Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2012 Nov 1.
Published in final edited form as: Alcohol Clin Exp Res. 2011 Jun 8;35(11):1928–1937. doi: 10.1111/j.1530-0277.2011.01544.x

Up-regulation of MicroRNAs in Brain of Human Alcoholics

Joanne M Lewohl **, Yury O Nunez *, Peter R Dodd ***, Gayatri R Tiwari *, R Adron Harris *, R Dayne Mayfield *
PMCID: PMC3170679  NIHMSID: NIHMS292033  PMID: 21651580

Abstract

Background

MicroRNAs (miRNAs) are small, non-coding oligonucleotides with an important role in post transcriptional regulation of gene expression at the level of translation and mRNA degradation. Recent studies have revealed that miRNAs play important roles in a variety of biological processes, such as cell proliferation, neuronal differentiation, developmental timing, synapse function and neurogenesis. A single miRNA can target hundreds of mRNA transcripts for either translation repression or degradation, but the function of many human miRNAs is not known.

Methods

MiRNA array analysis was performed on the prefrontal cortex of 27 individual human cases (14 alcoholics and 13 matched controls). Target genes for differentially expressed miRNAs were predicted using multiple target prediction algorithms and a consensus approach, and predicted targets were matched against differentially expressed mRNAs from the same samples. Over- and under-representation analysis was performed using hypergeometric probability and z-score tests.

Results

Approximately 35 miRNAs were significantly up-regulatedin the alcoholic group compared with controls. Target prediction showed a large degree of overlap with our published cDNA microarray data. Functional classification of the predicted target genes of the regulated miRNAs includes apoptosis, cell cycle, cell adhesion, nervous system development and cell-cell signaling.

Conclusions

This data suggests that the reduced expression of genes in human alcoholic cases may be due to the up-regulated miRNAs. Cellular processes fundamental to neuronal plasticity appear to represent major targets of the suggested miRNA regulation.

Keywords: Alcohol, Brain, Gene Expression, miRNA, Microarray

Introduction

In the brain, the acute effects of alcohol are mediated by direct interaction with neurotransmitter and signaling systems resulting in changes inthe number and type of receptors, and in the amount of neurotransmitter released into the synapse. In contrast, chronic alcohol abuse, results in persistent changes in brain function which are mediated, at least in part, by changes in gene expression (Nestler and Aghajanian, 1997).

Gene and protein expression profiling techniques have been used to identify alcohol-induced changes in gene expression in both cultured cells and animal models of alcoholism (reviewed in (Mayfield et al., 2008)). Alcohol alters the expression of genes involved in a wide variety of cellular functions including catecholamine metabolism, signal transduction cascades, protein trafficking, and oxidative stress responses. These studies suggest that specific patterns of gene expression may underlie alcohol-related phenotypes. In previous studies, we and others have used cDNA and oligonucleotide microarrays to identify genes with altered expression following long-term alcohol consumption(Lewohl et al., 2000; Liu et al., 2004; Liu et al., 2006; Mayfield et al., 2002) as well as the effects of concomitant diseases such as liver cirrhosis (Liu et al., 2007). These studies identified genes that likely underlie the adaptive response of neurons in the prefrontal cortex, a region of the brain that is particularly susceptible to the effects of long-term alcohol abuse and include genes involved in myelination, ubiquitination, apoptosis, cell adhesion, neurogenesis, and neural disease. Genes involved in neurodegenerative diseases such as Alzheimer disease were also significantly altered, suggesting a link between alcoholism and other neurodegenerative conditions. The mechanism by which chronic alcohol abuse results in these changes in gene expression remains unknown.

MicroRNAs (miRNAs) are small, non-coding RNAs that are thought to act as post-transcriptional modulators of gene expression, binding to miRNA Recognition Elements (MREs) in the 3′UTR of their target genes resulting in either the suppression of translation or degradation of mRNA transcripts or both (Filipowicz et al., 2008). However, recent reports have shown that MREs may also be found in the upstream elements (5′UTR) of a target mRNA (Lee et al., 2008; Lytle et al., 2007; Orom and Lund, 2010). Generally, miRNAs function as endogenous repressors of target mRNAexpression and/or translation although there are also instances where miRNAs have been shown to enhance mRNA translation (Manakov et al., 2009; Nielsen et al., 2009; Orom and Lund, 2010). The compounding effect of the multiple regulatory programs ultimately determines the protein profile of the cell.

Recent studies have revealed that miRNAs are highly abundant in brain and play important roles in a variety of biological processes such as neuronal differentiation (Cheng et al., 2009), brain development (Siegel et al., 2009), synapse formation and plasticity (Schratt et al., 2006), neuronal survival(Schaefer et al., 2007), and neurodegenerative diseases(Bushati and Cohen, 2008). MiRNAs may also mediate the cellular adaptations that occur in response to drug abuse and addiction. Specifically, changes in miRNA expression occur as a result of exposure to a number of drugs of abuse, including nicotine (Huang and Li, 2009; Shan et al., 2009), cocaine (Chandrasekar and Dreyer, 2009), morphine (Wu et al., 2008; Wu et al., 2009), and alcohol (Pietrzykowski et al., 2008; Sathyan et al., 2007; Tang et al., 2008).

Pietrzykowski et al (Pietrzykowski et al., 2008)showed that alcohol, acting via a specific miRNA, miR-9, regulates the expression of alternatively spliced mRNAs encoding the large-conductance calcium- and voltage-activated potassium channel (BK), which is a known target of alcohol’s actions in mediating molecular alcohol tolerance. Alcohol caused a rapid up-regulation in miR-9 expression, resulting in selective degradation of BK mRNAs containing a miR-9 target site in their 3′UTRs. The selective degradation of some splice variants but not others altered the profile of BK channels, consistent with the development of tolerance to alcohol (Pietrzykowski et al., 2008). This represents a new mechanism of gene regulation of splice variants that may underlie the neuroadaptive changes that occur at a cellular level to the long-term alcohol exposure.

In the present study, we used miRNA arrays to identify miRNAs with altered expression in the frontal cortex of 14 alcoholics and 13 age- and sex-matched controls that were used in our microarray studies (Liu et al., 2006).Since miRNAs are known to act in part as modulators of gene expression, we correlated mRNA and miRNA profiles across the entire genome to determine whether differentially expressed miRNAs could be influencing the mRNA expression changes observed in the frontal cortex of alcoholics. The predicted mRNA targets for each differentially expressed miRNA were determined and compared with the list of differentially expressed mRNAs reported in Liu et al (Liu et al., 2006)from the same samples.

Materials and Methods

Case selection

Tissues were received from the Queensland Brain Bank, School of Molecular & Microbial Sciences, The University of Queensland in collaboration with the Australian Brain Bank Network which is supported by the National Health and Medical Research Council (NHMRC) as well as the Australian Brain Donor Programs NSW Tissue Resource Centre which is supported by The University of Sydney, NHMRC, Schizophrenia Research Institute, National Institute of Alcoholism and Alcohol Abuse and New South Wales Department of Health. The brain samples were collected by qualified pathologists under full ethical clearance and informed written consent from the next of kin. This study was approved by the Institutional Review Board (IRB) of the University of Texas at Austin.

