Limits...
NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs.

Shirdel EA, Xie W, Mak TW, Jurisica I - PLoS ONE (2011)

Bottom Line: Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms.Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling.We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.

ABSTRACT

Background: MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome--referred to as the micronome--to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal--mirDIP (http://ophid.utoronto.ca/mirDIP).

Results: mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs.

Conclusions: Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level.

Show MeSH

Related in: MedlinePlus

MicroRNA interaction network for elements of the PI3K pathway.Mapping the elements of the PI3K pathway based on a literature review [66], produces a network where many genes are targeted by common microRNAs suggesting a novel microRNA role of pathway regulation.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3045450&req=5

pone-0017429-g004: MicroRNA interaction network for elements of the PI3K pathway.Mapping the elements of the PI3K pathway based on a literature review [66], produces a network where many genes are targeted by common microRNAs suggesting a novel microRNA role of pathway regulation.

Mentions: To further examine microRNA involvement in this pathway, we use a model of the downstream signalling components of this pathway as indicated in a recent review [66]. Here we unveil a second highly-connected microRNA network (Figure 4) (based on 2000 permutations: p<0.05 for number of nodes in the network, p<0.05 for number of interactions in the network, p<0.05 for number of nodes with degree ≥4). It is quite surprising to see the number of microRNAs that can co-target potent tumour suppressors and oncogenes. We find a microRNA – hsa-mir-19b – that concurrently targets PTEN-TSC1-PI3KCA-TP53, and others that co-target RPS6KB1-PDK1-TSC1-PTEN and PTEN-RPS6KB1-FOXO3-TSC1. In addition, there are many microRNAs that target pairs of elements of this pathway: 15 microRNAs target RPS6KB1 and PTEN, 8 microRNAs target both RPS6KB1 and TSC1, and 4 microRNAs target both EIF4E and RPS6KB1. Clearly, we are only beginning to understand the level of regulation possible with microRNAs co-targeting many different genes, but it is becoming increasingly evident that this level of network complexity governs some interesting and previously hidden relationships between potent oncogenes and tumour suppressors in the cell.


NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs.

Shirdel EA, Xie W, Mak TW, Jurisica I - PLoS ONE (2011)

MicroRNA interaction network for elements of the PI3K pathway.Mapping the elements of the PI3K pathway based on a literature review [66], produces a network where many genes are targeted by common microRNAs suggesting a novel microRNA role of pathway regulation.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3045450&req=5

pone-0017429-g004: MicroRNA interaction network for elements of the PI3K pathway.Mapping the elements of the PI3K pathway based on a literature review [66], produces a network where many genes are targeted by common microRNAs suggesting a novel microRNA role of pathway regulation.
Mentions: To further examine microRNA involvement in this pathway, we use a model of the downstream signalling components of this pathway as indicated in a recent review [66]. Here we unveil a second highly-connected microRNA network (Figure 4) (based on 2000 permutations: p<0.05 for number of nodes in the network, p<0.05 for number of interactions in the network, p<0.05 for number of nodes with degree ≥4). It is quite surprising to see the number of microRNAs that can co-target potent tumour suppressors and oncogenes. We find a microRNA – hsa-mir-19b – that concurrently targets PTEN-TSC1-PI3KCA-TP53, and others that co-target RPS6KB1-PDK1-TSC1-PTEN and PTEN-RPS6KB1-FOXO3-TSC1. In addition, there are many microRNAs that target pairs of elements of this pathway: 15 microRNAs target RPS6KB1 and PTEN, 8 microRNAs target both RPS6KB1 and TSC1, and 4 microRNAs target both EIF4E and RPS6KB1. Clearly, we are only beginning to understand the level of regulation possible with microRNAs co-targeting many different genes, but it is becoming increasingly evident that this level of network complexity governs some interesting and previously hidden relationships between potent oncogenes and tumour suppressors in the cell.

Bottom Line: Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms.Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling.We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.

ABSTRACT

Background: MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome--referred to as the micronome--to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal--mirDIP (http://ophid.utoronto.ca/mirDIP).

Results: mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs.

Conclusions: Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level.

Show MeSH
Related in: MedlinePlus