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. Author manuscript; available in PMC: 2014 Sep 10.
Published in final edited form as: Gene. 2013 Jun 6;526(2):10.1016/j.gene.2013.05.052. doi: 10.1016/j.gene.2013.05.052

Over-Expression of the miRNA Cluster at Chromosome 14q32 in the Alcoholic Brain Correlates with Suppression of Predicted Target mRNA Required for Oligodendrocyte Proliferation

AM Manzardo 1, S Gunewardena 3,4, MG Butler 1,2
PMCID: PMC3816396  NIHMSID: NIHMS490711  PMID: 23747354

Abstract

We examined miRNA expression from RNA isolated from the frontal cortex (Broadman area 9) of 9 alcoholics (6 males, 3 females, mean age 48 years) and 9 matched controls using both the Affymetrix GeneChip miRNA 2.0 and Human Exon 1.0 ST Arrays to further characterize genetic influences in alcoholism and the effects of alcohol consumption on predicted target mRNA expression. A total of 12 human miRNAs were significantly up-regulated in alcohol dependent subjects (fold change ≥1.5, false discovery rate (FDR) ≤0.3; p<0.05) compared with controls including a cluster of 4 miRNAs (e.g., miR-377, miR-379) from the maternally expressed 14q32 chromosome region. The status of the up-regulated miRNAs was supported using the high-throughput method of exon microarrays showing decreased predicted mRNA gene target expression as anticipated from the same RNA aliquot. Predicted mRNA targets were involved in cellular adhesion (e.g., THBS2), tissue differentiation (e.g., CHN2), neuronal migration (e.g., NDE1), myelination (e.g., UGT8, CNP) and oligodendrocyte proliferation (e.g., ENPP2, SEMA4D1). Our data support an association of alcoholism with up-regulation of a cluster of miRNAs located in the genomic imprinted domain on chromosome 14q32 with their predicted gene targets involved with oligodendrocyte growth, differentiation and signaling.

1 Introduction

RNA has emerged as a major component of the regulatory circuitry in cells of complex organisms including humans. Less than 2% of the mammalian genome encodes protein, thus RNA not coding for protein or “non-coding RNA” (ncRNA) is now recognized as a key area for study of human diversity and disease (Mattick and Makunin, 2005; Dinger et al., 2008; Mendell and Olson, 2012; Tal and Tanguay, 2012).

MicroRNAs (miRNAs) are small ncRNA molecules of approximately 22 nucleotides in size that regulate the expression of genes by binding to the 3′-untranslated regions (3′ UTR) of target mRNA which inhibits protein translation or facilitates degradation of mRNA (Lewis et al., 2005). MiRNAs are encoded by genes that are transcribed from DNA distributed throughout the genome but not translated into protein. Over 1,100 unique miRNAs have been identified with complementary binding for thousands of predicted mRNA targets. MicroRNAs are critical in development, differentially expressed in tissues, involved in viral infection processes and associated with oncogenesis (Mendell and Olson, 2012; Tal and Tanguay, 2012). Proliferating cells express mRNAs with shortened 3′ UTR and thus have fewer miRNA target sites (Sandberg et al., 2008). Different miRNAs are predicted to regulate the majority of human protein coding genes (Dinger et al., 2008; Mendell and Olson, 2012).

1.1 MiRNA in the Nervous System

Multiple classes of ncRNAs are highly represented in the nervous system (Rogelj and Giese, 2004; Cao et al., 2006) emphasizing that nervous system development and function is heavily dependent on RNA regulatory networks with alterations resulting in neurological and psychiatric diseases (Mehler and Mattick, 2006). NcRNAs appear to regulate the maintenance of mature neural traits and synaptic plasticity (Sempere et al., 2004; Conaco et al., 2006; Tal and Tanguay, 2012) and are heavily involved in synaptic function and memory formation (Ashraf and Kunes, 2006; Schratt et al., 2006). For example, dysregulation of miRNAs have been reported in association with Alzheimer disease, X-linked mental retardation, Parkinson disease, Tourette syndrome and schizophrenia (Dostie et al., 2003; Krichevsky et al., 2003; Ableson et al., 2005; Tal and Tanguay, 2012). However, less is known regarding miRNAs in the context of alcoholism.

1.2 MiRNA in Alcoholism

The number of serious adverse health effects associated with alcoholic drinking would predict a wide range of miRNA disturbances in individuals with alcohol dependence relative to healthy controls. Exposure to alcohol has been associated with disturbances in miRNA expression in neuronal cells from rodents (Sathyan et al., 2007; Guo et al., 2012) and in developing zebrafish which correlate with neurobehavioral and skeletal abnormalities (Tal et al., 2012; Soares et al., 2012). The expression of several miRNAs (miR-9, miR-21, miR-153 and miR-335) were reportedly suppressed by alcohol in fetal cultures of mouse cerebral cortical neuroepithelium (Sathyan et al., 2007). Preliminary studies have identified a putative role for miR-9 in alcohol tolerance possibly mediated through decreased expression of the BK channel, a high conductance calcium and voltage-dependent potassium channel (Pietrzykowski et al, 2008; Martin et al., 2008). However, it is not known how the effects of chronic heavy alcohol use on miR-9 or other miRNA expression may differ in the adult human brain.

