BACKGROUND

The incidence of type 1 diabetes mellitus (T1DM) is increasing globally, and as a consequence, more patients are affected by microvascular complications such as diabetic retinopathy (DR). The aim of this study was to elucidate possible associations between diabetes-related single-nucleotide polymorphisms (SNP) and the development of DR.

METHODS

Three hundred and thirty-nine patients with T1DM from the Danish Cohort of Pediatric Diabetes 1987 (DCPD1987) went through an ophthalmic examination in 1995; 185 of these were reexamined in 2011. The development of DR was assessed by comparison of overall DR level between baseline and follow-up in the worst eye at baseline. Patients were graded on a modified version of the Early Treatment Diabetic Retinopathy Study (ETDRS) scale, and 20 SNPs were genotyped in 130 of the 185 patients.

RESULTS

We found the CTSH/rs3825932 variant (C > T) was associated with reduced risk of progression to proliferative diabetic retinopathy (PDR) (OR [95 % CI] = 0.20 [0.07-0.56], p = 2.4 × 10(-3), padjust = 0.048) and ERBB3/rs2292239 variant (G > T) associated with increased risk of two-step progression (OR [95 % CI] = 2.76 [1.31-5.80], p = 7.5 × 10(-3), padjust = 0.15). The associations were independent of other known risk factors, such as HbA1c, sex, and diastolic blood pressure.

CONCLUSION

In conclusion, CTSH/rs3825932 and ERBB3/rs2292239 SNPs were associated with reduced risk of progression to PDR and two-step progression of DR on the ETDRS scale accordingly. The variant CTSH remained statistically significant after adjusting for multiple testing. Our results suggest an overlap between genetic variants that confer risk of T1DM and progression of DR.

No association between type 1 diabetes and genetic variation in vitamin D metabolism genes: a Danish study

BACKGROUND

Vitamin D, certain single nucleotide polymorphisms (SNPs) in the vitamin D-receptor (VDR) gene and vitamin D metabolism genes have been associated with type 1 diabetes (T1D).

OBJECTIVE

We wanted to examine if the most widely studied SNPs in genes important for production, transport, and action of vitamin D were associated with T1D or to circulating levels of vitamin D 25-hydroxyvitamin D [25(OH)D] in a juvenile Danish population.

METHODS

We genotyped eight SNPs in five vitamin D metabolism genes in 1467 trios. 25(OH)D status were analyzed in 1803 children (907 patients and 896 siblings).

RESULTS

We did not demonstrate association with T1D for SNPs in the following genes: CYP27B1, VDR, GC, CYP2R1, DHCR7, and CYP24A1. Though, variants in the GC gene were significantly associated with 25(OH)D levels in the joint model.

CONCLUSION

Some of the most examined SNPs in vitamin D metabolism genes were not confirmed to be associated with T1D, though 25(OH) levels were associated with variants in the GC gene.

Residual β-Cell function 3-6 years after onset of type 1 diabetes reduces risk of severe hypoglycemia in children and adolescents

OBJECTIVE

To determine the prevalence of residual β-cell function (RBF) in children after 3-6 years of type 1 diabetes, and to examine the association between RBF and incidence of severe hypoglycemia, glycemic control, and insulin requirements.

RESEARCH DESIGN AND METHODS

A total of 342 children (173 boys) 4.8-18.9 years of age with type 1 diabetes for 3-6 years were included. RBF was assessed by testing meal-stimulated C-peptide concentrations. Information regarding severe hypoglycemia within the past year, current HbA1c, and daily insulin requirements was retrieved from the medical records and through patient interviews.

RESULTS

Ninety-two children (27%) had RBF >0.04 nmol/L. Patients with RBF <0.04 nmol/L were significantly more likely to have severe hypoglycemia than patients with RBF >0.04 nmol/L (odds ratio, 2.59; 95% CI, 1.10-7.08; P < 0.03). HbA1c was significantly higher in patients with RBF <0.04 nmol/L compared with patients with RBF >0.04 nmol/L (mean, 8.49 ± 0.08% [69.3 ± 0.9 mmol/mol] vs. 7.92 ± 0.13% [63.1 ± 1.4 mmol/mol]; P < 0.01), and insulin requirements were significantly lower in patients with RBF >0.2 nmol/L (mean ± SE: 1.07 ± 0.02 vs. 0.93 ± 0.07 units/kg/day; P < 0.04).

