Integrating microRNA and mRNA expression profiles for identification of regulatory networks

The discovery of microRNA as an additional regulatory mechanism has been a revolution to the field of Developmental Biology. While early research has focused on the identification of miRNAs using a combination of experimental and computational techniques, subsequent studies have focused on identification of miRNA-target mRNA pairs as a means of identifying regulatory networks. It has been shown that the relationship between messenger RNA and microRNA (often an inverse relationship) plays a large role in cell functionality, especially in the early stages of cell development.

The identification of miRNAs, their target mRNAs, and the construction of their regulatory networks will provide new insights into complex biological procedures. There has been extensive computational analysis of transcriptome and microRNAome data; however, many of the datasets are derived from separate studies. There is a need for studies encompassing both datasets from cells within the same context for better understanding of miRNA – target interaction. Access to both datasets plus new bioinformatics capabilities represents a powerful new functional genomics tool for construction of cell development networks/pathways and also for unraveling the complex mechanism of action of miRNA.

In a recent study, researchers at the NIH used microarray analysis to show the correlation between mRNA and miRNA in neuronal cortical development1. Rat neuronal progenitors were obtained at days 11, 12, and 13 and their total RNA extracted. To evaluate the expression of mRNA a gene expression profile using the Affymetrix Rat Expression 230 2.0 microarray was used. Expression of miRNA was evaluated using miRNA microarrays (provided by LC Sciences). Both mRNA and miRNA expression was validated using qRT-PCR. Bioinformatics analysis was then carried out on the two datasets to show the negative correlation between predicted mRNA targets and miRNA expression, indicating a gene expression regulatory network at work. The negative correlation found in this study supports the theory that miRNAs play a larger role in modulating the expression of genes which are essential for cortical neurogenesis. As neuronal differentiation progresses, up-regulated miRNA may down-regulate the expression of genes which are no longer required.

The availability of mRNA and miRNA profiles from the same cell type represents a major limitation in the network analysis of genetic circuits. A platform capable of generating integrated datasets, not only from the same cell type, but from cells within the same context would be an important new tool for analysis of miRNA – target interaction. Such a tool may provide a solution for:

  • validation of computational methods of target prediction
  • discovery of novel regulatory pathways
  • elucidating the mechanism of action of miRNAs
  • providing datasets for the development of new pathway analysis algorithms
  • a diagnostic device for improving bio-molecular classification of human cancers

 Nielsen JA, Lau P, Maric D, Barker JL, Hudson LD. (2009) Integrating microRNA and mRNA  expression profiles of neuronal progenitors to identify regulatory networks underlying the onset of cortical neurogenesis. BMC Neurosci 10(1), 98. [abstract]

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