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Positively correlated miRNA-mRNA regulatory networks in mouse frontal cortex during early stages of alcohol dependence.

Nunez YO, Truitt JM, Gorini G, Ponomareva ON, Blednov YA, Harris RA, Mayfield RD - BMC Genomics (2013)

Bottom Line: We support the viewpoint that whole mirnome-transcriptome interaction analysis is required to better understand the mechanisms and potential consequences of miRNA regulation and/or deregulation in relevant biological models.This study provides new evidence for the role of miRNA regulation in brain homeostasis and sheds new light on current understanding of the development of alcohol dependence.To our knowledge this is the first report that activated expression of miRNAs correlates with activated expression of mRNAs rather than with mRNA downregulation in an in vivo model.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, Texas, USA. dayne.mayfield@austin.utexas.edu.

ABSTRACT

Background: Although the study of gene regulation via the action of specific microRNAs (miRNAs) has experienced a boom in recent years, the analysis of genome-wide interaction networks among miRNAs and respective targeted mRNAs has lagged behind. MicroRNAs simultaneously target many transcripts and fine-tune the expression of genes through cooperative/combinatorial targeting. Therefore, they have a large regulatory potential that could widely impact development and progression of diseases, as well as contribute unpredicted collateral effects due to their natural, pathophysiological, or treatment-induced modulation. We support the viewpoint that whole mirnome-transcriptome interaction analysis is required to better understand the mechanisms and potential consequences of miRNA regulation and/or deregulation in relevant biological models. In this study, we tested the hypotheses that ethanol consumption induces changes in miRNA-mRNA interaction networks in the mouse frontal cortex and that some of the changes observed in the mouse are equivalent to changes in similar brain regions from human alcoholics.

Results: miRNA-mRNA interaction networks responding to ethanol insult were identified by differential expression analysis and weighted gene coexpression network analysis (WGCNA). Important pathways (coexpressed modular networks detected by WGCNA) and hub genes central to the neuronal response to ethanol are highlighted, as well as key miRNAs that regulate these processes and therefore represent potential therapeutic targets for treating alcohol addiction. Importantly, we discovered a conserved signature of changing miRNAs between ethanol-treated mice and human alcoholics, which provides a valuable tool for future biomarker/diagnostic studies in humans. We report positively correlated miRNA-mRNA expression networks that suggest an adaptive, targeted miRNA response due to binge ethanol drinking.

Conclusions: This study provides new evidence for the role of miRNA regulation in brain homeostasis and sheds new light on current understanding of the development of alcohol dependence. To our knowledge this is the first report that activated expression of miRNAs correlates with activated expression of mRNAs rather than with mRNA downregulation in an in vivo model. We speculate that early activation of miRNAs designed to limit the effects of alcohol-induced genes may be an essential adaptive response during disease progression.

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Alcohol-induced miRNA-mRNA interaction networks. A: Interaction network among upregulated miRNAs (red squares) and downregulated mRNAs (blue circles); average number of neighbors 2.95. B: Interaction network among upregulated miRNAs (red squares) and upregulated mRNAs (pink circles); average number of neighbors 4.97. The average number of neighbors represents the average number of links (edges) a node has to other nodes. The size of the nodes is proportional to the number of edges (interactions, represented as lines) for each node.
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Figure 3: Alcohol-induced miRNA-mRNA interaction networks. A: Interaction network among upregulated miRNAs (red squares) and downregulated mRNAs (blue circles); average number of neighbors 2.95. B: Interaction network among upregulated miRNAs (red squares) and upregulated mRNAs (pink circles); average number of neighbors 4.97. The average number of neighbors represents the average number of links (edges) a node has to other nodes. The size of the nodes is proportional to the number of edges (interactions, represented as lines) for each node.

Mentions: In order to better understand miRNA-mRNA regulatory relationships, we constructed respective interaction networks among negatively and positively correlated interaction groups, using the miRNA-mRNA interaction universe generated from miRecords predicted (consensus of at least 4 tools) and validated interactions (described in Materials and Methods). We found that the negatively correlated network of interactions among upregulated miRNAs and downregulated mRNAs (average number of neighbors: 2.95, Figure 3A) was no different from randomized networks generated as a control tool (average number of neighbors: 3.03, shown in Additional file 1: Figure S2A). On the other hand, the corresponding network containing the miRNA-mRNA interactions that were positively correlated (upregulated miRNA-upregulated mRNA network) provided unexpected evidence that this network was twice as interconnected (average number of neighbors: 4.97, Figure 3B) as expected by chance (average number of neighbors: 2.52, shown in Additional file 1: Figure S2B).


