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Functional studies of PPP2R2A in breast cancer Ma, Qianli 2015

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  41 Table 2.5 Peptide dilution for MRM Peptides were synthesized commercially from JPT technologist, suspended in water. PH was adjusted with NH4OH (<50uL) and TFA (<50uL). Hydrophobicity was determined with C18 HPLC. Hydrophobicity: C18 HPLC exclusion time (min) Uniprot ID Gene Name Trypsin peptide # aa Notes Unique Water Solubility Properties Hydrophobicity1 P30153 PPP2R1A NLCSDDTPMVR 11 seen in MSMS Y good acidic 20.522 P30153 PPP2R1A VLAMSGDPNYLHR 13 seen in MSMS Y poor basic 21.383 P30153 PPP2R1A MAGDPVANVR 10 GPM Y good neutral 14.74 P30153 PPP2R1A TDLVPAFQNLMK 12 GPM Y poor neutral 35.725 P30153 PPP2R1A LTQDQDVDVK 10 GPM Y good acidic 14.516 P30154 PPP2R1B FGTEWAQNTIVPK 14 GPM Y poor neutral 28.077 P30154 PPP2R1B LGQDEDMDV 9 GPM Y good acidic 37.928 P30154 PPP2R1B ITLNDLIPAFQNLLK 15 GPM Y poor neutral 46.519 P63151 PPP2R2A VVIFQQEQENK 11 seen in MSMS Y good acidic 19.8310 P63151 PPP2R2A NAAQFLLSTNDK 12 GPM Y good neutral 25.4811 P63151 PPP2R2A YRDPTTVTTLR 11 seen in MSMS Y good basic 17.9212 Q00005 PPP2R2B DPATITTLR 9 GPM Y good neutral 19.4713 Q00005 PPP2R2B FFEEPEDPSNR 11 GPM Y good acidic 20.5714 Q00005 PPP2R2B DEISVDSLDFSK 12 GPM Y good acidic 31.1415 Q9Y2T4 PPP2R2C INLWHLAITDR 11 GPM Y poor basic 30.4716 Q9Y2T4 PPP2R2C SFFSEIISSVSDVK 14 GPM Y good acidic 42.3417 Q9Y2T4 PPP2R2C DDISVDSLDFTK 12 GPM Y good acidic 32.4818 Q66LE6 PPP2R2D AEGYNLKDEDGR 12 seen in MSMS Y good acidic 9.6119 Q66LE6 PPP2R2D GSDSAIMTGSYNNFFR 16 GPM Y poor neutral 32.2720 Q66LE6 PPP2R2D FFEEPEDPSSR 11 seen in MSMS Y good acidic 21.0621 Q06190 PPP2R3A GSTFQNTYNLK 11 No goodfor this protien Y poor basic 19.8822 Q06190 PPP2R3A IDNFSSGTDIK 11 No goodfor this protien Y good acidic 19.9323 Q06190 PPP2R3A ADIYEMGK 8 No goodfor this protien Y good 17.5424 Q9Y5P8 PPP2R3B ATMDDMGLVAK 11 No goodfor this protien Y good 24.4225 Q9Y5P8 PPP2R3B RHNDHALSTK 10 No goodfor this protien Y good 6.0126 Q9Y5P8 PPP2R3B DGDSGGPELSDWEK 14 No goodfor this protien Y good 23.3827 Q969Q6 PPP2R3C DEEMDLFTK 9 No goodfor this protien Y good 2628 Q969Q6 PPP2R3C RLPAEDEVLLQK 12 No goodfor this protien Y good 25.5629 Q969Q6 PPP2R3C TYLDFVLALENR 12 No goodfor this protien Y poor 44.88  43 Chapter 3: PPP2R2A Is Involved In Regulating Estrogen Receptor Signaling  3.1 Introduction and rationale Protein Phosphatase 2A (PP2A) is a tri-subunit complex consisting of scaffold, catalytic, and regulatory subunits. It has been reported that the abundance of PP2A family proteins makes up the most abundant phosphatase complex in cells (115). As one of the most abundantly expressed protein families in cells, PP2A has been discovered to be involved in regulating various cellular processes (45). Many subunits have been identified to present with mutations in certain types of cancer genomes (Table 1.1). PPP2R2A is one of the 16 PP2A regulatory subunits identified in cells. In the METABRIC genomic landscape, constructed based on 2000 breast cancers, copy number loss of PPP2R2A was associated with a strong and statistically significant (adjusted ANOVA p-value=1.15x10-63) reduction in expression (in-cis dependency of CNA with expression) and was found in a small (48 out of 1518) subset of almost exclusively ER+ subset of breast cancers (31). Cases in this subset were associated with high mitotic index and poorer prognosis, defined as luminal B cancers in the 5-subtype intrinsic classification of primary breast cancers and IntClust 1 and 9 groups in the 10 subtype classification (31). Generally, cancer cells with mutations giving rise to growth or survival advantages out compete surrounding normal cells, leading to overrepresentation of these mutations in the bulk genomic profile of tumours. On the other hand, specific genomic backgrounds may favour or lead to co-selection of certain mutations. The prevalence of PPP2R2A deletion in a discrete subset of ER positive breast cancers suggested that genomic background with activated ER signaling may cooperate or lead to co-selection of PPP2R2A copy number loss, raising the possibility that PPP2R2A deletion could also provide growth advantage in ER positive breast cancers.   44 Estrogen signaling can be regulated through several different mechanisms. Besides estrogen ligand binding, activation of the estrogen receptor also requires post-translational modifications on certain residues (64). Kinases in the PI3K/AKT/mTOR signaling pathway can cause ligand-independent ER activation in breast cancers through phosphorylation of its downstream regulators (116). After being activated and trans-located into the cell nucleus, estrogen receptor further recruits cofactors, which determine ER binding specificities and remodel chromatin to allow access of the transcriptional machinery as well as initiation of transcription (64). Therefore, regulation of ER cofactors also plays an important role in ER mediated transcriptional regulation. Many studies have suggested that ER signaling cross talks with other signaling pathways (117). PP2A was previously found to act as a tumour suppressor in cancers (50). As a phosphatase, PP2A modifies a large number of phospho-proteins and ultimately regulates the signaling pathways that these proteins are involved in. Several studies have investigated the role of PP2A in the activation of ER signaling from various aspects. In the study of Keen et al (2005), PP2A is involved in ER signaling through regulation of mRNA decay (118). In the study of Tan et al (2008), PP2A regulatory subunit STRN3 dephosphorylates ER and inhibits ER signaling pathway (119). Lu et al also reported that PP2A might directly interact with the estrogen receptor (120). Although these studies have shown evidence of potential interactions between PP2A function and ER signaling, there is still insufficient data to conclusively define the functional effects of PP2A on ER signaling.  Based on the METABRIC genomic landscape, correlations between PPP2R2A copy number loss and ER expression status imply a likely functional role of PPP2R2A loss in the high mitotic rate breast cancer subtypes, but the actual mechanistic relationships of PPP2R2A deletion   45 contribution to the regulation of ER signaling were unknown at the time. Therefore, I hypothesized that PP2R2A could be involved in the regulation of estrogen receptor (ERα) signaling and its copy number loss could provide growth or survival advantages in ER positive breast cancers.   In this chapter, I first tested the ER signaling hypothesis by investigating the effects of PPP2R2A deletion on ER signaling in an ER positive breast cancer cell model T47D cells (ATCC). This cell type grows in response to estrogen (indicating a functional ER responsive pathway) but independently of other growth factors, such as insulin or epidermal growth factor (EGF). This characteristic minimizes interference from other signaling pathways during the investigation of ER signaling. I employed an ER responsive luciferase reporter to detect changes of ER signaling in T47D cells. The reporter allowed measurement of ER activities in the cell model and the results showed enhanced ER signaling activity with PPP2R2A loss. As ER functions as a transcription factor that drives gene expression, we investigated how PPP2R2A copy number loss affects ER signaling on a transciptome-wide scale. I studied both early (3 hours) and late (6 hours) ER response gene expression using microarray and RNA-sequencing, respectively. I also employed Chromatin-Immunoprecipitation-sequencing (ChIP-seq) methods as a first step to identify ER binding sites to analyze alterations of ER binding specificity after PPP2R2A knockdown. The results of these combined bioinformatic analyses identified a list of direct ER target genes that were transcriptionally altered due to PPP2R2A loss. I validated a subset of genes for ER binding specificity and gene expression with ChIP-QCPR and RT-QPCR, respectively, and identified SPDEF, an ER cofactor and response gene, as one of the potential key mediators in PPP2R2A regulated ER signaling. I also studied the changes in the recruitment   46 of four ER cofactors, FOXA1, GATA3, p300, and SPDEF, as well as post-translational modifications on the ER and SPDEF. Last but not least, I investigated METABRIC transcriptome expression data regarding correlations between SPDEF expression and PPP2R2A copy number status, observing expression relationships in human cancers consistent with the in vitro/vivo cell line results.  3.2 Results 3.2.1 PPP2R2A is involved in estrogen receptor regulation PPP2R2A down regulation enhances ER responsive luciferase activity In the analysis of METABRIC tumours, 63% cases of PPP2R2A copy number losses are found in ER positive luminal breast cancers (48 out of 76 cases in ER positive breast cancers). As luminal breast cancers have active ER signaling, this high correlation frequency between mutation and cancer subtype suggested a possible connection between PPP2R2A function and ER signaling. Therefore, I hypothesized that reduced expression of PPP2R2A might alter ER signaling responses. ER response element sequences are considered enhancer elements because they act on promoters in an orientation and distance independent manner (67). To test this hypothesis, I employed an ER signaling reporter, containing ER response elements, to assay the levels of ER responses in the T47D cell model (Figure 3.1_A). This reporter assay provided information on the perturbation of ER regulated signaling activity on an artificial consensus ERE (Estrogen Response Element) in vitro (99). To do this experiment (Methods: Section 2.2), I established stable cell line models with lentiviral-packaged shRNA(s) targeting PPP2R2A or non-silencing control (Sequences: Table 2.1).  Compared to the shNS (non-silencing) control, shPPP2R2A was able to knockdown PPP2R2A by targeting its 3’ UTR (Table 2.1) and   47 significantly reduce PPP2R2A protein level regardless of ligand β-estradiol treatment (Figure 3.2A). When the control shNS cell line was treated with 100nM β-estradiol, the relative luciferase activity was increased on average by 2.8 (+/-0.4) fold (Figure 3.2B_i, ANOVA p<0.05). The same treatment also induced an increase by 3.2 (+/-0.4) fold in relative luciferase activity in the model with shPPP2R2A (Figure 3.2B_ii, ANOVA p<0.05). This result suggested that T47D cell models, under both shNS and shPPP2R2A conditions, have an intact and functional ER signaling pathway that actively responds to β-estradiol treatments. Comparing luciferase activity in cells with shPPP2R2A versus cells with shNS control, knockdown of PPP2R2A alone (without β-estradiol stimulation) significantly increased the transcription of mRNAs driven by consensus ER response elements (Figure 3.2B_iii, ANOVA p<0.05). The increase averaged approximately 20% in the absence of β-estradiol and 39% in the presence of β-estradiol (Figure 3.2B_iv, ANOVA p<0.05); the differences seen for the effects of β-estradiol treatment on shRNA knockdown were not significant when statistically analyzed using ANOVA with 95% confidence (6%-82% increase comparing shPPP2R2A+E2 to shNS+E2 versus 5%-39% increase comparing shPPP2R2A vs shNS, Figure 3.2B_iv vs Figure 3.2B_iii). These results suggested that PPP2R2A knockdown could enhance basal level ER signaling activity (Figure 3.2B, iii and iv) in the T47D cell model, but not with the same magnitude as β-estradiol activation (Figure 3.2B i and ii). In other words, PPP2R2A is likely to act as an inhibitor in ER signaling, but not the only inhibitor in the ER activation pathway, as ER signaling was still active with a normal level of PPP2R2A expression (Figure 3.2B_i). These results supported the hypothesis that reduced expression of PPP2R2A alters ER signaling activities, particularly leading to enhanced level of ER responses based on the reporter assay. However, the affinity of ER binding to specific elements (ERE) also depends on the nucleotides immediately flanking the   48 binding sequences (121). The results in this assay used a luciferase reporter, with consensus ER response elements flanked by TATA box and plasmid sequences, which might not fully recapitulate the magnitude of ER binding strength to actual ER-responsive elements in the cellular context. To address this question I next performed genome-wide analysis of ER responses. PPP2R2A down regulation alters ER responsive gene expression PPP2R2A down regulation alters early ER responsive gene expression with 3 hours ligand activation Since estrogen receptor functions as a transcription factor, changes in ER signaling could potentially affect the expression of downstream genes and their respective pathways.  As ER signaling could be regulated by other cellular mechanisms, particularly protein phosphorylation (75), expression changes of PPP2R2A, a regulatory subunit of PP2A phosphatase, may lead to alterations in ER signaling and ultimately in the expression of ER responsive genes. Thus, for the next step of investigation, I hypothesized that reduced PPP2R2A expression could affect ER responsive gene expression, either in magnitude or in scope.   I first analyzed the transcriptome expression in T47D in response to ER ligand activation after transcript knockdown of PPP2R2A. Generally, upon ligand binding, it takes about 45 minutes to 1 hour for ER to trans-locate and bind to its target sequences (32). Since many ER response genes are critical factors of other downstream signaling pathways, longer time ER ligand treatment will activate secondary signaling pathways. Gene expression changes within 6 hours are counted as the ER response; changes within the first 3 hours are considered the early ER   49 response (32), and changes after 6-hours are considered the effects of secondary signaling networks (32).  To carry out this analysis in an unbiased way, I treated T47D ER positive cells with β-estradiol for 3 or 6 hours and performed either transcriptome microarray hybridization (Figure 3.1_B) or whole transcriptome sequencing (Figure 3.1_C), respectively, to identify the ER-responsive genes that are preferentially regulated by PPP2R2A. First, I used Affymetrix Human Transcriptome microarrays 2.0 to investigate the changes in T47D transcriptome 3 hours after ligand addition (Method: Section 2.4) (Figure 3.1_B). We treated T47D cells with lipids delivering 50nM siRNA (Dharmacon) targeting PPP2R2A, and used cells treated with non-targeting siRNA (siNT) as the control. The experiment was done with 3 replicate sample pairs. After knockdown of PPP2R2A transcript (Figure 3.3A), the cells were activated with 100nM β-estradiol for 3 hours before collection. The microarray data was normalized and analyzed with the software “Affymetrix Human Transcriptome 2.0 console” that was specifically developed for this type of array. I compared transcript levels in the two conditions (siPPP2R2A vs siNT) and filtered the lists of differentially expressed genes with the threshold of |Fold Change|>1.5 (ANOVA, p<0.05). The analysis with transcriptome microarray data (Figure 3.3B) suggested that PPP2R2A transcript knockdown in T47D leads to a rapid (<3 hour) up-regulation of 170 genes (Figure 3.3B, red dots), as well as down-regulation (Figure 3.3B, green dots) of 135 genes in response to β-estradiol activation.   Among the differentially expressed genes, I immediately recognized a known ER cofactor SPDEF (122), with a 78% transcript increase (Appendix A.1, ANOVA p-value=0.0125) after   50 PPP2R2A knockdown. Furthermore, PBX1, was also up regulated by 97% (Appendix A.1, ANOVA p-value=0.0085), which was also previously found to be an ER response gene and a pioneer factor for the ER complex based on binding sequences analysis (69). Gene enrichment and network analysis of transcripts with more than 1.5 fold (50%) transcript expression changes revealed enriched genes in growth hormone signaling pathways, AKT signaling pathways, and interferon pathways, etc. (Figure 3.6A, Appendix D.1). The transcriptome microarray analysis revealed that knockdown of PPP2R2A in the ER positive T47D breast cancer cell model differentially alters the expressions of some genes even after short term activation with β-estradiol, including a few previously known ER response genes. PPP2R2A down regulation alters late ER responsive gene expression with 6 hours ligand activation To construct a fully comprehensive list of PPP2R2A modulated ER responsive genes, we also investigated the transcriptome expression ER response genes following 6 hours of β-estradiol treatment (Figure 3.1_C). To achieve maximum coverage I used an orthogonal method, RNA-sequencing, which has a larger dynamic range than array based methods. Two replicate pairs of RNA samples (siPPP2R2A+E2 vs siNT+E2) from T47D were prepared in the same way as those for transcriptome microarray (Methods: Section 2.5), except that the cells were treated with 100nM β-estradiol for 6 hours before collection. I also prepared a pair of samples without ligand treatment to study the expression of genes only in response to PPP2R2A expression level changes. With 60%-70% knockdown of PPP2R2A at the transcript level (Figure 3.4C), I analyzed the effects of ligand activation by comparing gene expression in samples treated with β-estradiol to that in samples without treatment. To analyze the effects of PPP2R2A knockdown,   51 we compared the samples with PPP2R2A siRNA knockdown to the samples with siNT non-targeting control, with or without estradiol activation, respectively. The results were filtered with the threshold of |Fold Change|>2 and false discovery rate (FDR) p<0.05. I also compared the list of deregulated genes in the data to previously identified ER response genes in MCF7 ER positive cells. Our results demonstrated that knockdown of PPP2R2A increased both the number of differentially expressed ER response genes (Figure 3.4) and the levels of differential expression (Figure 3.5). In the context of PPP2R2A knockdown followed by β-estradiol, 527 deregulated genes were identified based on our selection criteria (Figure 3.4A,B, siPPP2R2A+E2 vs siPPP2R2A, Red, 392 up regulated, 135 down regulated), whereas only 148 genes exhibited transcriptional deregulation in the β-estradiol treated control (siNT, non-targeting) samples (Figure 3.4A, 3.3B, siNT+E2 vs siNT, Blue, 125 up regulated, 23 down regulated). In other words, for 6 hours β-estradiol exposure, ligand alone induced a smaller number of differentially expressed genes in the control condition (siNT), while reduction of PPP2R2A expression increased the number of ER responsive genes upon ER stimulation. Among the 148 ER response genes induced by ligand only, 21 genes were induced by estrogen treatment of MCF7 cells in the study of Carroll et al. (Appendix G) (32). In the 6-hour β-estradiol treated condition, 1414 were genes were differentially regulated after PPP2R2A-knockdown compared to the control (Figure 3.4A, 3.3B, siPPP2R2A+E2 vs siNT+E2, Orange, 878 up regulated, 536 down regulated). Out of these 1414 genes, 80 were also previously identified as ER responsive genes in MCF7 cells (Appendix G) (32). Combining both transcriptome analyses (transcriptome microarray and RNA-seq), 83 genes were deregulated after PPP2R2A knockdown at 3 hours and 6 hours of ER ligand exposure (Appendix H). In non-ligand treated conditions, PPP2R2A knockdown also resulted in alterations of gene expression but only 200 loci were identified (Figure 3.4A, 3.3B,   52 siPPP2R2A vs siNT, Green 124 up regulated, 76 down regulated), among which, 11 genes were previously identified as ER responsive genes in MCF7 cells (Appendix G) (32). In addition, there were 7 genes that could be up regulated by either β-estradiol stimulated control condition or the condition of PPP2R2A knockdown without ligand activation in T47D cells (Green: siPPP2R2A vs siNT overlap Blue: siNT+E2 vs siNT). 