d9-tetrahydrocannabinol inhibits cytotrophoblast cell ......effects have not been elucidated but are...

Molecular Human Reproduction Vol.12, No.5 pp. 321–333, 2006Advance Access publication April 5, 2006 doi:10.1093/molehr/gal036

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D9-Tetrahydrocannabinol inhibits cytotrophoblast cell proliferation and modulates gene transcription

Manjiri Khare1, Anthony H.Taylor1,2,3, Justin C.Konje1,2 and Stephen C.Bell1,2

1Clinical Division of Obstetrics & Gynaecology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust and 2Reproductive Sciences Section, Department of Cancer Studies and Molecular Medicine, Leicester Medical School, University of Leicester, Leicester, UK3To whom correspondence should be addressed at: Reproductive Sciences Section, Department of Cancer Studies and Molecular Medicine, Robert Kilpatrick Clinical Sciences Building, Leicester Royal Infirmary, P.O. Box 65, Leicester, Leicestershire, LE2 7LX, UK. E-mail: [email protected]

Cannabis use in pregnancy is associated with a range of obstetrical conditions. The molecular mechanisms underlying theseeffects have not been elucidated but are attributed to the actions of delta-9-tetrahydrocannabinol (D9-THC). In this study, concen-trations of D9-THC equivalent to those found in the serum of cannabis users, i.e. ~20 mM, inhibited proliferation and activated arestricted tight transcriptional programme in the BeWo trophoblast cell line. Employing genome-wide expression profiling meth-ods, we found that the pattern of gene expression differs from that described in the placenta of patients with fetal growth restric-tion (FGR), associated with either hypoxia or discordant dichorionic twins, or of patients with pre-eclampsia. It was also dissimilarto the patterns obtained from the transcriptome of other tissues, such as the mouse brain, treated with D9-THC. The expression oftranscription factors, such as thyroid hormone receptor-b1 (TRb1), and transcriptional co-repressors, such as histone deactylase3 (HDAC3), was affected by D9-THC in a dose-dependent manner, whereby 15 mM D9-THC caused a 2.8-fold inhibition of TRb1expression, but a 3.5-fold increase in HDAC3 expression. These data were confirmed by end-point RT–PCR analyses and under-pin the observed D9-THC-induced inhibition of BeWo cell proliferation. Genes encoding for growth, apoptosis, cell morphologyand ion exchange pathways were modulated by 15 mM D9-THC. This study may provide insight into the mechanisms underlyingthe effects of D9-THC and cannabis use upon placental development during pregnancy.

Key words: cannabis/cytotrophoblast/marijuana/microarray/tetrahydrocannabinol

IntroductionMarijuana is one of the most commonly used illicit drugs in the UKand USA (Hutchings and Dow-Edwards, 1991; Parliamentary Officeof Science and Technology, 1996), and its use appears to be increasing(Knight et al., 1994; King, 1997). It is difficult to quantify the risks ofmarijuana use in pregnancy, as there are likely to be many confoundingvariables such as the use of other drugs and associated lifestyle factors.However, marijuana use has been associated with low birthweight,fetal growth restriction (FGR) (Zuckerman et al., 1989), placentalabruption, preterm birth, stillbirths and spontaneous miscarriages(Conner, 1984; Hatch and Bracken, 1986; Felder and Glass, 1998).

The most psychoactive agent in marijuana is delta-9-tetrahydrocan-nabinol (Δ9-THC). Δ9-THC is highly lipophilic (Leuschner et al.,1986) and has a half-life of about 8 days in fat deposits, and conse-quently, it may take up to 1 month for it to be eliminated entirely fromthe body after a single dose (Hatch and Bracken, 1986). In fact, fol-lowing inhalation from a single marijuana cigarette, blood levels ofΔ9-THC may remain detectable for up to 30 days (Jones, 1980). Thesepharmacokinetic characteristics pose a particular risk to the fetus,because maternal tissues may act as a reservoir for Δ9-THC (Hatchand Bracken, 1986; Leuschner et al., 1986); Δ9-THC readily crossesthe placenta (Hatch and Bracken, 1986), and this, combined with aslow fetal clearance, (Bailey et al., 1987) means prolonged fetal exposure,even when smoking is discontinued. Therefore, Δ9-THC potentially

influences the development of many organs, (Harclerode, 1980) espe-cially in the first trimester.

Δ9-THC has been found to have several cellular-binding sites (Felderand Glass, 1998), including one on DNA (Porcella et al., 1998), and canbind to the G-coupled endocannabinoid receptors CB1 and CB2. Theendometrium and myometrium possess the cannabinoid receptors CB1and CB2 and thus are potential targets for the action of Δ9-THC duringimplantation, early pregnancy (Paria et al., 2001) and labour (Dennedyet al., 2004). However, cannabinoid receptors are also expressed by pla-cental tissue at term (Park et al., 2003), and although when they are firstacquired during pregnancy is unknown, expression has been detected inearly first trimester placental tissues (Helliwell et al., 2004). Interest-ingly, levels of the endogenous cannabinoid, anandamide, fall progres-sively during pregnancy (Habayeb et al., 2004), supporting otherevidence that low systemic levels are required for normal pregnancyprogression (Maccarrone et al., 2002). Therefore, exposure to the exo-cannabinoid Δ9-THC could lead to inappropriate activation of the CB-mediated pathways in the placental trophoblast. At least one action ofΔ9-THC upon placental transport, i.e. mediated by the serotonin recep-tor and of α-amino isobutyric acid, has been proposed to be through theCB1 receptor (Fisher et al., 1987; Kenney et al., 1999).

The BeWo cell line, derived from human gestational choriocarcinoma,has been widely used as an in vitro model for trophoblast intercellularfusion and differentiation (Burres and Cass, 1986; Hohn et al., 1992;

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Cohran et al., 1996; Kudo et al., 2004) and in toxicology studies(Burres and Cass, 1987; Thibault et al., 2000; Nomura et al., 2004).Additionally, these cells have been used in genome-wide analyses(Aronow et al., 2001; Saito et al., 2001; Endo et al., 2002; Kudo et al.,2004) that have identified putative novel targets for the mechanism ofFGR that were subsequently confirmed in animal studies to be of sig-nificant experimental and clinical value (Loiselle et al., 2004; Nomuraet al., 2004; Ohara et al., 2004). The BeWo cell line thus provides anappropriate model in which to study some aspects of human trophob-last physiology and pathophysiology without the aspect of inter-patient variability and other confounding variables. In this study, wehave therefore employed this cell line to determine whether Δ9-THCwould specifically affect cytotrophoblast cell survival and gene tran-scription and whether extrapolation of such effects may be used toexplain the reported in-vivo effects of Δ9-THC upon clinical condi-tions affecting feto-placental development.

Materials and methods

MaterialsThe human choriocarcinoma cell line BeWo (ECACC 86082803, EuropeanCollection of Cell Cultures, Salisbury, Wiltshire, UK) was maintained inHam’s F12 medium (Invitrogen, Paisley, UK) supplemented with 10% fetalcalf serum (FCS) (Invitrogen) and cultured at 37°C in humidified atmosphereof 5% CO2 in air. Δ9-THC (Sigma-Aldrich, Poole, Dorset, UK) was diluted inethanol and stored under nitrogen at –20°C. Oligonucleotide primers were syn-thesized (Sigma-Genosys Ltd., Haverhill, Suffolk, UK) and desalted beforereconstitution in sterile dH2O at 200 pmol/μl and stored at –20°C.

