st john’s wort and imipramine-induced gene … john's wort and imipramine...immediate...

IMMEDIATE COMMUNICATION

St John’s wort and imipramine-induced gene expressionprofiles identify cellular functions relevant toantidepressant action and novel pharmacogeneticcandidates for the phenotype of antidepressant treatmentresponseM-L Wong1, F O’Kirwan1, J P Hannestad1, KJL Irizarry1, D Elashoff2 and J Licinio1,3

1Department of Psychiatry, Center for Pharmacogenomics, Neuropsychiatric Institute, David Geffen School of Medicine atUCLA, Los Angeles, CA, USA; 2Department of Biostatistics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA;3Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine at UCLA,Los Angeles, CA, USA

Both the prototypic tricyclic antidepressant imipramine (IMI) and the herbal product St John’swort (SJW) can be effective in the treatment of major depressive disorder. We studiedhypothalamic gene expression in rats treated with SJW or IMI to test the hypothesis thatchronic antidepressant treatment by various classes of drugs results in shared patterns ofgene expression that may underlie their therapeutic effects. Individual hypothalami werehybridized to individual Affymetrix chips; we studied three arrays per group treatment. Weconstructed 95% confidence intervals for expression fold change for genes present in at leastone treatment condition and we considered genes to be differentially expressed if they had aconfidence interval excluding 1 (or �1) and had absolute difference in expression value of 10or greater. SJW treatment differentially regulated 66 genes and expression sequence tags(ESTs) and IMI treatment differentially regulated 74 genes and ESTs. We found six commontranscripts in response to both treatments. The likelihood of this occurring by chance is1.14� 10�23. These transcripts are relevant to two molecular machines, namely the ribosomesand microtubules, and one cellular organelle, the mitochondria. Both treatments also affecteddifferent genes that are part of the same cell function processes, such as glycolytic pathwaysand synaptic function. We identified single-nucleotide polymorphisms in the human orthologsof genes regulated both treatments, as those genes may be novel candidates forpharmacogenetic studies. Our data support the hypothesis that chronic antidepressanttreatment by drugs of various classes may result in a common, final pathway of changes ingene expression in a discrete brain region.Molecular Psychiatry (2004) 9, 237–251. doi:10.1038/sj.mp.4001470Published online 13 January 2004

Keywords: antidepressants; major depression; imipramine; hypericum; treatment; microarray

The search for new molecules and biological targetsfor antidepressant drug discovery remains a challengefor contemporary psychiatry. Such work is needed tounderstand the fundamental molecular mechanismsunderlying major depressive disorder (MDD) and toidentify new directions for treatment. The investiga-tion of the mechanisms for the reported therapeuticactivity of traditional herbal products, such as StJohn’s wort (SJW) (see Figure 1), could uncover newmechanisms and novel treatments for MDD.

SJW (hypericum) extracts are among the mostwidely used antidepressants in Germany with amarket share of more than 25% in 1997.1 Despitethe controversy on the effectiveness of SJW as anantidepressant, the use of SJW has been widespreadand several preparations containing SJW are commer-cially available in Europe and in the US. Even thoughtwo recent multicenter trials in the USA havefailed,2,3 randomized clinical studies have shown thatSJW extracts are significantly superior to placebo andsimilarly effective as standard antidepressants in thetreatment of mild to moderately severe depression.4–6

Significant drug interactions have been reported withcyclosporine and protease inhibitors,7,8 but a com-monly cited advantage of SJW continues to be itsfavorable side effect profile. Although reports ofserious and potentially fatal adverse reactions are

Received 13 October 2003; revised 12 November 2003; accepted17 November 2003

Correspondence: Professor M-L Wong MD, Department of Psy-chiatry and Biobehavioral Sciences, David Geffen School ofMedicine at UCLA, 3357A Gonda Center; 695 Charles YoungDrive So., Los Angeles, CA 90095-1761, USA.E-mail: [email protected]

Molecular Psychiatry (2004) 9,237–251& 2004 Nature Publishing Group All rights reserved 1359-4184/04 $25.00

www.nature.com/mp

known, and probably underreported,9 the majority ofsignificant side effects described in clinical trials areconsidered mild to moderate or transient.

Preclinical studies suggest that SJW is effective inthree major biochemical systems relevant for anti-depressant activity: inhibition of the synaptic reup-take of serotonin, noradrenaline, and dopamine;10–12

it also produces monoamine reinhibition and long-term changes in receptors.10 However, there is con-troversy as to the exact mechanism of action for theantidepressant effects of SJW. It is probable that bothhypericin and hyperforin have antidepressant proper-ties. Hypericin inhibits monoamine oxidases (MAO-Aand MAO-B) and the antidepressant property ofhyperforin has been attributed to its capacity toincrease the extracellular levels of the monoaminesand glutamate in the synaptic cleft, probably as aconsequence of uptake inhibition.11,13–15 SJW extractscontain six major natural product groups that may

contribute to their pharmacological effects [acylphlor-oglucinols, biflavones, naphthodianthrones (hypericin),flavonol glycosides (hyperforin), proanthocyanidins,phenylpropanes, proanthcyanidins, and phenylpro-panes];16 therefore, compounds other than hypericinor hyperforin could also contribute to the antidepres-sant effects of hypericum. Repeated oral administra-tion of SJW for 3 consecutive days has been reportedto ameliorate behavior observations in some animalmodels of depression,14 and only recently the effectsof long-term administration of SJW have started to beexplored.15

Our hypothesis has been that there may be commonfinal molecular pathways that underlie the responseto various types of antidepressant treatment. Theidentification of transcripts that are regulated in thehypothalamus by prolonged administration of eitherimipramine (IMI) or fluoxetine treatment has sup-ported this hypothesis.17 Several investigators study-ing mood-stabilizing drugs have used this strategy of apresumed final common pathway of action.18–20 Thehypothalamus was used in this study because keybiologic manifestations of MDD include alterations inthe hypothalamic functions that regulate food intake,libido, circadian rhythms, and the synthesis andrelease of hypothalamic neurohormones.21 Patientswith MDD, with melancholic features, typically havedecrease appetite, decreased sexual interest, earlymorning awakening, diurnal variation in mood, andendocrine abnormalities, such as hypogonadism,hyposomatotropism, and hypercortisolism.22 Addi-tionally, several antidepressants, including extracts ofSJW, when administered in a long-term basis down-regulate corticotropin-releasing hormone gene (CRH)expression in the paraventricular nucleus of thehypothalamus (PVN).15,23–25

Here, we describe common transcripts in thehypothalamus related to the long-term administrationof IMI and SJW. We identified human orthologs forgenes that are regulated both by SJW and by IMI, asthey may be potential novel candidates for pharma-cogenetic studies. The ability to map rat genes to theircognate human genes provides a needed bridgebetween basic science and clinical research.