91 genes were significantly deregulated in response to β-estradiol treatment regardless of PPP2R2A expression level (Red: siPPP2R2A+E2 vs siPPP2R2A overlap Blue: siNT+E2 vs siNT, 83 up regulated, 8 down regulated), whereas 151 genes were deregulated in response to PPP2R2A knockdown regardless of β-estradiol stimulation (Orange: siPPP2R2A+E2 vs siNT+E2 overlap Green: siPPP2R2A vs siNT, 98 up regulated, 53 down regulated). Furthermore, with reduced expression of PPP2R2A, β-estradiol induced deregulation of an additional 1263 genes (Orange: siPPP2R2A+E2 vs siNT+E2 not overlapped with Green: siPPP2R2A vs siNT, 780 up regulated, 483 down regulated). Taken together, the data suggested that β-estradiol activation of ER response gene expression was more effective in a background with loss or reduction of PPP2R2A (Figure 3.4D, siPPP2R2A+E2 vs siPPP2R2A). There were also more up-regulated genes than the down-regulated genes in all pairs of comparisons (Figure 3.4D), which is consistent with the idea that β-estradiol acts as an ER agonist and PPP2R2A may be antagonistic to ER signaling.   Consistent with the pattern of ER responses in the context of reduced PPP2R2A expression, I observed higher expression levels for the transcripts appearing in each comparison with a greater overall dynamic range. Using a log2 scale, β-estradiol induced differential gene expression under the siNT control condition ranged from -1 to 3.5 fold (log2 value, Figure 3.5 B), whereas with PPP2R2A knockdown, the range was from - 4.5 to 4.5 fold (log2 value, Figure 3.5 A). When   53 comparing siPPP2R2A and siNT, PPP2R2A knockdown alone (without E2 treatment) resulted in gene expression changes ranging from log2 value of -2.5 to 3 (Figure 3.5 D), while this range was increased to -4 to 4 when treated with β-estradiol (log2 value, Figure 3.5 C).  I systematically examined the gene networks and functions represented in the differentially regulated gene expression sets above by gene ontology, using the enrichment map feature of Cytoscape (Figure 3.6) (Method: Section 2.5). I observed common networks between experiments with the 3-hour and 6-hour ER ligand exposure, comprising functions related to cell cycle control/progression, gene transcription/translation, cell adhesion and kinase receptor signaling. This identified a large immune function network in the 3-hour comparison (Figure 3.6A) and a single orphan node (interferon signaling) in the 6-hour comparison (Figure 3.6B). In contrast, a discrete multi-node cell cycle sub-network was observed in the 6 hours comparison. Reduced PPP2R2A expression alters ER binding specificity I next asked whether the synergistic modulation of gene expression due to ER ligand activation and reduced PPP2R2A expression was associated with altered patterns of ER binding to DNA response elements. To address this, I carried out chromatin IP experiments and collaborated with Dr. Jason Carroll from Cambridge UK to investigate the nature of the binding sequences retrieved on a genome wide basis (Figure 3.1_D) (104). Dr Carroll is a pioneer in development of ER genomic binding specificity assays using next generation sequencing methods applied to chromatin immunoprecipitation. PPP2R2A was knocked down in T47D cells as previously described (Method: Section 2.7 and 2.8) for microarray assay analysis and two replicate pairs of cells were treated with 100nM β-estradiol for one hour before cross-linking (siPPP2R2A+E2 vs   54 siNT+E2, Figure 3.7 A). One hour is just enough time for estrogen receptors to translocate to the nucleus and bind to their binding sites, but not long enough for transcript accumulation or secondary signaling activation (32) (personal communication, Dr. Jason Carroll). The cells were cross-linked with formaldehyde before collection and chromatin-immunoprecipitation with ER-targeting antibodies. The sequencing data of the precipitated DNA were filtered to remove artifacts and only peaks that appeared in both replicate pairs were called (using MACS) as real ER binding sequences (32). After PPP2R2A knock down and 1 hour β-estradiol activation (Figure 3.7A), there were 332 newly gained ER binding sites unique in the siPPP2R2A condition and 739 lost ER binding sites that is unique in siNT condition. 1237 binding sites remain shared in both the control (siNT) and PPP2R2A-knockdown (siPPP2R2A) conditions (Figure 3.7B). These changes in ER binding sites suggest that estrogen receptor does not completely change its binding target specificities, but rather shifted its binding site repertoires in T47D cells after PPP2R2A knockdown. This shift in DNA binding sites is consistent with the changes of ER responsive genes observed in the transcriptome expression analysis (Section  Based on the annotation of sequencing data with cis-Regulatory Element Annotation System (CEAS), the majority of the human genome is composed of introns or distal intergenic regions, with only 2.4% (1.1%+0.7%+0.6%) of the genome designated as promoter sequences (Figure 3.8, A, Genome) (106,123,124) (Methods: Section 2.8). When treated with β-estradiol, estrogen receptor ChIP in the siNT control cells resulted in a small enrichment of upstream promoter regions, 10.5% (8.5%+1.1%+0.9%) of all identified ER binding sites (Figure 3.8 A, siNT). After knocking down PPP2R2A, the enrichment for promoter regions was 5.5% (2.2%+1.9%+1.3%) of its identified ER binding sites, which is less than the control, (Figure 3.8 A, siPPP2R2A), but   55 still more than the 2.4% in the genome. The enriched binding to promoters in both conditions is reflective of ER function as a transcription factor; however, the majority of ER binding sites were not located within 3kb upstream of promoters under either condition. More ER binding was observed in the intronic regions in siPPP2R2A than siNT condition. In the PP2R2A knockdown condition, 44.5% of ER binding sites retrieved by ChIP were found in intron or exon regions, (Figure 3.8 B, siPPP2R2A), which was more than the 42.5% in the genome and contrasts with the 36.7% in the control samples (Figure 3.8 B, siNT). This profile suggested that PPPR2A knockdown led to a shift in ER binding sites to include more intronic and exonic regions.   To investigate the ER binding site sequences in detail, I aligned the sequences obtained from the unique binding sites to determine a consensus sequence for each condition (sequence logos in Figure 3.7). It is clear that the palindromic ER binding consensus sequence (AGGTCAnnnTGACCT) (32) was present in the uniquely gained (332 unique sites identified in siPPP2R2A) as well as the uniquely lost ER binding sites (739 unique sites identified in siNT) after PPP2R2A knockdown (Figure 3.7C and D). Other enriched motifs that appear to be slightly different are also known ER binding half-sites, which are parts of the palindromic consensus sequence. In short, PPP2R2A knockdown did not direct ER to de novo binding motifs, but shifted the specificity among known ER targets. Joint analysis of expression and chip-seq datasets identified SPDEF as a key regulator of PPP2R2A regulated ER signaling The list of ER binding sites obtained from ChIP-Sequencing was still large and I therefore asked which sites were associated with altered transcription of the nearest neighbor genes. To identify   56 the ER binding associated genes, I mapped the ChIP-Sequencing identified ER binding sequences to the hg18 human genome. The first genes with either their 3’UTR in front of their transcription start sites behind an ER binding sites were considered as the genes associated with these ChIP-Sequencing identified ER targeting sites (Appendix B). In order to find out how these unique ER-binding-sites associated genes were differentially regulated after PPP2R2A knockdown, we extracted and plotted the differential expression data of these ER-binding-site associated genes based on the transcriptome analysis data (From section Among the genes, associated with ER binding sites either gained or lost after PPP2R2A knockdown, there appeared to be both up regulated and down regulated genes at the 3 hour (Figure 3.9, A) and 6 hour (Figure 3.9, B) time points of β-estradiol (E2) treatment after PPP2R2A knockdown in T47D cells. As we were interested in bona fide genes that showed sustained expressions upon ER binding, we picked out the genes that were regulated in the same direction after both 3-hour and 6-hour β-estradiol treatment. For the uniquely “gained” ER binding sites, there were 34 genes whose expression was significantly up regulated (Linear fold change>1) at both time points when PPP2R2A was knocked down (Figure 3.10, Appendix E.1), whereas for the “lost” unique sites, 37 genes were up regulated (Figure 3.10, Appendix E.2). Eight genes appeared to be associated with more than one unique ER binding site in both conditions, meaning they lost one ER binding site, but gained another after PPP2R2A knockdown (Figure 3.10).   I decided to validate the expression modulation in additional experiments for a number of genes selected with the following criteria: (i): Genes associated with unique ER binding sites gained after PPP2R2A knockdown in T47D cells. (ii): Gene transcripts up regulated ((Linear fold change>1) after both 3 hours and 6 hours time points, in the β-estradiol treated condition (Figure   57 3.1_E). Ten genes with successfully designed primers were picked at random to validate their differential expression after PPP2R2A knockdown with QPCR (Method: Section 2.6). Based on triplicate QPCR results, when PPP2R2A transcript was successfully knocked down by ~50% with siRNA (Figure 3.11), these 10 gene transcripts were all up regulated in the ligand-activated condition after 6 hours as well as in the non-hormone treated control condition (Figure 3.11, 95% confidence intervals>1). AFF3, KCNMA1, and S3HR2, associated with both “lost” and “gained” ER binding sites, were also up regulated in both conditions. Thus, the results of RT-QPCR confirmed the correlations between the expression changes of these genes and their association with uniquely gained ER binding sites after PPP2R2A knockdown (Appendix F). Moreover, contrasting the E2 treated to vehicle control in the background of PPP2R2A knockdown, two genes CACNA1D and SPDEF, exhibited statistically significant increase in transcription upon E2 treatment.   In the annotation of ChIP-Sequencing data (Section, ER binding sites appear to be more enriched in the intron or exon regions after PPP2R2A knockdown (Figure 3.8). I therefore set out to validate the ER binding specificity in independent experiments. Among QPCR validated 10 genes, I picked 4 genes (ABCC3, CACNA1D, SPDEF, SPNS2) to assay ER binding specificity to their promoters by chromatin immune-precipitation (ChIP) based on the following selection criteria: (i) These 4 genes were associated with and only with uniquely “gained” ER binding sites that lie within their genomic loci (either intronic or exonic regions, not distal upstream or downstream regions) after PPP2R2A knockdown (Figure 3.12). (ii) Based on genome-wide ChIP-sequencing analysis, the inferred binding strength was among the greatest at these four loci (determined by ranking the MACS score – see methods in Section 2.8).   58 Among these 4 unique ER targeted genes, the binding site regions of interest after PPP2R2A knockdown overlapped within the respective non-coding intron regions of CANAC1D and SPDEF (Figure 3.12). The ER binding sites associated with ABCC3 and SPNS2 overlapped with exons and some surrounding intron sequences. CACNA1D encodes a membrane protein involved with ion transport, whereas, SPDEF encodes a transcription factor containing ETS (E26 transformation-specific) domains and has also been previously reported as an ER cofactor (122). ABCC3 is a member of ABC (ATP Binding Cassette) transporter involved in transporting molecules across cell membranes. Hundreds of studies have reported that ABC transporters could be responsible for drug resistance in cancers (125). Gene SPNS2 codes for the Sphingosine-1-phosphate (S1P) transporter previously associated with biological processes in breast cancers (126). Of these four genes, SPDEF exhibited strong up-regulation within 3 hours of E2 exposure with 78% increase (Figure 3.13_Top, Appendix A.1) based on microarray analysis, but the relative increase at 6 hours was only 33% (Figure 3.13_Bottom, Appendix A.2) based on RNA-seq analysis. PBX1, which encodes a pioneer factor of ER complexes, also followed a similar trend (Figure 3.13, Appendix A.1, A.2). The two other genes (CACNA1D and SPNS2) showed increased up-regulation with increased time of E2 exposure (Figure 3.13).   To confirm ER binding to the ChIP-Seq identified sequences, I validated the specificity of ER binding using an orthogonal ChIP-QPCR assay targeting each region (Method: Section 2.9, Amplicon length ranges ~100bp to 200bp, Appendix I) (Figure 3.1_F). I also included ER activation cofactors FOXA1 and p300 in the validation experiments since they have been commonly used for validation studies of ER binding (64). FOXA1 has also been suggested to regulate SPDEF expression in luminal breast epithelium cell models (68,127). SPDEF encodes a   59 previously identified ER cofactor, SPDEP/PDEF, and was up regulated after PPP2R2A knockdown. In the ChIP-QPCR assay for ER binding, INTG1, a previously described non-functional intergenic region, was used as a negative control (122) as it was not expected to be a target of DNA binding proteins. I compared the percentages of ER or cofactor recruitment to each targeting sites in T47D cells with reduced PPP2R2A expression (shPPP2R2A+E2), compared to cells in the control condition (shNS+E2). Each cofactor antibody was used separately as the bait  (Figure 3.14 B) to examine co-binding, in three replicates, in a pairwise contrast for each of the four genes. ER recruitment was significantly increased to the binding sites associated with ABCC3, SPDEF, and SPNS2 after PPP2R2A knockdown by more than 50% (Appendix-S) (Figure 3.14 B, two-paired t test, p<0.05, for CACNA1D, p=0.072). The results confirmed the enhanced ER binding to these sites. However, the recruitment of ER cofactor FOXA1 was only increased at the ER binding site for SPDEF. Recruitment of p300 was increased at all 4 binding sites, but only with 95% significance to CACNA1D associated binding site. SPDEF was only significantly more recruited to the binding sites associated with ABCC3 and SPDEF itself, but not CACNA1D or SPNS2 (Figure 3.14 B, two-paired t test, p<0.05).  Combining with the transcriptome (Section and ChIP-sequencing results (Section, after PPP2R2A deletion, SPDEF was not only transcriptionally up regulated, but also increasingly recruited to its own transcriptional regulatory site with ER. This further suggested that SPDEF is not only an ER cofactor, but also a cis-acting transcriptional regulator of itself creating a positive feedback loop with respect to its own transcription regulation (Figure 3.18). Taken together, the results of transcription factor binding analysis (ChIP-QPCR) confirmed that ER, along with some of its cofactors, were increasingly recruited to the regulatory sequences associated with their associated genes, ABCC3, CACNA1D, SPDEF, and SPNS2, after   60 knockdown of PPP2R2A, but the cofactor compositions appeared to be different in the recruited ER complexes.  Consistent with the above ER binding validation analysis, I identified binding motifs for ETS (SPDEF DNA binding domain) and P300 (or its binding factor C/EBPb) in the ABCC3, CACNA1D and SPDEF associated unique ER binding sequences, but not for SPNS2, suggesting that SPDEF and p300 could be the important cofactor mediators regulating ER binding specificities in this condition (Figure 3.15, TFSEARCH, Methods: Section 2.8). However, no FOXA1 motif was identified within the unique ER binding sites in these binding sites, although FOXA1 binding was enriched to the SPDEF regulatory site based on ChIP-QPCR. This could be that the FOXA1 binding motifs were de novo in this case that this result is not included in the software database or the effects of PPP2R2A reduction on FOXA1 binding is probably due to its interaction with other cofactors, rather than its own binding to specific motifs.   3.2.2 Mechanism of PPP2R2A reduction induced estrogen receptor regulation PPP2R2A deletion enhances ER cofactor recruitment Since the transcription factor binding analysis showed increased recruitment of FOXA1, P300, and SPDEF, to the ER binding sites recruited after PPP2R2A knockdown (Figure 3.14), I hypothesized that reduced PPP2R2A expression can regulate ER binding specificity by altering the amount of cofactors in recruited ER transcription complexes. To test this idea, I treated shRNA stable cells with 100nM β-estradiol for 6 hours and cross-linked ER with its cofactors before collection. I precipitated the ER complexes with antibodies and probed for ER cofactors on western blots (Method: Section 2.10) (Figure 3.1_G). With knockdown of PPP2R2A by 60%-  61 70% (Figure 3.16A) (Appendix-S), the input levels of cofactor proteins before immune-precipitation appeared relatively invariant (Figure 3.16, B). Based on densitometry analysis, the normalized ratios of cofactors to ER showed significant increase of SPDEF and FOXA1 in the ER complexes precipitated from cell models after PPP2R2A knockdown (Figure 3.16C, paired two-tailed t test, p<0.05), suggesting increased binding of these two cofactors in ER complexes. Although p300 appeared to show some level of increase (not significant based on paired two-tailed t test, paired two-tailed t test, p<0.05) in the precipitated ER complexes, I was not able to detect p300 while analyzing the input levels of cofactors before precipitation (5ug of input lysate was analyzed). Importantly, this increase was specific to certain co-factors since the levels of GATA3 and PBX1 in the respective precipitated ER complexes did not appear to have significant changes after PPP2R2A perturbation (Figure 3.16D). GATA3 is previously known as an ER cofactor and suppressor for SPDEF (122). PBX1 was also reported as a pioneer factor for ER complex formation (69). Unlike SPDEF, there was no uniquely gained ER binding site found associated with PBX1 or GATA3 after PPP2R2A knockdown. These results suggested that one mechanism of PPP2R2A modulation of ER signaling could involve the recruitment of ER cofactors, which ultimately affect the expression of the target genes. PPP2R2A knockdown affects phosphorylation states of ER and its cofactors Crystallography studies of estrogen receptors (ER) ligand binding domain (LBD) have revealed that phosphorylation of ER can effect allosteric structural changes and cofactor recruitment upon ligand binding (77). Since PP2A is a phosphatase, it is possible that another element of the mechanism involves direct or indirect phospho regulation of the ER complex. I proposed that if PP2A interacts with ER, it would be cross-linked and precipitated down along with other ER   62 cofactors. To test if PP2A complex interacts with ER directly, I probed for PPP2CA, the catalytic subunit of PP2A complex, in the precipitated ER complexes. However, with successful pull-down of ER complex, I could not detect any PPP2CA by western blot under the co-IP conditions used. Therefore, there is not sufficient data to conclude whether reduced level of PPP2R2A affects a direct interaction between ER and PP2A complexes.   