Original Article

Molecular Psychiatry (2007) 12, 167–189. doi:10.1038/sj.mp.4001897; published online 10 October 2006

Region-specific transcriptional changes following the three antidepressant treatments electro convulsive therapy, sleep deprivation and fluoxetine

B Conti1, R Maier2, A M Barr1,3, M C Morale1,4, X Lu1, P P Sanna1, G Bilbe2, D Hoyer2 and T Bartfai1

  1. 1Molecular and Integrative Neuroscience Department, The Harold L Dorris Neurological Research Institute, The Scripps Research Institute, La Jolla, CA, USA
  2. 2Neuroscience Research, Novartis Institutes for Biomedical Research, Basel, Switzerland

Correspondence: Dr T Bartfai, Molecular and Integrative Neuroscience Department, The Harold L Dorris Neurological Research Institute, The Scripps Research Institute, 10550 N Torrey Pines Rd, SR-307, La Jolla, CA 92037, USA. E-mail: tbartfai@scripps.edu

3Current address: Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.

4Current address: Dipartimento di Neurofarmacologia, OASI (IRCCS), Troina, Italy.

Received 14 April 2006; Revised 13 July 2006; Accepted 5 August 2006; Published online 10 October 2006.



The significant proportion of depressed patients that are resistant to monoaminergic drug therapy and the slow onset of therapeutic effects of the selective serotonin reuptake inhibitors (SSRIs)/serotonin/noradrenaline reuptake inhibitors (SNRIs) are two major reasons for the sustained search for new antidepressants. In an attempt to identify common underlying mechanisms for fast- and slow-acting antidepressant modalities, we have examined the transcriptional changes in seven different brain regions of the rat brain induced by three clinically effective antidepressant treatments: electro convulsive therapy (ECT), sleep deprivation (SD), and fluoxetine (FLX), the most commonly used slow-onset antidepressant. Each of these antidepressant treatments was applied with the same regimen known to have clinical efficacy: 2 days of ECT (four sessions per day), 24 h of SD, and 14 days of daily treatment of FLX, respectively. Transcriptional changes were evaluated on RNA extracted from seven different brain regions using the Affymetrix rat genome microarray 230 2.0. The gene chip data were validated using in situ hybridization or autoradiography for selected genes. The major findings of the study are:

  1. The transcriptional changes induced by SD, ECT and SSRI display a regionally specific distribution distinct to each treatment.
  2. The fast-onset, short-lived antidepressant treatments ECT and SD evoked transcriptional changes primarily in the catecholaminergic system, whereas the slow-onset antidepressant FLX treatment evoked transcriptional changes in the serotonergic system.
  3. ECT and SD affect in a similar manner the same brain regions, primarily the locus coeruleus, whereas the effects of FLX were primarily in the dorsal raphe and hypothalamus, suggesting that both different regions and pathways account for fast onset but short lasting effects as compared to slow-onset but long-lasting effects. However, the similarity between effects of ECT and SD is somewhat confounded by the fact that the two treatments appear to regulate a number of transcripts in an opposite manner.
  4. Multiple transcripts (e.g. brain-derived neurotrophic factor (BDNF), serum/glucocorticoid-regulated kinase (Sgk1)), whose level was reported to be affected by antidepressants or behavioral manipulations, were also found to be regulated by the treatments used in the present study. Several novel findings of transcriptional regulation upon one, two or all three treatments were made, for the latter we highlight homer, erg2, HSP27, the proto oncogene ret, sulfotransferase family 1A (Sult1a1), glycerol 3-phosphate dehydrogenase (GPD3), the orphan receptor G protein-coupled receptor 88 (GPR88) and a large number of expressed sequence tags (ESTs).
  5. Transcripts encoding proteins involved in synaptic plasticity in the hippocampus were strongly affected by ECT and SD, but not by FLX.

The novel transcripts, concomitantly regulated by several antidepressant treatments, may represent novel targets for fast onset, long-duration antidepressants.


electroconvulsive, sleep, fluoxetine, depression, antidepressant, microarray



The most commonly employed pharmacological treatments of Major Depression (MD) aim at the manipulation of the monoaminergic systems.1, 2 For the past 30 years, the prominent theories of mood disorders pointed to the pathological evidence of decreased monoamine levels, including the low 5-HIAA found in suicide victims.3, 4 Indeed, the monoamine oxidase inhibitors since iproniazide, the tricyclic antidepressants (TCAs), the selective serotonin reuptake inhibitors (SSRIs) and the serotonin/noradrenaline reuptake inhibitors (SNRIs) are all causing elevation of synaptic monoamine levels.5, 6 Such pharmacological treatments are widely and successfully used in alleviating the symptoms in ca 70% of patients with MD, but remain ineffective in ca 30% of them. In addition, treating MD with these drugs can require several months of therapy but, most importantly, commonly used antidepressants have a slow onset of action.7 For instance, the SSRIs and SNRIs require treatment for 14–21 days (or longer) before the Hamilton score returns towards normal values. Clinically, this slow onset of action can be problematic, especially as MD is a severely debilitating disorder whose symptoms should be treated promptly once diagnosis is made to reduce the high suicide risk, common in depressed patients. In terms of mechanistic understanding of the current drugs' antidepressant action, this delay in the onset of clinical improvements is in strong contrast to the rapid change in synaptic monoamine levels achieved upon the first or second dose of SSRIs/SNRIs. This has been interpreted as an indication of the need for large, multiple changes in several signaling systems in order to achieve therapeutic effect,6, 8, 9 although neuroprotection and neurogenesis mechanisms may be targeted by antidepressants contributing to the slow onset of action.10, 11, 12, 13, 14

Thus, the existence of a large group of depressed patients resistant to monoaminergic therapy and the slow onset of therapeutic effects of the SSRIs/SNRIs are two major reasons for the sustained search for new antidepressant mechanisms. In contrast to drugs affecting the monoaminergic system, sleep deprivation (SD) and electro convulsive therapy (ECT) are two clinically well-documented, robust, fast-acting methods for treatment of severely depressed patients.15, 16, 17, 18, 19, 20 Both treatments require hospitalization and have been extensively used over the past century. However, the molecular and cellular mechanisms underlying their antidepressant effects are poorly understood. Many studies have examined the effects of ECT on the CSF or serum level of selected markers including neurotransmitters, neuropeptide and hormones.20, 21, 22, 23 Similar work has been carried out on SD. Yet, no molecular and/or cellular mechanism has been identified that satisfactorily explains the rapid, although transient, antidepressant effects of ECT and/or SD.

