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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression. / Bergholdt, Regine; Brorsson, Caroline; Palleja, Albert; Berchtold, Lukas A; Fløyel, Tina; Bang-Berthelsen, Claus Heiner; Frederiksen, Klaus Stensgaard; Jensen, Lars Juhl; Størling, Joachim; Pociot, Flemming.

In: Diabetes, Vol. 61, No. 4, 2012, p. 954-62.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Bergholdt, R, Brorsson, C, Palleja, A, Berchtold, LA, Fløyel, T, Bang-Berthelsen, CH, Frederiksen, KS, Jensen, LJ, Størling, J & Pociot, F 2012, 'Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression', Diabetes, vol. 61, no. 4, pp. 954-62. https://doi.org/10.2337/db11-1263

APA

Bergholdt, R., Brorsson, C., Palleja, A., Berchtold, L. A., Fløyel, T., Bang-Berthelsen, C. H., Frederiksen, K. S., Jensen, L. J., Størling, J., & Pociot, F. (2012). Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression. Diabetes, 61(4), 954-62. https://doi.org/10.2337/db11-1263

Vancouver

Bergholdt R, Brorsson C, Palleja A, Berchtold LA, Fløyel T, Bang-Berthelsen CH et al. Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression. Diabetes. 2012;61(4):954-62. https://doi.org/10.2337/db11-1263

Author

Bergholdt, Regine ; Brorsson, Caroline ; Palleja, Albert ; Berchtold, Lukas A ; Fløyel, Tina ; Bang-Berthelsen, Claus Heiner ; Frederiksen, Klaus Stensgaard ; Jensen, Lars Juhl ; Størling, Joachim ; Pociot, Flemming. / Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression. In: Diabetes. 2012 ; Vol. 61, No. 4. pp. 954-62.

Bibtex

@article{ce56d28235c74c0a9485fb8309e26bc5,
title = "Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression",
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 {\ss}-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.",
keywords = "Diabetes Mellitus, Type 1, Gene Expression Profiling, Gene Expression Regulation, Genome, Human, Humans, Islets of Langerhans, Protein Interaction Maps",
author = "Regine Bergholdt and Caroline Brorsson and Albert Palleja and Berchtold, {Lukas A} and Tina Fl{\o}yel and Bang-Berthelsen, {Claus Heiner} and Frederiksen, {Klaus Stensgaard} and Jensen, {Lars Juhl} and Joachim St{\o}rling and Flemming Pociot",
year = "2012",
doi = "10.2337/db11-1263",
language = "English",
volume = "61",
pages = "954--62",
journal = "Diabetes",
issn = "0012-1797",
publisher = "American Diabetes Association",
number = "4",

}

RIS

TY - JOUR

T1 - Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression

AU - Bergholdt, Regine

AU - Brorsson, Caroline

AU - Palleja, Albert

AU - Berchtold, Lukas A

AU - Fløyel, Tina

AU - Bang-Berthelsen, Claus Heiner

AU - Frederiksen, Klaus Stensgaard

AU - Jensen, Lars Juhl

AU - Størling, Joachim

AU - Pociot, Flemming

PY - 2012

Y1 - 2012

N2 - 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 ß-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.

AB - 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 ß-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.

KW - Diabetes Mellitus, Type 1

KW - Gene Expression Profiling

KW - Gene Expression Regulation

KW - Genome, Human

KW - Humans

KW - Islets of Langerhans

KW - Protein Interaction Maps

U2 - 10.2337/db11-1263

DO - 10.2337/db11-1263

M3 - Journal article

C2 - 22344559

VL - 61

SP - 954

EP - 962

JO - Diabetes

JF - Diabetes

SN - 0012-1797

IS - 4

ER -

ID: 40290812