A total of 27 cases including 14 alcoholic and 13 control cases were selected for analysis. The cases were identical to those used in previous microarray studies (Liu et al., 2006). Alcoholics and controls were selected on the basis of alcohol intake. Controls were defined as individuals who consumed less than 20 g of ethanol per day on average. Alcoholics were defined by NHMRC/WHO criteria as individuals who consumed more than 80 g of ethanol per day for most of their adult lives. Many of the alcoholics used in this study had consumed over 200 g of ethanol per day and had been drinking for over 20 years. Alcoholics with a history of poly-drug abuse or other complicating conditions such as cirrhosis of the liver, Wernicke Korsakoff syndrome or Hepatic Encephalopathy were excluded.

MiRNA extraction and hybridization

MiRNAs were extracted using the mirVana™ miRNA Isolation Kit (Ambion, Austin TX, USA) according to the manufacturer’s instructions. Yield and quality of the miRNA-enriched fraction was determined using the Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA).

Microarray printing and hybridization were performed at the NIH-Duke University microarray facility. miRNA samples from 13 controls and 14 alcoholics were labeled with the mirVana miRNA Labeling Kit (Ambion) according to the manufacturer’s protocol. Expression profiling was performed using the miRNA-AI (Ambion mirVana set2 + Invitrogen NCode multispecies miRNA microarray V2), which contains probes targeting all of the known mature miRNAs in mirBase 9.0 for human, mouse, rat, Drosophila, C.elegans, and zebrafish. Images were analyzed using Axon GenePix 6.0.

MiRNA microarray data analysis

MiRNA array data analysis. Data preprocessing included normexp background correction (Ritchie et al., 2007) and print-tip loess normalization(Smyth and Speed, 2003). Statistical differences between groups was performed using the Bioconductor package limma (Smyth et al., 2005) using an empirical Bayes approach. Since each probe was spotted in duplicate on the arrays, within-array replication was assessed using the limmaduplicate Correlation function (Smyth et al., 2005). False discovery rate was assessed using the method of Benjamini and Hochberg (Benjamini and Hochberg, 1995). The raw data has been deposited in MIAME compliant Longhorn Array Database (LAD) (Killion et al., 2003) with the access number in progress.

Validation by real time RT-PCR analysis

Single-stranded cDNA was synthesized from total RNA using the TaqMan® MicroRNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Following reverse transcription, quantitative RT-PCR (qRT-PCR) was performed in duplicate, using TaqMan® MicroRNA Assays (Applied Biosystems) following manufacturer’s instructions. Assays used were: hsa-miR-7 (P/N: 4427975, ID: 000268), hsa-let-7g (P/N: 4427975, ID: 002282), hsa-miR-152 (P/N: 4427975, ID: 000475), hsa-miR-15b (P/N: 4427975, ID: 000390), hsa-miR-301a (P/N: 4427975, ID: 000528), hsa-miR-369-3p (P/N: 4427975, ID: 000557), RNU44 (P/N: 4427975, ID: 001094), and RNU6B (P/N: 4427975, ID: 001093). qRT-PCR reactions were carried out in a 7900HT Fast Real-Time PCR System and data collected using SDS software (Applied Biosystems). qRT-PCR results (absolute Ct data) were imported into GenExsoftware (MultiD Analyses AB, Gothenburg, Sweden). Data assembled from multiple experimental plates was subjected to normalization to common reference samples, autoscaling, and normalization to the average of two endogenous control genes (SNORD44 and U6BsnRNA). Kolomogorov-Smirnov test was performed to assess normality of the data and t-test to assess statistical significance.

MiRNA target prediction

Gene targets of the differentially expressed miRNAs were predicted using the Predicted Targets component of miRecords ((Xiao et al., 2009), http://miRecords.biolead.org), which integrates the predicted targets produced by 11 established miRNA target prediction programs (DIANA-microT, Micro Inspector, miRanda, MirTarget2, miTarget, NBmiRTar, PicTar, PITA, RNA22, RNAhybrid, and TargetScan/TargertScanS). Predictions were filtered to only consider those targets predicted by at least 4 out of the 11 prediction algorithms.

Integrative analysis of miRNA and mRNA microarray data

Hypergeometric tests for multiple experimental proportions extracted from the integrated dataset containing information about differentially expressed miRNAs, differentially expressed mRNAs, and putative miRNA targets were implemented in the R environment (R Development Core Team (2010); R: A language and environment for statistical computing; R Foundation for Statistical Computing, Vienna, Austria. Available from: http://www.R-project.org). Hypergeometric tests for each differentially expressed miRNA were conducted to determine whether corresponding putative targets were over- or under-represented among inversely correlated differentially expressed mRNAs from the same samples. Hypergeometric tests for each differentially expressed mRNA that is predicted to be targeted by more than one miRNA were conducted to determine whether any of such mRNAs could be subjected to “over-targeting” by miRNAs.

To verify the results from the hypergeometric tests on the experimental data, we generated 1000 randomized samples of non-modulated miRNAs and 1000 randomized samples of non-modulated mRNAs. The size of the samples were fixed and determined by the number of corresponding modulated miRNAs and mRNAs in the experimental dataset. These randomized samples were paired into a 1000 sets of randomized miRNAs and mRNAs and iteratively subjected to the same statistical analysis workflow as the experimental dataset. These represent a set of 1000 randomized trials, which represent our randomized control group. Proportions of interest (proportion of putative targets among randomly selected mRNAs; proportion of randomly selected mRNAs being targeted by more than 1 randomly selected miRNAs; proportion of randomly selected miRNAs that have an over-represented number of randomly selected mRNA targets; and proportion of randomly selected mRNAs that have an over-represented numbers of randomly selected miRNAs targeting them) were recorded for each trial, summarized after completion of 1000 trials, and statistically compared to corresponding experimental proportions using the z-score test (z-test), also implemented in the R environment. The download of miRecords’ predicted target information for every human miRNA accessible through the website was done using the RCurl package implemented in R.

Gene list functional enrichment analysis

Lists of statistically significant miRNAs and mRNAs were subjected to functional enrichment analysis using GO categories and KEGG pathways in DAVID (Dennis et al., 2003) and ToppFun(Chen et al., 2009).

Results

MiRNA up-regulation in the frontal cortex of alcoholics

MiRNA microarrays were used to measure miRNA expression profiles in the frontalcortex of control and well-characterized alcoholic cases. The cases used in the present study are the same as those used in previous cDNA microarray studies (Liu et al., 2006) allowing direct comparison between mRNA expression profiles and those of the predicted target mRNAs.

Approximately48 miRNAs were up-regulatedin the frontal cortex of alcoholics (p < 0.005, FDR < 15%) with a fold change of between 16% and 72% (mean = 27% ± 11%). Interestingly, miRNA down-regulation was not observed at this level of significance. The miRNA microarray contains probes targeting miRNAs for human, mouse, rat, Drosophila, C.elegans, and zebrafish; and, several miRNAs that met our significance cutoff for differential expression were from species other than human. For each of these miRNAs, the sequence of the probe representing the miRNA on the array was compared with all known miRNA sequences in miRBase using BLAST to identify the human equivalent. Where there was a greater than 90% homology in miRNA sequence across species, the human homologs for the miRNAs were used for target prediction. Six of the differentially expressed miRNAs had no homology to any known human miRNA and were not included in subsequent analyses.