Lewohl et al. (2011) recently reported significant up-regulation, but no significant down-regulation, of 35 miRNA constructs in the prefrontal cortex of human alcoholics compared to non-alcoholic control subjects. Predicted targets of up-regulated miRNAs showed a high level of overlap with published cDNA expression disturbances. The functional classification of disturbed predicted targets included nervous system development, cellular adhesion, and cell-cell signaling. Our study seeks to identify and further characterize the nature of functional miRNA disturbances observed in the frontal cortex of alcoholic men and women relative to age, race and gender-matched controls in relationship to target gene expression using the latest microarray technology.

2 Methods

2.1 Samples

MicroRNA and mRNA (exon) expression profiles were obtained from total RNA isolated using the Qiagen (Qiagen Inc, Maryland) kit from post-mortem human frontal cortex (Brodman Area 9) of 9 alcoholics [6 males, 3 females; mean (±SD) age = 49.1 (±6.0) yrs, range 41-57 yrs] and 9 age and gender-matched control subjects [6 males, 3 females; mean (±SD) age = 50.0 (±6.6) yrs, range 37-56 yrs]. The pre-frontal cortex was selected for study due to its role in the regulation of motivated behaviors, impulse dysregulation in addiction and as a region of the brain particularly susceptible to the effects of long-term alcohol abuse (Ross and Peselow, 2009). The average RNA integrity number (RIN) was 6 for the alcoholics and 5 for the controls and considered adequate for microarray analysis. The gender composition of the sample reflects the sex ratio distribution found in the general population of alcoholics. Samples were procured from the New South Wales Tissue Resource Centre (Sydney, Australia) and collected according to a standardized protocol (Sheedy et al., 2008) in compliance with ethical guidelines established by the Sydney South West Area Health Service Human Ethics Committee (X03-0074). Informed written consent was obtained from the nearest living relative. The mean (±SD) post-mortem interval (PMI) for our subjects was 26.3 (±9.6) hours with a range of 13 to 43 hours. The mean (±SD) sample pH was 6.6 (±0.22). All samples tested were negative for viral hepatitis and for the human immunodeficiency virus.

All subjects were of European descent and alcohol dependent subjects met the criteria described in the Diagnostic and Statistical Manual for Mental Disorders, Fourth Edition and National Health and Medical Research Council/World Health Organization criteria. Control subjects were social drinkers (non-abstainers for alcohol use) and did not meet criteria for alcohol abuse or dependence. The average estimated duration of alcohol dependence for case subjects was 19.5 (±8.1) years (range 10-30 years). The individual causes of death varied across participants with the most common causes due to cardiovascular and respiratory problems or infection. Direct alcohol toxicity or overdose was indicated in two deaths of alcoholic subjects. Family history of alcohol problems was either negative or unknown for all subjects.

2.2 Microarrays

The GeneChip miRNA 2.0 Array (Affymetrix, Inc.; Santa Clara, CA) was used to examine miRNA disturbances in Alcohol Dependent subjects relative to age and gender-matched controls. The array specifications included the following information: Array Type- GeneChip miRNA 2.0; Source - Sanger miBase miRNA (http://microrna.sanger.ac.uk) for mature and pre-miRNA; snoRNABase (www.snorna.biotoul.fr/coordinates.php) and Ensembl Archive (www.ensembl.org/biomart/martview) for small nucleolar RNA (snoRNA) and small Cajal body-specific RNA (scaRNA); Build- all probe locations used the human genome reference GRCh36/hg19 assembly (http://genome.ucsc.edu/cgi-bin/hgGateway?db=hg19); Probe Length-25mer or less. The GeneChip miRNA 2.0 array uses 20,287 total probe-sets to provide 100% coverage of miRBase v15 (15,644 probe-sets); 2,334 snoRNA and scaRNA; and 2,202 unique pre-miRNA hairpin sequences from 131 organisms. The human probe-set provides coverage for 1,105 mature miRNA; 1,121 pre-miRNA; 2,302 snoRNA; and 32 scaRNA and hybridization with processing was performed at the same time using the same batch of commercially generated microarrays. The scope of the present study was limited to an analysis of human miRNA disturbances excluding snoRNA and scaRNA.

The Human Exon 1.0 ST (sense target) Array (Affymetrix, Inc.; Santa Clara, CA) was used to identify disturbances in mRNA that inversely correlate with miRNA changes (i.e., increased expression of specific miRNAs should show decreased expression of the predicted target mRNAs) generated from miRNA arrays. The co-expression patterns of the predicted target mRNAs were used to assess the functional impact of observed miRNA disturbances obtained from the same RNA source, a practice gaining acceptance as an experimental validation method merging high-throughput techniques with existing gene-specific assays including advanced microarray screening as performed in our study in place of conventional quantitative RT-PCR (Vergoulis et al., 2012; Vlachos et al., 2012). For example, comparison of the number of targets per experimental validation method for the TarBase 5.0 and TarBase 6.0 prediction software programs indicates an increasing use of high-throughput techniques including microarray platforms which currently follows only sequencing as the most widely used techniques for this purpose (Vergoulis et al., 2012). Hence, the selection of human exon arrays for experimental validation of miRNA disturbances in our study. Array specifications included the following information: Array Type- Human Exon 1.0 ST; Source - cDNA-based content including the more established human RefSeq mRNAs, GenBank® mRNAs, and ESTs from dbEST. Additional annotations were created by mapping syntenic cDNAs to the human, mouse, and rat genomes using genome synteny maps from the UCSC Genome Bioinformatics group. Predicted gene structure sequences from GENSCAN; Ensembl; Vega; geneid and sgp; TWINSCAN; Exoniphy; microRNA Registry; MITOMAP; and structural RNA predictions; Build- all probe locations used the human genome reference GRCh36/hg19 assembly (http://genome.ucsc.edu/cgi-bin/hgGateway?db=hg19). Probe Length- 25mer or greater. The Human Exon 1.0 ST Array uses 1.4 million probe sets to interrogate exons at 28,869 well-annotated genes.

