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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.

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Related in: MedlinePlus

MicroRNA interaction network for assembly of PI3K subunits.Mapping PI3K subunits to microRNA interactions produces a network that is significantly more connected than at random (p<0.05). Green nodes are regulatory subunits and yellow nodes are catalytic subunits.
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pone-0017429-g003: MicroRNA interaction network for assembly of PI3K subunits.Mapping PI3K subunits to microRNA interactions produces a network that is significantly more connected than at random (p<0.05). Green nodes are regulatory subunits and yellow nodes are catalytic subunits.

Mentions: The PI3K family is divided into 3 classes. Members of each class of PI3K molecules comprise 2 subunits – a regulatory subunit and a catalytic subunit. These subunits are distinct proteins coded in different regions of the genome as either distinct genes or splice variants transcribed out of a similar locus producing translated proteins of varying sizes. The particular assembled combination of the 2 subunits of PI3K determine the molecule's structure and function, and varying combinations of subunits are active in entirely different cellular settings [69]. Using interactions at the 3+DB robustness level, we map the microRNAs targeting genes involved in the assembly of Class 1 PI3K (Figure 3). Immediately, it becomes evident that the possibility for PI3K subunit regulation at a post-transcriptional level is real. The network resulting from the input of all Class 1 PI3K subunit genes (PIK3CA/B/C/D, PIK3R1/2/3/4/5/6) contains five primary nodes (the other subunit genes are missing due to the lack of microRNAs targeting them in a sufficient number of databases), 181 secondary nodes and 206 interactions. Permutation analysis of randomly selected 5-node networks confirmed that this provides a significant enrichment (p<0.05) for number of nodes and interactions in the network. The most striking feature of the network is the participation of primary nodes in interactions with at minimum two other nodes – indicating that this network is significantly more connected through microRNAs than one would expect by chance alone (p<0.01).


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 assembly of PI3K subunits.Mapping PI3K subunits to microRNA interactions produces a network that is significantly more connected than at random (p<0.05). Green nodes are regulatory subunits and yellow nodes are catalytic subunits.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0017429-g003: MicroRNA interaction network for assembly of PI3K subunits.Mapping PI3K subunits to microRNA interactions produces a network that is significantly more connected than at random (p<0.05). Green nodes are regulatory subunits and yellow nodes are catalytic subunits.
Mentions: The PI3K family is divided into 3 classes. Members of each class of PI3K molecules comprise 2 subunits – a regulatory subunit and a catalytic subunit. These subunits are distinct proteins coded in different regions of the genome as either distinct genes or splice variants transcribed out of a similar locus producing translated proteins of varying sizes. The particular assembled combination of the 2 subunits of PI3K determine the molecule's structure and function, and varying combinations of subunits are active in entirely different cellular settings [69]. Using interactions at the 3+DB robustness level, we map the microRNAs targeting genes involved in the assembly of Class 1 PI3K (Figure 3). Immediately, it becomes evident that the possibility for PI3K subunit regulation at a post-transcriptional level is real. The network resulting from the input of all Class 1 PI3K subunit genes (PIK3CA/B/C/D, PIK3R1/2/3/4/5/6) contains five primary nodes (the other subunit genes are missing due to the lack of microRNAs targeting them in a sufficient number of databases), 181 secondary nodes and 206 interactions. Permutation analysis of randomly selected 5-node networks confirmed that this provides a significant enrichment (p<0.05) for number of nodes and interactions in the network. The most striking feature of the network is the participation of primary nodes in interactions with at minimum two other nodes – indicating that this network is significantly more connected through microRNAs than one would expect by chance alone (p<0.01).

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