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ABSTRACT: Diabetes is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The chronic hyperglycemia of diabetes is associated with long-term damage, dysfunction, and failure of different organs, especially the eyes, kidneys, nerves, heart, and blood vessels. Several pathogenic processes are involved in the development of diabetes. These range from autoimmune destruction of the beta cells of the pancreas with consequent insulin deficiency to abnormalities that result in resistance to insulin action (American Diabetes Association, 2011). The vast majority of cases of diabetes fall into two broad categories. In type 1 diabetes (T1D), the cause is an absolute deficiency of insulin secretion whereas in type 2 diabetes (T2D) the cause is a combination of resistance to insulin action and an inadequate compensatory insulin secretory response. However, the subdivision into two main categories represents a simplification of the real situation and research during the recent years has shown that the disease is much more heterogeneous than a simple subdivision into two major subtypes assumes. Worldwide prevalence figures estimate that there are 280 million diabetic patients in 2011 and more than 500 million in 2030 (http://www.diabetesatlas.org/). In Europe, about 6-8% of the population suffer from diabetes , of them about 90% has T2D and 10% T1D, thereby making T2D to the fastest increasing disease in Europe and worldwide. This epidemic has been ascribed to a collision between genes and the environment. While our knowledge about the genes is clearly better for T1D than for T2D given the strong contribution of variation in the HLA region to the risk of T1D the opposite is the case for T2D, where our knowledge about the environmental triggers (obesity, lack of exercise) is much better than the understanding of the underlying genetic causes. This lack of knowledge about the underlying genetic causes of diabetes is often referred to as missing heritability (Manolio, Collins, Cox et al., 2009) which exceeds 80% for T2D but less than 25% for T1D. In the following review we will discuss potential sources of this missing heritability which also includes the possibility that our definition of diabetes and its subgroups is imprecise and thereby makes the identification of genetic causes difficult.
Molecular and Cellular Endocrinology 04/2013; · 4.19 Impact Factor
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ABSTRACT: 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.
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.
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).
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.
PLoS ONE 01/2013; 8(3):e58236. · 4.09 Impact Factor
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Jannet Svensson,
Stefanie Eising,
David Michael Hougaard,
Henrik Bindesbøl Mortensen,
Kristin Skogstrand,
Lars Bjarke Simonsen,
Bendix Carstensen,
Anita Nilsson,
Ke Lernmark, Flemming Pociot,
Jesper Johannesen
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ABSTRACT: The cause of the worldwide increase in type 1 diabetes (T1D) is largely unknown. T cells are thought to play a role in disease progression. In contemporary research over the last decade, age- and gender-specific serum levels as well as changes of Th1 and Th2-related cytokines are not well described. From a population-based register of children diagnosed from 1997 to 2005 this study explores eight different cytokines at time of diagnosis. Only TGF-β and IL-18 showed higher levels in patients compared to siblings in an adjusted model (p<0.01); whereas the other seven cytokines were not significantly different. IL-1β, IL-18, IL-12, IL-10 and IL-4 were significantly higher among the youngest children and males had significantly lower levels of IL-10 and IL-12 but higher levels of TNF-α. During the nine-year study all of the cytokines increased except TGF-β, which showed a slight decrease over time. The cytokine levels tended to be highest during summer and were most pronounced for IL-1β and TNF-α. In conclusion, serum levels of known β-cell cytotoxic cytokines were indifferent in patients and siblings, while gender, age and season appear to exert some influence on the serum level and need to be explored further. The influence of time on systemic levels cannot be ignored and may reflect decay or environmental impact on the immune system.
Human immunology 08/2012; 73(11):1116-26. · 2.55 Impact Factor
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ABSTRACT: Large scale quantitative phosphoproteomics depends upon multidimensional strategies for peptide fractionation, phosphopeptide enrichment, and mass spectrometric analysis. Previously, most robust comprehensive large-scale phosphoproteomics strategies have relied on milligram amounts of protein. We have set up a multi-dimensional phosphoproteomics strategy combining a number of well-established enrichment and fraction methods: An initial TiO(2) phosphopeptide pre-enrichment step is followed by post-fractionation using sequential elution from IMAC (SIMAC) to separate multi- and mono-phosphorylated peptides, and hydrophilic interaction liquid chromatography (HILIC) of the mono-phosphorylated peptides (collectively abbreviated "TiSH"). The advantages of the strategy include a high specificity and sample preparation workload reduction due to the TiO(2) pre-enrichment step, as well as low adsorptive losses. We demonstrate the capability of this strategy by quantitative investigation of early interferon-γ signaling in low quantities of insulinoma cells. We identified ~6600 unique phosphopeptides from 300μg of peptides/condition (22 unique phosphopeptides/μg) in a duplex dimethyl labeling experiment, with an enrichment specificity>94%. When doing network analysis of putative phosphorylation changes it could be noted that the identified protein interaction network centered upon proteins known to be affected by the interferon-γ pathway, thereby supporting the utility of this global phosphoproteomics strategy. This strategy thus shows great potential for interrogating signaling networks from low amounts of sample with high sensitivity and specificity.