Microarray analysis of gene expression presents an opportunity to conduct an unbiased search for the transcriptional changes associated with antidepressant treatment in multiple brain regions. Several studies have been recently reported including those investigating transcriptional changes following treatment with an SSRI24, 25 and lithium25, 26 with respect to major depression and bipolar disorders,27 respectively.

In order to develop a strategy that would enable identification of molecular mechanisms suitable for pharmacotherapy for major depression, we compared the transcriptional changes occurring in seven rat brain regions implicated in control of mood and anxiety upon treatment with the two fast-onset/short-lived antidepressant paradigms, ECT and SD and the late-onset/prolonged-lasting SSRI fluoxetine (FLX).

The major weaknesses of today's gene chipping studies, the overwhelming amount of information that needs to be verified and organized, the experimental problems of control tissues and of dissection artefacts were uppermost in the design and execution of the study. Further separate control groups were used for each treatment. Inclusion of three different treatment paradigms permits analysis of common transcriptional changes and indeed resulted in a remarkable focusing/narrowing of the number of transcripts requiring verification, and subsequent follow up in behavioral models to validate the functional significance of the transcriptional change in mood regulation.

The results show that ECT and SD cause larger-scale transcriptional changes and affect similar brain regions, different from those found for SSRI treatment. Transcripts commonly regulated by two or all three treatments were identified and relevance as possible pharmacological targets for MD is discussed.


Materials and methods

Animals and treatment

All procedures were approved by the Institutional Animal Care and Use Committee of the Scripps Research Institute and were carried out on adult male Sprague–Dawley rats (250–300 g). Animals were housed two per cage, food and water were ad libitum and the light/dark cycle was of 12/12 h with light on at 0700 hours. The experimental design is summarized in Figure 1.

Figure 1.
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Schematic representation of experimental design. (a) The study was carried out using six groups of nine rats/group treated with ECT, SD and FLX; each treatment group was paralleled by its specific control. Following dissection and RNA extraction, samples were pooled into groups of three and used for microarray experiments in triplicate. (b) Representation of the brain regions dissected and (c) of the analysis of the microarray data. Abbreviations: E, electroconvulsive therapy; S, sleep deprivation; F, fluoxetine. PFC, prefrontal cortex; FC, frontal cortex; Amy, amygdala; Hypo, hypothalamus; Hipp, hippocampus; DR, dorsal raphe; LC, locus coeruleus.

Full figure and legend (124K)

For electroconvulsive shock (ECT) animals received four shock applications daily (70 pulses/s; pulse width of 0.5 ms; current of 90 mA; shock duration up to 8 s delivered with a 1 h time interval between applications) for 2 days and were killed 1 h after final treatment. Only animals exhibiting full tonic/clonic seizure after each application were utilized. The control group underwent the same manipulation of the ECT group with electrode application at same time and time intervals over 2 days, but did not receive electric shock. Instead, they received four sham applications per day for 2 days.

For SD, animals were kept awake by investigators by gentle disturbances during a 24 h cycle until they were killed. The regular light/dark cycle was not interrupted during experimental procedure, a dim red light was used to facilitate experimenters work during dark. Control animals were kept undisturbed in a separate room with same light/dark cycle of SD group.

For FLXtreatment, animals received daily intraperitoneal injections of a 10 mg/kg FLX HCl solution for 14 days and were killed 1 h after the last treatment. Control animals underwent the same regimen of daily intraperitoneal injection of saline (phosphate-buffered saline (PBS)) solution for 14 days and were killed 1 h after the last injection.


Brains were always dissected by the same investigator, using a wire brain slicer (Research Instruments & MFG, Corvallis, OR, USA) with the assistance of a brain atlas28 (Figure 1b). The frontal and medial prefrontal cortices, the amygdaloid complex, the hippocampus (Hipp) and hypothalamus (Hypo) were dissected free-handedly using established anatomical landmarks.28 A 16-gauge needle constructed from a spinal tap needle and equipped with a plunger to facilitate the transfer of the dissected tissue was used to collect the locus coeruleus (LC) and a similarly constructed 14-gauge needle for the dorsal raphe nucleus (DRN). Tissues were immediately frozen and stored at -80°C until further analysis.

Preparation of RNA and chipping

Total RNA was isolated and purified individually from the seven dissected brain areas obtained from a total of nine animals per condition using the Quiagen RNAeasy purification kit (total of 378 samples). Chipping analysis for each brain region and condition was performed in triplicate on pools of RNA from three animals per chipping sample. Specifically, pools of RNA were generated for each condition and brain area by randomly combining three equal amounts of total RNA isolated from individual animals. This resulted in a total of 126 RNA pools, which were subjected to microarray profiling (Figure 1a).

In total, 5 mug total RNA was used for cDNA synthesis and cRNA amplification and chips were hybridized to Rat Genome 230 2.0 arrays (Affymetrix) according to standard Affymetrix protocols (Affymetrix Expression Analysis Technical Manual, http://www.affymetrix.com/support/technical/manuals.affx). Data were first processed with MAS5 (Affymetrix) by global scaling to a target intensity of 150 for quality assessment. From a total of 126 microarrays, 121 met our quality criteria and the data were used for further analysis.

Evaluation of the chipping data, algorithms and cut offs

The analysis of differential gene expression was performed using Robust Multichip Average (RMA)-processed data.29 A combined expression value for each probe set was calculated as the 66th percentile of the respective expression values from the three data sets which were available for each condition. This strategy was chosen to obtain a data set that is more robust, less sensitive to outliers and subsequently provides a better estimate of differential expression. In the few cases where only two data sets were available, the mean value was used instead.