Cell cultureBeWo cells were plated into Nunc 6-well multi-well plates in triplicate foreach data point (Fisher Scientific, Loughborough, Leicestershire, UK) at a den-sity of 8 × 105 cells per well (Nomura et al., 2004). After 48 h culture, themedium was exchanged with one that contained 5% FCS and 15 μM Δ9-THC[final concentration of 0.1%(v/v) ethanol]. The control consisted of culturemedium containing 0.1%(v/v) ethanol. Cells were cultured with media plusadditives for 1 h to allow non-specific binding of Δ9-THC to plastic to reachequilibrium. The medium was then replaced and culture continued for 48 h,with fresh medium replaced at 24 h. Photomicrographs were obtained using aNikon Eclipse TE2000-U inverted microscope equipped with a DN-100 digitalcamera image capture system, and percentage confluency was measured byimage analysis of pixels representing cells and substratum.

To examine the effect of Δ9-THC on cell survival and cell proliferation indi-ces, we plated BeWo cells to Nunc 96-well plates (Fisher Scientific) at either4 × 104 or 1 × 104 cells per well, respectively, in 200 μl of normal growth mediumand allowed them to proliferate for 48 h until the higher density cultures reachedconfluency. The medium was then changed to one that contained 0–30 μMΔ9-THC for 1 h to allow for non-specific binding. The medium was thenreplaced and culture continued for 48 h, with fresh medium replaced at 24 h.After 48 h, cell numbers were assessed using the Cell Proliferation and Apop-tosis Kit II (Roche Diagnostics Ltd., Lewes, East Sussex, UK) as per the man-ufacturer’s instructions with measurements taken on a Multiskan AscentELISA plate reader (Labsystems Oy, Helsinki, Finland) with the detection fil-ter set at 420 nm and the reference set at 620 nm. Cell numbers were obtainedby calibration against a standard curve of untreated BeWo cell numbers grownin parallel (Taylor et al., 2002). To make direct comparisons between cultures,we then converted the cell numbers to a percentage of the untreated control.

The effect of Δ9-THC dose on gene expression was obtained by culturingBeWo cells with doses of Δ9-THC ranging from 0.3 to 30 μM in 0.1% ethanolfor 48 h, as described above.

Total RNA extraction, cRNA synthesis and microarray hybridizationTotal RNA was extracted using a combination of TRIZOL reagent (Invitro-gen), RNeasy mini kit (Qiagen Ltd., Crawley, UK) and ethanol precipitation.Briefly, aqueous total RNA from the TRIZOL reagent procedure was mixed

with ethanol applied to the RNeasy mini columns, subsequently purified andconcentrated through ethanol precipitation. RNA integrity was assessed usingagarose gel electrophoresis and Agilent 2100 Bioanalyser (Agilent Technolo-gies, UK, South Queensferry, UK). RNA from two separate experiments per-formed in triplicate was pooled and samples submitted to the MRCGeneservice (Babraham, Cambridge, UK) for further purification and produc-tion of biotinylated cRNA. Biotinylated cRNA was prepared from 10 μg oftotal RNA according to Affymetrix protocols. The integrity of the labelledcRNA and fragmentation products were assessed on the Agilent 2100 bioana-lyser. Next, 15 μg of biotinylated cRNA fragments were hybridized to humanHU133_plus 2 microarray chips overnight and then stringently washed, stainedand scanned using a GeneArray scanner (Agilent Technologies, Palo Alto, CA,USA), according to Affymetrix protocols.

Gene expression analysisFluorescence data were corrected for background fluorescence, reduced inintensity values before normalization against internal standards. All arrayswere scaled to the same target intensity and were normalized to housekeepinggenes on the U133_plus 2 arrays and analysed using dChip Analyzer softwareversion 1.4 (Harvard School of Public Health and Dana-Farber Cancer ResearchInstitute, Boston, MA, USA, 2004; http://biosun1.harvard.edu/complab/dchip/)using a 2-fold change in expression with an a of 0.90 and a b of 0.05 being thestatistical cut-off points for real expression changes from duplicate chips.Hierarchical clustering was used to obtain gene expression patterns and ontologyinformation.

End-point RT–PCRTo confirm the data from the microarray experiment, we performed end-pointRT–PCR upon the samples used in these experiments. One microgram of totalRNA was reversed transcribed with avian myeloblastosis virus-reverse tran-scriptase (AMV-RT; Promega, Southampton, UK) at 42°C for 1 h in the pres-ence of 5 units of RNase inhibitor (RNasin; Promega). A minus RT reactionwas obtained by omitting the AMV-RT enzyme. At the end of the reaction, theenzymes were denatured by heating at 95°C for 5 min and the cDNA was storedat –20°C. One microlitre of cDNA was subject to PCR using 10 pmol/μl of spe-cific primers for histone deactylase 3 (HDAC3), thyroid hormone receptor-β1(TRβ1) and glyceraldehyde-3-phosphatedehydrogenase (GAPDH) using theannealing temperatures given in Table I. The RNA from the dose-ranging studywas treated with RNAse-free DNAse 1 (Promega) before reverse transcriptionand PCR amplification. PCR products were resolved on 3% agarose gels andstained with ethidium bromide (2 μg/ml; ICN Biomedicals, Basingstoke, UK)for 15 min, before being destained with dH2O for 30 min. Gel images were cap-tured using a Syngene GeneGenius system (Syngene, Cambridge, UK)equipped with GeneSnap version 6 gel documentation software. Product densi-ties were assessed using the Scion Image beta version 4.0.2 software (ScionCorporation, Frederick, MD, USA, 1999; http://www.scioncorp.com).

Data analysisSubtraction of the densitometry values obtained from the –RT lanes from thosefrom the +RT lanes, and then division by the relative GAPDH transcript levels,was used to correct the PCR data. These data were then normalized to theuntreated control and expressed relative to the untreated controls. All data werethen analysed for differences using one-way ANOVA with Tukey’s honest sig-nificant difference (HSD) test with the InStat version 3.0 software package(GraphPad Software, San Diego, CA, USA, 1998; http://www.graphpad.com).Statistical significance was accepted when P < 0.05.

