Independent Component and Pathway-based Analysis of MiRNA-regulated Gene Expression in a Model of Type 1 Diabetes

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2011 Feb 8

PMID 21294859

Citations 16

Authors

Claus H Bang-Berthelsen

Lykke Pedersen

Tina Floyel

Peter H Hagedorn

Titus Gylvin

Flemming Pociot

Affiliations

Soon will be listed here.

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.

Citing Articles

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Jin F, Hu H, Xu M, Zhan S, Wang Y, Zhang H Front Immunol. 2018; 9:2381.

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MicroRNAs and histone deacetylase inhibition-mediated protection against inflammatory β-cell damage.

Lindelov Vestergaard A, Bang-Berthelsen C, Floyel T, Stahl J, Christen L, Taheri Sotudeh F PLoS One. 2018; 13(9):e0203713.

PMID: 30260972 PMC: 6160007. DOI: 10.1371/journal.pone.0203713.

MicroRNAs Involvement in Renal Pathophysiology: A Bird's Eye View.

Jaswani P, Prakash S, Dhar A, Sharma R, Prasad N, Agrawal S Indian J Nephrol. 2017; 27(5):337-341.

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Determining the optimal number of independent components for reproducible transcriptomic data analysis.

Kairov U, Cantini L, Greco A, Molkenov A, Czerwinska U, Barillot E BMC Genomics. 2017; 18(1):712.

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T2DiACoD: A Gene Atlas of Type 2 Diabetes Mellitus Associated Complex Disorders.

Rani J, Mittal I, Pramanik A, Singh N, Dube N, Sharma S Sci Rep. 2017; 7(1):6892.

PMID: 28761062 PMC: 5537262. DOI: 10.1038/s41598-017-07238-0.

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