The miRNA microarray also included a set of probes corresponding to unpublished miRNA sequences from Ambion. Seven of these miRNAs were differentially expressed in the frontal cortex of alcoholics (ambi_miR_12897, ambi_miR_9451,ambi_miR_8488,ambi_miR_9125,ambi_miR_7058, ambi_miR_8518, and ambi_miR_13196). The sequences corresponding to the probes on the microarrayfor each of these miRNAs were compared with the sequences of the known miRNAs registered in miRBase using the SSEARCH algorithm (http://www.ebi.ac.uk). Only one, ambi_miR_7058, showed homology to a known miRNA. This miRNA sequence exhibited a 100% match to the hsa-miR-423-5p sequence. The predicted targets for this miRNA were included in subsequent analyses. Thus, a total of 35 differentially expressed miRNAs were used for target predictions (Table 1).

Table 1.

Differentially expressed miRNAs used for target prediction.

miRNA % Change P.Value Adj. P.Value
miR-553 25 0.00009 0.0425
miR-369-3p 29 0.00014 0.0425
miR-18a 24 0.00014 0.0425
miR-339-5p 29 0.00015 0.0425
miR-1 45 0.00018 0.0440
miR-7 24 0.00023 0.0440
miR-196a 28 0.00026 0.0440
miR-301a 25 0.00031 0.0453
miR-144 24 0.00036 0.0453
let-7g 23 0.00043 0.0453
miR-153 26 0.00044 0.0453
let-7f 27 0.00050 0.0490
miR-203 21 0.00054 0.0500
miR-34c-5p 23 0.00068 0.0554
miR-101 25 0.00080 0.0554
miR-376c 27 0.00080 0.0554
miR-665 26 0.00084 0.0554
miR-152 33 0.00085 0.0554
miR-194 21 0.00098 0.0597
miR-423-5p 27 0.00122 0.0715
miR-515-3p 31 0.00135 0.0744
miR-374b 26 0.00149 0.0794
miR-140 72 0.00223 0.1065
miR-519b-3p 17 0.00256 0.1152
miR-586 20 0.00282 0.1162
miR_135b 25 0.00284 0.1162
miR-92a 51 0.00333 0.1283
miR-15b 18 0.00338 0.1283
miR-580 23 0.00350 0.1283
miR-146a 19 0.00391 0.1283
miR-454-3p 16 0.00398 0.1283
miR-380 26 0.00402 0.1283
miR-652 16 0.00404 0.1283
miR-802 18 0.00409 0.1283
miR-196b 20 0.00481 0.1432
Open in a new tab

Predicted miRNA targets are over-represented among down-regulated mRNAs

Genome-wide prediction algorithms can provide a variable picture of miRNA behavior, although it is difficult to assess which method is best for identifying miRNA targets. We relied on a consensus-based approach to increase our prediction confidence. For the 35up-regulated miRNAs, a total of 8,200targets were predicted by a consensus of any 4 of the 11 prediction tools provided by miRecords (see Materials and Methods).

Global expression profiling identified 531 differentially expressed mRNAs in the frontal cortex of alcoholics. Annotations are currently available for 342; 217 were down-regulated and 125 were up-regulated (Liu et al., 2006). In general, the expression levels of miRNAs and their mRNA targets are inversely correlated (Guo et al., 2010), with up-regulation of miRNAs correlated with down-regulation of their respective targets. Because all of the differentially expressed miRNAs in our dataset were up-regulated, we first considered only those mRNAs which were down-regulated in our microarray study and conducted over-representation analysis of predicted miRNA target sites in our set of down-regulated mRNAs. Predicted miRNA targets were significantly over-represented in our set of down-regulated mRNAs (experimental proportion 131/217 vs. mean randomized trial proportion 95/217, z-test P = 0.00059). There was no such over-representation amongst the set of up-regulated mRNAs. This finding supports a role for miRNA-regulation of these genes that is consistent with the current understanding that miRNA expression levels are generally inversely correlated with the expression levels of their respective mRNA targets (Guo et al., 2010).

Down-regulated mRNAs may be combinatorially regulated by multiple miRNAs

We used our list of consensus predictions to determine the proportion of down-regulated mRNAs potentially targeted by more than one of the up-regulated miRNAs. Of the 131 down-regulated mRNAs, 102 (78%) were predicted to have miRNA recognition sequences for more than one miRNA. This proportion is significantly higher than expected by chance (hypergeometric P = 2.7×10−5). Analysis of the corresponding proportion on the randomized control group (54/95, 57%) additionally evidenced the significant over-representation of putative targets regulated by multiple miRNAs (z-test P = 0.00068). There was no such over-representation amongst the set of up-regulated mRNAs. This suggests that post-transcriptional mRNA processing in the frontal cortex of alcoholics may be the result of the combined effects of multiple miRNAs acting on the same mRNA.

Further analysis showed that the predicted targets of 27 up-regulated miRNAs were over-represented in the group of down-regulated mRNAs (hypergeometric p< 0.05, FDR < 5%, Table 2). This was also corroborated by the analysis on the randomized control group (z-test P = 0.00099), where only 14 out of 35 randomly selected miRNAs display the equivalent behaviour. Remarkably, this core group of 27 miRNAs is predicted to regulate 128 out of the 131 (98%) down-regulated mRNAs that are putative miRNA-targets. This underscores the central role played by these 27 miRNAs in regulating gene expression in the frontal cortex in response to chronic alcohol consumption.

Table 2.

miRNAs whose targets are over-represented among down-regulated mRNAs.

Up-regulated miRNAs Down-regulated Targets % All Predicted Targets % Hyper-geometric P. Value Adjusted P. Value
hsa-miR-203 49 37.4 1759 15.1 8.3E-11 2.9E-09
hsa-miR-454 22 16.8 428 3.7 3.7E-10 6.4E-09
hsa-miR-586 37 28.2 1185 10.2 1.4E-09 1.6E-08
hsa-miR-301a 23 17.6 542 4.7 7.0E-09 6.1E-08
hsa-miR-144 22 16.8 547 4.7 3.9E-08 2.8E-07
hsa-miR-152 17 13.0 455 3.9 3.3E-06 1.9E-05
hsa-miR-380 31 23.7 1248 10.7 5.9E-06 2.9E-05
hsa-miR-374b 10 7.6 204 1.8 1.9E-05 8.2E-05
hsa-miR-101 16 12.2 513 4.4 6.2E-05 0.00024
hsa-miR-519b-3p 24 18.3 1003 8.6 0.00012 0.00042
hsa-miR-18a 8 6.1 193 1.7 0.00033 0.00100
hsa-miR-196a 8 6.1 194 1.7 0.00034 0.00100
hsa-miR-802 22 16.8 962 8.3 0.00043 0.00116
hsa-miR-1 13 9.9 453 3.9 0.00058 0.00145
hsa-miR-7 11 8.4 355 3.1 0.00066 0.00155
hsa-miR-196b 8 6.1 222 1.9 0.00091 0.00198
hsa-miR-15b 18 13.7 779 6.7 0.00114 0.00234
hsa-miR-153 10 7.6 375 3.2 0.00340 0.00661
hsa-miR-580 18 13.7 869 7.5 0.00393 0.00723
hsa-miR-665 7 5.3 228 2.0 0.00425 0.00744
hsa-miR-376c 12 9.2 544 4.7 0.00833 0.01388
hsa-let-7g 12 9.2 551 4.7 0.00922 0.01467
hsa-miR-194 8 6.1 352 3.0 0.01805 0.02746
hsa-let-7f 11 8.4 547 4.7 0.02052 0.02984
hsa-miR-135b 11 8.4 550 4.7 0.02132 0.02984
hsa-miR-92a 10 7.6 491 4.2 0.02279 0.03069
hsa-miR-369-3p 7 5.3 329 2.8 0.03259 0.04225
Open in a new tab

