Global transcriptomic analysis reveals CYP1A1 as a target of the GPER agonist G1

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Manjegowda, Anil Mukund Limaye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4291506/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : G1, a G-protein coupled estrogen receptor (GPER) agonist, has been instrumental in delineating the mechanisms and cellular consequences of GPER signal transduction. The effects of G1 on cell proliferation are controversial, including those that are demonstrably GPER-independent. It begs the question as to whether G1 has off-target effects. Methods and Results : Here, transcriptomic alterations in MCF-7 breast cancer cells treated with 1 mM G1 are presented. GSEA and GO analysis showed enrichment of gene-sets in G1-treated MCF-7 transcriptome, which align with loss of cell viability. Genes related to xenobiotic metabolism were regulated by G1. In this category, CYP1A1, was the topmost G1-induced gene. CH223191, an aryl hydrocarbon receptor inhibitor, blocked G1-mediated increase in CYP1A1 mRNA. Furthermore, the G1-mediated modulation of CYP1A1, and other genes, was also observed in the GPER-negative MDA-MB-453 cells. Conclusions : This study captures the transcriptomic alterations associated with G1-induced cell cycle arrest in MCF-7 cells, with the important caveat that these may not be entirely attributed to GPER activation. G1 GPER CYP1A1 RNA-seq MCF-7 MDA-MB-453 Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction 17b-estradiol induces short-term non-genomic effects via the seven transmembrane-spanning G-protein coupled estrogen receptor (GPER). Originally, it was identified as an estrogen receptor (ER)-a co-expressed gene (GPCR-Br/GPR30) in breast cancer cells and tissues [1]. Considered as an orphan receptor, it emerged as a mediator of EGF-like effects of estrogen, leading up to ERK1/2 activation [2]. It was subsequently shown to specifically bind estrogen to cause rapid intracellular calcium mobilization, and generation of phosphatidylinositol 3,4,5-trisphosphate [3, 4]. These developments led to the re-christening of this receptor as GPER [5]. A large body of work highlights its relevance in different physiological contexts, such as immune, reproductive, cardiovascular, neuroendocrine, urinary, and musculoskeletal systems [6]. A potential target in cardiovascular, immune and infectious diseases, obesity, and diabetes [7], GPER’s aberrant expression associated with established clinico-pathological variables, in endocrine or non-endocrine tumors, sets its relevance in carcinogenesis [8]. Being a typical G-protein coupled receptor, ligand-binding leads to signalling cascades downstream of G a and G bg subunits [8]. GPER activation is linked to EGFR transactivation via src-like tyrosine kinase-mediated activation of MMPs, HB-EGF ectodomain shedding, and activation of ERK1/2 [2]. Besides this, activation of adenylate cyclase, PI3K-AKT, and eNOS, and increased cAMP and intracellular calcium mobilization are well-demonstrated consequences of GPER activation [8, 9]. Both the aforementioned arms of GPER signalling ultimately lead to activation of transcription factors, and therefore, modulation of gene expression on a longer time-scale [9]. The identification of G1, a GPER-specific ligand provided the impetus to study specific consequences of GPER activation [10]. Prior to this, the role GPER would be inferred based on correlations between its expression, and the effects brought about by estrogen. However, G1 produces differential effects in cell culture models; proliferation [11, 12], or its inhibition [13–15]. Adding to the confusion, few studies have demonstrated G1-induced cell death in a GPER-independent manner by targeting tubulin polymerization, or microtubules [16–18]. G1 treatment in GPER-positive settings has enabled identification of transcriptional targets of GPER, such as c-fos, pS2, and the cyclins A, D1, and E, using candidate gene approaches [9], or the microarray technology [19]. We have analyzed RNA-seq data to ascertain transcriptomic alteration in MCF-7 cells treated with 1 mM G1 for a period of 48 h, which is associated with G1-induced loss of cell viability. Furthermore, we show that G1 increases CYP1A1 mRNA expression via the aryl hydrocarbon receptor (AHR). G1 also modulated the expression of CYP1A1, and other selected mRNA transcripts, in the GPER-negative MDA-MB-453 cells, thereby casting doubts over GPER-specificity of the observed effects. Materials and Methods Cell culture Cell culture plasticwares were from Eppendorf (Hamburg, Germany) and Nunc (Roskilde, Denmark). Phenol red-containing Dulbecco’s Modified Eagle’s medium (DMEM), Roswell Park Memorial Institute Medium (RPMI-1640), 100X Pen-Strep antibiotic solution, and trypsin-EDTA were purchased from Himedia (Mumbai, India). Fetal bovine serum (FBS) was from Gibco (NY, USA). MCF-7, and MDA-MB-453 cells were obtained from the National Centre for Cell Science (Pune, India). They were grown in phenol red-containing DMEM or RPMI-1640, respectively, supplemented with 10% FBS (heat-inactivated), 100 U/mL penicillin, and 100 μg/mL streptomycin, in a humidified chamber maintained at 37⁰C and 5% CO 2 . Treatment Typically, 0.2×10 6 cells were seeded in 35 mm dishes. After 48 h, the cells were washed with Dulbecco’s phosphate buffered saline (Himedia, Mumbai, India). They were then treated with 100 nM or 1 μM concentration of G1 (Cat. #10008933, CAS.#881639-98-1; Cayman Chemicals, Ann Arbor, USA), for 24 or 48 h depending upon the experiment. Cells treated with ethanol (0.1% v/v) served as controls. To test the effect of the AHR inhibitor CH223191 (Cat No. C8124, St. Louis, MO, USA), two additional treatment groups were added; comprising of cells treated with 10 mM CH223191 alone, or in combination with 1 mM G1. Flow cytometry Cells were trypsinized, washed with phosphate buffered saline (PBS), and permeabilized and fixed with ice-cold methanol. After 1 h, methanol was removed by PBS washes. The cells were treated with RNase A for 30 min at 37 o C, and stained with propidium iodide (PI, 20 mg/mL solution in PBS). The PI-stained cells were analyzed by FACSCalibur (Becton, Dickinson and Company, USA) in FL2 channel (585/42 bandpass filter). A dot plot of FL2 area vs. FL2 width was used to discriminate doublets. The distribution of cells at different stages of cell cycle was estimated using FCS Express 6 software. Histogram of FL2 area for the gated population was plotted to represent the distribution of cells in various stages of the cell cycle. Western Blotting Cells were lysed in RIPA lysis buffer containing protease inhibitor cocktail. Protein concentration was estimated by Lowry’s method [20]. 30 µg of total protein was fractionated on 10% reducing SDS-PAGE, and transferred to nitrocellulose membrane. After blocking in 1% gelatin in TBST for 1 h, the blots were probed with primary antibodies against histone-H3 (Cat. No. BB-AB0055, Bio Bharati Life Science, India), or GPER antibody generated as described earlier [21], which were diluted in 0.1% gelatin in TBST. Blots were washed for 30 min with TBST (3 × 10 min) to remove unbound primary antibody. They were incubated with the HRP-tagged secondary antibody (1:5000 dilution in 0.1% gelatin in TBST) for 1 h followed by three TBST washes of 10 min each. Blots were developed with enhanced chemiluminescence reagent (Bio-Rad Laboratories, Hercules, CA, USA). Total RNA isolation, and RT-qPCR Total RNA was isolated using a reagent, which was prepared in-house according to Chomcsynski and Sacchi [22] with minor modifications. 2 µg of total RNA was reverse transcribed using High Capacity cDNA Reverse Transcription kit (Invitrogen, USA) as per manufacturer’s instructions. 2 ml of diluted cDNA (1:10) was used as template for PCR reaction with PowerUP SYBR Green Master Mix (Cat No. A25743, Thermo Scientific, PA, USA) and gene-specific primers (Table 1). RPL35a served as an internal control. The PCR reactions were carried out in AriaMx real time PCR System (Agilent). Expression levels of genes were analyzed by ΔΔCt relative quantitation method [23], or using the ΔCt method as used earlier [24]. In the latter, the average Ct value for the test gene of interest (Ct test ), and the internal control RPL35a (Ct RPL35a ) were determined. The difference Ct test - Ct RPL35a , which is referred to as ΔCt, was considered as a measure of the normalized expression. Thus, higher the ΔCt value, lower is the normalized expression. Routine RT-PCR Total RNA was extracted, reverse transcribed, and subjected to PCR as described in the previous section. However, the reactions were carried out in Veriti 96 Well Thermal Cycler (Applied Bio Systems, USA). After the completion of the PCR, the products were analyzed on 2% agarose gels. The images of ethidium bromide-stained bands were captured using ChemiDoc™ XRS + System with Image Lab™ Software (Bio-Rad Laboratories, Hercules, CA, USA). MTT assay: Cells were seeded in 96-well plates. When the cells became 50% confluent, they were treated with the indicated concentrations of G1, with or without CH223191, for 48 h. The spent medium was removed, and the cells were washed with PBS. MTT (0.5 mg/ml) was added to the wells and incubated for 3 h at 37 ˚C. Thereafter, MTT was removed, and formazan crystals were dissolved in 100 µl of DMSO. The absorbance was measured at 570 nm (A 570nm ) and 690 nm (A 690nm ) using Varioskan LUX microplate reader (Thermo Fisher, PA, USA). The difference, A 570nm – A 690nm , was used as a measure of viability. RNA-seq The detailed protocol for treatment of cells, total RNA extraction, library preparation, and sequencing is already published [25]. The raw sequence data are freely accessible through the Gene Expression Omnibus (accession number GSE188706). The raw data were downloaded using the aforementioned accession number and re-analyzed. FASTQC tool [26], was used for read quality assessment. Adapters and low quality reads were removed using Trimmomatic [27]. The trimming parameters were- IlluminaClip: TruSeq3.fa:2:30:10:2:keepBothReads; LEADING:3; TRAILING:3; SLIDINGWINDOW:4:30; MINLEN: 50. Trimmed reads were aligned using STAR aligner [28] to generate BAM files. Read count data obtained using the featureCounts tool [29], were processed using the DESeq2 package in R [30]. Quality of the normalized count data was assessed and visualized by unsupervised clustering, correlation heatmap, and principal component analysis. The normalized counts were fitted on the negative binomial model. The normalized counts were subjected to statistical analyses by applying the Wald statistic (α = 0.05) and FDR correction with a 5% cut-off. Gene set enrichment and gene ontology analysis fGSEA package [31] was used for identification of enriched gene-sets using 25% cut-off for FDR. Normalized enrichment scores were generated, and plotted using additional packages in R. The clusterProfiler package [32] in R was used for GO analysis for identification of overrepresented biological terms associated with differentially expressed genes under three functional categories, namely biological processes (BP), cell components (CC), and molecular function (MF). Statistical Analysis Relative mRNA expression data for G1-treated cells compared to control were analyzed by one-sample one tailed t-tests to examine whether the mean relative expression was significantly greater or less than 1. Multiple group data were analysed by one-way ANOVA. In experiments where the interaction between two variables were analyzed, two-way ANOVA was applied. The data were examined for homogeneity of variance using the Levene’s test before applying ANOVA. TukeyHSD was used for multiple comparison between pairs of groups. All statistical tests were performed at 5% level of significance (p < 0.05). Results 1 m M G1 negatively affects MCF-7 cell viability A previous study from our laboratory showed that MCF-7 cells were more sensitive to G1-mediated cell cycle arrest compared to T47D cells, although both express GPER. 500 nM G1 significantly reduced the viability of MCF-7, but not T47D cells. 