ResultsEffect of D9-THC on gross BeWo cell growth, survival and morphologyBeWo cells were plated such that cultures achieved approximately70–80% confluency after 48 h, the point at which Δ9-THC treatmentswere initiated. Confluency was achieved within 24 h of the subse-quent 48 h culture period (data not shown). Cultures treated at 48 hwith a range of Δ9-THC demonstrated an inhibitory effect on theBeWo cell cultures (Figure 1A and B), but only at concentrations in



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excess of 3 μM Δ9-THC, where confluency was significantly reducedfrom ∼95 to ∼70% (*P < 0.05; one-way ANOVA with Tukey’s HSDtest; n = 4). Cultures treated with 15 and 30 μM Δ9-THC did not reachconfluence (Figure 1A and B). There did not appear to be anyincreased cell death or failure to attach to the substratum as evidencedby the lack of increase of shedding of cells in the spent medium. Anal-ysis of the effect of Δ9-THC on BeWo cell survival and proliferationrevealed that cultures that reached confluency before treatment

remained stable (1.32 ± 0.065 × 105 compared with 1.22 ± 0.063 × 105

cells per well; P = 0.37, n = 8 ANOVA with Tukey’s HSD test) andconfirmed the observation that the effect of Δ9-THC on BeWo cul-tures was not due to increased cell death. Sub-confluent culturestreated with Δ9-THC demonstrated a significant dose-dependent inhi-bition of cell numbers at concentrations above 3 μM Δ9-THC after 48 h(Figure 1C). These cultures failed to exceed the 30% confluency level(the untreated cultures reached a cell density of 3.7 ± 0.31 × 104 cells

Table I. Oligonucleotide primer sequences and expected amplicon sizes

*From Hall et al. 1998.

mRNA species Oligonucleotide sequence Product size (bp) Annealing temperature (°C)

Accession number

GAPDH5′ 671AGAACATCATCCCTGCCTC689 347 60 X01677*3′ 1017GCCAAATTCGTTGTCATACC998

HDAC35′ 1576GGGACATTATTGGCAGTG1593 233 55 U756973′ 1808GGATTCAGGTGTTAGGGAG1790

TRβ15′ 59GCGATTTCCTTCTGGTTG76 314 and 168 55 NM-000461.23′ 372AGTGCTTCGGTTTGTCCC355

Figure 1. The dose-dependent effect of Δ9-tetrahydrocannabinol (Δ9-THC) upon BeWo cell growth, morphology and survival. (A) Photomicrographs of BeWocells treated with 0.1% ethanol (control) or the indicated concentrations of Δ9-THC for 48 h. Areas not covered with cells are arrowed. Data are representative offive experiments performed in triplicate. Scale bar = 120 μm (B) Graph showing the dose-dependent effect of Δ9-THC on BeWo cell culture confluency. Data aremean ± SEM of four experiments performed in triplicate; *P < 0.05 one-way ANOVA with Tukey’s honest significant difference (HSD) test. (C) Graph showingthe dose-dependent effect of Δ9-THC on inhibiting BeWo cell numbers when the cells were grown to 25% confluency (sub-confluent) before treatment comparedwith a lack of effect on cell numbers grown to confluency. Data are the mean ± SEM of two experiments performed in quadruplicate; **P < 0.001 one-wayANOVA with Tukey’s HSD test.



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per well from an initial density of 1 × 104 cells per well). However,15 μM Δ9-THC inhibited cell proliferation so that the final cell densitywas 2.25 ± 0.19 × 104 cells per well after Δ9-THC treatment (P <0.001; n = 8; ANOVA with Tukey’s HSD test). Further morphologicalexamination of cell cultures treated with 15 μM Δ9-THC indicatedthat the inhibitory effect resulted from a decrease in growth rather thaninvolution of the culture after achieving confluency (Figure 2).

Microarray analysis: identification of D9-THC-regulated transcriptsThe use of microarray analysis facilitates the global assessment oftranscriptional profiles of cells. A number of studies have established

the very high degree of reproducibility across replicate GeneChipexperiments (Irizarry et al., 2003; Cope et al., 2004). In this study, toensure consistent data, we adopted both biological (experiments per-formed four times in triplicate) and technical replicates (hybridizationsperformed twice) for these studies, whereas others adopt either bio-logical (Hoffman et al., 2004) or technical replicates (Kudo et al.,2004), but seldom both (Kothapalli et al., 2002). Therefore, total RNAfrom two sample pools of the control and 15 μM Δ9-THC-treated cul-tures (n = 12) were used to generate biotinylated target cRNA andhybridized to HU133A_Plus 2 arrays, which represented approximately56 000 characterized transcripts and expressed sequence tags (ESTs).Scaling factors, noise and percentage of present and absent calls showedonly minor variations between arrays, an essential requirement whencomparing multiple arrays. These replicate gene chip experimentsrevealed an overall correlation (r2 = 0.89) with an average of only0.04% of probe sets showing more than a two-fold change between thereplicate experiments. The number of genes induced and repressed onmicroarray 1 at the two-fold level were 304 and 105, respectively,whereas the number induced and repressed on microarray 2 were 220and 50, respectively, representing a mean error measurement rate ofonly 0.5%. When the cut-off was reduced to 1.6-fold, the number ofgenes induced and repressed on microarray 1 were 1504 and 1127,respectively, whereas the number induced and repressed on microarray2 were 2291 and 5670, respectively, representing a mean error measure-ment rate of 26.3%. Under these conditions, it was considered that26.3% error lacked precision for detailed transcriptome analysis and theconventional two-fold cut-off point was used (Cope et al., 2004).Focusing on genes showing an increased level of expression on botharrays and using a two-fold change cut-off, we identified 134 genes ofwhich the top 20 genes are mentioned in Table II. Focusing on genesshowing a decreased level of expression on both arrays with a two-fold(50%) change cut-off, we identified only 18 genes (Table II).

Confirmation of microarray dataTo validate the microarray analysis data, we chose two candidategenes, histone deacetylase 3 (also known as MALAT-1; HDAC3) andTRb1, for further study as they were represented by several probe setson the HU133_plus 2 microarray chip and were significantly modu-lated more than two-fold and <50% respectively on both arrays. TRb1was of significant interest because it is associated with placental func-tion and development (Kilby et al., 1998; Yen, 2001; Barber et al.,2005), and direct perturbation of the maternal hypothalamic–pituitary–thyroid–placental axis is a direct cause of multiple obstetri-cal problems (Nickel and Cattini, 1991; Ohara et al., 2004). HDAC3 isof potential interest because it is associated with transcriptional regu-lation, cell cycle progression and developmental events, and perturba-tion of its activity is potentially linked with placental development(Jho et al., 2005).

The cycle numbers for each set of primers were obtained empiri-cally with an untreated BeWo cell extract (Figure 3), and subsequentreactions were performed at 35, 34 and 26 cycles for HDAC3, TRβ1and GAPDH, respectively, with the extension step for HDAC3 andTRβ1 starting at 1 min at 72°C and increasing by 5 s/cycle. End-pointRT–PCR with the pooled RNA extracts from the control and 15 μMΔ9-THC-treated BeWo cells showed a 2.34-fold change in HDAC3transcript levels from experiment 1 and 2.85-fold change from experi-ment 2 (Figure 4A); the mean was 2.58-fold (Figure 4B). By contrast,the mean fold change for HDAC3 on microarray 1 was 6.51-fold andon microarray 2 was 2.45-fold with a median fold change of 3.47-fold(Table II). The levels of transcripts for TRβ1 (Lazar, 1993), found bycombining the levels for 5′-mRNA variants ABCF (short form; 168 bp)and variants DE (long form; 314 bp) (Mannavola et al., 2004), were

Figure 2. Representative photomicrographs of (A) untreated BeWo cells ortreated with 15 μM Δ9-tetrahydrocannabinol (Δ9-THC) for (B) 24 h or (C) 48 h(five experiments performed in triplicate). Scale bar = 120 μm.



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shown to be decreased by 4.61-fold (78.3%) in experiment 1 and by3.36-fold (70.2%) in experiment 2 (Figure 4C); the mean was 3.90-fold (74.3%), which compared favourably with the 5.28-fold (81.1%)and 2.98-fold (66.5%) reductions in the two microarrays, respectively.The median change in TRβ1 levels was 2.79-fold (64.1%; Table II).Variant G (Frankton et al., 2004) was not observed in any BeWo extract.