There is increasing evidence to suggest that multiple miRNAs can work together either cooperatively or competitively to alter the expression of their mRNA targets (Grimson et al., 2007; Hua et al., 2006; Nachmani et al., 2010; Sathyan et al., 2007). Supporting this idea, we have shown that combinatorial miRNA targeting events are over-represented among down-regulated mRNAs. To identify the down-regulated mRNAs that may be targeted by multiple miRNAs in the frontal cortex of alcoholics we performed an over-representation analysis of miRNA targeting events for each individual down-regulated mRNA. Out of the 102 downregulated mRNAs that are putative targets of multiple miRNAs, 17 were predicted to be targeted by a significantly higher number of up-regulated miRNAs than expected by chance (hypergeometric p< 0.05 and FDR < 25%, Table 3). This proportion is also significantly different (z-test P = 0.00086) from the equivalent one obtained in the random control group, where only 1 out of 54 relevant mRNAs displayed such behaviour. These results suggest that, similarly to the upregulated miRNA core group, this downregulated “over-targeted” mRNA group may be important in mediating the effects of miRNA regulation due to chronic alcohol consumption.

Table 3.

mRNA targets with an over-represented number of miRNA targeting events.

Down-regulated Target Up-regulated miRs % All Predicted miRs % Hyper-geometric P. Value Adjusted P. Value
CXCR4 3 8.6 5 1.4 0.00036 0.01227
DICER1 13 37.1 59 16.5 0.00037 0.01227
BIRC6 8 22.9 29 8.1 0.00072 0.01875
SCARB2 8 22.9 36 10.1 0.00405 0.07250
EDIL3 3 8.6 8 2.2 0.00407 0.07250
SLAIN1 5 14.3 18 5.0 0.00445 0.07250
SSR1 8 22.9 37 10.3 0.00498 0.07250
GART 2 5.7 5 1.4 0.00750 0.09831
FAM108B1 5 14.3 21 5.9 0.01041 0.11368
RNF103 2 5.7 6 1.7 0.01400 0.13897
FBXL3 6 17.1 29 8.1 0.01485 0.13897
TEX2 5 14.3 23 6.4 0.01671 0.14592
FRYL 6 17.1 30 8.4 0.01799 0.14728
PAPD4 5 14.3 24 6.7 0.02069 0.15943
SESTD1 5 14.3 25 7.0 0.02528 0.18069
RIPK2 2 5.7 8 2.2 0.03413 0.21897
CDKN1B 4 11.4 20 5.6 0.03510 0.21897
Open in a new tab

Functional Enrichment Analysis

To investigate the functional significance of the group of 131 downregulated mRNA that are putative targets of the 35 upregulated miRNAs, and the functional significance of the core of 17 downregulated mRNAs that appear combinatorially over-targeted by the same miRNAs, we performed enrichment analysis using ToppFun (Table 4) and DAVID (Table 5).

Table 4.

ToppFun functional enrichment analysis (Bonferroni FDR < 0.2). Categories with 2 or more terms in query.

A. List of 131 downregulated genes
GO Biological Process
ID Name P-value Term in Query Term in Genome
GO:0032989 cellular component morphogenesis 0.0219 20 987
GO:0048468 cell development 0.0416 21 1120
GO:0002064 epithelial cell development 0.1847 4 37
GO Cellular Component
ID Name P-value Term in Query Term in Genome
GO:0012505 endomembrane system 0.0004 27 1481
GO:0016023 cytoplasmic membrane-bounded vesicle 0.0017 17 719
GO:0045177 apical part of cell 0.0796 8 253
GO:0012506 vesicle membrane 0.1167 8 268
GO:0005794 Golgi apparatus 0.1802 17 1056
Coexpression Gene Sets
Source Name P-value Term in Query Term in Genome
mSigDB POD1_KO_DN 0.0002 22 696
mSigDB ASTON_DEPRESSION_DN 0.0004 10 141
mSigDB ET743_SARCOMA_DN 0.0595 10 248
mSigDB ALZHEIMERS_DISEASE_UP 0.1318 27 1424
B. List of 17 over-targeted downregulated mRNAs
GO Molecular Function
ID Name P-value Term in Query Term in Genome
GO:0016879 ligase activity, forming carbon-nitrogen bonds 0.0063 4 290
GO:0004842 ubiquitin-protein ligase activity 0.0063 3 204
GO:0030145 manganese ion binding 0.0092 2 162
GO Cellular Component
ID Name P-value Term in Query Term in Genome
GO:0030426 growth cone 0.1135 2 89
Open in a new tab

Table 5.

DAVID functional enrichment analysis on list of 131 downregulated mRNA

Term Count Fold PValue Genes
fatty acid biosynthetic process 6 9.8 0.0003 FAR1, PLP1, ELOVL5, ELOVL4, SCD, ELOVL7
lipid biosynthetic process 9 3.6 0.0034 FAR1, PLP1, ELOVL5, SPTLC2, ELOVL4, LASS2, SCD, ELOVL7, SGMS1
cytoskeleton organization 10 3.0 0.0064 CDC42, FERMT2, LMO7, ABI2, TNKS, CNP, CAPN3, DST, SPAST, SGCB
cell cycle arrest 5 6.3 0.0081 KHDRBS1, CDKN1B, DST, TP53INP1, TGFB2
fatty acid metabolic process 6 3.9 0.0183 FAR1, PLP1, ELOVL5, ELOVL4, SCD, ELOVL7
sphingolipid metabolic process 4 6.9 0.0200 TEX2, SPTLC2, LASS2, SGMS1
cell death 12 2.2 0.0221 SGK1, PLP1, CDKN1B, SPG20, RYBP, BIRC6, RIPK2, SGMS1, DIDO1, SPAST, TP53INP1, TGFB2
ensheathment of axons in the central nervous system 2 85.9 0.0229 PLP1, C11ORF9
myelination in the central nervous system 2 85.9 0.0229 PLP1, C11ORF9
cell projection organization 8 2.8 0.0233 ALCAM, CDC42, CCDC88A, ULK2, ABI2, RDX, CNP, DST
sphingolipid biosynthetic process 3 12.5 0.0235 SPTLC2, LASS2, SGMS1
membrane lipid metabolic process 4 6.4 0.0244 TEX2, SPTLC2, LASS2, SGMS1
regulation of organelle organization 6 3.6 0.0260 CDC42, CDKN1B, CCDC88A, RDX, TNKS, DST
actin cytoskeleton organization 6 3.4 0.0302 CDC42, FERMT2, LMO7, ABI2, CAPN3, DST
Sphingolipid metabolism 3 8.9 0.0425 SPTLC2, PPAP2C, SGMS1
phosphorylation 12 1.9 0.0432 SRPK2, SGK1, ULK2, PIK3C2B, AAK1, GAB1, ABI2, RIPK2, TNKS, CDK7, MON2, TGFB2
Open in a new tab