For finding gene-expression differences between conditions, fold changes were calculated by dividing the expression value for the treated by the value of the respective control condition. Fold-change values <1 were inversed and negated. Therefore, positive values indicated a higher, whereas negative values indicated a reduced expression in the treated group.

The data were subsequently filtered and patterns determined according to the following criteria: (1) absolute fold change values were required to be greater than or equal to1.8; (2) expression values in at least one of the compared conditions (treated or control) were required to be above the estimated background value of 70; (3) the set criteria had to be met in at least one of the seven brain areas in order for a given probe set (gene) to remain listed.

In situ hybridization (ISH) analysis

Six groups of brains were processed: sleep-deprived rats (SD), control for SD, electroshock (ECT), control for ECT, FLX-treated rats, and control for FLX (saline). The brains from three controls and three treated animals were removed, frozen in isopentane and stored at -80°C. Rat brains were then sent by courier to NIBR, Basel, where they were processed upon arrival. Coronal tissue sections were cut in 10 mum-thick slices with a microtome-cryostat and were thaw-mounted on silane-coated microscope slides.

In situ probes were generated from cDNA fragments of the respective genes that were amplified by PCR from rat whole brain cDNA and cloned into pGEM-T Easy vector (Promega, Madison, WI, USA). Probes were generated for Cplx2 (NM_053878, bp 456–713), Ntrk2 (NM_012731, bp 2671–3135), BDNF (NM_012513, bp 249–580) and Camk2a (NM_012920, bp 962–1492) and confirmed by sequencing.

ISH was performed using sections fixed in 4% (w/v) ice-cold paraformaldehyde for 5 min and washed four times for 1 min in 1 times PBS at room temperature. The slides were then incubated for 2 min in 0.1 M triethanolamine pH 8 and in the same buffer supplemented with 0.25% (v/v) acetic anhydride for 10 min. After washing two times for 2 min in 2 times SSC (0.3 M NaCl, 0.03 M sodium citrate pH 7), the sections were dehydrated in 50, 70, 95 and 100% ethanol, air dried and used the same day for hybridization. Antisense and sense [35S]UTP-labeled RNA probes were synthesized by in vitro transcription using T7 or SP6 RNA polymerase ([35S]UTP-specific activity 1000 Ci/mmol; Amersham, UK). 35S-labeled antisense riboprobe detected mRNA, hybridizing adjacent sections with the corresponding [35S]-labeled sense riboprobe and did not reveal any mRNA signal and served as a control. The riboprobes were purified by using G-50 Sephadex Quick spin columns (Roche, Basel, Switzerland), diluted to 107 c.p.m./ml in hybridization buffer (50% formamide, 10% dextran sulfate, 1 times Denhardt's solution, 10 mM Tris-HCl pH 8, 0.3 M NaCl, 2 mM EDTA, 500 mug/ml t-RNA) and then were applied to the tissue sections and overlaid with coverslips. Slides were hybridized in an incubator at 55°C for 16–22 h. After hybridization, the slides were cooled down in 4 times SSC and the coverslips were removed, washed again four times for 4 min in 4 times SSC. The sections were treated with RNase A (20 mug/ml) for 30 min at 30°C, washed in decreasingly stringent solutions of SSC (down to 0.1 times SSC at 65°C for 30 min), dehydrated in 70, 95 and 100% ethanol and air dried. Slides were exposed to Biomax MR film (Eastman Kodak Company, Rochester, NY, USA) at 4°C, 1 day for Cplx2, 3 days for Ntrk2, 2 days for BDNF and Camk2a.

Autoradiography of [3H]MPPI binding to 5-HT1A receptor sites

[3H]MPPI-binding sites ([3H]4-(2'-methoxyphenyl)-1-[2'-[N-(2"-pyridinyl)-iodobenzamido] ethyl] piperazine, NEN Life Science Products, Boston, MA, USA) were determined by an autoradiographic assay as described previously.30 The assay was performed in 10-mum brain sections of rats subjected to the three different antidepressant treatments. The brain sections were pre-incubated for 30 min at room temperature in assay buffer (170 mM Tris-HCl, pH 7.6). The slides were then incubated with [3H]MPPI (10 nM in assay buffer) for 90 min at room temperature. Non-specific binding was defined using 10 muM WAY100,635 (Anawa, Wangen, Switzerland). The slides were then washed twice with assay buffer at 4°C and rinsed with cold double distilled H2O (ddH2O). After air blow-drying, the slides were exposed to 3H Hyperfilm (Amersham, Arlington Heights, IL, USA) for 4–8 weeks. 125I microscales (Amersham) were exposed with each slide film to calibrate the absorbance in the fmol/mg tissue equivalent.

Data and statistical analysis

Sections were finally counterstained with 0.5% Cresyl violet and nuclei localized according to Paxinos and Watson.28 Data from hybridization signals were analyzed by optic densitometry of Biomax MR films using a computerized image analysis system (MCID, Imaging Research, St Catherines, Ontario, Canada).



The Rat Genome 230 2.0 chip utilized in our studies provides information on over 31 000 probe sets, which combined with the number of treatments investigated (three plus respective controls) and the seven brain regions analyzed results in a set of information equivalent to over 3.7 million transcript levels. This provided the opportunity to search for transcripts/genes of interest as encoding possible new antidepressant drug targets and to analyze the extent of the region-specific action of each different antidepressant treatment.

Quality and validation of the gene-chipping data

Seven brain areas were dissected, RNA extracted and used for chipping: prefrontal cortex (PFC), frontal cortex (FC), amygdala (Amy), hippocampus (Hipp), hypothalamus (Hypo), locus coeruleus (LC) and dorsal raphe (DR) (Figure 1b). Reproducibility was facilitated by dissecting each region from the same selected brain slice obtained with a custom-made brain slicer. All dissections were performed by the same investigator and were straightforward for PFC, FC Amy, Hippo and Hypo. The presence of mRNA for the specific catecholaminergic marker dopamine beta hydroxylase (DBH) in all LC samples was used to demonstrate that LC dissections were consistent and reproducible (not shown). Similarly, the presence of tryptophan beta hydroxylase (TPH) mRNA was used as an indication for the quality of DRN dissections (not shown).

