A Model for Profiling the Emolecular Effects of Alcohol

ORIGINAL ARTICLE The synaptoneurosome transcriptome: a model for proﬁling the emolecular effects of alcohol

D Most1,2, L Ferguson1,2, Y Blednov1, RD Mayﬁeld1 and RA Harris1

Chronic alcohol consumption changes gene expression, likely causing persistent remodeling of synaptic structures via altered translation of mRNAs within synaptic compartments of the cell. We proﬁled the transcriptome from synaptoneurosomes (SNs) and paired total homogenates (THs) from mouse amygdala following chronic voluntary alcohol consumption. In SN, both the number of alcohol-responsive mRNAs and the magnitude of fold-change were greater than in THs, including many GABA-related mRNAs upregulated in SNs. Furthermore, SN gene co-expression analysis revealed a highly connected network, demonstrating coordinated patterns of gene expression and highlighting alcohol-responsive biological pathways, such as long-term potentiation, long-term depression, glutamate signaling, RNA processing and upregulation of alcohol-responsive genes within neuroimmune modules. Alterations in these pathways have also been observed in the amygdala of human alcoholics. SNs offer an ideal model for detecting intricate networks of coordinated synaptic gene expression and may provide a unique system for investigating therapeutic targets for the treatment of alcoholism.

The Pharmacogenomics Journal (2015) 15, 177–188; doi:10.1038/tpj.2014.43; published online 19 August 2014

INTRODUCTION mRNAs from SN15,16,18,19 and TH samples from mouse amygdala, a Alcohol dependence is a severe and widespread disease. Over 17 brain region known to be involved with the negative reinforce- 20 million Americans suffer from alcohol-related problems; total cost ment of alcohol and other drugs of abuse. The present findings estimates of substance abuse in the United States exceed $600 reveal greater expression of alcohol-responsive mRNAs in SN billion annually, with 39% of that cost related to alcohol.1,2 The compared with TH. Using gene expression patterns to generate pharmacotherapies available today are significantly limited due to biological networks, the SN preparation appears ideally suited for side effects and failure to relieve drug craving, leading to high detecting alcohol-responsive groups of genes that have been relapse rates. shown to be important in human alcoholism. The gene clusters Chronic alcohol use produces long-term neuroadaptations in isolated in SN could prove useful in developing targets for the synaptic structure and function, which are likely caused by persi- future treatment of alcoholism. stent changes in gene expression.3–6 This leads to a remodeling of 7–9 neural circuitry and is one of the main features of addic- MATERIALS AND METHODS tion.10–12 Synaptic translation of mRNA is a cardinal process underlying normal function,13–16 and perturbation by alcohol Animal housing and alcohol self-administration represents a mechanism contributing to synaptic neuroadap- Adult (2-month old) C57BL/6 J female mice were purchased from The 17 fi Jackson Laboratory (Bar Harbor, ME, USA) and were maintained at the tations. The composition of speci c mRNAs in the synaptic University of Texas at Austin Animal Research Center. Mice were given a compartment may give insight into the neurobiology of different 1-week acclimation period in combined housing and another week to states of addiction and is an unexplored avenue of research. acclimate to the bottle position in individual housing. Food and water were Given the role of synaptic plasticity in alcohol dependence, provided ad libitum and monitored daily, as were the temperature and selecting a biologically relevant model system for analysis of the light/dark cycles. Mice underwent a 30-day two-bottle choice paradigm synaptic transcriptome is of critical importance. Although total with continuous (24 h) access to one bottle of 20% ethanol and one bottle 21 homogenate (TH) preparations have been used for mRNA and of water, similar to that described previously (n = 8 alcohol group, n =13 control group). Bottle weights were recorded daily, and the amount of alcohol studies in the past, this method limits identification of − alcohol consumed throughout the 30 days was calculated as g kg 1 regional mRNAs and likely underestimates the number and (Supplementary Figure S1). Bottle positions were changed daily to control magnitude of alcohol-responsive transcripts in the synapse. for position preferences, and mice were weighed every 4 days. All pro- Synaptoneurosomes (SNs) contain membrane vesicles of presy- cedures were approved by the Institutional Animal Care and Use naptic and postsynaptic compartments composed of primarily Committee at the University of Texas at Austin and adhere to NIH neurons as well as astrocytes and microglia. SNs have been used Guidelines for the ethical care and use of animals in research. to study local translation of mRNAs in the synapse15,16 and may prove to be a superior model system for alcohol effects confined SN preparation and RNA extraction to synaptic regions of the cell. Mice were euthanized by cervical dislocation and then decapitated. Brains In order to measure discrete changes within the synaptic were removed and washed for 1 min with 1 ml of ice-cold Homogenizing − 1 transcriptome following chronic alcohol consumption, we profiled Buffer (HB) containing 20 mM Hepes, 1 mM EDTA (pH 7.4), 40 U ml

1Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, TX, USA and 2The Institute for Neuroscience (INS), University of Texas at Austin, Austin, TX, USA. Correspondence: D Most, Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, 2500 Speedway, Austin, TX 78712, USA. E-mail: [email protected] Received 21 January 2014; revised 29 April 2014; accepted 18 June 2014; published online 19 August 2014 Profiling alcohol-responsive synaptic mRNAs D Most et al 178 RNAseOut (Invitrogen, Carlsbad, CA, USA), phosphatase inhibitor cocktail 3 cutoff and were therefore removed from the analysis). This package was (Sigma, St Louis, MO, USA) and protease inhibitors ‘Complete’ (Roche, also used to generate the principal component analysis. Transcripts Indianapolis, IN, USA). Brains were then placed in a coronal Zivic mouse significantly detected on 80% of the arrays were used in the analysis brain slicer with a 0.5 mm resolution (Zivic Instruments, Pittsburgh, PA, (detection probability o0.05). The data presented in this publication have USA) and sliced in the following coordinates in order to isolate extended been deposited in NCBI's Gene Expression Omnibus30 and are accessible amygdala (two coronal slices were made for greater ease of dissection): through GEO Series accession number GSE51730. coronal level 56–66 (Bregma (−0.18)–(−1.155)) and 66–80 (Bregma The ‘Limma’ package31 was used for differential expression analysis between (−1.155)–(−2.55)). The extended amygdala was dissected, placed in ice- SN and paired TH samples (paired/dependent t-test) and between the cold HB (250 ml) and homogenized for 1 min using a VWR homogenizer alcohol and control samples in SN and TH (two independent t-tests). A list and pestle (VWR, Radnor, PA, USA). To minimize homogenate loss, pestles of alcohol-responsive mRNAs was compiled from the list of genes differ- were washed with 50 ml HB after use, and the wash was collected and entially expressed between alcohol and control samples. A weighted gene added to the sample. Ten percent of the homogenate (30 ml) was snap- correlation (co-expression) network analysis32 was generated for the com- frozen in liquid nitrogen and stored at − 80 °C for subsequent RNA TH bined control and alcohol data, using the weighted gene correlation (co- analysis. expression) network analysis (WGCNA) package.32 Alcohol-responsive Paired SNs18 were isolated from the rest of the homogenate (270 ml) in a mRNA enrichment analysis was performed for each module using an manner similar to that described previously.15 Briefly, homogenates were over-representation (hypergeometric) test with a cutoff P-valueo0.05. To filtered through a 100-μm pore filter and subsequently through a 5-μm determine alcohol-responsive SN and TH modules, we used the ‘alcohol- pore filter (Millipore, Billerica, MA, USA); filters were washed with HB before responsive mRNAs’ lists from our data. For details on the WGCNA param- use for protection from RNAse. To maximize yield, the filters were washed eters, see Supplementary Methods. We evaluated whether the correlation with 50 ml HB after use, and the wash was collected and added to the between alcohol consumption and TH modules would increase depending homogenate. The homogenate was then centrifuged at 14 000 g for on WGCNA parameters. We generated another TH WGCNA network and 20 min at 4 °C in order to pellet the cell fraction containing SNs.15,19,22 The optimized for the highest correlation of modules with consumption (top supernatant was removed and the pellet snap-frozen and stored at − 80 °C 10% of the modules). Enrichment and clustering analyses were performed for SN RNA analysis. Microscopy was used to further characterize the SN using KEGG pathways, Wikipathways, gene ontologies and protein interac- preparation (see Supplementary Methods). tions, part of the Database for Annotation Visualization and Integrated Total RNA was extracted from 21 SN and 21 paired TH samples with the Discovery (DAVID),33 WEB-based GEne SeT AnaLysis Toolkit (Webgestalt)34,35 Direct-Zol RNA extraction kit (Zymo Research Corporation, Irvine, CA, USA), and Ingenuity Pathway Analysis (IPA; Qiagen, Valencia, CA, USA). All using IC columns according to the manufacturer’s instructions. The RNA P-values from these analyses were adjusted using the Benjamini–Hochberg was quantified using NanoDrop1000 (Thermo Fisher Scientific Inc., method (BH). Synaptic mRNA enrichment was assessed using a list of Rockford, IL, USA) and assayed for quality using Agilent 2100Tape- Station (Agilent Technologies, Santa Clara, CA, USA). The cutoff criteria 4 4 4 were set on 280/260 1.7, RIN 6.5 and amount of total RNA 500 ng. Table 1. Comparison of the DAVID enrichment scores of synaptoneurosome (SN)- and total homogenate (TH)-enriched cellular Microarray hybridization, data quality assessments and analysis components RNA samples were processed at the University of Texas Southwestern Medical Center microarray facility in Dallas. mRNA was amplified and DAVID enrichment clustering SN Score TH Score biotin-labeled using the Illumina TotalPrep RNA Amplification kit (Ambion, Austin, TX, USA) and hybridized to Mouse WG-6 v2.0 Expression BeadChips Intracellular 20 (1370) 32.4 (1528) (Illumina, San Diego, CA, USA). Each array contained SN and paired TH Organelle 6.2 (438) 29.7 (621) samples from control and alcohol-treated mice. These were assigned Organelle membrane 3.5 (137) 13.2 (179) randomly to each array. The array data were analyzed using R environment Synapse 2.9 (64) 1.3 (NS) (21) 21,23 and Bioconductor packages, similar to our published studies. The Abbreviations: DAVID, Database for Annotation Visualization and Inte- 24,25 ‘Lumi’ package was used to preprocess the data using variance grated Discovery; NS, non-significant. The table illustrates reduction of 26,27 stabilization transformation (variance within array), quantile normaliza- somatic and intracellular components and preservation/enrichment of 24,25 tion (variance between arrays) and background subtraction. Quality synaptic mRNAs in SN (the number of genes detected in a cluster are measures were taken before and after preprocessing using the arrayQua- shown in parenthesis). All scores for enrichment clustering are significant 28,29 lityMetrics package to remove outliers determined by at least two out with a DAVID Benjamini–Hochberg method Po0.05. A NS score was of the three tests in the package, and care was taken that the normaliza- defined as a cluster containing only one significant group out of five. tion did not skew the data (two TH samples out of the 42 failed to pass the

Figure 1. (a) Principal component analysis of expression profiles from paired synaptoneurosome (SN) and total homogenate (TH) samples are shown in green and blue, respectively. Preparation difference is the first principal component and explains 17% of the variance. (b) SN- enriched mRNA network illustrating known protein interactions between the SN-enriched mRNAs. The bottom right portion of the figure is an overview of the entire network, and the highlighted portion of this network has been enlarged. The green nodes represent the mRNAs enriched in the SN compared with the TH preparation (fold-change threshold of 410%, Benjamini–Hochberg method Po0.05). Many known synaptic mRNAs are found in the center of this network, emphasizing enrichment of the synaptic components in the SN preparation.

Table 2. Functional clustering of synaptoneurosome (SN)-enriched mRNAs

Annotation DAVID Number P-value BH Gene symbols cluster enrichment of mRNAs P-value score

Regulation of 4.1 18 8.60E-06 1.60E-02 KCNMA1, MYO6, GNAI2, GRIK5, CTNND2, MECP2, GJA1, ATP1A2, CSPG5, system process ADORA1, RIMS1, PTPN11, HDAC4, SLC1A3, NTRK2, HOPX, DLG4, CAMK2A. Synapse 4.0 26 4.00E-07 1.20E-04 GRIK5, TIMP4, RIMS1, ADORA1, SLC1A2, GP1BB, SNPH, DLG4, CAMK2A, DLG2, MT3, KCNMA1, PHACTR1, ARC, MYO6, DLGAP3, SPARCL1, PSD3, SSPN, SHANK3, PPP1R9B, HDAC4, NTRK2, VAMP3, UNC13C, SNTA1. PDZ/DHR/GLGF 3.8 14 4.10E-05 3.40E-02 SNX27, PREX1, PDLIM4, PDLIM2, MPP6, SLC9A3R1, RIMS1, SHANK3, PPP1R9B, MAST2, SIPA1L1, DLG4, DLG2, SNTA1. Cytoskeleton 3.8 51 3.30E-05 2.00E-03 KIF23, KIFC2, GFAP, TUBB2B, AIF1, FERMT2, PDLIM2, ADORA1, CTNNB1, NDE1, EVI5, DLG4, DLG2, ARC, MYO6, INPPL1, KIF5A, KIF5C, PSD3, SPIRE1, MID1IP1, TBCEL, RB1, DNAIC1, CTNNA1, FMN2, KIF1A, MAST2, KIF1B, PDE4DIP, ADD3, CAPZB, LLGL1, KLC1, GP1BB, STRBP, CDC42EP4, ACTB, DLGAP3, CKAP5, CSRP1, COTL1, SIRT2, SHANK3, PTPN11, EPB4.1L2, PPP1R9B, HDAC4, EPB4.1L1, NTRK2, SNTA1. Transmission of 3.0 18 3.90E-05 2.40E-02 KCNMA1, SCD2, MYO6, ALDH5A1, GRIK5, MECP2, TIMP4, ATP1A2, ADORA1, nerve impulse CTNNB1, MBP, ATXN1, KIF1B, ABAT, LGI4, UNC13C, NCAN, DLG2. Four hundred forty-six mRNAs were enriched in the SN and there were 163 functional clusters. In order to ﬁnd the most synaptically-enriched pathways, we used a higher threshold fold-change of 25%. The top 5 clusters are shown (BH, Po0.05). Gene symbols are shown for each cluster.

Figure 2. (a) The number of alcohol-responsive annotated mRNAs identiﬁed in paired synaptoneurosome (SN) and total homogenate (TH) samples for Po0.05 are shown (n = 8 for alcohol and n = 13 for control). (b) Fold-change produced by alcohol consumption is shown as a function of the cumulative number of transcripts. Alcohol-induced changes in the number of transcripts and magnitude of fold-changes. (c) Venn diagram showing the overlap in alcohol-responsive unique mRNAs between the SN and TH preparations (Po0.05). mRNAs enriched in the synaptic neuropil36 and in process-localized bottle choice paradigm.21 For cell types and immune response enrichment, mRNAs.36 For alcohol-responsive mRNA enrichment, we used a human we used the following lists of genes: neuronal, astrocytic and oligoden- alcoholic mRNA data set23 from the amygdala, quantitative trait loci mRNA drocyte,38 microglial,39 glutamate/GABA40 and lipopolysaccharide (LPS)- list37 and a list of mRNAs from prefrontal cortex of C57BL6 after a two- regulated mRNAs.21

Table 3. Thirty alcohol-responsive mRNAs in synaptoneurosomes (SN) that were not signiﬁcantly changed in total homogenates (TH).