Among the relevant categories highlighted by the analysis of the 131 dowregulated target list (Table 4.A) is the process of cellular component morphogenesis, particularly in apical parts of the cells, which is intrinsically related to neuronal development and structural plasticity (Russo et al., 2010). Interestingly, Hamilton and collaborators have recently reported that alcohol exposure decreases dendritic complexity and density while consolidating pre-existing synapses in prefrontal cortex of postnatal rats (Hamilton et al., 2010). Our data shows additional significant enrichment in the growth cone cellular component category for the core of 17 over-targeted mRNAs (Table 4.B), which underscore the impact that miRNAs may play in this process. Vesicle-related and the endomembrane system categories, accounting for about 20% of the downregulated targets in the group of 131 downregulated mRNAs, also showed highly significant enrichments. This could represent additional evidence for the role that neuronal remodelling and plasticity might play during neuroadaptation to chronic alcohol consumption. Furthermore, ubiquitination-related activities were highlighted as significantly enriched molecular functions in the core of 17 over-targeted mRNAs, which underscores the importance of the proteasome system. An unexpected result from the functional enrichment analysis was the inverse relationship detected between a subset of 27 downregulated mRNA targets and upregulated Alzheimer’s disease genes (Blalock et al., 2004) (Table 4, Coexpression Gene Sets). Interestingly, there is growing body of evidence describing a neuroprotective role for moderate alcohol drinking that significantly reduces the risk of either cognitive decline or dementia, including Alzheimer’s disease (Collins et al., 2009). Table 5 shows the lists of downregulated genes that scored significantly enriched in DAVID GO categories and KEGG pathways.

Validation by real time RT-PCR analysis

To validate results from our integrated miRNA-mRNA microarray analysis we performed qRT-PCR. Due to limited sample availability, a representative subset(6out of 27)of up-regulated miRNAs wasassessed. Four of the six miRNAs (67%) were also found significantly upregulated by qRT-PCR: hsa-let-7g (P=0.00196), hsa-miR-15b (P=0.02394), hsa-miR-152 (P=0.02605), and hsa-miR-7 (P=0.04647). Two miRNAs, hsa-miR-301a and hsa-miR-369-3p, did not produce statistically significant differences. Although hsa-miR-301a did not achieve the level of significance desired, we were able to detect an expected 1.57 fold increase in expression in the alcoholic samples as compared to the control group. This particular miRNA assay was negatively affected by the distribution of the control group results, which failed the Kolomogorov-Smirnov test for normality.

Additional validation of these results was recently obtained by our group while conducting miRNA microarray studies in a mouse model of highethanol consumption (unpublished data). Mouse homologs of 5 out of the 6 human miRNAs assessed by qRT-PCR (hsa-let-7g, hsa-miR-15b, hsa-miR-152, hsa-miR-7, and hsa-miR-301a) were also found significantly upregulated in the mouse microarray study. This conservation across species represents further invaluable validation of the role played by these miRNAs in alcohol-related cellular mechanisms.

Discussion

Global microarray and proteomics studies have identified a number of gene groups and gene families that are differentially expressed in the prefrontal cortex of alcoholics. These gene groups include those involved in myelination, ubiquitination, apoptosis, cell adhesion, neurogenesis, and neural disease (Liu et al., 2006). The mechanism by which chronic alcohol abuse results in selective changes in gene expression in the prefrontal cortex is not known. Here we present the results of miRNA microarray analysis of the same samples used in our previous microarray studies. The aim of the study was to identify miRNAs which are differentially expressed in the prefrontal cortex. By profiling global miRNA expression changes and mRNA profiles in the same samples, we were able to determine the extent to which down-regulated genes may be targeted by miRNAs. In striking correspondence with the relatively small but abundant changes detected in mRNA expression, we now find that many miRNAs (approximately 35) change expression levels in the frontal cortex of the human alcoholics and that these changes are consistently small across the whole set of differentially expressed miRNAs. In addition, 27 of the differentially expressed miRNAs had an over-represented number of predicted targets correspondingly changing expression in the alcoholic group.

Using miRNA target prediction programs in combination with previous mRNA profiling, more than half of the annotated differentially expressed mRNAs (55%) were identified as potential targets. Putative targets of miRNAs were significantly over-represented among downregulated genes in alcoholics. This may indicate an inverse relationship among the over-expressed miRNAs and respective down-regulated target genes. Several studies have recently reported similar negatively correlated expression patterns relationships (Farh et al., 2005; Manakov et al., 2009; Nielsen et al., 2009; Sood et al., 2006), which may indicate a characteristic behavior of miRNA-mRNA regulatory networks. However, miRNAs may additionally function indirectlyover specific targets, e.g. through complex feed-forward and feedback loops (Hobert, 2008; Sales et al., 2010), or different combinations of miRNAs may coordinately regulate specific target genes by unknown mechanism to promote positively correlated mRNA expression patterns.

Among major findings reported by our group in Liu et al. (Liu et al., 2006) was the consistent down-regulation of particular biological processes such as myelination and cell adhesion, which play critical roles in the development of the CNS and synapse formation. These observations were supported by previous morphological and expression profiling studies (Charness et al., 1994; Lewohl et al., 2000; Liu et al., 2004; Mayfield et al., 2002). We have now found that up-regulated miRNAs appear to impact the down-regulation of genes relevant to the same processes, for example miR-203/miR-586/miR-146a targeting proteolipid protein 1 (PLP1), a major component of the myelin sheath; miR-194 targeting myelin gene regulatory factor C11ORF9 (a putative transcription factor); and miR-519b-3p/miR665 targeting minor myelin sheath component 2′,3′-cyclic nucleotide 3′phosphodiesterase (CNP). Important cell adhesion genes such as activated leukocyte cell adhesion molecule (ALCAM ) and scavenger receptor class B, member 2 (SCARB2) are apparently targeted by a myriad of up-regulated miRNAs including miR-586/miR-144/miR-34c-5p/miR-18a and miR-203/miR-586/miR-580/miR-144/miR-380/miR-802/miR-7/miR-339-5p respectively. Genes involved in cytoskeleton (CDC42, FERMT2, LMO7, ABI2, TNKS, CNP, CAPN3, DST, SPAST, SGBC) and cell projection (ALCAM, CDC42, CCDC88A, ULK2, ABI2, RDX, CNP, DST) organization were also among the functional groups enriched among the differentially expressed miRNA targets. Other neurogenesis-related genes such as TIMP metallopeptidase inhibitor 2 (TIMP2) and TGFB2 appear to be regulated by multiple miRNAs as well. These results support the consensus idea that remodelling of synaptic connections, dependent upon changes in gene expression in response to alcohol, is responsible for the structural and functional alterations seen in alcohol dependence (Pignataro et al., 2009; Wilke et al., 1994). It has also been suggested that the adaptations to alcohol resemble to some extent the brain plasticity taking place during long-term exposure to other drug of abuse such as cocaine and heroin (Pignataro et al., 2009).