However, it did not rule out the possibility that alternative PPP2R2A regulated phospho signaling could modify the phosphorylation status of ER or its cofactors. Structural studies have reported that phosphorylation of ER on residues Ser167, Ser118, and Ser104 are involved in its activation (79). However, clinical studies have identified phosphorylation on ER residue Ser305 to be related to tamoxifen resistance (78). Some studies reported that phosphorylation of ER cofactors could also affect ER signaling (74–76). Therefore, I decided to analyze the phospho status of ER in the next step. As noted above, since ER cofactor SPDEF was up regulated after PPP2R2A knockdown (Figure 3.13), we also included SPDEF in the analysis.  I immunoprecipitated ER and SPDEF from stable cells harboring shRNA targeting PPP2R2A and probed for the phosphorylation levels of these two immunoprecipitated proteins on western blots (Figure 3.1_H). The blots were analyzed by densitometry for their total and residue-specific phosphorylation levels (Figure 3.17) and compared to their phosphorylation in cells with reduced PPP2R2A (KD) to that in cells under the control condition (WT). The experiments were done in triplicate and each phosphorylation level was normalized to the amount of precipitated protein (eg. Phospho-ER normalized to total precipitated ER) (Methods: Section 2.11). With reduced expression of PPP2R2A (Figure 3.17 A, B), the level of phospho-ER on Serine-305 (S305)   63 appeared to be 46% (Mean=0.64, Fold Change with 95% confidence interval=0.53 to 0.79, ANCOVA, p<0.05) lower in PPP2R2A-knockdown cell model when comparing to the β-estradiol treated condition (Figure 3.17C_a_2, D_ii,) to its un-treated counterpart. No significant changes were found in the phosphorylation levels on the other four tested ER residues or the overall phosphorylation level of ER.  For SPDEF, since there was no commercially available phospho antibody for its specific residues, I only analyzed its overall phosphorylation with a Pan-phospho Serine/Threonine antibody. Without β-estradiol treatment, reduced PPP2R2A expression leads to 67% increase for pan-phosphorylation level on SPDEF (Mean=1.67, Fold Change with 95% confidence interval, 1.66-1.68, ANCOVA, P<0.05) (Figure 3.17C_b, D_i). On the other hand, ligand activation decreases SPDEF pan-phosphorylation level by 36% (Mean=0.64, Fold Change with 95% confidence interval, 0.46-0.87, ANCOVA, P<0.05) (Figure 3.17C_b, D_iv) in the condition with reduced PPP2R2A expression. No significant change of SPDEF pan-phosphorylation level was found in the other conditions in this study. In short, reduced expression of PPP2R2A changes phosphorylation status on ER and its cofactor SPDEF, but the conditions leading to these changes were also associated with β-estradiol stimulation.  3.2.3 Expression correlations of PPP2R2A deletions in breast cancers SPDEF up regulation is correlated with ER positive breast cancer with PPP2R2A deletions Since I observed that SPDEF is strongly co-regulated by ER and PPP2R2A in the ER positive cell line T47D, I asked whether an expression correlation between SPDEF expression and PPP2R2A copy number variation exists in human ER positive tumours. To test this idea, I studied the correlations between the expressions of SPDEF and PPP2R2A copy number status in   64 the METABRIC breast cancer tumour cases (31) (Method: Section 2.12) (Figure 3.1_I). Based on this analysis, the expression of SPDEF divided these cases into two groups, rather than showing obvious linear correlations with copy number status of PPP2R2A (Appendix J). Particularly, in ER positive cases with cis-acting PPP2R2A deletion, all 48 cases have increased SPDEF expression (Appendix J); whereas, other groups (ER negative cases, or cases with PPP2R2A amplification) have cases with both increased and reduced SPDEF expression (Appendix J).   To explore further, I analyzed SPDEF expression in clinical samples by dividing these METABRIC cases into 4 groups based on their ER expression and PPP2R2A copy number status and comparing the expression of SPDEF in cases with PPP2R2A copy number changes to others (Method: Section 2.12). Out of 1518 ER positive cases, there were 48 cases containing cis-acting PPP2R2A homozygous or heterozygous deletions (Table 3.1). There was a significant increase in SPDEF expression in these 48 cases compared to all other ER positive cases. This increase was small (1.17 fold), but statistically significant (p-value: 0.012, Figure 3.19_A, Kruskal-Wallis chi-squared test p=0.015). In contrast, only 5 ER positive cases contain high-level PPP2R2A amplification, and there was no significant difference of SPDEF expression when compared to other ER positive cases (Figure 3.19_B). In ER negative breast cancer cases, there were 28 cases containing a PPP2R2A homozygous or heterozygous deletion, but no significant difference of SPDEF expression was found in these cases compared to others (Figure 3.19_C). On the other hand, in the 7 ER negative cases that had high-level PPP2R2A amplification, there was a strong and statistically significant reduction in the expression of SPDEF when compared to other ER negative cases (Figure 3.19_D). The statistical correlations between SPDEF expression and   65 PPP2R2A copy number variation in these clinical cases with different ER statuses are consistent with the results in my in vitro studies, thus extending the observations from the cell lines used. PPP2R2A copy number loss is not associated with luminal B subtype differentiation  In the study of Buchwalter et al (2013), ectopic expression of SPDEF in basal breast cell line MCF10A pushed the cells to differentiate from a basal to luminal subtype (122). In the study of METABRIC cases, the biggest share of PPP2R2A deletions in the METABRIC landscape were found in Luminal B breast cancers (31), this relation suggested two possibilities: (i): PPP2R2A copy number loss could be selected for in the background of Luminal B breast cancer, or (ii) PPP2R2A deletion is a required mutation to drive Luminal B subtype differentiation. To find out whether the two pathways (ER and PP2A) determine the other, I further hypothesized that loss of PPP2R2A could be a driving force for ER positive cells to differentiate into luminal B subtype (Figure 3.1_J). To test this hypothesis, I first analyzed differential gene expression in Luminal B cases, compared with Luminal A in METABRIC samples and defined up regulated genes in Luminal B cases as Luminal B markers, and down regulated genes as Luminal A markers (these genes are up regulated in Luminal A cases).  I then analyzed the expression of these markers in the T47D model after PPP2R2A knockdown (based on RNA-sequencing data, 6 hours of β-estradiol treatment) to see if they also appeared to be differentially regulated in the same direction as they were in METABRIC cases (Method: section 2.13). T47D is classified as luminal breast cancer cell line based on the studies of expression status of endocrine receptors ER, PR, and HER2 (128,129). If these luminal B breast cancer markers, identified in METABRIC cases, also appeared to be regulated in the same direction as in my model after   66 PPP2R2A knockdown, then it would be consistent with a notion that the expression state shifts from luminal A to luminal B subtype after PPP2R2A knockdown. However, contrary to this hypothesis, the differentiated genes up regulated in Luminal B cases in METABRIC dataset were all down regulated in the cell model T47D in my analysis (Figure 3.20A). The same pattern was observed in the TCGA dataset (Figure 3.20B). These analyses suggested that the transcriptome expression pattern of T47D cells used in my studies normally appear to be more like a Luminal B subtype, but changed to be more like the Luminal A subtype after PPP2R2A knockdown. The results also suggested that despite high prevalence of PPP2R2A copy number loss in Luminal B breast cancers, this genomic aberration might not be a driving force in promoting Luminal B differentiation. On the other hand, it could be the transcriptomic background of Luminal B breast cancer that is more likely to select the mutation of PPP2R2A copy number loss. As the in vitro data suggested that PPP2R2A deletion enhanced ER signaling, this result, indicating cells with PPP2R2A loss behave more like Luminal A, was consistent with a previous report that the Luminal A subtype has higher ER expression status (130).   3.3 Discussion The aim of this research was to study if genomic aberrations identified in silico have functional impact in cells. More specifically in this chapter, I focused on investigating functional roles of PPP2R2A copy number loss in the development of ER positive breast cancers. The data presented in this chapter supported the hypothesis that PPP2R2A plays an important role in regulating ER signaling in breast cancer through alterations of ER binding specificity, ER cofactor recruitment, and phosphorylation status on ER and its cofactor SPDEF. I identified for   67 the first time SPDEF, a known ER cofactor, as one of the mediators in the mechanism of PPP2R2A regulated ER signaling.    Based on the measurements of ER responsive luciferase activity, reduced PPP2R2A expression led to increased ER signaling activity in ER positive T47D cells, suggesting PPP2R2A functions as a putative ER signaling inhibitor. The transcriptome expression analysis also supported this finding (Section As the data demonstrated (Figure 3.3), reduced PPP2R2A expression and β-estradiol stimulation regulated the expression of different but overlapping sets of genes. While ligand activation appeared to increase the variations and magnitudes of differential gene expression caused by PPP2R2A knockdown, cells with reduced PPP2R2A expression were also more sensitive to estrogen treatment.  Due to different durations of ligand activation (3 hours versus 6 hours), the sets of deregulated genes were enriched to pathways involved in different signaling networks (Section, but also including some shared pathways. These differences in deregulated gene sets could occur for several reasons. The platform differences between transcriptome microarray and RNA-sequencing could be one cause of these differences. More importantly, longer duration (6 hours) of β-estradiol treatment accumulated more and newer sets of deregulated genes, and their enriched pathways appeared dominant over the differentially regulated genes in the first 3 hours, while the expression of some early response genes might decline from their peaks between 3 and 6 hours time points. The results also reflected the idea that ER signaling functions in a time dependent manner (32,131). The network analysis of genes deregulated by PPP2R2A knockdown both after 3 hours and 6 hours of ligand activation were genes enriched in the   68 pathways regulating RNA stability, transcription, and translation. These are important pathways in promoting gene expression involved in regulating cell growth and proliferation (Figure 3.6). This was also consistent with my previous analysis that reduced PPP2R2A expression led to more up regulated than down regulated ER responsive genes (Figure 3.4D).  In the analysis of ER binding specificity, with reduced PPP2R2A expression, ER binding site repertoires were shifted. The joint analysis of ER binding specificity and transcriptome analysis identified a putative list of ER response genes that were both associated with unique ER binding sites (Appendix-E) and differentially regulated in the same direction upon short (3 hours) and long (6 hours) durations of ligand treatment. All of these genes were up regulated based on transcriptome analysis, even for those associated with ER binding sites that were lost in the condition of reduced PPP2R2A expression. This suggests that ER could also function as a transcription suppressor where loss of ER binding leads to up regulation of its response genes. Moreover, with reduced PPP2R2A expression, ER could bind to multiple regulatory subunits of a gene in the same condition, suggesting that ER could be regulating gene expression through multiple binding sites (Appendix E). There were also 8 genes associated with more than one unique ER binding sites. These findings suggested that ER could regulate gene expression through binding to multiple regulatory sites or even alternating the regulatory sites of a gene in different conditions. However, how ER is regulated in this process and what role PPP2R2A plays in the mechanism warrants further investigation.  In addition, in the condition of reduced PPP2R2A, ER binding sites were enriched more toward intron regions and ER cofactor composition changed in ER recruited transcription complexes.   69 SPDEF is a known ER cofactor (122). Based on the validation experiments after knocking down PPP2R2A, SPDEF itself was not only up regulated, but also increasingly recruited to its own transcription regulatory site, suggesting that SPDEF plays an important role in PPP2R2A regulated ER signaling. In the transcriptome analysis after PPP2R2A knockdown, there is 1.78 fold up regulation of SPDEF after 3 hours of β-estradiol treatment, but the level of up regulation dropped to 1.33 folds after 6 hours of ligand activation (Figure 3.13). Despite the relative differences in expression levels, SPDEF appeared to be regulated in the same direction on both array and sequencing platforms. The differences in expressions levels could also be due to both technical and biological reasons. In terms of technical reasons, the detection strengths of the different platforms vary. As a case-in-point, the expression validation analysis with QPCR indicated that the SPDEF transcript was up regulated after 6 hours of β-estradiol treatments by more than 2 fold (Figure 3.11), which was higher than the levels identified by either microarray or RNA-sequencing. In terms of biological reasons, it suggested that, as ER cofactors, SPDEF, as well as PBX1, could be early ER response genes.   My analysis of phosphorylation status demonstrated changes in post-translational modification of ER and SPDEF in the condition with reduced PPP2R2A, suggesting that PPP2R2A could regulate ER signaling through multiple mechanisms. Theoretically, upon PPP2R2A knockdown, the number of PP2A complexes that contain the subunit PPP2R2A would decrease and therefore it was predicted that phosphorylation levels of its direct targets would increase. The reduced phosphorylation level of ER Serine 305 may be resulted from a decreased activity of the associated kinase or an increased activity of the relevant phosphatase enzymes, respectively. Therefore, Serine 305 on ER may not be a direct target of PPP2R2A. It is unknown whether this   70 residue is still a direct target of PP2A, but specified by regulatory subunits other than PPP2R2A. Moreover, the phosphorylation level on ER-S305 was only significantly decreased in estradiol treated conditions, this suggested that only under conditions of ER-activation may loss of PPP2R2A result in significant alteration of the phosphorylation on this particular ER site. The change in pan-phosphorylation of SPDEF suggested it could be either a direct or indirect target of PPP2R2A. Alternatively, this could also be due to some other PPP2R2A regulated phosphorylation-dependent signaling pathway that targets SPDEF. Therefore further investigations of global phospho protein status could be expected to reveal a more comprehensive picture of the mechanisms. Furthermore, expression of SPDEF was significantly higher in ER positive cases with cis-acting PPP2R2A homozygous or heterozygous deletion in METABRIC, confirming the clinical significance of the in vitro results in this study.   Overall, I could propose a model of PPP2R2A regulated ER signaling (Figure 3.18). In ER positive breast cancers, reduced expression of PPP2R2A leads to reduced phosphorylation level on Serine 305 of ER, as well as alterations of ER binding specificities. These changes are accompanied with the composition changes of recruited ER co-factors, including SPDEF. Changes of ER binding specificity and cofactor compositions could lead to differential gene expression in the transcriptome, particularly those genes involved in transcription and translation. Increased recruitment of SPDEF, along with ER, to its own regulatory site could create a positive feedback loop, leading to enhanced ER signaling activity in the cell (Figure 3.18).   There were also some pitfalls associated with in this study. First of all, the level of PPP2R2A copy number loss varies in the patient breast cancers in METABRIC, with some samples   71 containing homozygous deletions, while others had only heterozygous deletions. While I made no distinction between the zygosity of deletions in the analyses, I still do not know if heterozygous deletions of PPP2R2A present the same phenotype as homozygous deletions in vivo. All of the in vitro studies are based on transient knockdown of PPP2R2A transcripts, rather than genetic deletion that would result in a complete loss; therefore, the reduced PPP2R2A expression might not fully represent all the conditions in clinical samples. Secondly, chromatin immunoprecipitation identified ER binding sites were based on sequences common between duplicate experiments, which were just a small portion of all sequences in each condition. There could still be real ER binding sites (ie false negatives) in the excluded sequences that did not satisfy the stringent filtering and were missed out in the study. In addition, we only analyzed a few selected ER cofactors and phosphorylation sites. Since PP2A modulates many signaling pathways, it would be better to analyze these aspects more comprehensively with mass spectrometry analysis of whole cell lysates for all differentially phosphorylated proteins followed by investigation of the deregulated signaling pathways. By combining existing information and mechanisms of identified phospho proteins, a potential signaling network could be constructed for PPP2R2A. Furthermore, it is unknown whether the altered downstream signaling pathways was due to deletion of one PP2A regulatory subunit alone or the resulting changes of PP2A subunit composition.  For further studies, it would be worth creating cell lines with a conditional PPP2R2A genomic knockout. Since ER signaling could be regulated indirectly through other signaling pathways, global analysis of phospho proteins in total cell lysates is expected to unravel the interconnecting network of signaling pathways perturbed by PPP2R2A deletion. Finally, to comprehensively   72 study the mechanisms of PPP2R2A regulated ER signaling, using quantitative mass spectrometry to analyze PP2A complex composition and immune-precipitated ER cofactors after PPP2R2A deletion, would provide more mechanistic insight.         