Effect of D9-THC dose on HDAC3 and TRb1 mRNA levelsTreatment of BeWo cells with Δ9-THC did not show a dose-dependentincrease on HDAC3 mRNA expression, but a threshold effect at 3 μM(Figure 5). At this dose, HDAC3 mRNA levels increased by 1.55-foldand reached a maximum of 1.70-fold change at 15 μM (Figure 5). Bycontrast, there was a dose-like effect on the repression of TRβ1 that

Table II. Putative target genes of Δ9-tetrahydrocannabinol (Δ9-THC) identified by microarray analysis

LOC, LocusLink; UG, Unigene; EST, expressed sequence tag; fold 1, mean fold change array 1; fold 2, mean fold change array 2; median fold change, median fold change for both arrays; % change, % movement from 100%. The top 20 most up-regulated and the 18 down-regulated probe sets are shown. Genes studied to confirm the microarray data are shown in bold.

Probe set Gene listing Fold 1 Fold 2 Median fold change

% Change Accession number

Up-regulated genes227062_at Trophoblast-derived non-coding RNA/plectin 1 6.81 2.07 5.23 423 AU155361229713_at cDNA FLJ13267 fis, clone OVARC1000964 7.79 3.00 3.69 269 AW665227224726_at DAPK-interacting protein 1 5.63 2.57 3.58 258 W80418224568_x_at Metastasis associated in lung adenocarcinoma transcript 1/

MALAT-1/HDAC36.51 2.45 3.47 247 AW005982

244804_at Sequestosome 1 4.74 2.63 3.44 244 AW293441208610_s_at Serine/arginine repetitive matrix 2 4.93 2.13 3.42 242 AI655799225640_at Hypothetical gene supported by AK091718 (LOC401504), mRNA 4.94 2.56 3.38 238 AA875998239451_at Tumour rejection antigen (gp96) 1 4.49 2.33 3.38 238 AI684643234605_at Homo sapiens cDNA: FLJ21233 fis, clone COL00789 3.51 3.15 3.37 237 AK024886239274_at Transcribed sequence with weak similarity to protein

NP_062553.1 hypothetical protein FLJ112674.99 2.18 3.29 229 AV729557

1554178_a_at Hypothetical protein MGC39518 5.11 2.69 3.26 226 BC0392951558080_s_at Hypothetical protein LOC144871 3.88 2.17 3.21 221 BG913589213593_s_at Transformer-2α 3.50 2.15 3.20 220 AW978896239742_at Tubby like protein 4 4.14 2.59 3.19 219 H152781561097_at Clone IMAGE:5270855, mRNA 2.48 3.84 3.14 214 BC040602237469_at Unknown protein; UG = Hs.242998; EST 3.23 2.52 3.07 207 T96523233819_s_at Zinc finger protein 294 4.79 3.27 3.04 204 AK023499236524_at cDNA FLJ37319 fis, clone BRAMY2018027 3.20 2.16 3.03 203 AA737437242528_at cDNA FLJ31668 fis, clone NT2RI2004916 2.08 5.98 3.03 203 AI473887212468_at Sperm-associated antigen 9 4.28 2.63 2.99 199 AK023512223940_x_at Metastasis associated in lung adenocarcinoma transcript 1/

MALAT-1/HDAC35.93 2.21 2.96 196 AF132202

227740_at Kinase interacting with leukemia-associated gene (stathmin) 4.91 3.32 2.95 195 AW173222236327_at Transcribed sequences 3.56 2.75 2.94 194 AA767373201730_s_at Translocated promoter region (to activated MET oncogene) 4.31 3.42 2.93 193 BF110993201996_s_at SMART/HDAC1-associated repressor protein 4.88 2.09 2.87 187 AL524033222413_s_at Myeloid/lymphoid or mixed-lineage leukemia 3 4.46 3.09 2.87 187 AW137099222576_s_at Eukaryotic translation initiation factor 2C, 1 4.70 4.08 2.86 186 AW071829213067_at Myosin, heavy polypeptide 10, non-muscle 5.76 2.41 2.85 185 AI382123211944_at HbxAg transactivated protein 2 3.65 2.41 2.80 180 BE729523216550_x_at Ankyrin repeat domain 12 3.61 2.87 2.78 178 X80821

Down-regulated genes211106_at Suppressor of Ty 3 homolog (S. cerevisiae) −3.49 −218.65 −89.12 98.8 AF064804204133_at RNA, U3 small nucleolar interacting protein 2 −2.49 −152.67 −69.80 98.6 NM_004704214729_at TWIST neighbour −2.06 −62.76 −22.89 95.6 AA400421216588_at Ribosomal protein L7 −2.62 −4.87 −4.07 75.4 AL031577212444_at Retinoic acid induced 3 −3.20 −1.92 −3.80 73.7 AA156240212838_at Dynamin-binding protein −2.25 −4.83 −3.15 68.3 AB023227209907_s_at Intersectin 2 −3.06 −4.41 −2.80 64.3 AF182198229657_at Thyroid hormone receptor, b [erythroblastic leukemia viral

(v-erb-a) oncogene homolog 2, avian]–5.28 –2.98 –2.79 64.2 BF431989

242229_at N-acyl-phosphatidylethanolamine-hydrolyzing phospholipase D .2.06 −5.39 −2.66 63.4 W01715241617_x_at EST moderately similar to 810024C cytochrome oxidase I −3.86 −4.27 −2.64 62.1 BE9619491555433_at Solute carrier family 39 (zinc transporter), member 14 −2.90 −3.07 −2.64 62.1 BC015770234179_at Homo sapiens cDNA: FLJ23200 fis, clone KAIA38871. .2.67 −7.00 −2.57 61.1 AK026853238493_at Zinc finger protein 506 −2.05 −4.71 −2.53 60.5 AI559570244752_at Hypothetical protein LOC220929 −2.09 −4.63 −2.49 59.8 AI563915238283_at Hypothetical protein LOC151658 −2.07 −3.84 −2.42 58.7 AI685344222341_x_at Transcribed sequences −3.50 −2.71 −2.10 52.4 AW973235224321_at Transmembrane protein with EGF-like and two follistatin-like

domains 2−2.11 −2.00 −2.07 51.7 AB004064

207350_s_at Vesicle-associated membrane protein 4 −2.00 −2.04 −2.02 50.5 NM_003762



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only reached a significant 75.3% decrease at 15 μM Δ9-THC (Figure 5)that was not further suppressed by 30 μM Δ9-THC.

Grouping of genes on the microarray by functionK-means and hierarchical clustering of the 134 up-regulated and 18down-regulated genes identified by the microarray analysis identifiednine major groupings: growth and apoptosis genes; cell morphology andcontraction genes; transcription factors; transcription regulation genes;

RNA processing and translation genes; protein trafficking genes; ionchannels and transducers genes; lipid metabolism genes; and hypotheticalproteins and EST genes. These groups are identified where possible inTable III. Because morphologically the BeWo cell cultures appeared to beaffected by either failure to proliferate, or increased apoptosis, or contrac-tion of cultures, and because the microarray is a measure of the transcrip-tional activity of the BeWo treated cultures, analyses of the modulation oftranscripts implicated in these pathways were performed.

