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

Bang-Berthelsen CH, Pedersen L, Fløyel T, Hagedorn PH, Gylvin T, Pociot F - BMC Genomics (2011)

Bottom Line: Applying ICA on the mRNA profiling data revealed five significant independent components (ICs) correlating to the experimental conditions.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 results suggest that ICA is better at identifying miRNA targets than negative correlation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Glostrup Research Institute, Glostrup University Hospital, DK-2600 Glostrup, Denmark.

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: 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).

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.

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miRNA regulatory network. miRNA regulatory networks were identified using a combined bioinformatics approach involving seed matching, promoter analysis, text mining and ICA. The nodes and links were established by linking miRNAs with potential mRNA targets or transcription factors.
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Figure 5: miRNA regulatory network. miRNA regulatory networks were identified using a combined bioinformatics approach involving seed matching, promoter analysis, text mining and ICA. The nodes and links were established by linking miRNAs with potential mRNA targets or transcription factors.

Mentions: Using a combined bioinformatics approach involving seed matching, promoter analysis, text mining and ICA we identified miRNA regulatory networks (Figure 5). Using the miRNA database http://www.miRbase.org, we found STAT3 binding sites in the promoter of the human miR-124 gene. This interaction was supported by ICA that showed that miR-124 and Stat3 both have negative contribution from IC 1 (Additional file 2 and Figure 2C) correlating with an up-regulation in response to Pdx-1 induction. Interestingly, the 3'UTR of Stat3 has seed matches for miR-124 (TargetScan and 6mer seed matching), indicating a potential feedback loop.


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

Bang-Berthelsen CH, Pedersen L, Fløyel T, Hagedorn PH, Gylvin T, Pociot F - BMC Genomics (2011)

miRNA regulatory network. miRNA regulatory networks were identified using a combined bioinformatics approach involving seed matching, promoter analysis, text mining and ICA. The nodes and links were established by linking miRNAs with potential mRNA targets or transcription factors.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3040732&req=5

Figure 5: miRNA regulatory network. miRNA regulatory networks were identified using a combined bioinformatics approach involving seed matching, promoter analysis, text mining and ICA. The nodes and links were established by linking miRNAs with potential mRNA targets or transcription factors.
Mentions: Using a combined bioinformatics approach involving seed matching, promoter analysis, text mining and ICA we identified miRNA regulatory networks (Figure 5). Using the miRNA database http://www.miRbase.org, we found STAT3 binding sites in the promoter of the human miR-124 gene. This interaction was supported by ICA that showed that miR-124 and Stat3 both have negative contribution from IC 1 (Additional file 2 and Figure 2C) correlating with an up-regulation in response to Pdx-1 induction. Interestingly, the 3'UTR of Stat3 has seed matches for miR-124 (TargetScan and 6mer seed matching), indicating a potential feedback loop.

Bottom Line: Applying ICA on the mRNA profiling data revealed five significant independent components (ICs) correlating to the experimental conditions.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 results suggest that ICA is better at identifying miRNA targets than negative correlation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Glostrup Research Institute, Glostrup University Hospital, DK-2600 Glostrup, Denmark.

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: 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).

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.

Show MeSH
Related in: MedlinePlus