73  Figure 3.1 Overall scheme of studies on PPP2R2A copy number loss and ER signaling I proposed this study based on frequent PPP2R2A copy number loss in ER positive breast cancers in METABRIC genomic landscape. I tested ER activity in response to reduced PPP2R2A expression in ER positive T47D cells (A). I then analyzed transcriptome expression (I.) and ER binding specificity (II.) in T47D cells treated with PPP2R2A targeting siRNA, and identified a list of genes whose expressions were preferentially regulated by ER upon PPP2R2A knockdown. I validated the transcript expression of 10 selected targets with QPCR. I then studied METABRIC Genomic Landscape Identifies PPP2R2A copy number loss (A.) ERE luciferase report assay I. Transcriptome Expression analysis (B.) Affymetrix Microarray (3 hours β-estradiol activation) (C.) RNA-sequencing (none or 6 hours β-estradiol activation) (D.) ER-ChIP-Sequencing (ER binding specificity) Short list of putative ER targeting genes (Combine ChIP-Sequencing + Transcriptome analysis) Unique ER binding sites + differentially regulated genes (E.) RT-QPCR validation  (Randomly selected10 genes)  (F.) ER-ChIP-QPCR validation (4 selected from the validated 10 targets) (G.) ER-ChIP Western blot (H.) ER-IP-Phospho ER, SPDEF-IP-Phospho SPDEF IV. Correlation studies with patient samples (J.) Differential gene expression heatmaps (Luminal A vs Luminal B in MTABRIC &TCGA data) (I.) Expression of ER cofactors in ER and PPP2R2A dichotomized groups in METABRIC III. ER binding mechanisms Differentially regulated genes identified in both (B.) and (C.) Altered ER binding specificity (Changes of ER binding sites associated genes) II. ER binding specificity analysis   74 changes of ER binding specificity with ER-ChIP-QPCR, ER-ChIP-WB, and phosphorylation status of ER and SPDEF (III.). Finally, I correlated my results with the data from clinical breast cancer samples (IV.)                    75  Figure 3.2 Reduced PPP2R2A expression enhances ER signaling measured by ERE luciferase activity T47D luminal breast cancer cells were transduced lentiviral packaged shRNA to develop stable cell lines. shNS: non silencing control; shPPP2R2A: shRNA targeting PPP2R2A. T47D stable cell lines harboring shRNA were cultured in medium supplemented charcoal/dextran treated serum for at least two days before experiments. Cells were transfected with ERE_Luciferase reporter using lipofectamine 2000. The cells subjected to ligand activation were treated with 100nM β-estradiol after transfection for 48 hours before luciferase activity assay using Promega Dual-Glo kit.  ERE_Luciferase reporter has 3 copies of a minimal consensus ER response elements (ERE) cloned in front of the firefly luciferase coding sequence. A TATA box promoter Ratio of Bend Intensity (PPP2R2A/Actin)#1 #2 #3PPP2R2A Actin 0 1 2 3 4(ii) shPPP2R2A+E2 vs shPPP2R2A(i) shNS+E2      vs shNSLinear Fold ChangeEffect of Ligand Actiation0 1 2 3 4(iv) shPPP2R2A+E2 vs shNS+E2(iii) shPPP2R2A     vs shNSLinear Fold ChangeEffect of PPP2R2A knockdownA B shNS shPPP2R2A shNS+E2 shPPP2R2A+E2Relative Ratio of  Band Intensity  (PPP2R2A/Actin)   76 and a transcription start site were placed next to the ERE consensus sequences upstream of the gene encoding the firefly luciferase enzyme to help the initiation of transcription. The consensus ERE sequence contains a13bp (5’GGTCAnnnTGACC) sequence (99). This sequence was first identified based on alignments of 5’ sequences of estrogen-regulated genes in Xenopus laevis and chicken (66). (A) Western blot analysis of PPP2R2A knockdown by shRNA in T47D stable cells and densitometry analysis of western blots (B) (i) and (ii): Effects of 100nM β-estradiol treatment on ER signaling measured by ERE_luciferase activity. n=3, ANOVA p<0.05. Dashed line: 95% statistical significance cut-off. (iii) and (iv): Effects of PPP2R2A knockdown on ER signaling measured by ERE_luciferase activity. n=3, ANOVA P<0.05. Refer to Appendix-R for details of densitometry analysis, image setting 8-bit.   77  A B -2 2-4 4 2134560Fold Change (Linear) p-value (-Log10) 0.0 0.5 1.0 1.5siPPP2R2A+E2 vs siNT+E2-3siPPP2R2A+E2 vs siNT+E2-2siPPP2R2A+E2 vs siNT+E2-1Linear Fold Change170$up$regulated$135$down$regulated$  78 Figure 3.3 PPP2R2A regulated differential transcriptome expressions in response to 3 hours of 100nM β-estradiol activation ER in T47D cells T47D cells were cultured in RPMI medium supplemented with charcoal/dextran treated (CDT) serum before siRNA treatment. 50nM of siRNA were transfected into T47D cells with lipofectamine 2000. The cells were cultured in CDT serum supplemented medium for 2 more days after siRNA knockdown. The cells subjected to ligand activation were treated with 100nM of β-estradiol for 3 hours before collection. The purified mRNA samples were analyzed on Affymetrix GeneChip Human Transcriptome microarray. (A). QPCR analysis of PPP2R2A transcript knockdown in T47D samples used in microarray analysis. (B) Volcano plot of transcriptome differential expression after PPP2R2A transcript knockdown measured by Affymetrix GeneChip Human Transcriptome microarray. Threshold: ANOVA p<0.05, FDR<0.5. |Linear fold change|>=1.5, n=3. Red: 170 up regulated transcripts. Green: 135 down regulated transcripts. Grey: genes with expression below the threshold. Refer to Appendix A.1 for details of deregulated genes.    79  Figure 3.4 Reduced PPP2R2A expression increases the number of deregulated ER response gene upon 6 hours of ligand activation in T47D cells T47D cells were cultured in RPMI medium supplemented with charcoal/dextran treated (CDT) serum before and after siRNA treatment. T47D cells were transfected with 50nM of siRNA using lipofectamine 2000. The cells were cultured in CDT serum supplemented medium for 2 days after siRNA knockdown treatment. The cells subjected to ligand activation were treated with 100nM β-estradiol for 6 hours before collection. The purified RNA samples were analyzed with strand-specific RNA sequencing. Threshold: FDR<0.05, |Log (Fold change)|>1. (A) Venn siPPP2R2A+E2 vs siNT+E2siPPP2R2A+E2 vs siPPP2R2AsiPPP2R2A vs siNTsiNT+E2 vs siNT050010001500Number of differentially regulated genesUpDownA B C D Up regulated Down regulated 0.0 0.5 1.0 1.5siPPP2R2A+E2 vs siNT+E2-2siPPP2R2A+E2 vs siNT+E2-1siPPP2R2A+E2 vs siNT-2siPPP2R2A+E2 vs siNT-1Linear Fold Change  80 diagram presenting up-regulated genes in each condition (B) Venn diagram for down-regulated gene in each condition; Conditions: (Orange: siPPP2R2A+E2 vs siNT+E2. Green: siPPP2R2A vs siNT. Blue: siNT+E2 vs siNT. Red: siPPP2R2A+E2 vs siPPP2R2A. (C): QPCR analysis of PPP2R2A transcript knockdown in RNA-sequencing samples; Dashed line: 95% statistical confidence cut-off. (D) Number of differentially regulated genes for each pair of comparison. Black: The number of up regulated genes. Grey: The number of down regulated genes. Threshold: n=2, FDR<0.05. Refers to table Appendix C.         81  Figure 3.5 Reduced PPP2R2A expression alteres the magnitude of ER responsive gene expression upon 6 hours of ligand activation in T47D cells T47D cells were cultured in RPMI medium supplemented with charcoal/dextran treated (CDT) serum before siRNA treatment. 50nM of siRNA were transfected to T47D cells with lipofectamine 2000. The cells were cultured in CDT serum supplemented medium for 2 more days after siRNA knockdown treatment. The cells subjected to ligand activation were treated with100nM of β-estradiol for 6 hours before collection. The purified RNA samples were analyzed with strand-specific RNA sequencing. Threshold: FDR<0.05, |Log (Fold change)|>1. siPPP2R2A+E2 vs siNT+E2 siPPP2R2A+E2 vs siPPP2R2A siPPP2R2A vs siNT siNT+E2 vs siNT   82 (A). siPPP2R2A+E2 vs siPPP2R2A (Appendix A.3). (B). siNT+E2 vs siNT (Appendix A.5). (C). siPPP2R2A+E2 vs siNT+E2 (Appendix A.2). (D). siPPP2R2A vs siNT (Appendix A.4). Threshold: |Log fold change|>=1. FDR<0.05. Red: Differentially expressed genes with statistical significance based on the threshold. Black: Genes with no significant differential expressions based on the threshold.     83  Measles (K) Leishmaniasis (K) Interferon Signaling (R) RIG-I/MDA5 mediated induction of IFN-alpha/beta pathways (R) Hepatitis C (K) Phagosome (K) Influenza A  (K) RIG-I-Like receptor signaling  (K) Pancreatic Cancer (K) HTLV-I Infection (K) Antigen Processing and Presentation (K) Autoimmune thyroid disease (K) Epstein-Barr Virusus Infection (K) Herpes Simplex Infection (K) Rheumatoid Arthritis (K) Cell Adhesion Molecules (K) Allograft Rejection (K) GRAFT-Viruses-Host Disease (K) 1 2 *1. Viral Myocarditis (K) *2. Type 1 diabetes mellitus (K)!Chemokine Signaling pathway (K) Growth Hormone Receptor Signaling (R) Signaling ERBB4 (R) GMCSF-Mediated signaling events (N) Platelet activation signaling and aggregation (R) PI3K/AKT Signaling in cancer (R) PIP3 Activates AKT Signaling (R) Signaling by PDGF (R) Signaling SCF-KIT (R) Signaling FGFR in disease (R) 5HT2 type receptor mediated signaling (P) Cytokine-cytokine interaction (K) Natural killer cell mediated cytotoxicity (K) Immuno-regulation interaction between lymphoid and non-lymphoid cells (R) Extracellular matrix organization (R)  EPO Signaling (N)!TCPTP mediated signaling (N) Endocytosis (K) Osteopotin mediated events (N) ALK1 Signaling events (N) EPHA Forward signaling (N) Chagas disease (K) TGF-beta receptor signaling (N) Class I MHC antigen processing and presentation (R) MHCII antigen presentation  EGF Signaling (B) Mitotic M-M/G1 phase (R) A   84   Synthesis of DNA (R) Regulation of Mitotic cell cycle (R) Cell cycle checkpoints (R) S phase (R) Mitotic G1-G1/S Phases (R) Mitotic M-M/G1 phases (R) *1. Regulation of DNA replication (R) Influenza infection (R) Nonsense-mediated decay (R) Translation (R) Ribosome (K) Hypertrophic cardiomyopathy (K) Dilated Cardiomyopathy (K) Cadherin signaling pathway (P) WNT signaling pathway (P) Pancreatic Cancer (K) Cell cycle (K) HTLV-1 Infection (K) Castrin-CREB signaling pathway via PKC and MAPK (R) Platelet activation, signaling and aggregation (R) E2F Transcription factor network (N) CDK Regulation of DNA replication (B) Dopaminergic synapse (K) Infection signaling (R) Heterotrimeric G-protein signaling pathway 1 B   85  Figure 3.6 Duration of ligand activation affectes types of downstream pathways regulated by ER responsive genes after PPP2R2A knockdown The pathway enrichment analysis was done with Reactome-FI plugin (132), and the analysis results were presented as enrichment maps with Cytoscape enrichment map plug in (133). The gene lists in each condition were analyzed with four databases. Network analysis used KEGG (K), Biocarta (B), Reactome (R), NCI (N) databases. Threshold for early ER responsive genes (3 hours of β-estradiol activation, Human Transcriptome Microarray): |Linear Fold change|>1, Eukaryotic translation initiation (R) Eukaryotic translation termination (R) Nonsense mediated decay (R) Ribosome (K) SRP-dependent co-translational protein targeting to membrane (R) Eukaryotic translation elongation (R) C   86 ANOVA p<0.05. Threshold for late ER responsive genes (6 hours of β-estradiol activation, Strand-specific RNA-Sequencing): |Log (Fold change)|>=1. FDR<0.05. (A) Pathways affected by differentially regulated ER responsive genes after PPP2R2A knockdown with 3 hours of 100nM β-estradiol treatment. (B) Pathways affected by differentially regulated ER responsive genes after PPP2R2A knockdown with 6 hours of 100nM β-estradiol treatment. (C) Pathways affected by ER responsive genes that were differentially regulated after PPP2R2A knockdown and with the treatment of β-estradiol at both 3 hours and 6 hours time points. Refer to Appendix D for the details of network analysis. Red: Pathways enriched with differentially regulated genes. Sizes of the red circles: the number of genes form the list of differential expression involved in the pathway. Darkness of red circles: significance of the pathway based on pathway enrichment analysis (lighter pink: low significance; dark red: high significance). Green line: connections among pathways.   87  Figure 3.7 Reduced PPP2R2A expression alters ER binding specificity T47D cells were cultured in CDT serum supplemented medium before transfected with 50nM of siRNA using lipofectamine 2000. The cells were cultured for another two days after transfection. Before collection, cells were treated with 100nM of β-estradiol for 1 hour and then cross-linked Ratio of band intensity (PPP2R2A/Actin)siNT+E2siPPP2R2A+E21237 739 332 siPPP2R2A + E2 siNT + E2 A B siNT +E2 siPPP2R2A+E2 PPP2R2A Actin   +        +       −       −   −        −       +       + Unique to siNT  (lost when PPP2R2A is silenced) Unique to siPPP2R2A  (new ER binding sites when PPP2R2A is silenced) ERE (Estrogen Responsive Element) ERE (Estrogen Responsive Element) C D Bits Bits   88 with 1% formaldehyde in serum-free medium. ER, along with its binding DNA, was precipitated by ChIP-grade anti-ER antibody and the DNA was reverse cross-linked and purified before sequencing. (A) Western blot analysis of PPP2R2A knockdown with siRNA and Western blot densitometry quantification analysis of PPP2R2A knockdown with siRNA with ImageJ.  (B). Venn diagram on the number of unique ER binding sites in T47D after PPP2R2A knockdown with one hour of β-estradiol activation. Genes appeared in both replicate samples are counted. Artifact sequences were filtered out with MACS. (C) Summary of consensus motifs of unique ER responsive sites in T47D with siNT+E2. n=739. (D) Summary of consensus motifs of unique ER responsive sites in T47D with siPPP2R2A+E2. n=332. Refer to Appendix B for the list of unique ER binding sites. Refer to Appendix-R for details of densitometry analysis of western blots, setting: 8–bit images.     89  Figure 3.8 Reduced PPP2R2A expression leads to more intron oriented ER binding T47D cells were cultured in CDT serum supplemented medium before transfected with 50nM of siRNA using lipofectamine 2000. The cells were cultured for another two days after transfection. Genome siNT siPPP2R2A siNT siPPP2R2A A B   90 Before collection, cells were treated with 100nM of β-estradiol for 1 hour and then cross-linked with 1% formaldehyde in serum-free medium. ER, along with its binding DNA, was precipitated by ChIP-grade anti-ER antibody and the DNA was reverse cross-linked and purified before sequencing.  ER binding sequences were sequenced, artifact signals were filtered out and ER binding peaks were called with software MACS. CEAS (Cis-regulatory Elements Annotation System) were used for sequence annotation. (A). Pie charts of genomic elements, ER binding elements in control T47D cells, and ER binding elements in T47D cells after PPP2R2A knockdown. (B) Annotations of ER binding sites in each condition (siNT and siPPP2R2A). ER binding is more preferentially enriched to intron regions in T47D after PPP2R2A knockdown.    91  Figure 3.9 Differential expression of genes associated with unique ER binding sites after PPP2R2A knockdown in T47D cells  The unique ER binding sites identified in T47D cells with ChIP-Sequencing after PPP2R2A transcript knockdown were overlapped with genomic sequences to identify associated genes. siPPP2R2A unique siNT unique A. Expression of unique ER binding sites associated genes (3 hours E2, Microarray) B. Expression of unique ER binding sites associated genes (6 hours E2, RNA-Seq) siPPP2R2A unique siNT unique False Discovery Rate (-Log10) ANOVA p-value (-Log10) Linear Fold Change Linear Fold Change Fold Change (Log2) Fold Change (Log2) ANOVA p-value (-Log10) False Discovery Rate (-Log10)   92 Genes with sequences overlapped with and immediately on either side of the ER binding sites were identified as potential unique ER targeting genes after PPP2R2A knockdown in T47D cells. The transcript expression of these genes was plotted based on Affymetrix Human Transcriptome microarray analysis and RNA-sequencing. X-axis: transcript level fold change, linear fold change for microarray analysis, and Log2 fold change for RNA-sequencing. Y-axis: FDR (log10). Dashed line: 95% significance cut-off. Black dots: Genes with significant differential expression based on the corresponding statistical tests. Grey dots: Genes with non-significant differential expression based on the corresponding statistical tests. (A). Differential expressions of putative ER response genes after PPP2R2A knockdown in T47D cells with 3 hours β-estradiol treatment. n=3, ANOVA p<0.05. (B) Expression of putative ER response genes after PPP2R2A knockdown in T47D with 6 hours β-estradiol treatment. n=2, FDR<0.05.           93  Figure 3.10 Reduced PPP2R2A expression leads to shifted ER binding specificity and differential expression regulation of the associated genes Distribution of deregulated genes with associated unique ER binding sites in siNT or siPPP2R2A treated conditions. Green: Differentially regulated genes in cells treated with siRNA non-targeting (siNT) control followed by 100nM β-estradiol treatment. Blue: Differentially regulated genes with PPP2R2A transcript knockdown followed by 100nM β-estradiol treatment. Genes differentially regulated in the same direction are 3 hours E2 treatment and 6 hours E2 treatment are counted. 8 genes are associated with more than one ER binding sites in conditions both before and after knockdown of PPP2R2A, but the binding sites are different in the two conditions (The 8 genes are AFF3, ASTN2, CD82, EHF, KCNMA1, PRICKLE2, SH3RF2, SLC39A11). All genes were up regulated. For details of genes in each condition, refer to Appendix E.      94  Figure 3.11 QPCR transcript analyses of some selected genes associated with gained unique ER binding sites after PPP2R2A knockdown in T47D cells T47D cells were treated with siRNA targeting PPP2R2A (siPPP2R2A) and non-targeting control (siNT). The cells were cultured in CDT serum supplemented medium. Cells subjected to ligand 0 1 2 3 4 5SPNS2SPDEFSH3RF2MLPHKCNMA1NEBCD44CACNA1DAFF3ABCC3PPP2R2ALinear Fold ChangeTraget genesT47D siPPP2R2A E2 VS siNT E2T47D siPPP2R2A vs siNT**  95 activation were treated with 100nM of β-estradiol before collection. Expression was analyzed based on ANOVA analysis of Ct values in each condition and normalized to the Ct value of GAPDH. Black: Transcript expression change of the gene after PPP2R2A knockdown without β-estradiol activation. Red: Transcript expression change of the gene after PPP2R2A knockdown with 6 hours of 100nM β-estradiol activation. Dashed line: 95% confidence interval cut-off, n=3. ANOVA P<0.05. *Transcript differential expression was higher in the condition with β-estradiol treatment than the condition without ligand activation. Refer to Appendix F for statistical details of QPCR results.             96  Figure 3.12 Genomic locations of the four selected unique ER binding sites gained after PPP2R2A knockdown in T47D cells Selection criteria of the four genes: in T47D cells, 1.) Genes were associated with unique ER binding sites after PPP2R2A knockdown based on ChIP-seqencing analysis. Genes were identified based on human genome hg18. 2.) Genes appeared to be up regulated both in Human Transcriptome microarray after 3-hours of β-estradiol activation and in Strand-specific sequencing after 6-hours of β-estradiol activation. 3.) The associated ER binding sites are located within the genomic sequences of the genes. Red circle with black bar: location of overlapped ER binding sites. 4.) SPDEF was selected due to its identification as ER cofactor. Other genes were randomly selected after filtering the list with the 3 above criteria. Refer to Appendix B.2 for coordinates of each gene.                        SPDEF  (chromosome 6) ABCC3  (Chromosome 17) SPNS2  (Chromosome 17) CACNA1D  (chromosome 3)   97  Transcriptome Microarray Linear Fold Change RNA-Sequencing p-value (-Log10) Fold Change (Log2) FDR (-Log10) ABCC3 CACNA1D SPDEF SPNS2 ABCC3 CACNA1D SPDEF SPNS2   98 Figure 3.13 The four ChIP-QPCR validated ER target genes are significantly up regulated upon ligand activation The unique ER binding sites identified in T47D cells with ChIP-Sequencing after PPP2R2A transcript knockdown were overlapped with genomic sequences to identify associated genes. Genes with sequences overlapped with and immediately on either side of the ER binding sites were identified as potential unique ER targeting genes after PPP2R2A knockdown in T47D cells. The transcript expression of these genes was plotted based on Affymetrix Human Transcriptome microarray analysis (ANOVA p-value<0.05) and RNA-sequencing (p-value: FDR<0.05).   