Chip hybridization reactions were performed utilizing three pooled tissue samples from a total of nine animals for each condition, a strategy that reduced the number of chips to be used and minimized the possibility that differences observed could reflect the quality of dissections rather than actual changes (Figure 1). To minimize differences that could arise from specific manipulation of the animals, each experimental group was compared to its own specific control as specified in the Materials and methods section. For electroconvulsive shock, animals that did not show seizures upon ECT were not used for analysis. For the 121 microarrays analyzed, the average 3'/5' glyceraldehydes 3-phosphate dehydrogenase (GAPDH) ratio was 1.15plusminus0.13 and 57plusminus2.5% of the probe sets (genes) indicated as presented by MAS5.

Evaluation of the expression profile of different probe sets for the same transcripts across the study was carried out on gene chip data filtered for a 'cutoff' of 1.3-fold change above or below control. The analysis revealed a high degree of reproducibility. In total, over 3900 genes were affected by one or more treatments. Thus, subsequent analysis of the chipping data was carried out using the higher cutoff criteria of 1.8-fold higher or lower than controls, with the exception of a few selected gene transcripts showing wide variation of regulation throughout brain regions and across treatments.

Changes in the expression levels of BDNF transcripts found by microarray analysis were confirmed by quantitative ISH that was extended to 19 brain regions for each antidepressant treatment (see Figure 2). Similarly, the expression profile of the neurotropin receptor NTRK2 was confirmed by ISH and the quantification was extended to 21 brain regions (Figure 3). The levels of serotonin 1A (5-HT1A) receptors determined by receptor autoradiography in six brain regions were not affected by any of the three treatments (Figure 4).

Figure 2.
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Region-specific changes in BDNF transcript. (a) Representative autoradiographic photoemulsion of in situ hybridization for BDNF on sections comparing containing prefrontal cortex (PFC) and hippocampus (Hipp) from animals under different treatment and controls as indicated. (b, c and d) Histograms of quantification of BDNF mRNA in 19 different brain regions. Abbreviations: AO, anterior olfactory nucleus; PFC, prefrontal cortex; Pir2, piriform cortex, layer 2; Cl, claustrum; Den, dorsal endopiriform nucleus; PVP, paraventricular thalamic nucleus, posterior part; GrDG, granular layer of the dentate gyrus; PoDG, polymorph layer of the dentate gyrus; CA1py, CA2py, CA3py, pyriform layer of fields CA1-3 of Ammon's horn; VMH, ventromedial hypothalamic nucleus; Me, medial amygdaloid nucleus; BL+LA, basolateral+lateral amygdaloid nuclei; PMC0+AHi, postero medial cortical amygdaloid nucleus and amygdalo hippocampal area; DR, dorsal raphe nucleus; Ent II, entorhinal cortex, layer II; GrCb, granule cell layer of the cerebellum. *P<0.05, **P<0.01, ***P<0.001.

Full figure and legend (230K)

Figure 3.
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Region-specific changes in Ntrk2 transcript. (a) Representative autoradiographic photoemulsion of ISH for Ntrk2 on sections comparing containing prefrontal cortex (PFC), hippocampus (Hipp) and amygdala (Amy) from animals under different treatment and controls as indicated. (b, c and d) Histograms of quantification of Ntrk2 mRNA in 21 different brain regions. Abbreviations: OB, olfactory bulb; PreFr, prefrontal cortex; AO, anterior olfactory nucleus; Pir, piriform cortex; Cl, claustrum; Den, dorsal endopiriform nucleus; LS, lateral septum; MPO, medial preoptic nucleus; HDB+MCPO, nucleus of the horizontal limb of the diagonal band+magnocellular preoptic nucleus; AHypo, anterior hypothalamus; ATh, anterior thalamus; M+Vth, medial+ventral thalamus; MHypo, medial hypothalamus; CA1+2+3, fields CA1-3 of Ammon's horn; GrDG, granular layer of the dentate gyrus; AHi+PMCo, amygdalo hippocampal area and postero medial cortical amygdaloid nucleus; DR, dorsal raphe nucleus; LC, locus coeruleus; GrCb, granule cell layer of the cerebellum. **P<0.01, ***P<0.001.

Full figure and legend (264K)

Figure 4.
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Measurement of 5-HT1A receptor level. Histograms showing the autoradiographic quantification of the 5-HT1A receptor binding following (a) ECS, (b) SD and (c) FLX, as indicated measured in the cerebral cortex, the fields CA-1 and the CA1-CA3 of the hippocampus, the dentate gyrus (DG), the dorsal (DR) and the median (MeR) raphe nuclei.

Full figure and legend (31K)

Number of transcriptional changes caused by ECT, SD and FLX treatment

All three antidepressant treatments resulted in both increases and decreases in the levels of a large number of transcripts (Figure 5a). The treatment causing overall the largest cumulative number of changes in all brain regions was ECT (3285 genes affected) followed by FLX (520 genes affected) and SD (440 genes affected). ECT induced the largest number of changes in all brain regions with the exception of the Hypo where the highest number of changes was induced by FLX (294 by FLX, 148 by ECT and 73 by SD). Each treatment induced a larger number of upregulated (Figure 5b) than downregulated transcripts (Figure 5c) with the exception of FLX in the Hypo (ECT: 2544 up and 741 down; FLX: 186 up and 336 down; SD: 281 up and 159 down).

Figure 5.
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Regional distribution of transcripts affected by each treatment. (a) Histogram showing the total number of transcripts whose level was changed more than 1.8-fold to the control by ECT, SD and FLX in the different brain regions investigated (ordered by anatomical rostral to caudal). (b) Number of transcripts upregulated and (c) downregulated.

