gene expression profiling of leiomyoma and myometrial smooth muscle cells in response to...

Gene Expression Profiling of Leiomyoma andMyometrial Smooth Muscle Cells in Response toTransforming Growth Factor-�

Xiaoping Luo, Li Ding, Jingxia Xu, and Nasser Chegini

Department of Obstetrics and Gynecology, University of Florida College of Medicine, Gainesville, Florida 32610

Altered expression of the TGF-� system is recognized to playa central role in various fibrotic disorders, including leiomy-oma. In this study we performed microarray analysis to char-acterize the gene expression profile of leiomyoma andmatched myometrial smooth muscle cells (LSMC and MSMC,respectively) in response to the time-dependent action ofTGF-� and, after pretreatment with TGF-� type II receptor(TGF-�RII) antisense oligomer-blocking/reducing TGF-�autocrine/paracrine actions. Unsupervised and supervisedassessments of the gene expression values with a false dis-covery rate selected at P < 0.001 identified 310 genes as dif-ferentially expressed and regulated in LSMC and MSMC in acell- and time-dependent manner by TGF-�. Pretreatmentwith TGF-�RII antisense resulted in changes in the expres-sion of many of the 310 genes regulated by TGF-�, with 54genes displaying a response to TGF-� treatment. Comparativeanalysis of the gene expression profile in TGF-�RII antisense-and GnRH analog-treated cells indicated that these treat-

ments target the expression of 222 genes in a cell-specificmanner. Gene ontology assigned these genes functions as cellcycle regulators, transcription factors, signal transducers, tis-sue turnover, and apoptosis. We validated the expression andTGF-� time-dependent regulation of IL-11, TGF-�-inducedfactor, TGF-�-inducible early gene response, early growth re-sponse 3, CITED2 (cAMP response element binding protein-binding protein/p300-interacting transactivator with ED-richtail), Nur77, Runx1, Runx2, p27, p57, growth arrest-specific 1,and G protein-coupled receptor kinase 5 in LSMC and MSMCusing real-time PCR. Together, the results provide the firstcomprehensive assessment of the LSMC and MSMC molecularenvironment targeted by autocrine/paracrine action ofTGF-�, highlighting potential involvement of specific geneswhose products may influence the outcome of leiomyomagrowth and fibrotic characteristics by regulating inflamma-tory response, cell growth, apoptosis, and tissue remodeling.(Endocrinology 146: 1097–1118, 2005)

TGF-� IS A multifunctional cytokine and key regulator ofcell growth and differentiation, inflammation, apopto-

sis, and tissue remodeling (1–5). Although under normalphysiological conditions, the expression and autocrine/paracrine actions of TGF-� are highly regulated, alterationsin TGF-� and TGF-� receptor expression and their signalingmechanisms often result in various pathological disorders,including fibrosis (1–5). Leiomyoma is a benign uterine tu-mor characterized by features typical of fibrotic disorder. Wehave previously identified altered expression of TGF-� iso-forms (TGF-�1, -�2, and -�3) and TGF-� receptors (types I,II, and III) in leiomyoma and their isolated smooth musclecells (LSMC) compared with normal myometrium (6–9). Re-cently, we also demonstrated that leiomyoma and LSMCexpress elevated levels of Smads, components of the TGF-�receptor signaling pathway, compared with myometrium

and myometrial smooth muscle cells (MSMC) (9, 10). TGF-�regulates its own expression and the expression of Smad inLSMC and MSMC, and through downstream signaling fromthis and MAPK pathways regulates the expression of c-Fos,c-Jun, fibronectin, collagen, and plasminogen activator in-hibitor 1 in these cells (7, 8, 11). Additionally, data from ourlaboratory and others have demonstrated the ability ofTGF-� to regulate LSMC and MSMC cell growth (12–15).

Because leiomyoma growth is dependent on ovariansteroids, GnRH analog (GnRHa) therapy and, most re-cently, selective estrogen and progesterone receptor mod-ulators have been used for their medical management. Wedemonstrated that GnRHa therapy markedly down-reg-ulates TGF-� and TGF-� receptor expression and alters theexpression and activation of Smads in leiomyoma as wellas LSMC (6, 8, 9). We have also shown that TGF-� ex-pression in LSMC and MSMC is inversely regulated byovarian steroids compared with their antagonists, ICI-182780, ZK98299, and RU486 (8). In addition, we haveshown that other cytokines, such as granulocyte-macroph-age colony-stimulating factor (GM-CSF), IL-13, and IL-15,which promote myofibroblast transition, granulation tis-sue formation, and inflammatory response, respectively,may mediate their actions either directly or indirectlythrough induction of TGF-� expression in LSMC andMSMC (7, 9, 16). From these observations we proposedthat the TGF-� system serves as a major autocrine/para-crine regulator of fibrosis in leiomyoma (6 –12, 17). Wehave provided evidence reflecting the molecular environ-

First Published Online December 16, 2004Abbreviations: ALK, Activin receptor-like kinase; CDK, cyclin-de-

pendent kinase; CITED2, cAMP response element binding protein-bind-ing protein/p300-interacting transactivator with ED-rich tail; ECM, ex-tracellular matrix; EGR3, early growth response 3; Gas1, growth arrest-specific 1; GM-CSF, granulocyte-macrophage colony-stimulating factor;GnRHa, GnRH analog; GPRK, G protein-coupled receptor kinase;LSMC, leiomyoma smooth muscle cell; MMP, matrix metalloprotease;MSMC, myometrial smooth muscle cell; TGF-�RII, TGF-� type II re-ceptor; TGIF, TGF-�-induced factor; TIEG, TGF-�-inducible early generesponse.Endocrinology is published monthly by The Endocrine Society (http://www.endo-society.org), the foremost professional society serving theendocrine community.

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ments directed by GnRHa therapy in leiomyoma and myo-metrium as well as by GnRHa direct action in LSMC andMSMC (18, 19). In the present study we evaluated theunderlying differences between molecular responses di-rected by TGF-� autocrine/paracrine actions in LSMC andMSMC and after interference with these actions usingTGF-� type II receptor (TGF-�RII) antisense oligomertreatment. Because TGF-� receptor expression is targetedby GnRHa in leiomyoma and myometrium, we also eval-uated the gene expression profiles in response to TGF-�RIIantisense treatment and GnRHa-treated LSMC and MSMCto identify the genes whose expression are the specifictargets of these treatments. Using this approach we iden-tified several differentially expressed and regulated genestargeted by TGF-� and validated the expression of 12genes in LSMC and MSMC in response to the time-de-pendent action of TGF-� using real-time PCR.

Materials and Methods

All materials used for this study, including isolation of leiomyomaand myometrial cells, are identical and were previously described indetail (19). Prior approval was obtained from the University of Floridainstitutional review board for the experimental protocol of this study.

To determine the effect of TGF-�1 on global gene expression in LSMCand MSMC, the cells were cultured in six-well plates at an approximatedensity of 106 cells/well in DMEM-supplemented medium containing10% fetal bovine serum. After reaching visual confluence, often after 2–3d, the cells were washed in serum-free medium and incubated for 24 hunder serum-free, phenol red-free conditions (10, 11). The cells werethen treated with 2.5 ng/ml TGF-�1 (R&D System, Inc., Minneapolis,MN) for 2, 6, and 12 h. To further profile the autocrine/paracrine actionof TGF-�1 on gene expression in LSMC and MSMC, the cells werecultured as described above and treated with 1 �m TGF-�RII antisenseor sense oligonucleotides for 24 h as previously described (10, 11). Thecells were washed and then treated with TGF-�1 (2.5 ng/ml) for 2 h.Parallel experiments using untreated cells were used as controls, in-cluding an additional control for TGF-�RII antisense and senseexperiments.