Blue: ChIP-QPCR validated ER response genes. Black dots: Genes with significant differential expression based on the specified statistical tests. Dashed line: 95% confidence interval cut-off based on the specified statistical tests. Top: Transcript differential expression of genes with associated unique ER binding genes after PPP2R2A knockdown and 3 hours of β-estradiol treatment, based on transcriptome microarray analysis (ANOVA p<0.05). Bottom: Transcript differential expression of genes associated with unique ER binding genes after PPP2R2A knockdown and 6 hours of β-estradiol treatment, based on RNA-sequencing analysis (FDR<0.05). Red colored genes: the associated unique ER binding sites of these genes were validated by ChIP-QPCR.     99 Ratio of band intensity (PPP2R2A/H3)A PPP2R2A H3 shNS +E2 shPPP2R2A+E2 +""""""−"""""+""""−"""""+""""−"−""""""+"""""−""""+"""""−""""+""B INTG1ABCC3CACNA1DSPDEFSPNS2051015FOXA1-ChIP% Input*ER-ChIP% InputINTG1ABCC3CACNA1DSPDEFSPNS2020406080****p300 ChIP% InputINTG1ABCC3CACNA1DSPDEFSPNS2051015*INTG1ABCC3CACNA1DSPDEFSPNS20246SPDEF ChIP% Input**shNS shPPP2R2A  100 Figure 3.14 Increased recruitments of ER and its cofactors to the selected unique ER binding sites gained after PPP2R2A knockdown in T47D cells T47D stable cells harboring shRNA were cultured in medium supplemented with CDT serum. Cells were treated with 100nM β-estradiol and cross-linked with 1% formaldehyde before collection. ER or its cofactors, along with their binding DNA, were immunoprecipitated with ChIP-grade antibodies (ER, FOXA1, p300, SPDEF). The precipitated complexes were reverse cross-linked and the collected DNA in each condition was analyzed with QPCR by normalizing to 1% input of immunoprecipitation. INTG 1 (intergenic region) is intergenic sequence that is not expected to be bond by transcription machinery. (A) Western blot analysis of PPP2R2A knockdown in T47D samples that were used in the ChIP-QPCR analysis and western blot densitometry quantification of PPP2R2A knockdown. The data was presented as relative pixel density ratio of PPP2R2A: Actin. (B) QPCR analysis of ER binding to the targeting sites in T47D, Y-axis: The percent recruitment of ER or its cofactors to the targeting sequences after PPP2R2A knockdown (shPPP2R2A) compared to non-silencing control (shNS). X- axis: the names of genes with associated ER binding sites. FOXA1and p300 are common ER cofactors. Statistics: Paired two-tailed t test. *p<0.05, **p<0.01. Refer Appendix I for amplicons used for QPCR validation. Refer to Appendix-R for details of western blot densitometry analysis, image setting: 8–bit. Refer to Appendix-S for details of ChIP-QPCR results.    101  Figure 3.15 Transcription factor binding sites analysis of ChIP-QPCR validated ER targeting sequences after PPP2R2A knockdown identifies p300 and c-Ets (SPDEF) binding sites Transcription factor search analysis with TFSEARCH of uniquely gained ER binding sites in ABCC3, CACNA1D, and SPDEF after PPP2R2A knockdown. Red arrow: SPDEF binding sites c-Ets. Green arrow: Binding sites for p300 or C/EBP, which recruits p300. (http://www.cbrc.jp/research/db/TFSEARCH.html)   ABCC3 CACNA1D SPDEF   102  Figure 3.16 Reduced PPP2R2A expression increases the recruitment of FOXA1 and SPDEF to ER complexes T47D cell models with stably integrated shRNA (shPPP2R2A vs shNS) were cross-linked with 1% formaldehyde after treatment 6 hours of 100nM β-estradiol and estrogen receptors were immunoprecipiated (ER-ChIP), along with its cofactors. The precipitated ER-cofactor complexes were analyzed with western blot and densitometry.  The data were presented as relative intensity of bands of protein of interest to the reference proteins (β-actin or precipitated estrogen receptor).  (A). Western blot analysis of PPP2R2A knockdown by shRNA in T47D and densitometry quantification of western blots. Densitometry data is presented as the pixel density ratio of Ratio of Bend Intensity (PPP2R2A/Actin)shNSshPPP2R2APPP2R2A H3 +""""""""−"""""""""+""""""""−""""""""+""""""−"−""""""""+"""""""""−""""""""+""""""""−""""""+"A #1 #2 #3 Ratio of Bend Intensity (Cofactors/H3)shNSshPPP2R2AER       FOXA1   SPDEF     PBX1     GATA3      H3 +""""−"""""""+"""""−""""""+""""""−"""""""+"""""−"""""""+"""""−"""""""+"""""−"−""""+"""""""−"""""+""""""−""""""+"""""""−"""""+"""""""−"""""+"""""""−"""""+"shNS+E2 shPPP2R2A +E2 #1 #2 #3 01234Relative Ratio of Bend Intensity (Cofactors/ER)shNSshPPP2R2A**012345Relative Ratio of Bend Intensity (Cofactors/ER)shNSshPPP2R2AFOXA1      SPDEF        p300           ER PBX1        GATA3      ER B C D #1  #2  #3 #1  #2  #3 shNS+E2 shPPP2R2A +E2 shNS+E2 shPPP2R2A +E2 shNS+E2 shPPP2R2A +E2 +"""""""−"""""""+"""""""−"""""""+""""""−"""""""+""""""−"−"""""""+"""""""−"""""""+"""""""−""""""+"""""""−""""""+" +"""""""−"""""""+""""""−"""""""+"""""−"−"""""""+"""""""−""""""+"""""""−"""""+"Relative Ratio of Band Intensity (PPP2R2A/H3) Relative Ratio of Band Intensity (Cofactor/H3) Relative Ratio of Band Intensity (Cofactor/ER) Relative Ratio of Band Intensity (Cofactor/ER)   103 corresponding bands (PPP2R2A/Actin) of each sample. (B). Western blot and densitometry analyses of each co-factors subjected for analysis in 5ug of input samples (3 replicates). Densitometry data was presented as pixel density ratio of bands (cofactors/H3) for the 3 replicate samples in each condition. Although p300 was detected in the precipitated ER complex, we were not able to detect p300 in the 5ug of input sample. (C). Western blot and densitometry analysis of the amount of ER cofactors (FOXA1, SPDEF, p300) with the respect to the immune-precipitated ER complexes after PPP2R2A knockdown. Densitometry data is presented as density ratio (Cofactors/ER) for 3 replicates of samples immunoprecipitaed with a ChIP-Grade Mouse-anti-ER antibody, n=3. Statistics: paired two-tailed t test, *p<0.05.  (D). Western blot and densitometry analysis of the amount of ER cofactors GATA3 and pioneer factor PBX1 in the immune-precipitated ER complexes after PPP2R2A knockdown. Densitometry data is presented as density ratio (Cofactors/ER) for 3 replicates of samples immunoprecipitaed by ChIP-Grade Rabbit-anti-ER antibody, n=3. Statistics: paired two-tailed t test. Refer to Appendix-R for details of densitometry data, image setting: 8–bit.    104  ER ER ER a_1 a_2 a_3 b #1 #2 #3   shNS shPPP2R2A     shNS+E2      shPPP2R2A+E20. Ratio of Bend Intensity (PPP2R2A/Actin)D A C Relative Ratio of  Band Intensity (PPP2R2A/Actin) B 0 1 2 3Pan-p-SPDEFER p104/106ER p305ER p167ER p118Pan-p-ERFold ChangeshPPP2R2A+E2 vs shNS+E2*ii 0 2 4 6 8 10Pan-p-SPDEFER p104/106ER p305ER p167ER p118Pan-p-ERFold ChangeshNS+E2 vs shNSiii 0 1 2 3 4 5 6 7Pan-p-SPDEFER p104/106ER p305ER p167ER p118Pan-p-ERFold ChangeshPPP2R2A+E2 vs shPPP2R2A*iv 0 1 2 3Pan-p-SPDEFER p104/106ER p305ER p167ER p118Pan-p-ERFold ChangeshPPP2R2A vs shNS**i   105 Figure 3.17 PPP2R2A expression reduction and β-estradiol activation change the phosphorylation status of ER and SPDEF T47D stable cell lines harboring shRNA were cultured in medium supplemented with CDT serum. The cells were treated with 100nM β-estradiol for 6 hours before collection. Cells were collected for immunoprecipitation without formaldehyde treatment. (A). Western blot analysis of PPP2R2A knockdown by shRNA in T47D. (B). Densitometry analysis of western blots with ImageJ on PPP2R2A knockdown, as the relative pixel density ratio of PPP2R2A/Actin in each sample. (C). Western blot analysis of phosphorylation levels on precipitated ER and SPDEF. (a_1): analysis of pan-phosphorylation level of ER. (a_2 and a_3): analysis of phosphorylation levels on specific ER residues. (b): analysis of pan-phosphorylation level on SPDEF. (D). Densitometry analysis of phosphorylation levels. Image setting: 8–bit. Statistics: Ratio paired t test normalized to the precipitated ER or SPDEF, n=3, dashed line: cut-off of adjusted p-value with 95% confidence interval. Red: Significant increase in the phosphorylation level in the respective condition. Green: Significant reduction of the phosphorylation level in the respective condition. * p<0.05. ** p<0.01. Refer to Appendix-R and T for details of densitometry data.          106  Figure 3.18 Proposed mechanism of PPP2R2A function in ER signaling Copy number loss of PPP2R2A in ER positive breast cancers results in changes of phosphorylation levels of downstream phospho proteins and their regulated gene expression. Expression changes of critical proteins, such as ER cofactor SPDEF, alter ER binding specificity and ER down stream gene expression. Reduced PPP2R2A expression and induction of ER ligand lead to up regulation of SPDEF, which further alters ER signaling. Red arrow: expression up regulation. Blue arrow: direction of the regulation.    PPP2R2A Differential gene expression (eg. SPDEF) Phospho level changes of ER and its cofactors Changes of ER binding specificity Differential expression of ER response genes (eg. SPDEF)   107  Figure 3.19 Up regulation of SPDEF in ER positive cancers is correlated with cis-acting PPP2R2A copy number loss in METABRIC samples Analysis of SPDEF expression in METABRIC samples, presented in bean plot of SPDEF expression in breast cancers samples divided into groups based on cis-acting PPP2R2A copy number status and ER expression. Y-axis: expression score of SPDEF is the direct readout of   108 Illumina HT-12 v3 microarray. Statistics: Kruskal-Wallis chi-squiared test, p<0.05. Dashed line: mean value representing SPDEF expression of all (either ER positive or ER negative) samples analyzed. Solid line: mean value representing SPDEF expression in the subgroup, either with PPP2R2A copy number variation or others. (A) Comparison of SPDEF mRNA expression between ER positive breast cancers with PPP2R2A copy number loss and other ER positive cancers (B) Comparison of SPDEF mRNA expression in ER positive breast cancers with PPP2R2A copy number gain and other ER positive cancers (C) Comparison of SPDEF mRNA expression in ER negative breast cancers with PPP2R2A copy number loss and other ER negative cancers (D) Comparison of SPDEF mRNA expression in ER negative breast cancers with PPP2R2A copy number gain and other ER negative cancers. Red: groups with statistically significant SPDEF expression change compared to its counterpart, p<0.05. Refer to Appendix-J for linear correlations of PPP2R2A and SPDEF in METABRIC breast cancer samples.             109 Table 3.1 SPDEF up regulation is correlated with PPP2R2A cis-acting copy number loss in METABRIC samples Expression analysis of SPDEF is based on direct readout from Illumina HT-12 v3 gene expression microarray of METABRIC breast cancer samples.  Statistics: Kruskal-Wallis chi-squared test PPP2R2A--: cis-acting homozygous or heterozygous deletion of PPP2R2A. PPP2R2A+: cis-acting gain or high-level amplification of PPP2R2A.  Condition Target N Fold change Kruskal Wallis P-value ConclusionER+ PPP2R2A- SPDEF 48 1.17 0.012 Incrased expressionER- PPP2R2A- SPDEF 28 1.92 0.043 Incrased expressionER+ PPP2R2A+ SPDEF 32 0.86 0.092 Not significantER- PPP2R2A+ SPDEF 7 0.25 0.011 Decreased expression  110  Control PPP2R2A knockdown A Luminal B Luminal A   111  Figure 3.20 Gene expression pattern of T47D after PPP2R2A knockdown is more correlated to that in Luminal A subtype rather than Luminal B Gene selection criteria: differentially expressed genes in Luminal B cases compared to Luminal A cases in METBRIC or TCGA databases. Selected genes were clustered based on the expression information of Strand-specific RNA-sequencing in T47D after PPP2R2A knockdown. Normalized expression (Z-score)= (Measured expression-Media (expression for the given probe set))/standard deviation among research centres.) (A). Genes selected from METABRIC samples. (B). Genes selected from TCGA samples. Up regulated genes in ER positive breast cancer cases with PPP2R2A copy number loss in METABRIC appeared to be down regulated in T47D cells after PPP2R2A knockdown. The same pattern also appeared in analysis of TCGA database. Red: up regulated in the respective condition. Blue: down regulated genes in the Luminal B Luminal A Control PPP2R2A knockdown B   112 respective condition. Blue: up regulated genes in Luminal B breast cancer cases in the given database, Yellow: up regulated genes in Luminal A breast cancer cases in the give database.                         113 Chapter 4: Reduced Expression of PPP2R2A Changes PP2A Complex Composition  4.1 Background and rationale PP2A is a hetero-trimeric complex made up of catalytic subunits, scaffolding subunits, and regulatory subunits. Two catalytic subunits are encoded in the genome (PPP2CA, PPP2CB) and either one may be incorporated in the holoenzyme. They catalyze the removal of the phospho residues from serine or threonine side chains. The scaffolding subunits (PPP2R1A, PPP2R1B) provide a platform to organize the other two functional parts of the holoenzyme. Based on crystallography structural studies, the scaffold subunits have 15 HEAT-repeats (Huntington, Elongation factor 3, PP2A, and Yeast kinase TOR1) that are responsible for their binding to catalytic subunits (52,134). The catalytic subunits and scaffold subunits make up the core P2A complex. The regulatory B subunits are responsible for determining the substrate specificity. There are 16 B subunits encoded in the genome. Based on their functional domains, the regulatory subunits are further divided into 4 major subgroups. Members of the B sub-group contain four to seven Tryptophan-Aspartate (WD40) repeat domains that form beta-propeller or beta-sheet structures responsible for binding to the scaffold subunit (52). B’ group members contain mostly helical structures. They possess the same central region but differ in their N- or C- domains (52). The B’’ group members are calmodulins that require calcium for their functional activation (52,61). Members of the recently identified B’’’ (Striatin) group also contain WD40 repeat domains (53). Structural studies suggested that striatin subunits bind   114 calmodulins in a calcium-dependent manner and are likely to function as modulator scaffolds (135) (Table 1.1).   In vitro, and likely in vivo, regulatory subunits compete for inclusion in the holoenzyme. Therefore, the permutations and combinations of subunits make the interpretation of the functional consequences, caused by perturbation of any single PP2A subunit, more complicated. Due to the structural differences, each regulatory subunit is expected to have overlapping but distinct substrate affinity. Usually, protein phosphatases work in a regulatory network involving other enzymes such as kinases, which have opposing functions, and it is the balance of these activities determines the functional outcome.  The deletion or reduction in any one of the regulatory subunits in a phosphatase complex could potentially lead to two very different effects on its bona fide substrates and hence the downstream signaling pathways. For example, the deletion of the gene encoding PPP2R2A leads to the concomitant reduction of its coded protein B55α (PPP2R2A). Assuming the activity of kinases do not change, in the first (direct) case, reduction of PPP2R2A results in reduced binding to its protein targets and ultimately results in higher steady-state phosphorylation level of these protein targets. In a second (indirect) case, assuming that PP2A holoenzyme subunit proteins are in excess and not prevented from binding by post-translational modifications or chaperones, reduction of PPP2R2A leaves its interacting complex core available for other regulatory subunits to compete for binding, which could result in higher relative abundance in the PP2A complex of other regulatory subunits that are not transcriptionally up regulated. This may result in an altered function, through the re-targeting of PP2A to a different set of substrates.    115 In the previous chapter, my results showed that PPP2R2A deletion led to decreased phosphorylation of ER on its Serine 305 residue. Since the direct mode of action of PPP2R2A deletion would predict an increase of phosphorylation level of its target, this result could be potentially due to the indirect effects of PPP2R2A deletion. This highlighted the need to better understand how PPP2R2A deletion affects the stoichiometry of holoenzyme complexes. Therefore, I hypothesized that PPP2R2A deletion could lead to changes of PP2A complex composition and relative abundance of regulatory subunits in the complex and set out to measure the extent to which this occurs.  In order to explore the hypothesis, I carried out the following studies. I first established a method of analyzing PP2A complex composition using HLPC (High liquid performance chromatography) and quantitative mass spectrometry (Multiple Reaction Monitoring, MRM). Different from tandem mass spectrometry (MS/MS) that aims to identify all the fragment ions after peptide fragment ionization, multiple reaction monitoring is a specified quantitative measurement of protein peptides that monitors a smaller set of pre-identified (by MS/MS) peptides after peptide fragment ionization. The method provides enhanced detection limits and allows rapid and continuous monitoring of specific fragment ions of interest (Figure 2.2) (136). The method is useful for quantitative assessment of peptide (protein) levels to assess a specific hypothesis. My studies confirmed that PP2A subunits existed in the cell in interchangeable states between complex and free monomers, but most of the proteins were in complexes. Further studies of the PP2A complex in the 3 cell models with reduced PPP2R2A expression levels showed relative increase of the STRN regulatory subunit in the PP2A complex. This is potentially significant because STRN has been linked to non-genomic ER function through direct binding (137).   116 Finally, I analyzed the transcript expression of each PP2A subunit in the three cell lines, several normal primary breast samples, both luminal and myoepithelial subtypes, as well as breast cancer samples from METABRIC dataset, and compared their relative expression differences with the respect to scaffold subunit PPP2R1A; however, the lack of a linear relationship between transcript and protein limits the ability to draw conclusions on relative PP2A subunit composition in large transcriptome cancer datasets.   4.2 Results  4.2.1 PP2A subunits exist in equilibrium between complex and monomers As noted above, the interactions among PP2A subunits could be in a state of dynamic equilibrium in which the regulatory subunits may be constantly competing with each other and partitioning between bound and free pools in cells. Therefore, before focusing on how specific subunits are recruited into the holoenzyme complex, I first defined the states in which PP2A subunits exist in cells, particularly, the relative abundance of free and complexed subunits. I exploited the predicted size differences of PP2A component as free monomers and trimeric complexes and performed an assay using size-exclusion chromatography (SEC) by HPLC, to separate PP2A proteins in cellular extracts based on their sizes. The sizes of most PP2A subunits range from 36kDa~65kDa (Table 4.1), while the trimeric complexes containing each of the three subunit groups were around 150kDa (Table 4.1). Except for PPP2R3A, which was 130kDa, all subunit monomers were smaller than 100kDa and all the tri-subunit complexes are larger than 100kDa. Therefore, by choosing 100kDa as the molecular weight cut off, I could identify the fractions containing the complexes or free subunit monomers in cell lysate.    117 Since size-exclusion chromatography (SEC) columns can be easily clogged with trace amount of DNA and most PP2A signaling functions happen outside of the nucleus, I therefore used only cytoplasmic fraction proteins in my studies. I disrupted the cell membrane with a tissue grinder to release the cytoplasmic proteins without disrupting the nuclear membrane. The cell debris were precipitated by centrifugation and discarded. Only the supernatant containing the cytoplasmic protein was collected. The lysates were then filtered on a spin column with a protein size cut-off filter at 100kDa (Method: Section 2.14), and the retentate was collected for the next step. The spin column also filtered out residual DNA (DNA was stuck on the column) and reduced the sample volume for SEC fractionation.   