3 Data Analysis

3.1 MiRNA Array Analyses

MiRNA expression profiling was performed using the Affymetrix GeneChip miRNA 2.0 Array. This array accommodates miRNA probesets from 131 organisms. Of this, only the 2,226 human miRNA probesets [1,105 mature miRNA; 1,121 pre-miRNA] were used for the current analysis. These probesets were background corrected, normalized and summarized using the Robust Multichip Average (RMA) procedure (Irizarry et al., 2003). The log (base 2) intensity values from the normalized miRNA probesets for individual miRNAs were fitted to a multiple linear regression model of the form, Y = μ + Alcoholism + Gender + Scan Date + Alcoholism × Gender + ℮, where the intercept μ models the common effect, the categorical variable ‘Alcoholism’ models a subject's state of either being alcoholic or non-alcoholic, the categorical variable ‘Gender’ models the subject's gender and the categorical variable ‘Scan Date’ models the random effect attributable to the different days on which the experiments were performed. The variable ‘℮’ represents the individual specific random error associated with the model and is assumed to be normally and independently distributed with mean zero. Principle Component Analysis was used to test for batch effects related to RIN number. RIN number was also included as a factor in our linear regression model where it had no significant influence and was removed from the final model. Additionally, Affymetrix Expression Console was used to perform a Wilcoxon Rank-Sum test for detection to compare the miRNA probe signal (signal) to the distribution of signals from GC content matched anti-genomic probes (background noise). Significance criteria for probe level hybridization is defined as significant detection of signal in at least 2/3 of the samples in the alcoholic group for up-regulated miRNAs or significant detection of signal in at least 2/3 of the samples in the control group for down-regulated miRNAs. The false discovery rate (FDR) was calculated using the Benjamini and Hochberg procedure and set at ≤ 0.3 for miRNA expression data to gauge for selective biological evidence.

3.2 Cluster Analysis

There were 16 miRNAs that showed significant differential expression (p-value ≤ 0.05; absolute fold change ≥1.5) between alcoholics compared to controls. These miRNAs were hierarchically clustered for visualization using the un-weighted average distance (UPGMA) algorithm applied to the euclidean distance matrix of pair-wise distances between miRNAs computed from the standardized log expression values of individual subjects (see Figure 1). All computations were performed using Matlab (R2009b, The MathWorks Inc, Natick, MA) and the Partek Genomic suite (v 6.5, Partek Inc., St. Louis, MO).

Figure 1.

Figure 1

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Heatmap of miRNAs clustered and based upon significant disturbances in the frontal cortex of alcoholics and control subjects.

3.4 Exon Array Analysis

Gene expression profiling was carried out using the Affymetrix GeneChip Human Exon 1.0 ST Array for determination of the inverse correlation known to exist between miRNA and gene expression using this high-throughput method. This array consists of 1.4 million probe-sets, of which around 300,000 are core exon probe-sets supported by putative full-length mRNA (RefSeq and full-length GenBank annotated alignments). These core probe-sets map to approximately 18,000 genes with high confidence. Gene expression level was determined by averaging the intensity signals of multiple probes to the individual exons per gene. The exon-arrays are RMA-background corrected, quantile-normalized and gene-level summarized using the Median Polish algorithm (Irizarry et al., 2003). The resulting log (base 2) transformed signal intensities (expression values) are used for ascertaining differentially expressed genes. Fold change statistics for individual genes are then calculated by taking the linear contrast between the least square means of the (log) alcoholic and (log) control groups and back transforming the result to a linear scale (this is the ratio of the geometric mean of the treatment samples to the geometric mean of the control samples). Corresponding significance scores (p-values) are assigned based on the t-statistic of the linear contrast. Our analysis was done on brain tissues obtained from biological replicates of seven alcoholic and seven control samples. The false discovery rate was set at ≤ 0.3 for exon (gene) expression data. The fold change cutoff for prediction target analysis was set to 1.5 due to the low number of up-regulated miRNA observed in our sample (3 with fold change ≥2.0). Target miRNA predictions were evaluated for all disturbed exons and miRNA meeting these criteria.

Three different well-respected, commonly-referenced web-based search engines were used to predict miRNA/target mRNA binding for all exons significantly disturbed in our sample (FDR <0.3 and fold change greater than 1.5; targetscan.org, microRNA.org and TarBase 6.0; Betel et al., 2008; Vergoulis et al., 2012; Vlachos et al., 2012). TargetScan predictions are based upon sequence complementarity giving priority to conserved sequences and binding in seed regions. Target predictions for microRNA.org rely on the miRanda algorithm emphasize sequence complementarity between mature miRNA and mRNA constructs by using a weighted score based upon sequence matching which allows for some mismatching (Betel et al., 2008) and considers the free energy of formation of the miRNA/mRNA complex. TarBase 6.0 provides relevant predictions of the largest collection of manually curated experimentally validated miRNA-gene interactions involving more than 65,000 targets for 1,572 miRNAs interacting with an expanding list of genes for three species (human, mouse and rat). Predictions are based upon validated associations drawn from a comprehensive database of experimentally supported human and animal miRNA targets including microarray technology similar to the exon array data used in the present study (Vergoulis et al., 2012; Vlachos et al., 2012).