1 mM G1 significantly affected cell viability; the reduction being greater in MCF-7, compared to T47D cells [33]. Fig. 1A shows the morphology of cells treated with 0.1% ethanol (vehicle control), 100 nM G1, or 1 mM G1, for 48 h. None of the concentrations of G1 affected the morphology of T47D cells. However, MCF-7 cells treated with 1 mM G1 appeared to lose their attachment with the growth surface, resulting in spherical morphology. They resembled those treated with colchicine ( data not shown ). This is mirrored by the distribution of cells in G1, S, and G2 phases of the cell cycle. After 24 h of vehicle treatment, the percentage of cells in G1, S, and G2 phases were 65.4, 20.6 and 14.0, respectively. However, the percentage of cells in the three phases after 24 h of 1 mM G1 treatment were 44.2, 11.6 and 44.1 respectively (Fig. 1B). G1-induced transcriptomic alterations in MCF-7 cells Total RNA samples from MCF-7 cells treated with vehicle (0.1% ethanol), 100 nM G1, or 1 mM G1, were subjected to RNA-seq using the Illumina platform [25]. The raw sequence data (GSE188706) were downloaded, and re-analyzed as described in materials and methods. DESeq2 analysis of the data using 5% cut-off for FDR showed that 100 nM G1 did not significantly affect the MCF-7 cell transcriptome. This is reflected in the correlation heatmap, or the PCA plot, which were generated using the regularised log-transformed counts. The three biological replicate samples of MCF-7 cells treated with 1 mM G1 emerged as a distinct group, which was well separated from the remaining samples (Supplementary data 1). 1 mM G1, however, caused a significant change in the transcriptome; modulating the expression of 2301 genes. These were comprised of 837 upregulated, and 1464 downregulated genes as illustrated in Fig. 2A (volcano plot), and Fig. 2B (expression heatmap). The complete list of significantly modulated genes using a log 2 FoldChange threshold of 0, and FDR cut-off of 5% is provided as Supplementary data 2. Genes listed in Table 2, and Table 3 show the functional diversity of the top 25 upregulated and downregulated genes, which include structural proteins, enzymes, signaling molecules, and cytokines. GSEA and GO analysis GSEA revealed positive enrichment of p53 pathway , TNF- a signalling , and apoptosis gene-sets, along with negative enrichment of E2F targets , MYC targets , and the G2M checkpoint gene-set (Fig. 2C). The normalized enrichment plots for the aforementioned gene sets, along with the names of their respective leading edge genes are provided as Supplementary data 3. Few G1 modulated genes were validated by RT-qPCR. Consistent with the RNA-seq data, AHRR and TIMP3 transcripts were significantly up-regulated, whereas BRCA1, CIT, KIF11, KIF20A, NCAPD3, SMC1A, and TOP2A transcripts were significantly downregulated upon 1 mM G1 treatment (Fig. 2D). GO analysis allowed identification of over-represented GO terms associated with the significantly modulated genes under three categories, namely biological processes (BP), cellular compartment (CC) and molecular function (MF). Under biological processes, the term positive regulation of programmed cell death was over-represented for genes upregulated by G1 (Fig. 2E, left panel ). On the other hand, the GO terms over-represented in genes downmodulated by G1 were chromosome , chromosomal region , condensed chromosome , centromeric region , and microtubule cytoskeleton under cellular compartment, and cell cycle , chromosome segregation , and cell cycle process under biological processes (Fig. 2E, right panel ). G1 induces CYP1A1 mRNA expression Among the genes modulated by G1, there was a positive enrichment of genes related to xenobiotic metabolism (Fig. 2C). Fig. 3A shows the enrichment plot for this gene-set. The expression heatmap shown in Fig. 3B shows that the leading edge genes in this set are induced by 1 mM G1. CYP1A1, one of the leading edge genes was the topmost G1-induced gene with a log 2 FoldChange of 3.98 (p ≈ 0). A dose-response experiment to validate this result showed that 100 nM G1 did not significantly affect CYP1A1 mRNA expression. However, 500 nM, and 1 mM G1 significantly increased CYP1A1 mRNA expression. The effect of 1 mM G1 was significantly greater compared to 500 nM G1 (Fig. 3C). CYP1A1 is a well-known transcriptional target of AHR [34]. AhR inhibitor CH223191, significantly reduced the basal levels of CYP1A1 mRNA. Moreover, it significantly blocked G1-stimulated increase of CYP1A1 mRNA (Fig. 3D). Notably, CH223191 alone did not cause any change in cell morphology. However, G1 in combination with CH233191 caused the cells to round-off due to detachment from the surface in a manner that was brought about by G1 alone (Fig. 3E). G1 induces CYP1A1 mRNA expression in MDA-MB-453 cells The effect of G1 treatment on cell viability and gene expression was also studied in the GPER-negative MDA-MB-453 cells (Fig. 4A,B). 1 mM G1 marginally but significantly reduced the viability of MDA-MB-453 cells, which was not affected by CH223191 (Fig. 4C). A dose-response study showed that 1 mM G1 significantly induced CYP1A1 mRNA expression (Fig. 4D). The induction of CYP1A1 mRNA was significantly blocked by CH223191 (Fig. 4E). In MDA-MB-453 cells, we examined the effect of G1 on those genes, which were modulated by G1 in MCF-7 cells. As shown in Fig. 4F, BRCA1, CIT, KIF11, NCAPD3 and TOP2A were downmodulated by G1, while AHRR, and TIMP3 were upregulated in a manner that was observed in MCF-7 cells. There was no effect on KIF20A, and SMC1A. Discussion The current understanding of GPER signalling, and its consequences in a cellular milieu dominated by other estrogen receptors, owes much to the identification of G1 as a GPER agonist [10]. The consequences known till date include short-term non-genomic effects, and long-term effects on gene expression, in relation to the effect on cell proliferation. Pandey and co-workers used microarray technology to elucidate transcriptomic changes induced by estrogen or 4-hydroxytamoxifen in ER-negative/GPER-positive SKBR3 cells, which were associated with proliferation and migration. They identified 36 GPER target genes after taking into account the gene expression changes in cells, which were depleted of GPER expression [35]. Schuler-Toprak and co-workers applied the Affymetrix GeneChip technology on ovarian cancer cells to show that G1 inhibited growth, and described the associated transcriptomic response [19]. Here we have employed RNA-seq technology to deduce transcriptomic alterations in MCF-7 cells induced by G1, assuming its GPER-specificity. Our results capture genomic effects induced by G1, which is associated with cell cycle arrest. Positive enrichment of p53 pathway, TNF-a signalling, and apoptosis related genes, and negative enrichment of E2F or MYC targets, and G2M checkpoint gene-set are consistent with G1-induced loss of cell viability. However, in view of the following discussion, it would be more appropriate to consider the observed transcriptomic changes as G1-induced, and not strictly GPER-mediated. CYP1A1 is a prominent drug-metabolizing enzyme, and a mediator of xenobiotic response [34]. It not only emerged as the most upregulated gene, but was also among the leading edge genes within the positively enriched xenobiotic response gene-set. This observation raises interesting possibilities. Given that perturbation of survival- or proliferation-related pathways leads to altered CYP1A1 expression [36], its induction by G1 may be an indirect result of cell cycle arrest and apoptosis. CYP1A1 is a direct transcriptional target of AHR [34]. The inability of G1 to induce CYP1A1 mRNA in the presence of CH223191 indicates that G1 activates the AHR pathway. Xenobiotics such as polycyclic aromatic hydrocarbons are known to induce their own metabolism via the AHR pathway. It will be interesting to investigate whether G1 is perceived by the cells as a xenobiotic. G1 or a metabolite of G1, resulting from the enzymatic activity of any one or more of the cellular drug-metabolizing enzymes, directly or indirectly interacts with AHR to activate its transactivation function. Such mechanisms would be GPER agnostic, since G1 appears to induce CYP1A1 mRNA in the GPER-negative MDA-MB-453 cells. The popularity of G1 as a pharmacological tool to activate GPER is evident from the plethora of published studies. However, the studies, which have applied G1 to probe GPER function have yielded controversial results. While some studies have shown enhanced proliferation [11, 12], others have demonstrated the opposite [13–15]. Few studies have demonstrated G1-mediated effects in the absence of GPER [16–18]. This controversy has led to the larger controversy about GPER’s role in cancer; tumor promoter or tumor suppressor. G1 inducing similar alterations in gene expression in the GPER-positive MCF-7 cells, and GPER-negative MDA-MB-453 cells, is an indication that off-target effects are likely. However, it does not disqualify G1 as a GPER agonist. Different cell culture models have different levels of GPER expression. The observed effect of a given concentration of G1 may be a function of GPER expression. It is possible that at low concentrations of G1, GPER-mediated effects dominate. But at higher concentrations, G1 may bind other low-affinity targets to generate what appears as GPER-independent effects. Declarations Ethical Statements Ethical Approval- It is not applicable Consent to participate- It is not applicable Consent to publish- It is not applicable Competing interests The authors declare no competing interests. Author Contributions J.H., U.P., M.C.M performed the experiments and collected data. J.H., A.M.L analyzed the data. M.C.M, U.P. and A.M.L conceptualized the experiments. U.P. wrote the first draft of the manuscript, and all authors contributed to the revision and editing. Funding Funding was from the Department of Biotechnology, Govt. of India (Sanction letter No. BT/506/NE/TBP/2013, and BT/PR16071/NER/95/63/2015). References Carmeci C, Thompson DA, Ring HZ, et al (1997) Identification of a Gene (GPR30) with Homology to the G-Protein-Coupled Receptor Superfamily Associated with Estrogen Receptor Expression in Breast Cancer. 