Total cellular RNA was isolated from LSMC- and MSMC-treatedand untreated controls and subjected to microarray analysis; a de-tailed description of all procedures was previously provided (19). Tomaintain standards and allow for comparative analysis, GeneChipsin this study were used and simultaneously processed, and their geneexpression values were subjected to global normalization and trans-formation with the GeneChips used in the other study (19). Afterthese unsupervised assessments, the coefficient of variation was cal-culated for each probe set across all chips used in this and the otherstudy (19), and the selected gene expression values of this study wereindependently subjected to supervised learning, including statisticalanalysis in R programming environment and ANOVA with falsediscovery rate selected at P � 0.001 (19). The genes identified in thesecohorts were analyzed for functional annotation and visualized usingDatabase for Annotation, Visualization, and Integrated Discovery(DAVID) software with integrated GoCharts as described in detailpreviously (19). After the analysis, we selected 12 of the differentiallyexpressed and regulated genes, including 10 identified and validatedin leiomyoma and myometrium from untreated and GnRHa-treatedcohorts as well as LSMC and MSMC treated in vitro with GnRHa (19),for validation in response to TGF-� time-dependent action usingreal-time PCR. They include IL-11, EGR3 (early growth response 3),TIEG (TGF-�-inducible early gene response), TGIF (TGF-�-inducedfactor), CITED2 (cAMP response element binding protein-bindingprotein/p300-interacting transactivator with ED-rich tail), Nur77,p27, p57, Gas-1 (growth arrest-specific 1), and GPRK5 (G protein-coupled receptor kinase). In addition, the expression of Runx1 andRunx2, transcription factors that interact with TGF-� receptor sig-naling pathways (20), was validated in LSMC and MSMC in responseto TGF-� and GnRHa action as well as in leiomyoma and myome-

trium from GnRHa-treated and untreated cohorts. Detail descriptionsof the materials and methods for real-time PCR as well as dataanalysis were provided in the previous study (11, 19).

ResultsGene expression profiles of leiomyoma and matchedmyometrium cells in response to TGF-�1

In this study we performed microarray analysis to fur-ther characterize the molecular environment of LSMC andMSMC directed by TGF-� autocrine/paracrine actions.Using the same cell preparations and culture conditions asthose described to study GnRHa action (19), LSMC andMSMC were treated with TGF-�1 (2.5 ng/ml) for 2, 6, and12 h, and total RNA was isolated and subjected to mi-croarray analysis. After unsupervised and supervisedlearning of array data (19), the gene expression values forthis study were independently subjected to statistical anal-ysis in R programming and ANOVA. At a false discoveryrate selected at P � 0.001, we identified 310 genes, or 2.46%of the genes on the array, as differentially expressed andregulated in response to the time-dependent action ofTGF-� in LSMC and MSMC. As illustrated in Fig. 1, hi-erarchical clustering analysis separated these genes intodistinctive clusters with sufficient difference in their pat-terns to allow each cohort to cluster into their respectivesubgroup. The genes were separated into five clusters inresponse to the time-dependent action of TGF-� in LSMCand MSMC, with genes in clusters A and B displaying alate response, genes in cluster D displaying an early re-sponse, and genes in clusters C and E showing biphasicregulatory behaviors (Fig. 1). Additional analysis of thevariance-normalized mean gene expression values di-vided the genes into six clusters, each displaying a dif-ferent level of response to time-dependent action of TGF-�,with overlapping behavior between LSMC and MSMC with theexception of genes in clusters E and F (Fig. 2).

Comparative analysis of gene expression profiles of LSMCand MSMC in response to TGF-� action with their corre-sponding leiomyoma and myometrium (tissues) from theuntreated group (19) revealed a substantial variabilityamong their profiles (data not shown). However, gene on-tology assessment and division into functional categoriesindicated that the majority of the genes (60–70%) are in-volved in transcriptional regulation and metabolism, cellcycle regulation, extracellular matrix and adhesion mole-cules, and signal transduction and transcription factors (19)(Fig. 3). The time-dependent action of TGF-� on the expres-sion profile of a selective group of these genes in the aboveclusters representing transcription factors, growth factors,cytokines, signal transduction pathways, extracellular ma-trix (ECM)/adhesion molecules, etc., in LSMC and MSMCare presented in Fig. 4. As illustrated, the expressions of thesegenes are regulated by TGF-�1 in LSMC and MSMC, dis-playing both overlapping and differential patterns of expres-sion. The profile of IL-11 was not included in the growthfactor/cytokine group in LSMC and MSMC, because therelative expression values were too high for graphic presen-tation (see Fig. 7).

1098 Endocrinology, March 2005, 146(3):1097–1118 Luo et al. • Gene Expression Profiling in Response to TGF-�


FIG. 1. Hierarchical clustering analy-sis of 310 genes identified in LSMC (f)and MSMC (m) in response to TGF-�1(2.5 ng/ml) treatment for 2, 6, and 12 hor in the untreated control (C). Thegenes were identified after unsuper-vised and supervised analyses of the ex-pression values, statistical analysis inthe R programming environment, andANOVA with a false discovery rate se-lected at P � 0.001. Each column rep-resents data from a single time pointusing two independent cell cultures,with shades of red and green indicatingup- or down-regulation of a given geneaccording to the color scheme shown be-low. Genes represented by rows wereclustered according to their similaritiesin expression patterns for each treat-ment and cell type. The dendrogramshowing similarity of gene expressionamong the treatments/cells is shown ontop of the overview image, and related-ness of the arrays is denoted by the dis-tance to the node linking the arrays.The gene tree shown at the left of theimage corresponds to the degree of sim-ilarity (Pearson correlation) of the pat-tern of expression for genes across theexperiments. The clustering depictsfive groups of genes, designated A–E,and their zoomed images are presentedin A–E. Genes that appear more thanonce are represented by multiple cloneson arrays. (Figure continues on nextpage.)

Luo et al. • Gene Expression Profiling in Response to TGF-� Endocrinology, March 2005, 146(3):1097–1118 1099


Gene expression profiles of LSMC and MSMC in responseto TGF-� after pretreatment with TGF-�RII antisense

To further evaluate the autocrine/paracrine action ofTGF-� in leiomyoma and myometrial microenvironments,LSMC and MSMC were pretreated with TGF-�RII anti-sense oligomers to block/reduce TGF-� receptor signal-

ing. After pretreatments, the cells were treated with orwithout TGF-� for 2 h, and their total RNA was subjectedto microarray analysis. Based on the same data analysisdescribed above with a false discovery rate of P � 0.001,we identified 54 genes whose expression was targeted byTGF-�1 (2.5 ng/ml for 2 h) in LSMC and MSMC after

FIG. 1. Continued



pretreatment with TGF-�RII antisense compared with the310 genes regulated by TGF-�. Interestingly, pretreatmentwith TGF-�RII antisense blocked or altered the expressionof many genes known to be the target of TGF-� action,including those validated in our study. Hierarchical clus-ter analysis distinctively separated these genes into three

clusters, with each cohort separated into their respectivesubgroups (Fig. 5). The genes in clusters A and C displayeddifferent responses to TGF-�RII antisense pretreatment,whereas genes in cluster B showed overlapping behaviorin LSMC and MSMC (Fig. 5). However, there was anoverlapping pattern between the gene expression profiles

FIG. 2. A k-means clustering analysis of genes in LSMC (f) and MSMC (m) in response to time-dependent action of TGF-�, as described in Fig.1. The gene expression values in these cohorts were combined and subjected to k-means clustering that grouped the genes into six clusters (A–F)based on similarity of expression over the three time points and an untreated control. The rows represent the genes, and the columns representthe samples, with shades of red and green indicating up- or down-regulation of a given gene; genes are clustered according to their similaritiesin expression patterns. The line graphs display the SD from the mean (y-axis) for each cluster in MSMC and LSMC in response to TGF-�time-dependent action for 2, 6, and 12 h (x-axis) compared with the untreated control (Ctrl).



in TGF-�RII sense- and antisense-treated cells that couldbe due to the inability of antisense pretreatment to blockall of the combined actions of autocrine/paracrine andexogenously added TGF-�. Gene ontology and divisioninto similar functional categories indicated that the ma-jority of these genes are involved in transcriptional reg-ulation and metabolism, cell cycle regulation, extracellularmatrix and adhesion molecules, and transcription factors(Figs. 3 and 4).

