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

ORIGINAL ARTICLE

The synaptoneurosome transcriptome: a model for profilingthe emolecular effects of alcoholD 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 alteredtranslation of mRNAs within synaptic compartments of the cell. We profiled the transcriptome from synaptoneurosomes (SNs) andpaired total homogenates (THs) from mouse amygdala following chronic voluntary alcohol consumption. In SN, both the number ofalcohol-responsive mRNAs and the magnitude of fold-change were greater than in THs, including many GABA-related mRNAsupregulated in SNs. Furthermore, SN gene co-expression analysis revealed a highly connected network, demonstrating coordinatedpatterns of gene expression and highlighting alcohol-responsive biological pathways, such as long-term potentiation, long-termdepression, 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 detectingintricate networks of coordinated synaptic gene expression and may provide a unique system for investigating therapeutic targetsfor the treatment of alcoholism.

The Pharmacogenomics Journal advance online publication, 19 August 2014; doi:10.1038/tpj.2014.43

INTRODUCTIONAlcohol dependence is a severe and widespread disease. Over 17million Americans suffer from alcohol-related problems; total costestimates of substance abuse in the United States exceed $600billion annually, with 39% of that cost related to alcohol.1,2 Thepharmacotherapies available today are significantly limited due toside effects and failure to relieve drug craving, leading to highrelapse rates.Chronic alcohol use produces long-term neuroadaptations in

synaptic structure and function, which are likely caused by persi-stent changes in gene expression.3–6 This leads to a remodeling ofneural circuitry7–9 and is one of the main features of addic-tion.10–12 Synaptic translation of mRNA is a cardinal processunderlying normal function,13–16 and perturbation by alcoholrepresents a mechanism contributing to synaptic neuroadap-tations.17 The composition of specific mRNAs in the synapticcompartment may give insight into the neurobiology of differentstates of addiction and is an unexplored avenue of research.Given the role of synaptic plasticity in alcohol dependence,

selecting a biologically relevant model system for analysis of thesynaptic transcriptome is of critical importance. Although totalhomogenate (TH) preparations have been used for mRNA andalcohol studies in the past, this method limits identification ofregional mRNAs and likely underestimates the number andmagnitude of alcohol-responsive transcripts in the synapse.Synaptoneurosomes (SNs) contain membrane vesicles of presy-naptic and postsynaptic compartments composed of primarilyneurons as well as astrocytes and microglia. SNs have been usedto study local translation of mRNAs in the synapse15,16 and mayprove to be a superior model system for alcohol effects confinedto synaptic regions of the cell.In order to measure discrete changes within the synaptic

transcriptome following chronic alcohol consumption, we profiled

mRNAs from SN15,16,18,19 and TH samples from mouse amygdala, abrain region known to be involved with the negative reinforce-ment of alcohol and other drugs of abuse.20 The present findingsreveal greater expression of alcohol-responsive mRNAs in SNcompared with TH. Using gene expression patterns to generatebiological networks, the SN preparation appears ideally suited fordetecting alcohol-responsive groups of genes that have beenshown to be important in human alcoholism. The gene clustersisolated in SN could prove useful in developing targets for thefuture treatment of alcoholism.

MATERIALS AND METHODSAnimal housing and alcohol self-administrationAdult (2-month old) C57BL/6 J female mice were purchased from TheJackson Laboratory (Bar Harbor, ME, USA) and were maintained at theUniversity of Texas at Austin Animal Research Center. Mice were given a1-week acclimation period in combined housing and another week toacclimate to the bottle position in individual housing. Food and water wereprovided ad libitum and monitored daily, as were the temperature andlight/dark cycles. Mice underwent a 30-day two-bottle choice paradigmwith continuous (24 h) access to one bottle of 20% ethanol and one bottleof water, similar to that described previously21 (n=8 alcohol group, n=13control group). Bottle weights were recorded daily, and the amount ofalcohol consumed throughout the 30 days was calculated as g kg− 1

(Supplementary Figure S1). Bottle positions were changed daily to controlfor position preferences, and mice were weighed every 4 days. All pro-cedures were approved by the Institutional Animal Care and UseCommittee at the University of Texas at Austin and adhere to NIHGuidelines for the ethical care and use of animals in research.

SN preparation and RNA extractionMice were euthanized by cervical dislocation and then decapitated. Brainswere removed and washed for 1 min with 1 ml of ice-cold HomogenizingBuffer (HB) containing 20mM Hepes, 1 mM EDTA (pH 7.4), 40 Uml− 1

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] 21 January 2014; revised 29 April 2014; accepted 18 June 2014