Journal of proteomics 08/2012; 75(18):5749-61. · 5.07 Impact Factor
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Joanna M M Howson,
Jason D Cooper,
Deborah J Smyth,
Neil M Walker,
Helen Stevens,
Jin-Xiong She,
George S Eisenbarth,
Marian Rewers,
John A Todd,
Beena Akolkar,
Patrick Concannon,
Henry A Erlich,
Cécile Julier,
Grant Morahan,
Jørn Nerup,
Concepcion Nierras, Flemming Pociot,
Stephen S Rich
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ABSTRACT: The common genetic loci that independently influence the risk of type 1 diabetes have largely been determined. Their interactions with age-at-diagnosis of type 1 diabetes, sex, or the major susceptibility locus, HLA class II, remain mostly unexplored. A large collection of more than 14,866 type 1 diabetes samples (6,750 British diabetic individuals and 8,116 affected family samples of European descent) were genotyped at 38 confirmed type 1 diabetes-associated non-HLA regions and used to test for interaction of association with age-at-diagnosis, sex, and HLA class II genotypes using regression models. The alleles that confer susceptibility to type 1 diabetes at interleukin-2 (IL-2), IL2/4q27 (rs2069763) and renalase, FAD-dependent amine oxidase (RNLS)/10q23.31 (rs10509540), were associated with a lower age-at-diagnosis (P = 4.6 × 10(-6) and 2.5 × 10(-5), respectively). For both loci, individuals carrying the susceptible homozygous genotype were, on average, 7.2 months younger at diagnosis than those carrying the protective homozygous genotypes. In addition to protein tyrosine phosphatase nonreceptor type 22 (PTPN22), evidence of statistical interaction between HLA class II genotypes and rs3087243 at cytotoxic T-lymphocyte antigen 4 (CTLA4)/2q33.2 was obtained (P = 7.90 × 10(-5)). No evidence of differential risk by sex was obtained at any loci (P ≥ 0.01). Statistical interaction effects can be detected in type 1 diabetes although they provide a relatively small contribution to our understanding of the familial clustering of the disease.
Diabetes 08/2012; 61(11):3012-7. · 8.29 Impact Factor
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ABSTRACT: BACKGROUND: In this study we evaluated the association of baseline levels of six different candidate proteins for the development of microalbuminuria and macroalbuminuria in type 1 diabetic patients, who were followed for approximately 30years. Two of the proteins are markers of inflammation: serum amyloid A (SAA) and C-reactive protein (CRP), three are involved in lipid metabolism: apolipoprotein A1, apolipoprotein E and adiponectin and the last protein, fibronectin, is related to structural changes. METHODS: A nested case control study population of 60 patients from an inception cohort of type 1 diabetic patients where 20 developed microalbuminuria followed by macroalbuminuria and 40 stayed normoalbuminuric during approximately 30years of follow-up time was used to evaluate baseline levels of the six candidate biomarkers. The proteins were quantified by multiplexed immunoassays. RESULTS: Log SAA levels were borderline predictor of microalbuminuria, HR 2.31 (1-5.4) p=0.053 in a univariate Cox regression model and predicted the development of macroalbuminuria HR 2.432 (1-6) p=0.049, also univariate. When adjusting for covariates, log SAA predicted the development of microalbuminuria with an HR 4.131 (1.1-15) p=0.03. Log CRP predicted the development of microalbuminuria, HR 2.928 (1.4-6.1) p=0.004, and macroalbuminuria, HR 2.785 (1.3-5.8) p=0.007 in univariate models. When adjusting for covariates, log CRP predicted the development of microalbuminuria with an HR 5.882 (1.7-20.9) p=0.006 and macroalbuminuria with an HR 3.233 (1.1-9.8) p=0.038. Apolipoprotein A1, apolipoprotein E, fibronectin and adiponectin were not associated with development of elevated albumin excretion rate. CONCLUSIONS: SAA and CRP baseline levels predicted development of micro- and macroalbuminuria during 30years of follow up, supporting the theory that inflammation is involved in the progression of diabetic nephropathy. Further studies are needed to fully establish the two proteins' potential as additional biomarkers for the development of diabetic nephropathy.