Illumina ID Gene Gene info SN correlation SN fold SN TH Fold TH symbol with alcohol change P-value change P-value consumption

ILMN_1229256 Bzrap1 Benzodiazepine receptor associated protein 1 − 0.48 0.80 2.00E-02 0.99 8.72E-01 ILMN_1222167 Gria2 Glutamate receptor, ionotropic, AMPA2 (alpha 2) − 0.47 0.81 2.37E-02 1.04 2.74E-01 ILMN_2483253 Dicer1 Dicer 1, ribonuclease type III − 0.54 0.86 5.71E-03 1.01 8.13E-01 ILMN_1240346 Socs5 Suppressor of cytokine signaling 5 − 0.62 0.86 1.14E-03 0.97 5.75E-01 ILMN_2644632 Stxbp1 Syntaxin binding protein 1 − 0.66 0.86 5.84E-04 1.01 8.89E-01 ILMN_1231506 Cnrip1 Cannabinoid receptor interacting protein 1 − 0.67 0.87 5.71E-04 1.00 9.94E-01 ILMN_3105417 Bdnf Brain derived neurotrophic factor − 0.60 0.88 2.98E-03 0.95 2.52E-01 ILMN_2622817 Kcnq2 Potassium voltage-gated channel, subfamily Q, member 2 − 0.46 0.88 2.77E-02 1.04 2.74E-01 ILMN_3061460 Ntrk2 Neurotrophic tyrosine kinase, receptor, type 2 − 0.42 0.88 4.04E-02 0.98 5.22E-01 ILMN_2724044 Sncb Synuclein, beta − 0.49 0.89 3.41E-02 1.02 6.21E-01 ILMN_2760927 Kcna6 Potassium voltage-gated channel, shakerrelated, subfamily, − 0.47 0.90 2.25E-02 0.97 5.43E-01 member 6 ILMN_2713841 Hspd1 Heat shock protein 1 (chaperonin) 0.47 1.00 4.01E-02 0.97 5.63E-01 ILMN_1217180 Iﬁtm1 Interferon induced transmembrane protein 1 0.46 1.11 2.96E-02 1.09 1.41E-01 ILMN_1242178 Adh5 Alcohol dehydrogenase 5 (class III), chi polypeptide 0.46 1.15 2.65E-02 0.99 7.31E-01 ILMN_2733179 Aldh2 Aldehyde dehydrogenase 2, mitochondrial 0.46 1.15 2.76E-02 0.96 5.25E-01 ILMN_1214715 Gfap Glial ﬁbrillary acidic protein 0.49 1.16 1.31E-02 1.03 4.81E-01 ILMN_1231625 Cyp4f14 Cytochrome P450, family 4, subfamily f, polypeptide 14 0.62 1.17 2.82E-03 1.01 6.59E-01 ILMN_2771956 Calu Calumenin 0.60 1.17 3.00E-03 1.03 1.21E-01 ILMN_2881480 Vamp3 Vesicle-associated membrane protein 3 0.54 1.17 1.16E-02 0.95 8.43E-02 ILMN_2945095 Tnfrsf10b Tumor necrosis factor receptor superfamily, member 10b 0.49 1.18 1.63E-02 1.03 5.29E-01 ILMN_1229720 Tollip Toll interacting protein 0.44 1.18 2.09E-02 1.07 9.35E-02 ILMN_2719908 Cyp2j9 Cytochrome P450, family 2, subfamily j, polypeptide 9 0.49 1.18 2.03E-02 1.06 6.38E-02 ILMN_2705777 Gstm5 Glutathione S-transferase, mu 5 0.61 1.20 2.32E-03 1.02 7.91E-01 ILMN_1255438 Cpped1 Calcineurin-like phosphoesterase domain containing 1 0.71 1.20 2.65E-04 1.00 9.80E-01 ILMN_2760800 Cxcl14 Chemokine (C-X-C motif) ligand 14 0.53 1.20 9.84E-03 1.03 6.64E-01 ILMN_2680745 Gabbr1 Gamma-aminobutyric acid (GABA) B receptor, 1 0.40 1.20 3.23E-02 1.00 9.82E-01 ILMN_2776008 Gstk1 Glutathione S-transferase kappa 1 0.52 1.21 1.36E-02 0.99 7.63E-01 ILMN_2619620 C1qb Complement component 1, q subcomponent, beta polypeptide 0.45 1.21 3.30E-02 0.99 8.93E-01 ILMN_3149251 Glud1 Glutamate dehydrogenase 1 0.40 1.23 3.84E-02 1.07 2.70E-01 ILMN_1239110 Eef2 Eukaryotic translation elongation factor 2 0.47 1.42 1.48E-02 1.08 2.69E-01 SN mRNA correlation with alcohol consumption, SN and TH treatment fold-change, and p-values are shown. Fold-change41 indicates an increase in expression and fold-changeo1 indicates a reduction.

Table 4. Over-representation of human alcoholic mRNA23, process mRNAs 36 and QTL genes 37 affected by alcohol are shown for the alcohol- responsive synaptoneurosome (SN) and total homogenate (TH) mRNAs

Human alcohol-responsive genes Cell process mRNAs Alcohol QTL genes

Number of mRNAs P-value Number of mRNAs P-value Number of mRNAs P-value

SN alcohol-responsive mRNAs 327 1.39E-04 1107 3.07E-03 358 8.74E-02 TH alcohol-responsive mRNAs 83 7.59E-01 358 7.54E-02 114 2.77E-01 Abbreviation: QTL, quantitative trait loci. The signiﬁcant over-representations are highlighted in bold.

RESULTS the SN- and TH-enriched cellular components and found that The SN transcriptome is composed of synaptic mRNAs and is the SN contained fewer somatic and intracellular components, distinct from the TH while preserving and enriching the synaptic mRNAs (Table 1). We compared SN and TH transcriptomes from mouse amygdala Among the SN-enriched transcripts, many known synaptic and detected 17 514 and 18 318 transcripts in the SN and TH mRNAs were over-represented in this preparation (Table 2). Most microarrays, respectively, with a high overlap of detected trans- of these synaptic functional groups were not detected in the TH, cripts (17 265). We studied the expression levels using principal indicating that enriched synaptic mRNAs are more readily component analysis and found a distinct clustering of the two detected in the SN. Webgestalt was used to investigate known types of preparations, while showing a homogenous sample pathways (KEGG and Wikipathways; Supplementary Table S3) population within each preparation (Figure 1a). The clustering was and to generate a network of known protein interactions enriched evident along the ﬁrst principal component, indicating that in the SN (Figure 1b). The network was associated with the largest variation stems from distinct expression levels in the axon guidance and cell leading edge and highlighted the Dlg two preparations. We identiﬁed 4539 differentially expressed family (also known as postsynaptic density proteins or PSDs). SN unique mRNAs (BH, Po0.05), with 2119 mRNAs enriched in (but not TH) transcripts were also over-represented with synaptic the SN (Supplementary Table S1) and 2420 mRNAs enriched mRNAs in a high-resolution study exploring the synaptic in the TH (Supplementary Table S2). We used DAVID to compare neuropil.36

Figure 3. (a) Results of a dendrogram-hierarchical cluster of mRNAs from synaptoneurosomes (SNs) (n = 21). In the cluster, each end point represents a gene, and the genes are arranged by similarity in covariance. Genes under the same branch of the dendrogram are more similar than those outside of the branch, and their dissimilarity is represented by the y axis. Gray represents genes unrelated to others. SN preparation contains groups of mRNAs that have highly coordinated patterns of expression as seen by the low dissimilarity values. (b) Results of a dendrogram-hierarchial cluster of mRNAs from total homogenate (TH; n = 21). High dissimilarity values (marked by the gray color) indicate that the TH contains fewer mRNAs with similar patterns of expression.