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RNAseOut (Invitrogen, Carlsbad, CA, USA), phosphatase inhibitor cocktail 3(Sigma, St Louis, MO, USA) and protease inhibitors ‘Complete’ (Roche,Indianapolis, IN, USA). Brains were then placed in a coronal Zivic mousebrain slicer with a 0.5 mm resolution (Zivic Instruments, Pittsburgh, PA,USA) and sliced in the following coordinates in order to isolate extendedamygdala (two coronal slices were made for greater ease of dissection):coronal level 56–66 (Bregma (−0.18)–(−1.155)) and 66–80 (Bregma(−1.155)–(−2.55)). The extended amygdala was dissected, placed in ice-cold HB (250ml) and homogenized for 1 min using a VWR homogenizerand pestle (VWR, Radnor, PA, USA). To minimize homogenate loss, pestleswere washed with 50ml HB after use, and the wash was collected andadded to the sample. Ten percent of the homogenate (30ml) was snap-frozen in liquid nitrogen and stored at − 80 °C for subsequent RNA THanalysis.Paired SNs18 were isolated from the rest of the homogenate (270ml) in a

manner similar to that described previously.15 Briefly, homogenates werefiltered through a 100-μm pore filter and subsequently through a 5-μmpore filter (Millipore, Billerica, MA, USA); filters were washed with HB beforeuse for protection from RNAse. To maximize yield, the filters were washedwith 50ml HB after use, and the wash was collected and added to thehomogenate. The homogenate was then centrifuged at 14 000 g for20min at 4 °C in order to pellet the cell fraction containing SNs.15,19,22 Thesupernatant was removed and the pellet snap-frozen and stored at − 80 °Cfor SN RNA analysis. Microscopy was used to further characterize the SNpreparation (see Supplementary Methods).Total RNA was extracted from 21 SN and 21 paired TH samples with the

Direct-Zol RNA extraction kit (Zymo Research Corporation, Irvine, CA, USA),using IC columns according to the manufacturer’s instructions. The RNAwas quantified using NanoDrop1000 (Thermo Fisher Scientific Inc.,Rockford, IL, USA) and assayed for quality using Agilent 2100Tape-Station (Agilent Technologies, Santa Clara, CA, USA). The cutoff criteriawere set on 280/26041.7, RIN46.5 and amount of total RNA4500 ng.

Microarray hybridization, data quality assessments and analysisRNA samples were processed at the University of Texas SouthwesternMedical Center microarray facility in Dallas. mRNA was amplified andbiotin-labeled using the Illumina TotalPrep RNA Amplification kit (Ambion,Austin, TX, USA) and hybridized to Mouse WG-6 v2.0 Expression BeadChips(Illumina, San Diego, CA, USA). Each array contained SN and paired THsamples from control and alcohol-treated mice. These were assignedrandomly to each array. The array data were analyzed using R environmentand Bioconductor packages, similar to our published studies.21,23 The‘Lumi’ package24,25 was used to preprocess the data using variancestabilization transformation (variance within array),26,27 quantile normaliza-tion (variance between arrays) and background subtraction.24,25 Qualitymeasures were taken before and after preprocessing using the arrayQua-lityMetrics package28,29 to remove outliers determined by at least two outof the three tests in the package, and care was taken that the normaliza-tion did not skew the data (two TH samples out of the 42 failed to pass the

cutoff and were therefore removed from the analysis). This package wasalso used to generate the principal component analysis. Transcriptssignificantly detected on 80% of the arrays were used in the analysis(detection probability o0.05). The data presented in this publication havebeen deposited in NCBI's Gene Expression Omnibus30 and are accessiblethrough GEO Series accession number GSE51730.The ‘Limma’ package31 was used for differential expression analysis between

SN and paired TH samples (paired/dependent t-test) and between thealcohol and control samples in SN and TH (two independent t-tests). A listof alcohol-responsive mRNAs was compiled from the list of genes differ-entially expressed between alcohol and control samples. A weighted genecorrelation (co-expression) network analysis32 was generated for the com-bined control and alcohol data, using the weighted gene correlation (co-expression) network analysis (WGCNA) package.32 Alcohol-responsivemRNA enrichment analysis was performed for each module using anover-representation (hypergeometric) test with a cutoff P-valueo0.05. Todetermine alcohol-responsive SN and TH modules, we used the ‘alcohol-responsive mRNAs’ lists from our data. For details on the WGCNA param-eters, see Supplementary Methods. We evaluated whether the correlationbetween alcohol consumption and TH modules would increase dependingon WGCNA parameters. We generated another TH WGCNA network andoptimized for the highest correlation of modules with consumption (top10% of the modules). Enrichment and clustering analyses were performedusing KEGG pathways, Wikipathways, gene ontologies and protein interac-tions, part of the Database for Annotation Visualization and IntegratedDiscovery (DAVID),33 WEB-based GEne SeT AnaLysis Toolkit (Webgestalt)34,35

and Ingenuity Pathway Analysis (IPA; Qiagen, Valencia, CA, USA). AllP-values from these analyses were adjusted using the Benjamini–Hochbergmethod (BH). Synaptic mRNA enrichment was assessed using a list of

Figure 1. (a) Principal component analysis of expression profiles from paired synaptoneurosome (SN) and total homogenate (TH) samples areshown 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 anoverview of the entire network, and the highlighted portion of this network has been enlarged. The green nodes represent the mRNAsenriched in the SN compared with the TH preparation (fold-change threshold of 410%, Benjamini–Hochberg method Po0.05). Many knownsynaptic mRNAs are found in the center of this network, emphasizing enrichment of the synaptic components in the SN preparation.