In addition, genes involved in lipid metabolism (PLP1,SPTLC2,SCD,PIK3C2B,SGMS1,FAR1,PLCL1,TEX2,ELOVL5,ELOVL4,LA SS2,ELOVL7,OSBPL11), ubiquitination (UFSP2,BIRC6,FBXL3,USP31), and cell death (SGK1,PLP1,CDKN1B,SPG20,RYBP,BIRC6,RIPK2,SGMS1,DIDO1,SPAST,TP53I NP1,TGFB2) (see Table 5) are differentially expressed and over-represented in our genome-wide mRNA profiling study as well as over-represented as predicted targets of miRNA changing expression in the same samples. Furthermore, out of a list of 27 genes previously identified in other alcohol studies using autopsy human brain (Flatscher-Bader et al., 2005; Lavoie and Butterworth, 1995; Lewohl et al., 2000; Liu et al., 2006; Mayfield et al., 2002; Sokolov et al., 2003), about half of them (13/27) were also identified as potential targets of differentially expressed miRNAs. These included: Calpain 3 (p94) (CAPN3); Lysosomal-associated membrane protein 2 (LAMP2); EGF-like repeats and discoidin I-like domains 3 (EDIL3); sparc/osteonectin, cwcv and kazal-like domains proteoglycan (testican) 1 (SPOCK1); TIMP metallopeptidase inhibitor 2 (TIMP2), SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 5 (SMARCA5); Calnexin (CANX); Unc-51-like kinase 2 (ULK2); proteolipid protein 1 (Pelizaeus-Merzbacher disease, spastic paraplegia 2, uncomplicated) (PLP1); 2′,3′-cyclic nucleotide 3′ phosphodiesterase (CNP); transketolase (Wernicke-Korsakoff syndrome) (TKT); stearoyl-CoA desaturase (delta-9-desaturase) (SCD); and acidic (leucine-rich) nuclear phosphoprotein 32 family, member E (AMP32E).

A recurring theme evidenced in our analysis is the extensive combinatorial targeting by which miRNA apparently exert an “over-zealous” control to fine tune the expression of specific targets. A combinatorial paradigm in miRNA function could help explain how miRNA could be achieving process-related specificity despite the fact that they are known to target a wide variety and number of distinct targets. Support for this idea has started to accumulate and its impact only recently highlighted. For example, while studying the global and local architecture of the mammalian miRNA-transcription factor (TF) regulatory network, Shalgi and collaborators found extensive interactions between miRNAs and between miRNAs and TFs, and suggested that “combinatorics may also have the advantage of allowing multiple sources of information, each represented by a single miR, to be integrated into the regulation of individual transcripts” (Shalgi et al., 2007). The potential combinatorial regulation of DICER1, the down-regulated alcohol-responsive gene with the greatest over-representation of miRNA targeting events in our dataset, could have important implications for understanding the regulatory events taking place under chronic alcohol abuse. Dicer is a key ribonuclease that processes precursor miRNAs and siRNA into their short mature forms and its knockdown has resulted in reduced expression of mature miRNA (Kumar et al., 2007). Targeting and down-regulation of Dicer by miRNAs could represent an important negative feedback mechanism by which miRNAs control their own availability and avoid potentially detrimental effects of excess deregulation. Supporting this line of reasoning is the recent report by Tokumaru et al. (Tokumaru et al., 2008) describing the possible existence of a novel negative feedback loop between let-7 and Dicer that may play a role in the tuning of mature miRNA expression and carcinogenesis. In addition, it has been reported that the magnitude of change in levels of endogenous miRNAs that mediate regulation of gene expression often occur over a narrow range of only 20-30% (Hobert, 2007; Peter, 2010). In our study we also found that the alcohol-induced up-regulation of miRNAs was constrained in a similar range, with only a few miRNAs varying levels in more than 30%. We speculate that negative feedback loops between Dicer and specific miRNAs could serve as a general mechanism for tight, global regulation of miRNA levels. The importance of feedback loops in homeostasis and cellular differentiation programs has been highlighted (Alon, 2007; Martinez et al., 2008) and the role of Dicer as a stress response component suggested (Wiesen and Tomasi, 2009). We reason that remodeling of cellular homeostasis could be taking place in the brain of chronic alcohol abusers as an adaptive mechanism, particularly in this specific cohort of alcoholic individuals, who had spent most of their adult lives consuming high quantities of ethanol without developing complicated alcohol-related disorders an indication of benign adaptation to the increased concentrations of alcohol in brain and other tissues.

This study highlights the relevant impact that miRNAs could be having in the development of alcohol-related changes in human brain. In general, our results suggest that up-regulation of miRNAs in the frontal cortex of human alcoholics may contribute to the deterioration and concomitant adaptation of neuronal functioning observed in cases of alcohol abuse. The overlap between putative targets of miRNAs and the differentially expressed genes from our previous study provides information on the biological functions and networks of genes regulated by specific miRNAs in human alcoholism. In the future, biochemical validation should be used to determine the extent to which gene regulation by miRNAs could be impacting specific protein levels.

Acknowledgments

Financial support was provided by the National Institute of Alcoholism and Alcohol Abuse (USA, NIH AA12404) (R01 AA012725-04) and the NHMRC.

The Brisbane Node of the National Health and Medical Research Council (NHMRC) Brain Bank and the New South Wales Tissue Resource Centre provided alcoholic and control brain tissue for analysis. Allison Eckert and Donna Sheedy provided detailed information on each of the cases used. We thank the next of kin for providing informed written consent for the studies.