4 Results

Of the 1,105 mature human miRNAs examined on the array, an average of 525 (±33) or 47% (range of 488 to 590) were expressed in the frontal cortex in the 9 alcoholic subjects and an average of 504 (±30) or 46% (range 443 to 539) were expressed in the 9 control subjects and were not different. Of the 1,105 mature miRNA probes, 402 unique miRNAs were expressed in all alcoholic subjects while 435 were not expressed in any alcoholics. The remaining 268 miRNA probes on the microarray were expressed in at least one alcoholic subject. Similarly, 377 unique miRNAs were expressed in all control subjects and 443 were not expressed in any controls. The remaining 285 miRNA probes were expressed in at least one control subject. Sixteen miRNAs (12 were increased and 4 were decreased) showed significant differential expression [absolute fold changes ≥1.5, FDR ≤ 0.3, p ≤ 0.05;] between alcoholic and control groups. These miRNAs were hierarchically clustered and visualized in a heat map (Figure 2). All differentially expressed miRNAs passed the significance criteria for probe level hybridization and detection except for 2 down-regulated miRNA (mir-1227 and miR-2355).

Figure 2.

Figure 2

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Venn diagram of messenger RNA predicted targets down-regulated in the frontal cortex of alcoholics grouped according to predicted miRNA binding. Solid lines group predicted mRNA predicted targets of miRNA from the 14q32 region. Dotted lines group predicted mRNA targets of miRNA from chromosomes other than chromosome 14.

4.1 Up-regulated miRNA Expression

A total of 12 miRNAs or ∼2% from an overall mean of 514 (±33) expressed miRNAs from our two subject groups were significantly up-regulated in the frontal cortex of alcohol dependent subjects (p<0.05, RMA followed by multiple linear regression) compared to age and gender-matched controls. These included 11 mature miRNAs and 1 low frequency, complementary (star) miRNA (miR-488*, 2.0 fold). A list of the 12 miRNAs that were up-regulated at ≥ 1.5 fold in alcoholics relative to non-alcoholic control subjects and with an FDR of ≤ 0.3 is presented in Table 1. The list includes three 5p derivative miRNAs (miR-3065-5p, miR-299-5p and miR-767-5p which were also up-regulated. Overall, functional disturbances were found to be greater among mature than immature miRNAs which did not exceed our criteria for disturbance. A possible disturbance in miRNA expression was noted at the paternally imprinted/maternally expressed DLK1-DIO3 domain on chromosome 14q32. Four of 16 or 25% of select miRNAs disturbed in alcoholism were located in this chromosome region (Table 1). Further, the direction of the disturbance was the same in all cases (up-regulated ≥1.5 fold, FDR ≤ 0.3).

Table 1. Increased miRNA Expression in Alcoholics Relative to Non-Alcoholic Controls.

miRNA Fold Change p-value Genomic Coordinates
hsa-miR-375 2.8 0.0007 2:219866367-219866430 (-)
hsa-miR-3065-5p 2.7 0.001 17:79099677-79099755 (+)
hsa-miR-488-star 2.0 0.003 1:176998499-176998581 (-)
hsa-miR-299-3p 2.0 0.003 14:101490131-101490193 (+)
hsa-miR-377 1.9 0.001 14:101528387-101528455 (+)
has-mir-516a-2* 1.7 0.002 19:54264387-54264476 (+)
hsa-miR-767-5p 1.7 0.002 X:151561893-151562001 (-)
hsa-miR-493 1.7 0.00005 14:101335397-101335485 (+)
hsa-miR-379 1.5 0.003 14:101488403-101488469 (+)
hsa-miR-105* 1.5 0.0009 Chromosome 7
hsa-miR-29b* 1.5 0.001 2 loci on chromosomes 7
hsa-miR-149 1.5 0.002 2:240227157-240227240 (+)
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Expression ≥1.5 fold increased among alcoholics relative to control brain. miRNAs linked to the maternally imprinted region 14q32 shown in italics.

*

represents those miRNAs whose precursors occur in a cluster of 2 (mir-105, mir-29b, mir-516a) transcripts at the same genetic coordinates. (+)=sense direction, (-)=anti-sense direction. Microarray data processed using RMA followed by multiple linear regression. FDR cutoff ≤ 0.3.

4.2 Down-regulated MircoRNA Expression

A total of two human miRNAs (miR-572, miR-3162) or <1% from an overall mean of 514 (±33) expressed miRNAs from our two subject groups were significantly down-regulated (p<0.05, RMA followed by multiple linear regression, less than or equal to -1.5 fold, FDR ≤ 0.3) in the frontal cortex of alcoholics relative to non-alcoholic control subjects. One of the two significantly down-regulated miRNAs (less than or equal to -1.5 fold) in alcoholism was matched to a disturbed mRNA target: miR-3162-3p (-2.8 fold disturbance) and BOK (-1.7 fold disturbance, Table 2). However, BOK is also a target for miR-3065 (up-regulated 2.7 fold) and miR-149 (up-regulated 1.5 fold) which are more likely mediators of the observed mRNA disturbance.