Table 1. Comparison of the DAVID enrichment scores ofsynaptoneurosome (SN)- and total homogenate (TH)-enriched cellularcomponents

DAVID enrichment clustering SN Score TH Score

Intracellular 20 (1370) 32.4 (1528)Organelle 6.2 (438) 29.7 (621)Organelle membrane 3.5 (137) 13.2 (179)Synapse 2.9 (64) 1.3 (NS) (21)

Abbreviations: DAVID, Database for Annotation Visualization and Inte-grated Discovery; NS, non-significant. The table illustrates reduction ofsomatic and intracellular components and preservation/enrichment ofsynaptic mRNAs in SN (the number of genes detected in a cluster areshown in parenthesis). All scores for enrichment clustering are significantwith a DAVID Benjamini–Hochberg method Po0.05. A NS score wasdefined as a cluster containing only one significant group out of five.

Profiling alcohol-responsive synaptic mRNAsD Most et al

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mRNAs enriched in the synaptic neuropil36 and in process-localizedmRNAs.36 For alcohol-responsive mRNA enrichment, we used a humanalcoholic mRNA data set23 from the amygdala, quantitative trait loci mRNAlist37 and a list of mRNAs from prefrontal cortex of C57BL6 after a two-

bottle choice paradigm.21 For cell types and immune response enrichment,we used the following lists of genes: neuronal, astrocytic and oligoden-drocyte,38 microglial,39 glutamate/GABA40 and lipopolysaccharide (LPS)-regulated mRNAs.21

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

Annotationcluster

DAVIDenrichment

score

Numberof mRNAs

P-value BHP-value

Gene symbols

Regulation ofsystem process

4.1 18 8.60E-06 1.60E-02 KCNMA1, MYO6, GNAI2, GRIK5, CTNND2, MECP2, GJA1, ATP1A2, CSPG5,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 ofnerve impulse

3.0 18 3.90E-05 2.40E-02 KCNMA1, SCD2, MYO6, ALDH5A1, GRIK5, MECP2, TIMP4, ATP1A2, ADORA1,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, weused 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 afunction 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).


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© 2014 Macmillan Publishers Limited The Pharmacogenomics Journal (2014), 1 – 12

RESULTSThe SN transcriptome is composed of synaptic mRNAs and isdistinct from the THWe compared SN and TH transcriptomes from mouse amygdalaand detected 17 514 and 18 318 transcripts in the SN and THmicroarrays, respectively, with a high overlap of detected trans-cripts (17 265). We studied the expression levels using principalcomponent analysis and found a distinct clustering of the twotypes of preparations, while showing a homogenous samplepopulation within each preparation (Figure 1a). The clustering wasevident along the first principal component, indicating thatthe largest variation stems from distinct expression levels in thetwo preparations. We identified 4539 differentially expressedunique mRNAs (BH, Po0.05), with 2119 mRNAs enriched inthe SN (Supplementary Table S1) and 2420 mRNAs enrichedin the TH (Supplementary Table S2). We used DAVID to compare

the SN- and TH-enriched cellular components and found thatthe SN contained fewer somatic and intracellular components,while preserving and enriching the synaptic mRNAs (Table 1).Among the SN-enriched transcripts, many known synapticmRNAs were over-represented in this preparation (Table 2). Mostof these synaptic functional groups were not detected in the TH,indicating that enriched synaptic mRNAs are more readilydetected in the SN. Webgestalt was used to investigate knownpathways (KEGG and Wikipathways; Supplementary Table S3)and to generate a network of known protein interactions enrichedin the SN (Figure 1b). The network was associated withaxon guidance and cell leading edge and highlighted the Dlgfamily (also known as postsynaptic density proteins or PSDs). SN(but not TH) transcripts were also over-represented with synapticmRNAs in a high-resolution study exploring the synapticneuropil.36

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

Illumina ID Genesymbol

Gene info SN correlationwith alcoholconsumption

SN foldchange

SNP-value

TH Foldchange

THP-value

ILMN_1229256 Bzrap1 Benzodiazepine receptor associated protein 1 − 0.48 0.80 2.00E-02 0.99 8.72E-01ILMN_1222167 Gria2 Glutamate receptor, ionotropic, AMPA2 (alpha 2) − 0.47 0.81 2.37E-02 1.04 2.74E-01ILMN_2483253 Dicer1 Dicer 1, ribonuclease type III − 0.54 0.86 5.71E-03 1.01 8.13E-01ILMN_1240346 Socs5 Suppressor of cytokine signaling 5 − 0.62 0.86 1.14E-03 0.97 5.75E-01ILMN_2644632 Stxbp1 Syntaxin binding protein 1 − 0.66 0.86 5.84E-04 1.01 8.89E-01ILMN_1231506 Cnrip1 Cannabinoid receptor interacting protein 1 − 0.67 0.87 5.71E-04 1.00 9.94E-01ILMN_3105417 Bdnf Brain derived neurotrophic factor − 0.60 0.88 2.98E-03 0.95 2.52E-01ILMN_2622817 Kcnq2 Potassium voltage-gated channel, subfamily Q, member 2 − 0.46 0.88 2.77E-02 1.04 2.74E-01ILMN_3061460 Ntrk2 Neurotrophic tyrosine kinase, receptor, type 2 − 0.42 0.88 4.04E-02 0.98 5.22E-01ILMN_2724044 Sncb Synuclein, beta − 0.49 0.89 3.41E-02 1.02 6.21E-01ILMN_2760927 Kcna6 Potassium voltage-gated channel, shakerrelated, subfamily,

