Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes. / Bang-Berthelsen, Claus H.; Pedersen, Lykke; Fløyel, Tina; Hagedorn, Peter; Gylvin, Titus; Pociot, Flemming.

In: B M C Genomics, Vol. 12, No. 1, 2011, p. 97.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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Bang-Berthelsen, Claus H.; Pedersen, Lykke; Fløyel, Tina; Hagedorn, Peter; Gylvin, Titus; Pociot, Flemming / Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes.

In: B M C Genomics, Vol. 12, No. 1, 2011, p. 97.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

Bibtex

@article{3bae5d63097b456d9c6e81dc60dcb22d,
title = "Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes",
publisher = "BioMed Central Ltd.",
author = "Bang-Berthelsen, {Claus H.} and Lykke Pedersen and Tina Fløyel and Peter Hagedorn and Titus Gylvin and Flemming Pociot",
year = "2011",
doi = "10.1186/1471-2164-12-97",
volume = "12",
number = "1",
pages = "97",
journal = "B M C Genomics",
issn = "1471-2164",

}

RIS

TY - JOUR

T1 - Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes

A1 - Bang-Berthelsen,Claus H.

A1 - Pedersen,Lykke

A1 - Fløyel,Tina

A1 - Hagedorn,Peter

A1 - Gylvin,Titus

A1 - Pociot,Flemming

AU - Bang-Berthelsen,Claus H.

AU - Pedersen,Lykke

AU - Fløyel,Tina

AU - Hagedorn,Peter

AU - Gylvin,Titus

AU - Pociot,Flemming

PB - BioMed Central Ltd.

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

U2 - 10.1186/1471-2164-12-97

DO - 10.1186/1471-2164-12-97

JO - B M C Genomics

JF - B M C Genomics

SN - 1471-2164

IS - 1

VL - 12

SP - 97

ER -