The greater resolution of SN preparation captures the molecular full list is shown in Supplementary Table S4. We examined the effects of alcohol number and magnitude of fold-changes (Figure 2b) as a potential We next identiﬁed mRNAs from the amygdala of 8 alcohol-treated means for identifying the most important mRNAs involved in and 13 control mice, for a total of 21 SN and paired TH samples. In alcohol-induced changes. SNs had three times more alcohol- SNs, 1531 alcohol-responsive mRNAs were identiﬁed, compared responsive mRNAs and larger fold-changes than THs. Twenty- with 462 in THs (Figure 2a). Examples of alcohol-responsive three percent of the TH alcohol-responsive mRNAs were also mRNAs, fold-changes and P-values are shown in Table 3, and the detected in SN, compared with only 7% of SN alcohol-responsive

Figure 4. Alcohol regulates RNA processing and translational machinery in the synapse. The ﬁgure illustrates examples of alcohol-responsive mRNAs related to known RNA processing pathways. The cartoon represents the postsynaptic compartment, which was enriched in the synaptoneurosome preparation.

mRNAs that were detected in THs (Figure 2c). A functional Figure S3c). We then determined the modules that were over- annotation of the top alcohol-responsive mRNAs revealed a higher represented with alcohol-responsive mRNAs (referred to as enrichment score for synaptic mRNAs in SNs than in THs. IPA was ‘alcohol-responsive modules’). In SNs, 10 out of the 54 alcohol- used to study the molecular and cellular functions of SN alcohol- responsive modules were detected (8 were upregulated and 2 responsive mRNAs, highlighting the following top five pathways: were downregulated; Supplementary Table S5). molecular transport, protein trafficking, RNA posttranscriptional These modules were significantly associated with biological modification, cell morphology, and DNA replication, recombina- pathways such as those associated with long-term potentiation tion, and repair. Importantly, the alcohol-responsive mRNA list and depression and RNA processing and contained many from SNs was strikingly similar to the mRNA list obtained from mRNAs associated with potassium channels, glutamate and GABA human alcoholics (Table 4), suggesting that the alcohol-drinking systems. Upregulated mRNAs include Camkk2, Camta1, Capn2, paradigm used in this study induced similar mRNA changes in Ntrk2, Ntsr2, Stx18, Stx8, Stxbp4, Syap1, Synj2bp, Prkcdbp and mouse amygdala to that observed in human amygdala23 and also Grk6. Downregulated mRNAs include brain-derived neurotrophic demonstrating that SNs are ideal for characterizing transcriptome factor (BDNF), Camsap3, Capn6, Negr1, Nptn, Ntrk2, Unc5c, Stx3, changes in chronic ethanol-treated mice that are relevant in Stxbp1, Stxbp2, Syncrip, Sst, Sstr2, Sncb and Timp4. The potassium human alcoholics. The SN alcohol-responsive mRNAs were highly channel family was also highly responsive to alcohol and includes enriched for neuron process-localized mRNAs36 and contained the voltage-gated potassium channels (Kcna6 and Kcnq2, down- many alcohol-consumption quantitative trait locus genes found in regulated), calcium-activated Kcnu1 (SLO-3-slowpoke3, down- mice,37 further highlighting this preparation as a tool to detect regulated) and inwardly rectifying potassium channels (Kcnj1 alcohol-responsive mRNAs related to synaptic function and and Kctd20, upregulated). The following glutamate- and GABA- structure. related transcripts were upregulated: Grk6, Glud1, Slc1a2, Slc1a3, Gabbr1, and Gabrb2, whereas the following were downregulated: Alcohol affects the transcriptome in a coordinated manner Grina, Gria2, Grip1, VGlut2 (Slc17a6), Grm7, and Narg2. 32 As mentioned, RNA processing machinery was a highly over- We used WGCNA to group genes into modules that have strong represented biological pathway associated with alcohol-respon- co-varying (similar) patterns of expression across the sample set. sive mRNAs (Figure 4). These mRNAs include RNA transcriptional, Hierarchical clustering showed that the SN preparation contains translational, spliceosomal and editing machineries, as well as groups of mRNAs that have highly coordinated patterns of many mRNAs for ribosomal proteins, suggesting that chronic expression (Figure 3a). When using the same parameters for THs, alcohol affects translational mechanisms in the synapse. the majority of mRNAs showed dissimilar levels of expression (marked by the gray color) (Figure 3b). We adjusted the TH network parameters to optimizing module correlation with SN alcohol-responsive effects are cell-type specific alcohol consumption. Allowing for less similarity between the A cell-type-specific enrichment analysis was performed for the mRNAs would enable greater detection of group-clustered alcohol-responsive mRNAs using neuronal, astrocytic, oligoden- modules in THs (Supplementary Figure S3a). However, the change drocyte, microglial, GABA and glutamate gene lists (see Methods in parameters did not significantly affect the correlation between for details). The upregulated alcohol-responsive mRNAs in SNs the TH modules and alcohol consumption (Supplementary Figure were enriched with microglial, astrocytic and GABAergic cell types, S3b). In SNs, 40% of the modules were significantly correlated with while the downregulated mRNAs were enriched in neuronal cell alcohol consumption (average r = 0.6, Po0.05) (Supplementary types (Table 5). This trend of upregulation of microglial cell types

The Pharmacogenomics Journal (2015), 177 – 188 © 2015 Macmillan Publishers Limited Proﬁling alcohol-responsive synaptic mRNAs D Most et al 183 was also true for the SN modules in the WGCNA network 1 02 22 01 01 01 (Figure 5a). Of the eight alcohol-responsive upregulated modules − − − − − − in SN, six were also positively correlated with alcohol consumption -value

P and enriched with microglia and astrocyte mRNAs (Table 6). The two downregulated modules were negatively correlated with

0.05) are bolded. alcohol consumption and enriched in neuronal mRNAs. DAVID

o fi P and Webgestalt were used to nd the KEGG and Wikipathways 74 4.88E 70 1.27E over-represented in each of the modules (Figure 5b). In the THs, only three modules were found to be cell specific (two astrocytic/ microglial and one neuronal). 20 03 152 4.71E 01 01 27 6.46E 01 99 1.30E 01 29 8.94E fi

cant values ( The immune system, speci cally the neuroimmune system, has − − ﬁ − − − − been recently implicated in alcohol dependence,41 and LPS -value GABA

P treatment, which activates an immune response, enhances alcohol consumption in mice.42 An over-representation analysis of the SN alcohol-responsive mRNAs with a list of LPS-regulated mRNAs showed a signiﬁcant representation of LPS mRNAs (Po0.05) and highlighted 55 common mRNAs found in chronic alcohol and 166 1.71E chronic LPS treatment. Many astrocyte and microglial transcripts related to neuroimmune signaling were upregulated by chronic alcohol consumption. Key immune/inﬂammatory genes, including 05 102 1.13E 01 60 4.36E 01 01 32 4.53E 01 99 4.10E 01 39 4.88E − − − − − − Tnfaip8l2, Tnfrsf10b, Traf4 and Tollip, were all upregulated by

-value Glutamate alcohol. In addition, chemokine and complement-related tran- P scripts were upregulated, including the following: Ccr5, C1qa, C1qb, Ccrn4l, CCR4, Cxcl14, Gfap and Gbas. The gluthathione and peroxisome pathways were altered by alcohol, almost all of which were upregulated (Gstk1, Gstm5, Gstm6, Gpt2, Gpx4, Gpx7, Pex5, Pex6, Prdx3). 5 9.95E 24 7.34E 45 6.10E

DISCUSSION We proﬁled mRNAs from SNs and paired TH preparations from the 13 07 01 7 8.60E 01 01 19 9.94E amygdala of ethanol-treated mice, using a within-subject compar- − − − − − ison, and found a robust difference between the SN and TH -value Oligodendrocytes

P alcohol-responsive mRNAs. A greater number of alcohol-responsive mRNAs with larger fold-changes were detected in the SNs as well as a greater enrichment of synaptic mRNAs. Our results suggest that the SN is a useful preparation for studying synaptic 8 1.00E+00 14 8.94E 39 8.68E 55 5.87E 47 5.12E 101 2.55E (both neuronal and glial) molecular changes associated with chronic alcohol consumption. Although there are other reports of RNA composition in 36,43–47 09 27 05 01 01 18 4.92E synaptic preparations, there have been no direct compar- − − − − − isons of synaptic vs paired TH. The SN preparation has been used -value Neuron