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EMBO J 28:523–532. https://doi.org/10.1038/emboj.2008.304 Krkoška M, Svobodová J, Kabátková M, et al (2021) Deregulation of signaling pathways controlling cell survival and proliferation in cancer cells alters induction of cytochrome P450 family 1 enzymes. Toxicology 461:152897. https://doi.org/10.1016/j.tox.2021.152897 Tables Table 1. List of primers used for gene expression analysis Gene Name Primer sequence (5´→3´) Amplicon (base pair) Annealing temperature (°C) RPL35a Forward- CGGCCTCCAAGCTCTCTAAG Reverse- CAGGTCCAGGGGCTTGTACT 131 60 KIF11 Forward- AAATCAGATGGACGTAAGGCAG Reverse- TAACTTTTCCTCTGTGGTGTCG 203 60 NCAPD3 Forward- GGAGCAAGAGTCGAATGGCG Reverse- CTTTGGCTGACGACGGAGTC 137 60 SMC1A Forward- CCAGGCCATCGTCATCTCTC Reverse- TGGTGAGGTCGAAGGTCAGG 119 60 CIT Forward- GTACCTGGACATCCCGAACC Reverse- CCTGGTATGAGGACGCCAAG 80 60 KIF20A Forward- GCCAAGCCACACACAGGTTC Reverse- TAGATGAGCCAGTTCTGCCC 127 60 TIMP3 Forward- CGCCTTCTGCAACTCCGACA Reverse- CTGCACATGGGGCATCTTGG 142 60 CYP1A1 Forward- ACCTTTGAGAAGGGCCACATCCG Reverse- TGACTGTGTCAAACCCAGCTCCAAAG 154 60 BRCA1 Forward- GATGCCTGGACAGAGGACAA Reverse- GGGATCTGGGGTATCAGGTA 140 60 AHRR Forward- GCGCCTCAGTGTCAGTTACC Reverse- CACTCACGACCAGAGCAAAGC 182 60 TOP2A Forward- GTGGCAAGGATTCTGCTAGTCC Reverse- ACCATTCAGGCTCAACACGCTG 135 60 GPER-v2 Forward- ATCTGGACGGCAGGTACC Reverse- GAAGAACAGATGCTCCTCACAC 149 60 GPER-v3 Forward- TGGACGGCAGCCCTGCTC Reverse- GCTGCTCACTCTCTGGGTAC 154 60 GPER-v4 Forward- GCGGGTCTCT TCCTCTCTC Reverse- GCTGCTCACTCTCTGGGTAC 166 60 Table 2: List of top 25 up-regulated genes Ensgene Symbol log2FC padj Description ENSG00000140465 CYP1A1 3.98 0.00 Cytochrome P450 Family 1 Subfamily A Member 1 ENSG00000130513 GDF15 2.78 0.00 Growth Differentiation Factor 15 ENSG00000108551 RASD1 2.28 0.00 Ras Related Dexamethasone Induced 1 ENSG00000102962 CCL22 2.20 0.00 C-C Motif Chemokine Ligand 22 ENSG00000107796 ACTA2 2.19 0.00 Actin Alpha 2, Smooth Muscle ENSG00000124762 CDKN1A 2.06 0.00 Cyclin Dependent Kinase Inhibitor 1A ENSG00000100292 HMOX1 1.99 0.00 Heme Oxygenase 1 ENSG00000138271 GPR87 1.95 0.00 G Protein-Coupled Receptor 87 ENSG00000173535 TNFRSF10C 1.90 0.00 TNF Receptor Superfamily Member 10c ENSG00000162772 ATF3 1.88 0.00 Activating Transcription Factor 3 ENSG00000205426 KRT81 1.87 0.00 Keratin 81 ENSG00000167772 ANGPTL4 1.84 0.00 Angiopoietin Like 4 ENSG00000063438 AHRR 1.77 0.00 Aryl Hydrocarbon Receptor Repressor ENSG00000116717 GADD45A 1.76 0.00 Growth Arrest and DNA Damage Inducible Alpha ENSG00000137868 STRA6 1.68 0.00 Signaling Receptor and Transporter of Retinol STRA6 ENSG00000100234 TIMP3 1.68 0.00 TIMP Metallopeptidase Inhibitor 3 ENSG00000128342 LIF 1.68 0.00 LIF Interleukin 6 Family Cytokine ENSG00000019186 CYP24A1 1.67 0.00 Cytochrome P450 Family 24 Subfamily A Member 1 ENSG00000105327 BBC3 1.64 0.00 BCL2 Binding Component 3 ENSG00000131080 EDA2R 1.61 0.00 Ectodysplasin A2 Receptor ENSG00000167755 KLK6 1.57 0.00 Kallikrein Related Peptidase 6 ENSG00000136378 ADAMTS7 1.52 0.00 ADAM Metallopeptidase with Thrombospondin Type 1 Motif 7 ENSG00000134013 LOXL2 1.52 0.00 Lysyl Oxidase Like 2 ENSG00000069812 HES2 1.49 0.00 Hes Family BHLH Transcription Factor 2 ENSG00000187134 AKR1C1 1.48 0.00 Aldo-Keto Reductase Family 1 Member C1 Table 3: List of top 25 down-regulated genes Ensgene Symbol log2 FC padj Description ENSG00000136824 SMC2 -2.69 0.00 Structural Maintenance Of Chromosomes 2 ENSG00000109805 NCAPG -2.65 0.00 Non-SMC Condensin I Complex Subunit G ENSG00000131747 TOP2A -2.58 0.00 DNA Topoisomerase II Alpha ENSG00000197299 BLM -2.54 0.00 BLM RecQ Like Helicase ENSG00000184661 CDCA2 -2.48 0.00 Cell Division Cycle Associated 2 ENSG00000174371 EXO1 -2.44 0.00 Exonuclease 1 ENSG00000156802 ATAD2 -2.43 0.00 ATPase Family AAA Domain Containing 2 ENSG00000174799 CEP135 -2.39 0.00 Centrosomal Protein 135 ENSG00000186871 ERCC6L -2.38 0.00 ERCC Excision Repair 6 Like, Spindle Assembly Checkpoint Helicase ENSG00000137812 KNL1 -2.30 0.00 Kinetochore Scaffold 1 ENSG00000138182 KIF20B -2.30 0.00 Kinesin Family Member 20B ENSG00000126787 DLGAP5 -2.30 0.00 DLG Associated Protein 5 ENSG00000011426 ANLN -2.30 0.00 Anillin, Actin Binding Protein ENSG00000148700 ADD3 -2.28 0.00 Adducin 3 ENSG00000119969 HELLS -2.26 0.00 Helicase, Lymphoid Specific ENSG00000092853 CLSPN -2.21 0.00 Claspin ENSG00000196757 ZNF700 -2.17 0.00 Zinc Finger Protein 700 ENSG00000145241 CENPC -2.16 0.00 Centromere Protein C ENSG00000021776 AQR -2.14 0.00 Aquarius Intron-Binding Spliceosomal Factor ENSG00000143476 DTL -2.13 0.00 Denticleless E3 Ubiquitin Protein Ligase Homolog ENSG00000065328 MCM10 -2.13 0.00 Minichromosome Maintenance 10 Replication Initiation Factor ENSG00000134352 IL6ST -2.11 0.00 Interleukin 6 Cytokine Family Signal Transducer ENSG00000114346 ECT2 -2.09 0.00 Epithelial Cell Transforming 2 ENSG00000102189 EEA1 -2.09 0.00 Early Endosome Antigen 1 ENSG00000024526 DEPDC1 -2.07 0.00 DEP Domain Containing 1 Additional Declarations No competing interests reported. Supplementary Files Supplementarydata1.pdf Supplementarydata2.csv Supplementarydata3.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4291506","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294463065,"identity":"2a2d5687-47f2-404f-95f2-50deab80d3fb","order_by":0,"name":"Juana Hatwik","email":"","orcid":"","institution":"Indian Institute of Technology Guwahati","correspondingAuthor":false,"prefix":"","firstName":"Juana","middleName":"","lastName":"Hatwik","suffix":""},{"id":294463066,"identity":"4c4cebb6-8cb4-4ca7-afcb-f42470d52b62","order_by":1,"name":"Uttariya Pal","email":"","orcid":"","institution":"Indian Institute of Technology Guwahati","correspondingAuthor":false,"prefix":"","firstName":"Uttariya","middleName":"","lastName":"Pal","suffix":""},{"id":294463067,"identity":"bf5d910c-b541-4941-8bff-63313ea6c669","order_by":2,"name":"Mohan C. Manjegowda","email":"","orcid":"","institution":"Indian Institute of Technology Guwahati","correspondingAuthor":false,"prefix":"","firstName":"Mohan","middleName":"C.","lastName":"Manjegowda","suffix":""},{"id":294463068,"identity":"bbc71bc5-4a02-49f7-aa65-4fe2ec18f0f1","order_by":3,"name":"Anil Mukund Limaye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYHACxgMfDCSgbAMi9RycYSAhQZqWwzwMDBKElcEA/7QzBodtCizq5B2YH35gKLhDWIvE7RyDwzlAhxkeYDOWYDB4RoQ1cC0NDGZAvxwmrEMepMUCrIX9G3FaDEBaGIBa5Bl4iLTF8HZawcEeAwnJDcw8xRIJxGiRu5288cGPP3X88u3tGz98+EOEFoQLQYoTSNAADIcGkpSPglEwCkbBSAIAPDIzTxad7yAAAAAASUVORK5CYII=","orcid":"","institution":"Indian Institute of Technology Guwahati","correspondingAuthor":true,"prefix":"","firstName":"Anil","middleName":"Mukund","lastName":"Limaye","suffix":""}],"badges":[],"createdAt":"2024-04-19 07:21:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4291506/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4291506/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55221818,"identity":"315ccb50-0edb-4f78-adfc-bc4fc8da6d23","added_by":"auto","created_at":"2024-04-24 09:20:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71365,"visible":true,"origin":"","legend":"\u003cp\u003eG1 induces cell cycle arrest in MCF-7 cells. \u003cstrong\u003eA\u003c/strong\u003e. Images of MCF-7 and T47D cells treated with 0.1% ethanol (vehicle control), 100 nM G1, or 1 μM G1 for a period of 48 h. The images were captured using a 20X objective on the EVOS XL core cell imaging system (Life Technologies, PA, USA). Scale bar is 200 microns. \u003cstrong\u003eB\u003c/strong\u003e. Flow cytometry of MCF-7 cells treated with 0.1% ethanol (\u003cem\u003eleft panel\u003c/em\u003e) or 1 mM G1 (\u003cem\u003eright panel\u003c/em\u003e) for 24 h.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4291506/v1/b81d9e2a52ae2f7b634e3fbd.jpg"},{"id":55222343,"identity":"3a049c80-8e06-45e5-8c37-740f4d48ff18","added_by":"auto","created_at":"2024-04-24 09:28:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":206045,"visible":true,"origin":"","legend":"\u003cp\u003eG1 induces genome-wide transcriptomic alterations in MCF-7 cells. Total RNA samples were subjected to RNA-seq analysis as described earlier [25]. The raw count data were downloaded, processed and analyzed as described in materials and methods. \u003cstrong\u003eA\u003c/strong\u003e. Volcano plot. \u0026nbsp;Dots represent genes regulated (colored), or un-regulated (grey) by 1 mM G1, using cut-off values of 0, and 5% for log2FoldChange, and padj, respectively. Yellow and red dots represent down-regulated (n = 1464) and up-regulated (n = 837) genes, respectively. \u003cstrong\u003eB\u003c/strong\u003e. Heatmap showing the two major clusters corresponding to down-regulated (n = 1464), and up-regulated (n = 837). The color scale represents log-transformed normalized counts. \u003cstrong\u003eC\u003c/strong\u003e. Summarized results of GSEA. Bars represent the normalized enrichment scores of the gene-sets, which are indicated on the vertical axis. \u0026nbsp;Significant enrichment was based on 25% FDR cut-off. \u003cstrong\u003eD\u003c/strong\u003e. RT-qPCR validation of selected genes indicated on the x-axis. Total RNA samples isolated from MCF-7 cells treated with 0.1% ethanol or 1 mM G1 were subjected to RT-qPCR analysis. The data were analyzed by ΔCt method [23], using RPL35a as an internal control. The expression in 0.1% ethanol treated cells (controls) were set to 1, and those determined for 1 mM G1-treated cells were expressed relative to control. Bars represent mean relative expression ± SD (n = 3). For each gene, the data were analyzed by a one-sample one-tailed t-test to ascertain whether the mean relative expression was significantly different from 1. Asterisks represent significance. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001. \u003cstrong\u003eE\u003c/strong\u003e. Summarized results of GO analysis. Data were analyzed using clusterProfiler package in R using an 25% FDR cut-off. Bars represent gene counts for the enriched GO terms indicated in y-axes associated with upregulated (left panel) and downregulated (right panel) genes. BP- biological processes, CC- cellular components, MF- and molecular functions.