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Researchers Report on Model for the Study of Competitive Endogenous RNAs


A team of researchers from Beth Israel Deaconess Medical Center and Harvard Medical School has published a report on the design and testing of a computational model built to examine the behavior of microRNA targets that cross-regulate each other and to establish a framework for their study.

The work, which appeared last month in the Proceedings of the National Academy of Sciences, showed that these so-called competitive endogenous RNAS, or ceRNAs, are “tightly intertwined” with transcription factors in both physiological and pathological conditions, with the alteration of one ceRNA having “striking effects” on integrated ceRNA and transcriptional networks.

According to the investigators, it has been hypothesized that miRNA activity is regulated by the abundance of targets and, as a result, the concentration of a target RNA may affect the activity of a given miRNA toward its other targets.

“Thus, miRNA targets may cross-regulate each other by competing for shared miRNAs,” they wrote in PNAS.

Such ceRNA crosstalk is based on an miRNA response element, or MRE, language that may be applied to any MRE-containing RNA molecule, including non-coding RNAs such as pseudogenes and long noncoding RNAs, they added. “As most transcripts harbor several MREs and most miRNAs target numerous transcripts, ceRNAs likely act in complex networks, which can be deconvoluted on the basis of their MRE language.”

Based on the theory that the expression levels of individual ceRNAs and miRNAs influence cross-regulation, the team posited that when the number of transcripts “vastly exceeds” the number of miRNAs, overall ceRNA activity is minimal due to the limited number of available miRNAs. By the same token, if miRNAs are far more abundant than ceRNAs, cross-regulation is unlikely as most transcripts are fully repressed.

“We therefore hypothesized that optimal ceRNA-mediated cross-regulation occurs at a near-equimolar equilibrium of all ceRNAs and miRNAs within a network,” they wrote, proposing a mathematical mass-action model to determine the optimal conditions for ceRNA activity in silico and validating it using phosphatase and tensin homolog, PTEN, and its ceRNA vesicle-associated membrane protein-associated protein A as paradigmatic examples.

Overall, the investigators showed that “relative abundance of ceRNAs and miRNAs as well as their stoichiometry, the number of MREs, and indirect interactions all contribute to ceRNA crosstalk,” according to the paper.

Further, the balance between miRNAs and ceRNAs is critical for ceRNA activity, and disruption of this balance affects ceRNA crosstalk and could potentially promote diseases such as cancer.

“Intriguingly, miRNA expression is globally decreased in cancers and Dicer1 is a haploinsufficient tumor suppressor whose expression is regulated during differentiation,” the team wrote.

“In addition, alternative polyadenylation signal utilization in cancer and during embryogenesis results in 3’ UTRs with different lengths,” they added. “Moreover, 3’ UTRs may be expressed independently of their coding sequence. Thus, although Dicer1 expression could serve as a critical rheostat for ceRNA interactions, aberrant expression of miRNAs and/or 3’ UTRs may critically alter regulation of ceRNETs and play important roles in normal physiology and disease.”

Notably, the researchers found that ceRNA and transcriptional networks are intertwined as transcription factor mRNAs and their target mRNAs “crosstalk with other RNA transcripts,” they noted in their paper. “This suggests the intriguing possibility that a transcription factor’s regulatory potency is much larger than anticipated. In turn, this has important implications not only for cellular and organismal physiology, but also for the pathogenesis of cancer and other diseases where aberrant expression of [transcription factors] occurs.”