member 6− 0.47 0.90 2.25E-02 0.97 5.43E-01

ILMN_2713841 Hspd1 Heat shock protein 1 (chaperonin) 0.47 1.00 4.01E-02 0.97 5.63E-01ILMN_1217180 Ifitm1 Interferon induced transmembrane protein 1 0.46 1.11 2.96E-02 1.09 1.41E-01ILMN_1242178 Adh5 Alcohol dehydrogenase 5 (class III), chi polypeptide 0.46 1.15 2.65E-02 0.99 7.31E-01ILMN_2733179 Aldh2 Aldehyde dehydrogenase 2, mitochondrial 0.46 1.15 2.76E-02 0.96 5.25E-01ILMN_1214715 Gfap Glial fibrillary acidic protein 0.49 1.16 1.31E-02 1.03 4.81E-01ILMN_1231625 Cyp4f14 Cytochrome P450, family 4, subfamily f, polypeptide 14 0.62 1.17 2.82E-03 1.01 6.59E-01ILMN_2771956 Calu Calumenin 0.60 1.17 3.00E-03 1.03 1.21E-01ILMN_2881480 Vamp3 Vesicle-associated membrane protein 3 0.54 1.17 1.16E-02 0.95 8.43E-02ILMN_2945095 Tnfrsf10b Tumor necrosis factor receptor superfamily, member 10b 0.49 1.18 1.63E-02 1.03 5.29E-01ILMN_1229720 Tollip Toll interacting protein 0.44 1.18 2.09E-02 1.07 9.35E-02ILMN_2719908 Cyp2j9 Cytochrome P450, family 2, subfamily j, polypeptide 9 0.49 1.18 2.03E-02 1.06 6.38E-02ILMN_2705777 Gstm5 Glutathione S-transferase, mu 5 0.61 1.20 2.32E-03 1.02 7.91E-01ILMN_1255438 Cpped1 Calcineurin-like phosphoesterase domain containing 1 0.71 1.20 2.65E-04 1.00 9.80E-01ILMN_2760800 Cxcl14 Chemokine (C-X-C motif ) ligand 14 0.53 1.20 9.84E-03 1.03 6.64E-01ILMN_2680745 Gabbr1 Gamma-aminobutyric acid (GABA) B receptor, 1 0.40 1.20 3.23E-02 1.00 9.82E-01ILMN_2776008 Gstk1 Glutathione S-transferase kappa 1 0.52 1.21 1.36E-02 0.99 7.63E-01ILMN_2619620 C1qb Complement component 1, q subcomponent, beta polypeptide 0.45 1.21 3.30E-02 0.99 8.93E-01ILMN_3149251 Glud1 Glutamate dehydrogenase 1 0.40 1.23 3.84E-02 1.07 2.70E-01ILMN_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 inexpression 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-02TH 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.


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The greater resolution of SN preparation captures the moleculareffects of alcoholWe next identified mRNAs from the amygdala of 8 alcohol-treatedand 13 control mice, for a total of 21 SN and paired TH samples. InSNs, 1531 alcohol-responsive mRNAs were identified, comparedwith 462 in THs (Figure 2a). Examples of alcohol-responsivemRNAs, fold-changes and P-values are shown in Table 3, and the

full list is shown in Supplementary Table S4. We examined thenumber and magnitude of fold-changes (Figure 2b) as a potentialmeans for identifying the most important mRNAs involved inalcohol-induced changes. SNs had three times more alcohol-responsive mRNAs and larger fold-changes than THs. Twenty-three percent of the TH alcohol-responsive mRNAs were alsodetected in SN, compared with only 7% of SN alcohol-responsive

Figure 3. (a) Results of a dendrogram-hierarchical cluster of mRNAs from synaptoneurosomes (SNs) (n= 21). In the cluster, each end pointrepresents a gene, and the genes are arranged by similarity in covariance. Genes under the same branch of the dendrogram are more similarthan those outside of the branch, and their dissimilarity is represented by the y axis. Gray represents genes unrelated to others. SNpreparation contains groups of mRNAs that have highly coordinated patterns of expression as seen by the low dissimilarity values. (b) Resultsof 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.


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mRNAs that were detected in THs (Figure 2c). A functionalannotation of the top alcohol-responsive mRNAs revealed a higherenrichment score for synaptic mRNAs in SNs than in THs. IPA wasused to study the molecular and cellular functions of SN alcohol-responsive mRNAs, highlighting the following top five pathways:molecular transport, protein trafficking, RNA posttranscriptionalmodification, cell morphology, and DNA replication, recombina-tion, and repair. Importantly, the alcohol-responsive mRNA listfrom SNs was strikingly similar to the mRNA list obtained fromhuman alcoholics (Table 4), suggesting that the alcohol-drinkingparadigm used in this study induced similar mRNA changes inmouse amygdala to that observed in human amygdala23 and alsodemonstrating that SNs are ideal for characterizing transcriptomechanges in chronic ethanol-treated mice that are relevant inhuman alcoholics. The SN alcohol-responsive mRNAs were highlyenriched for neuron process-localized mRNAs36 and containedmany alcohol-consumption quantitative trait locus genes found inmice,37 further highlighting this preparation as a tool to detectalcohol-responsive mRNAs related to synaptic function andstructure.