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4291506/v1/7724984bb80a5aea9bd8ee61.png"},{"id":55221820,"identity":"f86bb4e1-4acb-44e5-a0ba-8761150b2358","added_by":"auto","created_at":"2024-04-24 09:20:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60924,"visible":true,"origin":"","legend":"\u003cp\u003eG1 modulates the expression of xenobiotic response gene CYP1A1 in MCF-7 cells. \u003cstrong\u003eA\u003c/strong\u003e. Enrichment plot for xenobiotic metabolism genes. \u003cstrong\u003eB\u003c/strong\u003e. Heatmap showing the expression pattern of the leading edge genes of the xenobiotic metabolism gene-set. The color scale on the right represents log transformed normalized counts. \u003cstrong\u003eC\u003c/strong\u003e. 1 mM G1 induces the expression of CYP1A1 mRNA in a dose-dependent manner. Total RNA samples isolated from MCF-7 cells treated with 0.1% ethanol (control), or the indicated concentrations of G1 for 48 h, were subjected to RT-qPCR. The data were analyzed by ΔCt method as described in materials and methods. Note that higher the ΔCt value, lower the normalized expression of CYP1A1 mRNA in a given sample. Boxplot shows the distribution of ΔCt values for each treatment group. The data were analyzed by one-way ANOVA followed by TukeyHSD (n = 3 biological replicates). \u003cstrong\u003eD\u003c/strong\u003e. CH223191 blocks G1 mediated induction of CYP1A1 mRNA. The cells were treated with 1 mM G1 in the presence or absence of the 10 mM AHR antagonist CH223191 for 48 h, and CYP1A1 mRNA expression was analyzed by RT-qPCR as described in \u003cstrong\u003eC\u003c/strong\u003e. The ΔCt values were analyzed by two-way ANOVA to examine the main effects of G1, CH223191, or their interaction. This was followed by TukeyHSD to ascertain pairs of groups with significant differences in mean ΔCt values. Boxplot shows the distribution of ΔCt (n = 3 biological replicates) for each of the treatment groups. \u003cstrong\u003eE\u003c/strong\u003e. MCF-7 cells were treated with 1 mM G1 alone or in combination with 10 mM CH223191 for 48 h. Thereafter the images of the cells were captured using a 20X objective on the EVOS XL core cell imaging system. Scale bar represents 200 mM. The lower case letters in panels \u003cstrong\u003eC\u003c/strong\u003e and \u003cstrong\u003eD\u003c/strong\u003e indicate statistical difference between pairs of treatments.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4291506/v1/561c28c8a107b1eeecc26f44.jpg"},{"id":55221822,"identity":"4d251fa6-3c85-4ce0-99b5-4d1ffe8b91a4","added_by":"auto","created_at":"2024-04-24 09:20:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":63463,"visible":true,"origin":"","legend":"\u003cp\u003eG1 affects cell viability and gene expression in MDA-MB-453 cells. \u003cstrong\u003eA\u003c/strong\u003e. RT-PCR analysis of GPER mRNA in MCF-7 and MDA-MB453 cells. GPER-v2, GPER-v3, and GPER-v4 are three transcript variants. \u003cstrong\u003eB\u003c/strong\u003e. Western blotting analysis of GPER protein expression. Total protein isolated from MCF-7 and MA-MB-453 cells were subjected to western blotting analysis using primary antibody against GPER. Histone H3 was used as an internal control. \u003cstrong\u003eC\u003c/strong\u003e. MTT assay to determine the effect of 1 mM G1 alone, or in combination with CH223191 in MDA-MB-453 cells. Bars represent mean viability±SD (n = 12 wells). The data were analysed by one-way ANOVA followed by TukeyHSD. \u003cstrong\u003eD\u003c/strong\u003e. Dose-response study of the effect of G1 on CYP1A1 mRNA in MDA-MB-453 cells. The cells were treated with indicated concentrations of G1 for 48 h. The total RNA was subjected to RT-qPCR analysis, and the data were analyzed using the ΔCt method as described in materials and methods. The boxplots show the distribution of ΔCt values in each of the treatment groups (n = 3). The data were analyzed by one-way ANOVA, followed by TukeyHSD to identify pairs of groups with significant difference. \u003cstrong\u003eE\u003c/strong\u003e. Effect of CH223191 on G1-induced expression of CYP1A1. The cells were treated with 1 mM G1 alone or in combination with 10 mM CH223191 for a period of 48 h. The total RNA was subjected to RT-qPCR analysis followed by analysis of the data using two-way ANOVA to examine the main effect of G1, CH233191, or their interaction. This was followed by TukeyHSD for pair-wise comparison of groups. Boxplots show the distribution of ΔCt values (n = 3). The lower case letters in panels \u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eD\u003c/strong\u003e and \u003cstrong\u003eE\u003c/strong\u003e represent statistical difference in pairs of treatment groups. F. Effect of G1 on mRNA expression levels in MDA-MB-453 cells. Total RNA samples isolated from MDA-MB-453 cells treated with 0.1% ethanol or 1 mM G1 were subjected to RT-qPCR analysis. The data were analyzed by ΔCt method [23], using RPL35a as an internal control. The expression in 0.1% ethanol treated cells (controls) were set to 1, and those determined for 1 mM G1-treated cells were expressed relative to control. Bars represent mean relative expression ± SD (n = 3). For each gene, the data were analyzed by a one-sample one-tailed t-test to ascertain whether the mean relative expression was significantly different from 1. Asterisks represent significance. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4291506/v1/e3568cf9330c3e6eef049e03.png"},{"id":72770907,"identity":"c74a17a4-4dcb-4ced-8308-d1d67adf4689","added_by":"auto","created_at":"2025-01-02 03:16:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1074674,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4291506/v1/32d24fe7-7080-4310-a815-a76871d24746.pdf"},{"id":55221821,"identity":"2e961562-e097-4c85-9116-543059351c96","added_by":"auto","created_at":"2024-04-24 09:20:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":115301,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4291506/v1/ec9e37fed12e0641c7d61015.pdf"},{"id":55221824,"identity":"b27754b1-40f5-47aa-a5aa-2068d61e4e50","added_by":"auto","created_at":"2024-04-24 09:20:05","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":201720,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata2.csv","url":"https://assets-eu.researchsquare.com/files/rs-4291506/v1/fd0f44e85f957d096ad84f94.csv"},{"id":55221825,"identity":"f9787c1c-779c-4632-8c2e-cb6a64fb0af3","added_by":"auto","created_at":"2024-04-24 09:20:05","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":169538,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4291506/v1/c216b4ff013bb058bdb35590.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global transcriptomic analysis reveals CYP1A1 as a target of the GPER agonist G1","fulltext":[{"header":"Introduction","content":"\u003cp\u003e17b-estradiol induces short-term non-genomic effects via the seven transmembrane-spanning G-protein coupled estrogen receptor (GPER). Originally, it was identified as an estrogen receptor (ER)-a\u0026nbsp;co-expressed gene (GPCR-Br/GPR30) in breast cancer cells and tissues\u0026nbsp;[1]. Considered as an orphan receptor, it emerged as a mediator of EGF-like effects of estrogen, leading up to ERK1/2 activation\u0026nbsp;[2]. It was subsequently shown to specifically bind estrogen to cause rapid intracellular calcium mobilization, and generation of phosphatidylinositol 3,4,5-trisphosphate\u0026nbsp;[3, 4]. \u0026nbsp;These developments led to the re-christening of this receptor as GPER\u0026nbsp;[5]. A large body of work highlights its relevance in different physiological contexts, such as immune, reproductive, cardiovascular, neuroendocrine, urinary, and musculoskeletal systems\u0026nbsp;[6]. A potential target in cardiovascular, immune and infectious diseases, obesity, and diabetes\u0026nbsp;[7], GPER’s aberrant expression associated with established clinico-pathological variables, in endocrine or non-endocrine tumors, sets its relevance in carcinogenesis\u0026nbsp;[8].\u003c/p\u003e\n\u003cp\u003eBeing a typical G-protein coupled receptor, ligand-binding leads to signalling cascades downstream of G\u003csub\u003ea\u003c/sub\u003e and G\u003csub\u003ebg\u003c/sub\u003e subunits\u0026nbsp;[8]. GPER activation is linked to EGFR transactivation via src-like tyrosine kinase-mediated activation of MMPs, HB-EGF ectodomain shedding, and activation of ERK1/2\u0026nbsp;[2]. Besides this, activation of adenylate cyclase, PI3K-AKT, and eNOS, \u0026nbsp;and increased cAMP and intracellular calcium mobilization are well-demonstrated consequences of GPER activation\u0026nbsp;[8, 9]. Both the aforementioned arms of GPER signalling ultimately lead to activation of transcription factors, and therefore, modulation of gene expression on a longer time-scale\u0026nbsp;[9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe identification of G1, a GPER-specific ligand provided the impetus to study specific consequences of GPER activation\u0026nbsp;[10]. Prior to this, the role GPER would be inferred based on correlations between its expression, and the effects brought about by estrogen. However, G1 produces differential effects in cell culture models; proliferation\u0026nbsp;[11, 12], or its inhibition\u0026nbsp;[13–15]. Adding to the confusion, few studies have demonstrated G1-induced cell death in a GPER-independent manner by targeting tubulin polymerization, or microtubules\u0026nbsp;[16–18]. G1 treatment in GPER-positive settings has enabled identification of transcriptional targets of GPER, such as c-fos, pS2, and the cyclins A, D1, and E, using candidate gene approaches\u0026nbsp;[9], or the microarray technology\u0026nbsp;[19].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe have analyzed RNA-seq data to ascertain transcriptomic alteration in MCF-7 cells treated with 1 mM G1 for a period of 48 h, which is associated with G1-induced loss of cell viability. Furthermore, we show that G1 increases CYP1A1 mRNA expression via the aryl hydrocarbon receptor (AHR). \u0026nbsp;G1 also modulated the expression of CYP1A1, and other selected mRNA transcripts, in the GPER-negative MDA-MB-453 cells, thereby casting doubts over GPER-specificity of the observed effects. \u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eCell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell culture plasticwares were from Eppendorf (Hamburg, Germany) and Nunc (Roskilde, Denmark). Phenol red-containing Dulbecco\u0026rsquo;s Modified Eagle\u0026rsquo;s medium (DMEM),\u0026nbsp;Roswell Park Memorial Institute Medium (RPMI-1640),\u0026nbsp;100X Pen-Strep antibiotic solution, and trypsin-EDTA were purchased from Himedia (Mumbai, India). Fetal bovine serum (FBS) was from Gibco (NY, USA). MCF-7, and MDA-MB-453 cells were obtained from the National Centre for Cell Science (Pune, India). They were grown in phenol red-containing DMEM or RPMI-1640, respectively, supplemented with 10% FBS (heat-inactivated), 100 U/mL penicillin, and 100 \u0026mu;g/mL streptomycin, in a humidified chamber maintained at 37⁰C and 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTypically, 0.2\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells were seeded in 35 mm dishes. After 48 h, the cells were washed with Dulbecco\u0026rsquo;s phosphate buffered saline (Himedia, Mumbai, India). They were then treated with 100 nM or 1 \u0026mu;M concentration of G1 (Cat. #10008933, CAS.