P to identify synaptic networks related to neurodegenerative disorders,48 mental retardation,13 schizophrenia49 and cocaine addiction.50 However, this is the ﬁrst alcohol study utilizing SN preparations. We used a model of alcohol consumption that produces intoxicating blood ethanol concentrations51 and induces 115 5.33E

-values are shown for microglia, astrocyte, neuron, oligodendrocyte, glutamate and GABA mRNA lists. The signi 21 P mRNA expression changes in the prefrontal cortex of mice, as well as functional changes in the nucleus accumbens.51 Our results, showing that the THs from amygdala contained 500 03 91 2.51E 04 250 1.03E 01 24 9.52E 02 01 43 1.00E+00 01 26 4.95E − − − − − − differentially expressed mRNAs, are consistent with previous ﬁ 21 ﬁ

-value Astrocytes ndings. A key nding is that the chronic alcohol paradigm P used here in mice induces changes in the transcriptome of the amygdala that are similar to those observed in the amygdala of human alcoholics (Table 4).23 Our current results also highlight the ’

5 8.52E utility of SNs compared with THs in studying alcohol s molecular 20 5.81E 28 5.02E effects, given that the overlapping expression changes between mouse and human were observed using SNs but not in our previous studies using THs.21 There are two possible explanations for the difference between the SN and TH preparations. First, restricting gene expression proﬁling to the synaptic compartments (preventing dilution with the somatic transcriptome) should facilitate detection of speciﬁc mRNAs that are localized to the synapse. Our results from the Over-representation of the cell types for the alcohol-responsive mRNAs in synaptoneurosomes (SN) and total homogenates (TH) s effect on SN 25 5.40E s effect on TH 7 3.94E ’ ’ weighted gene co-expression networks showed many mRNAs with similar or overlapping patterns of expression in SNs, while the TH network contained few overlapping networks. The similarity in Alcohol-responsive downregulated SN Alcohol Alcohol-responsive upregulated SN SN enriched Alcohol TH enriched 15 5.97E Data set Microglia Table 5. The number of overlapping mRNAs and over-representation Fold changes above 10% were used for the SN- and TH-enriched lists. functional gene networks in SNs facilitates the detection of

Figure 5. (a) Alcohol-responsive synaptic modules, correlation with alcohol consumption and cell specificity. The dendrogram shows the hierarchical relationship between the gene modules for the synaptoneurosome (SN) mRNA network. Below the dendrogram, a heatmap shows the module correlation to amount of alcohol consumed; red and green indicate strong positive and negative correlations, respectively. The boxed correlations represent the significant alcohol-responsive modules (over-representation-hypergeometric test), and the letters inside the boxes represent the cell type over-represented in the modules: N = neuron, M = microglia, and A = astrocyte. The different color under the dendrogram correspond to the different modules, and these match with the colors in Figure 3a. (b) Alcohol-responsive modules, cell specificity and biological pathways. The KEGG and Wikipathways and biological functions identified as alcohol-responsive are shown for each of the modules in SN, illustrating the most critical biological functions in groups of co-expressed mRNAs (DAVID (Database for Annotation Visualization and Integrated Discovery) and Webgestalt, BH (Benjamini–Hochberg method) corrected Po0.05). Pathways from modules with similar expression were grouped together for each of the over-represented cell types. The arrow in each box represents the direction of alcohol response (that is, upregulation or downregulation by alcohol treatment). The text-box border colors correspond to the module colors. Boxes A and B are enriched for neurons; Box C is enriched for microglia and astrocytes; Box D is enriched for astrocytes. Cell type illustrations were modified from Kettenman et al., 2013.73

alcohol actions that are speciﬁc to the synaptic region. Second, processing enzymes show synaptic localization, suggesting a well- alcohol could selectively target synaptic mRNAs, ultimately orchestrated microRNA regulation in the synapse.53–55 In fact, our changing gene expression in the synapse. This is supported by data show that synaptic microRNA enzymes such as Dicer1 and the ﬁnding that RNA processing machinery was responsive to Eif2c3 are alcohol sensitive. Previous studies from our group alcohol. For example, RNA transcriptional, translational, spliceoso- anticipated that Dicer would be a predicted target of microRNAs mal and editing machineries, as well as many ribosomal proteins, in human alcoholic brain samples.56 It is appealing to propose were over-represented in the alcohol-responsive mRNAs and the that alcohol affects synaptic microRNA machinery, allowing for different modules, suggesting that chronic alcohol use affects targeted regulation of gene expression in the synapse. translation in the synapse. Studies of synaptic compartments show We found that BDNF and its receptor TrkB as well as potassium involvement of microRNAs in regulating synaptic translation channels were all altered by alcohol, corroborating well-docu- of mRNAs.15,16,52 Furthermore, microRNAs, their precursors and mented alcohol-induced changes in these receptors. BDNF was

Table 6. Over-representation of cell type speciﬁc mRNA in the synaptoneurosome (SN) modules based on a hypergeometric test with microglia, astrocyte, neuron and oligodendrocyte mRNA lists

Module No. of P-value Average Microglia P-value Astrocytes P-value Neuron P-value Oligodendrocytes P-value Correlation P-value KEGG Wikipathways signiﬁcant fold pathways mRNAs change in module

Tan 237 4.27E − 64 1.08 11 6.52E − 04 27 1.71E − 05 3 1.00E+00 7 4.47E − 01 0.05 8.30E − 01 Oxidative phosphorylation, metabolic Electron transport chain, complement activation, pathways, Parkinson's disease, systemic classical pathway, oxidative phosphorylation, lupus erythematosus, peroxisome, mitochondrial LC-fatty acid beta-oxidation Huntington's disease, Alzheimer's disease, valine, leucine and isoleucine degradation, pyrimidine metabolism, ribosome, Staphylococcus aureus infection, purine metabolism, proteasome Ivory 39 1.88E − 08 1.07 4 8.82E − 03 15 3.95E − 08 1 9.83E − 01 3 1.90E − 01 0.50 2.23E − 02 Keap1-Nrf2, glutathione metabolism, Glutathione metabolism, B-cell receptor signaling glutathione and one-carbon metabolism pathway Blue 753 1.42E − 49 1.06 18 4.59E − 01 50 8.05E − 01 110 2.29E − 03 23 9.68E − 01 − 0.75 9.88E − 05 Metabolic pathways, proteasome, oxidative Proteasome degradation, translation factors, mRNA phosphorylation, protein processing in processing, oxidative phosphorylation, electron endoplasmic reticulum, phagosome, transport chain, TCA cycle aminoacyl-tRNA biosynthesis, protein export, lysosome, ubiquitin-mediated proteolysis, Parkinson's disease, collecting duct acid secretion, glycolysis/ Pro gluconeogenesis, RNA transport, nucleotide Most D