Table 2. Expression of Target Genes by Up-regulated miRNAs in Alcoholism.

miRNA Fold Change mRNA Expression Target Fold Change Chromosome Band
miR-375(2q35) 2.8 transferrin (TF) -3.9 3q22.1
blood vessel epicardial substance (BVES) -2.5 6q21
chloride channel accessory 4 (CLCA4) -2.1 1p31-p22
transmembrane and tetratricopeptide repeat containing 2 (TMTC2) -1.9 12q21.31
phosphodiesterase 8A (PDE8A) -1.9 15q25.3
SLAIN motif family, member 1 (SLAIN1) -1.8 13q22.3
EGF-like repeats and discoidin I-like domains 3 (EDIL3) -1.8 5q14
chimerin (chimaerin) 2 (CHN2) -1.6 7p15.3
inositol hexakisphosphate kinase 3 (IP6K3) -1.5 6p21.31
calcineurin-like phosphoesterase domain containing1 (CPPED1) -1.5 16p13.12
miR-3065(17q25.3) 2.7 UDP glycosyltransferase 8 (UGT8) -3.7 4q26
catenin (cadherin-associated protein), alpha 3 (CTNNA3) -3.3 10q22.2
thrombospondin 2 (THBS2) -2.1 6q27
transmembrane protein 206 (TMEM206) -1.7 1q32.3
BCL2-related ovarian killer (BOK) -1.7 2q37.3
miR-488-star(1q25.2) 2.0 ribonuclease, RNase A family, 1 (RNASE1) -2.6 14q11.2
dedicator of cytokinesis 5 (DOCK5) -2.5 8p21.2
lysophosphatidic acid receptor 1 (LPAR5) -2.3 9q31.3
Ras association (RalGDS/AF-6) domain family member 2 (RASSF2) -2.3 20p13
solute carrier family 44, member 1 (SLC44A1) -2.2 9q31.2
chromosome 13 open reading frame 31 (C13ORF31) -2.1 13q14.11
inducible T-cell co-stimulator ligand (ICOSLG) -2.0 21q22.3
sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4D (SEMA4D) -2.0 9q22.2
phosphodiesterase 8A (PDE8A) -1.9 15q25.3
EF-hand domain family, member D1 (EFHD1) -1.9 2q37.1
transmembrane and tetratricopeptide repeat containing 2 (TMTC2) -1.9 12q21.31
transmembrane and coiled-coil domain family 3 (TMCC3) -1.8 12q22
EGF-like repeats and discoidin I-like domains 3 (EDIL3) -1.8 5q14
proline rich 5 like (PRR5L) -1.7 11p13-p12
chimerin (chimaerin) 2 (CHN2) -1.6 7p15.3
SHC (Src homology 2 domain containing) family, member 4 (SHC4) -1.6 15q21.1-21.2
presenilin 1 (PSEN1) -1.6 14q24.3
miR-299-3p(14q32) 2.0 chromosome 21 open reading frame 91 (C21orf91) -3.1 21q21.1
ectonucleotide pyrophosphatase 2 (ENPP2) -3.4 8q24.1
carnosine dipeptidase 1 (metallopeptidase M20 family) (CNDP1) -2.6 18q22.3
lysophosphatidic acid receptor 1 (LPAR1) -2.4 9q31.3
engulfment and cell motility 1 (ELMO1) -1.8 7p14.1
dihydropyrimidinase-like 5 (DPYSL5) -1.7 2p23.3
DIX domain containing 1 (DIXDC1) -1.5 11q23.1
miR-377(14q32) 1.9 UDP glycosyltransferase 8 (UGT8) -3.7 4q26
chromosome 21 open reading frame 91 (C21orf91) -3.1 21q21.1
anillin, actin binding protein (ANLN) -3.5 7p15-p14
ring finger protein 125 (RNF125) -2.4 18q12.1
chloride channel accessory 4 (CLCA4) -2.1 1p31-p22
nudE nuclear distribution gene (NDE1) -2.1 16p13.11
chromosome 13 open reading frame 31 (C13orf31) -2.1 13q14.11
sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4D (SEMA4D) -2.0 9q22.2
transmembrane and tetratricopeptide repeat containing 2 (TMTC2) -1.9 12q21.31
transmembrane and coiled-coil domain family 3 (TMCC3) -1.8 12q22
EGF-like repeats and discoidin I-like domains 3(EDIL3) -1.8 5q14
engulfment and cell motility 1 (ELMO1) -1.8 7p14.1
chimerin (chimaerin) 2 (CHN2) -1.6 7p15.3
MID1 interacting protein 1 (gastrulation specific G12 homolog (zebrafish), MID1IP1) -1.6 Xp11.4
miR-767-5p(Xq28) 1.7 ectonucleotide pyrophosphatase/phosphodiesterase2 (ENPP2) -3.4 8q24.1
miR-379(14q32) 1.5 sema domain, immunoglobulin domain (Ig), -2.0 9q22.2
transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4D (SEMA4D)
2′,3′-cyclic nucleotide 3′ phosphodiesterase (CNP) -2.0 17q21
transmembrane and coiled-coil domain family 3 (TMCC3) -1.8 12q22
miR-105(Xq28, 2 genes) 1.5 phosphatidic acid phosphatase type 2C (PPAP2C) -1.7 19p13
miR-29b(7q32.3 or1q32.2) 1.5 ectonucleotide phosphodiesterase 2 (ENPP2) -3.4 8q24.1
carnosine synthase 1 (CARNS1) -2.9 11q13.2
myelin oligodendrocyte glycoprotein (MOG) -2.9 6p22.1
thrombospondin 2 (THBS2) -2.1 6q27
CKLF-like MARVEL transmembrane domain containing 5 (CMTM5) -2.1 14q11.2
sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic -2.1 14q11.2
domain, (semaphorin) 4D (SEMA4D) -2.0 9q22.2
dishevelled associated activator of morphogenesis 2 (DAAM2) -2.0 6p21.2
proteolipid protein 1 (PLP1) -1.8 Xq22
dihydropyrimidinase-like 5 (DPYSL5) -1.7 2p23.3
DIP2 disco-interacting protein 2 homolog B (DIP2B) -1.6 12q13.12
presenilin 1 (PSEN1) -1.6 14q24.3
MAP6 domain containing 1 (MAP6D1) -1.6 3q27.1
DIX domain containing 1 (DIXDC1) -1.5 11q23.1
miR-149 (2q37.2) 1.5 potassium voltage-gated channel, subfamily H (eag-related), member 8 (KCNH8) -2.8 3p24.3
CD22 molecule (CD22) -2.7 19q13.1
solute carrier organic anion transporter family, member 1A2 (SLCO1A2) -2.7 12p12
v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) (ERBB3) -2.7 12q13
chromosome 11 open reading frame 9 (C11orf9) -2.5 11q12-q13.1
dedicator of cytokinesis 10 (DOCK10) -2.1 2q36.2
CKLF-like MARVEL transmembrane domain containing 5 (CMTM5) -2.1 14q11.2
PX domain containing serine/threonine kinase (PXK) -2.0 3p14.3
EF-hand domain family, member D1 (EFHD1) -1.9 2q37.1
kinesin family member 13B (KIF13B) -1.9 8p12
UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 6 (GalNAc-T6) (GALNT6) -1.7 12q13
BCL2-related ovarian killer (BOK) -1.7 2q37.3
arrestin domain containing 2 (ARRDC2) -1.6 19p13.11
ring finger protein 220 (RNF220) -1.5 1p34.1
miR-3162-3p (11q12.1) -2.8 BCL2-related ovarian killer (BOK) -1.7 2q37.3
miR-1227 (19p13.3) -1.5 sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4D (SEMA4D) -2.0 9q22.2
phosphodiesterase 6B, cGMP-specific, rod, beta (PDE6B) -1.6 4p16.3
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miRNA data were processed using RMA followed by multiple linear regression. Exon-arrays were RMA-background corrected, quantile-normalized and gene-level summarized using the Median Polish algorithm followed by linear regression. Target expression source information from: microRNA.org in normal text; targetscan.org shown in italics; or identified in both search engines shown in bold.