#881639-98-1; Cayman Chemicals, Ann Arbor, USA), for 24 or 48 h depending upon the experiment. Cells treated with ethanol (0.1% v/v) served as controls. To test the effect of the AHR inhibitor CH223191 (Cat No. C8124, St. Louis, MO, USA), two additional treatment groups were added; comprising of cells treated with 10 mM CH223191 alone, or in combination with 1 mM G1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow cytometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were trypsinized, washed with phosphate buffered saline (PBS), and permeabilized and fixed with ice-cold methanol. After 1 h, methanol was removed by PBS washes. The cells were treated with RNase A for 30 min at 37 \u003csup\u003eo\u003c/sup\u003eC, and stained with propidium iodide (PI, 20\u0026nbsp;mg/mL solution in PBS). The PI-stained cells were analyzed by FACSCalibur (Becton, Dickinson and Company, USA) in FL2 channel (585/42 bandpass filter). A dot plot of FL2 area vs. FL2 width was used to discriminate doublets. The distribution of cells at different stages of cell cycle was estimated using FCS Express 6 software. Histogram of FL2 area for the gated population was plotted to represent the distribution of cells in various stages of the cell cycle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern Blotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were lysed in RIPA lysis buffer containing protease inhibitor cocktail. Protein concentration was estimated by Lowry\u0026rsquo;s method [20]. 30 \u0026micro;g of total protein was fractionated on 10% reducing SDS-PAGE, and transferred to nitrocellulose membrane. After blocking in 1% gelatin in TBST for 1 h, the blots were probed with primary antibodies against histone-H3 (Cat. No. BB-AB0055, Bio Bharati Life Science, India), or GPER antibody generated as described earlier [21], which were diluted in 0.1% gelatin in TBST. Blots were washed for 30 min with TBST (3 \u0026times; 10 min) to remove unbound primary antibody. They were incubated with the HRP-tagged secondary antibody (1:5000 dilution in 0.1% gelatin in TBST) for 1 h followed by three TBST washes of 10 min each. Blots were developed with enhanced chemiluminescence reagent (Bio-Rad Laboratories, Hercules, CA, USA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTotal RNA isolation, and RT-qPCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was isolated using a reagent, which was prepared in-house according to Chomcsynski and Sacchi [22] with minor modifications. 2 \u0026micro;g of total RNA was reverse transcribed using High Capacity cDNA Reverse Transcription kit (Invitrogen, USA) as per manufacturer\u0026rsquo;s instructions. 2 ml of diluted cDNA (1:10) was used as template for PCR reaction with PowerUP SYBR Green Master Mix (Cat No. A25743, Thermo Scientific, PA, USA) and gene-specific primers (Table 1). RPL35a served as an internal control. The PCR reactions were carried out in AriaMx real time PCR System (Agilent). Expression levels of genes were analyzed by \u0026Delta;\u0026Delta;Ct relative quantitation method [23], or using the \u0026Delta;Ct method as used earlier [24]. In the latter, the average Ct value for \u003cem\u003ethe test gene of interest\u003c/em\u003e (Ct\u003csup\u003etest\u003c/sup\u003e), and the internal control \u003cem\u003eRPL35a\u003c/em\u003e (Ct\u003csup\u003eRPL35a\u003c/sup\u003e) were determined. The difference Ct\u003csup\u003etest\u003c/sup\u003e - Ct\u003csup\u003eRPL35a\u003c/sup\u003e, which is referred to as \u0026Delta;Ct, was considered as a measure of the normalized expression. Thus, higher the \u0026Delta;Ct value, lower is the normalized expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRoutine RT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted, reverse transcribed, and subjected to PCR as described in the previous section. However, the reactions were carried out in Veriti 96 Well Thermal Cycler (Applied Bio Systems, USA). After the completion of the PCR, the products were analyzed on 2% agarose gels. The images of ethidium bromide-stained bands were captured using ChemiDoc\u0026trade; XRS + System with Image Lab\u0026trade; Software (Bio-Rad Laboratories, Hercules, CA, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMTT assay:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were seeded in 96-well plates. When the cells became 50% confluent, they were treated with the indicated concentrations of G1, with or without CH223191, for 48 h. The spent medium was removed, and the cells were washed with PBS. MTT (0.5 mg/ml) was added to the wells and incubated for 3 h at 37 ˚C. Thereafter, MTT was removed, and formazan crystals were dissolved in 100 \u0026micro;l of DMSO. The absorbance was measured at 570 nm (A\u003csub\u003e570nm\u003c/sub\u003e) and 690 nm (A\u003csub\u003e690nm\u003c/sub\u003e) using Varioskan LUX microplate reader (Thermo Fisher, PA, USA).\u0026nbsp;The difference, A\u003csub\u003e570nm\u003c/sub\u003e \u0026ndash; A\u003csub\u003e690nm\u003c/sub\u003e, was used as a measure of viability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA-seq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe detailed protocol for treatment of cells, total RNA extraction, library preparation, and sequencing is already published\u0026nbsp;[25]. The raw sequence data are freely accessible through the Gene Expression Omnibus (accession number GSE188706).\u003c/p\u003e\n\u003cp\u003eThe raw data were downloaded using the aforementioned accession number and re-analyzed. FASTQC tool [26], \u0026nbsp;was used for read quality assessment. Adapters and low quality reads were removed using Trimmomatic [27]. The trimming parameters were- IlluminaClip: TruSeq3.fa:2:30:10:2:keepBothReads; LEADING:3; TRAILING:3; SLIDINGWINDOW:4:30; MINLEN: 50. Trimmed reads were aligned using STAR aligner [28] to generate BAM files. Read count data obtained using the featureCounts tool [29], were processed using the DESeq2 package in R [30]. Quality of the normalized count data was assessed and visualized by unsupervised clustering, correlation heatmap, and principal component analysis. The normalized counts were fitted on the negative binomial model. The normalized counts were subjected to statistical analyses by applying the Wald statistic (\u0026alpha; = 0.05) and FDR correction with a 5% cut-off.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene set enrichment and gene ontology analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003efGSEA package [31] was used for identification of enriched gene-sets using 25% cut-off for FDR. Normalized enrichment scores were generated, and plotted using additional packages in R. The clusterProfiler package [32] in R was used for GO analysis for identification of overrepresented biological terms associated with differentially expressed genes under three functional categories, namely biological processes (BP), cell components (CC), and molecular function (MF).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelative mRNA expression data for G1-treated cells compared to control were analyzed by one-sample one tailed t-tests to examine whether the mean relative expression was significantly greater or less than 1. Multiple group data were analysed by one-way ANOVA. In experiments where the interaction between two variables were analyzed, two-way ANOVA was applied. The data were examined for homogeneity of variance using the Levene\u0026rsquo;s test before applying ANOVA. TukeyHSD was used for multiple comparison between pairs of groups. All statistical tests were performed at 5% level of significance (p \u0026lt; 0.05).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eM G1 negatively affects MCF-7 cell viability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA previous study from our laboratory showed that MCF-7 cells were more sensitive to G1-mediated cell cycle arrest compared to T47D cells, although both express GPER. 500 nM G1 significantly reduced the viability of MCF-7, but not T47D cells. 1\u0026nbsp;mM G1 significantly affected cell viability; the reduction being greater in MCF-7, compared to T47D cells \u0026nbsp;[33]. Fig. 1A shows the morphology of cells treated with 0.1% ethanol (vehicle control), 100 nM G1, or 1\u0026nbsp;mM G1, for 48 h. None of the concentrations of G1 affected the morphology of T47D cells. However, MCF-7 cells treated with 1\u0026nbsp;mM G1 appeared to lose their attachment with the growth surface, resulting in spherical morphology. They resembled those treated with colchicine (\u003cem\u003edata not shown\u003c/em\u003e). This is mirrored by the distribution of cells in G1, S, and G2 phases of the cell cycle. After 24 h of vehicle treatment, the percentage of cells in G1, S, and G2 phases were 65.4, 20.6 and 14.0, respectively. However, the percentage of cells in the three phases after 24 h of 1\u0026nbsp;mM G1 treatment were 44.2, 11.6 and 44.1 respectively (Fig. 1B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG1-induced transcriptomic alterations in MCF-7 cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA samples from MCF-7 cells treated with vehicle (0.1% ethanol), 100 nM G1, or 1\u0026nbsp;mM G1, were subjected to RNA-seq using the Illumina platform\u0026nbsp;[25]. The raw sequence data (GSE188706) were downloaded, and re-analyzed as described in materials and methods. DESeq2 analysis of the data using 5% cut-off for FDR showed that 100 nM G1 did not significantly affect the MCF-7 cell transcriptome. This is reflected in the correlation heatmap, or the PCA plot, which were generated using the regularised log-transformed counts. The three biological replicate samples of MCF-7 cells treated with 1\u0026nbsp;mM G1 emerged as a distinct group, which was well separated from the remaining samples (Supplementary data 1). 1\u0026nbsp;mM G1, however, caused a significant change in the transcriptome; modulating the expression of 2301 genes. These were comprised of 837 upregulated, and 1464 downregulated genes as illustrated in Fig. 2A (volcano plot), and Fig. 2B (expression heatmap). The complete list of significantly modulated genes using a log\u003csub\u003e2\u003c/sub\u003eFoldChange threshold of 0, and FDR cut-off of 5% is provided as Supplementary data 2. Genes listed in Table 2, and Table 3 show the functional diversity of the top 25 upregulated and downregulated genes, which include structural proteins, enzymes, signaling molecules, and cytokines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGSEA and GO analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSEA revealed positive enrichment of \u003cem\u003ep53 pathway\u003c/em\u003e, \u003cem\u003eTNF-\u003c/em\u003e\u003cem\u003ea\u003c/em\u003e\u003cem\u003e\u0026nbsp;signalling\u003c/em\u003e, and \u003cem\u003eapoptosis\u003c/em\u003e gene-sets, along with negative enrichment of \u003cem\u003eE2F targets\u003c/em\u003e, \u003cem\u003eMYC targets\u003c/em\u003e, and the \u003cem\u003eG2M checkpoint\u003c/em\u003e gene-set (Fig. 2C). The normalized enrichment plots for the aforementioned gene sets, along with the names of their respective leading edge genes are provided as Supplementary data 3. Few G1 modulated genes were validated by RT-qPCR. Consistent with the RNA-seq data, AHRR and TIMP3 transcripts were significantly up-regulated, whereas BRCA1, CIT, KIF11, KIF20A, NCAPD3, SMC1A, and TOP2A transcripts were significantly downregulated upon 1\u0026nbsp;mM G1 treatment (Fig. 2D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO analysis allowed identification of over-represented GO terms associated with the significantly modulated genes under three categories, namely biological processes (BP), cellular compartment (CC) and molecular function (MF). Under biological processes, the term \u003cem\u003epositive regulation of programmed cell death\u003c/em\u003e was over-represented for genes upregulated by G1 (Fig. 2E, \u003cem\u003eleft panel\u003c/em\u003e). On the other hand, the GO terms over-represented in genes downmodulated by G1 were \u003cem\u003echromosome\u003c/em\u003e, \u003cem\u003echromosomal region\u003c/em\u003e, \u003cem\u003econdensed chromosome\u003c/em\u003e, \u003cem\u003ecentromeric region\u003c/em\u003e, and \u003cem\u003emicrotubule cytoskeleton\u003c/em\u003e under cellular compartment, and \u003cem\u003ecell cycle\u003c/em\u003e, \u003cem\u003echromosome segregation\u003c/em\u003e, and \u003cem\u003ecell cycle process\u003c/em\u003e under biological processes (Fig. 2E, \u003cem\u003eright panel\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG1 induces CYP1A1 mRNA expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the genes modulated by G1, there was a positive enrichment of genes related to xenobiotic metabolism (Fig. 2C). Fig. 3A shows the enrichment plot for this gene-set. The expression heatmap shown in Fig. 3B shows that the leading edge genes in this set are induced by 1\u0026nbsp;mM G1. CYP1A1, one of the leading edge genes was the topmost G1-induced gene with a log\u003csub\u003e2\u003c/sub\u003eFoldChange of 3.98 (p\u0026nbsp;≈\u0026nbsp;0). A dose-response experiment to validate this result showed that 100 nM G1 did not significantly affect CYP1A1 mRNA expression. However, 500 nM, and 1\u0026nbsp;mM G1 significantly increased CYP1A1 mRNA expression. The effect of 1\u0026nbsp;mM G1 was significantly greater compared to 500 nM G1 (Fig. 3C). CYP1A1 is a well-known transcriptional target of AHR\u0026nbsp;[34]. \u0026nbsp;AhR inhibitor CH223191, significantly reduced the basal levels of CYP1A1 mRNA. Moreover, it significantly blocked G1-stimulated increase of CYP1A1 mRNA (Fig. 3D). Notably, CH223191 alone did not cause any change in cell morphology. However, G1 in combination with CH233191 caused the cells to round-off due to detachment from the surface in a manner that was brought about by G1 alone (Fig. 3E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG1 induces CYP1A1 mRNA expression in MDA-MB-453 cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effect of G1 treatment on cell viability and gene expression was also studied in the GPER-negative MDA-MB-453 cells (Fig. 4A,B). 1 mM G1 marginally but significantly reduced the viability of MDA-MB-453 cells, which was not affected by CH223191 (Fig. 4C). A dose-response study showed that 1 mM G1 significantly induced CYP1A1 mRNA expression (Fig. 4D). The induction of CYP1A1 mRNA was significantly blocked by CH223191 (Fig. 4E). In MDA-MB-453 cells, we examined the effect of G1 on those genes, which were modulated by G1 in MCF-7 cells. As shown in Fig. 4F, BRCA1, CIT, KIF11, NCAPD3 and TOP2A were downmodulated by G1, while AHRR, and TIMP3 were upregulated in a manner that was observed in MCF-7 cells. There was no effect on KIF20A, and SMC1A.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current understanding of GPER signalling, and its consequences in a cellular milieu dominated by other estrogen receptors, owes much to the identification of G1 as a GPER agonist [10]. \u0026nbsp;The consequences known till date include short-term non-genomic effects, and long-term effects on gene expression, in relation to the effect on cell proliferation. Pandey and co-workers used microarray technology to elucidate transcriptomic changes induced by estrogen or 4-hydroxytamoxifen in ER-negative/GPER-positive SKBR3 cells, which were associated with proliferation and migration. They identified 36 GPER target genes after taking into account the gene expression changes in cells, which were depleted of GPER expression [35]. Schuler-Toprak and co-workers applied the Affymetrix GeneChip technology on ovarian cancer cells to show that G1 inhibited growth, and described the associated transcriptomic response [19]. Here we have employed RNA-seq technology to deduce transcriptomic alterations in MCF-7 cells induced by G1, assuming its GPER-specificity. Our results capture genomic effects induced by G1, which is associated with cell cycle arrest. Positive enrichment of p53 pathway, TNF-a signalling, and apoptosis related genes, and negative enrichment of E2F or MYC targets, and G2M checkpoint gene-set are consistent with G1-induced loss of cell viability. However, in view of the following discussion, it would be more appropriate to consider the observed transcriptomic changes as G1-induced, and not strictly GPER-mediated.\u003c/p\u003e\n\u003cp\u003eCYP1A1 is a prominent drug-metabolizing enzyme, and a mediator of xenobiotic response\u0026nbsp;[34]. It not only emerged as the most upregulated gene, but was also among the leading edge genes within the positively enriched xenobiotic response gene-set. This observation raises interesting possibilities. Given that perturbation of survival- or proliferation-related pathways leads to altered CYP1A1 expression\u0026nbsp;[36], its induction by G1 may be an indirect result of cell cycle arrest and apoptosis. CYP1A1 is a direct transcriptional target of AHR\u0026nbsp;[34]. The inability of G1 to induce CYP1A1 mRNA in the presence of CH223191 indicates that G1 activates the AHR pathway. Xenobiotics such as polycyclic aromatic hydrocarbons are known to induce their own metabolism via the AHR pathway. It will be interesting to investigate whether G1 is perceived by the cells as a xenobiotic. G1 or a metabolite of G1, resulting from the enzymatic activity of any one or more of the cellular drug-metabolizing enzymes, directly or indirectly interacts with AHR to activate its transactivation function. Such mechanisms would be GPER agnostic, since G1 appears to induce CYP1A1 mRNA in the GPER-negative MDA-MB-453 cells.\u003c/p\u003e\n\u003cp\u003eThe popularity of G1 as a pharmacological tool to activate GPER is evident from the plethora of published studies. However, the studies, which have applied G1 to probe GPER function have yielded controversial results. While some studies have shown enhanced proliferation [11, 12], others have demonstrated the opposite [13\u0026ndash;15]. Few studies have demonstrated G1-mediated effects in the absence of GPER [16\u0026ndash;18]. This controversy has led to the larger controversy about GPER\u0026rsquo;s role in cancer; tumor promoter or tumor suppressor. G1 inducing similar alterations in gene expression in the GPER-positive MCF-7 cells, and GPER-negative MDA-MB-453 cells, is an indication that off-target effects are likely. However, it does not disqualify G1 as a GPER agonist. Different cell culture models have different levels of GPER expression. The observed effect of a given concentration of G1 may be a function of GPER expression. It is possible that at low concentrations of G1, GPER-mediated effects dominate. But at higher concentrations, G1 may bind other low-affinity targets to generate what appears as GPER-independent effects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical Approval- It is not applicable\u003c/p\u003e\n\u003cp\u003eConsent to participate- It is not applicable\u003c/p\u003e\n\u003cp\u003eConsent to publish- It is not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.H., U.P., M.C.M performed the experiments and collected data. J.H., A.M.L analyzed the data. M.C.M, U.P. and A.M.L conceptualized the experiments. U.P. wrote the first draft of the manuscript, and all authors contributed to the revision and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding was from the Department of Biotechnology, Govt. of India (Sanction letter No. BT/506/NE/TBP/2013, and BT/PR16071/NER/95/63/2015).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCarmeci C, Thompson DA, Ring HZ, et al (1997) Identification of a Gene (GPR30) with Homology to the G-Protein-Coupled Receptor Superfamily Associated with Estrogen Receptor Expression in Breast Cancer. Genomics 45:607\u0026ndash;617. https://doi.org/10.1006/geno.1997.4972\u003c/li\u003e\n\u003cli\u003eFilardo EJ, Quinn JA, Bland KI, Frackelton AR (2000) Estrogen-Induced Activation of Erk-1 and Erk-2 Requires the G Protein-Coupled Receptor Homolog, GPR30, and Occurs via Trans-Activation of the Epidermal Growth Factor Receptor through Release of HB-EGF. Mol Endocrinol 14:1649\u0026ndash;1660. https://doi.org/10.1210/mend.14.10.0532\u003c/li\u003e\n\u003cli\u003eThomas P, Pang Y, Filardo EJ, Dong J (2005) Identity of an Estrogen Membrane Receptor Coupled to a G Protein in Human Breast Cancer Cells. Endocrinology 146:624\u0026ndash;632. https://doi.org/10.1210/en.2004-1064\u003c/li\u003e\n\u003cli\u003eRevankar CM, Cimino DF, Sklar LA, et al (2005) A Transmembrane Intracellular Estrogen Receptor Mediates Rapid Cell Signaling. Science (80- ) 307:1625\u0026ndash;1630. https://doi.org/10.1126/science.1106943\u003c/li\u003e\n\u003cli\u003eProssnitz ER, Arterburn JB (2015) International Union of Basic and Clinical Pharmacology. XCVII. G Protein\u0026ndash;Coupled Estrogen Receptor and Its Pharmacologic Modulators. Pharmacol Rev 67:505\u0026ndash;540. https://doi.org/10.1124/pr.114.009712\u003c/li\u003e\n\u003cli\u003eProssnitz ER, Barton M (2011) The G-protein-coupled estrogen receptor GPER in health and disease. Nat Rev Endocrinol 7:715\u0026ndash;726. https://doi.org/10.1038/nrendo.2011.122\u003c/li\u003e\n\u003cli\u003eBarton M (2016) Not lost in translation: Emerging clinical importance of the G protein-coupled estrogen receptor GPER. Steroids 111:37\u0026ndash;45. https://doi.org/10.1016/j.steroids.2016.02.016\u003c/li\u003e\n\u003cli\u003eProssnitz ER, Barton M (2023) The G protein-coupled oestrogen receptor GPER in health and disease: an update. Nat Rev Endocrinol 19:407\u0026ndash;424. https://doi.org/10.1038/s41574-023-00822-7\u003c/li\u003e\n\u003cli\u003eProssnitz ER, Maggiolini M (2009) Mechanisms of estrogen signaling and gene expression via GPR30. Mol Cell Endocrinol 308:32\u0026ndash;38. https://doi.org/10.1016/j.mce.2009.03.026\u003c/li\u003e\n\u003cli\u003eBologa CG, Revankar CM, Young SM, et al (2006) Virtual and biomolecular screening converge on a selective agonist for GPR30. Nat Chem Biol 2:207\u0026ndash;212. https://doi.org/10.1038/nchembio775\u003c/li\u003e\n\u003cli\u003eAlbanito L, Madeo A, Lappano R, et al (2007) G Protein\u0026ndash;Coupled Receptor 30 (GPR30) Mediates Gene Expression Changes and Growth Response to 17\u0026beta;-Estradiol and Selective GPR30 Ligand G-1 in Ovarian Cancer Cells. Cancer Res 67:1859\u0026ndash;1866. https://doi.org/10.1158/0008-5472.CAN-06-2909\u003c/li\u003e\n\u003cli\u003eLiu H, Yan Y, Wen H, et al (2014) A novel estrogen receptor GPER mediates proliferation induced by 17\u0026beta;-estradiol and selective GPER agonist G-1 in estrogen receptor \u0026alpha; (ER\u0026alpha;)-negative ovarian cancer cells. Cell Biol Int 38:631\u0026ndash;8. https://doi.org/10.1002/cbin.10243\u003c/li\u003e\n\u003cli\u003eWang C, Lv X, He C, et al (2013) The G-protein-coupled estrogen receptor agonist G-1 suppresses proliferation of ovarian cancer cells