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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 expressions and mixes of independent components. There are three experimental conditions: Pdx-1 induction (dox treatment), IL-1β treatment and time (samples are taken 2 h and 24 h after treatment). (A) Log2-transformed fold changes (mean and standard deviations) between experimental and control (untreated cells) conditions. *: 0.05 > q > 0.01, **: 0.01 > q > 0.001, ***: 0.001 > q > 0. (B) For each independent component (IC) the average of mixes are shown for each condition. Bars represent mean and standard deviation. IC1 is a Pdx-1 component showing mixes correlating to Pdx-1 induction. IC 2 and 3 are cytokine components with mixes correlating to IL-1β treatment. IC 4 shows mixes correlating to induction of Pdx-1 and treatment with IL-1β after 24 h. IC 5 has mixes increasing from 2 h to 24 h in all three conditions. (C) The coefficient for the linear combination of the ICs giving the best fit of the miRNA expressions. The coefficients are scaled to have an absolute sum of one.
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Figure 2: miRNA expressions and mixes of independent components. There are three experimental conditions: Pdx-1 induction (dox treatment), IL-1β treatment and time (samples are taken 2 h and 24 h after treatment). (A) Log2-transformed fold changes (mean and standard deviations) between experimental and control (untreated cells) conditions. *: 0.05 > q > 0.01, **: 0.01 > q > 0.001, ***: 0.001 > q > 0. (B) For each independent component (IC) the average of mixes are shown for each condition. Bars represent mean and standard deviation. IC1 is a Pdx-1 component showing mixes correlating to Pdx-1 induction. IC 2 and 3 are cytokine components with mixes correlating to IL-1β treatment. IC 4 shows mixes correlating to induction of Pdx-1 and treatment with IL-1β after 24 h. IC 5 has mixes increasing from 2 h to 24 h in all three conditions. (C) The coefficient for the linear combination of the ICs giving the best fit of the miRNA expressions. The coefficients are scaled to have an absolute sum of one.

Mentions: miRNA expression profiling resulted in identification of eight miRNAs with differential expression in response to Pdx-1 induction and/or IL-1β exposure. All eight miRNAs (miR-124/128/192/194/204/375/672/708) showed significant (p < 0.05) changes in expression between conditions and/or time points (Figure 2A). The eight miRNA expression profiles capture all three experimental conditions: dox-induced Pdx-1 expression, IL-1β exposure and time. For example, the miR-672 expression decreased significantly (p < 0.05) with time independent of Pdx-1 induction and/or IL-1β treatment. The reversed pattern was seen for miR-204, though only for cells with induced Pdx-1 expression. A third example was the up-regulating effect of IL-1β treatment on the expressions of miR-128/192/194.


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 expressions and mixes of independent components. There are three experimental conditions: Pdx-1 induction (dox treatment), IL-1β treatment and time (samples are taken 2 h and 24 h after treatment). (A) Log2-transformed fold changes (mean and standard deviations) between experimental and control (untreated cells) conditions. *: 0.05 > q > 0.01, **: 0.01 > q > 0.001, ***: 0.001 > q > 0. (B) For each independent component (IC) the average of mixes are shown for each condition. Bars represent mean and standard deviation. IC1 is a Pdx-1 component showing mixes correlating to Pdx-1 induction. IC 2 and 3 are cytokine components with mixes correlating to IL-1β treatment. IC 4 shows mixes correlating to induction of Pdx-1 and treatment with IL-1β after 24 h. IC 5 has mixes increasing from 2 h to 24 h in all three conditions. (C) The coefficient for the linear combination of the ICs giving the best fit of the miRNA expressions. The coefficients are scaled to have an absolute sum of one.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3040732&req=5

Figure 2: miRNA expressions and mixes of independent components. There are three experimental conditions: Pdx-1 induction (dox treatment), IL-1β treatment and time (samples are taken 2 h and 24 h after treatment). (A) Log2-transformed fold changes (mean and standard deviations) between experimental and control (untreated cells) conditions. *: 0.05 > q > 0.01, **: 0.01 > q > 0.001, ***: 0.001 > q > 0. (B) For each independent component (IC) the average of mixes are shown for each condition. Bars represent mean and standard deviation. IC1 is a Pdx-1 component showing mixes correlating to Pdx-1 induction. IC 2 and 3 are cytokine components with mixes correlating to IL-1β treatment. IC 4 shows mixes correlating to induction of Pdx-1 and treatment with IL-1β after 24 h. IC 5 has mixes increasing from 2 h to 24 h in all three conditions. (C) The coefficient for the linear combination of the ICs giving the best fit of the miRNA expressions. The coefficients are scaled to have an absolute sum of one.
Mentions: miRNA expression profiling resulted in identification of eight miRNAs with differential expression in response to Pdx-1 induction and/or IL-1β exposure. All eight miRNAs (miR-124/128/192/194/204/375/672/708) showed significant (p < 0.05) changes in expression between conditions and/or time points (Figure 2A). The eight miRNA expression profiles capture all three experimental conditions: dox-induced Pdx-1 expression, IL-1β exposure and time. For example, the miR-672 expression decreased significantly (p < 0.05) with time independent of Pdx-1 induction and/or IL-1β treatment. The reversed pattern was seen for miR-204, though only for cells with induced Pdx-1 expression. A third example was the up-regulating effect of IL-1β treatment on the expressions of miR-128/192/194.

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