Comparative analysis of gene expression profiles in responseto TGF-�RII antisense and GnRHa treatments in LSMCand MSMC

Because GnRHa alters the expression of TGF-� and TGF-�receptors in leiomyoma and myometrium as well as in LSMCand MSMC, we compared the gene expression profile ofTGF-�RII antisense-treated vs. GnRHa-treated LSMC andMSMC, searching for common genes whose expressions are

affected by these treatments. Based on the same data analysisdescribed above with a false discovery rate selected at P �0.001, we identified 222 genes as differentially expressed andregulated in LSMC and MSMC in response to TGF-�RIIantisense- and GnRHa-treated cells (19) (Tables 1 and 2).Hierarchical clustering analysis separated these genes intofour clusters displaying different patterns of regulation, al-lowing their separation into respective subgroups (Fig. 6).The genes in clusters A, B, and D displayed different re-sponses to TGF-�RII antisense and GnRHa treatments, withgenes in cluster C showing overlapping behavior in LSMCand MSMC (Fig. 6).

Verification of gene transcripts in TGF-�-treated LSMCand MSMC

Using real-time PCR, we validated the expression of 12genes in response to time-dependent action of TGF-� inLSMC and MSMC (Figs. 7 and 8). They include IL-11, TIEG,TGIF, EGR3, CITED2, Nur77, p27, p57, GAS-1, and GPRK5,

FIG. 3. Gene ontology assessment anddivision of genes identified in LSMCand MSMC in response to TGF-� treat-ment into similar functional categoriesillustrated as bar graphs, with the per-centage of the total number of genes ineach group shown in the front of eachbar. A and B, Gene ontology assessmentfor LSMC and MSMC treated withTGF-� (A) and pretreatment with TGF-�RII antisense (B) for 24 h, followed byTGF-� treatment as indicated in Mate-rials and Methods.



whose expression was also validated in leiomyoma andmatched myometrium from untreated and GnRHa-treatedcohorts as well as in LSMC and MSMC treated in vitro withGnRHa as previously described (19). In addition, we verifiedthe expression of Runx1 and Runx2 in leiomyoma and myo-metrium as well as in LSMC and MSMC in response totime-dependent actions of TGF-� and GnRHa (Fig. 8). As

illustrated, TGF-�, in a time-dependent manner, differen-tially regulated the expression of these genes in LSMC andMSMC, with a pattern of expression displaying significantoverlap between real-time PCR and microarray analysis(Figs. 7 and 8). However, the expression values of GPRK5and Runx2 genes in microarray analysis of LSMC and MSMCand of Runx2 in leiomyoma and myometrium did not meet

FIG. 4. The expression profile of a group of genes representing transcription factors (row 1), growth factors/cytokines/polypeptide hormones/receptors (row 3), intracellular signal transduction pathways (rows 3 and 4), cell cycle (row 5), oncogenes/tumor suppressers (row 6), and celladhesion/ECM/cytoskeletons (row 7) in response to the time-dependent action of TGF-� in LSMC and MSMC. Values on the y-axis representan arbitrary unit derived from the mean gene expression value for each factor after supervised analysis, statistical analysis in R programmingenvironment, and ANOVA as described in Fig. 1, with gene expression values for the untreated controls (Ctrl) set at 1. (Figure continues onnext page.)



FIG. 4. Continued



the standard of analysis used in our study. However, Runx2mRNA was detectable by real-time PCR in leiomyoma andmyometrium from untreated and GnRHa-treated tissues atlow levels compared with Runx1, and GnRHa therapyseemed to increase its expression in these tissues (Fig. 8). Theexpression of Runx1 and Runx2 not only was the target ofTGF-� regulatory action, but was also regulated by GnRHain a cell- and time-dependent manner in LSMC and MSMCin vitro (Fig. 8).

We verified the expression of IL-11, TIEG, TGIF, EGR3,CITED2, Nur77, p27, p57, and Gas-1 by Western blotting andtheir cellular distribution using immunohistochemistry inleiomyoma and myometrium (19). These findings provideadditional support for real-time PCR data showing that theproducts of these genes are expressed in leiomyoma andmyometrium. We are currently investigating time- and dose-dependent regulation of these genes in response to TGF-�.

Discussion

By extending our previous work on the role of TGF-� inleiomyoma, in the present study we provided the first ex-ample of gene expression fingerprints of LSMC and MSMCin response to TGF-� action. We also characterized the mo-lecular environment of these cells after pretreatment withTGF-� type IIR (TGF-�RII) antisense as a tool to interferewith the autocrine/paracrine action of TGF-� isoforms,

and comparatively assessed their expression profiles withGnRHa-treated cells, which also inhibits TGF-� receptor ex-pression in these cells (6, 8). Since the aim of our study wasto capture the early and late autocrine/paracrine action ofTGF-� in these cells, we selected a treatment strategy basedon our previous observations reflecting TGF-� time-depen-dent regulation of c-Fos, c-Jun, fibronectin, collagen, andplasminogen activator inhibitor-1 expression (11). Promotersof these genes are known to contain TGF-� response ele-ments (20, 21) and are regulated in LSMC and MSMCthrough TGF-� receptor activation of Smad and MAPK path-ways (3, 10, 11). Our study design is also consistent withother microarray studies profiling gene expression in re-sponse to TGF-� action in human dermal fibroblasts, theHaCaT keratinocyte cell line, and NMuMG, a mouse mam-mary gland epithelial cell line, in which the cells were treatedfor 1, 2, 6, and 24 h, displaying a Smad-mediated regulationof selected number of genes (22–24).

Cluster and Tree-View analysis revealed a considerablesimilarity in overall gene expression patterns between LSMCand MSMC in response to TGF-� action, with sufficient dif-ference allowing their separation into respective subgroups.The genes in these clusters displayed different regulatoryresponse to TGF-� action in a cell- and time-specific manner,with genes in clusters A and B displaying a late response,genes in cluster D displaying early responsiveness, and

FIG. 5. Hierarchical clustering analysis of gene expression values in untreated and TGF-�-treated LSMC and MSMC after pretreatmentwith TGF-�RII antisense or sense oligomers. The cells were cultured in serum-free, phenol-red free medium for 24 h, washed, and treated withTGF-�RII antisense or sense oligomers for an additional 24 h. The cells were then washed and treated with TGF-� (2.5 ng/ml) for 2 h, withuntreated cells serving as controls. Supervised analysis of the gene expression values and statistical analysis in R programming and ANOVAidentified 54 genes at a false discovery rate of rate of P � 0.001, with expression levels discriminated among the treatment groups and theuntreated control. Each column represents data from a single time point using two independent cell cultures (f314 and f316 for LSMC and m314and m316 for MSMC), with shades of red and green indicating up- or down-regulation of a given gene according to the color scheme shown below.Genes represented by rows were clustered according to their similarities in expression patterns for each treatment and cell type. The dendrogramshowing similarity of gene expression among the treatments/cells is shown on top of the overview image, and relatedness of the arrays is denotedby the distance to the node linking the arrays. The gene tree shown at the left of the image corresponds to the degree of similarity (Pearsoncorrelation) of the pattern of expression for genes across the experiments. The clustering depicts three groups of genes (A–C), and their zoomedimages are presented in A–C. Genes appearing more than once are represented by multiple clones on arrays.