excision repair, SNARE interactions in fi vesicular transport, vasopressin-regulated mRNAs synaptic alcohol-responsive ling water reabsorption Sienna3 42 6.84E − 06 1.06 2 2.60E − 01 11 3.40E − 04 0 1.00E+00 0 1.00E+00 0.50 1.98E − 02 Systemic lupus erythematosus NA al et Lightgreen 82 9.00E − 06 1.06 10 5.29E − 05 24 8.66E − 08 2 1.00E+00 2 9.06E − 01 0.52 1.55E − 02 Ribosome, oxidative phosphorylation, Electron transport chain, cytoplasmic ribosomal Huntington's disease, Parkinson's disease, proteins, oxidative phosphorylation Alzheimer's disease, SNARE interactions in vesicular transport, metabolic pathways, peroxisome Steelblue 40 3.51E − 03 1.05 5 5.75E − 03 14 1.78E − 05 1 9.97E − 01 1 8.83E − 01 0.38 9.06E − 02 NA NA Green 141 1.41E − 04 1.03 4 6.96E − 01 26 5.56E − 03 33 2.54E − 02 5 9.40E − 01 − 0.57 7.16E − 03 NA NA Floralwhite 24 2.08E − 02 1.03 0 1.00E+00 6 2.21E − 02 0 1.00E+00 0 1.00E+00 0.52 1.51E − 02 Amino-acid metabolism, mRNA Spliceosome, alanine, aspartate and glutamate processing, TCA cycle metabolism Yellow 149 1.07E − 04 0.96 0 1.00E+00 4 1.00E+00 49 3.63E − 07 6 8.64E − 01 0.32 1.57E − 01 NA Hypothetical network for drug addiction, TNF-alpha NF-kB signaling pathway, PluriNetWork, mRNA processing, splicing factor NOVA-regulated synpatic proteins Turquoise 747 4.62E − 33 0.94 5 9.98E − 01 15 1.00E+00 101 2.04E − 05 22 7.90E − 01 − 0.05 8.17E − 01 Long-term potentiation, long-term mRNA processing, hypothetical network for drug depression addiction The significant values (Po0.05) are bolded. Average fold-change41 indicates an increase in gene expression in a module and fold-changeo1 indicates reduction in gene expression. – 188 185 Profiling alcohol-responsive synaptic mRNAs D Most et al 186

Figure 6. Theoretical model for metabolism of alcohol in the brain. Alcohol-responsive mRNAs associated with alcohol metabolism and the glutamate metabolic pathway in synaptoneurosomes. Upregulated mRNAs are shown in red, and downregulated mRNAs are in green.

downregulated, whereas one TrkB transcript was downregulated glutamate and GABA metabolizing enzymes, receptors and and one was upregulated. These results might be due to probe transporters and tumor necrosis factor-alpha receptors and their hybridization differences stemming from different splice variants. interacting proteins. This suggests that the upregulated astrocyte- BDNF is involved in synaptic plasticity,57,58 obesity59 and addic- specific genes could induce a wide range of effects following tion,60 and potassium channels have been associated with chronic alcohol consumption, ranging from neuroimmune to increased sensitivity and tolerance to the sedative effects of plasticity responses. The SN preparation also enriches for peri- ethanol,61 seizure susceptibility,62 neonatal familial convulsions synaptic microglial and astroglial processes.67,68 As microglia and and epilepsy63 and may be important in the withdrawal-induced astrocytes can actively engage in synaptic function68 and have seizures caused by chronic alcohol consumption in humans. been associated with alcoholism,23 the SN preparation may be Chronic alcohol use might result in increased metabolism of useful in investigating alcohol’s effects on the neuroimmune alcohol or acetate in the brain.64 A study in human alcoholics system. Because the cell-type specificity in our SN preparation is showed an increase in glutamate–glutamine and GABA labeling in not known, we used a comprehensive bioinformatics approach heavy compared with light drinkers.64 In our study, we found similar to Ponomarev et al.,23 including identifying mRNAs which many alcohol-metabolizing enzymes that were upregulated by are co-expressed with known astro-glial markers and examining alcohol consumption, in agreement with a previous study.65 the astro-glia mRNAs altered by alcohol consumption. Furthermore, alcohol-responsive glutamate and GABA mRNAs Extra-nuclear splicing has been discovered as a process were detected in SNs (GABA-related mRNAs were associated with involved in synaptic structure and function,69 and this process the upregulated alcohol-responsive mRNAs, see Table 5). Figure 6 may explain why nuclear mRNAs were found in the SN prepara- illustrates how the alcohol-responsive mRNAs can participate in tion. Alternatively, there could be some nuclear contamination. alcohol degradation to produce metabolites that can enter the The estimate of nuclear contamination in our SN preparation is TCA cycle and be converted into glutamate, which may contribute based on two main measurements: (1) DAPI (4',6-diamidino-2- to the dysregulation in the glutamate system seen in alcoholics. phenylindole) staining of nuclear DNA, showing no detected The sensitivity of the SN preparation compared with the TH also staining in the SNs (Supplementary Figure S2).70 (2) Neun (a allowed for improved cell-type enrichment analysis, enabling the nuclear protein) western blots, showing the SN preparation detection of a high positive correlation of astrocyte and microglial decreases 75% of the Neun found in the TH.70 mRNAs with alcohol consumption in the SNs. All of the alcohol- Although there is evidence for synaptic translation in post- responsive genes in these astrocyte/microglia modules were synaptic neuronal compartments,71 translation might also take upregulated. Given that these cell types are generally associated place in the presynaptic compartment (axonal terminal).72 The SN with neuroinflammation, a potential consequence of chronic preparation enriches for both the presynaptic and the postsynap- alcohol use is activation of neuroimmune signaling. The adapta- tic fractions, and further research is warranted to determine tion of the neuroimmune system is consistent with data from the whether alcohol differentially affects these two compartments. amygdala of human alcoholics23 and supports the emerging In summary, we identified coordinated changes in mRNA expres- concept that there is a neuroimmune response to chronic alcohol sion in SNs and THs following chronic alcohol consumption. The use.41 In addition, astrocytes might have role in regulating expression changes in SNs from mouse amygdala corroborate that synaptic plasticity by altering the levels of glutamate, GABA and seen in the amygdala of human alcoholics and include over- tumor necrosis factor-alpha available in the synapse.66 We found lapping changes in GABA, glutamate and neuroimmune pathways. that all three of these systems were sensitive to alcohol, including Our results demonstrate that (1) the mouse chronic-drinking