Positively correlated miRNA-mRNA regulatory networks in mouse frontal cortex during early stages of alcohol dependence.

Nunez YO, Truitt JM, Gorini G, Ponomareva ON, Blednov YA, Harris RA, Mayfield RD - BMC Genomics (2013)

Alcohol-induced miRNA-mRNA interaction networks. A: Interaction network among upregulated miRNAs (red squares) and downregulated mRNAs (blue circles); average number of neighbors 2.95. B: Interaction network among upregulated miRNAs (red squares) and upregulated mRNAs (pink circles); average number of neighbors 4.97. The average number of neighbors represents the average number of links (edges) a node has to other nodes. The size of the nodes is proportional to the number of edges (interactions, represented as lines) for each node.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3924350&req=5

Figure 3: Alcohol-induced miRNA-mRNA interaction networks. A: Interaction network among upregulated miRNAs (red squares) and downregulated mRNAs (blue circles); average number of neighbors 2.95. B: Interaction network among upregulated miRNAs (red squares) and upregulated mRNAs (pink circles); average number of neighbors 4.97. The average number of neighbors represents the average number of links (edges) a node has to other nodes. The size of the nodes is proportional to the number of edges (interactions, represented as lines) for each node.
Mentions: In order to better understand miRNA-mRNA regulatory relationships, we constructed respective interaction networks among negatively and positively correlated interaction groups, using the miRNA-mRNA interaction universe generated from miRecords predicted (consensus of at least 4 tools) and validated interactions (described in Materials and Methods). We found that the negatively correlated network of interactions among upregulated miRNAs and downregulated mRNAs (average number of neighbors: 2.95, Figure 3A) was no different from randomized networks generated as a control tool (average number of neighbors: 3.03, shown in Additional file 1: Figure S2A). On the other hand, the corresponding network containing the miRNA-mRNA interactions that were positively correlated (upregulated miRNA-upregulated mRNA network) provided unexpected evidence that this network was twice as interconnected (average number of neighbors: 4.97, Figure 3B) as expected by chance (average number of neighbors: 2.52, shown in Additional file 1: Figure S2B).

Bottom Line: We support the viewpoint that whole mirnome-transcriptome interaction analysis is required to better understand the mechanisms and potential consequences of miRNA regulation and/or deregulation in relevant biological models.This study provides new evidence for the role of miRNA regulation in brain homeostasis and sheds new light on current understanding of the development of alcohol dependence.To our knowledge this is the first report that activated expression of miRNAs correlates with activated expression of mRNAs rather than with mRNA downregulation in an in vivo model.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, Texas, USA. dayne.mayfield@austin.utexas.edu.

ABSTRACT

Background: Although the study of gene regulation via the action of specific microRNAs (miRNAs) has experienced a boom in recent years, the analysis of genome-wide interaction networks among miRNAs and respective targeted mRNAs has lagged behind. MicroRNAs simultaneously target many transcripts and fine-tune the expression of genes through cooperative/combinatorial targeting. Therefore, they have a large regulatory potential that could widely impact development and progression of diseases, as well as contribute unpredicted collateral effects due to their natural, pathophysiological, or treatment-induced modulation. We support the viewpoint that whole mirnome-transcriptome interaction analysis is required to better understand the mechanisms and potential consequences of miRNA regulation and/or deregulation in relevant biological models. In this study, we tested the hypotheses that ethanol consumption induces changes in miRNA-mRNA interaction networks in the mouse frontal cortex and that some of the changes observed in the mouse are equivalent to changes in similar brain regions from human alcoholics.

Results: miRNA-mRNA interaction networks responding to ethanol insult were identified by differential expression analysis and weighted gene coexpression network analysis (WGCNA). Important pathways (coexpressed modular networks detected by WGCNA) and hub genes central to the neuronal response to ethanol are highlighted, as well as key miRNAs that regulate these processes and therefore represent potential therapeutic targets for treating alcohol addiction. Importantly, we discovered a conserved signature of changing miRNAs between ethanol-treated mice and human alcoholics, which provides a valuable tool for future biomarker/diagnostic studies in humans. We report positively correlated miRNA-mRNA expression networks that suggest an adaptive, targeted miRNA response due to binge ethanol drinking.

Conclusions: This study provides new evidence for the role of miRNA regulation in brain homeostasis and sheds new light on current understanding of the development of alcohol dependence. To our knowledge this is the first report that activated expression of miRNAs correlates with activated expression of mRNAs rather than with mRNA downregulation in an in vivo model. We speculate that early activation of miRNAs designed to limit the effects of alcohol-induced genes may be an essential adaptive response during disease progression.

Show MeSH
Related in: MedlinePlus