Full figure and legend (85K)

Brain areas preferentially affected by ECT, SD and FLX treatment

We evaluated the relative efficacy of each treatment in inducing changes in each brain region. To do so, we compared the percentage of genes affected relative to the total number of genes modulated in all regions (Table 1). Each treatment affects each brain area differently. As summarized in Table 1, ECT affects the largest number of transcripts in the LC and the lowest number of transcripts in the DRN, with the following order: LC>FC>PFC>Amy>Hipp>Hypo>DRN. SD was found to induce most changes in LC and least in FC: LC>Hypo>Hipp>PFC>DRN>Amy>FC. FLX mostly affected Hypo and DRN, but had little effect on PFC: Hypo>DRN>Amy>LC>FC>Hipp>PFC.

Interesting differences were found by comparing the percentage of transcript changes by the fast-onset antidepressant treatments SD and ECT and the slow-onset antidepressant FLX. Specifically, SD and ECT appear similarly effective in producing changes in the LC compared to FLX (SD, ECT, FLX: 44, 31 and 8%, respectively). In contrast, FLX appeared more effective than SD and ECT in inducing changes in the DRN (FLX, SD, ECT: 14, 7 and 3%, respectively) and in Hypo (FLX, SD, ECT: 56, 16 and 4%, respectively). By contrast, FLX and SD were poorly effective in FC compared to ECT (ECT, SD, FLX: 17, 3 and 3%, respectively).

Transcripts commonly affected by two antidepressant treatments

A summary of the number of changes of the levels of transcripts in two out of three treatments is presented in Table 2. The brain regions most affected by multiple transcriptional changes were, in decreasing order, LC, Hypo, Hipp, DRN, PFC, Amy and FC. The highest total number of changes was induced by the two fast-acting antidepressant treatments, ECT and SD (136). Similarly, the number of transcripts decreased to similar values when compared to transcript changes induced by FLX (50 for ECT/FLX and 57 for SD/FLX).

In the LC, 83 transcripts were co-regulated by ECT and SD, many more than FLX. A similar pattern emerges in PFC and Hippo. In the DRN, Hypo or Amy, FLX shows more co-regulation with either of the two other treatments. Of the total of 193 genes affected by two treatments, 24 or 12% were regulated in at least two brain regions. Remarkably, with the exception of the acidic nuclear phosphoprotein 32 (Anp32a), which was mildly upregulated by ECT and FLX in the DRN and downregulated in PFC, the other 23 transcripts were all upregulated by both ECT and SD. Among those transcripts that were upregulated are those encoding the brain-derived neurotrophic factor (BDNF), the neuronal immediate early gene homer (Homer1), the early growth response 2 gene (Egr2), the activity and neurotransmitter-induced early gene protein 4 (Ania-4), the heat shock 27 kDa protein 1 (Hspb1) and the ret proto-oncogene (Ret). These transcripts were all upregulated by ECT and SD, but not by FLX in two brain regions: PFC and HIPP (Table 3). This list represents 25% of the genes regulated by two treatments in PFC.

A complete list of gene transcripts affected in two out of three treatments is presented in Table 4.

Transcripts regulated by all three treatments

A summary of the number of changes in transcriptional levels evoked by all three treatments is presented in Table 2; the complete list is presented in Table 5. In total, only 19 gene transcripts were found to be up- or downregulated greater than or equal to1.8-fold by all three antidepressant treatments in at least one brain region. Thus, the strategy to identify common changes to several treatments reduces dramatically the number of transcripts to be validated. Of these, 13 (about 70%) occurred in the Hypo, three in LC, two in DRN and one in Amy, whereas no changes common to the three treatments were found in PFC, FC and HIPP. However, when taking into account changes with a cutoff of 1.5-fold in one of the treatments, it was found that serum/glucocorticoid regulated kinase (Sgk1) was regulated by all treatments in LC and DRN, sulfotransferase family 1A (Sult1a1) in LC and FC, and glycerol 3-phosphate dehydrogenase (GPD3) was upregulated in all regions by all three treatments. Further, we identified G protein-coupled receptor 88 (GPR88), an orphan GPCR, to be co-regulated by all three treatment modalities in the Hypo.



The present study compared the changes in gene expression profiles induced in rat brain by three different, clinically proven antidepressant treatments: electroconvulsive shock (ECT), SD and the SSRI FLX. These treatments differ in the onset of their therapeutic action which is fast in ECT and SD (2 days and 1 day, respectively) compared to FLX (14–21 days). ECT, SD and FLX treatments were employed in rats with the same therapeutic regimen used in human patients. The rationale for choosing FLX as the SSRI studied was the large amount of published data collected with this SSRI both clinically and pre-clinically31, 32, 33 that would increase our ability to make comparisons with other SSRI-affected parameters. The brain regions investigated were selected based on their presumed involvement in regulating mood states, fear and anxiety. There are no 'control' regions in the sense that none of the treatments used was known to not affect specifically one brain region. Including a peripheral tissue in the study was not considered to be meaningful to the study.

Number of transcripts affected by single and multiple treatments per brain region

The investigation of the comprehensive gene expression profile in several different brain regions provided the opportunity to use the microarray data as an indicator for the biological activity of each brain region in response to each treatment. This approach allows the 'visualization' of the brain regions affected by the treatment, information especially interesting for ECT and SD, whose antidepressant mechanisms of action are far less characterized than that of FLX. The experimental model utilized was aimed at reducing the number of artefacts that could confound the results. The study was carried out by analyzing triplicates of pools of tissues from three animals per treatment and by adopting a specific control for each treatment, thus minimizing a possible confound by stress-dependent specific manipulation of the animals. Further, analysis with a 1.8-fold cutoff above or below control values, similar to other microarray studies,34, 35, 36, 37 greatly limited the number of genes considered as differentially expressed and strongly increased the stringency of the analysis. Combined with some regional-specific markers, evaluation of the results obtained with independent probes for the same transcripts across the study as well as independent ISH validation for selected transcripts served to validate the data set. Nevertheless, we are aware that this study presents limitations that need to be considered for critical interpretation of the results.