References

  1. Alon U. Network motifs: theory and experimental approaches. Nat Rev Genet. 2007;8(6):450–61. doi: 10.1038/nrg2102. [DOI] [PubMed] [Google Scholar]
  2. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B. 1995;57:289–300. [Google Scholar]
  3. Blalock EM, Geddes JW, Chen KC, Porter NM, Markesbery WR, Landfield PW. Incipient Alzheimer’s disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc Natl Acad Sci U S A. 2004;101(7):2173–8. doi: 10.1073/pnas.0308512100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bushati N, Cohen SM. MicroRNAs in neurodegeneration. Curr Opin Neurobiol. 2008;18(3):292–6. doi: 10.1016/j.conb.2008.07.001. [DOI] [PubMed] [Google Scholar]
  5. Chandrasekar V, Dreyer JL. microRNAs miR-124, let-7d and miR-181a regulate cocaine-induced plasticity. Mol Cell Neurosci. 2009;42(4):350–62. doi: 10.1016/j.mcn.2009.08.009. [DOI] [PubMed] [Google Scholar]
  6. Charness ME, Safran RM, Perides G. Ethanol inhibits neural cell-cell adhesion. J Biol Chem. 1994;269(12):9304–9. [PubMed] [Google Scholar]
  7. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37(Web Server issue):W305–11. doi: 10.1093/nar/gkp427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cheng LC, Pastrana E, Tavazoie M, Doetsch F. miR-124 regulates adult neurogenesis in the subventricular zone stem cell niche. Nat Neurosci. 2009;12(4):399–408. doi: 10.1038/nn.2294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Collins MA, Neafsey EJ, Mukamal KJ, Gray MO, Parks DA, Das DK, Korthuis RJ. Alcohol in moderation, cardioprotection, and neuroprotection: epidemiological considerations and mechanistic studies. Alcohol Clin Exp Res. 2009;33(2):206–19. doi: 10.1111/j.1530-0277.2008.00828.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Dennis G, Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4(5):P3. [PubMed] [Google Scholar]
  11. Farh KK, Grimson A, Jan C, Lewis BP, Johnston WK, Lim LP, Burge CB, Bartel DP. The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science. 2005;310(5755):1817–21. doi: 10.1126/science.1121158. [DOI] [PubMed] [Google Scholar]
  12. Filipowicz W, Bhattacharyya SN, Sonenberg N. Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet. 2008;9(2):102–14. doi: 10.1038/nrg2290. [DOI] [PubMed] [Google Scholar]
  13. Flatscher-Bader T, van der Brug M, Hwang JW, Gochee PA, Matsumoto I, Niwa S, Wilce PA. Alcohol-responsive genes in the frontal cortex and nucleus accumbens of human alcoholics. J Neurochem. 2005;93(2):359–70. doi: 10.1111/j.1471-4159.2004.03021.x. [DOI] [PubMed] [Google Scholar]
  14. Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell. 2007;27(1):91–105. doi: 10.1016/j.molcel.2007.06.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Guo H, Ingolia NT, Weissman JS, Bartel DP. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature. 2010;466(7308):835–40. doi: 10.1038/nature09267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hamilton GF, Whitcher LT, Klintsova AY. Postnatal binge-like alcohol exposure decreases dendritic complexity while increasing the density of mature spines in mPFC Layer II/III pyramidal neurons. Synapse. 2010;64(2):127–35. doi: 10.1002/syn.20711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hobert O. miRNAs play a tune. Cell. 2007;131(1):22–4. doi: 10.1016/j.cell.2007.09.031. [DOI] [PubMed] [Google Scholar]
  18. Hobert O. Gene regulation by transcription factors and microRNAs. Science. 2008;319(5871):1785–6. doi: 10.1126/science.1151651. [DOI] [PubMed] [Google Scholar]
  19. Hua Z, Lv Q, Ye W, Wong CK, Cai G, Gu D, Ji Y, Zhao C, Wang J, Yang BB, Zhang Y. MiRNA-directed regulation of VEGF and other angiogenic factors under hypoxia. PLoS One. 2006;1:e116. doi: 10.1371/journal.pone.0000116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Huang W, Li MD. Nicotine modulates expression of miR-140*, which targets the 3′-untranslated region of dynamin 1 gene (Dnm1) Int J Neuropsychopharmacol. 2009;12(4):537–46. doi: 10.1017/S1461145708009528. [DOI] [PubMed] [Google Scholar]
  21. Killion PJ, Sherlock G, Iyer VR. The Longhorn Array Database (LAD): an open-source, MIAME compliant implementation of the Stanford Microarray Database (SMD) BMC Bioinformatics. 2003;4:32. doi: 10.1186/1471-2105-4-32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kumar MS, Lu J, Mercer KL, Golub TR, Jacks T. Impaired microRNA processing enhances cellular transformation and tumorigenesis. Nat Genet. 2007;39(5):673–7. doi: 10.1038/ng2003. [DOI] [PubMed] [Google Scholar]
  23. Lavoie J, Butterworth RF. Reduced activities of thiamine-dependent enzymes in brains of alcoholics in the absence of Wernicke’s encephalopathy. Alcohol Clin Exp Res. 1995;19(4):1073–7. doi: 10.1111/j.1530-0277.1995.tb00991.x. [DOI] [PubMed] [Google Scholar]
  24. Lee EJ, Baek M, Gusev Y, Brackett DJ, Nuovo GJ, Schmittgen TD. Systematic evaluation of microRNA processing patterns in tissues, cell lines, and tumors. RNA. 2008;14(1):35–42. doi: 10.1261/rna.804508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Lewohl JM, Wang L, Miles MF, Zhang L, Dodd PR, Harris RA. Gene expression in human alcoholism: microarray analysis of frontal cortex. Alcohol Clin Exp Res. 2000;24(12):1873–82. [PubMed] [Google Scholar]
  26. Liu J, Lewohl JM, Dodd PR, Randall PK, Harris RA, Mayfield RD. Gene expression profiling of individual cases reveals consistent transcriptional changes in alcoholic human brain. J Neurochem. 2004;90(5):1050–8. doi: 10.1111/j.1471-4159.2004.02570.x. [DOI] [PubMed] [Google Scholar]
  27. Liu J, Lewohl JM, Harris RA, Dodd PR, Mayfield RD. Altered gene expression profiles in the frontal cortex of cirrhotic alcoholics. Alcohol Clin Exp Res. 2007;31(9):1460–6. doi: 10.1111/j.1530-0277.2007.00444.x. [DOI] [PubMed] [Google Scholar]
  28. Liu J, Lewohl JM, Harris RA, Iyer VR, Dodd PR, Randall PK, Mayfield RD. Patterns of gene expression in the frontal cortex discriminate alcoholic from nonalcoholic individuals. Neuropsychopharmacology. 2006;31(7):1574–82. doi: 10.1038/sj.npp.1300947. [DOI] [PubMed] [Google Scholar]
  29. Lytle JR, Yario TA, Steitz JA. Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5′ UTR as in the 3′ UTR. Proc Natl Acad Sci U S A. 2007;104(23):9667–72. doi: 10.1073/pnas.0703820104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Manakov SA, Grant SG, Enright AJ. Reciprocal regulation of microRNA and mRNA profiles in neuronal development and synapse formation. BMC Genomics. 2009;10:419. doi: 10.1186/1471-2164-10-419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Martinez NJ, Ow MC, Barrasa MI, Hammell M, Sequerra R, Doucette-Stamm L, Roth FP, Ambros VR, Walhout AJ. A C. elegans genome-scale microRNA network contains composite feedback motifs with high flux capacity. Genes Dev. 2008;22(18):2535–49. doi: 10.1101/gad.1678608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Mayfield RD, Harris RA, Schuckit MA. Genetic factors influencing alcohol dependence. Br J Pharmacol. 2008;154(2):275–87. doi: 10.1038/bjp.2008.88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Mayfield RD, Lewohl JM, Dodd PR, Herlihy A, Liu J, Harris RA. Patterns of gene expression are altered in the frontal and motor cortices of human alcoholics. J Neurochem. 2002;81(4):802–13. doi: 10.1046/j.1471-4159.2002.00860.x. [DOI] [PubMed] [Google Scholar]