Journal of diabetes and its complications 08/2012; · 2.11 Impact Factor
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ABSTRACT: Microparticles and exosomes are two of the most well characterized membrane-derived microvesicles released either directly from the plasma membrane or released through the fusion of intracellular multivesicular bodies with the plasma membrane, respectively. They are thought to be involved in many significant biological processes such as cell to cell communication, rescue from apoptosis, and immunological responses. Here we report for the first time a quantitative study of proteins from β-cell-derived microvesicles generated after cytokine induced apoptosis using stable isotope labeled amino acids in cell culture combined with mass spectrometry. We identified and quantified a large number of β-cell-specific proteins and proteins previously described in microvesicles from other cell types in addition to new proteins located to these vesicles. In addition, we quantified specific sites of protein phosphorylation and N-linked sialylation in proteins associated with microvesicles from β-cells. Using pathway analysis software, we were able to map the most distinctive changes between microvesicles generated during growth and after cytokine stimulation to several cell death and cell signaling molecules including tumor necrosis factor receptor superfamily member 1A, tumor necrosis factor, α-induced protein 3, tumor necrosis factor-interacting kinase receptor-interacting serine-threonine kinase 1, and intercellular adhesion molecule 1.
Molecular & Cellular Proteomics 02/2012; 11(8):230-43. · 7.40 Impact Factor
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ABSTRACT: 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.
Diabetes 02/2012; 61(4):954-62. · 8.29 Impact Factor
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Lotte B Nielsen,
Cheng Wang,
Kaspar Sørensen,
Claus H Bang-Berthelsen,
Lars Hansen,
Marie-Louise M Andersen,
Philip Hougaard,
Anders Juul,
Chen-Yu Zhang, Flemming Pociot,
Henrik B Mortensen
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ABSTRACT: 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.
Experimental Diabetes Research 01/2012; 2012:896362. · 1.20 Impact Factor
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Jannet Svensson,
Stefanie Eising,
Henrik Bindesbøl Mortensen,
Michael Christiansen,
Inga Laursen,
Åke Lernmark,
Anita Nilsson,
Lars Bjarke Simonsen,
Bendix Carstensen, Flemming Pociot,
Jesper Johannesen
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ABSTRACT: The incidence of type 1 diabetes (T1D) is increasing, either because of environmental factors accelerating onset of the disease or because of inducement of autoimmune diabetes in children who previously were at lower risk. High levels of immunoglobulin (Ig), specifically, IgM and IgA, and a low level of IgG were reported in adult patients; however no studies have analyzed the increasing incidence in relation to Ig levels. Our aim was to describe Ig in children newly diagnosed with diabetes and in their healthy siblings. Children with T1D expressed significantly lower IgG (p < 0.01) and higher IgA levels (p = 0.045), whereas no differences in IgE or IgM (p > 0.5) levels were found. Age-specific levels were unchanged over a 9-year period. In patients and siblings IgG, IgA and IgE increased by age (p < 0.001); which was in contrast to IgM (p > 0.05). The continued increase in IgG levels by age indicates that adult levels are reached later than in previously studied cohorts, thereby indicating a slower maturation of the immune system.