One limitation is that the three antidepressant treatments were used on naïve rather than 'depressed' animals. Thus, it was not possible to identify and eventually eliminate non-responders from the study. In addition, although equivalent treatment modalities were applied to the animals in this study, transcriptional changes caused by clinically effective antidepressants may differ between patients and rodents. Thus, for the functional validation of some transcripts identified as possible drug targets, further studies will be necessary to assess the effect of transcriptional regulation in one or more rodent models of depression.38, 39, 40, 41, 42, 43 Another apparent limitation is that transcriptional profiles for the different treatments were compared only at the time points corresponding to clinical efficacy, which differed for each treatment being 2 days for ECT, 1 day for SD and 14 days for FLX. Thus, a comparative analysis of transcriptional changes induced at the same time points for all treatments is missing. This may be particularly interesting for FLX, as its effects evaluated at 2 weeks distance from ECS and SD may be different if evaluated at the same time points. However, as amply discussed, the time points were chosen to mimic clinical conditions.

Despite such limitations, the data obtained collectively reveal a very peculiar scenario demonstrating that ECT, SD and FLX induce changes in all seven brain regions investigated, albeit with a very distinct pattern. Thus, while common molecular mechanisms may exist for all these treatments, important differences were noticed. Most interesting is the finding that treatment with the two fast-acting antidepressants, ECT and SD, is accompanied by large transcriptional changes in the LC, a region barely affected by chronic FLX treatment, which instead caused a large number of changes in the Hypo and the DRN. By contrast, ECT and SD had little effects on the DRN.

An important correlate to the stress of ECT and SD treatments is the large increase in CRF transcripts in the LC by ECT and SD, whereas the 14-day FLX downregulates the mRNA levels for CRF in the LC.

Thus, ECT and SD, the two openly stressful treatments, appear to be mostly effective on noradrenergic neurons, but poorly on serotonergic ones, which in turn are expectedly targeted by FLX. Although this finding is not novel, to the best of our knowledge, it was never demonstrated in a comparative study investigating several brain regions. Such an analysis clearly indicates that fast-onset, but short-lived, antidepressant action is associated with 'activation' of brain regions containing noradrenergic neurons, whereas targeting of serotonergic neurons is associated with late antidepressant onset. This indicates that fast-onset and long-lasting antidepressant effects may be achieved by targeting both systems. However, that interesting concept may be an oversimplification as it is not necessarily supported by existing clinical data. SNRIs are believed to have a more 'robust' effect than SSRIs, yet their time of onset is similar to that of SSRIs. The situation is similar for tricyclic antidepressants like chlorimipramine which affect both NE and 5-HT uptake. Nevertheless, it is worth noting that in depression it is assumed that the LC is overactive and thus targeting it strongly with ECT and SD may achieve a paradoxical inhibition, with resulting reduction in the LC-mediated inhibition of DRN 5HT-neurons. It is unclear why the robust and rapid antidepressant effects of ECT and SD are lost rapidly, but it is likely that the very high level of transcriptional activation in the LC cannot be maintained. In addition, acute FLX treatment was demonstrated to increase circulating dopamine and norepinephrine levels in rat PFC 44, 45 and to elevate circulating levels of catecholamines in depressed individuals,46 yet without therapeutic effects. Further, it is reported that SSRIs affect sleep patterns and reduce rapid eye movement (REM) sleep during early phases of treatment, as are reports of increased anxiety and nervousness.47, 48, 49, 50, 51

A number of transcripts show up- and downregulation depending on brain region and/or treatment

All three antidepressant treatments induced up- and downregulation of transcripts level. While upregulation was more frequent overall, downregulation was recorded with similar frequency in all brain regions following all three treatments. The only exception was the Hypo where FLX induced primarily downregulation of gene expression. These observations suggest transcriptional suppression or change in mRNA stability as major mechanisms that may be involved in antidepressant action, a finding consistent to that reported for treatments with imipramine and citalopram.52 The mechanisms mediating such changes may include chromatin remodeling through histone modification as recently demonstrated in a chronic model of ECT.53

When comparing the three treatments, ECT and SD affect transcription more robustly in the LC than does FLX. This is partly explained by the selectivity of FLX, a serotonin reuptake blocker that can affect the serotonergic projections that are not the most important drivers of neuronal activity for the noradrenergic cells in the LC. ECT induces convulsions by depolarization of neurons in several areas, including the LC. The fact that both SD and ECT can affect the LC are in line with earlier observations on the effects of ECT54, 55, 56 and of sleep deprivation on LC.57, 58, 59 The strong stress generated by both treatments and the similarly short duration of these two treatments as compared to the 14-day long SSRI treatment also suggest that of the three treatments, the two faster-acting stress-based treatments, ECT and SD, will bear similarities and one of these similar features is the strong effect on the LC, most likely on the noradrenergic neurons of the LC. The strong upregulation of the early gene cFos by ECT and SD but not by the SSRI is reflecting the shorter time point for the former treatments as well as differences in the neurochemical substrates.

FLX induced a large number of transcriptional changes in the Hypo. There is extensive literature on the chronic FLX-mediated neuroendocrine changes based on acute upregulation of serotonergic receptor occupancy followed by desensitization during the chronic treatment (reviewed in Lanfumey et al.,60 Simansky,61 Hernandez et al.,62 and Fuller63). In the Hypo, FLX changes not only 5-HT but also noradrenaline and dopamine and these may jointly contribute to the large transcriptional changes in this brain area. A better understanding of the observed transcriptional profile in the Hypo might require mapping them to specific hypothalamic nuclei, an analysis not contemplated in this study.

Selected genes whose transcription was similarly affected by two of the three treatments in one or several brain regions

The similarity between transcriptional effects of ECT and SDs and difference to the effects of FLX as revealed by investigating the number of changes, is illustrated by a closer look at the following targets: BDNF, Homer1, Egr2 (Krox20), Ania-4, heat shock 27 kDa protein 1 (Hspb1) (Hsp 27) and Ret, which forms a cluster of functionally correlated genes that were found to be upregulated by both ECT and SD, but not by FLX.

