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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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Related in: MedlinePlus

miRNA target-enriched pathways. Diagram showing a selection of the significant pathways for the miRNA targets in the five ICs when using KEGG annotations (blue text), MSigDB annotations (magenta text) or both annotations (purple text). The color of the bars indicate positive loads (red line) and negative loads (green line) of the miRNA targets in the ICs.
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Figure 4: miRNA target-enriched pathways. Diagram showing a selection of the significant pathways for the miRNA targets in the five ICs when using KEGG annotations (blue text), MSigDB annotations (magenta text) or both annotations (purple text). The color of the bars indicate positive loads (red line) and negative loads (green line) of the miRNA targets in the ICs.

Mentions: When using KEGG terms only, 25 pathways were significantly enriched for miRNA targets in the five ICs (q < 0.05). The most significant and diabetes-relevant pathways are shown in Figure 4 (all are listed in Additional file 6). Notable was the significance of genes with low loads in IC 3 belonging to the T1D pathway. IC 3 had a clear correlation with stimulation of IL-1β. Furthermore, the pathways maturity onset diabetes of the young (MODY), type 2 diabetes (T2D) mellitus and oxidative phosphorylation were significant. The first two have an obvious relation to diabetes, and the oxidative phosphorylation pathway has been shown to be related to both type 1 and type 2 diabetes [39]. When using MSigDB annotations, 150 pathways were significantly enriched for miRNA targets belonging to the five ICs (q < 0.05). A selection of the pathways is shown in Figure 4 (all are listed in Additional file 6). Since KEGG is part of MSigDB it comes as no surprise that T2D, MODY and oxidative phosphorylation again showed up as significant. However, T1D did not show up as a significant pathway, probably due to correction of multiple testing, since MSigDB is a much larger repository. Also, dysregulation of genes involved in the p53 signaling pathway have been suggested to sensitize the cells to apoptotic stimuli [32,40]. In accordance with this, we find genes annotated with the p53-signalling pathway (using MSigDB) having significant low loads in IC 3.


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 target-enriched pathways. Diagram showing a selection of the significant pathways for the miRNA targets in the five ICs when using KEGG annotations (blue text), MSigDB annotations (magenta text) or both annotations (purple text). The color of the bars indicate positive loads (red line) and negative loads (green line) of the miRNA targets in the ICs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: miRNA target-enriched pathways. Diagram showing a selection of the significant pathways for the miRNA targets in the five ICs when using KEGG annotations (blue text), MSigDB annotations (magenta text) or both annotations (purple text). The color of the bars indicate positive loads (red line) and negative loads (green line) of the miRNA targets in the ICs.
Mentions: When using KEGG terms only, 25 pathways were significantly enriched for miRNA targets in the five ICs (q < 0.05). The most significant and diabetes-relevant pathways are shown in Figure 4 (all are listed in Additional file 6). Notable was the significance of genes with low loads in IC 3 belonging to the T1D pathway. IC 3 had a clear correlation with stimulation of IL-1β. Furthermore, the pathways maturity onset diabetes of the young (MODY), type 2 diabetes (T2D) mellitus and oxidative phosphorylation were significant. The first two have an obvious relation to diabetes, and the oxidative phosphorylation pathway has been shown to be related to both type 1 and type 2 diabetes [39]. When using MSigDB annotations, 150 pathways were significantly enriched for miRNA targets belonging to the five ICs (q < 0.05). A selection of the pathways is shown in Figure 4 (all are listed in Additional file 6). Since KEGG is part of MSigDB it comes as no surprise that T2D, MODY and oxidative phosphorylation again showed up as significant. However, T1D did not show up as a significant pathway, probably due to correction of multiple testing, since MSigDB is a much larger repository. Also, dysregulation of genes involved in the p53 signaling pathway have been suggested to sensitize the cells to apoptotic stimuli [32,40]. In accordance with this, we find genes annotated with the p53-signalling pathway (using MSigDB) having significant low loads in IC 3.

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