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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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Correlations of module eigengenes vs. consumption and top 20 differentially expressed miRNA traits. Significant miRNA targeting is evident primarily against red and turquoise modules. Brown and yellow modules show significant correlations to a lower but relevant number of differentially expressed miRNAs.
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Figure 6: Correlations of module eigengenes vs. consumption and top 20 differentially expressed miRNA traits. Significant miRNA targeting is evident primarily against red and turquoise modules. Brown and yellow modules show significant correlations to a lower but relevant number of differentially expressed miRNAs.

Mentions: Five modules (yellow, red, turquoise, pink, and brown) presented significant average correlations with the ethanol-related traits (Table 2, P < 0.01; Figure 5A and 5B; P < 0.05), thus warranting further investigation. The alcohol-responsive modules also presented highly significant correlations between module membership (MM) and gene significance (GS) to the ethanol-consumption trait (Figure 5C), which strengthen the inference of biological functions of the alcohol-significant genes by analyzing ontology category enrichment in the respective modules. Moreover, the five modules significantly correlated with ethanol drinking were significantly enriched with differentially expressed genes (Table 2). We also detected significant correlations (FDR < 0.1) between individual gene expression patterns and individual differentially expressed miRNA expression patterns (Figure 4, Additional file 1: Table S8), which provide indirect experimental validation for a group of predicted miRNA-mRNA interactions. By correlating the module eigengenes with the differentially expressed miRNA expression patterns (Figure 6) and considering the individual significant correlations shown in Figure 4, three modules (red and brown –positively correlated with ethanol drinking, and turquoise –negatively correlated with ethanol drinking), were found to be preferentially targeted by the differentially expressed miRNAs. To assess the validity of this approach, we corroborated the positively correlated expression levels of interacting miRNAs and mRNAs (i.e., mmu-let-7 vs. Syt11 and mmu-let-7 vs. Tom1) by qPCR. Additional confirmation for WGCNA-suggested interactions was obtained from TarBase, the database for validated interactions, where we found validated interactions between miRNAs and mRNAs that were positively correlated in our study but negatively correlated in previous publications: mmu-miR-34a-5p --/ ACTB, mmu-miR-200b-3p --/ ZEB2, mmu-miR-30a-5p --/ TNRC6A, mmu-miR-152-3p --/ CAMK2A, mmu-miR-200c-3p --/ FLT1, mmu-miR-20a-5p --/ ZBTB7A. Also, we found evidence in TarBase for validated interactions between miRNAs and mRNAs that were negatively correlated in our study as well as in previous publications: mmu-miR-29b-5p --/ COL4A2 and mmu-miR-30a-5p5p --/ AK1. Apparent spurious correlation was observed among some differentially expressed miRNAs and the black module. However, this module did not show significant correlation with the alcohol consumption phenotype. Hence the correlations detected among the black module and differentially expressed miRNAs are understood as pleiotropic interactions, akin to off-target effects (in this case referring to the miRNA targeting of non-alcohol-relevant transcripts).


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)

Correlations of module eigengenes vs. consumption and top 20 differentially expressed miRNA traits. Significant miRNA targeting is evident primarily against red and turquoise modules. Brown and yellow modules show significant correlations to a lower but relevant number of differentially expressed miRNAs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Correlations of module eigengenes vs. consumption and top 20 differentially expressed miRNA traits. Significant miRNA targeting is evident primarily against red and turquoise modules. Brown and yellow modules show significant correlations to a lower but relevant number of differentially expressed miRNAs.
Mentions: Five modules (yellow, red, turquoise, pink, and brown) presented significant average correlations with the ethanol-related traits (Table 2, P < 0.01; Figure 5A and 5B; P < 0.05), thus warranting further investigation. The alcohol-responsive modules also presented highly significant correlations between module membership (MM) and gene significance (GS) to the ethanol-consumption trait (Figure 5C), which strengthen the inference of biological functions of the alcohol-significant genes by analyzing ontology category enrichment in the respective modules. Moreover, the five modules significantly correlated with ethanol drinking were significantly enriched with differentially expressed genes (Table 2). We also detected significant correlations (FDR < 0.1) between individual gene expression patterns and individual differentially expressed miRNA expression patterns (Figure 4, Additional file 1: Table S8), which provide indirect experimental validation for a group of predicted miRNA-mRNA interactions. By correlating the module eigengenes with the differentially expressed miRNA expression patterns (Figure 6) and considering the individual significant correlations shown in Figure 4, three modules (red and brown –positively correlated with ethanol drinking, and turquoise –negatively correlated with ethanol drinking), were found to be preferentially targeted by the differentially expressed miRNAs. To assess the validity of this approach, we corroborated the positively correlated expression levels of interacting miRNAs and mRNAs (i.e., mmu-let-7 vs. Syt11 and mmu-let-7 vs. Tom1) by qPCR. Additional confirmation for WGCNA-suggested interactions was obtained from TarBase, the database for validated interactions, where we found validated interactions between miRNAs and mRNAs that were positively correlated in our study but negatively correlated in previous publications: mmu-miR-34a-5p --/ ACTB, mmu-miR-200b-3p --/ ZEB2, mmu-miR-30a-5p --/ TNRC6A, mmu-miR-152-3p --/ CAMK2A, mmu-miR-200c-3p --/ FLT1, mmu-miR-20a-5p --/ ZBTB7A. Also, we found evidence in TarBase for validated interactions between miRNAs and mRNAs that were negatively correlated in our study as well as in previous publications: mmu-miR-29b-5p --/ COL4A2 and mmu-miR-30a-5p5p --/ AK1. Apparent spurious correlation was observed among some differentially expressed miRNAs and the black module. However, this module did not show significant correlation with the alcohol consumption phenotype. Hence the correlations detected among the black module and differentially expressed miRNAs are understood as pleiotropic interactions, akin to off-target effects (in this case referring to the miRNA targeting of non-alcohol-relevant transcripts).

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