CONCLUSIONS

We demonstrated considerable phenotypic diversity in RBF among children after 3-6 years of type 1 diabetes. Children with RBF are at lower risk for severe hypoglycemia, have better diabetes regulation, and have lower insulin requirements compared with children without RBF. There appears to be a lower limit for stimulated RBF of ∼0.04 nmol/L that confers a beneficial effect on hypoglycemia and metabolic control.

Identification of a SIRT1 mutation in a family with type 1 diabetes

Type 1 diabetes is caused by autoimmune-mediated β cell destruction leading to insulin deficiency. The histone deacetylase SIRT1 plays an essential role in modulating several age-related diseases. Here we describe a family carrying a mutation in the SIRT1 gene, in which all five affected members developed an autoimmune disorder: four developed type 1 diabetes, and one developed ulcerative colitis. Initially, a 26-year-old man was diagnosed with the typical features of type 1 diabetes, including lean body mass, autoantibodies, T cell reactivity to β cell antigens, and a rapid dependence on insulin. Direct and exome sequencing identified the presence of a T-to-C exchange in exon 1 of SIRT1, corresponding to a leucine-to-proline mutation at residue 107. Expression of SIRT1-L107P in insulin-producing cells resulted in overproduction of nitric oxide, cytokines, and chemokines. These observations identify a role for SIRT1 in human autoimmunity and unveil a monogenic form of type 1 diabetes.

Islet autoantibodies and residual beta cell function in type 1 diabetes children followed for 3-6 years

AIMS

To test if islet autoantibodies at diagnosis of type 1 diabetes (T1DM) and after 3-6 years with T1D predict residual beta-cell function (RBF) after 3-6 years with T1D.

METHODS

T1D children (n=260, median age at diagnosis 9.4, range 0.9-14.7 years) were tested for GAD65, IA-2, ZnT8R, ZnT8W and ZnT8Q autoantibodies (A) at diagnosis, and 3-6 years after diagnosis when also fasting and stimulated RBF were determined.

RESULTS

For every 1-year increase in age at diagnosis of TID, the odds of detectable C-peptide increased 1.21 (1.09, 1.34) times for fasting C-peptide and 1.28 (1.15, 1.42) times for stimulated C-peptide. Based on a linear model for subjects with no change in IA-2A levels, the odds of detectable C-peptide were 35% higher than for subjects whose IA-2A levels decreased by half (OR=1.35 (1.09, 1.67), p=0.006); similarly for ZnT8WA (OR=1.39 (1.09, 1.77), p=0.008) and ZnT8QA (OR=1.55 (1.06, 2.26) p=0.024). Such relationship was not detected for GADA or ZnT8RA. All OR adjusted for confounders.

CONCLUSIONS

Age at diagnosis with T1D was the major predictor of detectable C-peptide 3-6 years post-diagnosis. Decreases in IA-2A, and possibly ZnT8A, levels between diagnosis and post-diagnosis were associated with a reduction in RBF post-diagnosis.

Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression

Genome-wide association studies (GWAS) have heralded a new era in susceptibility locus discovery in complex diseases. For type 1 diabetes, >40 susceptibility loci have been discovered. However, GWAS do not inevitably lead to identification of the gene or genes in a given locus associated with disease, and they do not typically inform the broader context in which the disease genes operate. Here, we integrated type 1 diabetes GWAS data with protein-protein interactions to construct biological networks of relevance for disease. A total of 17 networks were identified. To prioritize and substantiate these networks, we performed expressional profiling in human pancreatic islets exposed to proinflammatory cytokines. Three networks were significantly enriched for cytokine-regulated genes and, thus, likely to play an important role for type 1 diabetes in pancreatic islets. Eight of the regulated genes (CD83, IFNGR1, IL17RD, TRAF3IP2, IL27RA, PLCG2, MYO1B, and CXCR7) in these networks also harbored single nucleotide polymorphisms nominally associated with type 1 diabetes. Finally, the expression and cytokine regulation of these new candidate genes were confirmed in insulin-secreting INS-1 β-cells. Our results provide novel insight to the mechanisms behind type 1 diabetes pathogenesis and, thus, may provide the basis for the design of novel treatment strategies.