4.3 Exon (Gene) Expression

Exon and gene expression analysis of mRNAs taken from the same brain specimens of alcohol dependent and control subjects showed less than or equal to -1.5 fold down-regulation of 132 genes and greater than or equal to 1.5 fold up-regulation of 38 genes. Disturbances in down-regulated genes preferentially impacted neuronal growth and proliferation (19 of 132 genes, 14%), lipid biosynthesis and myelinization (15 genes, 11%), and cellular signaling (13 genes, 10%) suggesting a suppression of neuronal outgrowth and myelinization specifically impacting oligodendrocyte proliferation, function and survival. Sixteen genes (12%) coded for biological transport proteins and ion channels impacting metal, lipid and ion transport were also down-regulated. Other down-regulated proteins function in cellular metabolism [11 genes (8%)] and adhesion [6 genes (5%)] or play a role in immune or cytokine function [6 genes (5%)] and the remaining genes were either unknown or unclassified. Ten of 38 (26%) up-regulated genes function as transporters while 9 of the 38 (24%) were inflammatory mediators such as cytokines and/or oncogenes. Minor categories of up-regulated genes include cell signaling [4 genes (11%)], cellular structure and metabolism [8 genes (21%)] and cardiac function [3 genes (8%)] while the remaining were either unknown or unclassified. Analysis of predicted miRNA targets showed a less than or equal to -1.5 fold decrease in the expression of multiple predicted cellular mRNA targets for 10 miRNA up-regulated in alcoholism (Table 2). Three miRNAs were located within a genetic locus in the maternally imprinted chromosome 14q32 region and showed alignment with 19 separate mRNAs with down-regulation in the exon expression microarray performed on our subjects. Of these, miR-377 (-1.9 fold) had the greatest number of disturbed predicted mRNA targets (16 predicted targets) possibly indicating increased functional importance of disturbances in these particular miRNAs and the involvement of the chromosome 14q32 region as a whole.

Validated target predictions based upon TarBase 6.0 were only possible for 2 miRNA disturbed in our study (miR-375 and miR-572). Linkage to miR-375 was widely reported in TarBase showing 433 validated targets including TMTC4 and TGFB2 disturbed in our study. The microRNA, miR-572, showed one validated mRNA target (CNKN1A) which was not disturbed in our sample. Predicted binding profiles (based upon Targetscan.org and microRNA.org) were found for 7 disturbed miRNAs which appeared to converge upon several common target mRNAs in overlapping biochemical pathways of potential relevance to alcoholism (Figure 2): ELMO1 (miR-377, miR-299-3p), TMCC3 and SEMA4D1 (miR-377, miR-379), UGT8 (miR-3065-5p, miR-377), and CLCA4, TMTC2, CHN2 and EDIL3 (miR-377, miR-375). Additional overlap was also noted for miR-29b and miR-448* (Table 2). The down-regulated predicted mRNA targets of the disturbed miRNAs have functional roles in cellular adhesion (e.g., THBS2), tissue differentiation (e.g., CHN2), neuronal migration (e.g., NDE1) and myelination (e.g., UGT8, MOG). Several of the down-regulated mRNAs (e.g., MOG, TF, CHN2 ENPP2, UGT8) code for proteins associated with oligodendrocyte differentiation (Dugas et al., 2006).