Material and methods

Hypericum extractsThe SJW extract was prepared by Botanicals Interna-tionals (Long Beach, CA, USA) and generouslydonated to us. Hypericum perforatum native plantwas extracted in ethanol/water at a ratios of 3–6 : 1,dried, and stored protected from light and moisture at41C. All SJW extract used in this study came from asingle lot and was free of pesticide, heavy metals, andmicrobiological contaminants. The SJW extract wasanalyzed at the initiation and every 6 months there-after. High-performance liquid chromatography(HPLC) analysis of SJW was performed by thePharmanex Research Center (Redwood City, CA,USA), and the extract we used had the following

Figure 1 Warty St John’s wort (United States Departmentof Agriculture, Agricultural Research Service, Image Num-ber K9982-4).

Antidepressant-induced gene expressionM-L Wong et al

238

Molecular Psychiatry

composition: total hypericins 0.33%; hypericin andpseudohypericin 0.013%; hyperforin 3.03%. Theconcentration and composition of other flavonoidsin the SJW extract was not determined.

AnimalsStudies were carried out in accordance with animalprotocols approved by University of California, LosAngeles. Virus- and antibody-free male Sprague–Dawley rats (200–250 g, Harlan, Indianapolis, IN,USA) were housed 2/cage at 241C with lights on from0600 to 1800. Animals were housed 3/cage in a stress-free environment for at least 5 days before theinitiation of experimental procedures. This acclima-tization period was used to minimize the effects ofenvironmental and social stress in our experiments.Rats were randomly separated into four groups: saline(a control for IMI), carboxymethylcellulose (CMC, acontrol for SJW), IMI, or SJW groups. Animals treatedwith IMI (Sigma, St Louis, MO, USA) received 0.5 mlintraperitoneal (i.p.) injections daily for 8 weeks with5.0 mg/kg drug dissolved in 0.9% saline. Animalstreated with SJW extract were gavaged daily, for 8weeks with 120 mg/kg, in 1 ml of 0.3% CMC (Sigma)in distilled water. CMC has been used to dissolve SJWas described previously.14,26 Control animals con-sisted of rats injected with vehicle (0.9% saline) i.p.for the IMI-treated group or gavaged with vehicle(0.3% CMC) for the SJW-treated group. Long-termadministration has been utilized because of the well-documented findings that antidepressants are gener-ally effective after 3–6 weeks of administration.24

Animals received their last treatment 24 h beforetermination of experiments. To prevent the confound-ing effects of stress on gene expression, animals wereremoved from their home cages by an animal handlernot involved in the decapitation process and weredecapitated within 45 s of individual removal fromhome cages. Immediately after sacrificing the animals,brains were removed. To avoid possible confoundingvariations caused by circadian rhythms, all animalswere sacrificed at 10:00 to 12:00 noon. All hypotha-lamic tissues were dissected by the same investigator (M-L W), frozen, and stored at �801C.

Microarray studiesThe following groups of animals were studied: (1)saline; (2) CMC; (3) IMI; and (4) SJW. Total RNA wasextracted from each hypotamus using TRIzol (Qiagen,Valencia, CA, USA) reagent, cleaned with RNeasy(Qiagen), and its quality was verified by gel. TotalRNA from individual hypothalamus was used foreach array; three animals per group were studied. Weused three to five array experiments per group. Dchipsoftware27 was used to do a stringent quality controlof our arrays; array data that did not pass our qualitycontrol were not used in our analyses. Array experi-ments were repeated until three arrays per group metour quality control requirements, with three indepen-dent replications of our microarray experiments asrecommended by Lockhart and Barlow.28

Protocols recommended by Affymetrix Inc. (SantaClara, CA, USA) were followed. Briefly, 15mg of eachRNA sample pool were submitted to reverse tran-scription with SuperScript Choice System (GibcoBRL, Rockville, MD, USA) and a T7-(dT)24 primer,followed by in vitro transcription and biotin-labelingwith the Enzo BioArray High Yield RNA TranscriptLabeling Kit (Enzo Diagnostics, Farmingdale, NY,USA). Labeled cRNA was fragmented and 15mg ofthe fragmented cRNA of each sample was used forhybridization in a 2-(N-Morpholino) ethanesulfonicacid-based buffer containing bovine serum albumin,herring sperm DNA, control oligonucleotide B2, andEukaryotic hybridization controls BioB, BioC, BioD,and Cre (Affymetrix). We used GeneChip Rat GenomeU34A arrays (Affymetrix), which contain sequencesfor 8799 genes and expressed sequences tags (EST).The samples were hybridized at 451C for 16 h underconstant rotation. After the hybridization, microar-rays were washed, stained with streptavidin phycoer-ythrin, and scanned in a GMS 418 Array Scanner(Affymetrix).

Data analysis: statistical methodsMicroarray image files were loaded into the Dchipsoftware package. This package implements the Li &Wong algorithm and computes two summary mea-sures for each gene.27 The first measure is the model-based expression index (MBEI) that summarizes theapproximate expression level over the 20 individualprobes. The second measure is the present/absentcall. This call is a decision rule utilizing a number ofdetails of the probe pairs to determine if there was orwas not any mRNA corresponding to that particulargene in the sample. The data set had 8799 genes. Ouranalysis plan consisted of three filtering steps toidentify a list of differentially expressed genes. Geneswere filtered in both of the treatment vs. controlseparately. The first step of the analysis was to removegenes that were absent in all arrays in all four of theconditions (two treatments� two controls). This stepremoved 3051 genes from the analysis. Step twoinvolved the construction of 95% confidence inter-vals for the expression fold-change for each of theremaining 5748 genes. The expression fold-changevalue is a ratio between expression values in treat-ment and control conditions, thus fold-change of 1 or�1 is not different. Only those genes that hadconfidence intervals excluding 1 (or �1) wereincluded in the final gene lists. Step three excludedgenes where the absolute difference between condi-tions was less than 10 (the median expression acrossthe arrays was 55). This last step helps remove geneswith very low levels of expression in which the fold-change can be large due to very small expressiondifferences.

The Dchip software provides automated qualitycontrol by determining the genes or probes that areoutliers. These outliers are treated as missing data inthe subsequent data analysis steps. Dchip suggeststhat chips with greater than 5% outliers are of poor


239


quality and should be repeated. In our study wefound three chips to have greater than 5% outliersand these experimental conditions were re-run onnew chips.