TABLE 1. Categorical list of differentially expressed genes in LSMC treated with GnRHa (2–12 h) compared with TGF-� type II receptorantisense-pretreated cells

Gene functionGnRHa 2 h vs. TGF-�RII

antisense (P � 0.001)GnRHa 6 h vs. TGF-�RII


antisense (P � 0.001)

Accession no. Symbol Accession no. Symbol Accession no. Symbol

Growth factor and chemokine AL833597 CSF2RA � AF056979 IFNGR1 � BC008816 CCBP2 �receptors/hormone receptors AY449732 PTHR1 � AJ271736 IL9R � AL833597 CSF2RA �

AB023192 NISCH � AF245699 AGTR1 � AB004066 BHLHB2 �AK130009 FRZB � AF004230 LILRB1 � AB017498 LRP5 �AC004663 NOTCH3 � AB084454 ANGPT1 � BC000125 TGFB1 �AB084454 ANGPT1 � AC004634 DTR � AB020410 SHH �AB005060 NRG2 � AC005369 IK � AF052181 EPIM �AC005369 IK � AJ238420 PDGFA � BC001429 ANXA5 �AJ238420 PDGFA � BC000125 TGFB1 � AB014887 ORM1 �BC000125 TGFB1 � AL031659 GHRH � IGF I �AF003521 JAG2 � AB014887 ORM1 �

Intracellular transducers/ AB014764 COPS7A � AK022142 GAB1 � AB014605 AIP1 �modulators/kinase/protein AB015718 STK10 � BC016618 LCP2 � AF254253 PHKG1 �phosphatases/GTP/GDP BC014243 TYK2 � AF055581 LNK � AK091875 PPP2CB �exchangers and G-protein BC012085 STK38 � AF345987 PRKCG � AI800682 PTPN21 �GTPase modulators BC002438 RAB4A � NM_005491 CXorf6 � BC011645 RRAD �

AF070597 GNB1 � AF119836 RAB6A � AF103905 EPAC �BC002829 S100A2 � AF493902 GNA13 � AF144713 GDI2 �AK022697 CBARA1 � AF055026 RPIP8 � NM_000855 GUCY1A2 �BC015800 DXYS155E � AB014560 G3BP2 � AK026690 MADH3 �AF168956 APLP2 � BC009050 BTG1 �

AF085357 FLOT1 �BC047502 PPP1R3D �AF220656 PHLDA1 �AF027964 MADH2 �

Transcription/transcription BC028412 ELL2 � NM_005593 MYF5 � NM_005593 MYF5 �activators and repressor/RNA D10570 RUNX1 � AF055077 ZNF42 � AB017335 MAZ �polymerase AF139463 EGR2 � AF060865 ZNF205 � D10570 RUNX1 �

AB004066 BHLHB2 � BC012088 TAF10 � AF139463 EGR2 �AF064804 SUPT3H � AF139463 EGR2 � N66802 EGR3 �AK025458 NUCB1 � N66802 EGR3 � AF050110 TIEG �AA846273 PRCC � AF000670 ELF4 � AJ000051 SF1 �AL031228 RING1 � AF050110 TIEG � AF169253 GATA2 �BC030810 ZNF230 � AB004066 BHLHB2 � AF264785 HES1 �AF080561 RBM14 � AF169253 GATA2 � BC003151 TCFL1 �

AF264785 HES1 � BC009610 PC4 �M95929 PRRX1 � AB030905 CBX3 �BC002609 CBX1 � AK096917 SREBF2 �AF019413 RDBP � AF019413 RDBP �

Cell cycle proteins BI547129 ZW10 � AF007140 ILF3 � BC032518 CCNG2 �AF213700 CDKN1B � GAS1 �

AF088046 DNAJA2 �

Oncogenes and tumor suppressors BC002646 JUN � AB025247 MAFF � NM_005354 JUND �AF508978 MTA1 � AF508978 MTA1 � AB025247 MAFF �

AF083633 EXTL1 � AF508978 MTA1 �

Cell adhesion receptors/matrix BC020740 SGCD � BC020740 SGCD � AU130185 CDH6 �adhesion receptors/cell-cell AK125542 SRPX � BC026329 GJA1 � AJ011497 CLDN7 �adhesion receptors AF136185 COL17A1 �

L25851 ITGAE �

Extracellular matrix proteins AC002366 AMELX � AA053720 EDIL3 � BC005159 COL6A1 �BC014410 EFEMP1 � AC002366 AMELX �AI290070 THBS1 � AF019413 TNXB �

AF018081 COL18A1 �

Apoptosis-associated proteins AB015051 DAXX � AB051763 POR �AF111344 CASP10 �AF022224 BAG1 �BC019307 BCL2L1 �AB051763 POR �AF002697 BNIP3 �

DNA replication/DNA damage/ AY032603 MCM3 � AF087143 TOP2B �repair proteins and DNA ligases AK092872 ERCC2 � BC001122 MSH2 �



TABLE 1. Continued






AK098615 CRY1 � AK098588 APEX1 �AK098615 CRY1 �

Metabolism AK130664 MTHFD2 � AC006020 AASS � AF025439 PKM2 �BC035837 HAS1 � AK130664 MTHFD2 � AF047181 NDUFB5 �AL831821 ACADSB � D12775 AMPD3 � M12271 ADH1A �AJ251501 GAD2 � AB062290 TYMS � AF038954 ATP6V1G1 �AK000379 ASNS � AK055119 PDK2 � BC001390 QP-C �AK094782 GLUD1 � AB029821 PEMT �AF044206 PTGS2 � BC000171 AMD1 �

AC000051 GGT1 �

Cytoskeleton/motility/ AF081484 K-ALPHA-1 � AB062484 CALD1 � BC003576 ACTN1 �intermediate filament BC009971 KRTHA3B � AB020673 MYH11 � BC039596 DNM2 �

AY117678 TPT1 � AY351902 IQGAP2 � AB025186 MAPRE3 �AB025186 MAPRE3 �

Translation factors AC004976 GARS � AF174496 EEF1A1 � AF134726 VARS2 �AF086557 RPL10A �AB007147 RPS2 �AF208852 EIF4A2 �

RNA processing, turnover, andtransport AB061546 SRP14 � AF391283 SSA1 � AK023529 PCBP2 �

BC003608 RBPMS � AB017018 HNRPDL � BC004534 SFPQ �AF000177 LSM1 � AK097315 SF3B4 �

BC003608 RBPMS �

Chromatin proteins AK128741 CHD4 � AC005943 MBD3 �AF002713 CENPB �AC005943 MBD3 �

Drug resistance proteins AB005754 POLS � AK025459 TRA1 � AB005754 POLS �

Cell surface antigens AF038953 ITM2A �

Protein turnover AB003730 UBC � AF062536 CUL1 � AJ006267 CLPX �AJ313463 DF � AB003730 UBC � AF019413 BF �

AF013611 CTSW �BC005927 SERPINE1 �

Nuclear receptors AF109134 OGFR � BC028038 PTPRD �

Facilitated diffusion proteins/ AF061809 KRT16 � AF052693 GJB5 � BC000165 VDAC2 �symporters and antiporters AY142112 SLC4A3 � AF001787 UCP3 � AB001328 SLC15A1 �

AC004000 SLC25A5 �

Targeting/exocytosis AC006378 BET1 � AF135372 VAMP2 � AF060538 VAMP1 �AL021917 BTN3A3 � BC017263 LMAN2 � AK092006 ANXA2 �

BC000326 COPB2 � AF001434 EHD1 �

Chaperones/protein modification AK097824 HSPA2 � BC001786 FKBP4 � AF042385 PPIE �enzymes AF047432 ARF6 � AF042385 PPIE � BC003384 FKBP2 �

AK096243 RPN2 � AK125499 P5 � AC002481 TUSC4 �AF002224 UBE3A �

Functionally unclassified AF053134 SNCB � AY358917 FSTL3 � AB020680 BAG5 �BC009356 CDC42EP1 � AB029013 WHSC1 � AF070614 SCHIP1 �AB002301 KIAA0303 � AY065346 TNFAIP1 � AY358917 FSTL3 �AF007132 ABHD5 � BC014450 B7 � BC000738 EMD �

