Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method

Background

MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.

Results

We proposed a network propagation based method to infer the perturbed miRNAs and their key target genes by integrating gene expressions and global gene regulatory network information. The method used random walk with restart in gene regulatory networks to model the network effects of the miRNA perturbation. Then, it evaluated the significance of the correlation between the network effects of the miRNA perturbation and the gene differential expression levels with a forward searching strategy. Results show that our method outperformed several compared methods in rediscovering the experimentally perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory networks of colorectal cancer, including several known oncogenic or tumor-suppressive miRNAs, such as miR-17, miR-26 and miR-145.

Conclusions

Our network propagation based method takes advantage of the network effect of the miRNA perturbation on its target genes. It is a useful approach to infer the perturbed miRNAs and their key target genes associated with the studied biological processes using gene expression data.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-255) contains supplementary material, which is available to authorized users.     

Origin and evolution of a placental-specific microRNA family in the human genome

Background  

MicroRNAs (miRNAs) are a class of short regulatory RNAs encoded in the genome of DNA viruses, some single cell organisms, plants and animals. With the rapid development of technology, more and more miRNAs are being discovered. However, the origin and evolution of most miRNAs remain obscure. Here we report the origin and evolution dynamics of a human miRNA family.     

TAM: A method for enrichment and depletion analysis of a microRNA category in a list of microRNAs

Background  

MicroRNAs (miRNAs) are a class of important gene regulators. The number of identified miRNAs has been increasing dramatically in recent years. An emerging major challenge is the interpretation of the genome-scale miRNA datasets, including those derived from microarray and deep-sequencing. It is interesting and important to know the common rules or patterns behind a list of miRNAs, (i.e. the deregulated miRNAs resulted from an experiment of miRNA microarray or deep-sequencing).     

Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine

Background  

MicroRNAs (miRNAs) are a group of short (~22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking. Being able to classify real vs. pseudo pre-miRNAs is important both for understanding of the nature of miRNAs and for developing ab initio prediction methods that can discovery new miRNAs without known homology.     

MapMi: automated mapping of microRNA loci

Background  

A large effort to discover microRNAs (miRNAs) has been under way. Currently miRBase is their primary repository, providing annotations of primary sequences, precursors and probable genomic loci. In many cases miRNAs are identical or very similar between related (or in some cases more distant) species. However, miRBase focuses on those species for which miRNAs have been directly confirmed. Secondly, specific miRNAs or their loci are sometimes not annotated even in well-covered species. We sought to address this problem by developing a computational system for automated mapping of miRNAs within and across species. Given the sequence of a known miRNA in one species it is relatively straightforward to determine likely loci of that miRNA in other species. Our primary goal is not the discovery of novel miRNAs but the mapping of validated miRNAs in one species to their most likely orthologues in other species.     

Altered microRNA expression in patients with non-obstructive azoospermia

Background  

MicroRNAs (miRNAs), a class of small non-coding RNA molecules, are indicated to play essential roles in spermatogenesis. However, little is known about the expression patterns or function of miRNAs in human testes involved in infertility.     

Improving performance of mammalian microRNA target prediction

Background  

MicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing. Constructing a richer and more reliable training data set and developing an algorithm that properly exploits this data set would be the key to improve the performance current prediction algorithms.     

Detecting microRNA activity from gene expression data

Background  

MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.     

Circulating microRNA signature of steroid‐induced osteonecrosis of the femoral head

Objectives

Steroid‐induced osteonecrosis of the femoral head (ONFH) is a common orthopaedic disease of which early detection remains clinically challenging. Accumulating evidences indicated that circulating microRNAs (miRNAs) plays vital roles in the development of several bone diseases. However, the association between circulating miRNAs and steroid‐induced ONFH remains elusive.

Materials and methods

miRNA microarray was performed to identify the differentially abundant miRNAs in the serums of systemic lupus erythematosus (SLE) patients with steroid‐induced ONFH as compared with SLE control and healthy control group. We predicted the potential functions of these differentially abundant miRNAs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and reconstructed the regulatory networks of miRNA‐mRNA interactions.

Results

Our data indicated that there were 11 differentially abundant miRNAs (2 upregulated and 9 downregulated) between SLE‐ONFH group and healthy control group and 42 differentially abundant miRNAs (14 upregulated and 28 downregulated) between SLE‐ONFH group and SLE control group. We also predicted the potential functions of these differentially abundant miRNAs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and reconstructed the regulatory networks of miRNA‐mRNA interactions.

Conclusions

These findings corroborated the idea that circulating miRNAs play significant roles in the development of ONFH and may serve as diagnostic markers and therapeutic targets.     

SAGE detects microRNA precursors

Background

MicroRNAs (miRNAs) have been shown to play important roles in regulating gene expression. Since miRNAs are often evolutionarily conserved and their precursors can be folded into stem-loop hairpins, many miRNAs have been predicted. Yet experimental confirmation is difficult since miRNA expression is often specific to particular tissues and developmental stages.

Results

Analysis of 29 human and 230 mouse longSAGE libraries revealed the expression of 22 known and 10 predicted mammalian miRNAs. Most were detected in embryonic tissues. Four SAGE tags detected in human embryonic stem cells specifically match a cluster of four human miRNAs (mir-302a, b, c&d) known to be expressed in embryonic stem cells. LongSAGE data also suggest the existence of a mouse homolog of human and rat mir-493.

Conclusion

The observation that some orphan longSAGE tags uniquely match miRNA precursors provides information about the expression of some known and predicted miRNAs.