Expression profiling of human genetic and protein interaction networks in type 1 diabetes

Regine Bergholdt; Caroline Brorsson; Kasper Lage; Jens Høiriis Nielsen; Søren Brunak; Flemming Pociot

doi:10.1371/journal.pone.0006250

Expression profiling of human genetic and protein interaction networks in type 1 diabetes

Regine Bergholdt, Caroline Brorsson, Kasper Lage, Jens Høiriis Nielsen, Søren Brunak, Flemming Pociot

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

12 Citationer (Scopus)

Abstract

Udgivelsesdato: 2009-null

Originalsprog	Engelsk
Tidsskrift	PLoS ONE
Vol/bind	4
Udgave nummer	7
Sider (fra-til)	e6250
ISSN	1932-6203
DOI	https://doi.org/10.1371/journal.pone.0006250
Status	Udgivet - 2009

Bibliografisk note

Keywords: Adolescent; Adult; Case-Control Studies; Child; Diabetes Mellitus, Type 1; Female; Gene Expression Profiling; Humans; Male; Middle Aged; Polymorphism, Single Nucleotide; Protein Binding; Proteins; Young Adult

Adgang til dokumentet

10.1371/journal.pone.0006250

Citationsformater

@article{e754372035ab11df8ed1000ea68e967b,

title = "Expression profiling of human genetic and protein interaction networks in type 1 diabetes",

abstract = "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.",

author = "Regine Bergholdt and Caroline Brorsson and Kasper Lage and Nielsen, {Jens H{\o}iriis} and S{\o}ren Brunak and Flemming Pociot",

note = "Keywords: Adolescent; Adult; Case-Control Studies; Child; Diabetes Mellitus, Type 1; Female; Gene Expression Profiling; Humans; Male; Middle Aged; Polymorphism, Single Nucleotide; Protein Binding; Proteins; Young Adult",

year = "2009",

doi = "10.1371/journal.pone.0006250",

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T1 - Expression profiling of human genetic and protein interaction networks in type 1 diabetes

AU - Bergholdt, Regine

AU - Brorsson, Caroline

AU - Lage, Kasper

AU - Nielsen, Jens Høiriis

AU - Brunak, Søren

AU - Pociot, Flemming

N1 - Keywords: Adolescent; Adult; Case-Control Studies; Child; Diabetes Mellitus, Type 1; Female; Gene Expression Profiling; Humans; Male; Middle Aged; Polymorphism, Single Nucleotide; Protein Binding; Proteins; Young Adult

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pone.0006250

DO - 10.1371/journal.pone.0006250

M3 - Journal article

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VL - 4

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JO - PLoS ONE

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SN - 1932-6203

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