Homer1 is a scaffolding protein involved in the activity-dependent alteration of synaptic structure and function modulating neuroplasticity in glutamatergic synapses.64 Homer proteins also regulate sensitivity to cocaine and may be involved in the pathogenesis of schizophrenia.65, 66

Erg2, also known as Krox20, is a transcription factor which plays a role in peripheral myelination.67 and is involved, together with homer and BDNF, in the stabilization of long-term potentiation (LTP).68, 69, 70 Interestingly, Krox20 expression is upregulated exclusively during the dark phase in the cortex and Hippo of rats exposed to an enriched environment.71

The activity and neurotransmitter-induced early gene protein 4 (Ania-4) shares high homology with doublecortin-like kinase, CaM-Kinase, the CaMK-related peptide (CARP) and the candidate plasticity gene 16 (CPG16), a protein serine/threonine kinase.72, 73 Ania-4, together with Homer and Erg2, is upregulated in the striatum by dopaminergic stimulation.74 Ania-4 expression is also elevated in the cerebral cortex after traumatic brain injury and in the striatum following treatment with the putative D1 agonist/D2 antagonist LEK8829.75, 76

Hpb1, better known as Hsp27, is a heat shock protein with antiapoptotic effects believed to be important for neuronal survival following axotomy or trophic factor withdrawal. Hpb1 and BDNF expression are elevated following spinal cord injury77 or in the CNS after cortical spreading depression,78 whereas it is reduced after BDNF treatment in retinal ganglion following axotomy.79

The proto-oncogene Ret belongs to the receptor-like tyrosine kinase superfamily and is strongly elevated in motor neurons following axotomy and during neuronal differentiation.80, 81, 82

Together with BDNF, these genes are involved in synaptic plasticity and possibly in neurogenesis, two mechanisms that have been proposed to participate in antidepressant action.83 Such studies have focused mainly on the effects of BDNF gene upregulation following antidepressant treatment. Finally, a large number of ESTs were found to be regulated by more than one antidepressant treatment.

Transcripts regulated by all three antidepressant treatments

Few transcripts were found to be consistently modulated by all three antidepressant treatment. These include GPD3, Sgk1, Sult1a1 and GPR88. This is encouraging from an antidepressant drug target search point of view, if one could show that non-antidepressant psychoactive drugs do not affect transcript levels, that is, that there is a selectivity to the transcriptional changes in GPD3, Sgk1, Sult1a1 and GPR88.

GPD3, the mitochondrial flavoprotein-dependent glycerol-3-phosphate dehydrogenase is the only target in the present study which is consistently upregulated by all three treatment modalities, in all seven brain regions studied. The activity of GPD3 is considered a reliable marker of thyroid status both in the liver, and in the brain and thyroid hormone supplementation is a long-known adjunct therapy in depression.84, 85 The altered metabolic state in the brain regions examined during depression and antidepressant treatments has been demonstrated by 18-fluorodeoxyglucose PET studies.86, 87 GPD3 was found to be transcriptionally regulated in relation to changes in neuronal activity and antidepressant drug treatment.88 Enhanced expression of GPD3 might reflect increased metabolic requirements and contribute to a higher Na/K ATPase activity that can accelerate the rate of neuronal repolarization.

The serum and glucocorticoid-regulated kinase 1 (SGK, Sgk1) is upregulated consistently by all treatment modalities in six out of seven brain regions, whereas in the PFC, ECT leads to reduced mRNA expression, suggesting Sgk1 is involved not only in stress but indeed also in mood regulation. Sgk1 belongs to a family of serine/threonine kinases that is under acute transcriptional control by serum and glucocorticoids, as well as by an expanding set of hormones, growth factors and cellular stress pathways.89 Sgk1 is implicated in the regulation of ion channel conductance, cell volume, cell cycle progression and apoptosis.90 Sgk1 is activated through the phosphatidylinositol 3 kinase (PI3-kinase) pathway by growth factors such as insulin, insulin-like growth factor-1 (IGF1), fibroblast growth factor (FGF), platelet-derived growth factor (PDGF) or tumor growth factor beta (TGF-beta), which activate extracellular signal-regulated kinases (ERKs).91, 92 Sgk1 is similar in primary structure to protein kinase B (PKB), protein kinase C (PKC) and protein kinase A (PKA). In the brain or in brain cells, transcription of Sgk1 is increased in several animal models of Parkinson's disease93 and a transgenic model of amyotrophic lateral sclerosis (ALS), following brain injury94 and after transient global cerebral ischemia.95 Increased expression of Sgk1 has been observed in the Hippo of fast-learning rats as compared with slow-learning rats.96 Enrichment-induced Sgk1 expression is specifically mediated through alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors97 and Sgk1 can directly phosphorylate the transcription factor cyclic AMP response element-binding protein (CREB) on serine 133.98 Sgk1 expression level is increased by acute amphetamine36 and lysergic acid diethylamide (LSD) treatment,36 and it is increased in the brain and peripheral tissues following psychosocial stress.40 The upregulation of Sgk1 strongly correlates with the occurrence of cell death. In Mecp2-null mice, a model for Rett syndrome,99 increased levels of Sgk1 mRNA are reported before and after onset of neurological symptoms. Fear conditioning is accompanied by changes in Sgk1 expression.100 Sgk1 is also upregulated during lactation as were Sult1a1 and GPR88.101 Taken together, the published data and our own data strongly suggest a role for Sgk1 in stress, cell survival, and also learning and memory consolidation and other central functions, like reward and depression.96, 102

Sulfotransferase 1a (Sult1a1) specifically catalyzes the sulfonation of the catecholamines, dopamine, adrenaline and noradrenaline as well as of drugs such as apomorphine. Sult1a1 is involved in drug metabolism, cancer, hormone regulation and neurotransmitter synthesis/metabolism.103 Sult1a1 was also upregulated during lactation as was Sgk1 and GPR88.101 Both Sgk1 and Sult1a1 are transcriptionally regulated in studies on bipolar disorder and lithium treatment.37 The effects of Sult1a1 on thyroid hormone metabolism are also compatible with involvement in antidepressant effects and altered metabolic rates.104 Given the widespread and rather extensive regulation of Sult1a1 mRNA expression by all three antidepressant treatments applied in the present study, this enzyme appears to play a significant role in mood states. Interestingly, FLX and SD promote upregulation in all brain regions tested, whereas ECT induces upregulation in some and downregulation in other brain areas.