Fewer predicted mRNA targets were up-regulated than down-regulated in our study of alcoholism and to a lesser degree - possibly a consequence of disease-related impairments in protein and nucleic acid synthesis reported in alcoholism (Lang, 2001; Vary et al, 2001; Karinch et al., 2008; Wani et al., 2012). Similarly, disturbances in miRNA expression appeared to have little influence over the observed increases in mRNA expression in alcoholism identified in the exon expression data.

5 Discussion

Our results suggest that alcoholism may be associated with a disturbance in the expression of select miRNAs isolated from the medial frontal cortex of alcoholics relative to age and gender-matched non-alcoholic controls. These disturbances may reflect an up-regulation of miRNAs from the biologically relevant chromosome region, 14q32. A paternally imprinted/maternally expressed DLK1-DIO3 domain of the 14q32 region that has previously been associated with disease (e.g., cancer, lupus, and schizophrenia; Felsberg et al., 2006; Moon et al., 2006; Vinuesa and Rigby, 2009; Gardiner et al., 2012; Luk et al., 2011). Up-regulation of miRNAs in the 14q32 region in our study was associated with a significant disturbance in the expression of predicted mRNA targets for 19 mRNA with functional roles in cellular adhesion (e.g., THBS2), tissue differentiation (e.g., CHN2), and neuronal migration (e.g., NDE1). Collectively, genes associated with oligodendrocyte maturation appeared to be suppressed in alcoholism and predicted targets for up-regulated miRNA appeared to be enriched in mRNA related to these processes (Dugas et al., 2006). Disturbances in the 14q32 equivalent region have been reported in other mammals (e.g., callipyge mutation in sheep; Wylie et al., 2000; Takeda et al., 2006) and thus imprinting errors (e.g., transmission ratio distortion, Yang et al., 2008) may play a role in the observed association with alcoholism in our study.

5.1 Up-regulated miRNAs in Alcoholism

Twelve miRNAs were significantly up-regulated in our study including miR-375, miR-29b, miR-377 and miR-379. MiR-375 has been implicated in the regulation of glucose metabolism and the suppression of glucose-mediated insulin secretion while miR-29b has been shown to suppress catabolism of branched-chain amino acids (Chen et al., 2012). Over-expression of miR-377 in mesangial cells is associated with increased levels of superoxide dismutases (SOD1 and SOD2) and p21 -activated kinase 1 (PAK1) which may enhance susceptibility to oxidative stress and contribute to nephropathy in diabetes and alcoholism (Wang et al., 2008). Over-expression of miR-377 in our study was associated with the greatest exon or gene expression disturbance in our alcoholics. Predicted messenger RNA targets of miR-377 included neuronal growth factors necessary for cellular adhesion, synaptogenesis, neuronal growth and maturation (Janssens et al., 2001; Christopherson et al., 2005). Up-regulation of miR-377 has been observed in animal models of diabetic nephropathy and may be induced under the condition of high glucose and by TGF-β1 (Wang, et al., 2008; Saal and Harvey, 2009). MiR-379, also found in the 14q32 region and up-regulated in our study, is believed to be a key regulator of transforming growth factor- beta (TGF-β) signaling in bone (Pollari et al., 2012). Thus, miRNAs may play pluripotent roles in a range of biological and pathological processes in alcoholism.

5.2 Suppression of Oligodendrocyte Differentiation in Alcoholism

Many of the mRNAs down-regulated in our study are also highly expressed in brain oligodendrocytes (e.g., UGT8, TF) and have been linked to oligodendrocyte differentiation in previous reports (UGT8, CHN2, TF, ENPP2, and MOG; Baumann and Pham-Dihn, 2001; Dugas et al., 2006). Oligodendrocytes are glial cells which control the growth and maintenance of white matter tracts within the central nervous system (Baumann and Pham-Dihn, 2001) and generated from oligodendrocyte precursor cells which migrate and proliferate before differentiating into mature myelinating cells. Dugas et al. (2006) characterized the gene expression profile of oligodendrocyte differentiation into 2 distinct early and late phase processes and our data supports miRNA disturbances associated with the down-regulation of genes from both the early (UGT8, ENPP2) and late phases (TF, MOG) of oligodendrocyte differentiation in alcoholism. Of interest, is the thrombospondin (THBS2) gene which is expressed in immature but not in mature astrocytes plays a role in the assembly of synapses within the developing central nervous system (Christopherson et al., 2005). Binding domains for disturbed miRNAs with possible functional significance in alcoholism appeared to converge on several common predicted target mRNAs with similar functional roles implicated in neuronal growth and functioning in alcoholics.