Microarray-based experiments involve exceedinglymore measurements than samples even in experi-ments containing the largest practical sample size. Ina recent review, Slonim discussed the difficulties ofextracting meaningful information out of array data.Unfortunately, there is no ‘one-size-fits-all’ solutionfor the analysis and interpretation of wide expressionarray data.29 In fact, data analysis techniques formicroarray are still in the early phases of develop-ment.30 Bonferroni method, one proposed solution tothe multiple comparisons problem, requires the use ofstatistical test with known distribution and offers aconservative method for significance assessment.31

The implementation of Bonferroni method to arrayanalyses is generally not appropriate and it yieldsoverly conservative critical values; therefore, alter-native solutions to the multiple comparisons problemhave been used.32 Furthermore, methods for P-valuecorrection are not generally appropriate for explora-tory data analysis as false negatives are of potentiallygreater concern than false positives. Our analyses arecomparable to those performed in many studies thatmeasured differential expression by the ratio ofexpression levels between two samples; in whichgenes with ratios above a fixed cutoff were said to bedifferentially expressed.33–38 The advantage of ourmethod is that the Li & Wong algorithm estimates thestandard error of the expression measurement, whichallows us to make statistical statements about thefold-change rather than simply using a predeterminedcutoff.27

Final gene lists were functionally classified usingthe batch query in the NetAffxt Analysis Center onthe affymetrix website (http://www.affymetrix.com/analysis/index.affx). Probability calculations werecarried out to understand the likelihood of findinggenes and ESTs common to the list of transcripts thatwere differentially regulated by IMI and SJW. In thesecalculations, we excluded genes that were absent inall conditions; therefore, we calculated the probabil-ity of finding six common genes in lists of 66 and 74randomly selected group of genes from a total of 5748genes.

Hierarchical clustering was performed using CLUS-TER and TREEVIEW software39 on the sets of genesthat had significant treatment fold changes. Genesand arrays were clustered using Average LinkageClustering. Expression values in the cluster diagramswere standardized by subtracting the mean expres-sion and dividing by the standard deviation.

Human ortholog and SNP identificationRat genes were mapped to their human orthologsusing a combination of web based databases andsequence alignment tools. We mapped orthologsacross genomes using one or more locus specificidentifiers as a foreign key in the LocusLink database

(http://www.ncbi.nlm.nih.gov/LocusLink/). Many ratgenes have obvious human orthologs, but in someinstances, some rat genes could not be mapped to ahuman ortholog using Locus Link. We used at leasttwo independent computational sequence compari-son methods for genes that we could not map usingLocusLink. Single-nucleotide polymorphisms (SNPs)were identified by querying the dbSNP database,(http://www.ncbi.nlm.nih.gov/SNP/), via a web inter-face available on Locus Link.

Results

We used an expression profile approach to comparetranscriptional changes related to prolonged admin-istration of IMI and SJW. Transcripts that weresignificantly altered by IMI and SJW treatments arepresented in Figures 2 and 3, respectively; thesefigures show the cluster analyses of our results. Table 1lists all the transcripts significantly changed by eithertreatment grouped by functional classification. Ourfindings are compatible with a predominance ofdownregulation after chronic IMI, but not after SJWtreatment.

Microarray analysesOur analysis filtered out 66 genes as differentiallyexpressed in the SJW versus CMC (control) compar-ison and 74 in the IMI versus saline comparison. Sixgenes were found to be common in the two lists ofgenes with a significant fold change. We calculatedthe probability that this was due to chance alone. Theprobability of finding one gene in common in tworandomly selected lists of genes (n¼ 66 and 74) is0.00015, hence the probability of finding six genes incommon is 1.14� 10�23. Four of those six transcriptswere similarly downregulated, one transcript wassimilarly upregulated and another one had differentregulation. Figure 4 shows the cluster analysis ofthese common genes. The ribosomal proteins S29(X59051) and S4 (X14210); rat long interspersedrepetitive DNA sequence LINE3 (M13100); andEST202275 which is similar to the sequence of thecomplete mitochondrial genome of the R. novergicuswere downregulated in both IMI and SJW treatments;microtube-associated protein 1A (MAP1A, M83196)was upregulated; and rat carnitine palmitoyltransfer-ase Ib (AF063302) was downregulated by SJW andupregulated by IMI treatment.

The functional classification of transcript revealedthat differentially regulated genes and ESTs for SJWand IMI could be separated into 12 main functionalgroups, namely genes related to the following process:calcium, cell structure, energy production/expendi-ture, fatty acid metabolism, hormonal, ion concentra-tion, neurotransmission, protein synthesis/degradation, repetitive DNA sequences, signal trans-duction, synaptic transmission, and transcriptionregulation. Genes that could not be classified in thosecategories were included in miscellaneous. Severalgenes in the same pathway can be modulated by one


240


of the treatment drugs (Table 1). Also, different genesin the same pathway can be modulated by differentantidepressant treatments. A very illustrative exampleof this phenomenon is our findings involving ele-

ments of the synaptic transmission: three genes aremodulated by IMI and other three different genes aremodulated by SJW (Table 1).

Human ortholog identificationA number of rat genes, including ribosomal proteinS29, ribosomal protein S4, and MAP1A, was readilymapped to their associated human genes throughLocus Link using the OMIM (Online MendelianInheritance in man) identifier to query the Homo-logene ortholog database.

Homologene results for rat ribosomal protein S29(GenBank identifier X59051), ribosomal protein S4(identifier X14210), microtubule associated protein,including the Homo sapiens MAP1A, were also easilyidentified. The rat gene carnitine palmitoyltransferase(CPT) Ib is the ortholog of the human carnitinepalmitoyltransferase 1B gene.

The rat long interspersed repetitive DNA sequenceLINE3 (M13100) proved difficult to map to thecorresponding human ortholog using gene identifiersalone, so we performed a BLAST search against theNon-Redundant (NR) Genbank protein database.None of the top 40 blast hits mapped to a humangene; among the best three scoring human hits wasthe human LINE-1, which belongs to the reversetranscriptase family. We are unable to unambiguouslyclassify the human LINE-1 homolog as the ortholog ofthe rat LINE-3 gene.

Finally, we wanted to identify the human orthologof the rat expressed sequence fragment, EST202275,which is annotated as having similarity to mitochon-drial genome. The results of a BLAST query againstthe NR database at NCBI showed that none of the highscoring homologous sequences mapped to the humangenome. We utilized the entire finished humangenome sequence, including the mitochondrial gen-ome, in another round of sequence comparisonagainst the rat gene fragment using the indexedsequence comparison method called BLAT (available

Figure 2 Cluster analysis of transcripts that were signifi-cantly altered by imipramine when compared to saline. Ineach treatment group, genes colored in red are upregulatedand genes colored in green are downregulated whencompared to the average gene expression across all condi-tions. The CMC and St John’s wort groups have beenincluded just as illustration here, as most of the genes inthis cluster analysis were not significantly changed by StJohn’s wort treatment (See Figure 4). Note the predomi-nance of downregulation after imipramine treatment. Thedendogram of treatment clustering is displayed above theimage and describes the degree of relatedness betweentreatment groups; the length of branches denotes the degreeof similarity, short branches indicate high similarity. Activetreatment groups are more similar between themselveswhen compared to control groups. Expression values werestandardized by subtracting the mean expression acrossgroups and dividing by the standard deviation. CMC,carboxmethylcellulose; IMIP, imipramine; SAL, saline;SJW, St John’s wort.


241


at UCSC, http://genome.ucsc.edu/cgi-bin/hgBlat).The top three BLAT hits indicated strong homologyto the rat sequence within a region of the mitochon-drial genome as well as intervals within chromo-somes 11. We focused on the human mitochondrialinterval since the rat sequence was also associated

with mitochondrial sequence. A gene within thisregion (AY029066) translates into a protein 24 aminoacids in length, MAPRGFSCLLLLTSEIDLPVKRRA.By traversing the genomic annotation back to thetranscript level, we were able to identify the locus asbelonging to Humanin and the mitochondrial riboso-mal 16S RNA.

SNP identificationSNP information on the six genes that are regulated bySJW and IMI treatments is presented in Table 2.

Discussion

This study focused on surveying a number ofgenes and EST to elucidate the molecular mechan-isms of the action of antidepressants beyondthe receptor level and to identify a presumedfinal common pathway of antidepressant action.Figure 5 synthesizes our approach to identify cellularfunctions and genes that are regulated by antidepres-sants of different classes. The identification of such

Figure 3 Cluster analysis of transcripts that were signifi-cantly altered by St John’s wort. In each treatment group,genes colored in red are upregulated and genes colored ingreen are downregulated when compared to the averagegene expression across all conditions. The saline andimipramine groups have been included just as illustrationhere, as most of the genes in this cluster analysis were notsignificantly changed by imipramine treatment (See Figure4). The dendogram of treatment clustering is displayedabove the image and describes the degree of relatednessbetween treatment groups; the length of branches denotesthe degree of similarity, short branches indicate highsimilarity. Active treatment groups are more similarbetween themselves when compared to control groups.Expression values were standardized by subtracting themean expression across groups and dividing by thestandard deviation. CMC, carboxmethylcellulose; IMIP,imipramine; SAL, saline; SJW, St John’s wort.

Figure 4 Cluster analysis of six genes that are significantlyregulated by St John’s wort and imipramine treatments.Note that four of the six transcripts were downregulated bySt John’s wort and imipramine treatments. In each treatmentgroup, genes colored in red are upregulated and genescolored in green are downregulated when compared to theaverage gene expression across all conditions. Expressionvalues were standardized by subtracting the mean expres-sion across groups and dividing by the standard deviation.CMC, carboxmethylcellulose; IMIP, imipramine; SAL, sal-ine; SJW, St John’s wort.


242



243



244


genes is useful for neuroscience research,pharmacogenetic association studies, and drugdevelopment.

Several groups, including ours, have used stress-free animals to study the effects of chronic antide-pressant treatment.15,17,23,24 This is useful in theunderstanding of chronic antidepressant effects, asnondepressed humans treated with antidepressantshave been reported to have physiological responses tosuch treatments.40 We expected that SJW wouldregulate many transcripts in a similar way to aprototypic antidepressant drug. Contrary to expecta-

tion, our study identified only six genes that areregulated by IMI and SJW in the hypothalamus, butwe found that treatment with both IMI and SJW elicittranscripts that are important in the same cellularfunctions of building (ribosomal protein S4 and S29),scaffolding/intracellular transport (microtubule-associated protein 1A), and fueling the cell (carnitinepalmitoyltransferase Ib and EST202275). Althoughthese findings require further confirmation, we dis-cuss their relevance, as these transcripts arefair representatives of their respective functionalgroups.


245


Table 2 SNP information on six genes that are regulated by St John’s wort and imipramine treatments

Gene/protein SNP link

(1) Ribosomal protein S29 http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId¼ 6235GenBank GI: 13904868

Number detected SNPs: 12a

Number Unclassified SNPs: 11a

Number intronic SNPs: N/Ab

Number exonic SNPs: 1b

Number coding SNPs: 1b

Number amino-acid changes: 1 (nonconservative)b

(2) Ribosomal protein S4 http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId¼ 6191Genbank GI: 17981705


Number unclassified SNPs: 66a

Number intronic SNPs: 4b Some inconsistencies because of two diff annotations:Number exonic SNPs: 4b (1) Contig annotationNumber coding SNPs: 3b (2) GenBank mappingNumber amino-acid changes: 3b

(3) Microtube-associated protein 1a http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId¼ 4130Genbank: GI: 21536457


Number Unclassified SNPs: 176 (genbank mapping)a

Number intronic SNPs: 1b



Number amino-acid changes:78b

(4) LINE1 rev transcr. homolog http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?type¼ rs&rs¼ 792629GenBank: 126295

Number Detected SNPs: 1a

Number Unclassified SNPs: N/Aa


Number exonic SNPs: N/Ab

Number coding SNPs: N/Ab

Number amino-acid changes: N/Ab

(5) Carnitine palmitoyltransferase 1B http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId¼ 1375GenBank GI: 23238252

Number detected SNPs:52a

Number unclassified SNPs: 46a

Number intronic SNPs:18b



Number amino-acid changes: 8b

(6) Humanin 1 http://www.giib.or.jp/mtsnp/index_e.htmlhttp://www.giib.or.jp/mtsnp/search_mtSNP_e.htmlMitochondrial SNP Project

GenBank GI: 14017399Number detected SNPs: 81a

Number Unclassified SNPs: N/Aa


Number exonic SNPs: N/Ab These SNPs were collected from 16S rRNANumber coding SNPs: N/Ab

Number amino-acid changes: N/Ab

N/A, not available.aGenBank Mapping Annotation Statistics.bContig Assembly Annotation Statistics.


246


Antidepressant effect in the function of cellbuilding—down regulation of ribosomal proteinsmRNA: ribosomal protein S4 and S29Several lines of evidence indicate that ribosomalRNAs are responsible for protein synthesis; therefore,ribosomal proteins could have been recruited duringevolution to stabilize the configuration of ribosomalRNA and improve protein synthesis.41 Thus, riboso-mal proteins may also have extraribosomal functions,which are still largely unknown. Protein productionis important for several cellular functions, and it isessential for cell proliferation and neurogenesis.Evidence that treatment with antidepressants canalter neuronal cell proliferation in vivo and in vitrohas already been documented. In vitro experimentssuggest that fluoxetine is capable of inhibiting andstimulating cell proliferation in non-neuronalcells.42,43 Prolonged treatment with fluoxetine in-creased neurogenesis in adult rat hippocampus, butin vitro findings showed a dual effect of antidepres-sant in cerebellar cell cultures.44 The role possible ofaltered functioning of ribosomal protein in humandisease is largely unexplored.

Antidepressant effect in the function of cellscaffolding/transport—upregulation of microtubulemRNA: microtubule-associated protein 1A(MAP1A)

Microtubules have a crucial organizing role in alleucaryotic cells. A variety of accessory proteins bindto microtubules and serve various functions. MAPscan stabilize microtubules against disassembly orcross-link microtubules in the cytosol. MAP assem-blies can be separated in two types, type I MAP(MAP1A and 1B) and type II (MAP2, 4 and Tau). TypeI MAPs are large filamentous molecules found inaxons and dendrites of neurons. Chronic but not acutetreatment with desipramine inhibited microtubuleassembly in vivo and the degree of phosphorylation ofserine residues of MAP2 was significantly increasedafter chronic administration of desipramine withoutchanges in the total concentration of MAP2.45 Giventhat MAP2 and Tau are major cytoskeletal proteins inneurons, which are involved in the pathogenesis ofAlzheimer’s and other neuropsychiatric diseases,46,47

and that it is not uncommon that antidepressants areincluded in the treatment regimen of those condi-tions, a better understanding of the effects ofantidepressant treatment on microtubule proteinswould be relevant.

Antidepressant effect in the function of cell fueling:downregulation of EST202275 (similar tomitochondrial genome mRNA) and modulation of ratcarnitine palmitoyltransferase Ib 1, 2, and 3 genes.Antidepressants can affect brain energy metabolismand substrate oxidation pattern. It has been longsuggested that antidepressants may act by makingmore energy available to overcome the depressedstated.48 Initial observations supported that treatmentwith desmethylimipramine for 7 days resulted inincrease in glucose utilization in several brain areas,

but that treatment for 28 days was inhibitory.49 Dataon oxidative energy metabolism suggest that IMI hada maximum stimulation of respiration in the brainmitochondria that was sustained after 1- and 2-weektreatment.50 It should however be noted that IMI athigh concentrations can inhibit ATPase activity;51

moreover, uncoupling effects on oxidative phosphor-ylation in rat liver mitochondria have been describedwith melipramine.52 It is possible that the humanortholog of the gene represented in rat EST202275 isthe mitochondrial 16S rRNA gene. This gene seem toalso encode humanin, a unique oncopeptide thatinhibits neuronal death by an antagonistic interactionwith members of the apoptotic signaling machinery.53

This finding would support the hypothesis thatantidepressants might be neuroprotective agents,possibly exerting their neuroprotective effectsthrough diverse cellular and molecular mechanisms.

Additional mitochondrial process can also beaffected by antidepressants, such as monoamineoxidase activity and the oxidation of fatty acids.CNS and peripheral mitochondrial monoamine oxi-dase (MAO) are influenced by antidepressants.54,55

Long-term effects of several antidepressant drugs,including IMI, can inhibit brain mitochondrial MAOactivities of MAO-A and MAO-B forms.56 Oxidationof fatty acids in the mitochondria is initiated by asequence of events, of which CPT I and CPTII areimportant elements. Mitochondrial b-oxidation oflong-chain fatty acids produces a large amount ofATP; it is the main source of energy for skeletalmuscle during exercise and for cardiac muscle, andduring prolonged fasting. In our experiments, mod-ulation of carnitine palmitoyltransferase Ib 1,2, and 3genes by SJW and IMI treatments were of oppositeeffect. It has been known that antidepressants disturblipid turnover in different cell types, such aslymphocytes, monocytes, and human histiocyticlymphoma cell line U-937.57,58

There is evidence that mitochondrial DNA poly-morphisms may increase the susceptibility to Alzhei-mer disease, indicating that mitochondrial functionmight be relevant to a psychiatric phenotype.59–61

Repeated DNA sequences: long interspersed elements(LINE)Rat LINE3 (or L1Rn) was included among the fewgenes that were differentially regulated by chronicantidepressant treatment with both IMI and SJW. Thisgene belongs to a family of repetitive DNA elements,which is ubiquitous in mammals, namely the L1family. Rat LINE3 has six putative open readingframes; the functions of these putative proteins areunknown. About 50 000 copies of L1 elements occurin the human genome, it accounts for about 5% of thetotal human DNA. L1 elements transpose throughreverse transcription of an RNA intermediate.62 Giventhat over 40% of the mammalian genome can becomposed of repetitive elements of four major classes,could it be predictable that repetitive DNA sequenceswould be implicated in paradigms involving long-


247


term treatments? The function of most families ofrepeated DNA is unknown: only 1–2% of thosecorrespond to families of known function (ribosomalRNA, histones).

Intriguingly, the theme of repeated DNA sequencesis present in a significant proportion of differentiallyregulated transcripts. For instance, several copies ofribosomal proteins sequences can be found in thegenome. Several copies of the mitochondrial DNAgenome are located in the mitochondrial matrix; eachcell contains hundreds of mitochondrias and thou-sands of mtDNA copies. Type I MAP has severalrepeats on the amino-acid sequence KKEX, which isimplicated as a binding site for tubulin.

Additional relevant systems identified by thefunctional characterization of differentially regulatedgenesAs we focused on hypothalamic tissue, it should notbe surprising that our results have not shown manysignificant changes in classical neurotransmittersystems. Nevertheless, our results support the con-cept that the cellular and molecular effects ofantidepressants might involve neurohormones (vaso-

pressin, growth hormone-releasing factor, proenke-phalin, pituitary glycoprotein hormone alphasubunit, and promelanin-concentrating hormone),neuroimmune factors (transthyretin, thymosinbeta-10, prothymosin alpha), glutamate (N-methylD-aspartate 2A receptor), metabolic pathway (glyco-lytic) genes, protein synthesis and degradation (pro-teasome) and synaptic function (SC2 synapticglycoprotein, synaptojanin 1, synaptosomal asso-ciated protein (SNAP-25A), syntaxin 1a and 2, andvesicle-associated membrane protein 1) (refer to Table1). Interestingly, differential expression of vesicle-associated membrane protein 2 (VAMPs/synaptobre-vin-2) has been found after antidepressant andelectroconvulsive treatment in rat frontal cortex.63

Additionally, signaling and calcium-related compo-nents appear to be modulated by long-term antide-pressant treatment. These systems have been recentlyimplicated in the pathophysiology of major depres-sion and in the mechanism of action of antide-pressant drugs.21,64

Transcriptional regulationThe changes in gene expression that are observed inresponse to both acute and chronic antidepressanttreatment provide insight into the underlying regula-tory networks that modulate neural plasticity neces-sary to respond to chronic stress and subsequentdepression induced by such chronic stress. Theresults of chronic antidepressant treatment ought tobe conceptualized in the context of the transcriptionalregulators, which ultimately mediate the observedchanges in gene expression. Specific effects ofantidepressants or electroconvulsive treatment havealready been reported for immediate early genes andtranscription factors such as c-fos, zif268, NGF1-A,phosphorylation of CRE-binding protein, DfosB, andNarp.65–72

In our analysis, we identified four genes that weredownregulated in response to both IMI and SJW. Suchgenes could be potential markers for predictingresponse to antidepressant treatment; however, thefact that genes are coordinately downregulated sug-gests there are specific combinations of transcrip-tional regulators that control the expression of thesefour genes. Even though the transcription factorsthemselves may not exhibit significant differences inexpression levels, changes in their ability to regulategene expression can still occur. We have identifiedfour transcription factors that undergo changes inexpression specifically in response to IMI. Of thesefour transcription factors, AI23025 is a knowninhibitor of DNA binding via dominant-negativeeffects that prevent other transcription factors frombuilding a suitable scaffold for the recruitment ofpolymerase.

Additionally, the combination of transcriptionfactors, including a known repressor, in combinationwith the modulation of other genes, includinghumanin, which inhibits specific pathways, such asapoptosis, suggests a real shift in the genetic program

Figure 5 Summary of the study’s strategy, findings, andpotential applications.


248


after antidepressant treatment. Chronic antidepres-sants may modify the expression levels of genesknown to antagonize various pathways and cellularsystems.

These results represent the net changes of many celltypes present in hypothalamic tissue; therefore, it isnot unexpected that most of the changes we identifiedare below two-fold. It was not within the scope of thiswork to replicate the relatively small changes of CRHgene expression in specific hypothalamic areas, suchas in the PVN (reduction of 40%) that have consis-tently been reported after chronic antidepressant orSJW treatment.15,23–25

Of note, two transcripts that displayed large down-regulation belong to the immune/inflammation re-lated group; they are the transthyretin (for IMItreatment) and the interleukin-3 beta subunit (forSJW treatment) genes.

The findings reported here do not replicate thosereported by Landgrebe et al.73 as they characterizedthe effects of treatment with mirtazapine or parox-etine in total brain RNA of mice using a customizedmicroarray. Differences in study design could accountfor these discrepancies. We studied a specific brainregion (hypothalamus), while they studied wholebrain; our study examined rats, theirs, mice. Finally,the duration of our long-term treatment (8 weeks) wasdifferent from the paradigm used in that study (1 or 4weeks). In spite of these methodological differences,which preclude direct comparison of results, it isnoteworthy that Landgrebe et al’s data provideevidence that some antidepressants can predomi-nantly downregulate gene expression, and that wefound similar downregulation effects after chronicIMI treatment.

We need to consider the possibility that ourresults underreport actual changes because we didnot focus on a specific neuronal nucleus; conse-quently, additional genes could be identified in futurestudies by adopting specific dissection strategies.Furthermore, as it is the case in most microarraystudies, the number of genes queried in our studywas disproportionally larger than the number ofsamples per group. To address that key issue, weused analyses approaches described in the literatureto define differentially expressed genes. Furtherstudies are needed to characterize the localization,time course, and intensity of changes we report here.It is unclear how many of these changes in mRNA willreflect into protein expression and be functionallyrelevant.

A novel observation emerging from this study isthat several essential general cell functions may beaffected by chronic antidepressant treatments (seefigure 5). These functions include protein synthesisand degradation, cellular scaffolding and intracellulartransport, mitochondrial and glycolytic energy meta-bolism, and cellular signaling through modulation ofcalcium biding proteins. Pathways that may bemodulated by chronic antidepressant treatmentsinclude functions that may protect neurons from

cellular injury. Our experiments provide a frameworkfor the changes that occur during long-term adminis-tration of hypericum and IMI. It is possible that theregulation of genes in the same functional groupcould result in the same final outcome along asignaling pathway. These findings could form thebasis of subsequent bioinformatics investigations intothe pathways underlying both the progression andtreatment of depression.

The ability to understand the results of microarrayexperiments and translate them into novel tools andtargets for advancing the state of knowledge in thestudy of depression and response to antidepressanttreatment can be greatly enhanced by identifyingneuronal signaling pathways. Such models couldserve as a framework for testing our hypotheses andrefining them where necessary. The lack of completeunderstanding requires model building and hypoth-esis testing. The fact that several of the genesidentified in this study contain dense numbers ofSNPs is exciting because those SNPs may representpotential new candidates for the pharmacogenomicassessment of the phenotype of antidepressant treat-ment response. The combination of carefully plannedgenetic screens and informatics mining of expressiondata to extend and complete candidate pathways caneventually lead to the development of a new genera-tion of treatment strategies and possibly diagnosticapproaches for depression.

Acknowledgements

We thank Aaron Bertalmio, Xueying Gu, Che Hutson,and Dr Joao R Oliveira for their technical help. Weexpress our gratitude to Botanicals International forproviding a large amount of SJW extract from a singlelot for our studies, and to Dr. H. Howardsun fromPharmanox for his analysis of SJW extract. We thankDrs. David Heber and Vay Lian W. Go for theirsupport.

This work was supported by NIH grants AT00151,MH062777, RR00865, and RR017365 (M-LW), RR-017611, RR016996, HL004526, DK058851, DK063240,HG002500, and GM061394 (JL), and by awards from

NARSAD (ML-W), Dana Foundation, and StanleyFoundation (JL), and the Neuropsychiatric Institute(University of California, Los Angeles, CA, USA).

References

1 Muller WE, Kasper S. Clinically used antidepressant drugs.Pharmacopsychiatry 1997; 30(Suppl 2): 71.

2 Group HDTS. Effect of Hypericum perforatum (St John’s wort) inmajor depressive disorder: a randomized controlled trial. JAMA2002; 287: 1807–1814.

3 Shelton RC, Keller MB, Gelenberg A, Dunner DL, Hirschfeld R,Thase ME et al. Effectiveness of St John’s wort in majordepression: a randomized controlled trial. JAMA 2001; 285:1978–1986.

4 Linde K, Ramirez G, Mulrow CD, Pauls A, Weidenhammer W, MelD. St John’s wort for depression—an overview and meta-analysisof randomised clinical trials. BMJ 1996; 313: 253–258.


249


5 Stevinson C, Ernst E. Hypericum for depression. An update of theclinical evidence. Eur Neuropsychopharmacol 1999; 9: 501–505.

6 Lecrubier Y, Clerc G, Didi R, Kieser M. Efficacy of St. John’s wortextract WS 5570 in major depression: a double-blind, placebo-controlled trial. Am J Psychiatry 2002; 159: 1361–1366.

7 Piscitelli SC, Kress DR, Bertz RJ, Pau A, Davey R. The effect ofritonavir on the pharmacokinetics of meperidine and normeper-idine. Pharmacotherapy 2000; 20: 549–553.

8 Ruschitzka F, Meier PJ, Turina M, Luscher TF, Noll G. Acute hearttransplant rejection due to Saint John’s wort. Lancet 2000; 355:548–549.

9 Barnes J, Mills SY, Abbot NC, Willoughby M, Ernst E. Differentstandards for reporting ADRs to herbal remedies and conventionalOTC medicines: face-to-face interviews with 515 users of herbalremedies. Br J Clin Pharmacol 1998; 45: 496–500.

10 Muller WE, Rolli M, Schafer C, Hafner U. Effects of hypericumextract (LI 160) in biochemical models of antidepressant activity.Pharmacopsychiatry 1997; 2: 102–107.