The Pharmacogenomics Journal (2015), 177 – 188 © 2015 Macmillan Publishers Limited Profiling alcohol-responsive synaptic mRNAs D Most et al 187 paradigm used in this study is sufficient to produce the same 18 Hollingsworth EB, McNeal ET, Burton JL, Williams RJ, Daly JW, Creveling CR. Bio- expression changes previously seen in human alcoholics and (2) chemical characterization of a filtered synaptoneurosome preparation from gui- the parallel changes are evident for the SN but not TH mouse nea pig cerebral cortex: cyclic adenosine 3':5'-monophosphate-generating 5 – transcriptome. Our results highlight the advantage of the mouse systems, receptors, and enzymes. J Neurosci 1985; : 2240 2253. 19 Wang H, Kim S, Ryu WS. DDX3 DEAD-Box RNA helicase inhibits hepatitis B virus SN preparation for identifying therapeutic gene targets for alcohol reverse transcription by incorporation into nucleocapsids. J Virol 2009; 83 dependence that are relevant in humans and for studying 5815–5824. synaptic plasticity under normal and disease conditions. 20 Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacology 2010; 35:217–238. 21 Osterndorff-Kahanek E, Ponomarev I, Blednov YA, Harris RA. Gene expression in CONFLICT OF INTEREST brain and liver produced by three different regimens of alcohol consumption in mice: comparison with immune activation. PLoS One 2013; 8: e59870. The authors declare no conflict of interest. 22 Wang CY, Kim HH, Hiroi Y, Sawada N, Salomone S, Benjamin LE et al. Obesity increases vascular senescence and susceptibility to ischemic injury through 2 ACKNOWLEDGMENTS chronic activation of Akt and mTOR. Sci Signal 2009; ra11. 23 Ponomarev I, Wang S, Zhang L, Harris RA, Mayfield RD. Gene coexpression net- We thank Kimberly Raab-Graham for valuable instruction in the isolation of works in human brain identify epigenetic modifications in alcohol dependence. synaptoneurosomes and for providing the PSD-95 antibody. We thank Jill Benavidez J Neurosci 2012; 32: 1884–1897. and Kathryn Ondricek for help with the behavioral paradigm and synaptoneurosome 24 Wang Y, Adachi Y, Imsumran A, Yamamoto H, Piao W, Li H et al. Targeting for preparation. We thank Joseph Corey for the generous assistance with computational insulin-like growth factor-I receptor with short hairpin RNA for human digestive/ analysis, Marianna Grenadier for help with the illustrations and Jody Mayfield for gastrointestinal cancers. J Gastroenterol 2010; 45: 159–170. helpful scientific editing. This work was supported by the National Institute of Health 25 Wang Y, Brahmakshatriya V, Zhu H, Lupiani B, Reddy SM, Yoon BJ et al. Grants 1F31-AA022557-01, AA-UO1-13520, AA012404, RC2AA019382 and AA020683. Identification of differentially expressed miRNAs in chicken lung and trachea with avian influenza virus infection by a deep sequencing approach. BMC Genomics 2009; 10:512. REFERENCES 26 Dunning MJ, Ritchie ME, Barbosa-Morais NL, Tavare S, Lynch AG. Spike-in vali- dation of an Illumina-specific variance-stabilizing transformation. BMC Res Notes 1 Wang Y, Kim JD, Saetre R, Pyysalo S, Tsujii J. Investigating heterogeneous protein 2008; 1:18. annotations toward cross-corpora utilization. BMC Bioinformatics 2009; 10: 403. 27 Lin SM, Du P, Huber W, Kibbe WA. Model-based variance-stabilizing transforma- 2 Rehm J, Mathers C, Popova S, Thavorncharoensap M, Teerawattananon Y, Patra J. tion for Illumina microarray data. Nucleic Acids Res 2008; 36: e11. Global burden of disease and injury and economic cost attributable to alcohol use 28 Kauffmann A, Gentleman R, Huber W. arrayQualityMetrics--a bioconductor pack- 373 – and alcohol-use disorders. Lancet 2009; : 2223 2233. age for quality assessment of microarray data. Bioinformatics 2009; 25:415–416. 3 Rimondini R, Arlinde C, Sommer W, Heilig M. Long-lasting increase in voluntary 29 Kauffmann A, Huber W. Microarray data quality control improves the detection of ethanol consumption and transcriptional regulation in the rat brain after inter- differentially expressed genes. Genomics 2010; 95:138–142. 16 – mittent exposure to alcohol. FASEB J 2002; :27 35. 30 Wang H, Knaub LA, Jensen DR, Young Jung D, Hong EG, Ko HJ et al. Skeletal 4 Arlinde C, Sommer W, Bjork K, Reimers M, Hyytia P, Kiianmaa K et al. A cluster of muscle-specific deletion of lipoprotein lipase enhances insulin signaling in ske- fi differentially expressed signal transduction genes identi ed by microarray ana- letal muscle but causes insulin resistance in liver and other tissues. Diabetes 2009; 4 – lysis in a rat genetic model of alcoholism. Pharmacogenomics J 2004; :208 218. 58:116–124. 5 Liu J, Lewohl JM, Harris RA, Iyer VR, Dodd PR, Randall PK et al. Patterns of gene 31 Wang K, Zhang H, Kugathasan S, Annese V, Bradfield JP, Russell RK et al. Diverse expression in the frontal cortex discriminate alcoholic from nonalcoholic indivi- genome-wide association studies associate the IL12/IL23 pathway with Crohn 31 – duals. Neuropsychopharmacology 2006; : 1574 1582. Disease. Am J Hum Genet 2009; 84: 399–405. fi 6 Melendez RI, McGinty JF, Kalivas PW, Becker HC. Brain region-speci c gene 32 Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network expression changes after chronic intermittent ethanol exposure and early with- analysis. BMC Bioinformatics 2008; 9: 559. 17 – drawal in C57BL/6 J mice. Addict Biol 2012; : 351 364. 33 Wang PC, Wester BA, Rajaraman S, Paik SJ, Kim SH, Allen MG. Hollow polymer fi 7Mayeld RD, Lewohl JM, Dodd PR, Herlihy A, Liu J, Harris RA. Patterns of gene microneedle array fabricated by photolithography process combined with expression are altered in the frontal and motor cortices of human alcoholics. micromolding technique. Conf Proc IEEE Eng Med Biol Soc 2009; 2009: 7026–7029. J Neurochem 2002; 81:802–813. 34 Wang K, Tang Z, Yang CJ, Kim Y, Fang X, Li W et al. Molecular engineering of DNA: 8 Kerns RT, Ravindranathan A, Hassan S, Cage MP, York T, Sikela JM et al. Ethanol- molecular beacons. Angew Chem Int Ed Engl 2009; 48: 856–870. responsive brain region expression networks: implications for behavioral 35 Wang Y, O'Connell JR, McArdle PF, Wade JB, Dorff SE, Shah SJ et al. From the responses to acute ethanol in DBA/2 J versus C57BL/6 J mice. J Neurosci 2005; 25: Cover: Whole-genome association study identifies STK39 as a hypertension 2255–2266. susceptibility gene. Proc Natl Acad Sci USA 2009; 106:226–231. 9 Hansson AC, Rimondini R, Neznanova O, Sommer WH, Heilig M. Neuroplasticity in 36 Cajigas IJ, Tushev G, Will TJ, tom Dieck S, Fuerst N, Schuman EM. The local brain reward circuitry following a history of ethanol dependence. Eur J Neurosci transcriptome in the synaptic neuropil revealed by deep sequencing and high- 2008; 27: 1912–1922. resolution imaging. Neuron 2012; 74: 453–466. 10 Nestler EJ. Molecular basis of long-term plasticity underlying addiction. Nat Rev 37 Mulligan MK, Ponomarev I, Hitzemann RJ, Belknap JK, Tabakoff B, Harris RA et al. Neurosci 2001; 2: 119–128. Toward understanding the genetics of alcohol drinking through transcriptome 11 Kauer JA, Malenka RC. Synaptic plasticity and addiction. Nat Rev Neurosci 2007; 8: meta-analysis. Proc Natl Acad Sci USA 2006; 103: 6368–6373. 844–858. 38 Cahoy JD, Emery B, Kaushal A, Foo LC, Zamanian JL, Christopherson KS et al. 12 Kertes DA, Kalsi G, Prescott CA, Kuo PH, Patterson DG, Walsh D et al. Neuro- A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new transmitter and neuromodulator genes associated with a history of depressive resource for understanding brain development and function. J Neurosci 2008; 28: symptoms in individuals with alcohol dependence. Alcohol Clin Exp Res 2011; 35: 264–278. 496–505. 39 Wang K, Zhang H, Ma D, Bucan M, Glessner JT, Abrahams BS et al. Common 13 Wang DO, Kim SM, Zhao Y, Hwang H, Miura SK, Sossin WS et al. Synapse- and genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 2009; stimulus-specific local translation during long-term neuronal plasticity. Science 459:528–533. 2009; 324: 1536–1540. 40 Sugino K, Hempel CM, Miller MN, Hattox AM, Shapiro P, Wu C et al. Molecular 14 Wang L, Wang Z, Xu Y, Joo SH, Kim SK, Xue Z et al. OsGSR1 is involved in crosstalk taxonomy of major neuronal classes in the adult mouse forebrain. Nat Neurosci between gibberellins and brassinosteroids in rice. Plant J 2009; 57:498–510. 2006; 9:99–107. 15 Wang XQ, Kravchuk O, Liu PY, Kempf M, Boogaard CV, Lau P et al. The evaluation 41 Mayfield J, Ferguson L, Harris RA. Neuroimmune signaling: a key component of of a clinical scar scale for porcine burn scars. Burns 2009; 35: 538–546. alcohol abuse. Curr Opin Neurobiol 2013; 23:513–520. 16 Raab-Graham KF, Haddick PC, Jan YN, Jan LY. Activity- and mTOR-dependent 42 Blednov YA, Benavidez JM, Geil C, Perra S, Morikawa H, Harris RA. Activation of suppression of Kv1.1 channel mRNA translation in dendrites. Science 2006; 314: inflammatory signaling ptby lipopolysaccharide produces a prolonged increase of 144–148. voluntary alcohol intake in mice. Brain Behav Immun 2011; 25(Suppl 1) : 17 Barak S, Liu F, Ben Hamida S, Yowell QV, Neasta J, Kharazia V et al. Disruption of S92–S105. alcohol-related memories by mTORC1 inhibition prevents relapse. Nat Neurosci 43 Tian QB, Nakayama K, Okano A, Suzuki T. Identification of mRNAs localizing in the 2013; 16: 1111–1117. postsynaptic region. Brain Res Mol Brain Res 1999; 72:147–157.