From a drug development perspective, one of the most interesting candidates to emerge from our study is GPR88 (an orphan GPCR). GPCRs are attractive drug targets and dozens of GPCRs have been targeted for safe, chronic therapies of several disorders including hypertension, allergy, acid secretion and schizophrenia. GPR88 is an orphan GPCR of the family A, which shows highest homology to 5HT1D-receptor and the beta 3 adrenergic receptor, respectively. GPR88 was recently described to be selectively expressed in the striatum;105 however, its mRNA is also present in cerebral cortex, nucleus accumbens, Amy, Hippo, Hypo, thalamus, and LC (Allen Brain Atlas, GNF Atlas and our own unpublished data). Several lines of evidence suggest that GPR88 mRNA is regulated in a variety of psychiatric conditions including bipolar disorder and major depression. In an adult rat cortex slice model, inositol depletion by lithium (IMPase block) and carbachol (muscarinic receptor agonist) co-treatment increased GPR88 transcript levels approx2.2-fold, whereas repletion of inositol decreased GPR88 transcripts in the same magnitude (approx2.3-fold). Lithium inhibits inositol monophosphatase and downstream GSK3beta at therapeutically effective concentrations, and it has been hypothesized that depletion of brain inositol levels is an important neurochemical change forming the molecular basis of lithium's therapeutic efficacy in bipolar disorder.106 GPR88 is upregulated by methamphetamine and valproate, a clinically effective drug in treatment of bipolar disorders, in mouse pharmacogenomic models for bipolar disorders.37 GPR88 expression on the transcriptional level is lower in BDNF-deficient mice.107 GPR88 transcript levels were increased in the arcuate/ventromedial nucleus of the Hypo during lactation (approx1.7-fold). Removal of pups for 48 h decrease GPR88 mRNA levels. Incidentally, in that study, similar observations were made for Sgk1 and Sult1a1 mRNA regulation during lactation.101 This observation is particularly interesting as lactating mothers can be considered to be in a non-depressed state; it has been reported that withdrawal of pups from nursing mothers induces anhedonia, whereas the presence of pups and maternal care increase reward in a number of paradigms. Qualitative trait linkage (QTL) analysis has resulted in high logarithm of odds (LOD) scores on Chr1q, which is synthenic to that mapping rodent emotionality. Genes of interest in these regions include GPR88.108 Taken together, our data and that reported in the literature suggest that GPR88 is regulated in a number of conditions linked to reward, anhedonia, depression and bipolar disorders. Intriguingly, both down- and upregulation was seen after the different treatments, depending on the brain regions (Amy, Hippo, Hypo, LC and PFC).



In summary, the major conclusion of this unbiased search for molecular changes occurring during fast and slower antidepressant treatments is that the faster acting ECT and SD predominantly affects the LC and much less the DRN. Not surprisingly, the SSRI FLX affects predominantly the DRN, but even more profoundly the Hypo.

As for the identification of new molecules or molecular pathways that may be of interest for development of antidepressant treatments, this study confirms transcriptional regulation of several genes known to be affected by more than one antidepressant treatment. These include BDNF whose transcription is upregulated by both ECT and SD as previously reported,13, 21, 109, 110, 111 although unlike other studies13, 112 both microarray and ISH analysis demonstrated that BDNF transcription is not affected by FLX. Similarly, NTRK2 upregulation also confirm earlier reports that neutropin signaling is activated by antidepressant treatments.21, 109, 113, 114 This, together with the finding that ECS and SD elevate the levels Ania-4, Erg-2, Homer1, Hspb and Ret may reflect the growing assertion that antidepressant treatments are associated with neuroprotection, neurogenesis and neuronal plasticity.10, 11, 12, 13, 14

A very limited number of transcripts was found to be affected by all three antidepressant treatments and only functional validation will reveal their relevance with respect to depression. Among them, three enzymes: GPD3, Sgk1 and Sult1a1 can be modulated by several hormonal states making them difficult drug targets in terms of specificity. GPD3 is distributed isotropically and its general metabolic role renders it an unlikely drug target. Sgk1 is activated by glucocorticoids that are induced by the activation of HPA axis by chronic stress such as may underlie depressed states. It is likely that in a feedback regulatory arrangement, many substrates of this protein kinase may act to limit the effects of stress and thus may exert an antidepressant like effect. Thus, identification of Sgk1 substrates in key brain areas may be important to determine its candidacy as drug target for antidepression. The fact that Sult1a1 utilizes catecholamines as substrates renders it an interesting candidate for a possible significant involvement in antidepressant action. The simultaneous change in the levels of the three catecholamines adrenaline, noradrenaline and dopamine may represent a set of neurochemical stimuli in several brain regions not dissimilar to the effect of changes in monoamine oxidase (MAO) activity. The rise of this transcript upon treatment with all three antidepressant modalities in the LC, the major noradrenergic nucleus with significant dendritic release, may be important for the regulation of catecholamine levels in this nucleus.



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We acknowledge all our collagues for their help: colleagues in the Bartfai lab: Hedie Badieh, Marga Behrens, Svetlana Gaidarova, Jeffrey Kinney, Janell Laca, Jacinta Lucero, Shuei Sugama, Iustin Tabarean and Sebastian Wirz all spent countless hours in assisting us with the treatments described. Colleagues in the Hoyer lab: Dominique Fehlmann, Edi Schuepbach, Sabine Leonhard and Deepak Thakker have performed ISH, autoradiography and data analysis thereof. Colleagues in the Maier lab: Jose Luis Crespo and Doris Rueegg have performed the RNA isolations and cloning of the in situ probes. We also thank Nicole Hartmann and her team from the Genomics Factory at NIBR Basel for the microarray work.