11 Muller WE, Singer A, Wonnemann M, Hafner U, Rolli M, SchaferC. Hyperforin represents the neurotransmitter reuptake inhibitingconstituent of hypericum extract. Pharmacopsychiatry 1998; 1:16–21.

12 Neary JT, Bu Y. Hypericum LI 160 inhibits uptake of serotonin andnorepinephrine in astrocytes. Brain Res 1999; 816: 358–363.

13 Kaehler ST, Sinner C, Chatterjee SS, Philippu A. Hyperforinenhances the extracellular concentrations of catecholamines,serotonin and glutamate in the rat locus coeruleus. Neurosci Lett1999; 262: 199–202.

14 Chatterjee SS, Bhattacharya SK, Wonnemann M, Singer A, MullerWE. Hyperforin as a possible antidepressant component ofhypericum extracts. Life Sci 1998; 63: 499–510.

15 Butterweck V, Winterhoff H, Herkenham M. St John’s wort,hypericin, and imipramine: a comparative analysis of mRNAlevels in brain areas involved in HPA axis control following short-term and long-term administration in normal and stressed rats.Mol Psychiatry 2001; 6: 547–564.

16 Erdelmeier CA. Hyperforin, possibly the major non-nitrogenoussecondary metabolite of Hypericum perforatum L. Pharmacopsy-chiatry 1998; 1: 2–6.

17 Wong ML, Khatri P, Licinio J, Esposito A, Gold PW. Identificationof hypothalamic transcripts upregulated by antidepressants.Biochem Biophys Res Commun 1996; 229: 275–279.

18 Berridge MJ, Downes CP, Hanley MR. Neural and developmentalactions of lithium: a unifying hypothesis. Cell 1989; 59: 411–419.

19 Manji HK, Chen G. PKC, MAP kinases and the bcl-2 family ofproteins as long-term targets for mood stabilizers. Mol Psychiatry2002; 7(Suppl 1): S46–S56.

20 Williams RS, Cheng L, Mudge AW, Harwood AJ. A commonmechanism of action for three mood-stabilizing drugs. Nature2002; 417: 292–295.

21 Wong M-L, Licinio J. Research and treatment approaches todepression. Nature Rev Neurosci 2001; 2: 343–351.

22 Gold PW, Wong ML, Chrousos GP, Licinio J. Stress systemabnormalities in melancholic and atypical depression: molecular,pathophysiological, and therapeutic implications. Mol Psychiatry1996; 1: 257–264.

23 Brady LS, Whitfield Jr HJ, Fox RJ, Gold PW, Herkenham M. Long-term antidepressant administration alters corticotropin-releasinghormone, tyrosine hydroxylase, and mineralocorticoid receptorgene expression in rat brain. Therapeutic implications. J ClinInvest 1991; 87: 831–837.

24 Brady LS, Gold PW, Herkenham M, Lynn AB, Whitfield Jr HJ. Theantidepressants fluoxetine, idazoxan and phenelzine alter cortico-tropin-releasing hormone and tyrosine hydroxylase mRNA levelsin rat brain: therapeutic implications. Brain Res 1992; 572: 117–125.

25 Reul JM, Stec I, Soder M, Holsboer F. Chronic treatment of ratswith the antidepressant amitriptyline attenuates the activity of thehypothalamic–pituitary–adrenocortical system. Endocrinology1993; 133: 312–320.

26 Bhattacharya SK, Chakrabarti A, Chatterjee SS. Activity profiles oftwo hyperforin-containing hypericum extracts in behavioralmodels. Pharmacopsychiatry 1998; 1: 22–29.

27 Li C, Wong WH. Model-based analysis of oligonucleotide arrays:expression index computation and outlier detection. Proc NatlAcad Sci USA 2001; 98: 31–36.

28 Lockhart DJ, Barlow C. Expressing what’s on your mind: DNAarrays and the brain. Nat Rev Neurosci 2001; 2: 63–68.

29 Slonim DK. From patterns to pathways: gene expression dataanalysis comes of age. Nat Genet 2002; 32(Suppl): 502–508.

30 Zhang MQ. Large-scale gene expression data analysis: a newchallenge to computational biologists. Genome Res 1999; 9: 681–688.

31 Rice JA. Mathematical Statistics and Data Analysis. WadsworthPublishing Company: Pacific Grove, CA, 1988.

32 Olshen AB, Jain AN. Deriving quantitative conclusionsfrom microarray expression data. Bioinformatics 2002; 18: 961–970.

33 Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown PO etal. The transcriptional program of sporulation in budding yeast.Science 1998; 282: 699–705.

34 DeRisi JL, Iyer VR, Brown PO. Exploring the metabolic and geneticcontrol of gene expression on a genomic scale. Science 1997; 278:680–686.

35 Schena M, Shalon D, Heller R, Chai A, Brown PO, Davis RW.Parallel human genome analysis: microarray-based expressionmonitoring of 1000 genes. Proc Natl Acad Sci USA 1996; 93:10614–10619.

36 Wodicka L, Dong H, Mittmann M, Ho MH, Lockhart DJ. Genome-wide expression monitoring in Saccharomyces cerevisiae. NatBiotechnol 1997; 15: 1359–1367.

37 Fambrough D, McClure K, Kazlauskas A, Lander ES. Diversesignaling pathways activated by growth factor receptors inducebroadly overlapping, rather than independent, sets of genes. Cell1999; 97: 727–741.

38 Holstege FC, Jennings EG, Wyrick JJ, Lee TI, Hengartner CJ, GreenMR et al. Dissecting the regulatory circuitry of a eukaryoticgenome. Cell 1998; 95: 717–728.

39 Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysisand display of genome-wide expression patterns. Proc Natl AcadSci USA 1998; 95: 14863–14868.

40 Michelson D, Galliven E, Hill L, Demitrack M, Chrousos G, Gold P.Chronic imipramine is associated with diminished hypothalamic–pituitary–adrenal axis responsivity in healthy humans. J ClinEndocrinol Metab 1997; 82: 2601–2606.

41 Wool IG. Extraribosomal functions of ribosomal proteins. TrendsBiochem Sci 1996; 21: 164–165.

42 Edgar VA, Sterin-Borda L, Cremaschi GA, Genaro AM. Role ofprotein kinase C and cAMP in fluoxetine effects on human T-cellproliferation. Eur J Pharmacol 1999; 372: 65–73.

43 Genaro AM, Edgar VA, Sterin-Borda L. Differential effects offluoxetine on murine B-cell proliferation depending on thebiochemical pathways triggered by distinct mitogens. BiochemPharmacol 2000; 60: 1279–1283.

44 Manev H, Uz T, Smalheiser NR, Manev R. Antidepressants altercell proliferation in the adult brain in vivo and in neural culturesin vitro. Eur J Pharmacol 2001; 411: 67–70.

45 Miyamoto S, Asakura M, Sasuga Y, Osada K, Bodaiji N, Imafuku Jet al. Effects of long-term treatment with desipramine onmicrotubule proteins in rat cerebral cortex. Eur J Pharmacol1997; 333: 279–287.