Alcohol affects the transcriptome in a coordinated mannerWe used WGCNA32 to group genes into modules that have strongco-varying (similar) patterns of expression across the sample set.Hierarchical clustering showed that the SN preparation containsgroups of mRNAs that have highly coordinated patterns ofexpression (Figure 3a). When using the same parameters for THs,the majority of mRNAs showed dissimilar levels of expression(marked by the gray color) (Figure 3b). We adjusted the THnetwork parameters to optimizing module correlation withalcohol consumption. Allowing for less similarity between themRNAs would enable greater detection of group-clusteredmodules in THs (Supplementary Figure S3a). However, the changein parameters did not significantly affect the correlation betweenthe TH modules and alcohol consumption (Supplementary FigureS3b). In SNs, 40% of the modules were significantly correlated withalcohol consumption (average r= 0.6, Po0.05) (Supplementary

Figure S3c). We then determined the modules that were over-represented with alcohol-responsive mRNAs (referred to as‘alcohol-responsive modules’). In SNs, 10 out of the 54 alcohol-responsive modules were detected (8 were upregulated and 2were downregulated; Supplementary Table S5).These modules were significantly associated with biological

pathways such as those associated with long-term potentiationand depression and RNA processing and contained manymRNAs associated with potassium channels, glutamate and GABAsystems. Upregulated mRNAs include Camkk2, Camta1, Capn2,Ntrk2, Ntsr2, Stx18, Stx8, Stxbp4, Syap1, Synj2bp, Prkcdbp andGrk6. Downregulated mRNAs include brain-derived neurotrophicfactor (BDNF), Camsap3, Capn6, Negr1, Nptn, Ntrk2, Unc5c, Stx3,Stxbp1, Stxbp2, Syncrip, Sst, Sstr2, Sncb and Timp4. The potassiumchannel family was also highly responsive to alcohol and includesthe voltage-gated potassium channels (Kcna6 and Kcnq2, down-regulated), calcium-activated Kcnu1 (SLO-3-slowpoke3, down-regulated) and inwardly rectifying potassium channels (Kcnj1and Kctd20, upregulated). The following glutamate- and GABA-related transcripts were upregulated: Grk6, Glud1, Slc1a2, Slc1a3,Gabbr1, and Gabrb2, whereas the following were downregulated:Grina, Gria2, Grip1, VGlut2 (Slc17a6), Grm7, and Narg2.As mentioned, RNA processing machinery was a highly over-

represented biological pathway associated with alcohol-respon-sive mRNAs (Figure 4). These mRNAs include RNA transcriptional,translational, spliceosomal and editing machineries, as well asmany mRNAs for ribosomal proteins, suggesting that chronicalcohol affects translational mechanisms in the synapse.

SN alcohol-responsive effects are cell-type specificA cell-type-specific enrichment analysis was performed for thealcohol-responsive mRNAs using neuronal, astrocytic, oligoden-drocyte, microglial, GABA and glutamate gene lists (see Methodsfor details). The upregulated alcohol-responsive mRNAs in SNswere enriched with microglial, astrocytic and GABAergic cell types,while the downregulated mRNAs were enriched in neuronal celltypes (Table 5). This trend of upregulation of microglial cell types

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


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was also true for the SN modules in the WGCNA network(Figure 5a). Of the eight alcohol-responsive upregulated modulesin SN, six were also positively correlated with alcohol consumptionand enriched with microglia and astrocyte mRNAs (Table 6). Thetwo downregulated modules were negatively correlated withalcohol consumption and enriched in neuronal mRNAs. DAVIDand Webgestalt were used to find the KEGG and Wikipathwaysover-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).The immune system, specifically the neuroimmune system, has

been recently implicated in alcohol dependence,41 and LPStreatment, which activates an immune response, enhances alcoholconsumption in mice.42 An over-representation analysis of the SNalcohol-responsive mRNAs with a list of LPS-regulated mRNAsshowed a significant representation of LPS mRNAs (Po0.05) andhighlighted 55 common mRNAs found in chronic alcohol andchronic LPS treatment. Many astrocyte and microglial transcriptsrelated to neuroimmune signaling were upregulated by chronicalcohol consumption. Key immune/inflammatory genes, includingTnfaip8l2, Tnfrsf10b, Traf4 and Tollip, were all upregulated byalcohol. In addition, chemokine and complement-related tran-scripts were upregulated, including the following: Ccr5, C1qa,C1qb, Ccrn4l, CCR4, Cxcl14, Gfap and Gbas. The gluthathione andperoxisome pathways were altered by alcohol, almost all of whichwere upregulated (Gstk1, Gstm5, Gstm6, Gpt2, Gpx4, Gpx7, Pex5,Pex6, Prdx3).

DISCUSSIONWe profiled mRNAs from SNs and paired TH preparations from theamygdala of ethanol-treated mice, using a within-subject compar-ison, and found a robust difference between the SN and THalcohol-responsive mRNAs. A greater number of alcohol-respon-sive mRNAs with larger fold-changes were detected in the SNs aswell as a greater enrichment of synaptic mRNAs. Our resultssuggest that the SN is a useful preparation for studying synaptic(both neuronal and glial) molecular changes associated withchronic alcohol consumption.Although there are other reports of RNA composition in

synaptic preparations,36,43–47 there have been no direct compar-isons of synaptic vs paired TH. The SN preparation has been usedto identify synaptic networks related to neurodegenerativedisorders,48 mental retardation,13 schizophrenia49 and cocaineaddiction.50 However, this is the first alcohol study utilizing SNpreparations. We used a model of alcohol consumption thatproduces intoxicating blood ethanol concentrations51 and inducesmRNA expression changes in the prefrontal cortex of mice,21 aswell as functional changes in the nucleus accumbens.51 Ourresults, showing that the THs from amygdala contained 500differentially expressed mRNAs, are consistent with previousfindings.21 A key finding is that the chronic alcohol paradigmused here in mice induces changes in the transcriptome of theamygdala that are similar to those observed in the amygdala ofhuman alcoholics (Table 4).23 Our current results also highlight theutility of SNs compared with THs in studying alcohol’s moleculareffects, given that the overlapping expression changes betweenmouse and human were observed using SNs but not in ourprevious studies using THs.21

There are two possible explanations for the difference betweenthe SN and TH preparations. First, restricting gene expressionprofiling to the synaptic compartments (preventing dilution withthe somatic transcriptome) should facilitate detection of specificmRNAs that are localized to the synapse. Our results from theweighted gene co-expression networks showed many mRNAswith similar or overlapping patterns of expression in SNs, while theTH network contained few overlapping networks. The similarity infunctional gene networks in SNs facilitates the detection ofTa

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r-representationofthecelltypes

forthealco

hol-responsive

mRNAsin

synap

toneu

rosomes

(SN)an

dtotalhomogen

ates

(TH)

Dataset

Microglia

P-value

Astrocytes

P-value

Neuron

P-value

Oligod

endrocytes

P-value

Glutamate

P-value

GABA

P-value

SNen

rich

ed28

5.02E−04

250

1.03E−27

398.68

E−01

456.10E−05

102

1.13E−03

152

4.71E−22

THen

rich

ed15

5.97

E−01

431.00

E+00

101

2.55E−13

247.34

E−01

166

1.71E−20

744.88

E−01

Alcohol’s

effect

onSN

255.40

E−02

115

5.33E−05

555.87

E−01

199.94

E−01

994.10

E−01

991.30

E−01

Alcohol’s

effect

onTH

73.94

E−01

264.95

E−01

184.92

E−01

78.60

E−01

324.53

E−01

276.46

E−01

Alcohol-responsive

upregulatedSN

20

5.81E−03

91

2.51E−09

81.00

E+00

148.94

E−01

604.36

E−01

70

1.27E−02

Alcohol-responsive

downregulatedSN

58.52

E−01

249.52

E−01

47

5.12E−07

59.95

E−01

394.88

E−01

298.94

E−1

Thenumber

ofove

rlap

pingmRNAsan

dove

r-representationP-values

areshownformicroglia,astrocyte,n

euron,o

ligoden

drocyte,g

lutamatean

dGABAmRNAlists.Thesignificantvalues

(Po

0.05

)arebolded

.Fo

ldch

anges

above

10%

wereusedfortheSN

-an

dTH

-enrich

edlists.


7


alcohol actions that are specific to the synaptic region. Second,alcohol could selectively target synaptic mRNAs, ultimatelychanging gene expression in the synapse. This is supported bythe finding that RNA processing machinery was responsive toalcohol. For example, RNA transcriptional, translational, spliceoso-mal and editing machineries, as well as many ribosomal proteins,were over-represented in the alcohol-responsive mRNAs and thedifferent modules, suggesting that chronic alcohol use affectstranslation in the synapse. Studies of synaptic compartments showinvolvement of microRNAs in regulating synaptic translationof mRNAs.15,16,52 Furthermore, microRNAs, their precursors and

processing enzymes show synaptic localization, suggesting a well-orchestrated microRNA regulation in the synapse.53–55 In fact, ourdata show that synaptic microRNA enzymes such as Dicer1 andEif2c3 are alcohol sensitive. Previous studies from our groupanticipated that Dicer would be a predicted target of microRNAsin human alcoholic brain samples.56 It is appealing to proposethat alcohol affects synaptic microRNA machinery, allowing fortargeted regulation of gene expression in the synapse.We found that BDNF and its receptor TrkB as well as potassium

channels were all altered by alcohol, corroborating well-docu-mented alcohol-induced changes in these receptors. BDNF was

Figure 5. (a) Alcohol-responsive synaptic modules, correlation with alcohol consumption and cell specificity. The dendrogram shows thehierarchical relationship between the gene modules for the synaptoneurosome (SN) mRNA network. Below the dendrogram, a heatmapshows 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 insidethe boxes represent the cell type over-represented in the modules: N=neuron, M=microglia, and A= astrocyte. The different color under thedendrogram correspond to the different modules, and these match with the colors in Figure 3a. (b) Alcohol-responsive modules, cellspecificity and biological pathways. The KEGG and Wikipathways and biological functions identified as alcohol-responsive are shown for eachof the modules in SN, illustrating the most critical biological functions in groups of co-expressed mRNAs (DAVID (Database for AnnotationVisualization and Integrated Discovery) and Webgestalt, BH (Benjamini–Hochberg method) corrected Po0.05). Pathways from modules withsimilar expression were grouped together for each of the over-represented cell types. The arrow in each box represents the direction ofalcohol 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 illustrationswere modified from Kettenman et al., 2013.73


8


Table6.

Ove

r-representationofcelltypespecificmRNAin

thesynap

toneu

rosome(SN)modulesbased

onahyp

ergeo

metrictest

withmicroglia,astrocyte,n

euronan

dolig

oden

drocyte

mRNAlists

Mod

ule

No.

ofsign

ificant

mRN

As

inmod

ule

P-value

Average

fold

chan

ge

Microglia

P-value

Astrocytes

P-value

Neuron

P-value

Oligod

endrocytes

P-value

Correlation

P-value

KEGG

pathways

Wikipathw

ays

Tan

237

4.27E−64

1.08

11

6.52E−04

27

1.71E−05

31.00

E+00

74.47

E−01

0.05

8.30

E−01

Oxidativephosphorylation,metab

olic

pathways,Pa

rkinson'sdisease,systemic

lupuserythem

atosus,perox

isome,

Huntington'sdisease,A

lzheimer'sdisease,

valin

e,leucinean

disoleucinedeg

radation,

pyrim

idinemetab

olism,ribosome,

Stap

hyloco

ccusau

reusinfection,purine

metab

olism,p

roteasome

Electrontran

sport

chain,complemen

tactivation,

classicalpathway,oxidativephosphorylation,

mitoch

ondrial

LC-fatty

acid

beta-oxidation

Ivory

39

1.88E−08

1.07

48.82E−03

15

3.95E−08

19.83

E−01

31.90

E−01

0.50

2.23E−02

Keap1-Nrf2,

glutathionemetab

olism,

glutathionean

done-carbonmetab

olism

Glutathionemetab

olism,B

-cellreceptorsignaling

pathway

Blue

753

1.42E−49

1.06

184.59

E−01

508.05

E−01

110

2.29E−03

239.68

E−01

−0.75

9.88E−05

Metab

olic

pathways,proteasome,

oxidative

phosphorylation,p

rotein

processingin

endoplasm

icreticu

lum,p

hag

osome,

aminoacyl-tRNAbiosynthesis,p

rotein

export,lysosome,

ubiquitin-m

ediated

proteolysis,Pa

rkinson'sdisease,collecting

duct

acid

secretion,g

lyco

lysis/

gluco

neo

gen

esis,R

NAtran

sport,nucleo

tide

excisionrepair,SN

AREinteractionsin

vesicu

lartran

sport,vasopressin-reg

ulated

water

reab

sorption

Proteasomedeg

radation,translationfactors,m

RNA

processing,o

xidativephosphorylation,e

lectron

tran

sport

chain,TC

Acycle

Sien

na3

42

6.84E−06

1.06

22.60

E−01

11

3.40E−04

01.00

E+00

01.00

E+00

0.50

1.98E−02

System

iclupuserythem

atosus

NA

Lightgreen

82

9.00E−06

1.06

10

5.29E−05

24

8.66E−08

21.00

E+00

29.06

E−01

0.52

1.55E−02

Ribosome,

oxidativephosphorylation,

Huntington'sdisease,P

arkinson'sdisease,

Alzheimer'sdisease,S

NAREinteractionsin

vesicu

lartran

sport,metab

olic

pathways,

peroxisome

Electrontran

sport

chain,cytoplasm

icribosomal

proteins,oxidativephosphorylation

Stee

lblue

40

3.51E−03

1.05

55.75E−03

14

1.78E−05

19.97

E−01

18.83

E−01

0.38

9.06E−02

NA

NA

Green

141

1.41E−04

1.03

46.96

E−01

26

5.56E−03

33

2.54E−02

59.40

E−01

−0.57

7.16E−03

NA

NA

Floralwhite

24

2.08E−02

1.03

01.00

E+00

62.21

E−02

01.00

E+00

01.00

E+00

0.52

1.51E−02

Amino-acidmetab

olism,m

RNA

processing,T

CAcycle

Spliceo

some,

alan

ine,

aspartate

andglutamate

metab

olism

Yello

w149

1.07E−04

0.96

01.00

E+00

41.00

E+00

49

3.63E−07

68.64

E−01

0.32

1.57

E−01

NA

Hyp

otheticalnetwork

fordrugad

diction,TNF-alpha

NF-kB

signalingpathway,P

luriNetWork,m

RNA

processing,splicingfactorNOVA

-reg

ulatedsynpatic

proteins

Turquoise

747

4.62E−33

0.94

59.98

E−01

151.00

E+00

101

2.04E−05

227.90

E−01

−0.05

8.17

E−01

Long-term

potentiation,long-term

dep

ression

mRNAprocessing,h

ypothetical

network

fordrug

addiction

Thesignificantvalues

(Po

0.05

)arebolded

.Ave

ragefold-chan

ge4

1indicates

anincrease

ingen

eexpressionin

amodule

andfold-chan

geo

1indicates

reductionin

gen

eexpression.