Severe traumatic brain injury: consequences of early adverse events

BACKGROUND

Several factors associated with an unfavourable outcome after severe traumatic brain injury (TBI) have been described: prolonged pre-hospital time, secondary referral to a level 1 trauma centre, the occurrence of secondary insults such as hypoxia, hypotension or low end-tidal carbon dioxide (ETCO(2)). To determine whether adverse events were linked to outcome, patients with severe TBI were studied before arrival at a level 1 trauma centre.

METHODS

Prospective, observational study design. Patients with severe TBI (n = 48), admitted to Umeå University Hospital between January 2002 to December 2005 were included. All medical records from the site of the accident to arrival at the level 1 trauma centre were collected and evaluated.

RESULTS

A pre-hospital time of >60 min, secondary referral to a level 1 trauma centre, documented hypoxia (oxygen saturation <95%), hypotension (systolic blood pressure <90 mmHg), hyperventilation (ETCO(2) <4.5 kPa) or tachycardia (heart rate >100 beats/min) at any time before arrival at a level 1 trauma centre were not significantly related to an unfavourable outcome (Glasgow Outcome Scale 1-3).

CONCLUSION

Early adverse events before arrival at a level 1 trauma centre were without significance for outcome after severe TBI in the trauma system studied.

The type 1 diabetes - HLA susceptibility interactome--identification of HLA genotype-specific disease genes for type 1 diabetes

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.

No association of the IRS1 and PAX4 genes with type I diabetes

To reassess earlier suggested type I diabetes (T1D) associations of the insulin receptor substrate 1 (IRS1) and the paired domain 4 gene (PAX4) genes, the Type I Diabetes Genetics Consortium (T1DGC) evaluated single-nucleotide polymorphisms (SNPs) covering the two genomic regions. Sixteen SNPs were evaluated for IRS1 and 10 for PAX4. Both genes are biological candidate genes for T1D. Genotyping was performed in 2300 T1D families on both Illumina and Sequenom genotyping platforms. Data quality and concordance between the platforms were assessed for each SNP. Transmission disequilibrium testing neither show T1D association of SNPs in the two genes, nor did haplotype analysis. In conclusion, the earlier suggested associations of IRS1 and PAX4 to T1D were not supported, suggesting that they may have been false positive results. This highlights the importance of thorough quality control, selection of tagging SNPs, more than one genotyping platform in high throughput studies, and sufficient power to draw solid conclusions in genetic studies of human complex diseases.

Expression profiling of human genetic and protein interaction networks 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.

Functional SOCS1 polymorphisms are associated with variation in obesity in whites

AIMS/HYPOTHESIS

The suppressor of cytokine signalling 1 (SOCS1) is a natural inhibitor of cytokine and insulin signalling pathways and may also play a role in obesity. In addition, SOCS1 is considered a candidate gene in the pathogenesis of both type 1 diabetes (T1D) and type 2 diabetes (T2D). The objective was to perform mutation analysis of SOCS1 and to test the identified variations for association to T2D-related quantitative traits, T2D or T1D.

METHODS

Mutation scanning was performed by direct sequencing in 27 white Danish subjects. Genotyping was carried out by TaqMan allelic discrimination. A total of more than 8100 individuals were genotyped.

RESULTS

Eight variations were identified in the 5' untranslated region (UTR) region. Two of these had allele frequencies below 1% and were not further examined. The six other variants were analysed in groups of T1D families (n = 1461 subjects) and T2D patients (n = 1430), glucose tolerant first-degree relatives of T2D patients (n = 212) and normal glucose tolerant (NGT) subjects. The rs33977706 polymorphism (-820G > T) was associated with a lower body mass index (BMI) (p = 0.004). In a second study (n = 4625 NGT subjects), significant associations of both the rs33977706 and the rs243330 (-1656G > A) variants to obesity were found (p = 0.047 and p = 0.015) respectively. The rs33977706 affected both binding of a nuclear protein to and the transcriptional activity of the SOCS1 promoter, indicating a relationship between this polymorphism and gene regulation.

