Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes
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- 作者：Claus H Bang-Berthelsen (1) (2)
Lykke Pedersen (3)
Tina Fl?yel (1) (2)
Peter H Hagedorn (4) (7)
Titus Gylvin (5)
Flemming Pociot (1) (2) (6) - 刊名：BMC Genomics
- 出版年：2011
- 出版时间：December 2011
- 年：2011
- 卷：12
- 期：1
- 全文大小：798KB
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Lykke Pedersen (3)
Tina Fl?yel (1) (2)
Peter H Hagedorn (4) (7)
Titus Gylvin (5)
Flemming Pociot (1) (2) (6)
1. Glostrup Research Institute, Glostrup University Hospital, DK-2600, Glostrup, Denmark
2. Hagedorn Research Institute, Niels Steensensvej 6, DK-2820, Gentofte, Denmark
3. Center for Models of Life, University of Copenhagen, Blegdamsvej 17, DK-2100, Copenhagen, Denmark
4. Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800, Lyngby, Denmark
7. Department of Molecular Biomedicine, LEO Pharma A/S, Industriparken 55, DK-2750, Ballerup, Denmark
5. Steno Diabetes Center, Niels Steensensvej 2, DK-2820, Gentofte, Denmark
6. University of Lund, CRC, Sk?ne University Hospital, SE-20502, Malmoe, Sweden
文摘
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.