The analyses revealed that several genes implicated in the controlof cell cycle progression were affected by 48 h Δ9-THC treatment.These included insulin-like growth 1 receptor, which has beenimplicated in cancer cell survival through p53-dependent pathways(Girnita et al., 2003), cyclin-dependent kinase 11, which associateswith cyclin type L and initiates pre-mRNA splicing events (Huet al., 2003), and HDAC1-associated repressor protein (also knownas SMART/Msx1/SHARP/SMART/SPEN), which is involved inzebrafish neurogenesis (Cunliffe, 2004) and murine neuromuscu-loskeletal development (Ishii et al., 2003). By contrast, transcriptsthat are involved with apoptosis tended to be pro-apoptopic ratherthan anti-apoptopic, e.g. death-associated protein kinase (DAPK)-interacting protein 1 (Dip1), which antagonizes the anti-apoptoticfunction of DAPK in Hela cells to promote caspase-dependent apop-tosis (Jin et al., 2002) and cell division and apoptosis regulator 1(CARP-1), which promotes retinoid-stimulated cell apoptosis (Rishiet al., 2003) (Table III).

Although the major cell contraction genes actin and myosin were notregulated by Δ9-THC, non-muscle myosin (heavy polypeptide 10) andprotein phosphatase 1, which are predicted to function in a similarmanner to myosin, were both up-regulated (Table III), as were severalgenes that regulate cell morphology by providing or destroyingcytoskeletal frameworks, e.g. plectin (trophoblast-derived non-codingRNA/Hemidesmosomal protein 1 intermediate filament-binding pro-tein 500 kDa) (Uitto and Pulkkinen, 1996), whereas intersectin 2 anddynamin-binding protein (McGavin et al., 2001) were down-regulated.

The main effect of Δ9-THC was to regulate transcription factors andregulators of transcription with 50 up-regulated and five down-regulated genes (Table III). Transcripts for the ‘orphan’ nuclear receptors(NR1D2/EAR-1R/Rev-erb-beta/RVR and Nor-1) and the β-isoformof the TR that interact with receptors for retinoic acid to control genetranscription (Wolf, 2002) were up-regulated as were several tran-scriptional co-regulators, such as histone deacetylase 3 (MALAT-1/HDAC3/PRO1073) that interacts with TRs and other nuclear recep-tors to enhance transcriptional activity and interacts with Phox2 toregulate the dopamine β hydroxylase promoter (Xu et al., 2003).Ribosomal subunit expression was also represented on the microarraychips and several of these genes such as U3 small nucleolar inter-acting protein 2 and RNA motif-binding protein 25 (Table III) wereregulated. Additionally, the transcripts for several ‘initiation of trans-lation’ proteins were increased.

A number of other transcripts for genes involved in trophoblastfunction were noted to be affected by Δ9-THC treatment, e.g. solutecarrier family 40 also known as ferroportin 1 (Wallace et al., 2002),which is involved in iron transport and solute carrier family 4 member7 (SLC4A7) which is involved in bicarbonate and small anion trans-port (Loiselle et al., 2004). The most down-regulated membrane pro-tein was the orphan G-protein coupled receptor family-related retinoicacid-induced protein, RAIG3/GPRC5C (Robbins et al., 2000), thatmay be involved in calcium sensing (Brauner-Osborne et al., 2001).

Interestingly, an enzyme involved in the synthesis of the endocan-nabinoid, anandamide, N-acyl-phosphatidylethanolamine-hydrolyzingPhospholipase D (NAPE-PLD; Habayeb et al. 2002) and an uncharac-terized lipoprotein lipase, associated with fatty acid transport (Garnicaand Chan, 1996), were both down-regulated.

Figure 3. Optimization of cycle numbers for HDAC3 and TRβ1 end-pointRT–PCR assays. RNA extracts from BeWo cells was reverse transcribed intocDNA and subjected to PCR with gene-specific primers (Table I) and resolvedon 3% agarose ethidium bromide-stained gels. The relative levels of ampliconwere determined as described in the Methods section. Data are the mean of thetwo experiments.



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The microarray analysis also revealed the regulation of several tran-scripts for which the predicted protein has no known function (Table III).

DiscussionEmploying concentrations of Δ9-THC that are detected in patientsusing cannabis for recreational use (Cherlet and Scott, 2002), we havedemonstrated that 3–30 μM Δ9-THC inhibited BeWo cell proliferationin a dose-dependent manner. Conversely, the same doses of Δ9-THCshowed no significant effect on confluent cell cultures indicating notoxic or pro-apoptopic effect of Δ9-THC upon BeWo cells. Throughmicroarray analysis, we have also demonstrated distinct changes inthe pattern of transcription by the BeWo cells in response to 15 μMΔ9-THC. However, only a relatively low number of transcripts wereconsistently modulated that suggested a tight transcriptional controlbe manifested in the Δ9-THC-treated BeWo cultures. The reason forthis is not readily apparent but may be part-way due to the conven-tional two-fold cut-off point used in microarray analyses. However,although ‘raising the bar’ to 1.6-fold increased the number of tran-scripts that were regulated by Δ9-THC in the BeWo cell, an unaccept-able significant increase in the amount of experimental variabilityfrom 0.5 to 26.3% was also introduced which would lead to false-positive identification of non-regulated transcripts. These data mean

that several transcripts that might be considered relevant could havebeen omitted from these analyses in an attempt to obtain precision andidentify the most likely mediators involved in Δ9-THC-induced BeWocell-growth inhibition. The tight regulation of the transcriptome is notlimited to this study, as similar tight transcriptional control mecha-nisms have been demonstrated in neurons undergoing apoptosis thatdo not modulate ‘classical’ apoptosis proteins (Desagher et al., 2005).

The effects of Δ9-THC upon BeWo cells also appeared to be cellspecific in that although expression of CPNE6 (copine VI), the lipidmetabolizing, neuronal form of the copine vesicle transport family ofcalcium-sensing proteins, and LlGI 1, a cytoskeletal protein thatassociates with non-muscle myosin II heavy chain to control asym-metrical cellular polarization in neuroblasts (Klezovitch et al., 2004),was repressed in both BeWo cells and the brain of Δ9-THC-treatedmice (Parmentier-Batteur et al., 2002), no overlap in the expressionpattern of other genes was noted. The altered transcripts belonged toseven functional groups with the largest number being transcriptionalregulators, such as TRβ1, their co-regulators, such as HDAC3 (Table III),suggesting that one of the major effects of Δ9-THC on the BeWo cellis the regulation of gene transcription and RNA processing.

As cannabis use during pregnancy is associated with FGR, the presentmicroarray data were compared with such data derived from placentaltissue obtained from conditions associated with FGR. Interestingly,

Figure 4. Comparison of the transcript levels in the microarray and the end-point RT–PCR of the pooled RNA samples used in the microarray experiments.HDAC3 mRNA levels determined by RT–PCR (A) from control Array 1 and Array 2 (+RT; lanes 1 and 5), control Array 1 and Array 2 (–RT; lanes 2 and 6), 15 μMΔ9-tetrahydrocannabinol (Δ9-THC) Array 1 and Array 2 (+RT; lanes 3 and 7), 15 μM Δ9-THC Array 1 and Array 2 (–RT; lanes 4 and 8). (B) Graphical representa-tion of HDAC3 transcript levels in the microarray (fluorescence) compared with those of the end-point RT–PCR (corrected for GAPDH levels). TRβ1 mRNA levelsdetermined by RT–PCR (C) from control Array 1 and Array 2 (+RT; lanes 3 and 5), control Array 1 and Array 2 (–RT; lanes 4 and 6), 15 μM Δ9-THC Array 1 andArray 2 (+RT; lanes 1 and 7), 15 μM Δ9-THC Array 1 and Array 2 (–RT; lanes 2 and 8). (D) Graphical representation of TRβ1 transcript levels in the microarray(fluorescence) compared with those of the end-point RT–PCR (combined long and short-form transcripts, corrected for GAPDH levels). Data are mean ± SD from15 fluorescence points in duplicate (microarray) and two data points for the end-point RT–PCR (C).