Alcoholism is characterized by white matter abnormalities including white matter hyper-intensities visualized on functional magnetic resonance imaging (Rosenbloom et al., 2003; Pfefferbaum et al., 2009). These abnormalities are attributed to both direct alcohol-mediated neurological injury as well as indirect effects of nutritional deficiency, particularly a deficiency of the vitamin, thiamine (Manzardo and Penick, 2006; He et al., 2007; Kashem et al., 2008). White matter abnormalities and related neurodevelopmental problems that pre-date and predict alcohol abuse have also been reported in studies of children of alcoholics (Manzardo et al., 2005; Herting et al., 2011). Lewohl et al. (2011) observed a significant upregulation of miRNA in the prefrontal cortex of alcoholics that is correlated with disturbances in overlapping mRNA predicted targets important for fatty acid and lipid biosynthesis, myelination in the central nervous system and neuronal development. Our data suggest that white matter abnormalities in alcoholism may be influenced by the up-regulation of miRNAs from the chromosome 14q32 region.

Disturbances in up-regulated genes suggest the activation of biological transport mechanisms and inflammatory mediators possibly as a response to disease-related stressors including impairments in protein production, transport and metabolism along with nucleic acid synthesis, findings commonly reported in acute alcoholism (Lang, 2001; Vary et al, 2001; Karinch et al., 2008; Wani et al., 2012). Under normal conditions, reductions in mature miRNA constructs would be expected to disinhibit mRNA translation resulting in an increase in both mRNA and protein levels. But down-regulation of select miRNAs in alcoholism is not associated with a meaningful change in mRNA expression of miRNA predicted targets for the most highly down-regulated miRNAs (≤1.5 fold change). It is possible that the disinhibitory effects of miRNA down-regulation are blunted in the context of severe alcohol dependence along with nutritional deficiencies where protein and nucleotide synthesis is often severely impaired. Similarly, the inhibitory influences of up-regulated miRNA may be amplified.

5.3 Regulation of Chromosome 14q32 Gene Expression

The paternally imprinted/maternally expressed DLK1-DIO3 gene domain of chromosome 14q32 is home to a large cluster of non-coding RNA (miRNA and snoRNA; Royo et al., 2006). Genetic expression in this region is epigenetically silenced on the paternal chromosome under normal conditions by DNA methylation of the promoter region at the intergenic germline derived differentially methylated region (IG-DMR; Wylie et al., 2000). Loss of imprinting at the 14q32 genetic loci has been associated with increased expression of gene transcripts from the normally silent paternal strand and may account for increased expression of miRNA transcripts observed in our study of alcoholic subjects (Takeda et al., 2006).

Folic acid is the primary source of methyl groups which serve as the biological substrate for DNA methylation processes (Crider et al., 2012; Hamid, 2012). Folate deficiency has the potential to alter methylation profiles within the 14q32 region and other imprinted DNA domains (Crider et al., 2012; Hamid, 2012). Severely dependent alcoholic drinkers are at high risk of developing nutritional deficiencies, particularly B vitamins including folic acid, as a result of decreased food intake, impaired absorption and cellular metabolism which could contribute to the observed miRNA disturbance (Manzardo and Penick, 2006; Hamid, 2012). There is also the possibility of inheriting disease specific differences in DNA sequence or copy number variation impacting on miRNA transcripts within this region. Genetic factors are known to contribute to alcoholism. In addition, human genome-wide association studies in alcoholism have reported linkage to the KIAA1409 gene found in the 14q32 region (Lind et al., 2011).

The present study suggests that alcoholism is associated with the up-regulation of a cluster of miRNA in the imprinted maternally expressed chromosome 14q32 region. These and other miRNA disturbances are associated with decreased expression of cell growth mediators that may target oligodendrocyte differentiation and increased apoptotic mechanisms suggesting imprinting at the 14q32 region in alcoholism by an unknown mechanism(s). Further studies are needed to characterize the role of miRNA clusters from this region and the effect of their over expression on alcoholism and related behaviors.

Highlights.

  1. Significant up-regulation of 12 human miRNAs in alcoholics relative to controls.

  2. Increased expression of 4 miRNAs from maternally expressed 14q32 chromosome region.

  3. Decreased mRNA target gene expression associated with oligodendrocyte proliferation.

  4. Down-regulated miRNAs not correlated with target mRNA gene expression disturbances.

Acknowledgments

Tissues were received from the New South Wales Tissue Resource Centre at the University of Sydney which is supported by the National Health and Medical Research Council of Australia, Schizophrenia Research Institute and the National Institute of Alcohol Abuse and Alcoholism [NIH (NIAAA) R24AA012725]. This investigation was supported by a grant from the Hubert & Richard Hanlon Trust, NICHD HD02528 and NIAAA K01-AA015935.

This investigation was supported by a grant from the Hubert & Richard Hanlon Trust, NICHD HD02528 and NIAAA K01-AA015935.

Abbreviations

cDNA

Complementary deoxyribonucleic acid

DNA

Deoxyribonucleic acid

FDR

False discovery rate

GC content

Guanine-cytosine content

mRNA

Messenger ribonucleic acid

miRNA

Micro ribonucleic acid

NIAAA

National Institute on Alcohol Abuse and Alcoholism

NICHD

National Institute of Child Health and Human Development

ncRNA

Non-coding ribonucleic acid

PMI

Post mortem interval

PCR

Polymerase chain reaction

RIN

Ribonucleic acid integrity number

RNA

Ribonucleic acid

RMA

Robust multichip average

RT-PCR

Reverse transcription polymerase chain reaction

SD

Standard deviation

scaRNA

Small Cajal body-specific ribonucleic acid

snoRNA

Small nucleolar ribonucleic acid

UCSC

University of California, Santa Cruz

UPGMA

Un-weighted average distance

3′UTR

3′ untranslated regions

Footnotes

The authors of this study have no competing financial interests pertaining to this work.

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