AJ005821 DMXL1 �

Not classified AB031083 AKR1C1 � BC000379 UBB � AF125532 MKNK2 �AB014595 CUL4B � AF019225 APOL1 � AB006746 PLSCR1 �AF077204 GTPBP1 � AB014528 KIAA0628 � A24059 PNLIP �AB018266 MATR3 � AB023214 ZBTB1 � AL110214 SFRS6 �AB014528 KIAA0628 � AB023172 CARD8 � AF061261 MBNL2 �AB014610 USP52 � AB007893 KIAA0433 � AC002400 UBPH �AF056022 KATNA1 � AB014581 L3MBTL � AF103884 HB-1 �AB011096 SARM1 � AF211969 LENG4 � D86989 IGL �AK023082 GORASP2 � AJ440721 TXNDC5 � AF057354 MTMR1 �AB014581 L3MBTL � BC005318 MYL1 � AB002308 KIAA0310 �



genes in clusters C and E showing a biphasic regulatorybehavior. These results suggest that the same factors and/ormechanisms coregulate the expression of these genes in eachcluster, possibly due to the presence of common regulatoryelements in their promoters. Whether the expression profileof these genes in LSMC and MSMC respond differently tovarying concentrations of TGF-� or other TGF-� isoforms isnot established. However, the concentration of TGF-� usedin this and our other studies as well as those reported byothers examining the effect of TGF-� on the expression ofother genes (10–13, 22–24) is comparable with level of TGF-�produced by these cells, although LSMC produces moreTGF-�1 than MSMC (7, 8). Moreover, based on the expres-sion profile of TGF-� isoforms in leiomyoma, we have pre-viously proposed that TGF-�1 and TGF-�3 play more criticalroles in leiomyoma (7), and in vitro studies have indicated ahigher growth response to TGF-�1 (Chegini, N., unpublishedobservations) and TGF-�3 in LSMC compared with MSMC(14, 15). However, TGF-� isoforms mediate their actionsthrough TGF-�RII, and alterations in the TGF-� receptorsystem may serve as a more accurate indicator of their overallactions in these and other cell types. We have shown thatleiomyoma overexpresses TGF-�RII compared with myo-metrium (6, 9), and pretreatment of LSMC with TGF-�RIIantisense oligomers and/or neutralizing antibodies pre-vented TGF-� receptor-mediated actions (8, 10).

These observations as well as identification of specificgenes whose expression exhibited sensitivity to pretreatmentwith TGF-�RII antisense, among them genes containingTGF-� regulatory response elements in their promoters, pro-vide additional support for the idea that TGF-� receptorsmediated signaling in regulating the overall expression ofthese genes in LSMC and MSMC and possibly in leiomyomaand myometrium. The lack of response of other TGF-�-tar-geted genes to TGF-�RII antisense pretreatment could be dueto the inability of antisense to block all autocrine/paracrineactions as well as exogenously added TGF-�. However, theexpression of these genes may also be regulated as a conse-

quence of overexpression of TGF-� receptor and/or theiraltered intracellular signaling. Interestingly, activin receptor-like kinases (ALK) ALK1 and ALK5, which serve as TGF-�RIand are activated by TGF-�RII, have been shown to regulatethe expressions of different genes in endothelial cells in re-sponse to TGF-� action (25). We have identified the expres-sion of all components of the TGF-� receptor system, in-cluding ALK5 and Smads in leiomyoma and myometrium aswell as LSMC and MSMC (9, 10). However, TGF-�-mediatedaction through ALK1 could result in the regulation of adifferent set of genes not involving ALK5. In addition, analteration in Smad expression has also been considered toinfluence the outcome of several disorders targeted byTGF-�, including tissue fibrosis (2).

Gene ontology revealed that the majority of the genestargeted in response to TGF-� treatment of LSMC and MSMCare functionally associated with cellular metabolism, cellgrowth regulation (cell cycle and apoptosis), cell and tissuestructure (ECM, adhesion molecules, and microfilaments),signal transduction, and transcription factors. Despite dif-ferences in their expression profiles, there was a substantialdegree of similarity in functional annotation among thegenes identified at tissue (leiomyoma and myometrium) (19)and cellular (LSMC and MSMC) levels in response to TGF-�1. The differences between gene expression profiles at tissueand cellular (LSMC/MSMC) levels in response to TGF-�could be due to the contributions of other cell types to thegene pool and the influence of other autocrine/paracrineregulators on the overall gene expression at the tissue level.Previous studies from our laboratory and others have re-ported the expression of a few other genes targeted by TGF-�action in LSMC and MSMC (10, 11, 17). However, to ourknowledge this is the first example of a large scale geneexpression profiling of these cells in response to TGF-�. Wevalidated the expression of several of these genes in responseto time-dependent action of TGF-� in LSMC and MSMC,including the expression of 10 genes validated in leiomyo-ma/myometrium as well as in LSMC/MSMC in response to

TABLE 1. Continued

Gene functionGnRHa 2 h vs. TGF-�RII antisense

(P � 0.001)GnRHa 6 h vs. TGF-�RII

antisense (P � 0.001)GnRHa 12 h vs. TGF-�RII antisense

(P � 0.001)


AF062174 TRIAD3 � AB028956 KIAA1033 � AB014765 JWA �AB040969 KIAA1536 � AF519179 SMOX � AB028976 SAMD4 �AF452623 CRELD1 � K03460 H2-ALPHA � AB011179 NCDN �BC009696 IFITM2 � BC012396 MGC40157 � AB014548 KIAA0648 �AK025504 KIAA0251 � AK095843 LOC169834 � AK025446 DKFZP564M182 �AB014548 KIAA0648 � AF519179 SMOX �AB078417 RIS1 � X83412 HAB1 �AK000818 FLJ20811 � AF261072 TCBAP0758 �AJ245587 ZNF248 � BC012396 MGC40157 �AJ306929 AFURS1 � Adenylyl

cyclase-associatedprotein

�

AB040972 KIAA1539 �AB059429 BUCS1 �AK074486 FLJ90005 �AJ420534 C6orf145 �

Glycoprotein �nuclear receptor coactivator 2 �



TABLE 2. Categorical list of differentially expressed genes in MSMC treated with GnRHa (2–12 h) compared with TGF-� type II receptorantisense-pretreated cells (continued on next page)






Growth factor and chemokine receptors/ BC002793 IFNAR2 � BC014972 IL2RG � AY497547 CMKLR1 �hormone receptors AY136751 HTR2B � BC015961 ADM � AF015257 GPR30 �

AB010710 OLR1 � AB014887 ORM1 � AF245699 AGTR1 �AB005298 BAI2 � AF498961 DRD1 �BC036787 CTF1 �AB038670 BDNF �AF003837 JAG1 �AY189737 OVGP1 �

Intracellular transducers/modulators/ AK124738 MAP4K5 � AB025105 CDH1 � D45915 ALK �kinase network/protein phosphatases/ NM_006256 PRKCL2 � AL137667 MAPK8 � AK057845 EFNA1 �GTP/GDP exchangers and G-protein AF000231 RAB11A � AF078803 CAMK2B � AL137667 MAPK8 �GTPase modulators AF125393 RAB27A � AF153609 SGK � AY245544 TRB2 �

BC002585 RAB7L1 � BC014243 TYK2 � AK055139 PTK2 �D38076 RANBP1 � BC038231 DUSP8 � AF008591 RAC3 �NM_000855 GUCY1A2 � AK023775 PTPRF � BC005334 CETN2 �AF129756 AIF1 � AF000231 RAB11A � AF129756 AIF1 �AF029081 SFN � AF129756 AIF1 � AF029081 SFN �AF361886 KEAP1 � AF361886 KEAP1 � AF135158 JIK �AA114994 ARGBP2 � AB026491 PRKCABP � BC013154 PPP2R5E �AB058690 GPS2 � AF010193 MADH7 � AF233882 JUP �

AF010193 MADH7 �

Transcription/transcription activators BC001325 FUBP3 � AF055028 POLR2B � BC007102 RQCD1 �and repressor/RNA polymerase BC007102 RQCD1 � J03066 EN2 � AB002107 PER1 �

AJ549245 TAF1 � AB032481 HOXD13 � AK095191 POU6F1 �BC029891 TFEC � AL137295 MLLT10 � AF115510 LRRFIP1 �AK095191 POU6F1 � AF299094 HSF1 � AL701206 RARG �AF072825 RREB1 � AF053318 CNOT8 � BC015752 IRF4 �AF299094 HSF1 � AF113003 NCOR2 � AF113003 NCOR2 �BC034041 LMO2 � AB030304 HUMGT198A � BX537824 TXNIP �AF016270 BRD8 � AB016485 LDB1 �AB112074 RBBP6 �AF113003 NCOR2 �

Oncogenes and tumor suppressors BC002706 ERBB3 �

AF000959 CLDN5 � AK074047 ITGAX �Cell adhesion/matrix adhesion recep BC006491 MPZ � AB001090 CDH13 � AF023476 ADAM12 �

tors/cell-cell adhesion receptors BC006491 MPZ �AB002155 UPK1B �

BPAG1 �

Extracellular transport/proteins BC014781 LCAT � AF122922 WIF1 �AF015224 SCGB2A2 � AB032372 CKTSF1B1 �

Caspases BC015799 CASP7 � BC015799 CASP7 �

DNA replication/polymerases, replica BC011539 ORCIL � BC011539 ORCIL �tion factors, and topoisomerases AY032603 MCM3 � BC000290 IGHMBP2 �

AF017146 TOP3B �

Metabolism AB009598 B3GAT3 � AL117618 PDHB �AF102544 MOCS3 � AK122769 CKMT2 � AC002389 GAPDS �AF011889 IDS � AF013160 NDUFS2 � AF361370 DIA1 �AF044958 NDUFB8 � BC004537 ATP6V0C � AF013160 NDUFS2 �AF280107 CYP3A5 � AF020351 NDUFS4 � AK058013 HPGD �BC000485 DDC � BC001390 QP-C � AF037335 CA12 �BC041164 SMPD1 � BC037246 PNMT �

AL133324 GSS �AB017103 YWHAE �

Translation factors AB018284 EIF5B � BC015460 QPCT � AB007148 RPS3A �

Posttranslational modification AL133335 PFDN4 � AF038009 TPST1 �AL133335 PFDN4 � AY341427 AP2B1 � BC000398 PAFAH1B2 �AK025599 MAN1A1 �



TABLE 2. Continued






RNA processing, turnover, and trans-port

AK055170 RAE1 � AF001042 ADARB1 �

AF117108 IMP-3 �AF000177 LSM1 �

Chromatin proteins AJ419231 PHC2 � BC029516 TNP1 �

Cell surface antigens BC009409 TACSTD2 � BC009409 TACSTD2 �

Heat shock proteins AF275719 HSPCB � NM_001886 CRYBA4 �AF253417 EPHX1 �

Symporters and antiporters AF078544 SLC25A14 � AF070589 CACNA1C �AK127319 SLC16A3 � AK127096 SLC30A3 �

AK000416 SLC16A5 �AY142112 SLC4A3 �AF078544 SLC25A14 �

Targeting/exocytosis BC006268 PEX7 � AK057883 AP2M1 �AC006378 BET1 � BC016804 TRAM2 � AF001307 ARNT �

BX537448 SEC14L1 �BC022069 CRABP1 �AK093558 PFDN1 �

Inhibitors of proteases BC000866 TIMP1 � BC005927 SERPINE1 �BC000866 TIMP1 � BC000866 TIMP1 �

Intermediate filament proteins BC000292 ACTG1 �BC002421 NEF3 � BC062602 PNN �AB037663 MYLK � BC005240 HAX1 �

Protein phosphatases AF263016 PTPRR �

Functionally unclassified AB012853 INGIL � AF368279 SGTA �AC004381 SAH � NM_004998 MYO1E � AI879202 ETHE1 �AI879202 ETHE1 � BC020746 DXS1283E � AF218026 PTOV1 �BC014450 B7 � AF127761 RBM8A � AF127761 RBM8A �BC015335 ICT1 � AF072814 M96 � BC009362 SETDB1 �

AI879202 ETHE1 �

Not classified AB046613 MYL6 � AB018327 ADNP �AF045569 PRKCH � BC031043 PRH1 � AK096489 BZW1 �AB018271 BPAG1 � AB051340 MRPL23 � AB029331 C6orfl8 �AF090947 BBS4 � AF142482 TEAD3 � AF103803 H41 �AB030036 ST14 � BC003015 DGCR14 � AB020638 KIAA0831 �AB037859 MKL1 � AB007887 KIAA0427 � AK025602 MGC2747 �AF006501 POLR2F � AF124491 GIT2 � AF125532 MKNK2 �AB029331 C6orf18 � AF155595 RCOR � AB051340 MRPL23 �AK074531 PRR3 � AB029331 C6orf18 � AF155595 RCOR �AC000391 BRD3 � AB037187 CHST7 � AB007897 SETBP1 �AF139460 ZNF288 � AK074531 PRR3 � AF142482 TEAD3 �AY136740 GPSM2 � AF241235 FXYD2 � AF104927 TTLL1 �AK025602 MGC2747 � BC013789 NHLH1 � AK023088 ARL6IP �AF070532 SUPT6H � AF332577 PSMA6 � X83412 HAB1 �

N-Cym � BC008861 ATP6V0D1 � AB006630 TCF20 �AF072164 C9orf33 � AB028953 KIAA1030 �X83412 HAB1 � BC014932 KIAA0280 �AJ420534 C6orf145 � AB028980 USP24 �AB014564 KIAA0664 � BC005407 KIAA0169 �

HERV-K � BC009899 PIK3R4 �AK074703 LOC89944 � AF441399 HSGP25L2G �AF332577 PSMA6 � GGF2 �AC002543 CAPZA2 � NF1A �AK000002 ABCC10 �AB046845 SMURF1 �AF072467 JRK �AF007157 PRNPIP �AB014608 PARC �



GnRHa treatment (19). Because detailed discussion of thegenes identified in this study is beyond the scope of thismanuscript, we focused on genes whose expression wasconfirmed and present only some major aspects of theirpossible function in leiomyoma and myometrium.

We demonstrated that LSMC express a significantlyhigher level of IL-11 compared with MSMC and a majortarget of TGF-� regulatory action. Although the biologicalsignificance of IL-11 expression in leiomyoma and myome-trial environments and the consequence of its overexpressionin leiomyoma await investigation, IL-11, alone, or throughinteraction with TGF-�, is considered to play a critical role inthe progression of fibrotic disorders (26–28). Equally, othermembers of the IL family, IL-4 and IL-13, and their interac-tions with TGF-� are reported to influence the outcome oftissue fibrosis (29). We have identified IL-13 expression inleiomyoma and discovered that exposure of LSMC to IL-13up-regulates the expression of TGF-� and TGF-�RII inLSMC, suggesting a direct and/or indirect regulatory func-tion for IL-13 in mediating events leading to the progressionof tissue fibrosis in leiomyoma (16). Other cytokines in thiscategory, including IL-4, IL-6, IL-8, IL-15, IL-17, TNF-�, andGM-CSF, are also expressed in leiomyoma and myometrium(16–19). These cytokines are classified as type 1/type 2-related subsets, and predominance toward type II directionis considered to result in inflammatory/immune responsesleading to the progression of tissue fibrosis (27–29). A recentreport has further elaborated the participation of IL-11,TGF-�, and transcription factor EGR1 in tissue fibrosis,through a mechanism involving regulation of balance be-

tween the rate of cellular apoptosis and inflammatory re-sponse (30). We have previously identified EGR1 among thedifferentially expressed genes in leiomyoma and myome-trium (18) and here demonstrated the expression of EGR2and EGR3 in these tissues (19) and regulation of EGR3 inresponse to TGF-� action in LSMC and MSMC. Elevatedexpression and preferential phosphorylation of EGR1 lead toregulation of target genes whose products are involved inapoptosis as well as angiogenesis and cell survival, includingIL-2, TNF-�, Flt-1, Fas, Fas ligand, cyclin D1, p15, p21, p53,platelet-derived growth factor-A, angiotensin II-dependentactivation of platelet-derived growth factor and TGF-�, vas-cular endothelial growth factor, tissue factor, 5-lipoxygenase,intercellular adhesion molecule-1, fibronectin, urokinase-type plasminogen activator, and matrix metalloproteinase-1(MMP-1) (31–36). The expression of many of these genes hasbeen documented in myometrium and leiomyoma and isknown to be the target of TGF-� regulatory action (1, 3, 18,20, 21). EGR1 also acts as a transcriptional repressor of TGF-�RII through direct interaction with specificity protein-1 andEts-like ets-related transcription factor sites in the proximalpromoter of the gene (36). Transfection of EGR1 expressionvector into a myometrial cell line expressing low levels ofEGR1 resulted in a rapid growth inhibition of these cells (37).To our knowledge, our study is the first to report a regulatoryfunction of TGF-� on EGR3 expression not only in LSMC andMSMC, but in any other cell type. Based on our previous andpresent observations, we propose that there is a local in-flammatory response mediated through individual and com-bined actions of TGF-�, IL-11, and IL-13 as well as a regu-latory function of TGF-� on EGRs expression, resulting in thelocal expression of genes whose products promote apoptoticand nonapoptotic cell death, further enhancing an inflam-matory reaction that orchestrates various events leading tothe progression of fibrosis in leiomyoma.

Additional genes identified as differentially expressed andregulated by TGF-� autocrine/paracrine action in LSMC andMSMC in this functional category include TGIF, TIEG,CITED2, Nur77, Runx1, and Runx2. These transcription fac-tors possess key regulatory functions in the expression of awide range of genes in response to various stimuli, specif-ically TGF-�. The expression of TGIF, TIEG, CITED2, andNur77 is highly regulated in LSMC and MSMC, and with theexception of CITED2, TGF-� transiently increased their ex-pression in a time-dependent manner. TGIF is a transcrip-tional corepressor that directly associates with Smads andinhibits Smad-mediated transcriptional activation by com-peting with p300 for Smad association (38, 39). CITED2,induced by multiple cytokines, growth factors, and hypoxia,also interacts with p300 and functions as a coactivator for thetranscription factor activating protein-2 (40). CITED2-medi-ated action is reported to result in down-regulation ofMMP-1 and MMP-13 through interactions with CBP/p300and other transcription factors, such as c-Fos, Ets-1, nuclearfactor-�B, and Smads, that control MMP promoter activities(41, 42). TGF-� targets the expression of these transcriptionfactors and MMPs in many cell types, including LSMC andMSMC (2, 5, 11, 42); thus their differential regulation andinteractions with CITED2 and TGIF may regulate the out-come of TGF-� actions in leiomyoma involving cell growth,

FIG. 6. Hierarchical clustering analysis of gene expression in LSMCand MSMC pretreated with TGF-�RII antisense for 24 h, followed byTGF-� treatment for 2 h (f 314, f316, m314, and m316 antisense),GnRHa-treated cells for 2 h (f-314G, f316G, m314G, and m316G), anduntreated control (C). Supervised analysis of the gene expressionvalues and statistical analysis in R programming and ANOVA iden-tified 222 genes with a false discovery rate of rate of P � 0.001, whoseexpression levels discriminated among the treatment groups and theuntreated control. The clustering depicts four groups of genes (A–D),and their zoomed images are presented in A–D. Genes appearingmore than once are represented by multiple clones on arrays.



inflammation, apoptosis, and tissue turnover. Unlike TGIF,TIEG is rapidly induced by TGF-� and enhances TGF-� ac-tions through Smad2/3 activation (43–45). However, TIEGhas no effect on gene transcription in the absence of Smad4or due to overexpression of Smad7, although it is capable ofincreasing Smad2/3 activity in the absence of Smad7 (43, 46).We showed that TGF-� induced a rapid, but transient, ex-

pression of TIEG in LSMC and MSMC, and we recentlydemonstrated the expression of Smad2/3, Smad4, andSmad7 and their differential regulation by TGF-� in thesecells (10, 11). Based on these observations, we also proposethat TGF-�, through a mechanism involving TGIF, TIEG, andSmads, self-regulates its own autocrine/paracrine action inleiomyoma/myometrium. Estrogen has also been shown to

FIG. 7. Comparative analysis of the ex-pression profile of 12 genes identified asdifferentially expressed and regulatedin response to time-dependent action ofTGF-�1 in LSMC and matched MSMCby microarray and real-time PCR. Val-ues on the y-axis represent an arbitraryunit derived from the mean expressionvalue for each gene, and values on the x-axis represent the time course of TGF-�(2.5 ng/ml) treatment (2, 6, and 12 h),with untreated control (Crtl) gene ex-pression values set at 1. Total RNA iso-lated from these cells was used for bothmicroarray analysis and real-time PCR,validating the expression of IL-11,EGR3, CITED2, Nur77, TIEG, TGIF,p27, p57, Gas-1, and GPRK5.



increase TIEG expression in breast tumor cell (43, 47). Be-cause estrogen, a major growth-promoting factor for leiomy-oma, induces TGF-� expression in LSMC and MSMC (7, 8),either estrogen directly or through estradiol-induced TGF-�may regulate TIEG expression in leiomyoma. TIEG is alsoreported to trigger apoptotic cell programs by a mechanisminvolving the formation of reactive oxygen species (45), often

created as a result of a local inflammatory response. WhetherTGF-�-induced TIEG through the above mechanism resultsin apoptotic response in leiomyoma is not known; however,the formation of reactive oxygen species may enhance thelocal inflammatory response, serving as an additional me-diator of tissue fibrosis in leiomyoma.

Nur77 regulates the expression of a group of genes

FIG. 7. Continued



whose products are involved in cell cycle regulation, dif-ferentiation, apoptosis, and malignant transformation (48,49). We provided evidence that Nur77 is the target of theregulatory action of TGF-� in LSMC and MSMC, with apattern of expression resembling that observed in leiomy-oma and myometrium, respectively (18, 19). Although thenature and functional significance of Nur77 expression inleiomyoma and its regulation by TGF-� are unknown,malignant transformation in leiomyoma is rare, suggest-ing that Nur77 may function as a regulator of the cell cyclein leiomyoma and myometrium. In addition to Nur77, wediscovered that the expression of various genes function-ally associated with cell cycle regulation and apoptosis isinfluenced by the autocrine/paracrine action of TGF-�,and the balance of their expression may be a critical factorin leiomyoma growth and regression. Additional tran-scription factors whose expression was the target of TGF-�action in LSMC and MSMC are Runx1 and Runx2. Thisfamily of transcriptional factors, consisting of Runx1 toRunx3, is an integral component of signaling cascadesmediated by TGF-� and bone morphogenetic proteins reg-ulating various biological processes, including cell growthand differentiation, hemopoiesis, and angiogenesis (20, 46,50). We provide the first evidence for regulatory action ofGnRHa therapy and GnRHa direct action on Runx1 andRunx2 expression in leiomyoma, myometrium, as well asLSMC and MSMC, with GnRHa significantly inhibitingtheir expression, specifically in MSMC. Although Runx2 isexpressed at low levels in leiomyoma and myometrium,Runx1 and Runx2 expression in LSMC and MSMC dis-played a rapid response to TGF-� action in vitro, withRunx1 showing a significantly higher response. TGF-�activation of Smad and MAPK cascades regulates the ex-pression of Runx2; however, interaction with Smad3causes repression of Runx2 and downstream transcriptionactivation of specific genes (20, 46). We recently reportedthat TGF-� and GnRH activate the MAPK pathway (11),and GnRHa alters TGF-�-activated Smad in LSMC andMSMC (10), a signaling cascade that may regulate theexpression of Runx1 and Runx2 in these cells. Differentialregulation of Runx1 and Runx2 by TGF-� and GnRHaimplies their potential biological implication, specificallyin regulating TGF-� action in the leiomyoma microenvi-ronment. This is particularly interesting because estrogenis also reported to enhance Runx2 activity through a mech-

anism involving TGF-�RI gene promoter, which containsseveral Runx binding sequences (51). Together, the iden-tification of these and several other key transcription fac-tors in LSMC and LSMC and their regulation by TGF-�,serving as integral components of inflammatory, cell cycle,and apoptotic processes, support our hypothesis that aregulatory balance between these events is a key factorin the progression of fibrosis mediated by TGF-� inleiomyoma.