Human immunology 10/2011; 73(1):17-25. · 2.55 Impact Factor
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Lukas Adrian Berchtold,
Zenia Marian Størling,
Fernanda Ortis,
Kasper Lage,
Claus Bang-Berthelsen,
Regine Bergholdt,
Jacob Hald,
Caroline Anna Brorsson,
Decio Laks Eizirik, Flemming Pociot,
Søren Brunak,
Joachim Størling
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ABSTRACT: Type 1 diabetes (T1D) is a complex disease characterized by the loss of insulin-secreting β-cells. Although the disease has a strong genetic component, and several loci are known to increase T1D susceptibility risk, only few causal genes have currently been identified. To identify disease-causing genes in T1D, we performed an in silico "phenome-interactome analysis" on a genome-wide linkage scan dataset. This method prioritizes candidates according to their physical interactions at the protein level with other proteins involved in diabetes. A total of 11 genes were predicted to be likely disease genes in T1D, including the INS gene. An unexpected top-scoring candidate gene was huntingtin-interacting protein (HIP)-14/ZDHHC17. Immunohistochemical analysis of pancreatic sections demonstrated that HIP14 is almost exclusively expressed in insulin-positive cells in islets of Langerhans. RNAi knockdown experiments established that HIP14 is an antiapoptotic protein required for β-cell survival and glucose-stimulated insulin secretion. Proinflammatory cytokines (IL-1β and IFN-γ) that mediate β-cell dysfunction in T1D down-regulated HIP14 expression in insulin-secreting INS-1 cells and in isolated rat and human islets. Overexpression of HIP14 was associated with a decrease in IL-1β-induced NF-κB activity and protection against IL-1β-mediated apoptosis. Our study demonstrates that the current network biology approach is a valid method to identify genes of importance for T1D and may therefore embody the basis for more rational and targeted therapeutic approaches.
Proceedings of the National Academy of Sciences 06/2011; 108(37):E681-8. · 9.68 Impact Factor
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Lotte B Nielsen,
Fariba Vaziri-Sani,
Sven Pörksen,
Marie-Louise M Andersen,
Jannet Svensson,
Regine Bergholdt, Flemming Pociot,
Philip Hougaard,
Carine de Beaufort,
Luis Castaño,
Henrik B Mortensen,
Ake Lernmark,
Lars Hansen
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ABSTRACT: Autoantibodies against the newly established autoantigen in type 1 diabetes, zinc transporter 8, ZnT8, are presented as two types, ZnT8RAb and ZnT8WAb. The rs13266634 variant of the SLC30A8 gene has recently been found to determine the type of ZnT8Ab. The aim of this study was to explore the impact of this genetic variant and the ZnT8Ab on the residual beta-cell function during disease progression the first year after disease diagnosis in children with newly diagnosed type 1 diabetes. This cohort consists of 257 children aged < 16 years, all patients were newly diagnosed with type 1 diabetes. A Boost-test was carried out at 1, 6, and 12 months to characterize the residual beta-cell function. Carriers of the CC and CT genotype groups of the rs13266634 SNP of the SLC30A8 gene had higher stimulated C-peptide levels the first year after onset compared with those of the TT genotype group (29%, p = 0.034). CC genotype carriers were highly associated with the presence of ZnT8RAb subtype during disease progression (compared with TT, p < 0.0001). On the other hand, the TT genotype was associated with the presence of ZnT8WAb subtype during disease progression (compared with CC, p < 0.0001). The C allele of the SLC30A8 gene is associated with preserved beta-cell function in type 1 diabetes patients. The genetic determination of the rs13266634 variant on the ZnT8Ab specificity is sustained the first 12 months after the diagnosis of type 1 diabetes in a pediatric cohort.
Autoimmunity 05/2011; 44(8):616-23. · 2.47 Impact Factor
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ABSTRACT: High S-ACE levels have been shown to predispose to increased risk of hypoglycemia, however; some inconsistency relates to the risk of the ACE genotype. We investigated the association between S-ACE level at diagnosis and ACE genotype to long-term risk of severe hypoglycemia in more than 1000 children and adolescents with type 1 diabetes being part of the Danish Registry of Childhood diabetes over a 10-yr period.
The Registry provided annual registration of clinical data, e.g., HbA1c, blood glucose monitoring, insulin type and dosage and acute diabetic complications (hypoglycemia and DKA). A BioBank coupled to the Registry comprised serum for measuring S-ACE levels and DNA for ACE genotyping.
A total of 1037 individuals were included, aged 9.97 yr (SD 3.84). A total of 622 severe hypoglycemic episodes were observed in 270 individuals. Associations to increased risk of hypoglycemia generated from a negative binominal model were long diabetes duration (p < 0.0001) and high S-ACE level (p = 0.0497) when adjusted for ACE genotype. In the stratified analysis, S-ACE and insulin dosage were associated with hypoglycemia in girls (p = 0.026 and 0.028, respectively). An association of S-ACE level to ACE genotype was identified; however, no difference in the frequency of hypoglycemia, diabetes duration or HbA1c was demonstrated between ACE genotypes.