To fractionate the concentrated cellular proteins, I first calibrated the SEC column with a standard mixture of 6 proteins of different sizes (Workflow shown in Figure 4.1_ A). I optimized the flow rate at 0.2mL/min, which allowed sufficient protein separation to determine the 100kDa cut-off region and also a reasonable time for fraction collection. The protein standards contained proteins with sizes ranging from 669kDa to 29kDa. At 0.2mL/min, it took about 50 minutes to fractionate a sample of total cytoplasmic proteins (Figure 4.2). By collecting one fraction per minute, the fractions containing protein of 100kDa were between the two standard peaks of 150kDa (fraction #35) and 66kDa (fraction #37), (Figure 4.2A, B). Slower flow rates significantly increased the collection time, while the impact on separation between the 150kDa and 66kDa peaks remained negligible. I therefore determined my optimum flow rate to be 0.2mL/min for SEC fractionation. Based on the sizes of protein standards and the time they were detected, I established the calibration curve for the column by performing linear regression   118 modeling (Figure 4.2C). The calibration curve demonstrated that the log value of protein sizes (molecular mass) was linearly and inversely correlated with the elution time.  With the calibrated size-exclusion column, I tested a sample of T47D lysate with the calibrated 0.2mL/min flow rate and probed for PP2A subunits in the collected fractions (Figure 4.1, B and C) to separate the fractions containing PP2A complexes from those containing PP2A monomer subunits. On the SEC spectrum, protein (as assayed by Absorbance at 280 nm) appeared by fraction #24 and disappeared after fraction #55 (Figure 4.3 A). I took 10% of each fraction to probe for the presence of PP2A subunits by western blot. I picked one representative subunit member from each subunit category, PPP2R1A for the scaffold subunits, PPP2CA for the catalytic subunits, and PPP2R2A for the regulatory subunits. On the western blot, PP2A subunits are only detectable starting from fraction #27 (Figure 4.4A). Based on densitometry analysis (Figure 4.4) of each fraction, the amount of the three PP2A subunits increased with time and peaked around fraction #33 to #35. However, in fraction #37 and #38, the level of PPP2CA significantly dropped and PPP2R1A or PPP2R2A were almost undetectable. Starting from fraction #39, each PP2A subunit started to reappear and increased again, but quickly dropped to the background level after fraction #42 or #45. The sudden drop of the amount of PP2A subunits in fractions #37 and #38 created a “gap” in time that can be used to separate the total protein of each subunit into two populations. The first population before the “gap” appears to be where the bulk of the proteins of interest are, while the second population is after the “gap” and has significantly smaller amount of proteins. Based on our standard curve calculated from the calibration of the 6 protein standards, fraction #37 and #38 contain proteins at the sizes around 105kDa, and 87kDa, respectively (Figure 4.3 B). Therefore, fraction #37 and #38 are right   119 around the 100kDa potential cut-off point (Figure 4.4 A, red dashed box) for PP2A complexes and monomers. Therefore, fractions #24 to #37 are PP2A complexes and fraction #39 and up are monomers.  Based on western blot and densitometry quantitation of the protein in the fractions on the two sides of the “gap”, the majority of PP2A subunit proteins were part of a multi protein complex in cells. In these experiments, 70.2% of PPP2CA, 82.9% of PPP2R1A, and 89.4% of PPP2R2A were in the complex-containing fractions  (fractions before and including #37), while only 28.8% of PPP2CA, 17.1% of PPP2R1A, and 10.6% of PPP2R2A were detected in the monomer-containing fractions (fractions including and after #38) (Figure 4.4B)  Moreover, this potential cut-off point also overlapped with the second highest peak on the SEC fractionation 280nm UV spectrum (Figure 4.3 A). In repeated size exclusion runs, the amount of protein in each fraction appeared to have a similar pattern. While the starting time for the chromatography and detection were not always the same and thus the exact fraction numbers of the western blot identified cut-off point varied, the location of the gaps always aligned to the second highest peak on the detection spectrum, which I used as an indicator to identify the approximate locations of the gap fractions based on UV protein detection.   Therefore, based on size-exclusion chromatography and western blot densitometry analysis of three representative subunits, PP2A subunits were present as either part of complexes or as free monomers in cell lysates (Figure 4.5). While the exact percentages vary, the majority of each subunit member is part of a PP2A complex. By combining size-exclusion chromatography and   120 western blot analysis, we identified and collected PP2A-complex-containing fractions to study PP2A holocomplex composition.    4.2.2 Quantitative mass spectrometry analysis identified increased relative abundance of STRN in the PP2A complex after PPP2R2A knockdown in ER+ cells As the fractionation experiments demonstrated, there are both PP2A complexes, as well as free monomeric PP2A subunits, in cell lysates. Deletion of PPP2R2A could potentially change the equilibrium between subunits in the PP2A complex and free monomers, which could ultimately change the subunit composition in the complexes (Figure 4.5). To test this hypothesis, I chose T47D, MCF7, and 184-hTERT as our cell models. T47D and MCF7 are both ER positive breast cancer cells, which is the most diagnosed breast cancer subtype, whereas, 184-hTERT is an ER negative, telomerase immortalized normal breast cell model (138). I established stable knockdown models of each line by drug-selecting stably integrated lenti-virus expressing either shNS non-silencing control or shRNA targeting PPP2R2A (Figure 4.7A). Based on western blot and densitometry analysis, the protein levels of PPP2R2A in the samples expressing shPPP2R2A were reduced to 43.1% in T47D, 50.2% in MCF7, and 40.2% in 184-hTERT, compared to their respective shNS controls (Figure 4.7A). Using the same method performed on the wild-type (parental) T47D samples, I fractionated the lysates of each sample on size-exclusion chromatography (Figure 4.1_D, Appendix-K) and analyzed 10% of each fraction from #35 to #40, which are around the predicted cut-off point (around the second highest peak), for the presence of PP2A subunits by western blot. Since samples containing shPPP2R2A would have reduced the levels of PPP2R2A, I only probed for PPP2CA and PPP2R1A to determine the accurate cut-off points. The “gap” of most samples appeared after fraction #37, some were after   121 fraction #38 or #40 (Figure 4.6). These western blot analyses confirmed the presence of the two discrete populations containing hetero-trimeric complexes and free monomers of PP2A subunits, respectively, in these cell models. Samples were collected from each condition and analyzed in biological triplicates that were grown up from the same original lines. The effectiveness of PPP2R2A knockdown in the cell models was confirmed by western blot (Figure 4.7A), and also confirmed later by Multiple Reaction Monitoring (MRM) mass spectrometry.  After determining the exact cut-off point for each sample, I pooled the rest of each fraction (90%) before the “gap” (containing PP2A complexes) and precipitated the proteins with ice-cold acetone. The re-suspended protein lysates were then assayed for protein quantity and digested into peptides with trypsin. After digestion, the peptides were Stage-tip (C18) purified before quantitative analysis by MRM. In collaboration with the proteomics core lab at the BCCRC, I chose at least 1 to 3 unique experimentally identified peptide signatures for each of the 20 PP2A protein subunits (Chapter 2, Table 2.5).   Based on the MRM signatures that Dr. Vincent Chen (Dr. Gregg Morin lab) developed for this analysis (Section 2.15), the PP2A subunit peptides in each sample were introduced into the MS by reverse phase chromatography and nano-electrospray ionization. Ionized peptides were scanned by MS, and the desired parent ions for the peptide were then selected and the collected ions were fragmented by collision-induced-dissociation (CID), then the desired fragment ions were further selected in the third quadruple and ion counts recorded by the detector. Each protein had 1-3 peptides that were analyzed. For each peptide, ion intensities of 3 fragments were recorded. Each parent ion/fragment ion pair is termed a transition. Thus, there were 3 transitions   122 for each peptide of interest (Figure 2.2). Quantification of each transition represents an independent measurement of the peptide, as well as each protein. Due to the variations during protein digestion and ionization, not all peptide measurements are reliable. To select reliable peptides,I performed the following analysis. For each peptide, I plotted the relative mass to charge ratio of each transition to the total measurements of all 3 transitions (Appendix-P). This analysis presented the relative abundance of each transition among the three analyzed. For a reliable peptide for measurement, the relative abundance of each transition from the same peptide should be consistent across all samples in the same cell line.  Based on the spectrum of measurements, I also assigned a quality score for each measurement (Method: Section 2.15) and further identified the most abundant transitions from the reliable peptides for each protein, based on the analyses using the Multi-Quant software (AB Sciex). The measurements with the highest values and quality score above “2” were selected as the final reliable measurement for each protein. For proteins with more than one abundant reliable measurement, the average measurement of each transition (up to 4 measurements) was taken. The peptide reliabilities were analyzed separately in each cell line (Appendix-P). For the subunits with no reliable measurements based on the above criteria, no analysis will be performed in the following step.   PPP2R1A is the most abundant subunit among various PP2A complexes. As a scaffold subunit, PPP2R1A is also anticipated to remain relatively unchanged between complexes. Therefore, to analyze the relative abundance of complexed PP2A subunits, I normalized the measurement of each subunit with PPP2R1A as the internal standard. For the analysis of PP2A complex composition, I used linear regression modeling and analysis of covariance (ANCOVA) with respect to PPP2R1A to look for changes of relative abundance of each subunit in PP2A   123 complexes.  Since PPP2R3A is the only subunit that is larger than 100kDa, the collected fractions could contain free PPP2R3A subunits, as well as complexed PPP2R3A. To avoid confounding free and complexed pools in the analysis of PPP2R3A, these peptides were omitted from the analysis. All conditions were the results of three biological replicates, except for the shNS non-silencing control condition in MCF7 and the shPPP2R2A experimental condition in 184-hTERT, where technical issues with clogging of the SEC column invalidated one of the replicates leaving two replicates, for analysis for those two conditions.   First, based on the analysis of the MRM data, the relative quantity of PPP2R2A was significantly and consistently reduced in shPPP2R2A samples compared to their corresponding controls, confirming effective knockdown of PPP2R2A with shRNA (Figure 4.7B, green bar). The protein levels of PPP2R2A in PP2A complexes were reduced by shPPP2R2A to 31% of its value in the control in T47D (Linear scale, Mean: 0.31, 95% CI: 0.092 to 1.046), 17.2% in MCF7 (Linear scale, Mean: 0.172, 95% CI: 0.041 to 0.726), and 46.1% in 184-hTERT (Linear scale, Mean: 0.461, 95% CI: 0.310 to 0.685) (Appendix-N).  The relative abundance of most PP2A subunits in the complexes remained unchanged after knocking down PPP2R2A. However, regulatory subunit STRN (PR110) showed a significant increase of relative abundance in PP2A complexes in both ER positive cell lines, T47D and MCF7 (Figure 4.7B, red bar), but not in the immortalized normal cell line 184-hTERT. Based on ANCOVA analysis with PPP2R1A as reference, the relative abundance of STRN in PP2A complexes increased by 69.5% in T47D (Linear scale, Mean: 1.695, 95% CI: 0.061 to 2.706) and 74.8% in MCF7 (Linear scale, Mean: 1.748, 95% CI: 0.071 to 2.856) (Appendix-N). STRN is a regulatory subunit belonging to the B’’’ (striatin) subunit family and its function is calcium-dependent (97). Its protein structure also   124 contains WD40 domains that allow it to interact with the PP2A core complex (scaffold and catalytic subunits). Based on our previous RNA-sequencing analysis, STRN is not significantly up regulated at the transcript level after PPP2R2A knockdown in T47D (Fold change=1.1, p=0.005, FDR=0.123, Chapter 3). Therefore, this relative increase of STRN in the PP2A protein complexes was unlikely to be due to transcriptional regulation, but rather posttranslational alteration of the complexed proteins.  In addition, with reduced protein levels of PPP2R2A, PPP2R2D also decreased in relative abundance (Mean: 0.633, 95% CI: 0.437 to 0.918) in PP2A complexes in MCF7, but not in the other two cell lines (Figure 4.7B).  These results suggested that PPP2R2A knockdown changed PP2A complex compositions and that some of these changes were cell type dependent.  4.2.3 The relative abundance of PP2A transcripts and complex subunits in cell lines and clinical samples Finally, I sought to determine how transcript abundance and protein abundance are related in cell lines and to understand the relative abundance of PP2A subunits in human tumours. I analyzed the transcript expression data of PP2A subunits filtered from published transcriptome sequencing data of the three cell lines (139), and in clinical samples including 6 primary breast samples that are 3 luminal and 3 myoepithelial fraction samples, as well as breast cancer samples from the METBRIC dataset (31) (Figure 4.1_F). These were divided into 4 groups based on their ER expression and PPP2R2A copy number status. I normalized all the measurements to PPP2R1A transcript levels in each sample.    125 The analyses of transcript levels revealed different patterns in between cell lines and primary breast samples. The transcripts of PPP2R2A and PPP2R2C, the two subunits both from B (PPP2R2) subgroup, had similar relative transcript levels in the ER positive breast cancer cell lines T47D and MCF7 (Table 4.2 and Table 4.3). However, in basal cell line 184-hTERT, the relative expression of PPP2R2A was the highest in the B subgroup, whereas PPP2R2C was almost undetectable (Table 4.4). The pattern of this subgroup in 184-hTERT is similar to that in primary breast samples. In addition, PPP2R3A and STRN4 had the highest relative expression in their own sub-group in primary breast samples. The relative expression of subunits in PPP2R5 subgroup were similar in all 3 cell lines, as well as primary samples (Table 4.5). Based on relative transcript expression analysis, 184-hTERT was a closer representative of primary breast samples.  The comparisons of transcript expression and protein relative abundance in the complex also demonstrated different patterns between overall expression level and their complexed form for each subunit. Comparing to transcript levels, in the PP2A complexes, the relative protein abundance of PPP2R2A dropped significantly and PPP2R2C was the most abundant subunits in PPP2R2 group in the three cell lines.  A similar pattern was also observed in STRN subgroup, where STRN4 had the highest transcript expression level and was the most abundant subunit in the complexes in the three cell lines. These patterns suggested that transcript expression levels of PP2A genes do not linearly represent the relative abundance of their encoded proteins in their functional form in cells (Table 4.2, Table 4.3, and Table 4.4).     126 With the above caveat in mind, I nevertheless attempted to examine the relationship between transcript expression level for each PP2A subunit in the METABRIC breast cancer transcriptome data, sorted into four groups based on their estrogen receptor expression and PPP2R2A copy number status (Figure 4.1_G). The expression levels of each subunit were normalized to PPP2R1A in each group. As expected, the relative expression level of PPP2R2A in samples with PPP2R2A copy number loss was lower than other groups (Figure 4.1_G). The relative expression of other PP2A subunits, including STRN, did not exhibit obvious changes across the four groups. Taken together with the cell line information, this comparison suggested that transcript expression analysis was not useful for inferring relationships in the complex (Table 4.6) and illustrated the limitations of understanding gene function in human tumours without accompanying protein level measurements.  4.3 Discussion In this chapter, my analysis of PP2A subunits with size-exclusion chromatography and western blot analysis demonstrated that PP2A subunits exist as either subunit complexes, or free monomers. Based on the western blot analysis, the first fraction containing PP2A subunits appeared in fraction #27, which corresponds to about 500kDa. This is larger than the expected 150kDa sizes of PP2A complexes. Since the inputs of the HPLC samples were whole cytoplasmic lysates, there could be other PP2A substrates or modifying proteins interacting with PP2A complexes during the chromatography. PP2A in these fractions could be in aggregates with modifying proteins or its enzymatic targets. Regardless, the most abundant peak in terms of PP2A subunits was in fractions #33-#35 (Figure 4.3), which corresponded to 150kDa-200kDa.   127 This is consistent with the fact that the majority of PP2A subunits are trimeric complexes as predicted (Table 4.1).  Although densitometry analysis showed that the majority of the PP2A subunits are in the complexed forms (Figure 4.4B), the possibility of subunits exchanging between the two pools suggested that expression change of one regulatory subunit could potentially alter the relative subunit abundance in the complex. Using Multiple Reaction Monitoring (MRM) mass spectrometry, I found increased relative abundance of striatin (STRN) in PP2A complexes in the ER positive cell lines T47D and MCF7 when the protein level of PPP2R2A was reduced by shRNA. Several studies have reported functional links between ER and STRN. In the studies of Lu et al (2004), striatin (STRN) interacts with ERα directly through binding of the ER N-terminal domain and functions as a modulator scaffold during localization of ERα to the cell membrane (137). STRN is also involved in regulating non-genomic activity of ERα through activation of the downstream mitogen activated AKT pathway and endothelial NO synthase in smooth muscle cells (137). Also, in the studies of Tan et al (2008), a splicing variant of rat striatin-3 (rSTRN3γ), another member of the same subgroup, was found by yeast-two hybrid assay to interact directly with ERα in a ligand-dependent manner in nuclear extracts of rat uterus and human cell lines (119). Overexpression of rSTRN3γ can lead to decreased transcriptional activity of ERα (119). Therefore, my results suggested that reduced PPP2R2A expression could not only alter ER signaling activities, but also affect non-genomic ER activities in ER positive breast cancers.    