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expressions of genes that were altered in placental tissue fromhypoxia-induced FGR, such as adipophilin and vascular endothelialgrowth factor (VEGF) (Roh et al., 2005), or from discordant dichori-onic twins-associated FGR, (Endo et al., 2002) such as SMAD4 andCDC46 (Endo et al., 2002), were not altered in Δ9-THC-stimulatedBeWo cells. Similarly, many of the changes found in our analyseswere not present or found on cDNA arrays used in these relatedgenome-wide studies, providing an incomplete analysis. Althoughexpressions of genes that were altered in association with in vitro syn-cytial formation, such as integrin-α1 (Reimer et al., 2002), or in pla-cental tissue in association with pre-eclampsia, such as hCG (Kudo et al.,2004), were elevated by Δ9-THC-stimulation, this increase did notachieve the two-fold cut-off value used for selection (data not shown)but could be of biological significance in that several genes onlyrequire a modest increase in transcript levels to provide a more sub-stantial increase in protein product (Storey and Storey, 2004). Thesedata, if reflective of the in vivo situation, may suggest that the mecha-nisms underlying FGR associated with hypoxia and/or pre-eclampsia

are fundamentally different to the mechanisms involved in the FGRassociated with in vivo Δ9-THC exposure.

In addition to the robust approach to microarray analysis (Li et al.,2004), we validated the results from the microarray analysis by exam-ining the expression of two identified transcripts, TRβ1 and HDAC3,that are both regulators of transcriptional programming and are impli-cated in cell growth and development. The effect of Δ9-THC onHDAC3 mRNA levels was not dose-dependent but appeared to showa hormetic effect (Calabrese, 2005) that exceeded control levels at∼3 μM Δ9-THC. HDAC3 is a transcriptional co-regulator protein thatinteracts with other members of the histone deacetylase family ofgenes, such as HDAC7 or HDAC10. It is a subfamily 1 member whichcontains proteins that regulate the G1-phase of the cell cycle and isknown to complex with NCOR1 and NCOR2, TBL1X, TBL1R,CORO2A and GPS2 to form large multi-protein complexes that con-stitute the N-Cor repressor complex, responsible for the deacetylationof lysine residues on the N-terminal part of the core histones (H2A,H2B, H3 and H4) (Guenther and Lazar, 2003). Histone deacetylationin turn gives a tag for epigenetic repression and plays an importantrole in transcriptional regulation, cell cycle progression and develop-mental events. The observed increase of HDAC3 expression inresponse to Δ9-THC may underlie its effects upon BeWo cell cultures(Figure 3) and if present in vivo suggests it may be linked to an inhibi-tion of cytotrophoblast cell-cycle progression and subsequent placentaldevelopment.

Of significance in this study was the observed Δ9-THC suppressionof TRβ1 expression, which was repressed in a dose-dependent man-ner. Linear-trend analysis of the TRβ1 mRNA levels (data not shown)indicated that this effect reached significance at 15 μM Δ9-THC. Tri-iodothyronine (T3) plays a key role in the developing placenta andfetus (Ohara et al., 2004), and loss of its action by maternal hypothy-roidism, placental deiodinase deficiency or mutations in the TRs maylead to FGR and placental insufficiency (Ohara et al., 2004). TR isinvolved in the normal proliferation and function of the trophoblastcell (Ohara et al., 2004), and although all four TR isoforms, TRα1,TRα2, TRβ1 and TRβ2 are found in the human placenta (Kilby et al.,1998), expression of only TRβ1 has been consistently reported (Chanet al., 2004), indicating that TRβ1 may be the most important TR inthe placenta. Although there are multiple TRβ1 5′-untranslated region(UTR) transcripts (Frankton et al., 2004), they produce two main tran-scripts, a short form and a long form (Mannavola et al., 2004). Wedesigned PCR primers that would distinguish between these twoisoforms and thus determined whether there was any differential regu-lation of the long and short forms and found that both the long andshort forms of the TRβ1 mRNA transcripts were repressed by Δ9-THC(Figure 5), indicating a common mechanism of action, presumablythrough the TRβ1 promoter, although other intermediary moleculesmay be involved.

Other functions proposed for T3 in the placenta have included stim-ulation of placental lactogen production as demonstrated in normaltrophoblast (Stephanou and Handwerger, 1995) and BeWo cell cul-tures (Nickel and Cattini, 1991) and increased progesterone produc-tion by early placental tissue (Maruo et al., 1991). Therefore,repression of TRβ1 expression may lead to loss of T3-regulated func-tions in the trophoblast. Additionally, any loss of TRβ1 would allowits promiscuous partners RXR and RAR (Stephanou and Handwerger,1995) to interact with retinoid-induced genes such as the two pro-apoptotic genes, retinoic acid induced 3 (RAIG3/GPRC5C) andCARP-1, the expression of which were both found to be up-regulatedby Δ9-THC in this study. If caused in vivo, this may manifest asincreased apoptosis, and hence compromised placental growth andfunction in patients.

Figure 5. Dose effect of Δ9-tetrahydrocannabinol (Δ9-THC) on HDAC3 (A)and TRβ1 (B) mRNA transcript levels. Cell extracts from BeWo cells treatedwith the indicated concentrations of Δ9-THC were subjected to RT–PCR andamplicon levels corrected for input GAPDH levels. Data are normalized to thecontrol (0) levels. Data are mean ± SEM of five experiments performed in tripli-cate. *P < 0.05, **P < 0.01 one-way ANOVA with Tukey’s HSD test.



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Table III. Classification of differentially expressed genes and their putative functions

Putative classification and ID Accession number Fold change % Change

Apoptosis and growth genesDAPK-interacting protein 1 NM_020774 3.58 258Sperm-associated antigen 9 AK023512 2.99 199Kinase interacting with leukemia-associated gene (stathmin) AW173222 2.95 195SMART/HDAC1-associated repressor protein AL524033 2.87 187Mortality factor 4 like 2 AI700608 2.67 167Insulin-like growth factor 1 receptor H05812 2.34 134Hypothetical protein FLJ14624 BC018707 2.32 132Cyclin-dependent kinase (CDC2-like) 11 AA994004 2.25 125Cell division cycle and apoptosis regulator 1 W73136 2.19 119TRK-fused gene AI908188 2.15 115Splicing factor, arginine/serine-rich 2, interacting protein W084759 2.06 106M-phase phosphoprotein, mpp8 BF678375 2.03 103Hypothetical protein FLJ11362 (similar to BCL-6 interacting corepressor isoform 1) NM_021946 2.01 101Splicing factor, arginine/serine-rich 2, interacting protein AW084759 2.06 106Suppressor of Ty 3 homolog (S. cerevisiae) AF064804 −89.12 98.9