by blocking tubulin polymerization. Cell Death Dis 4:e869. https://doi.org/10.1038/cddis.2013.397\u003c/li\u003e\n\u003cli\u003eChan QKY, Lam H-M, Ng C-F, et al (2010) Activation of GPR30 inhibits the growth of prostate cancer cells through sustained activation of Erk1/2, c-jun/c-fos-dependent upregulation of p21, and induction of G2 cell-cycle arrest. Cell Death Differ 17:1511\u0026ndash;1523. https://doi.org/10.1038/cdd.2010.20\u003c/li\u003e\n\u003cli\u003eAriazi EA, Brailoiu E, Yerrum S, et al (2010) The G Protein\u0026ndash;Coupled Receptor GPR30 Inhibits Proliferation of Estrogen Receptor\u0026ndash;Positive Breast Cancer Cells. Cancer Res 70:1184\u0026ndash;1194. https://doi.org/10.1158/0008-5472.CAN-09-3068\u003c/li\u003e\n\u003cli\u003eWang C, Lv X, Jiang C, Davis JS (2012) The putative G-protein coupled estrogen receptor agonist G-1 suppresses proliferation of ovarian and breast cancer cells in a GPER-independent manner. Am J Transl Res 4:390\u0026ndash;402\u003c/li\u003e\n\u003cli\u003eHolm A, Gr\u0026auml;nde P-O, Ludue\u0026ntilde;a RF, et al (2012) The G protein-coupled oestrogen receptor 1 agonist G-1 disrupts endothelial cell microtubule structure in a receptor-independent manner. Mol Cell Biochem 366:239\u0026ndash;249. https://doi.org/10.1007/s11010-012-1301-3\u003c/li\u003e\n\u003cli\u003eHirtz A, Lebourdais N, Rech F, et al (2021) GPER Agonist G-1 Disrupts Tubulin Dynamics and Potentiates Temozolomide to Impair Glioblastoma Cell Proliferation. Cells 10:3438. https://doi.org/10.3390/cells10123438\u003c/li\u003e\n\u003cli\u003eSch\u0026uuml;ler-Toprak S, Skrzypczak M, Ignatov T, et al (2020) G protein-coupled estrogen receptor 1 (GPER-1) and agonist G-1 inhibit growth of ovarian cancer cells by activation of anti-tumoral transcriptome responses: impact of GPER-1 mRNA on survival. J Cancer Res Clin Oncol 146:3175\u0026ndash;3188. https://doi.org/10.1007/s00432-020-03333-4\u003c/li\u003e\n\u003cli\u003eLOWRY OH, ROSEBROUGH NJ, FARR AL, RANDALL RJ (1951) Protein measurement with the Folin phenol reagent. J Biol Chem 193:265\u0026ndash;75\u003c/li\u003e\n\u003cli\u003eManjegowda MC, Gupta PS, Limaye AM (2016) Validation data of a rabbit antiserum and affinity purified polyclonal antibody against the N-terminus of human GPR30. Data Br 7:1015\u0026ndash;20. https://doi.org/10.1016/j.dib.2016.03.054\u003c/li\u003e\n\u003cli\u003eChomczynski P, Sacchi N (1987) Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem 162:156\u0026ndash;9. https://doi.org/10.1006/abio.1987.9999\u003c/li\u003e\n\u003cli\u003eLivak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25:402\u0026ndash;8. https://doi.org/10.1006/meth.2001.1262\u003c/li\u003e\n\u003cli\u003eHatwik J, Patil HN, Limaye AM (2023) Proliferative response of ER\u0026alpha;-positive breast cancer cells to 10 \u0026mu;M enterolactone, and the associated alteration in the transcriptomic landscape. Gene 881:147640. https://doi.org/10.1016/j.gene.2023.147640\u003c/li\u003e\n\u003cli\u003ePal U, Sahu A, Barah P, Limaye AM (2022) Transcriptomic data of MCF-7 breast cancer cells treated with G1, a G-protein coupled estrogen receptor (GPER) agonist. Data Br 41:107948. https://doi.org/10.1016/j.dib.2022.107948\u003c/li\u003e\n\u003cli\u003eAndrews S (2010) FastQC: A Quality Control Tool for High Throughput Sequence Data\u003c/li\u003e\n\u003cli\u003eBolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114\u0026ndash;2120. https://doi.org/10.1093/bioinformatics/btu170\u003c/li\u003e\n\u003cli\u003eDobin A, Davis CA, Schlesinger F, et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15\u0026ndash;21. https://doi.org/10.1093/bioinformatics/bts635\u003c/li\u003e\n\u003cli\u003eLiao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923\u0026ndash;930. https://doi.org/10.1093/bioinformatics/btt656\u003c/li\u003e\n\u003cli\u003eLove MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https://doi.org/10.1186/s13059-014-0550-8\u003c/li\u003e\n\u003cli\u003eKorotkevich G, Sukhov V, Budin N, et al (2021) Fast gene set enrichment analysis. bioRxiv 60012. https://doi.org/10.1101/060012\u003c/li\u003e\n\u003cli\u003eYu G, Wang L-G, Han Y, He Q-Y (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284\u0026ndash;7. https://doi.org/10.1089/omi.2011.0118\u003c/li\u003e\n\u003cli\u003ePal U, Manjegowda MC, Singh N, et al (2023) The G-protein-coupled estrogen receptor, a gene co-expressed with ER\u0026alpha; in breast tumors, is regulated by estrogen-ER\u0026alpha; signalling in ER\u0026alpha; positive breast cancer cells. Gene 877:147548. https://doi.org/10.1016/j.gene.2023.147548\u003c/li\u003e\n\u003cli\u003eAndroutsopoulos VP, Tsatsakis AM, Spandidos DA (2009) Cytochrome P450 CYP1A1: wider roles in cancer progression and prevention. BMC Cancer 9:187. https://doi.org/10.1186/1471-2407-9-187\u003c/li\u003e\n\u003cli\u003ePandey DP, Lappano R, Albanito L, et al (2009) Estrogenic GPR30 signalling induces proliferation and migration of breast cancer cells through CTGF. EMBO J 28:523\u0026ndash;532. https://doi.org/10.1038/emboj.2008.304\u003c/li\u003e\n\u003cli\u003eKrko\u0026scaron;ka M, Svobodov\u0026aacute; J, Kab\u0026aacute;tkov\u0026aacute; M, et al (2021) Deregulation of signaling pathways controlling cell survival and proliferation in cancer cells alters induction of cytochrome P450 family 1 enzymes. Toxicology 461:152897. https://doi.org/10.1016/j.tox.2021.152897\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. List of primers used for gene expression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer sequence (5\u0026acute;\u0026rarr;3\u0026acute;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicon (base pair)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnealing temperature (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eRPL35a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- \u0026nbsp;CGGCCTCCAAGCTCTCTAAG\u003c/p\u003e\n \u003cp\u003eReverse- \u0026nbsp;CAGGTCCAGGGGCTTGTACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eKIF11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- AAATCAGATGGACGTAAGGCAG\u003c/p\u003e\n \u003cp\u003eReverse- TAACTTTTCCTCTGTGGTGTCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eNCAPD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- GGAGCAAGAGTCGAATGGCG\u003c/p\u003e\n \u003cp\u003eReverse- CTTTGGCTGACGACGGAGTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eSMC1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- CCAGGCCATCGTCATCTCTC\u003c/p\u003e\n \u003cp\u003eReverse- TGGTGAGGTCGAAGGTCAGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eCIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- GTACCTGGACATCCCGAACC\u003c/p\u003e\n \u003cp\u003eReverse- CCTGGTATGAGGACGCCAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eKIF20A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- GCCAAGCCACACACAGGTTC\u003c/p\u003e\n \u003cp\u003eReverse- TAGATGAGCCAGTTCTGCCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eTIMP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- CGCCTTCTGCAACTCCGACA\u003c/p\u003e\n \u003cp\u003eReverse- CTGCACATGGGGCATCTTGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eCYP1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- ACCTTTGAGAAGGGCCACATCCG\u003c/p\u003e\n \u003cp\u003eReverse- TGACTGTGTCAAACCCAGCTCCAAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eBRCA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- GATGCCTGGACAGAGGACAA\u003c/p\u003e\n \u003cp\u003eReverse- GGGATCTGGGGTATCAGGTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eAHRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- GCGCCTCAGTGTCAGTTACC\u003c/p\u003e\n \u003cp\u003eReverse- CACTCACGACCAGAGCAAAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eTOP2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- GTGGCAAGGATTCTGCTAGTCC\u003c/p\u003e\n \u003cp\u003eReverse- ACCATTCAGGCTCAACACGCTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eGPER-v2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- ATCTGGACGGCAGGTACC\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eReverse- GAAGAACAGATGCTCCTCACAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eGPER-v3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- TGGACGGCAGCCCTGCTC\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eReverse- GCTGCTCACTCTCTGGGTAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eGPER-v4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.19834710743802%\" valign=\"top\"\u003e\n \u003cp\u003eForward- GCGGGTCTCT TCCTCTCTC\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eReverse- GCTGCTCACTCTCTGGGTAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214876033057852%\" valign=\"top\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.537190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: List of top 25 up-regulated genes\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnsgene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSymbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003elog2FC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003epadj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000140465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eCYP1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eCytochrome P450 Family 1 Subfamily A Member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000130513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eGDF15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eGrowth Differentiation Factor 15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000108551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eRASD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eRas Related Dexamethasone Induced 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000102962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eCCL22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eC-C Motif Chemokine Ligand 22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000107796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eACTA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eActin Alpha 2, Smooth Muscle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000124762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eCDKN1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eCyclin Dependent Kinase Inhibitor 1A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000100292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eHMOX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eHeme Oxygenase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000138271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eGPR87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eG Protein-Coupled Receptor 87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000173535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eTNFRSF10C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eTNF Receptor Superfamily Member 10c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000162772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eATF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eActivating Transcription