46 Spillantini MG, Murrell JR, Goedert M, Farlow MR, Klug A, GhettiB. Mutation in the tau gene in familial multiple system tauopathywith presenile dementia. Proc Natl Acad Sci USA 1998; 95: 7737–7741.

47 Hutton M, Lendon CL, Rizzu P, Baker M, Froelich S, Houlden Het al. Association of missense and 50-splice-site mutations intau with the inherited dementia FTDP-17. Nature 1998; 393: 702–705.

48 Mata M, Fink DJ, Gainer H, Smith CB, Davidsen L, Savaki H et al.Activity-dependent energy metabolism in rat posterior pituitaryprimarily reflects sodium pump activity. J Neurochem 1980; 34:213–215.

49 Gerber III JC, Choki J, Brunswick DJ, Reivich M, Frazer A. Theeffect of antidepressant drugs on regional cerebral glucoseutilization in the rat. Brain Res 1983; 269: 319–325.


250


50 Katyare SS, Rajan RR. Effect of long-term in vivo treatment withimipramine on the oxidative energy metabolism in rat brainmitochondria. Comp Biochem Physiol C Pharmacol ToxicolEndocrinol 1995; 112: 353–357.

51 Eto K, Fukuda T, Araki Y, Inoue B, Ogata M. Effect of tricyclicdrugs on mitochondrial membrane. Acta Med Okayama 1985; 39:289–295.

52 Burbenskaya NM, Nartsissov YR, Tsofina LM, Komissarova IA.The uncoupling effect of some psychotropic drugs on oxidativephosphorylation in rat liver mitochondria. Biochem Mol Biol Int1998; 45: 261–268.

53 Maximov V, Martynenko A, Hunsmann G, Tarantul V. Mitochon-drial 16S rRNA gene encodes a functional peptide, a potentialdrug for Alzheimer’s disease and target for cancer therapy. MedHypotheses 2002; 59: 670–673.

54 Roth JA, Gillis CN. Inhibition of lung, liver and brain monoamineoxidase by imipramine and desipramine. Biochem Pharmacol1974; 23: 1138–1140.

55 Nag M, Nandi N. In vitro and in vivo effect of chloropromazine,imipramine and lithium chloride on monoamine oxidase activityin rat brain mitochondria. Biosci Rep 1987; 7: 701–704.

56 Egashira T, Takayama F, Yamanaka Y. Effects of long-termtreatment with dicyclic, tricyclic, tetracyclic, and noncyclicantidepressant drugs on monoamine oxidase activity in mousebrain. Gen Pharmacol 1996; 27: 773–778.

57 Kodavanti UP, Mehendale HM. Cationic amphiphilic drugsand phospholipid storage disorder. Pharmacol Rev 1990; 42:327–354.

58 Xia Z, Appelkvist EL, DePierre JW, Nassberger L. Tricyclicantidepressant-induced lipidosis in human peripheral monocytesin vitro, as well as in a monocyte-derived cell line, as monitored byspectrofluorimetry and flow cytometry after staining with Nile red.Biochem Pharmacol 1997; 53: 1521–1532.

59 Lin FH, Lin R, Wisniewski HM, Hwang YW, Grundke-Iqbal I,Healy-Louie G et al. Detection of point mutations in codon 331 ofmitochondrial NADH dehydrogenase subunit 2 in Alzheimer’sbrains. Biochem Biophys Res Commun 1992; 182: 238–246.

60 Corral-Debrinski M, Horton T, Lott MT, Shoffner JM, McKee AC,Beal MF et al. Marked changes in mitochondrial DNA deletionlevels in Alzheimer brains. Genomics 1994; 23: 471–476.

61 Davis RE, Miller S, Herrnstadt C, Ghosh SS, Fahy E, Shinobu LA etal. Mutations in mitochondrial cytochrome c oxidase genessegregate with late-onset Alzheimer disease. Proc Natl Acad SciUSA 1997; 94: 4526–4531.

62 Boeke JD, Garfinkel DJ, Styles CA, Fink GR. Ty elements transposethrough an RNA intermediate. Cell 1985; 40: 491–500.

63 Yamada M, Takahashi K, Tsunoda M, Nishioka G, Kudo K, OhataH et al. Differential expression of VAMPs/synaptobrevin-2 afterantidepressant and electroconvulsive treatment in rat frontalcortex. Pharmacogenomics J 2003; 2: 377–382.

64 Manji HK, Drevets WC, Charney DS. The cellular neurobiology ofdepression. Nat Med 2001; 7: 541–547.

65 Dahmen N, Fehr C, Reuss S, Hiemke C. Stimulation of immediateearly gene expression by desipramine in rat brain. Biol Psychiatry1997; 42: 317–323.

66 Torres G, Horowitz JM, Laflamme N, Rivest S. Fluoxetine inducesthe transcription of genes encoding c-fos, corticotropin-releasingfactor and its type 1 receptor in rat brain. Neuroscience 1998; 87:463–477.

67 Johansson IM, Bjartmar L, Marcusson J, Ross SB, Seckl JR,Olsson T. Chronic amitriptyline treatment induces hippocampalNGFI-A, glucocorticoid receptor and mineralocorticoid receptormRNA expression in rats. Brain Res Mol Brain Res 1998; 62:92–95.

68 Bjartmar L, Johansson IM, Marcusson J, Ross SB, Seckl JR, OlssonT. Selective effects on NGFI-A, MR, GR and NGFI-B hippocampalmRNA expression after chronic treatment with different sub-classes of antidepressants in the rat. Psychopharmacology (Berlin)2000; 151: 7–12.

69 Thome J, Sakai N, Shin K, Steffen C, Zhang YJ, Impey S et al.cAMP response element-mediated gene transcription is upregu-lated by chronic antidepressant treatment. J Neurosci 2000; 20:4030–4036.

70 Chen J, Nye HE, Kelz MB, Hiroi N, Nakabeppu Y, Hope BT et al.Regulation of delta FosB and FosB-like proteins by electroconvul-sive seizure and cocaine treatments. Mol Pharmacol 1995; 48:880–889.

71 Reti IM, Baraban JM. Sustained increase in Narp protein expres-sion following repeated electroconvulsive seizure. Neuropsycho-pharmacology 2000; 23: 439–443.

72 Nibuya M, Nestler EJ, Duman RS. Chronic antidepressant admin-istration increases the expression of cAMP response elementbinding protein (CREB) in rat hippocampus. J Neurosci 1996; 16:2365–2372.

73 Landgrebe J, Welzl G, Metz T, van Gaalen MM, Ropers H, Wurst Wet al. Molecular characterisation of antidepressant effects in themouse brain using gene expression profiling. J Psychiatr Res 2002;36: 119–129.


251


st john’s wort and imipramine-induced gene … john's wort and imipramine...immediate...

Documents