  34. Nachmani D, Lankry D, Wolf DG, Mandelboim O. The human cytomegalovirus microRNA miR-UL112 acts synergistically with a cellular microRNA to escape immune elimination. Nat Immunol. 2010;11(9):806–13. doi: 10.1038/ni.1916. [DOI] [PubMed] [Google Scholar]
  35. Nestler EJ, Aghajanian GK. Molecular and cellular basis of addiction. Science. 1997;278(5335):58–63. doi: 10.1126/science.278.5335.58. [DOI] [PubMed] [Google Scholar]
  36. Nielsen JA, Lau P, Maric D, Barker JL, Hudson LD. Integrating microRNA and mRNA expression profiles of neuronal progenitors to identify regulatory networks underlying the onset of cortical neurogenesis. BMC Neurosci. 2009;10:98. doi: 10.1186/1471-2202-10-98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Orom UA, Lund AH. Experimental identification of microRNA targets. Gene. 2010;451(1–2):1–5. doi: 10.1016/j.gene.2009.11.008. [DOI] [PubMed] [Google Scholar]
  38. Peter ME. Targeting of mRNAs by multiple miRNAs: the next step. Oncogene. 2010;29(15):2161–4. doi: 10.1038/onc.2010.59. [DOI] [PubMed] [Google Scholar]
  39. Pietrzykowski AZ, Friesen RM, Martin GE, Puig SI, Nowak CL, Wynne PM, Siegelmann HT, Treistman SN. Posttranscriptional regulation of BK channel splice variant stability by miR-9 underlies neuroadaptation to alcohol. Neuron. 2008;59(2):274–87. doi: 10.1016/j.neuron.2008.05.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Pignataro L, Varodayan FP, Tannenholz LE, Harrison NL. The regulation of neuronal gene expression by alcohol. Pharmacol Ther. 2009;124(3):324–35. doi: 10.1016/j.pharmthera.2009.09.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, Holloway A, Smyth GK. A comparison of background correction methods for two-colour microarrays. Bioinformatics. 2007;23(20):2700–7. doi: 10.1093/bioinformatics/btm412. [DOI] [PubMed] [Google Scholar]
  42. Russo SJ, Dietz DM, Dumitriu D, Morrison JH, Malenka RC, Nestler EJ. The addicted synapse: mechanisms of synaptic and structural plasticity in nucleus accumbens. Trends Neurosci. 2010;33(6):267–76. doi: 10.1016/j.tins.2010.02.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Sales G, Coppe A, Bicciato S, Bortoluzzi S, Romualdi C. Impact of probe annotation on the integration of miRNA-mRNA expression profiles for miRNA target detection. Nucleic Acids Res. 2010;38(7):e97. doi: 10.1093/nar/gkp1239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Sathyan P, Golden HB, Miranda RC. Competing interactions between micro-RNAs determine neural progenitor survival and proliferation after ethanol exposure: evidence from an ex vivo model of the fetal cerebral cortical neuroepithelium. J Neurosci. 2007;27(32):8546–57. doi: 10.1523/JNEUROSCI.1269-07.2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Schaefer A, O’Carroll D, Tan CL, Hillman D, Sugimori M, Llinas R, Greengard P. Cerebellar neurodegeneration in the absence of microRNAs. J Exp Med. 2007;204(7):1553–8. doi: 10.1084/jem.20070823. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Schratt GM, Tuebing F, Nigh EA, Kane CG, Sabatini ME, Kiebler M, Greenberg ME. A brain-specific microRNA regulates dendritic spine development. Nature. 2006;439(7074):283–9. doi: 10.1038/nature04367. [DOI] [PubMed] [Google Scholar]
  47. Shalgi R, Lieber D, Oren M, Pilpel Y. Global and local architecture of the mammalian microRNA-transcription factor regulatory network. PLoS Comput Biol. 2007;3(7):e131. doi: 10.1371/journal.pcbi.0030131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Shan H, Zhang Y, Lu Y, Pan Z, Cai B, Wang N, Li X, Feng T, Hong Y, Yang B. Downregulation of miR-133 and miR-590 contributes to nicotine-induced atrial remodelling in canines. Cardiovasc Res. 2009;83(3):465–72. doi: 10.1093/cvr/cvp130. [DOI] [PubMed] [Google Scholar]
  49. Siegel G, Obernosterer G, Fiore R, Oehmen M, Bicker S, Christensen M, Khudayberdiev S, Leuschner PF, Busch CJ, Kane C, Hubel K, Dekker F, Hedberg C, Rengarajan B, Drepper C, Waldmann H, Kauppinen S, Greenberg ME, Draguhn A, Rehmsmeier M, Martinez J, Schratt GM. A functional screen implicates microRNA-138-dependent regulation of the depalmitoylation enzyme APT1 in dendritic spine morphogenesis. Nat Cell Biol. 2009;11(6):705–16. doi: 10.1038/ncb1876. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Smyth GK, Michaud J, Scott HS. Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics. 2005;21(9):2067–75. doi: 10.1093/bioinformatics/bti270. [DOI] [PubMed] [Google Scholar]
  51. Smyth GK, Speed T. Normalization of cDNA microarray data. Methods. 2003;31(4):265–73. doi: 10.1016/s1046-2023(03)00155-5. [DOI] [PubMed] [Google Scholar]
  52. Sokolov BP, Jiang L, Trivedi NS, Aston C. Transcription profiling reveals mitochondrial, ubiquitin and signaling systems abnormalities in postmortem brains from subjects with a history of alcohol abuse or dependence. J Neurosci Res. 2003;72(6):756–67. doi: 10.1002/jnr.10631. [DOI] [PubMed] [Google Scholar]
  53. Sood P, Krek A, Zavolan M, Macino G, Rajewsky N. Cell-type-specific signatures of microRNAs on target mRNA expression. Proc Natl Acad Sci U S A. 2006;103(8):2746–51. doi: 10.1073/pnas.0511045103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Tang Y, Banan A, Forsyth CB, Fields JZ, Lau CK, Zhang LJ, Keshavarzian A. Effect of alcohol on miR-212 expression in intestinal epithelial cells and its potential role in alcoholic liver disease. Alcohol Clin Exp Res. 2008;32(2):355–64. doi: 10.1111/j.1530-0277.2007.00584.x. [DOI] [PubMed] [Google Scholar]
  55. Tokumaru S, Suzuki M, Yamada H, Nagino M, Takahashi T. let-7 regulates Dicer expression and constitutes a negative feedback loop. Carcinogenesis. 2008;29(11):2073–7. doi: 10.1093/carcin/bgn187. [DOI] [PubMed] [Google Scholar]
  56. Wiesen JL, Tomasi TB. Dicer is regulated by cellular stresses and interferons. Mol Immunol. 2009;46(6):1222–8. doi: 10.1016/j.molimm.2008.11.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Wilke N, Sganga M, Barhite S, Miles MF. Effects of alcohol on gene expression in neural cells. EXS. 1994;71:49–59. doi: 10.1007/978-3-0348-7330-7_6. [DOI] [PubMed] [Google Scholar]
  58. Wu Q, Law PY, Wei LN, Loh HH. Post-transcriptional regulation of mouse mu opioid receptor (MOR1) via its 3′ untranslated region: a role for microRNA23b. FASEB J. 2008;22(12):4085–95. doi: 10.1096/fj.08-108175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Wu Q, Zhang L, Law PY, Wei LN, Loh HH. Long-term morphine treatment decreases the association of mu-opioid receptor (MOR1) mRNA with polysomes through miRNA23b. Mol Pharmacol. 2009;75(4):744–50. doi: 10.1124/mol.108.053462. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009;37(Database issue):D105–10. doi: 10.1093/nar/gkn851. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES