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. 2015 Feb 1;172:63–73. doi: 10.1016/j.jad.2014.09.024

Molecular signatures of mood stabilisers highlight the role of the transcription factor REST/NRSF

Alix Warburton a, Abigail L Savage a, Paul Myers a, David Peeney b, Vivien J Bubb a, John P Quinn a,⁎
PMCID: PMC4271744  PMID: 25451397

Abstract

Background

The purpose of this study was to address the affects of mood modifying drugs on the transcriptome, in a tissue culture model, using qPCR arrays as a cost effective approach to identifying regulatory networks and pathways that might coordinate the cell response to a specific drug.

Methods

We addressed the gene expression profile of 90 plus genes associated with human mood disorders using the StellARray™ qPCR gene expression system in the human derived SH-SY5Y neuroblastoma cell line.

Results

Global Pattern Recognition (GPR) analysis identified a total of 9 genes (DRD3⁎, FOS†, JUN⁎, GAD1⁎†, NRG1⁎, PAFAH1B3⁎, PER3⁎, RELN⁎ and RGS4⁎) to be significantly regulated in response to cellular challenge with the mood stabilisers sodium valproate (⁎) and lithium (†). Modulation of FOS and JUN highlights the importance of the activator protein 1 (AP-1) transcription factor pathway in the cell response. Enrichment analysis of transcriptional networks relating to this gene set also identified the transcription factor neuron restrictive silencing factor (NRSF) and the oestrogen receptor as an important regulatory mechanism.

Limitations

Cell line models offer a window of what might happen in vivo but have the benefit of being human derived and homogenous with regard to cell type.

Conclusions

This data highlights transcription factor pathways, acting synergistically or separately, in the modulation of specific neuronal gene networks in response to mood stabilising drugs. This model can be utilised in the comparison of the action of multiple drug regimes or for initial screening purposes to inform optimal drug design.

Abbreviations: AP-1, activator protein 1; ENCODE, Encyclopaedia of DNA Elements; ERK, extracellular-signal-regulated kinase; GPR, Global Pattern Recognition; G×E, Gene×Environment; NRSF, neuron restrictive silencing factor; REST, repressor element-1 silencing transcription factor

Keywords: Global Pattern Recognition, Mood disorders, Mood-modifying drugs, Neuronal signalling, NRSF, Pathway analysis

1. Introduction

Mental health is in part dependent upon transcriptional responses to cues which can be environmental, chemical, physiological and psychological; this is termed the Gene×Environment (G×E) component. These changes not only affect our health in the short term, but can have medium to long term impact via epigenetic modulation of gene expression, altering our response to environmental challenges. Genetic polymorphism can modulate the G×E response and offer insight into the mechanisms underpinning such pathways (Quinn et al., 2013). Earlier genetic studies targeted association of one genetic variant to a specific disorder; this had limited success and focused predominantly on candidate genes such as those in the monoaminergic pathways. These correlations are now being readdressed by analysing multiple variants in such pathways or by stratification of the cohorts based on environmental factors. Our recent work on a promoter polymorphism of the monoamine oxidase A gene and maternal parameters affecting infant behaviour is an example of the latter (Hill et al., 2013). It is difficult to address the signal cascade in response to specific challenges in vivo due to the heterogeneity of cells involved in processing the environmental signals mediating a cellular response. However, in vitro cell line models offer an opportunity to address in fine detail the signal pathways modulated in response to a specific challenge. In this study we analysed the response to distinct drugs in the human neuroblastoma cell line SH-SY5Y targeting a commercially available compilation of mood disorder genes to address whether they leave a molecular signature of transcriptional change to the challenge. The drugs chosen for comparison included two psychostimulant challenges, amphetamine and cocaine, and two mood stabilisers, sodium valproate and lithium. All of these drugs have been shown previously to modulate signal pathways in SH-SY5Y cells at the transcriptional and/or post-transcriptional level (Asghari et al., 1998, Di Daniel et al., 2005, Kantor et al., 2002, Lew, 1992, Pan et al., 2005, Warburton et al., 2014). Our analysis identified similarities and differences in the networks modified by the drug challenge which suggested an overlap in the pathways of the mood stabilisers. These changes reflect one window for the spectrum of changes that could occur in vivo, but nonetheless outline the potential for a concerted cellular response to drug exposure.

2. Materials and methods

2.1. Cell culture and drug treatment

Human derived SH-SY5Y neuroblastoma cells (American Type Culture Collection) were maintained in Earle׳s modified Eagle׳s medium (EMEM) (Sigma) and HAM׳s F12 (Sigma) at a ratio of 1:1, supplemented with 10% foetal calf serum (FCS) (Sigma), 1% 200 mM l-glutamine, 1% 100 mM sodium pyruvate and 100 U/ml penicillin/100 ug/ml streptomycin at 37 °C and 5% CO2. Amphetamine, cocaine hydrochloride, lithium chloride and valproic acid sodium salt were purchased from Sigma and stock solutions made using sterile filtered dH2O. Drug regimes were 1 h treatment with either: vehicle control (sterile filtered dH2O), 10 µM amphetamine (Jones and Kauer, 1999, Shyu et al., 2004), 10 µM cocaine (Warburton et al., 2014), 1 mM lithium (Hing et al., 2012, Roberts et al., 2007) or 5 mM sodium valproate (Pan et al., 2005, Phiel et al., 2001, Zhang et al., 2003). For each drug treatment, n=4. Basal (untreated) cells were also included.

2.2. RNA extraction and quantitative polymerase chain reaction (qPCR) analysis

Total RNA was extracted using Trizol reagent (Invitrogen) and the resulting RNA pellets resuspended in RNase-free water. 500 ng RNA was reverse transcribed into cDNA using the GoScript™ RT system (Promega). qPCR analysis was performed on an iQ5 real-time PCR system (Bio-Rad) using 1 µl of cDNA per reaction and GoTaq® qPCR Master Mix (Promega) with the addition of Fluorescene Calibration Dye (Bio-Rad) at a final concentration of 10 nM. Changes in gene expression were analysed on the Lonza Web site (http://array.lonza.com/gpr), using the Global Pattern Recognition™ (GPR) analysis software designed by Bar Harbor Biotechnology (https://www.bhbio.com/BHB/dw/home.html). This algorithm internally normalised the real-time qPCR data set of each gene with respect to all genes within the experiment and generated a list of genes that are ranked on the basis of the difference between the test and control expression levels and the consistency of the data between the biological replicates. This proprietary software calculated both the fold-change data and the respective p-values. The results are displayed as change with respect to the genes that showed minimal changes, which were defined on Ct values obtained using the Global Pattern Recognition analysis software (Akilesh et al., 2003).

A list of genes on the mood array is presented in Table 1.

Table 1.

Gene name and description for the Human Mood Disorder 96-well qPCR StellARray™.

Gene name Entrez gene Description
ACE 1636 Angiotensin I converting enzyme (peptidyl-dipeptidase A) 1
ADCYAP1 116 Adenylate cyclase activating polypeptide 1 (pituitary)
ADRBK2 157 Adrenergic, beta, receptor kinase 2
ARNTL 406 Aryl hydrocarbon receptor nuclear translocator-like
ATP2A2 488 ATPase, Ca++ transporting, cardiac muscle, slow twitch 2
BCR 613 Breakpoint cluster region
BDNF 627 Brain-derived neurotrophic factor
CASP8 841 Caspase 8, apoptosis-related cysteine peptidase
CCND2 894 Cyclin D2
CHRNA7 1139 Cholinergic receptor, nicotinic, alpha 7
CIT 11113 Citron rho-interacting serine/threonine kinase
CLOCK 9575 Clock circadian regulator
COMT 1312 Catechol-O-methyltransferase
CREB1 1385 CAMP responsive element binding protein 1
CRH 1392 Corticotropin releasing hormone
CRHBP 1393 Corticotropin releasing hormone binding protein
DAO 1610 d-amino-acid oxidase
DISC1 27185 Disrupted in schizophrenia 1
DLX1 1745 Distal-less homeobox 1
DRD1 1812 Dopamine receptor D1
DRD3 1814 Dopamine receptor D3
DRD4 1815 Dopamine receptor D4
DTNBP1 84062 Dystrobrevin binding protein 1
ERBB3 2065 V-erb-b2 erythroblastic leukemia viral oncogene homolog 3
FAT1 2195 FAT atypical cadherin 1
FKBP5 2289 FK506 binding protein 5
FOS 2353 FBJ murine osteosarcoma viral oncogene homolog
GABRA5 2558 Gamma-aminobutyric acid (GABA) A receptor, alpha 5
GAD1 2571 Glutamate decarboxylase 1 (brain, 67 kDa)
GCH1 2643 GTP cyclohydrolase 1
GPR50 9248 G protein-coupled receptor 50
GRIK3 2899 Glutamate receptor, ionotropic, kainate 3
GRIK4 2900 Glutamate receptor, ionotropic, kainate 4
GRIN2B 2904 Glutamate receptor, ionotropic, N-methyl d-aspartate 2B
GRM3 2913 Glutamate receptor, metabotropic 3
GRM4 2914 Glutamate receptor, metabotropic 4
GSK3B 2932 Glycogen synthase kinase 3 beta
Hs18s – Human 18S ribosomal RNA
HS Genomic – Human genomic DNA control
HSP90B1 7184 Heat shock protein 90 kDa beta (Grp94), member 1
HSPA5 3309 Heat shock 70 kDa protein 5 (glucose-regulated protein, 78 kDa)
HTR1B 3351 5-hydroxytryptamine (serotonin) receptor 1B
HTR2A 3356 5-hydroxytryptamine (serotonin) receptor 2A
IL1RN 3557 Interleukin 1 receptor antagonist
IMPA1 3612 Inositol(myo)-1(or 4)-monophosphatase 1
IMPA2 3613 Inositol(myo)-1(or 4)-monophosphatase 2
INPP1 3628 Inositol polyphosphate-1-phosphatase
ISYNA1 51477 Myo-inositol 1-phosphate synthase A1
JUN 3725 Jun oncogene
KCNN3 3782 Potassium intermediate/small conductance calcium-activated channel, subfamily N, member 3
MAG 27307 Malignancy-associated gene
MAL 4118 Mal, T-cell differentiation protein
MAOA 4128 Monoamine oxidase A
MLC1 23209 Megalencephalic leukoencephalopathy with subcortical cysts 1
MOBP 4336 Myelin-associated oligodendrocyte basic protein
MOG 4340 Myelin oligodendrocyte glycoprotein
MTHFR 4524 5,10-methylenetetrahydrofolate reductase (NADPH)
NAPG 8774 N-ethylmaleimide-sensitive factor attachment protein, gamma
NCAM1 4684 Neural cell adhesion molecule 1
ND4 4538 Mitochondrially encoded NADH dehydrogenase 4
NDUFV1 4723 NADH dehydrogenase (ubiquinone) flavoprotein 1, 51 kDa
NDUFV2 4729 NADH dehydrogenase (ubiquinone) flavoprotein 2, 24 kDa
NOS1AP 9722 Nitric oxide synthase 1 (neuronal) adaptor protein
NR1D1 9572 Nuclear receptor subfamily 1, group D, member 1
NR3C1 2908 Nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor)
NRG1 3084 Neuregulin 1
NTRK2 4915 Neurotrophic tyrosine kinase, receptor, type 2
OLIG2 10215 Oligodendrocyte lineage transcription factor 2
P2RX7 5027 Purinergic receptor P2X, ligand-gated ion channel, 7
PAFAH1B1 5048 Platelet-activating factor acetylhydrolase, isoform Ib, alpha subunit 45 kDa
PAFAH1B3 5050 Platelet-activating factor acetylhydrolase, isoform Ib, gamma subunit 29 kDa
PCNT 5116 Pericentrin
PDLIM5 10611 PDZ and LIM domain 5
PER3 8863 Period circadian clock 3
PIP4K2A 5305 Phosphatidylinositol-5-phosphate 4-kinase, type II, alpha
PLA2G1B 5319 Phospholipase A2, group IB (pancreas)
PLA2G4A 5321 Phospholipase A2, group IVA (cytosolic, calcium-dependent)
PLCG1 5335 Phospholipase C, gamma 1
PLP1 5354 Proteolipid protein 1
POLG 5428 Polymerase (DNA directed), gamma
PTGS2 5743 Prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase)
RELN 5649 Reelin
RFX4 5992 Regulatory factor X, 4 (influences HLA class II expression)
RGS4 5999 Regulator of G-protein signaling 4
SLC12A6 9990 Solute carrier family 12 (potassium/chloride transporters), member 6
SLC6A2 6530 Solute carrier family 6 (neurotransmitter transporter, noradrenalin), member 2
SLC6A3 6531 Solute carrier family 6 (neurotransmitter transporter, dopamine), member 3
SLC6A4 6532 Solute carrier family 6 (neurotransmitter transporter, serotonin), member 4
SULT1A1 6817 Sulfotransferase family, cytosolic, 1A, phenol-preferring, member 1
SYNGR1 9145 Synaptogyrin 1
TAAR6 319100 Trace amine associated receptor 6
TF 7018 Transferrin
TIMELESS 8914 Timeless circadian clock
TPH1 7166 Tryptophan hydroxylase 1 (tryptophan 5-monooxygenase)
TPH2 121278 Tryptophan hydroxylase 2
XBP1 7494 X-box binding protein 1
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2.3. Bioinformatic analysis

Gene expression data generated from GPR analysis was uploaded into the online biological pathway analysis software MetaCore™, version 6.15 build 62452. Functional enrichment of the experimental dataset was performed using: 1) the Pathway Map analysis tool to identify significantly associated pathways based on p-value and GPR Fold-change and 2) Build Network for Your Experimental Data feature using the Transcription Factor Targets Modelling algorithm with default settings under Analyse Networks (Transcription Factors) to generate sub-networks based on the presence of transcription factors and/or receptor targets within the original input file. Such genes/proteins uploaded from experimental datasets and from which pathways were built upon were termed ‘seed nodes’.

In silico analysis of NRSF binding sites over the significantly altered genes across the different treatment conditions from the qPCR array data were identified using Transcription Factor ChIP-seq from ENCODE (Encyclopaedia of DNA Elements), version 4, available on the UCSC Genome Browser (http://genome.ucsc.edu/index.html). Upstream and downstream flank sequences (10 Kb) were included and the position of NRSF binding sites calculated. For genes with multiple transcripts, the locus for the largest isoform was used. The full list of NRSF binding sites is detailed in Table 3.

Table 3.

Predicted NRSF regulation of genes affecting mood.

Gene Locus Strand NRSF site Size (Bp) Position
ACE chr17:61554422–61575741 + chr17:61553914–61554174 260 −508
ACE chr17:61554422–61575741 + chr17:61554504–61554774 270 82
ACE chr17:61554422–61575741 + chr17:61556270–61556594 324 1848
ACE chr17:61554422–61575741 + chr17:61557174–61557444 270 2752
ACE chr17:61554422–61575741 + chr17:61558309–61558579 270 3887
ADRBK2 chr22:25960861–26125258 + chr22:25961290–25961560 270 429
ADRBK2 chr22:25960861–26125258 + chr22:26052841–26053085 244 91980
ADRBK2 chr22:25960861–26125258 + chr22:26097050–26097320 270 136189
ARNTL chr11:13277734–13387266 + chr11:13283216–13283586 370 5482
ARNTL chr11:13277734–13387266 + chr11:13298458–13299341 883 20724
ARNTL chr11:13277734–13387266 + chr11:13310624–13311040 416 32890
ARNTL chr11:13277734–13387266 + chr11:13312905–13313275 370 35171
ARNTL chr11:13277734–13387266 + chr11:13351630–13351900 270 73896
ARNTL chr11:13277734–13387266 + chr11:13361071–13361575 504 83337
ARNTL chr11:13277734–13387266 + chr11:13364729–13364973 244 86995
ARNTL chr11:13277734–13387266 + chr11:13365612–13366116 504 87878
BCR chr22:23522552–23660224 + chr22:23525622–23525892 270 3070
BCR chr22:23522552–23660224 + chr22:23546679–23546949 270 24127
BCR chr22:23522552–23660224 + chr22:23562075–23562399 324 39523
BCR chr22:23522552–23660224 + chr22:23566052–23566322 270 43500
BCR chr22:23522552–23660224 + chr22:23591914–23592184 270 69362
BCR chr22:23522552–23660224 + chr22:23624008–23624332 324 101456
BCR chr22:23522552–23660224 + chr22:23647903–23648174 271 125351
BCR chr22:23522552–23660224 + chr22:23651156–23651400 244 128604
BDNF chr11:27676442–27743605 − chr11:27667673–27667943 270 −8499
BDNF chr11:27676442–27743605 − chr11:27671454–27671716 262 −4726
BDNF chr11:27676442–27743605 − chr11:27680076–27680346 270 63259
BDNF chr11:27676442–27743605 − chr11:27721240–27721484 244 22121
BDNF chr11:27676442–27743605 − chr11:27723005–27723329 324 20276
BDNF chr11:27676442–27743605 − chr11:27739843–27740167 324 3438
BDNF chr11:27676442–27743605 − chr11:27740692–27741122 430 2483
BDNF chr11:27676442–27743605 − chr11:27741795–27742502 707 1103
BDNF chr11:27676442–27743605 − chr11:27742701–27743071 370 534
BDNF chr11:27676442–27743605 − chr11:27743607–27744258 651 +2
BDNF chr11:27676442–27743605 − chr11:27744566–27744890 324 +961
CASP8 chr2:202098166–202152434 + chr2:202096900–202097280 380 −1266
CASP8 chr2:202098166–202152434 + chr2:202098061–202098441 380 −105
CASP8 chr2:202098166–202152434 + chr2:202122713–202123093 380 24547
CRH chr8:67088612–67090846 − chr8:67089099–67090281 1182 565
CRH chr8:67088612–67090846 − chr8:67090287–67090659 372 187
CRH chr8:67088612–67090846 − chr8:67090956–67091280 324 +110
CRH chr8:67088612–67090846 − chr8:67091915–67092285 370 +1069
CRH chr8:67088612–67090846 − chr8:67098519–67098889 370 +7673
DISC1 chr1:231762561–232177019 + chr1:231795960–231796330 370 33399
DISC1 chr1:231762561–232177019 + chr1:231814930–231815200 270 52369
DISC1 chr1:231762561–232177019 + chr1:231925791–231926295 504 163230
DISC1 chr1:231762561–232177019 + chr1:231963016–231963520 504 200455
DISC1 chr1:231762561–232177019 + chr1:231964053–231964309 256 201492
DISC1 chr1:231762561–232177019 + chr1:232067746–232067990 244 305185
DISC1 chr1:231762561–232177019 + chr1:232148522–232148892 370 385961
DRD3 chr3:113847557–113918254 − chr3:113871366–113871690 324 46564
DRD3 chr3:113847557–113918254 − chr3:113874262–113874642 380 43612
DRD3 chr3:113847557–113918254 − chr3:113897607–113898013 406 20241
DRD3 chr3:113847557–113918254 − chr3:113898443–113898813 370 19441
DRD4 chr11:637305–640705 + chr11:640330–640654 324 3025
DTNBP1 chr6:15523032–15663289 − chr6:15552018–15552288 270 111001
DTNBP1 chr6:15523032–15663289 − chr6:15621994–15622224 230 41065
DTNBP1 chr6:15523032–15663289 − chr6:15662506–15662830 324 459
FKBP5 chr6:35541362–35696397 − chr6:35656504–35656848 344 39549
FKBP5 chr6:35541362–35696397 − chr6:35687515–35687759 244 8638
FKBP5 chr6:35541362–35696397 − chr6:35695292–35695562 270 835
FKBP5 chr6:35541362–35696397 − chr6:35695873–35696103 230 294
FKBP5 chr6:35541362–35696397 − chr6:35699743–35700105 362 −3346
FOS chr14:75745481–75748937 + chr14:75743830–75744074 244 −1651
FOS chr14:75745481–75748937 + chr14:75745296–75745800 504 −185
GABRA5 chr15:27111866–27194357 + chr15:27110041–27110545 504 −1825
GABRA5 chr15:27111866–27194357 + chr15:27111625–27112129 504 −241
GAD1 chr2:171673200–171717659 + chr2:171670663–171671101 438 −2537
GAD1 chr2:171673200–171717659 + chr2:171671290–171671546 256 −1910
GAD1 chr2:171673200–171717659 + chr2:171672190–171672567 377 −1010
GAD1 chr2:171673200–171717659 + chr2:171679546–171679776 230 6346
GAD1 chr2:171673200–171717659 + chr2:171701873–171702253 380 28673
GRIK3 chr1:37261128–37499844 − chr1:37269486–37269856 370 229988
GRIK3 chr1:37261128–37499844 − chr1:37301874–37302144 270 197700
GRIK3 chr1:37261128–37499844 − chr1:37329834–37330078 244 169766
GRIK3 chr1:37261128–37499844 − chr1:37331752–37332256 504 167588
GRIK3 chr1:37261128–37499844 − chr1:37332540–37332784 244 167060
GRIK3 chr1:37261128–37499844 − chr1:37388506–37388750 244 111094
GRIK3 chr1:37261128–37499844 − chr1:37389788–37390253 465 109591
GRIK3 chr1:37261128–37499844 − chr1:37411488–37411732 244 88112
GRIK3 chr1:37261128–37499844 − chr1:37431706–37432281 575 67563
GRIK3 chr1:37261128–37499844 − chr1:37486267–37486654 387 13190
GRIK3 chr1:37261128–37499844 − chr1:37494616–37494860 244 4984
GRIK3 chr1:37261128–37499844 − chr1:37504779–37505043 264 −4935
GRM3 chr7:86273230–86494192 + chr7:86290343–86290599 256 17113
GRM3 chr7:86273230–86494192 + chr7:86322086–86322456 370 48856
GRM3 chr7:86273230–86494192 + chr7:86476174–86476554 380 202944
GRM3 chr7:86273230–86494192 + chr7:86497476–86497720 244 +3284
JUN chr1:59246463–59249785 − chr1:59249472–59249885 413 −100
MAG chr19:35782989–35820133 + chr19:35796870–35797100 230 13881
MAG chr19:35782989–35820133 + chr19:35809956–35810280 324 26967
MAOA chrX:43515409–43606068 + – – –
MLC1 chr22:50497820–50523781 − – – –
MOBP chr3:39543557–39567857 + chr3:39540121–39540386 265 −3436
MOBP chr3:39543557–39567857 + chr3:39558349–39558719 370 14792
MOBP chr3:39543557–39567857 + chr3:39574318–39574698 380 +6461
MTHFR chr1:11845787–11866160 − chr1:11845214–11845454 240 +573
MTHFR chr1:11845787–11866160 − chr1:11850982–11851306 324 14854
MTHFR chr1:11845787–11866160 − chr1:11856563–11856793 230 9367
MTHFR chr1:11845787–11866160 − chr1:11857775–11857960 185 8200
MTHFR chr1:11845787–11866160 − chr1:11858618–11858699 81 7461
MTHFR chr1:11845787–11866160 − chr1:11863764–11864034 270 2126
MTHFR chr1:11845787–11866160 − chr1:11865502–11865882 380 278
MTHFR chr1:11845787–11866160 − chr1:11866038–11866425 387 −265
NAPG chr18:10525873–10552766 + chr18:10525815–10526242 427 −58
NCAM1 chr11:112831969–113092626 + chr11:112831909–112832179 270 −60
NCAM1 chr11:112831969–113092626 + chr11:112977293–112977549 256 145324
NCAM1 chr11:112831969–113092626 + chr11:113008930–113009200 270 176961
NCAM1 chr11:112831969–113092626 + chr11:113011853–113012123 270 179884
NCAM1 chr11:112831969–113092626 + chr11:113023160–113023664 504 191191
NCAM1 chr11:112831969–113092626 + chr11:113074175–113074445 270 242206
NR1D1 chr17:38249037–38256973 − chr17:38244467–38244847 380 +4570
NR1D1 chr17:38249037–38256973 − chr17:38254215–38254595 380 2378
NR1D1 chr17:38249037–38256973 − chr17:38255228–38255666 438 1307
NR1D1 chr17:38249037–38256973 − chr17:38256685–38257094 409 −121
NR1D1 chr17:38249037–38256973 − chr17:38257324–38257828 504 −351
NR1D1 chr17:38249037–38256973 − chr17:38264445–38264769 324 −7472
NR3C1 chr5:142657496–142783254 − chr5:142784785–142785394 609 −2140
NRG1 chr8:31496911–32622558 + chr8:31499444–31499814 370 2533
NRG1 chr8:31496911–32622558 + chr8:31612484–31612740 256 115573
NRG1 chr8:31496911–32622558 + chr8:31629195–31629565 370 132284
NRG1 chr8:31496911–32622558 + chr8:31652781–31653242 461 155870
NRG1 chr8:31496911–32622558 + chr8:31691004–31691508 504 194093
NRG1 chr8:31496911–32622558 + chr8:31817830–31818086 256 320919
NRG1 chr8:31496911–32622558 + chr8:31896212–31896582 370 399301
NRG1 chr8:31496911–32622558 + chr8:32084240–32084744 504 587329
NRG1 chr8:31496911–32622558 + chr8:32122327–32122831 504 625416
NRG1 chr8:31496911–32622558 + chr8:32189091–32189595 504 692180
NRG1 chr8:31496911–32622558 + chr8:32191794–32192298 504 694883
NRG1 chr8:31496911–32622558 + chr8:32200953–32201685 732 704042
NRG1 chr8:31496911–32622558 + chr8:32245491–32245735 244 748580
NRG1 chr8:31496911–32622558 + chr8:32276508–32276752 244 779597
NRG1 chr8:31496911–32622558 + chr8:32284202–32284706 504 787291
NRG1 chr8:31496911–32622558 + chr8:32392615–32392985 370 895704
NRG1 chr8:31496911–32622558 + chr8:32405958–32406282 324 909047
NRG1 chr8:31496911–32622558 + chr8:32406492–32406892 400 909581
NRG1 chr8:31496911–32622558 + chr8:32411341–32411845 504 914430
NRG1 chr8:31496911–32622558 + chr8:32487206–32487506 300 990295
NRG1 chr8:31496911–32622558 + chr8:32488853–32489109 256 991942
NRG1 chr8:31496911–32622558 + chr8:32503654–32504024 370 1006743
NRG1 chr8:31496911–32622558 + chr8:32546371–32546746 375 1049460
NRG1 chr8:31496911–32622558 + chr8:32572641–32573145 504 1075730
NRG1 chr8:31496911–32622558 + chr8:32581201–32581705 504 1084290
NRG1 chr8:31496911–32622558 + chr8:32582687–32583047 360 1085776
PAFAH1B3 chr19:42801185–42806952 − chr19:42806435–42806939 504 −13
PER3 chr1:7844714–7905237 + ~14 Kb upstream of 5׳UTR – –
PDLIM5 chr4:95373038–95509370 + chr4:95372903–95373283 380 −135
PDLIM5 chr4:95373038–95509370 + chr4:95406777–95407007 230 33739
PDLIM5 chr4:95373038–95509370 + chr4:95418920–95419164 244 45882
PDLIM5 chr4:95373038–95509370 + chr4:95455973–95456203 230 82935
PDLIM5 chr4:95373038–95509370 + chr4:95456267–95456511 244 83229
PDLIM5 chr4:95373038–95509370 + chr4:95471601–95471831 230 98563
PDLIM5 chr4:95373038–95509370 + chr4:95499407–95499663 256 126369
RELN chr7:103112231–103629963 − chr7:103127865–103128245 380 501718
RELN chr7:103112231–103629963 − chr7:103276613–103276992 379 352971
RELN chr7:103112231–103629963 − chr7:103297949–103298179 230 331784
RELN chr7:103112231–103629963 − chr7:103301028–103301258 230 328705
RELN chr7:103112231–103629963 − chr7:103354935–103355205 270 274758
RELN chr7:103112231–103629963 − chr7:103438111–103438481 370 191482
RELN chr7:103112231–103629963 − chr7:103451010–103451107 97 178856
RELN chr7:103112231–103629963 − chr7:103484281–103484449 168 145514
RELN chr7:103112231–103629963 − chr7:103491745–103492249 504 137714
RELN chr7:103112231–103629963 − chr7:103559848–103560078 230 69885
RELN chr7:103112231–103629963 − chr7:103580845–103581215 370 48748
RELN chr7:103112231–103629963 − chr7:103636658–103636861 203 −6898
RFX4 chr12:106976685–107156582 + chr12:106975282–106975646 364 −1403
RFX4 chr12:106976685–107156582 + chr12:106975776–106976119 343 −909
RFX4 chr12:106976685–107156582 + chr12:107147300–107147544 244 170615
RGS4 chr1:163038396–163046592 + chr1:163039054–163039341 287 658
SLC12A6 chr15:34522197–34630265 − chr15:34516950–34517512 562 +5247
SLC12A6 chr15:34522197–34630265 − chr15:34610582–34611086 504 19179
SLC12A6 chr15:34522197–34630265 − chr15:34630069–34630393 324 −128
SLC12A6 chr15:34522197–34630265 − chr15:34634991–34635543 552 −4726
SLC6A2 chr16:55689542–55737700 + chr16:55686047–55686317 270 −3495
SLC6A2 chr16:55689542–55737700 + chr16:55689638–55689908 270 96
SLC6A2 chr16:55689542–55737700 + chr16:55690575–55690845 270 1033
SLC6A2 chr16:55689542–55737700 + chr16:55693927–55694197 270 4385
SLC6A2 chr16:55689542–55737700 + chr16:55695818–55696088 270 6276
SLC6A2 chr16:55689542–55737700 + chr16:55696686–55696956 270 7144
SLC6A2 chr16:55689542–55737700 + chr16:55744402–55744761 359 +7061
SLC6A2 chr16:55689542–55737700 + chr16:55746277–55746521 244 +8821
SLC6A4 chr17:28523378–28562954 − – – –
SULT1A1 chr16:28616908–28634907 − chr16:28621167–28621407 240 13500
TF chr3:133419211–133497850 + chr3:133461483–133461863 380 42272
TF chr3:133419211–133497850 + chr3:133465027–133465407 380 45816
TF chr3:133419211–133497850 + chr3:133472690–133472920 230 53479
TIMELESS chr12:56810157–56843200 − chr12:56811537–56811907 370 31293
TIMELESS chr12:56810157–56843200 − chr12:56842752–56843263 511 −63
TPH2 chr12:72332626–72426221 + chr12:72332400–72332889 489 −226
TPH2 chr12:72332626–72426221 + chr12:72374868–72375372 504 42242
TPH2 chr12:72332626–72426221 + chr12:72410895–72411165 270 78269
XBP1 chr22:29190548–29196560 − chr22:29196394–29196960 566 −400
XBP1 chr22:29190548–29196560 − chr22:29198252–29198482 230 −1922
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NRSF binding sites over top 10 affected genes across all drug treatments from Transcription Factor ChIP-seq from ENCODE version 4. Bold font indicates genes significantly affected by drug challenge. Negative and positive values under Position represent the location of the NRSF site upstream of the gene transcriptional start site and downstream of the 3׳UTR, respectively. Values not assigned +/− represent binding sites within the gene sequence. For genes with multiple transcripts, binding site positions are with respect to the largest isoform.

3. Results

3.1. Gene expression profiling of human SH-SY5Y cells in response to mood-modifying drugs using Global Pattern Recognition analysis

To investigate the effects of mood modifying drugs on the expression of a panel of genes associated with mood disorders (Human Mood Disorder 96 StellARray™), SH-SY5Y neuroblastoma cells after treatment for 1 h under one of the specified conditions were analysed using the proprietary Global Pattern Recognition (GPR) algorithm which compares the change in expression of a gene normalised to the expression of every other gene in the array (Akilesh et al., 2003). This software calculates both the fold-change data and the respective p-values with respect to genes that showed minimal changes. We and others have recently demonstrated that drugs used in the treatment of mood disorders can differentially affect the expression stability of traditionally used housekeeping genes, impacting upon their usefulness as normalising factors (D’Souza et al., 2013, Powell et al., 2013, Sugden et al., 2010). Unfortunately, these large changes in gene expression may mask small but biologically important changes in gene expression, such as master regulator genes (e.g., transcription factors). The data in Table 2 therefore represents a more appropriate display of the genes most changed within the experiment by comparing all genes against themselves. As the array contains validated mood genes we addressed the top 10 genes which significantly changed in response to each drug to define pathways and networks within the larger gene list.

Table 2.

Gene expression profiling of SH-SY5Y cells following exposure to drugs affecting mood.

Lithium
 Sodium valproate
Gene Description p Fold change Gene Description p Fold change
FOS FBJ murine osteosarcoma viral oncogene homolog 0.012 −2.57 DRD3 Dopamine receptor D3 0.001 −7.98
GAD1 Glutamate decarboxylase 1 0.023 −3.48 RGS4 Regulator of G-protein signaling 4 0.007 −2.08
RGS4 Regulator of G-protein signaling 4 0.063 −1.51 JUN Jun oncogene 0.008 2.49
PER3 Period circadian clock 3 0.067 −1.38 RELN Reelin 0.012 −1.78
NRG1 Neuregulin 1 0.068 −1.43 PER3 Period circadian clock 3 0.026 −1.48
NR1D1 Nuclear receptor subfamily 1, group D, member 1 0.069 −1.48 PAFAH1B3 Platelet-activating factor acetylhydrolase, isoform Ib, gamma subunit 29 kDa 0.034 1.61
RELN Reelin 0.078 −1.94 GAD1 Glutamate decarboxylase 1 0.035 −7.45
ACE Angiotensin I converting enzyme (peptidyl-dipeptidase A) 1 0.099 1.31 NRG1 Neuregulin 1 0.044 −1.34
Hs18s Human 18S ribosomal RNA 0.105 1.64 MTHFR Methylenetetrahydrofolate reductase (NADPH) 0.083 1.53
BDNF Brain-derived neurotrophic factor 0.106 −1.39 RFX4 Regulatory factor X, 4 (influences HLA class II expression) 0.092 −1.49



Cocaine Amphetamine
Gene Description p Fold change Gene Description p Fold change
SULT1A1 Sulfotransferase family, cytosolic, 1A, phenol-preferring, member 1 0.088 1.68 MOBP Myelin-associated oligodendrocyte basic protein 0.080 2.08
DRD3 Dopamine receptor D3 0.110 −2.08 XBP1 X-box binding protein 1 0.093 1.34
FOS FBJ murine osteosarcoma viral oncogene homolog 0.142 −1.45 NR1D1 Nuclear receptor subfamily 1, group D, member 1 0.109 −1.35
MOBP Myelin-associated oligodendrocyte basic protein 0.161 1.85 MAG Malignancy-associated gene 0.138 2.81
SLC6A2 Solute carrier family 6 (neurotransmitter transporter, noradrenalin), member 2 0.176 −1.28 PAFAH1B3 Platelet-activating factor acetylhydrolase, isoform Ib, gamma subunit 29 kDa 0.141 1.33
GRIK3 Glutamate receptor, ionotropic, kainate 3 0.194 −1.67 FKBP5 FK506 binding protein 5 0.159 −1.34
TIMELESS Timeless circadian clock 0.200 −1.20 RELN Reelin 0.198 −1.30
NCAM1 Neural cell adhesion molecule 1 0.206 −1.20 BCR Breakpoint cluster region 0.207 1.22
ND4 Mitochondrially encoded NADH dehydrogenase 4 0.232 1.15 MLC1 Megalencephalic leukoencephalopathy with subcortical cysts 1 0.208 2.52
NR1D1 Nuclear receptor subfamily 1, group D, member 1 0.233 −1.28 GABRA5 Gamma-aminobutyric acid (GABA) A receptor, alpha 5 0.213 −1.78
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Top 10 changes in gene expression levels between treated (10 µM amphetamine, 10 µM cocaine, 1 mM lithium and 5 mM sodium valproate) and untreated conditions measured using qPCR arrays (Human Mood Disorder 96 StellARrayTM) and Global Pattern Recognition (GPR) statistical analysis. Fold change values are represented as treated conditions normalised to the drug vehicle. Bold font indicates significant changes in gene expression, p<0.05.

Following treatment with the mood stabiliser sodium valproate, 8 genes were significantly (p<0.05) up- or down-regulated compared to the vehicle control; 2 up-regulated (JUN and PAFAH1B3) and 6 down-regulated (DRD3, GAD1, NRG1, PER3, RELN and RGS4). When compared to the results obtained after treatment with another common mood stabiliser, lithium, similarities in the gene expression profile with respect to the top 10 altered genes were observed; namely down-regulation of GAD1, NRG1, PER3, RELN and RGS4, but, only GAD1 reached statistical significance at this time point for lithium treatment. In addition, FOS was significantly down-regulated in response to lithium. Treatment with the two psychomotor stimulants cocaine and amphetamine demonstrated no statistically significant changes in gene expression following 1 h treatment. Furthermore the genes with the lowest p-values were distinct between the psychostimulants apart from MOBP (Table 2) demonstrating that these drugs might be preferentially targeting distinct pathways for their action. However due to the low p-values obtained under these experimental conditions we did not pursue their analysis further.

3.2. Network analysis of genes significantly modulated in response to mood stabilisers

To further explore potential gene networks important in the response to drug challenge, we analysed only the genes whose expression was most affected by lithium and sodium valproate using the Analyse Networks (Transcription Factors) algorithm from MetaCore™. This generates sub-networks through relative enrichment of the uploaded dataset based on the presence of transcription factors and/or receptor targets within the original input file. The gene set used was composed of GAD1, NRG1, PER3, RELN, RGS4, PAFAH1B3, DRD3, FOS and JUN, the first five of which were observed for both lithium and sodium valproate and the remaining were those significantly modified in response to either exposure.

A network containing NRSF, ErbB2 and ErbB3 as seed nodes was the highest ranked using this approach, and was defined as genes/proteins uploaded from experimental datasets or genes/proteins directly linked to uploaded gene lists from which networks are built (Fig. 1). It included 7 of our 9 input genes (DRD3, FOS, GAD1, JUN, NRG1, PAFAH1B3 and RELN) and had a p-value of 5.24×10−29 based on hypergeometric distribution which calculated the probability of a particular pathway map arising by chance given the number of genes across all gene pathways, within a particular pathway or sub-network and within the present experimental dataset. The transcription factors identified as being important regulators of this network were c-Fos and c-Jun (collectively AP-1), c-Myc, ESR1, NRSF, PR, RAR-alpha and SP3.

Fig. 1.

Fig. 1

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Network analysis of genes significantly modulated in response to mood stabilisers. Genes shown to be significantly up or down regulated in human SH-SY5Y cells in response to 1 h treatment with the mood stabilisers sodium valproate and lithium were uploaded into MetaCore™ for network analysis. The gene list was analysed under the Build Network feature using the Transcription Factor Targets Modelling algorithm. Seed nodes from which the network was built upon are encompassed by a large circle; blue circles represent genes from the experimental data, green circles represent molecules from which the pathway is expanded from and red circles represent molecules on which the pathway terminates. Genes uploaded from the experimental data are also marked with a smaller circle in their top right hand corner; red circles represent genes that were significantly up-regulated, whereas blue circles represent genes significantly down-regulated. Connecting arrows indicate interactions; green arrows represent activation, red arrows represent inhibition and blue arrows are unspecified. Overlaid cyan lines represent canonical pathways. Gene names/symbols within the network from top to bottom, left to right: Neuregulin 1, Dopamine D3 receptor, RELN, ErbB3, ErbB2, EGFR, Shc, GRB2, MEK1/2, c-Raf-1, GAD1 PAFAH gamma, SOS, c-Src, H-Ras, ERK1/2, NRSF, SP3, c-Myc, ESR1 (nuclear), c-Fos, c-Jun/c-Fos, JunD/c-Fos, RARalpha, PR (nuclear) c-Jun, and AP-1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

As our gene expression data showed that 7/9 of the significantly modulated genes were down-regulated (Table 2) and NRSF which predominantly functions as a transcriptional repressor was identified as an important regulator of our gene set, we addressed predicted NRSF binding sites using ENCODE data from the Transcription Factor ChIP-seq track (The ENCODE Project Consortium, 2011; Rosenbloom et al., 2013) on the UCSC Genome Browser. This identified NRSF binding at the promoter regions (within 5 Kb of the transcriptional start site) of DRD3 (transcript variant a, e and g), FOS, GAD1, JUN, NRG1 (transcript variant HRG-gamma1/2/3, HRG-beta1/d-, 2- and 3b, ndf43/b/c, HRG-alpha and SMDF), PAFAH1B3 and RGS4 (transcript variant 2/3) which, with the exception of JUN and PAFAH1B3, were all down-regulated in response to 1 h treatment with sodium valproate (or lithium with respect to FOS).

To determine how these regulatory pathways were most relevant for mood disorders, we filtered our dataset using the MetaCore™ ‘Filter by Disease’ feature which traces all of the known associated interactions for a particular disease process. This assigned 46.15% of our network, not unexpectedly to disease processes relating to mood (Fig. 2A). Furthermore, it identified NRSF and ERK1/2 signalling along the oestrogen receptor pathway as important regulators of processes relevant to mood disorders involving this subset of genes. In addition to disorders of the CNS, filtering of our dataset by disease showed there to be significant associations (96.15%) with breast, skin and gastrointestinal neoplasia; GAD1 being the only gene not to be involved in these cancer-related pathologies (Fig. 2B). To further assess which signalling pathways may be operating in response to challenge with these mood stabilisers, we also filtered our experimental network for Drug Responses under the Gene Ontology (GO) Processes filter. This identified the fibroblast growth factor, ERBB and neurotrophin TRK receptor signalling pathways as important cellular responses, with the dopamine D3 receptor, EGFR, ErbB2, ErbB3 and c-Src highlighted as therapeutic targets (Fig. 2C).

Fig. 2.

Fig. 2

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Network analysis filters for disease and gene ontology processes. The network generated in relation to genes significantly regulated in response to SH-SY5Y cell treatment with sodium valproate and lithium (Fig. 1) was filtered to show the relevant disease pathways (A and B) and gene ontology processes (C). (A and B) Disease processes relevant to mood disorders (A), represents 46.15% of the gene network; and breast, skin and gastrointestinal neoplasms (B), represents 96.15% of the gene network. (C) Gene ontology processes relevant to drug response. Seed nodes from which the network was built upon are encompassed by a large blue circle. Genes uploaded from the experimental data are also marked with a smaller circle in their top right hand corner; red circles represent genes that were significantly up-regulated, whereas blue circles represent genes significantly down-regulated. Connecting blue arrows indicate direct interactions, yellow arrows indicate interactions that are in the base but do not form part of the network and overlaid cyan lines represent canonical pathways. Gene names/symbols within network A, from top to bottom, left to right: Neuregulin 1, Dopamine D3 receptor, Reelin, ERK1/2, MEK1/2, NRSF, ESR1 (nuclear), c-Fos, c-Jun/c-Fos, JunD/c-Fos, PR (nuclear) and AP-1; B, from top to bottom, left to right: Neuregulin 1, Dopamine D3 receptor, Reelin, ErbB3, ErbB2, EGFR, SOS, Shc, GRB2, c-Raf-1, PAFAH gamma, H-Ras, c-Src, ERK1/2, MEK1/2, NRSF, SP3, c-Myc, ESR1 (nuclear), c-Fos, c-Jun/c-Fos, JunD/c-Fos, RARalpha, PR (nuclear), c-Jun and AP-1; and C, from top to bottom, left to right: Dopamine D3 receptor, Reelin, ErbB3, ErbB2, EGFR, GAD1, c-Src, NRSF, c-Myc, c-Fos, c-Jun/c-Fos, JunD/c-Fos, c-Jun and AP-1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

4. Discussion

Understanding the mechanism of action for a drug to alter the cell phenotype, in addition to the initial cellular targets recognised by the drug, is important for both clinical application and pharmaceutical development. Transcriptome profiling allows for global scale interrogation of potential regulatory mechanisms involved in modulating cellular responses to a particular drug through the use of pathway analysis tools. The aim of this study was to address the effects of mood modifying drugs on the expression profile of a commercially available panel of genes associated with mood disorders by network analysis to compare and contrast their mode of action.

We used two mood stabilisers (lithium and sodium valproate) and two mood stimulants (cocaine and amphetamine). Only the mood stabilisers reached statistical significance and interestingly they shared 5 genes in their top 9 most modified genes, Table 2; we therefore focused on this set of genes for further analysis. Valproate significantly modified 8 genes, lithium only two, GAD1 and FOS, with GAD1 being significantly down-regulated for both drugs. GAD1 encodes one of several forms of glutamic acid decarboxylase which is a key enzyme for the synthesis of the inhibitory neurotransmitter GABA. GAD1 is implicated from both genetic and functional analysis as a modulator of mood (Domschke et al., 2013, Hettema et al., 2006, Karolewicz et al., 2010, Lundorf et al., 2005, Thompson et al., 2009, Weber et al., 2012). FOS and JUN proteins constitute the AP-1 transcription factor complex which was a target for modulation. These factors represent a family of proteins that heterodimerise to regulate the AP-1 DNA site (Quinn, 1991, Quinn et al., 1989a, Quinn et al., 1989b, Takimoto et al., 1989). Lithium and sodium valproate have both been demonstrated to modulate the AP-1 complex (Chen et al., 2008, Ozaki and Chuang, 2002). The genes shared in common by the mood stabilisers sodium valproate and lithium were GAD1, NRG1, PER3, RELN and RGS4. The remainder, DRD3, JUN and PAFAH1B3 were specific for sodium valproate. Although some of these genes were modified with cocaine and amphetamine, the statistical significance was low, certainly lower than all the genes in the 9 most differentially expressed genes in Table 2. We have previously used cocaine and amphetamine in SH-SY5Y and found that we can observe significant changes in genes involved in mental health. For example, recently in the approximate same passage number of cells as used in this experiment, we have demonstrated that cocaine altered the expression of the schizophrenia candidate gene MIR137 (Warburton et al., 2014). However under the current experimental conditions this gene set targeting mood disorders is not responding as robustly to cocaine and amphetamine as lithium and sodium valproate. We therefore attempted to determine whether the significant mood stabiliser gene set defined a specific pathway or network of genes to explain their concerted response to drug exposure.

Pathway analysis using both the Analyse Networks (Transcription Factors) and Filter by Disease algorithms available on the online pathway analysis software MetaCore™ identified the transcription factor NRSF, also termed REST (repressor element-1 silencing transcription factor), to be strongly associated with the pathways supporting these networks of genes. NRSF has a direct association with DRD3, GAD1 and RELN genes based on the network analysis, Fig. 1. Bioinformatic analysis of predicted NRSF binding sites using ENCODE (Encyclopaedia of DNA Elements) data from the Transcription Factor ChIP-seq track (The ENCODE Project Consortium, 2011; Rosenbloom et al., 2013) on the UCSC Genome Browser identified NRSF binding at the promoter regions (within 5 Kb of the transcriptional start site) of the FOS, NRG1 and RGS4 genes, Table 3. This ENCODE analysis also demonstrated NRSF binding sites in similar genomic locations on DRD3, GAD1, JUN, PAFAH1B3 and RELN. Aberrant signalling of NRSF and its target genes has been shown to be involved in the pathophysiology of several CNS disorders including schizophrenia (Loe-Mie et al., 2010), major depressive disorder (Otsuki et al., 2010) and alcoholism and depression (Ukai et al., 2009), with genetic variants influencing age-related cognitive function (Miyajima et al., 2008). More recently it has been highlighted as a major player in Alzheimer׳s disease (Lu et al., 2014). NRSF has the properties to modulate epigenetic factors in its target genes due to its association with a plethora of co-activators, such as members of the SWI/SNF family, which can modify histones by post-translational modifications (Loe-Mie et al., 2010). These epigenetic modifications could result in medium to long term changes in gene expression that underlie drug exposure in addition to the immediate modulation of the transcriptome. Our data suggest that lithium and sodium valproate, with different initial cellular targets, may modulate related signalling pathways leading to overlapping cellular responses mediated in part by the NRSF pathway. It should be noted that we performed this experiment at 1 h postexposure to capture an early response of the cell to the drug. As in any stimulus induction modification of gene expression many of these changes will be transient, especially in the short term for transcription factors such as AP-1 and NRSF. This is in keeping with the transient response of AP-1 and NRSF in stimulus inducible gene expression models we have previously observed at 1 h postexposure (Gillies et al., 2009, Howard et al., 2008, Quinn, 1991, Spencer et al., 2006). A more extensive timescale would perhaps have demonstrated a different or related set of genes, nevertheless, our strategy allowed the observation of the differential gene set acting as a signature for the mood stabilisers and allows for future optimisation.

Filtering our dataset by disease also identified ERK1/2 signalling along with the oestrogen receptor pathway as a potentially important regulatory network for this gene set (Fig. 2). Oestrogen receptor signalling has been well documented in the modulation of behaviours relating to aggression (Nomura et al., 2002), anxiety and depression (Furuta et al., 2013). The action of sex hormones may in part explain why in conditions such as panic disorder these phenotypes are more prevalent among females. Our data would be consistent with GAD1 SNP variation being tentatively associated for the higher susceptibility of females to panic disorder (Weber et al., 2012) via modulation by oestrogen. This oestrogen pathway could overlap with other transcription factor pathways identified in our analysis, for example synergistic action of the oestrogen and AP-1 pathways on gene expression (Fujimoto and Kitamura, 2004). The extended networks identified in this study (AP-1, oestrogen and NRSF) may also work synergistically, for example NRSF activity is important for E2 stimulation of the cell cycle (Bronson et al., 2010) and oestrogen receptor B is enriched at NRSF binding sites (Le et al., 2013). Such interactions between these three pathways can be further modified by the glucocorticoid receptor, so linking these pathways to a major driver of mood (Abramovitz et al., 2008, Karmakar et al., 2013). Glucocorticoid sensitivity is strongly associated with several mood related disorders (Spijker and van Rossum, 2012) and anti-glucocorticoid drugs have been used in the treatment of such conditions (Gallagher et al., 2008, Wolkowitz and Reus, 1999, Wolkowitz et al., 1999). Mood disorder susceptibility has also been linked to glucocorticoid signalling through its modulation of the stress response along the hypothalamic–pituitary–adrenal (HPA) axis (Lupien et al., 2009, Spijker and van Rossum, 2012).

Our data points to a cost effective and rapid assessment of expression changes in selected genes using GPR analysis, which can help delineate the pathways targeted by drugs to modify mood. In particular, we have identified dopamine and glutamine pathways as being important; perhaps not unexpectedly as the gene set is enriched for known genes involved in mood disorders. Alteration in the regulation of these pathways would be expected to modulate mood and is reflected in the range of drugs currently used in targeting these pathways. However the modulation of the AP-1 pathway and the involvement of factors such as NRSF and ERK1/2 highlight a more general modulation of neurotransmitter pathways in response to mood modifying drugs. Our model can therefore be used to determine mechanisms associated with off target and long term affects of particular drugs and can be extrapolated to predict in vivo responses, utilised in the comparison of multiple drug regimes or used as an initial screening process to inform optimal drug design.

Role of funding source

Warburton, Peeney, Bubb and Quinn are funded by the Biotechnology and Biological Sciences Research Council (BBSRC) (BB/F016905/1), Myers and Quinn are funded by the Wellcome Trust (grant no. WT091483/Z) and Savage was funded by the University of Liverpool (UoL). BBSRC, Wellcome Trust and UoL had no role in the experimental design; acquisition, analysis and interpretation of data; writing of the manuscript and decision to submit the paper for publication.

Conflicts of interest

The authors report no conflicts of interest

Contribution of authors

Warburton was involved in experimental design, data acquisition, analysis of data and manuscript preparation. Savage and Myers were involved in experimental design and data acquisition. Peeney was involved in analysis of data. Quinn and Bubb were involved in experimental design, analysis of data and manuscript preparation. All authors have approved the final manuscript.

Acknowledgements

The authors wish to thank Kate Haddley for laboratory assistance and the Biotechnology and Biological Sciences Research Council (BBSRC), Wellcome Trust and University of Liverpool for funding.

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