© 2015 Macmillan Publishers Limited The Pharmacogenomics Journal (2015), 177 – 188 Profiling alcohol-responsive synaptic mRNAs D Most et al 188 44 Eberwine J, Belt B, Kacharmina JE, Miyashiro K. Analysis of subcellularly localized 59 Cao L, Lin EJ, Cahill MC, Wang C, Liu X, During MJ. Molecular therapy of obesity mRNAs using in situ hybridization, mRNA amplification, and expression profiling. and diabetes by a physiological autoregulatory approach. Nat Med 2009; 15: Neurochem Res 2002; 27: 1065–1077. 447–454. 45 Sung YJ, Weiler IJ, Greenough WT, Denman RB. Selectively enriched mRNAs in rat 60 Lobo MK, Covington HE 3rd, Chaudhury D, Friedman AK, Sun H, Damez-Werno D synaptoneurosomes. Brain Res Mol Brain Res 2004; 126:81–87. et al. Cell type-specific loss of BDNF signaling mimics optogenetic control of 46 Poon MM, Choi SH, Jamieson CA, Geschwind DH, Martin KC. Identification of cocaine reward. Science 2010; 330: 385–390. process-localized mRNAs from cultured rodent hippocampal neurons. J Neurosci 61 Wang Y, Roche O, Yan MS, Finak G, Evans AJ, Metcalf JL et al. Regulation of 2006; 26: 13390–13399. endocytosis via the oxygen-sensing pathway. Nat Med 2009; 15:319–324. 47 Wang XQ, Liu PY, Kempf M, Cuttle L, Mill J, Kimble RM. Burn wound infection in a 62 Wang P, Bang JK, Kim HJ, Kim JK, Kim Y, Shin SY. Antimicrobial specificity and porcine burn model. J Burn Care Res 2009; 30:369–370. mechanism of action of disulfide-removed linear analogs of the plant-derived 48 Wang L, Shi J, Kim YS, Zhai S, Jia B, Zhao H et al. Improving tumor-targeting Cys-rich antimicrobial peptide Ib-AMP1. Peptides 2009; 30: 2144–2149. capability and pharmacokinetics of (99 m)Tc-labeled cyclic RGD dimers with PEG 63 Wang J, Liu HB, Wang W, Kim I, Ha CS. A thiazoline-containing cobalt(II) complex (4) linkers. Mol Pharm 2009; 6:231–245. based colorimetric fluorescent probe: ‘turn-on’ detection of fluoride. Dalton Trans 49 Smalheiser NR, Lugli G, Zhang H, Rizavi H, Cook EH, Dwivedi Y. Expression of 2009; 47: 10422–10425. microRNAs and other small RNAs in prefrontal cortex in schizophrenia, bipolar 64 Wang JH, Bae JH, Lim HC, Shon WY, Kim CW, Cho JW. Medial open wedge high disorder and depressed subjects. PLoS One 2014; 9: e86469. tibial osteotomy: the effect of the cortical hinge on posterior tibial slope. Am J 50 Eipper-Mains JE, Kiraly DD, Palakodeti D, Mains RE, Eipper BA, Graveley BR. Sports Med 2009; 37: 2411–2418. microRNA-Seq reveals cocaine-regulated expression of striatal microRNAs. RNA 65 Liu J, Lewohl JM, Dodd PR, Randall PK, Harris RA, Mayfield RD. Gene expression 2011; 17: 1529–1543. profiling of individual cases reveals consistent transcriptional changes in alcoholic 51 Middaugh LD, Szumlinski KK, Van Patten Y, Marlowe AL, Kalivas PW. Chronic human brain. J Neurochem 2004; 90: 1050–1058. ethanol consumption by C57BL/6 mice promotes tolerance to its interoceptive 66 Wang C, Kang SG, Lee J, Sun Z, Kim CH. The roles of CCR6 in migration of Th17 cues and increases extracellular dopamine, an effect blocked by naltrexone. cells and regulation of effector T-cell balance in the gut. Mucosal Immunol 2009; 2: Alcohol Clin Exp Res 2003; 27: 1892–1900. 173–183. 52 Rajasethupathy P, Fiumara F, Sheridan R, Betel D, Puthanveettil SV, Russo JJ et al. 67 Gerstner JR, Vanderheyden WM, LaVaute T, Westmark CJ, Rouhana L, Pack AI et al. Characterization of small RNAs in Aplysia reveals a role for miR-124 in con- Time of day regulates subcellular trafficking, tripartite synaptic localization, straining synaptic plasticity through CREB. Neuron 2009; 63:803–817. and polyadenylation of the astrocytic Fabp7 mRNA. J Neurosci 2012; 32: 53 Lugli G, Larson J, Martone ME, Jones Y, Smalheiser NR. Dicer and eIF2c are enri- 1383–1394. ched at postsynaptic densities in adult mouse brain and are modified by neuronal 68 Halassa MM, Fellin T, Haydon PG. The tripartite synapse: roles for gliotransmission activity in a calpain-dependent manner. J Neurochem 2005; 94: 896–905. in health and disease. Trends Mol Med 2007; 13:54–63. 54 Lugli G, Torvik VI, Larson J, Smalheiser NR. Expression of microRNAs and their 69 Glanzer J, Miyashiro KY, Sul JY, Barrett L, Belt B, Haydon P et al. RNA splicing precursors in synaptic fractions of adult mouse forebrain. J Neurochem 2008; 106: capability of live neuronal dendrites. Proc Natl Acad Sci USA 2005; 102: 650–661. 16859–16864. 55 Huang YW, Ruiz CR, Eyler EC, Lin K, Meffert MK. Dual regulation of miRNA bio- 70 Sosanya NM, Huang PP, Cacheaux LP, Chen CJ, Nguyen K, Perrone-Bizzozero NI genesis generates target specificity in neurotrophin-induced protein synthesis. et al. Degradation of high affinity HuD targets releases Kv1.1 mRNA from miR-129 Cell 2012; 148:933–946. repression by mTORC1. J Cell Biol 2013; 202:53–69. 56 Lewohl JM, Nunez YO, Dodd PR, Tiwari GR, Harris RA, Mayfield RD. Up-regulation 71 Zalfa F, Eleuteri B, Dickson KS, Mercaldo V, De Rubeis S, di Penta A et al. Anew of microRNAs in brain of human alcoholics. Alcohol Clin Exp Res 2011; 35: function for the fragile X mental retardation protein in regulation of PSD-95 1928–1937. mRNA stability. Nat Neurosci 2007; 10:578–587. 57 Wang Y, Booth CJ, Kim H, Qiu M, Constable RT. Evaluation of hepatic fibrosis with 72 Akins MR, Berk-Rauch HE, Fallon JR. Presynaptic translation: stepping out of the portal pressure gradient in rats. Magn Reson Med 2009; 61: 1185–1192. postsynaptic shadow. Front Neural Circuits 2009; 3:17. 58 Im HI, Hollander JA, Bali P, Kenny PJ. MeCP2 controls BDNF expression and 73 Wang S, Kim S, Chawla S, Wolf RL, Zhang WG, O'Rourke DM et al. Differentiation cocaine intake through homeostatic interactions with microRNA-212. Nat Neurosci between glioblastomas and solitary brain metastases using diffusion tensor 2010; 13: 1120–1127. imaging. Neuroimage 2009; 44:653–660.

Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website (http://www.nature.com/tpj)