The balance between cell proliferation and apoptosis iscritical to tissue homeostasis and central to leiomyomagrowth and regression. Because both positive and negativesignals determine the outcomes of these events, we iden-tified several genes in this category in our previous andcurrent study as differentially expressed and regulated inleiomyoma and myometrium, as well as in LSMC andMSMC in response to TGF-�. Our primary focus here wason p27Kip1, p57Kip2, and Gas-1 expression, because oftheir regulation by GnRHa (19). We found that TGF-�suppressed the expression of these genes in LSMC and ina biphasic fashion, accompanied by suppression of Gas-1expression in MSMC. TGF-� is known to regulate theexpression of several cell cycle regulatory proteins, in-cluding p27, which binds cyclin-dependent kinase (CDK),and by inhibiting the catalytic activity of the CDK-cyclincomplex, regulate cell cycle progression and apoptosis(52). However, TGF-� regulation of p57 expression is lim-ited (20, 21, 53), and available data suggest that TGF-�enhances p57 degradation through the ubiquitin-protea-some pathway and Smad-mediated signaling (54). TGF-�-induced p57 degradation, CDK2 activation, and cellproliferation are blocked by proteasome inhibitors or over-expression of Smad7 (54 –57). TGF-� also induces cellgrowth by influencing c-Myc expression and activation ofG1, G2, CDK, and cyclins, and their inhibitors p15IN�4band p21 (20, 21, 46), and we identified them among TGF-�-targeted genes in LSMC and MSMC (18, 19). To ourknowledge, our study is the first to demonstrate Gas-1expression in human uterine tissue and its regulation byTGF-�. Gas-1 acts as a negative regulator of the cell cycleand has been positively correlated with the inhibition ofendothelial cell apoptosis and the integrity of resting en-dothelium (58). Gas-1 is reported to suppress the growthand tumorigenicity of human tumor cells, and overex-pression of c-Myc and murine double-minute clone 2 pro-

FIG. 7. Continued



tein or a p53 mutation inhibits Gas-1-mediated action (59 –61). Estrogen has also been reported to regulate Gas-1 inrat uterus (62). Because TGF-�s stimulate DNA synthesis,but not cell division, in LSMC and MSMC, p27, p57, Gas-1,and other cell cycle regulators may influence TGF-� action

on leiomyoma cell growth late in S to M phases of the cellcycle transition.

We also identified several genes functionally belonging tosignal transduction pathways as the target of TGF-� actionin LSMC and MSMC. Among them are members of family

FIG. 8. Comparative analysis of the gene expression profiles of Runx1 and Runx2 in leiomyoma (LM) and matched myometrium (MM) fromuntreated subjects (un-Trt) and women who received GnRHa therapy (GnRHa-Trt) as well as in LSMC and MSMC in response to thetime-dependent action (2, 6, and 12 h) of GnRHa (0.1 �M) as described in detail previously (19) and in response to the time-dependent (2, 6,and 12 h) action of TGF-�1 (2.5 ng/ml) determined by real-time PCR. In microarray analysis, Runx2 expression was not included because itsexpression value did not reach the study standard. Values on the y-axis represent an arbitrary unit derived from the mean expression valuefor each gene, and values on the x-axis represent the time course of TGF-� and GnRHa treatments, with untreated control (Crtl) gene expressionvalues set at 1. Total RNA isolated from these cells was used for both microarray analysis and real-time PCR validation.



of Ras/Rho, Smads, MAPK, protein tyrosine kinase 2, S100calcium-binding proteins, LIM protein and LIM domain ki-nase 2, serine/threonine kinase 17� (apoptosis-inducing),focal adhesion kinase 2, signal transducers and activators oftranscription, etc. Although Smad and MAPK pathways arerecruited and activated by TGF-� receptors, including inLSMC and MSMC, components of other pathways are notknown to be targeted by TGF-�. However, many growthfactors, cytokines, chemokines, polypeptide hormones, andadhesion molecules expressed by LSMC and MSMC, eitheralone or through cross-talk with TGF-� receptor signaling,may activate various components of the other pathways,although only the expression and activation of a few havebeen demonstrated in these cells. Because GPRK5 expressionwas detected in leiomyoma and myometrium and was thetarget of GnRHa action in LSMC and MSMC (19), we foundthat GPRK5 expression is regulated by TGF-�. The biologicalimplication of GPRK5 and its regulation by TGF-� in LSMCand MSMC is unclear; however, GPKs serve as negativeregulators of G protein-coupled receptor-mediated actionthrough the generation of second messengers, such as cAMPand calcium/calmodulin, and down-regulation of their ac-tivity (desensitization) (63–65). Activation of calcium/cal-modulin is reported to alter Smad function, influencing theoutcome of TGF-�’s action (46). This result suggests thatGPRK may act as a downstream regulator of TGF-� receptorsignaling, possibly through modulation of protein kinase C,MAPK, and/or calmodulin and hence influencing TGF-�action in leiomyoma.

Tissue remodeling is a critical event in the progression offibrotic disorders and modulation of ECM, adhesion mole-cules, and protease expression, and phenotypic changes to-ward a myofibroblastic phenotype are essential componentsof this process (1, 2, 66–69). In this and our previous studywe identified the expression of several genes in this categoryin leiomyoma/myometrium and LSMC/MSMC, includingfibronectin, collagens, decorin, versican, desmin, vimentin,fibromodulin, several members of the integrin family, des-moplakin, extracellular matrix protein 1, porin, SPARC (se-creted protein acidic and rich in cysteine)-like 1, syndecan 4,endothelial cell-specific molecule 1, as well as MMPs, TIMPs,and ADAMs (A disintigran and metalloprotease) (18, 19). Wehave previously demonstrated the expression of fibronectin,vimentin, collagen, fibromodulin, MMPs, and TIMPs inleiomyoma and myometrium and their regulation by TGF-�through the activation of MAPK (11, 42, 70). Of particularinterest is the elevated expression of decorin, vimentin, andfibromodulin in leiomyoma, because of their ability to bindTGF-� and control TGF-� autocrine/paracrine action, amechanism considered to regulate the outcome of tissuefibrosis (1, 5, 71, 72). Because leiomyoma is believed to derivefrom transformation of myometrial connective tissue fibro-blast and/or smooth muscle cells, the expression of vimentinin leiomyoma/LSMC implies that these cells have adopteda myofibroblastic characteristic. Although granulation tissuemyofibroblasts are derived from local fibroblasts, other celltypes, including smooth muscle cells, have the potential toacquire a myofibroblastic phenotype (30, 66–68). These cellsexpress various cytokines, including GM-CSF, IL-11, andTGF-�, of which GM-CSF is considered to participate in

fibroblast transformation into myofibroblasts and to enhancetheir TGF-� expression (66–68). We have shown that GM-CSF regulates TGF-� expression in LSMC, and their inter-action with other cytokines, such as IL-11 and IL-13, mayplay a key role in events leading to leiomyoma formation andthe outcome of fibrosis (7, 11, 16). IL-11, either alone orthrough induction by TGF-�, alters ECM turnover in myo-fibroblasts, resulting in the progression of tissue fibrosis (30,73). Despite the importance of tissue turnover in the patho-physiology of leiomyoma, few data are currently availableabout the extent of ECM expression and differences that mayexist compared with myometrium that contribute to the fi-brotic character of leiomyoma.

In conclusion, as a continuation of work with TGF-�, weprovided the first large scale example of a gene expressionprofile of LSMC and MSMC, identifying a specific cluster ofgenes whose expression is targeted by the autocrine/para-crine action of TGF-�. We validated the expression of aselective number of these genes functionally recognized toregulate inflammatory response, angiogenesis, cell cycle, ap-optotic and nonapoptotic cell death, and ECM matrix turn-over, events central to leiomyoma pathobiology. Based onthe present and our previous work with TGF-�, we proposethat the individual and combined actions of TGF-� withother profibrotic cytokines, such as IL-11, IL-13, and GM-CSF, orchestrate local inflammatory responses mediatedthrough and influenced by the expression of genes whoseproducts regulate the above processes, providing an envi-ronment leading to the progression of fibrosis.

Acknowledgments

Received October 20, 2004. Accepted December 1, 2004.Address all correspondence and requests for reprints to: Dr. Nasser

Chegini, Department of Obstetrics and Gynecology, University of Flor-ida, Box 100294, Gainesville, Florida 32610. E-mail: [email protected].

This work was supported by National Institutes of Health GrantHD-37432. Presented in part at the 51st Annual Meeting of the Societyfor Gynecological Investigation, Houston, TX, 2004.

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