Transcription factorsHypothetical protein F23149_1 NM_019088 137.09 —Sequestosome 1 AW293441 3.44 244Tubby-like protein 4 H15278 3.19 219Zinc finger protein 294 AK023499 3.04 204Nuclear factor of activated T-cells 5, tonicity-responsive NM_006599 2.77 177Bobby sox homolog (Drosophila) BF448315 2.59 159Myeloid/lymphoid or mixed-lineage leukemia AF272384 2.44 144Zinc finger, CCHC domain containing 2 BE676543 2.41 141Hypothetical protein LOC200933 BF590021 2.21 121Zinc finger protein 21 (KOX 14) T67481 2.18 118CTD-binding SR-like protein rA9 BC004950 2.16 116Zinc finger protein 198 AL136621 2.14 114Zinc finger protein 91 homolog (mouse) AA758013 2.09 109Ubinuclein 1 T70262 2.04 104Basic transcription element binding protein 1 AI690205 2.03 103Nuclear receptor subfamily 1, group D, member 2 N32859 2.01 101Hypothetical protein LOC220929 AI563915 −2.49 59.9Thyroid hormone receptor, beta BF431989 −2.79 64.2TWIST neighbour AA400421 −22.89 95.6

Transcription co-regulatorsPRO1073 protein (Histone deacetylase 3/HDAC3) AL037917 2.47 147Hypothetical protein MGC39518 BC039295 3.26 226Metastasis associated in lung adenocarcinoma transcript 1 (MALAT-1/HDAC3) AW005982 3.47 247Serine/arginine repetitive matrix 2 AI655799 3.42 242Myeloid/lymphoid or mixed-lineage leukemia 3 AW137099 2.87 187Ankyrin repeat domain 12 X80821 2.78 178Homeodomain interacting protein kinase 1 AI393355 2.39 139Transcriptional coactivator tubedown-100 NM_025085 2.23 123Nuclear receptor coactivator 3 NM_006534 2.11 111Hypothetical protein FLJ25778 AW967956 2.10 110Topoisomerase (DNA) II alpha 170 kDa AU159942 2.05 105

RNA processing and translationtRNA nucleotidyl transferase, CCA-adding, 1 BC005184 322.97 —Tumour rejection antigen (gp96) 1 AI684643 3.38 238Eukaryotic translation initiation factor 2C, 1 AW071829 2.86 186Hypothetical protein FLJ14281 BF590675 2.62 162Eukaryotic translation initiation factor 2C, 2 AI832074 2.58 158Poly(rC)-binding protein 2 AW103422 2.58 158Tetratricopeptide repeat domain 3 AI652848 2.56 156U2-associated SR140 protein BE464843 2.44 144Natural killer-tumour recognition sequence AI688640 2.44 144Spinocerebellar ataxia 1 (ataxin 1) AW235612 2.39 139Full length insert cDNA YQ11E04 AI076351 2.32 132DnaJ (Hsp40) homolog, subfamily C, member 3 AL119957 2.27 127Golgi apparatus protein 1 NM_012201 2.25 125Quaking homolog, KH domain RNA binding (mouse) AA935633 2.23 123RNA-binding motif protein 25 BE466128 2.22 122NP220 nuclear protein AI308174 2.21 121Hypothetical protein MGC12103 BE328312 2.20 120RNA-binding motif protein 4 NM_002896 2.20 120Ribosome-binding protein 1 homolog 180 kDa (dog) BE646396 2.13 113Kinase interacting with leukemia-associated gene (stathmin) AI249980 2.12 112Protein kinase, interferon-inducible double stranded RNA dependent activator AA279462 2.11 111Aldehyde dehydrogenase 6 family, member A1 AF130089 2.03 103



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Table III. Continued


Ribosomal protein L7 AL031577 −4.07 75.4RNA, U3 small nucleolar interacting protein 2 NM_004704 −69.80 98.6

Protein traffickingTranslocated promoter region (to activated MET oncogene) BF110993 2.93 193Sorting nexin family member 27 NM_030918 2.20 120Vesicle-associated membrane protein 4 NM_003762 −2.02 50.5Intersectin 2 AF182198 −2.80 64.3Dynamin-binding protein AB023227 −3.15 68.3

Structural and morphogenesis proteinsTrophoblast-derived non-coding RNA (plectin) AU155361 5.23 423Myosin, heavy polypeptide 10, non-muscle AI382123 2.85 185PTPRF interacting protein, binding protein 1 (liprin beta 1) AI962377 2.64 164Protein phosphatase 1, regulatory (inhibitor) subunit 12 A BE737620 2.42 142Caldesmon 1 AU145225 2.26 126Serine/threonine protein kinase TAO1 homolog (microtubule affinity regulating kinase kinase) AB037782 2.07 107

Membrane proteins, ion channels, transducersSolute carrier family 40 (iron-regulated transporter), member 1 AU156956 10.17 917Intracellular hyaluronic acid-binding protein 4 AK024886 3.37 237Protein tyrosine phosphatase, receptor type, F AU158443 2.56 156Sideroflexin 1 AA960991 2.45 145Casein kinase 1 alpha 1 AI377389 2.44 144Myozenin 2 AI475544 2.40 140Dual-specificity tyrosine-(Y)-phosphorylation-regulated kinase 2 NM_006482 2.36 136Fetal Alzheimer antigen NM_004459 2.33 133KIAA0152 gene product BC000371 2.30 130Guanine nucleotide-binding protein (G protein), beta polypeptide 4 AW504458 2.28 128Rap guanine nucleotide exchange factor (GEF) 6 BF677986 2.27 127v-Ki-ras2 Kirsten rat sarcoma 2 viral oncogene homolog BF673699 2.24 124Activin A receptor, type IIB NM_001106 2.24 124Polymerase (DNA directed), eta AW665155 2.22 122TBC1 domain family, member 8 (with GRAM domain) AI034387 2.16 116Protein kinase C-like 2 AI633689 2.14 114Solute carrier family 4, sodium bicarbonate cotransporter, member 7 NM_003615 2.13 113Small glutamine-rich tetratricopeptide repeat (TPR)-containing, beta BE671098 2.11 111Leucyl/cystinyl aminopeptidase (oxytocinase) AA767440 2.06 106Putative NFkB-activating protein HNLF AI472339 2.01 101Transmembrane protein with EGF-like and two follistatin-like domains 2 AB004064 −2.07 52.0Solute carrier family 39 (zinc transporter), member 14 BC015770 −2.64 62.1Retinoic acid induced 3 AA156240 −3.80 73.7

Lipid metabolismN-acyl-phosphatidylethanolamine-hydrolyzing phospholipase D W01715 −2.66 62.4Homo sapiens cDNA: FLJ23200 fis, clone KAIA38871 (uncharacterized membrane bound lipoprotein lipase)

