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Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.

Radfar MH, Wong W, Morris Q - PLoS ONE (2011)

Bottom Line: Host genes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3' UTRs and are significantly more likely to have predicted Pol II and Pol III promoters in their introns.We provide a dataset of 1,935 predicted mRNA targets for 22 intronic miRNAs.These prediction are supported both by sequence features and expression.By combining our results with previous reports, we distinguish three classes of intronic miRNAs: Those that are tightly regulated with their host gene; those that are likely to be expressed from the same promoter but whose host gene is highly regulated by miRNAs; and those likely to have independent promoters.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada. h.radfar@utoronto.ca

ABSTRACT
Approximately half of known human miRNAs are located in the introns of protein coding genes. Some of these intronic miRNAs are only expressed when their host gene is and, as such, their steady state expression levels are highly correlated with those of the host gene's mRNA. Recently host gene expression levels have been used to predict the targets of intronic miRNAs by identifying other mRNAs that they have consistent negative correlation with. This is a potentially powerful approach because it allows a large number of expression profiling studies to be used but needs refinement because mRNAs can be targeted by multiple miRNAs and not all intronic miRNAs are co-expressed with their host genes.Here we introduce InMiR, a new computational method that uses a linear-Gaussian model to predict the targets of intronic miRNAs based on the expression profiles of their host genes across a large number of datasets. Our method recovers nearly twice as many true positives at the same fixed false positive rate as a comparable method that only considers correlations. Through an analysis of 140 Affymetrix datasets from Gene Expression Omnibus, we build a network of 19,926 interactions among 57 intronic miRNAs and 3,864 targets. InMiR can also predict which host genes have expression profiles that are good surrogates for those of their intronic miRNAs. Host genes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3' UTRs and are significantly more likely to have predicted Pol II and Pol III promoters in their introns.We provide a dataset of 1,935 predicted mRNA targets for 22 intronic miRNAs. These prediction are supported both by sequence features and expression. By combining our results with previous reports, we distinguish three classes of intronic miRNAs: Those that are tightly regulated with their host gene; those that are likely to be expressed from the same promoter but whose host gene is highly regulated by miRNAs; and those likely to have independent promoters.

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The simplified DAG.The simplified DAG of Fig. 1 in which host genes have a direct interaction with the target.
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pone-0019312-g002: The simplified DAG.The simplified DAG of Fig. 1 in which host genes have a direct interaction with the target.

Mentions: Our model uses host gene expression as a surrogate for the expression level(s) of its intronic miRNAs. This requires us to resolve some of the host gene / intronic miRNA relationships that are not one-to-one, because some host genes contain multiple intronic miRNAs and some intronic miRNAs are duplicated in more than one host gene. Fig. 1 shows a directed acyclic graph (DAG) representing these relationship for eight intronic miRNAs that are possible regulators for the expression of gene LSM12 whose protein product accumulates in stress granules [70]. This DAG can be interpreted as a graphical model in which the expression patterns of intronic miRNAs are hidden. Because our goal is not only to predict miRNA targets but also to determine which host genes are good surrogates for their intronic miRNAs, we assign weights directly to host genes rather than miRNAs. So, the host genes of duplicated miRNAs get separate weights. Also, when a host gene contains more than one intronic miRNA with putative targets in a given mRNA, we assign this host gene weight to each of these miRNAs. The host gene / target mRNA model that we fit for LSM12 after making these adjustments is shown in Fig. 2.


Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms.

Radfar MH, Wong W, Morris Q - PLoS ONE (2011)

The simplified DAG.The simplified DAG of Fig. 1 in which host genes have a direct interaction with the target.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3111417&req=5

pone-0019312-g002: The simplified DAG.The simplified DAG of Fig. 1 in which host genes have a direct interaction with the target.
Mentions: Our model uses host gene expression as a surrogate for the expression level(s) of its intronic miRNAs. This requires us to resolve some of the host gene / intronic miRNA relationships that are not one-to-one, because some host genes contain multiple intronic miRNAs and some intronic miRNAs are duplicated in more than one host gene. Fig. 1 shows a directed acyclic graph (DAG) representing these relationship for eight intronic miRNAs that are possible regulators for the expression of gene LSM12 whose protein product accumulates in stress granules [70]. This DAG can be interpreted as a graphical model in which the expression patterns of intronic miRNAs are hidden. Because our goal is not only to predict miRNA targets but also to determine which host genes are good surrogates for their intronic miRNAs, we assign weights directly to host genes rather than miRNAs. So, the host genes of duplicated miRNAs get separate weights. Also, when a host gene contains more than one intronic miRNA with putative targets in a given mRNA, we assign this host gene weight to each of these miRNAs. The host gene / target mRNA model that we fit for LSM12 after making these adjustments is shown in Fig. 2.

Bottom Line: Host genes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3' UTRs and are significantly more likely to have predicted Pol II and Pol III promoters in their introns.We provide a dataset of 1,935 predicted mRNA targets for 22 intronic miRNAs.These prediction are supported both by sequence features and expression.By combining our results with previous reports, we distinguish three classes of intronic miRNAs: Those that are tightly regulated with their host gene; those that are likely to be expressed from the same promoter but whose host gene is highly regulated by miRNAs; and those likely to have independent promoters.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada. h.radfar@utoronto.ca

ABSTRACT
Approximately half of known human miRNAs are located in the introns of protein coding genes. Some of these intronic miRNAs are only expressed when their host gene is and, as such, their steady state expression levels are highly correlated with those of the host gene's mRNA. Recently host gene expression levels have been used to predict the targets of intronic miRNAs by identifying other mRNAs that they have consistent negative correlation with. This is a potentially powerful approach because it allows a large number of expression profiling studies to be used but needs refinement because mRNAs can be targeted by multiple miRNAs and not all intronic miRNAs are co-expressed with their host genes.Here we introduce InMiR, a new computational method that uses a linear-Gaussian model to predict the targets of intronic miRNAs based on the expression profiles of their host genes across a large number of datasets. Our method recovers nearly twice as many true positives at the same fixed false positive rate as a comparable method that only considers correlations. Through an analysis of 140 Affymetrix datasets from Gene Expression Omnibus, we build a network of 19,926 interactions among 57 intronic miRNAs and 3,864 targets. InMiR can also predict which host genes have expression profiles that are good surrogates for those of their intronic miRNAs. Host genes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3' UTRs and are significantly more likely to have predicted Pol II and Pol III promoters in their introns.We provide a dataset of 1,935 predicted mRNA targets for 22 intronic miRNAs. These prediction are supported both by sequence features and expression. By combining our results with previous reports, we distinguish three classes of intronic miRNAs: Those that are tightly regulated with their host gene; those that are likely to be expressed from the same promoter but whose host gene is highly regulated by miRNAs; and those likely to have independent promoters.

Show MeSH