CONCLUSIONS/INTERPRETATION

This study demonstrates that functional variations in the SOCS1 promoter may associate with alterations in BMI in the general white population.

Posttranslational Protein Modifications in Type 1 Diabetes - Genetic Studies with PCMT1, the Repair Enzyme Protein Isoaspartate Methyltransferase (PIMT) Encoding Gene

BACKGROUND

Posttranslational protein modifications have been implicated in the development of autoimmunity. Protein L-isoaspartate (D-aspartate) O-methyltransferase (PIMT) repairs modified proteins and is encoded by PCMT1, located in a region linked to type 1 diabetes (T1D), namely IDDM5.

AIM

To evaluate the association between genetic variations in the PCMT1 gene and T1D.

METHODS

Firstly, PCMT1 was sequenced in 26 patients with T1D (linked to IDDM5) and 10 control subjects. The variations found in PCMT1 were then tested (alone and interacting with a functional polymorphism in SUMO4 and with HLA) for association with T1D in 253 families (using transmission disequilibrium test). In a third step, the association of the functional variation in PCMT1 (rs4816) with T1D was analyzed in 778 T1D patients and 749 controls (using chi-square test). In vitro promoter activity was assessed by transfecting INS-1E cells with PCMT1 promoter constructs and a reporter gene, with or without cytokine stimulation.

RESULTS

Four polymorphisms in complete linkage disequilibrium were identified in PCMT1 (5' to the gene (rs11155676), exon 5 (rs4816) and exon 8 (rs7818 and rs4552)). In the whole cohort of 253 families, the allele associated with increased PIMT enzyme activity (rs4816, allele A) was less frequently transmitted to the affected than to the non-affected offspring (46% vs. 53%, p = 0.099). This finding was even more evident in the subset of families where the proband had high-risk SUMO4 (p = 0.069) or low-risk HLA (p = 0.086). Surprisingly, in the case-control study with 778 cases and 749 controls, an inverse trend was found (40.36% of patients and 36.98% of controls had the allele, p = 0.055). PCMT1 promoter activity increased with cytokine stimulation, but no differences were detected between the constructs adjacent to rs11155676.

CONCLUSION

PCMT1 was virtually associated with T1D in groups defined by other risk genes (SUMO4 and HLA). A general association in a not further defined sample of T1D patients was not evident. Verification in a larger population is needed.

Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

AIM

To develop novel methods for identifying new genes that contribute to the risk of developing type 1 diabetes within the Major Histocompatibility Complex (MHC) region on chromosome 6, independently of the known linkage disequilibrium (LD) between human leucocyte antigen (HLA)-DRB1, -DQA1, -DQB1 genes.

METHODS

We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein-protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein modules were statistically evaluated using permutation.

RESULTS

A total of 151 genes could be mapped to nodes within the protein interaction network and their interaction partners were identified. Five protein interaction modules reached statistical significance using this approach. The identified proteins are well known in the pathogenesis of T1D, but the modules also contain additional candidates that have been implicated in beta-cell development and diabetic complications.

CONCLUSIONS

The extensive LD within the MHC region makes it important to develop new methods for analysing genotyping data for identification of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D.

A non-synonymous variant in SLC30A8 is not associated with type 1 diabetes in the Danish population

Genome-wide association scans in type 2 diabetes (T2D) have identified a risk variant, rs13266634 (Arg325Trp), in SLC30A8 on chromosome 8. SLC30A8 encodes a beta-cell specific zinc-ion transporter and rs13266634 has been shown to affect insulin secretion. Recently, autoantibodies for Slc30A8 with high predictive value were demonstrated in individuals with type 1 diabetes (T1D), making this gene an interesting T1D candidate gene. We genotyped rs13266634 in 3008 cases and controls and 246 families from Denmark. Association to T1D could not be demonstrated.