9


downregulated, whereas one TrkB transcript was downregulatedand one was upregulated. These results might be due to probehybridization differences stemming from different splice variants.BDNF is involved in synaptic plasticity,57,58 obesity59 and addic-tion,60 and potassium channels have been associated withincreased sensitivity and tolerance to the sedative effects ofethanol,61 seizure susceptibility,62 neonatal familial convulsionsand epilepsy63 and may be important in the withdrawal-inducedseizures caused by chronic alcohol consumption in humans.Chronic alcohol use might result in increased metabolism of

alcohol or acetate in the brain.64 A study in human alcoholicsshowed an increase in glutamate–glutamine and GABA labeling inheavy compared with light drinkers.64 In our study, we foundmany alcohol-metabolizing enzymes that were upregulated byalcohol consumption, in agreement with a previous study.65

Furthermore, alcohol-responsive glutamate and GABA mRNAswere detected in SNs (GABA-related mRNAs were associated withthe upregulated alcohol-responsive mRNAs, see Table 5). Figure 6illustrates how the alcohol-responsive mRNAs can participate inalcohol degradation to produce metabolites that can enter theTCA cycle and be converted into glutamate, which may contributeto the dysregulation in the glutamate system seen in alcoholics.The sensitivity of the SN preparation compared with the TH also

allowed for improved cell-type enrichment analysis, enabling thedetection of a high positive correlation of astrocyte and microglialmRNAs with alcohol consumption in the SNs. All of the alcohol-responsive genes in these astrocyte/microglia modules wereupregulated. Given that these cell types are generally associatedwith neuroinflammation, a potential consequence of chronicalcohol use is activation of neuroimmune signaling. The adapta-tion of the neuroimmune system is consistent with data from theamygdala of human alcoholics23 and supports the emergingconcept that there is a neuroimmune response to chronic alcoholuse.41 In addition, astrocytes might have role in regulatingsynaptic plasticity by altering the levels of glutamate, GABA andtumor necrosis factor-alpha available in the synapse.66 We foundthat all three of these systems were sensitive to alcohol, including

glutamate and GABA metabolizing enzymes, receptors andtransporters and tumor necrosis factor-alpha receptors and theirinteracting proteins. This suggests that the upregulated astrocyte-specific genes could induce a wide range of effects followingchronic alcohol consumption, ranging from neuroimmune toplasticity responses. The SN preparation also enriches for peri-synaptic microglial and astroglial processes.67,68 As microglia andastrocytes can actively engage in synaptic function68 and havebeen associated with alcoholism,23 the SN preparation may beuseful in investigating alcohol’s effects on the neuroimmunesystem. Because the cell-type specificity in our SN preparation isnot known, we used a comprehensive bioinformatics approachsimilar to Ponomarev et al.,23 including identifying mRNAs whichare co-expressed with known astro-glial markers and examiningthe astro-glia mRNAs altered by alcohol consumption.Extra-nuclear splicing has been discovered as a process

involved in synaptic structure and function,69 and this processmay explain why nuclear mRNAs were found in the SN prepara-tion. Alternatively, there could be some nuclear contamination.The estimate of nuclear contamination in our SN preparation isbased on two main measurements: (1) DAPI (4',6-diamidino-2-phenylindole) staining of nuclear DNA, showing no detectedstaining in the SNs (Supplementary Figure S2).70 (2) Neun (anuclear protein) western blots, showing the SN preparationdecreases 75% of the Neun found in the TH.70

Although there is evidence for synaptic translation in post-synaptic neuronal compartments,71 translation might also takeplace in the presynaptic compartment (axonal terminal).72 The SNpreparation enriches for both the presynaptic and the postsynap-tic fractions, and further research is warranted to determinewhether alcohol differentially affects these two compartments.In summary, we identified coordinated changes in mRNA expres-

sion in SNs and THs following chronic alcohol consumption. Theexpression changes in SNs from mouse amygdala corroborate thatseen in the amygdala of human alcoholics and include over-lapping changes in GABA, glutamate and neuroimmune pathways.Our results demonstrate that (1) the mouse chronic-drinking

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


10


paradigm used in this study is sufficient to produce the sameexpression changes previously seen in human alcoholics and (2)the parallel changes are evident for the SN but not TH mousetranscriptome. Our results highlight the advantage of the mouseSN preparation for identifying therapeutic gene targets for alcoholdependence that are relevant in humans and for studyingsynaptic plasticity under normal and disease conditions.

CONFLICT OF INTERESTThe authors declare no conflict of interest.

ACKNOWLEDGMENTSWe thank Kimberly Raab-Graham for valuable instruction in the isolation ofsynaptoneurosomes and for providing the PSD-95 antibody. We thank Jill Benavidezand Kathryn Ondricek for help with the behavioral paradigm and synaptoneurosomepreparation. We thank Joseph Corey for the generous assistance with computationalanalysis, Marianna Grenadier for help with the illustrations and Jody Mayfield forhelpful scientific editing. This work was supported by the National Institute of HealthGrants 1F31-AA022557-01, AA-UO1-13520, AA012404, RC2AA019382 and AA020683.

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


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

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