128 In addition, based on transcriptome microarray data, STRN steady state mRNA levels were not deregulated in T47D (STRN: FC=1.1, ANOVA p-value= 0.005, n=3) or 184-hTERT (STRN: FC=1.21, ANOVA p-value= 0.082, n=3), which rejects the hypothesis that the relative increase of STRN in PP2A complexes was due to transcriptional up regulation. However, the mechanism leading to increased recruitment of STRN at the protein level into PP2A complexes after reduction of PPP2R2A needs further investigation. Although it is still unclear if background gene expression in ER positive breast cancer cells had functional influence on PP2A composition, the specific presence of increased STRN only in ER positive cell lines could potentially imply a functional and regulatory interaction between subunit recruitment into PP2A complexes and ER signaling.    Changes in PP2A complex composition due to reduced expression of one regulatory subunit have also been reported in the study of Batut et al (2008). In zebrafish, reduced expression of PPP2R2A not only led to reduced activity of its own downstream signaling pathway, but also activated pathways governed by PPP2R2D, which is the second abundant regulatory subunit in the same subgroup in zebrafish (55). This study is another example suggesting that expression changes of one subunit may lead to changes in the relative abundance of others in the complexes.   Overall, my data supported the possibility of secondary effects (ie beyond direct effects on the phosphorylation status of target sites) of genomic copy number changes of proteins that function as part of a complex, such as reduced protein level of PPP2R2A leads to increased recruitment of STRN into complexes. However, my correlation studies of transcript data in the cell line and   129 clinical samples suggested that transcript expression information does not linearly represent the relative presence of PP2A subunits in holoenzyme complexes at the protein level.  There were several pitfalls in this study requiring attention in future studies. My results were based on the stable shRNA cell models, which proved to be the only practical method of obtaining enough cells for the protein experiments. However the stable shRNA knockdown lines average about 50% reduction of the protein levels of PPP2R2A. Complete depletion of PPP2R2A expression could lead to other alterations in PP2A complex composition, although this could also prove deleterious to cells. Since PPP2R2A is an abundant protein in cells, further reduction at protein level in bulk samples would require engineering of a conditional genomic knockout in an estrogen responsive cell line, rather than relying on RNA interference.   My analyses of PP2A complex composition were all based on relative quantification of MRM data normalized to PPP2R1A. Theoretically, the levels of the scaffold subunit PPP2R1A should stay unchanged versus the other subunits involved in PP2A enzymatic functions; however, relative normalization, as is frequently used in transcript and protein studies, can still lead to unanticipated biases in the analysis of protein abundance. For future analysis, using absolute quantification to measure each subunit could avoid potential biases in the analysis.  Finally, due to technical issues, the analysis of relative quantity of PP2A subunits were based on triplicate, or even duplicate data, which barely satisfied the criteria for statistical analysis. Generally, linear regression modeling and analysis of covariance require a minimum of three replicates for at least one sample to properly fit in linear models for statistical analysis.   130 Originally in my study, three replicate samples were prepared for each condition, but two samples failed due to column clogging during MRM data acquisition. The lack of replicate data made it harder to draw conclusions with statistical significance. The large error bars for 95 % confidence intervals in the results could be due to small sample sizes. Therefore, for further analysis with Multiple Reaction Monitoring, more replicates are needed for validation.       131  Figure 4.1 Flow scheme of mass spectrometry study of PP2A complex composition In this study, I first established the method of separating PP2A complexes from free monomers with calibrated size-exclusion chromatography (SEC) (I.) and western blot densitometry. I then analyzed PP2A complex in SEC fractionated cell lysates in 3 cell lines, T47D, MCF7, and 184-hTERT, with or without reduced levels of PPP2R2A by shRNA (II). Finally, I correlated my analysis of PP2A subunits at protein level to their transcript expression analysis in clinical samples (III.). I. Method establishment for studying PP2A (A.) Size exclusion column calibration Analysis of PP2A complex and monomers  T47D fractionated cell lysates II. SEC and MRM of PP2A complex composition (F.) Correlation studies  with cell lines T47D, MCF7, 184-hTERT (G.) Correlation studies with patient samples (METABRIC) (D.) Fractionation and collection of PP2A complex (PPP2R2A knockdown vs Wild type) T47D, MCF7, 184-hTERT)  (E.) Quantitative mass spectrometry analysis of PP2A subunits in the complex with MRM (STRN increase in PP2A complex in ER+ cells) (B.) Separation of PP2A complex and monomers (C.) Relative quantification of PP2A subunits in complex and monomers III. Correlation studies of PP2A subunits expressions in cell lines and patient sample   132  Figure 4.2 Size-exclusion chromatography fractionates protein mixtures based on their sizes A mixture of six protein standards was run on the size-exclusion column (Phenomenex, Yarra 3u SEC-2000) for calibration. Method: Section: 2.14. (A.) Protein detection spectrum (Detection Component( (Time((Min)( Log10((size)(Sizes((KDa)(Thyroglobulin( 28.1( 2.8( 669(ApoferriEn( 30.6( 2.7( 443(betaIamylase( 33.2( 2.3( 200(Alcohol(Dehydrogenase( 35.5( 2.2( 150(Albumin( 37.8( 1.8( 66(Carbonic(anhydrase( 44.4( 1.5( 29(Size Exclusion Chromatography Calibriation25 30 35 40 450123Detection Time (Minutes)Log (Sizes of standards)Y=I0.0868+5.2366(R2=0.9736(A B C 669#443#200#150#66#29#100#kDa#Log10#(Sizes#of#Standards)#  133 voltage based on UV absorbance at wavelength of 280nm) of size-exclusion chromatography with protein standards at a flow rate of 0.2mL/minute (B.) Detection times and sizes of each protein standards (C.) Linear calibration curve of size-exclusion chromatography based on the log sizes of protein standards and their detection time      134  Figure 4.3 PP2A subunits exist in cell lysates as either free subunits or part of PP2A complexes T47D cell lysate was fractionated by size-exclusion chromatography at flow rate of 0.2mL/min and the fractions were collected at 1 fraction per minute. Fractions #27 to #48 were probed for PPP2CA PPP2R1A PPP2R2A 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 A PP2A$Subunit$ Time$(min)$ Log10$(Size)$ Size$PPP2R1A$ 39.4$ 1.8$ 65$PPP2R2A$ 40.3$ 1.7$ 55$PPP2CA$ 42.4$ 1.6$ 36$Gap$FracFon$(#37)$ 37$ 2.0$ 106$Gap$FracFon$$(#38)$ 38$ 1.9$ 87$Complex/monomer$cut$off$point$ 37.3$ 2$ 100$B   135 PP2A subunits PPP2R1A, PPP2CA, and PPP2R2A. Method: 2.14. (A.) The protein detection spectrum was obtained by detecting UV absorbance at wavelength of 280nm, measured as voltage. Red dashed boxes (red arrow): fractions #37 and #38 are the gap fractions. The “gap” region appeared to overlap with the second highest peak on the spectrum. X-axis: Time/Fraction (min); Y-axis: Absorbance at 280nm. (B.) The “Time/Fraction (min)” of the three PP2A subunits and the sizes of proteins in “gap” fractions #37 and #38 were predicted based on the calibration curve in Figure 4.2C.          136  05000100001500020000250003000035000Bend IntensityPPP2CAPPP2R1APPP2R2APPP2CA PPP2R1A PPP2R2A PPP2CA PPP2R1A PPP2R2A0.  of total cytoplasma PP2A proteins (%)ComplexMonomerA B 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Fractions Complex Monomer Complex Monomer Complex Monomer70.2% 29.8% 82.9% 17.1% 89.4% 10.6%PPP2CA PPP2R1A PPP2R2ABand Intensity Percentage of total PP2A proteins (%)   137 Figure 4.4 The majority of PP2A subunits exist as part of PP2A complexes, rather than free monomers in T47D cells T47D cytoplasm proteins were fractionated at the flow rate of 0.2mL/minute and collected at 1 fraction per minute. The amount of PP2A subunits in each fraction was plotted based on western blot densitometry using ImageJ (Appendix-M.1). The plot was based on densitometry of the three PP2A subunits from 10% of each SEC fraction. Red arrow: gap fractions #37 and #38. (A.) Densitometry analysis of PP2A subunits PPP2CA, PPP2R1A, and PPP2R2A in T47D cell lysates fractionated with size-exclusion chromatography  (B.) Relative percentage of each subunit (PPP2CA, PPP2R1A, and PPP2R2A) exists as part of PP2A complex or as free monomers in T47D. Calculation is based on densitometry analysis of each subunit.       138  Figure 4.5 Proposed model of PP2A subunit free monomers and complexes in dynamic equilibrium PP2A subunits in cells exist as either subunit monomers or part of the tri-subunit complexes and the status of the subunit could be interchangeable in cells.     A"C"B"A"C"B"B’"Complexes" Monomers"  139   05000100001500020000IntesityT47D-shNS-1PPP2R1APPP2CAPPP2CA PPP2R1A 35#####36#####37#####38#####39####40# 35#####36#####37#####38#####39####40#05000100001500020000T47D-shPPP2R2A-135#####36#####37#####38#####39####40# 35#####36#####37#####38#####39####40#PPP2CA PPP2R1A 05000100001500020000IntesityT47D-shNS-2PPP2R1APPP2CA05000100001500020000T47D-shPPP2R2A-235#####36#####37#####38#####39####40# 35#####36#####37#####38#####39####40#PPP2CA PPP2R1A 050001000015000IntesityT47D-shNS-3PPP2R1APPP2CA0500010000150002000025000T47D-shPPP2R2A-3A Intensity Intensity Intensity   140  B 35#####36#####37#####38#####39####40# 35#####36#####37#####38#####39####40#0500010000150002000025000IntesityMCF7-shNS-1PPP2R1APPP2CA05000100001500020000MCF7-shPPP2R2A-1PPP2CA PPP2R1A 35#####36#####37#####38#####39####40# 35#####36#####37#####38#####39####40#0500010000150002000025000IntesityMCF7-shNS-2PPP2R1APPP2CA010000200003000040000MCF7-shPPP2R2A-2PPP2CA PPP2R1A 35#####36#####37#####38#####39####40# 35#####36#####37#####38#####39####40#050001000015000IntesityMCF7-shNS-3PPP2R1APPP2CAPPP2CA PPP2R1A 05000100001500020000MCF7-shPPP2R2A-3Intensity Intensity Intensity   141   35#####36#####37#####38#####39######40# 35#####36#####37#####38#####39######40#PPP2CA PPP2R1A 05000100001500020000Intesity184-shNS-1PPP2R1APPP2CA05000100001500020000184-shPPP2R2A-135#####36#####37#####38#####39######40# 35#####36#####37#####38#####39######40#0500010000150002000025000Intesity184-shNS-2PPP2R1APPP2CA050001000015000184-shPPP2R2A-2PPP2CA PPP2R1A 35#####36#####37#####38#####39######40# 35#####36#####37#####38#####39######40#PPP2CA PPP2R1A C 05000100001500020000Intesity184-shNS-3PPP2R1APPP2CA0500010000150002000025000184-shPPP2R2A-3Intensity Intensity Intensity   142 Figure 4.6 Western blot analyses of size-exclusion fractions around the predicted “gap” regions in T47D, MCF7, and 184-hTERT samples to determine the specific cut-off points Cells membranes were disrupted with a tissue grinder to collect total cytoplasmic lysates (3x15cm dishes of cells for each sample) and the cytoplasmic lysates were filtered through columns (GE Vivaspin 100MWCO) at 6000g.  Each sample lysate was fractionated with size-exclusion column (calibrated in Figure 4.2C) 0.2mL/min and 1 fraction per minute. For each sample, only the fractions around the predicted “gap region”, or the second peak on the spectrum (Figure Appendix-M.1, red arrow) were analyzed by western blot. Red arrow: cut-off points. Figures were plotted based on densitometry of analyzed fractions with ImageJ (Appendix-M.2). (A.) T47D, (B.) MCF7, (C.) 184-hTERT, Two conditions were analyzed for each cell type; scrambled shRNA control and PPP2R2A targeting shRNA. Three biological replicates were collected for each condition. Fractions before the cut-off points were collected and processed for mass spectrometry analysis. Method: section 2.14. Refer to Appendix-K for spectrum of mass spectrometry of each sample.   143  A B PPP2R2A/Actin shNS shPPP2R2A Relative Intensity 1.567 0.676 Normalization 1.000 0.431 PPP2R2A/Actin shNS shPPP2R2A Relative Intensity 1.175 0.590 Normalization 1.000 0.502 PPP2R2A/Actin shNS shPPP2R2A Relative Intensity 1.216 0.489 Normalization 1.000 0.402 PPP2R2APPP2R1BPPP2CAPPP2R2BPPP2R2CPPP2R2DPPP2R3BPPP2R4PPP2R5APPP2R5BPPP2R5CPPP2R5DPPP2R5ESTRNSTRN3STRN407Log2 Fold ChangeT47DPPP2R2APPP2CAPPP2R2BPPP2R2DPPP2R3BPPP2R3CPPP2R4PPP2R5APPP2R5BPPP2R5BPPP2R5CPPP2R5DPPP2R5ESTRNSTRN3STRN4-505Log2 Fold ChangeMCF7PPP2R2APPP2CAPPP2R2BPPP2R2CPPP2R2DPPP2R4PPP2R5APPP2R5BPPP2R5CPPP2R5DPPP2R5ESTRNSTRN3STRN4-808Log2 Fold Change184-hTERT*"*"*"*"Relative Ratio of  Band Intensity (PPP2R2A/Actin) Relative Ratio of  Band Intensity (PPP2R2A/Actin) Relative Ratio of  Band Intensity (PPP2R2A/Actin)   144 Figure 4.7 Reduced PPP2R2A expression led to increased relative abundance of STRN in PP2A complexes in T47D and MCF7 cells Reduced expression of PPP2R2A led to increased relative abundance of STRN in ER positive breast cancer cells T47D and MCF7, but not 184-hTERT. (A.) Western blot and densitometry analysis of PPP2R2A levels in each cell type. Replicates for each condition were expanded from the same batch. (B.) Fold change, in log2 value, of PP2A subunits protein abundance with 95% confidence interval in each cell type after PPP2R2A shRNA knockdown compared to the scrambled control. Data was analyzed with ANCOVA (analysis of covariance, Appendix-N) on each PP2A subunit with respect to the scaffold subunit PPP2R1A. Green bar: Decreased relative abundance. Red bar: Increased relative abundance. * p<0.05. T47D, three replicates for each condition either with scrambled shRNA or PPP2R2A targeting shRNA. MCF7, two replicates for the condition with scrambled shRNA and three replicates for the condition with PPP2R2A targeting shRNA. 184-hTERT, three replicates for the condition with scrambled shRNA and two replicates for the condition with PPP2R2A targeting shRNA.             145 Table 4.1 Sizes of PP2A complexes are larger than PP2A free monomers Maximum/Minimum sizes of PP2A complexes were calculated based on sums of the largest/smallest subunits from each of Scaffold and catalytic subcategories, and the respected regulatory subunits. Red: smallest complex size (excluding PPP2R3A). Green: size of PPP2R3A.           PP2A subunits Size (Kda)PPP2R1A 65PPP2R1B 66PPP2CA 36PPP2CB 36PPP2R2A 52PPP2R2B 52PPP2R2C 52PPP2R2D 52PPP2R3A 130PPP2R3B 65PPP2R3C 53PPP2R4 41PPP2R5A 56PPP2R5B 57PPP2R5C 61PPP2R5D 70PPP2R5E 55# of Amino Acids Minimum complex size Maximum complex size589 101 102601309309447 153 154443 153 154447 152 153453 153 1541150 231 232575 166 167453 154 155358 142 142486 157 158497 158 159524 162 163602 171 172467 156 157  146 Table 4.2 Relative mRNA and protein abundance of PP2A subunits in T47D cells Relative mRNA abundance of PP2A subunits in T47D parental cells is based on RNA-sequencing and the measurements were normalized to PPP2R1A by division. Relative protein abundance of PP2A subunits in T47D shRNA samples is based on MRM mass spectrometry analyses, and the measurements were normalized to PPP2R1A by division and presented as mean +/- SD (standard deviation) in each condition. The rankings were in descending order based on of values of relative mRNA abundance or Mean values of relative protein abundance of each subunit. Refer to Appendix-L.2 for original RNA-sequencing data, and Appendix-O, P for MRM data.    T47DGene name Expression Ranking Mean (+/-SD) Ranking Mean (+/-SD) RankingPPP2R1A 1.00 1.00 (+/- 0.000) 1.00 (+/-0.000)PPP2R1B 0.102 11 0.071 (+/-0.055) 6 0.039 (+/-0.012) 10PPP2CA 0.476 2 0.612 (+/-0.410) 3 0.585 (+/-0.278) 3PPP2CB 0.084 12PPP2R2A 0.156 8 0.031 (+/-0.009) 9 0.011 (+/-0.006) 13PPP2R2B 0.016 (+/-0.017) 12 0.020 (+/-0.006) 11PPP2R2C 0.208 6 0.082 (+/-0.052) 5 0.088 (+/-0.052) 5PPP2R2D 0.078 13 0.014 (+/-0.004) 13 0.011 (+/-0.006) 13PPP2R3A 0.061 15PPP2R3BPPP2R3C 0.026 2 0.062 (+/-0.042) 7 0.062 (+/-0.023) 7PPP2R4 0.850 1 0.010 (+/-0.006) 15 0.006 (+/-0.005) 16PPP2R5A 0.169 4 0.034 (+/-0.010) 8 0.067 (+/-0.046) 6PPP2R5B 0.063 14 0.239 (+/-0.14) 4 0.291 (+/-0.094) 4PPP2R5C 0.336 4 17.7 (+/-12.3) 1 18.1 (+/-13.6) 1PPP2R5D 0.356 3 0.923 (+/-0.423) 2 1.66 (+/-1.08) 2PPP2R5E 0.195 7 0.027 (+/-0.003) 10 0.057 (+/-0.034) 8STRN 0.105 10 0.024 (+/-0.009) 11 0.044 (+/-0.023) 9STRN3 0.113 9 0.010 (+/-0.004) 15 0.013 (+/-0.007) 12STRN4 0.326 5 0.014 (+/-0.005) 13 0.009 (+/-0.008) 15Relative protein abundance by MRM analysisRelative mRNA abundance shNS (n=3) shPPP2R2A (n=3)Parental  147 Table 4.3 Relative mRNA and protein abundance of PP2A subunits in MCF7 cells Relative mRNA abundance of PP2A subunits in MCF7 parental cells is based on RNA-sequencing and the measurements were normalized to PPP2R1A by division. Relative protein abundance of PP2A subunits in MCF7 shRNA samples is based on MRM mass spectrometry analyses, and the measurements were normalized to PPP2R1A by division and presented as mean +/- SD (standard deviation) in each condition. The rankings were in descending order based on of values of relative mRNA abundance or Mean values of relative protein abundance of each subunit. Refer to Appendix-L.2 for original RNA-sequencing data, and Appendix-O, P for MRM data.    MCF7Gene name Expression Ranking Mean (+/-SD) Ranking Mean (+/-SD) RankingPPP2R1A 1.00 1.00 (+/-0) 1.00 (+/-0.000)PPP2R1B 0.038 16PPP2CA 0.461 2 1.02 (+/-0.249) 2 1.54 (+/-0.180) 2PPP2CB 0.060 13PPP2R2A 0.100 9 0.120 (+/-0.071) 7 0.029 (+/-0.12) 8PPP2R2B 0.2022 (+/-0.012) 4 0.020 (+/-0.007) 9PPP2R2C 0.153 7 0.123 (+/-0.100) 6 0.169 (+/-0.020) 4PPP2R2D 0.126 8 0.021 (+/-0.002) 9 0.014 (+/-0.002) 11PPP2R3A 0.047 14PPP2R3B 0.016 (+/-0.000) 10 0.006 (+/-0.002) 15PPP2R3C 0.035 17 0.052 (+/-0.038) 7 0.073 (+/-0.016) 6PPP2R4 0.787 1 0.011 (+/-0.011) 13 0.018 (+/-0.000) 10PPP2R5A 0.086 11 0.012 (+/-0.001) 12 0.013 (+/-0.004) 12PPP2R5B 0.033 15 0.126 (+/-0.092) 5 0.031 (+/-0.009) 7PPP2R5C 0.228 4 25.4 (+/-6.6) 1 34.1 (+/-5.69) 1PPP2R5D 0.226 5 0.552 (+/-0.064) 3 0.485 (+/-0.039) 3PPP2R5E 0.196 6 0.008 (+/-0.002) 15 0.007 (+/-0.001) 14STRN 0.083 12 0.044 (+/-0.003) 8 0.077(+/-0.001) 5STRN3 0.087 10 0.015 (+/-0.002) 11 0.010 (+/-0.002) 13STRN4 0.421 3 0.010 (+/-0.006) 10 0.006 (+/-0.001) 15Relative mRNA abundance Relative protein abundance by MRM analysisParental shNS (n=2) shPPP2R2A (n=3)  148 Table 4.4 Relative mRNA and protein abundance of PP2A subunits in 184-hTERT-L9 cells Relative mRNA abundance of PP2A subunits in 184-hTERT parental cells is based on RNA-sequencing and the measurements were normalized to PPP2R1A by division. Relative protein abundance of PP2A subunits in 184-hTERT shRNA samples is based on MRM mass spectrometry analyses, and the measurements were normalized to PPP2R1A by division and presented as mean +/- SD (standard deviation) in each condition. The rankings were in descending order based on of values of relative mRNA abundance or Mean values of relative protein abundance of each subunit. Refer to Appendix-L.2 for original RNA-sequencing data, and Appendix-O, P for MRM data.   184-hTERTGene name Expression Ranking Mean (+/-SD) Ranking Mean (+/-SD) RankingPPP2R1A 1.00 1.00 (+/-0.000) 1.00 (+/-0.000)PPP2R1B 0.055PPP2CA 0.219 3 1.06 (+/-0.527) 2 2.02 (+/-1.31) 2PPP2CB 0.177 4PPP2R2A 0.330 2 0.330 (+/-0.102) 6 0.150 (+/-0.108) 7PPP2R2B 0.009 (+/-0.002) 14 0.016 (+/-0.001) 13PPP2R2C 0.000 0.819 (+/-0.403) 4 0.670 (+/-0.112) 4PPP2R2D 0.080 11 0.038 (+/-0.011) 11 0.079 (+/-0.011) 8PPP2R3A 0.033 14 0.046 (+/-0.006) 10 0.058 (+/-0.043) 10PPP2R3B 0.030 15PPP2R3C 0.022 16PPP2R4 0.099 9PPP2R5A 0.092 10 0.052 (+/-0.003) 9 0.073 (+/-0.036) 9PPP2R5B 0.067 12 1.02 (+/-0.379) 3 0.590 (+/-0.238) 5PPP2R5C 0.140 6 32.8 (+/-3.93) 1 51.5 (+/-22.7) 1PPP2R5D 0.138 7 0.560 (+/-0.391) 5 1.06 (+/-0.150) 3PPP2R5E 0.134 8 0.014 (+/-0.008) 13 0.009 (+/-0.001) 14STRN 0.177 4 0.125 (+/-0.096) 7 0.407 (+/-0.215) 6STRN3 0.064 13 0.056 (+/-0.006) 8 0.044 (+/-0.020) 11STRN4 0.456 1 0.015 (+/-0.008) 12 0.024 (+/-0.015) 12Relative mRNA abundance Relative protein abundance by MRM analysisParental shNS (n=3) shPPP2R2A (n=2)  149 Table 4.5 Relative mRNA abundance of PP2A subunits in cell lines and primary breast samples The relative mRNA expression in cell lines was based on RNA-sequencing. The relative mRNA expression in primary breast samples was based on transcript microarray. The expression levels were normalized to PPP2R1A by division in each sample. Refer to Appendix-L.1 for raw data without normalization.          