Factor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000205426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eKRT81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eKeratin 81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000167772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eANGPTL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eAngiopoietin Like 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000063438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eAHRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eAryl Hydrocarbon Receptor Repressor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000116717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eGADD45A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eGrowth Arrest and DNA Damage Inducible Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000137868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eSTRA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eSignaling Receptor and Transporter of Retinol STRA6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000100234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eTIMP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eTIMP Metallopeptidase Inhibitor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000128342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eLIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eLIF Interleukin 6 Family Cytokine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000019186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eCYP24A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eCytochrome P450 Family 24 Subfamily A Member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000105327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eBBC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eBCL2 Binding Component 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000131080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eEDA2R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eEctodysplasin A2 Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000167755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eKLK6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eKallikrein Related Peptidase 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000136378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eADAMTS7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eADAM Metallopeptidase with Thrombospondin Type 1 Motif 7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000134013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eLOXL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eLysyl Oxidase Like 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000069812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eHES2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eHes Family BHLH Transcription Factor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.432859399684045%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000187134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.849921011058452%\" valign=\"top\"\u003e\n \u003cp\u003eAKR1C1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.006319115323855%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.28436018957346%\" valign=\"top\"\u003e\n \u003cp\u003eAldo-Keto Reductase Family 1 Member C1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: List of top 25 down-regulated genes\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnsgene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSymbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003elog2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003epadj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000136824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eSMC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eStructural Maintenance Of Chromosomes 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000109805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eNCAPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eNon-SMC Condensin I Complex Subunit G\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000131747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eTOP2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eDNA Topoisomerase II Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000197299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eBLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eBLM RecQ Like Helicase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000184661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eCDCA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eCell Division Cycle Associated 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000174371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eEXO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eExonuclease 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000156802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eATAD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eATPase Family AAA Domain Containing 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000174799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eCEP135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eCentrosomal Protein 135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000186871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eERCC6L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eERCC Excision Repair 6 Like, Spindle Assembly Checkpoint Helicase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000137812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eKNL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eKinetochore Scaffold 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000138182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eKIF20B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eKinesin Family Member 20B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000126787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eDLGAP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eDLG Associated Protein 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000011426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eANLN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eAnillin, Actin Binding Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000148700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eADD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eAdducin 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000119969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eHELLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eHelicase, Lymphoid Specific\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000092853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eCLSPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eClaspin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000196757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eZNF700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eZinc Finger Protein 700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000145241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eCENPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eCentromere Protein C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000021776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eAQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eAquarius Intron-Binding Spliceosomal Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000143476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eDTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eDenticleless E3 Ubiquitin Protein Ligase Homolog\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000065328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eMCM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eMinichromosome Maintenance 10 Replication Initiation Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000134352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eIL6ST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eInterleukin 6 Cytokine Family Signal Transducer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000114346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eECT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eEpithelial Cell Transforming 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000102189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eEEA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eEarly Endosome Antigen 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.244147157190636%\" valign=\"top\"\u003e\n \u003cp\u003eENSG00000024526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.545150501672241%\" valign=\"top\"\u003e\n \u003cp\u003eDEPDC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531772575250836%\" valign=\"top\"\u003e\n \u003cp\u003e-2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2876254180602%\" valign=\"top\"\u003e\n \u003cp\u003eDEP Domain Containing 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"G1, GPER, CYP1A1, RNA-seq, MCF-7, MDA-MB-453","lastPublishedDoi":"10.21203/rs.3.rs-4291506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4291506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: G1, a G-protein coupled estrogen receptor (GPER) agonist, has been instrumental in delineating the mechanisms and cellular consequences of GPER signal transduction. The effects of G1 on cell proliferation are controversial, including those that are demonstrably GPER-independent. It begs the question as to whether G1 has off-target effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and Results\u003c/strong\u003e: Here, transcriptomic alterations in MCF-7 breast cancer cells treated with 1 mM G1 are presented. GSEA and GO analysis showed enrichment of gene-sets in G1-treated MCF-7 transcriptome, which align with loss of cell viability. Genes related to xenobiotic metabolism were regulated by G1. In this category, CYP1A1, was the topmost G1-induced gene. CH223191, an aryl hydrocarbon receptor inhibitor, blocked G1-mediated increase in CYP1A1 mRNA. Furthermore, the G1-mediated modulation of CYP1A1, and other genes, was also observed in the GPER-negative MDA-MB-453 cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: This study captures the transcriptomic alterations associated with G1-induced cell cycle arrest in MCF-7 cells, with the important caveat that these may not be entirely attributed to GPER activation.\u003c/p\u003e","manuscriptTitle":"Global transcriptomic analysis reveals CYP1A1 as a target of the GPER agonist G1","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-24 09:20:00","doi":"10.21203/rs.3.rs-4291506/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa4afed4-eb6c-49b0-a81b-2825bce2a847","owner":[],"postedDate":"April 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-02T03:08:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-24 09:20:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4291506","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4291506","identity":"rs-4291506","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}