AK026853 −2.57 61.1

Hypothetical proteinsHypothetical gene supported by AK091718 (LOC401504), mRNA AA875998 3.38 238Hypothetical protein LOC144871 BG913589 3.21 221Clone IMAGE:5270855, mRNA BC040602 3.14 214HBxAg transactivated protein 2 BE729523 2.80 180Hypothetical protein FLJ21924 AW195525 2.69 169Hypothetical protein FLJ13456 N21008 2.68 168MRNA; cDNA DKFZp586E2317 (from clone DKFZp586E2317) AL117451 2.58 158FLJ35934 protein BC033201 2.49 149KIAA0776 AW298092 2.42 142Expressed in hematopoietic cells, heart, liver AB019490 2.39 139Hypothetical protein FLJ11273 AV705186 2.36 136Transcribed sequences BF508843 2.35 135Hypothetical protein FLJ10287 AK001149 2.31 131CDNA FLJ42565 fis, clone BRACE3007472 AI478268 2.27 127Adult retina protein AI307750 2.24 124Hypothetical protein FLJ22557 AU144048 2.23 123Hypothetical protein FLJ10613 NM_019067 2.14 114LOC392730 (LOC392730), mRNA AK021477 2.13 113Cartilage-associated protein BC008745 2.06 106KIAA0888 protein AB020695 2.03 103CDNA FLJ12005 fis, clone HEMBB1001565 AK022067 2.01 101Hypothetical protein LOC151658 AI685344 −2.42 58.7

ESTsCDNA FLJ13267 fis, clone OVARC1000964 AW665227 3.69 269Transcribed sequence with weak similarity to protein ref:NP_062553.1 (H.sapiens) hypothetical protein FLJ11267

AV729557 3.29 229



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Of note was the down-regulation in expression of N-acyl-phosphatidylethanolamine-hydrolyzing Phospholipase D, the enzymeresponsible for the production of anandamide, the principal endog-enous cannabinoid with major effects on human reproductive function(Wenger et al., 1999; Habayeb et al., 2002; Maccarrone et al., 2002).As anandamide and the exocannabinoid Δ9-THC exhibit differentaffinities for the cannabinoid and vanilloid receptors, in vivo exposureto Δ9-THC may further exacerbate its effects by switching from anan-damide to Δ9-THC action. Also of note was the suppression of expressionof an uncharacterized lipoprotein lipase (Garnica and Chan, 1996),because recently it has been suggested that lipoprotein lipase defi-ciency can lead to FGR (Magnusson et al., 2004) or fetal death (Tsaiet al., 2004).

We have identified a restricted alteration in the expression of genesin BeWo cells exposed to Δ9-THC. The alterations in the expressionof a number of characterized genes are consistent with its effects uponthe morphology of cells in culture, whereby the cultures were pre-vented from achieving confluency not through increased syncytialformation or culture involution, but through inhibition of cell prolifer-ation. Because BeWo cells are considered an appropriate model forhuman in vivo trophoblast action and function (Sullivan, 2004) and

similar effects are observed in vivo, these data suggest that Δ9-THCuse during human pregnancy may similarly inhibit trophoblast prolif-eration and that placentae during early development when the cytotro-phoblastic populations predominate may be more sensitive to theadverse effects of marijuana use and lead to a failure to achieve fullplacental development, and hence fetal growth. The lack of cell prolif-eration and migration associated with early placental developmentmight therefore go some way to explain some of the clinical observa-tions of miscarriage and placental abruption associated with marijuanause in early pregnancy and also explain why marijuana use in latepregnancy is not anti-gestational (Conner, 1984; Hatch and Bracken,1986; Zuckerman et al., 1989). Although the regulatory network thatunderlies the temporal control of transcript expression and genefunction remains to be determined, the identification of Δ9-THC-modulated trophoblast transcripts is likely to shed light on the mechanismsunderlying trophoblast response to marijuana use in pregnancy.

AcknowledgementsThe authors thank H. Longland and E. Beighton of the RNA Laboratory, MRCGeneservice, Babraham Bioincubator, Babraham, Cambridge, for the produc-tion of cRNA, hybridization of probes to Affymetrix genechips and the

Table III. Continued

LOC, LocusLink; UG, Unigene; EST, expressed sequence tag; fold 1, mean fold change array 1; median fold change, median fold change for both arrays; % change, % movement from 100%.


Gi:735147/DB_XREF=ye49c09.s1/CLONE=IMAGE:121072/Hs.242998 T96523 3.07 207CDNA FLJ37319 fis, clone BRAMY2018027 AA737437 3.03 203CDNA FLJ31668 fis, clone NT2RI2004916 AI473887 3.03 203Transcribed sequences AA767373 2.94 194gi:11593079/DB_XREF=UI-H-BI4-apg-f-05-0-UI.s1/CLONE=IMAGE:3087488/Hs.137551

BF509781 2.64 164

Transcribed sequences AI569482 2.55 155Hypothetical protein FLJ10618 AW514168 2.51 151Transcribed sequences BF512491 2.50 150Transcribed sequence with weak similarity to protein sp:P39194 (H. sapiens) ALU7_HUMAN Alu subfamily SQ sequence contamination warning entry

H57111 2.46 146

gi:2719066/DB_XREF=zf98g04.s1/CLONE=IMAGE:385014/Hs.140963.0/Weakly similar to ALUC_HUMAN ALU CLASS C WARNING ENTRY !!! (H.sapiens)

AA709148 2.37 137

CDNA FLJ41762 fis, clone IMR322004768 AW968465 2.34 134Transcribed sequences AA007336 2.32 132Transcribed sequence with weak similarity to protein sp:P39191 (H. sapiens) ALU4_HUMAN Alu subfamily SB2 sequence contamination warning entry

BE156563 2.32 132

Transcribed sequences AI656728 2.26 126Hypothetical protein FLJ38348 BG251692 2.16 116Transcribed sequences AI860360 2.13 113Transcribed sequence with weak similarity to protein ref:NP_060312.1 (H. sapiens) hypothetical protein FLJ20489 (H. sapiens)

AW629289 2.13 113

Chromosome 14 open reading frame 106 AW103300 2.13 113KIAA1961 protein AW263086 2.12 112Transcribed sequences AW511239 2.12 112Transcribed sequence with moderate similarity to protein ref:NP_071385.1 (H. sapiens) hypothetical protein FLJ20958 (H. sapiens)

AW382006 2.12 112

Transcribed sequences BE219036 2.09 109Transcribed sequence with weak similarity to protein sp:P39191 (H. sapiens) ALU4_HUMAN Alu subfamily SB2 sequence contamination warning entry

AA579047 2.07 107

Chromosome 6 open reading frame 111 BF591408 2.05 105Gi:10821309/DB_XREF=7k77g10.x1/CLONE=IMAGE:3481554/Hs.202042.0 BF062399 2.04 104Transcribed sequences W86781 2.02 102Transcribed sequences BF508288 2.02 102Transcribed sequence with moderate similarity to protein pir:I60307 (E. coli) I60307 beta-galactosidase, alpha peptide—Escherichia coli

AI823917 2.01 101

Transcribed sequences BF433200 2.01 101Transcribed sequences AW973235 −2.10 52.4Zinc finger protein 506 (possible transcription factor) AI559570 −2.53 60.5gi:11764352/DB_XREF=601655369R1/CLONE=IMAGE:3845872/Hs.295011/Moderately similar to 810024C cytochrome oxidase I (H.sapiens)

BE961949 −2.64 62.1



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production of scanned images and Dr E. Halligan, Genomic InstabilityResearch Group, Department of Cancer Studies and Molecular Medicine, forassistance with the dChip software.

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Submitted on May 25, 2005; resubmitted on December 15, 2005; accepted onMarch 1, 2006



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