This large nationwide cohort has identified an increased risk for hypoglycemia associated with higher S-ACE level, however only in girls. A strong association was found between ACE genotype and S-ACE levels, but ACE genotype was not related to risk of hypoglycemia.
Pediatric Diabetes 03/2011; 12(2):100-6. · 2.16 Impact Factor
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ABSTRACT: We hypothesised that the correlation between autoantibody specificity for the ZnT8 Arg325Trp isoforms and the type 2 diabetes-associated rs13266634 may affect β-cell function at type 1 diabetes (T1D) onset. To study this, we tested 482 newly diagnosed diabetic probands and 478 healthy siblings from the Danish population-based T1D registry for autoantibodies to ZnT8 (ZnT8A) in addition to GAD65 and IA-2. The prevalence and titres of autoantibodies were correlated with genotypes for rs13266634 and HLA-DQB1, age at diagnosis (AAD) and insulin dose-adjusted HbA1c (IDAA1c), as a proxy for residual β-cell function. We replicated the correlation between rs13266634 genotypes and specificity for the ZnT8-Argenine (ZnT8R) and ZnT8-Tryptophan (ZnT8W) isoforms previously reported. ZnT8A overlapped substantially with autoantibodies to glutamate decarboxylase 65 (GADA) and IA-2 (IA-2A) and correlated significantly with IA-2A prevalence (p < 2e-16). No effect on IDAA1c was demonstrated for ZnT8A or rs13266634. We found a correlation between ZnT8R positivity and HLA-DQB1*0302 genotypes (p = 0.016), which has not been shown previously. Furthermore, significantly lower ZnT8R and GADA prevalence and titres was found among probands with AAD < 5 years (prevalence: p = 0.004 and p = 0.0001; titres: p = 0.002 and p = 0.001, respectively). The same trend was observed for IA-2A and ZnT8W; however, the difference was non-significant. Our study confirms ZnT8 as a major target for autoantibodies at disease onset in our Danish T1D cohort of children and adolescents, and we have further characterised the relationship between autoantibody specificity for the ZnT8 Arg325Trp epitopes and rs13266634 in relation to established autoantibodies, AAD, measures of β-cell function and HLA-DQB1 genotypes in T1D.
Autoimmunity 03/2011; 44(2):107-14. · 2.47 Impact Factor
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ABSTRACT: 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.
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.
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.
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.
European Journal of Endocrinology 02/2011; 164(2):247-52. · 3.42 Impact Factor
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ABSTRACT: Genetic susceptibility is considered an important factor for the development and progression to diabetic nephropathy (DN), and for more than 20 years researchers have tried tounravel the genetic determinants of the disease. It is now clear that the pathogenesis of DN is most likely multifactorial and attributed to several genetic and environmental risk factors. Several candidate genes have been shown to be associated with the disease, but the results have not been consistent and most of the genes conferring risk to DN remain to be identified. In addition, studies have suggested that there might be differences in susceptibility loci and/or alleles between diverse populations. Recent developments in genotyping technology and increased information on the human genome have facilitated genome-wide association scans (GWAS) for investigating novel disease susceptibility across the entire human genome. The few GWAS performed for DN so far in combination with improved understanding of the human genome have identified novel risk loci and emphasized the importance of performing detailed genetic studies across diverse ethnic populations to fully unravel the genetic susceptibility to DN.
Contributions to nephrology 01/2011; 170:8-18. · 1.49 Impact Factor
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Grant Morahan,
Munish Mehta,
Ian James,
Wei-Min Chen,
Beena Akolkar,
Henry A Erlich,
Joan E Hilner,
Cécile Julier,
Jørn Nerup,
Concepcion Nierras, Flemming Pociot,
John A Todd,
Stephen S Rich
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ABSTRACT: 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.
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.
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.
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.
Diabetes 01/2011; 60(3):1030-40. · 8.29 Impact Factor
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ABSTRACT: 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.
Microarray 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).
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.
BMC Genomics 01/2011; 12:97. · 4.07 Impact Factor
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ABSTRACT: Abstract 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.
BMC Genomics. 01/2011;
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ABSTRACT: 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.
Clinical Proteomics 12/2010; 6(4):105-114.