Ensemble ID Gene name A01029 HS1187 HS1823 A01030 HS1188 HS1824 184--hTERT T47D MCF7ENSG00000105568 PPP2R1A 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000ENSG00000137713 PPP2R1B 0.336 1.061 0.190 0.337 0.354 0.157 0.055 0.102 0.038ENSG00000113575 PPP2CA 2.508 2.837 0.581 2.223 1.851 0.320 0.219 0.476 0.461ENSG00000104695 PPP2CB 1.077 2.048 0.423 1.374 1.453 0.312 0.177 0.084 0.060ENSG00000221914 PPP2R2A 0.736 1.555 0.157 0.726 0.889 0.149 0.330 0.156 0.100ENSG00000156475 PPP2R2B 0.000 0.000 0.001 0.002 0.007 0.004ENSG00000074211 PPP2R2C 0.001 0.010 0.003 0.001 0.002 0.000 0.000 0.208 0.153ENSG00000175470 PPP2R2D 0.266 0.296 0.099 0.217 0.170 0.073 0.080 0.078 0.126ENSG00000073711 PPP2R3A 0.314 0.803 0.094 0.269 0.274 0.144 0.033 0.061 0.047ENSG00000167393 PPP2R3B 0.027 0.049 0.035 0.011 0.010 0.002 0.030ENSG00000092020 PPP2R3C 0.153 0.463 0.100 0.093 0.160 0.085 0.022 0.026 0.035ENSG00000119383 PPP2R4 0.218 1.021 0.728 0.178 0.264 0.176 0.099 0.850 0.787ENSG00000066027 PPP2R5A 0.279 0.954 0.153 0.114 0.251 0.080 0.092 0.169 0.086ENSG00000068971 PPP2R5B 0.803 0.440 0.232 0.323 0.121 0.156 0.067 0.063 0.033ENSG00000078304 PPP2R5C 0.228 0.457 0.107 0.116 0.147 0.048 0.140 0.336 0.228ENSG00000112640 PPP2R5D 0.230 0.386 0.226 0.239 0.182 0.160 0.138 0.356 0.226ENSG00000154001 PPP2R5E 0.155 0.516 0.062 0.160 0.268 0.062 0.134 0.195 0.196ENSG00000115808 STRN 0.506 1.427 0.097 0.113 0.211 0.106 0.177 0.105 0.083ENSG00000196792 STRN3 0.343 0.852 0.112 0.368 0.443 0.236 0.064 0.113 0.087ENSG00000090372 STRN4 0.867 0.839 0.452 0.789 0.635 0.630 0.456 0.326 0.421Luminal sample Myoepithelial sample Breast cancer cell line  150 Table 4.6 Relative mRNA abundance of PP2A subunits in METABRIC breast cancer samples The transcript expressions of METABRIC breast cancer samples were analyzed with Illumina HT-12 v3 gene expression chip. The expression levels of each subunit were normalized to PPP2R1A by division. Refer to Appendix-L.3 for the original data without normalization.    Genes Name PPP2R2A copy number loss (N = 48)Others       (N = 1470) PPP2R2A copy number loss  (N = 28)Others   (N = 446)PPP2R1A_rawexp.ILMN_1810467 1.000 1.000 1.000 1.000PPP2R1B_rawexp.ILMN_1704656 0.554 0.558 0.576 0.567PPP2CA_rawexp.ILMN_1722858 1.034 1.026 1.010 1.010PPP2CB_rawexp.ILMN_1712659 0.753 0.807 0.783 0.804PPP2R2A_rawexp.ILMN_1788961 0.718 0.825 0.744 0.810PPP2R2B_rawexp.ILMN_1660732 0.562 0.571 0.580 0.595PPP2R2C_rawexp.ILMN_2401344 0.664 0.605 0.590 0.581PPP2R2D_rawexp.ILMN_1778587 0.771 0.761 0.788 0.768PPP2R3A_rawexp.ILMN_1656393 0.631 0.630 0.659 0.647PPP2R3B_rawexp.ILMN_1712257 0.565 0.552 0.558 0.564PPP2R3C_rawexp.ILMN_2214603 0.716 0.704 0.720 0.726PPP2R4_rawexp.ILMN_1729123 0.924 0.918 0.935 0.905PPP2R5A_rawexp.ILMN_1738784 0.787 0.814 0.789 0.782PPP2R5B_rawexp.ILMN_2124082 0.578 0.570 0.587 0.584PPP2R5C_rawexp.ILMN_2364971 0.779 0.774 0.803 0.795PPP2R5D_rawexp.ILMN_1780940 0.713 0.695 0.739 0.726PPP2R5E_rawexp.ILMN_1666761 0.859 0.855 0.870 0.863STRN3_rawexp.ILMN_1772946 0.747 0.732 0.729 0.713STRN4_rawexp.ILMN_1696190 0.628 0.620 0.644 0.632STRN_rawexp.ILMN_1749882 0.625 0.618 0.637 0.635ER positive ER negative  151 Chapter 5: PPP2R2A Regulates Cellular Structure And Cell Motility  5.1 Background and rationale In the previous chapters, I uncovered some of the molecular mechanisms behind PPP2RA deletion with respect to ER signaling and PP2A complex composition. Since the loss of PPP2R2A is associated with cancers having a higher rate of relapse, another natural set of questions is whether PPP2R2A also regulates differentiation and/or migration in the survival of mammary epithelial cells.  To test these ideas, I carried out studies using 184-hTERT and MCF10A cells as cell line models capable of recapitulating aspects of mammary epithelial differentiation. 184-hTERT is an immortalized normal diploid mammary ER negative breast cell line, which is partially ER responsive (140,141). The line used in this study is clonal line #9 (184hTERT-L9), isolated and characterized in the Aparicio lab, which shares many characteristics with primary mammary epithelium (138). 184-hTERT-L9 has a diploid chromosome copy number content in low passages (below passage 20, after which a slow gain of Chr 20 can occur) (140,141). I therefore only used the cells up to passage #19 in this study. 184-hTERT cells retain strong contact dependent inhibition of growth and the capacity to differentiate into multilayered epithelial structures in 3-D growth conditions. MCF10A is a non-tumour ER negative, spontaneously immortalized, and chromosomally abnormal breast epithelial cell line (ATCC CRL-10317), which has been extensively utilized for studies of polarity and epithelial growth in 3-dimension (98). PPP2R2A is located on chromosome 8p21.1 (31) and is diploid copy neutral in the MCF10A genome (142). Unlike 184-hTERT, MCF10A cells grow to confluence in two-  152 dimensional culture condition and start to form dome structures when confluent. Both 184-hTERT and MCF10A are mammary breast cell models traditionally used for studying breast cell morphologies (98,140,142). They are both able to form semi-spherical, hollow epithelial structures in 3D cultures that are reminiscent of the acinar structures observed when normal human breast epithelial cells isolated from reduction mammoplasties are grown in 3D culture (98). MCF10A acini are usually single layered, whereas 184hTERT acini are multilayered. 184-hTERT-L9 cells have minimal (~1% on average) matrix or transwell invasion capacity (138), so as MCF10A cells, unless certain invasion associated genes, such as ERBB2 or c-myc, are overexpressed (140,143).   Several cancer-associated phenotypes have been reported for the PP2A complex. PP2A has been reported to be involved in cell cycle regulation and mechanisms of mitotic entry and exit (59). Inactivation of PP2A by okadaic acid or SV40 small antigen is involved in regulation of cell adhesion, motility, and transformation (144). I hypothesized that as one of the most abundantly expressed regulatory subunit of PP2A, the regulatory subunit PPP2R2A is specifically involved in the mechanisms regulating cell proliferation, morphology, and capacities of migration and invasion across membranes.  In this chapter, I first analyzed the transcriptome of 184-hTERT-L9 cells with PPP2R2A knockdown by siRNA (mimicking the loss of PPP2R2A in cancers by deletion) and identified a number of deregulated genes and signaling networks involved in the regulation of morphology and motility. Second, I investigated the effects of loss of PPP2R2A on cell structural changes by comparing the morphological changes of 184-hTERT and MCF10A cells in control and   153 PPP2R2A knockdown conditions. In the last section, based on transcript microarray analysis and signaling network analysis of gene expression profiles, I assayed cell motility and invasiveness in these two cell models with in vitro cell-based assays.  5.2 Results 5.2.1 PPP2R2A deletion leads to differential gene expression As a regulatory subunit of the ubiquitous PP2A protein phosphatase enzyme, reduced expression of PPP2R2A is expected to alter the phosphorylation states of multiple downstream targets. Therefore, I carried out transcriptome expression analysis to look for genes possibly associated with PPP2R2A loss of expression (Figure 5.1_A). I reduced PPP2R2A transcript in 184-hTERT cells with siRNA. Based on the analysis with QPCR (Method: Chapter 2.6), the levels of transcripts were reduced to about 30% in siPPP2R2A treated cells compared with siNT (non-targeting) control samples (Figure 5.2A). The transcriptome was assayed on GeneChip Human Transcriptome Microarrays v2.0 with three biological replicates (Method: Section 2.4). Based on the microarray data, the mean fold-change score for PPP2R2A mRNA on the microarray was -1.8 (ANOVA p=0.0015, Appendix-A.1), which represented ~80% of transcript reduction on average in the triplicate samples treated with PPP2R2A targeting siRNA. With the threshold of linear |fold changes|>=1.8 and ANOVA p<0.05, there were 133 genes differentially regulated after PPP2R2A knockdown in 184-hTERT-L9 (Figure 5.2B, 100 up regulated, 33 down regulated). I took these 133 differentially regulated genes and used the pathway enrichment method (See section 2.5) of Cytoscape (132,133,145) to statistically group the biological functions involved based on several protein and function interaction gene ontology databases (Figure 5.3, Database: Biocarta, KEGG, NCI, Reactome) (133,145). Strikingly, several cytokine   154 networks were significantly altered by PPP2R2A knockdown, including immune cytokines and cell motility/differentiation cytokines. Several singleton nodes representing TGF-beta signaling were repeatedly identified in KEGG, Reactome, and NCI databases (Appendix-D.4) (blue box with “*”, Figure 5.3). E2F5, CNK2B, and CTGF, are differentially regulated in response to PPP2R2A knockdown (Appendix-A.6). TGF-beta signaling pathway has been reported to be involved in regulating cell proliferation, cell surface protein regulation, epithelial to mesenchyme transition (EMT) mechanisms, and cell migration and invasion, which are phenotypes commonly found in cancer (146,147). Moreover, this signaling pathway analysis also identified G1/S phase cell cycle checkpoint and several stress response pathways, including inflammatory response, oxidative stress response, and IFN-alpha pathway. The transcriptome expression and signaling pathway analyses suggested that reduced PPP2R2A expression could possibly cause changes in cell proliferation, morphology, and motilities, which I set out to test in the following experiments.  5.2.2 Reduced PPP2R2A expression leads to loss of cell contact inhibition phenotype in 184-hTERT  First I asked whether loss of PPP2R2A leads to morphological changes and proliferation. I therefore assayed immortalized 184-hTERT cells for growth pattern alterations after PPP2R2A knockdown (Figure 5.4). I treated 184-hTERT cells with siRNA, and kept the cells growing for 7 days in 2-D culture in medium for growing primary breast cells (See section 2.20). Western blot analysis confirmed that siRNA knockdown on PPP2R2A protein level was still effective on Day 7 (Figure 5.4A, B). I observed the cell growth and morphology with phase contrast microscopy (Figure 5.1_B). As previously characterized, 184-hTERT-L9 cells stop growing when they reach ~70% confluence in monolayer culture and they then enter a quiescent state (140). The cells in   155 the non-targeting (siNT) control condition reached ~70% confluence on Day7 (6 days after siRNA treatment after plating) (Figure 5.4C), and the cells appeared to be flat and spreading out. However, the cells in the PPP2R2A knockdown condition were oval shaped and almost reached full confluence by Day 7 (Figure 5.4C). I investigated the relative cell growth rate of 184-hTERT with the WST-1 growth assay (Figure 5.1_D, E) (Method: Section 2.20). This assay measures metabolic cell proliferation based on colorimetric absorbance reading of converted tetrazolium salt WST-1 by mitochondria dehydrogenase enzymes in cells (148). However, I did not find significant changes in cell proliferation in 184-hTERT after PPP2R2A knock down in this assay (Figure 5.4D, E). In monolayer cultures, cells signal their neighboring cells during proliferation. When non-transformed epithelial cells grow to certain level of confluence, cell growth is inhibited via contact inhibition.  Since 184-hTERT cells do not normally grow to 100% confluence (ie they are strongly contact inhibited), this change in cell growth behaviour after PPP2R2A knockdown is reminiscent of loss of cell contact mediated growth inhibition, which is a common characteristic of cancer cells.   5.2.3 Reduced PPP2R2A expression leads to deformation of breast cell 3D structures  The immortalized cell models 184-hTERT and MCF10A grow into semi-spherically shaped acini in Matrigel embedded culture (98,140). The proliferating cells in the centres of acini lose cell contact from the extra-cellular matrix and undergo apoptosis, resulting in hollow structured lumens (98). However during the process of malignant transformation, epithelial cells acquire contact independent growth survival characteristics and when placed into 3D growth conditions they form structures, but do not hollow out by cell death (98,140).  To address whether PPP2R2A is involved in this form of epithelial transformation, I assayed 184-hTERT and   156 MCF10A cells for their 3D colony structure forming capacities after knocking down PPP2R2A with siRNA (Method: Section 2.16) (Figure 5.1_D). In Matrigel-embedded culture, 184-hTERT-L9 cells usually require up to 21 days to form multi-layered acini with hollow structures (140). Unlike 184-hTERT-L9 cells, MCF10A cells tend to form single cell-layered spherical acini and the hollow structures can usually be observed 16 days after plating (98). However, siRNA is only able to transiently knockdown PPP2R2A at the protein level (up to 7 days based on western blot analysis, Figure 5.2A, B). Therefore, I sought to assay the early effects of reduced PPP2R2A expression on longer term the 3D acini structure formation (138). I performed siRNA treatment targeting PPP2R2A with confirmed protein level knockdown (45.2%, assayed two days after siRNA treatment) before plating cells in 3D Matrigel culture (Figure 5.5C). I collected the colonies after 3 weeks for 184-hTERTT-L9 and 16 days for MCF10A, fixed them with paraformaldehyde, and stained with phaloidin-green and Draq5 to visualize actin and DNA, respectively (Figure 5.5A, B). I counted both types of colonies, hollow or non-hollow, in each chamber and compared them in the two siRNA treated conditions. For 184-hTERT, under the siNT control condition, an average of 70% of the colonies formed a hollow lumen (Figure 5.5D, 184-hTERT, n=4). After knocking down PPP2R2A, the percentage of colonies with hollow structures dropped to an average of 44% (Figure 5.5D, 184-hTERT, n=4). Also, the structures of acini with PPP2R2A knockdown appeared qualitatively less spherically shaped than their control counterparts (Figure 5.5A, siPPP2R2A vs siNT, n=4) and these acini seemed to be still in the early phase of ”hollowing out”. For MCF10A cells, the effects of PPP2R2A knockdown on hollow structure formation were much more pronounced than in 184-hTERTcells. In the control condition, 61% of MCF10A acini on average formed hollow structures; whereas, this type of colony fell to about 19% on average after PPP2R2A was knocked down before plating (Figure   157 5.5D, MCF10A, n=3). The colonies with cell filled cores, on the other hand, increased from 39% on average in control condition to 81% on average with PPP2R2A knockdown (Figure 5.5D, MCF10A, n=3). Overall, both cell lines exhibited reduced percentages of hollow shaped colonies and an increased number of solid colonies after PPP2R2A knockdown. These data was consistent with the initial observation of loss of contact dependent inhibition in cell growth and suggested that PPP2R2A function might be required to regulate cell-cell contact signals and/or aspects of cellular polarity and apoptosis.  5.2.4 Reduced PPP2R2A expression leads to deregulation of cell surface protein localization The impairment of the formation of hollow acini structures could be due to either attenuated apoptosis of the central cells or loss of contact inhibition. Both cell contact inhibition and apoptosis mechanisms could be induced and regulated through cell surface receptors that sense signals from other surrounding cells and induce intracellular signaling pathways (149,150). I therefore hypothesized that reduced PPP2R2A expression could result in the deregulation of cell surface adhesion molecules that bind to the extracellular matrix or the deregulation of cell junction proteins that favor cell-cell contact over cell-matrix contact. I hence investigated the expression and subcellular localization of several cell surface proteins, E-cadherin (CDH1), ALK4, and ITG5A that have been previously reported to be involved in receptor mediated signaling (Method: Section 2.17) (Figure 5.1_E). Based on transcriptome microarray data and pathway analysis, MMP1, which is involved in cell-tight junction assembly (151), is up regulated, but none of these three surface proteins appeared transcriptionally deregulated (CDH1: FC=-1.02, p-value=0.534; ALK4: FC=-1.1, p-value=0.206; ITGA5: FC=1.11, p-value=0.827). Then I   158 investigated sub-cellular localization of these proteins. Under regular monolayer culture growth conditions, at 63x magnification, 184-hTERT-L9 cells in the control conditions (siNT) appeared to be flat, spreading out in all directions, whereas, cells knocked down with PPP2R2A were rounded up and oval shaped (Figure 5.6A-C, siPPP2R2A). E-cadherin (CDH1), the receptor involved in cadherin mediated adhesion and signaling, was localized on the surface and in the cell-cell junctions in the control condition (Figure 5.6A, siPPP2R2A). However, in the cells with PPP2R2A knockdown, E-cadherin was partially mis-localized into granules in the cytoplasm, rather than on the cell surface (Figure 5.6A, siPPP2R2A). The overall presentation of E-cadherin was lower than the control, which could have been due to down-regulation of surface receptors. The other two receptors, ALK4, the receptor mediating TGF-beta signaling, and integrin alpha5, responsible for cell-cell adhesion, also appeared to have similar patterns as E-cadherin after PPP2R2A knockdown (Figure 5.6B, C). (55). The similar phenotypes were also observed in MCF10A cells (Figure 5.6D). Taken together, the data showed that while epithelial organization was not extensively disrupted, there was significant mis-localization of several cell-cell interaction surface proteins. This supported my hypothesis that PPP2R2A is involved in the regulation of some cell surface receptors, such as CDH1, ALK4 and ITGA5, through either cytoplasmic aggregation or transcriptional down regulation.  5.2.5 Reduced PPP2R2A expression leads to enhanced cell migration The surface protein mislocalization and cell contact inhibition effects observed also prompted questions about cell migration and wound healing behaviours, which are classically disrupted in transformed cells (152). TGF-beta signaling, which appeared several times in network analysis after PPP2R2A knockdown in 184-hTERT, is involved in regulating the mechanisms of cell   159 migration and epithelial mesenchymal transition (EMT) (146,147). Therefore, the next question I was interested in was if these cells exhibit enhanced motility in the condition of reduced PPP2R2A expression (Figure 5.1_IV). To test this question, I assayed the wound-healing ability in these cells (Method: Section 2.18) (Figure 5.1_F). I knocked down PPP2R2A with siRNA in both cell lines and re-plated the cells in glass bottom 96 well plates 2 days after siRNA treatments. I created a wound 0.8mm wide using a 96 head wound maker (Essen Bioscience) and removed cell debris by washing with PBS. I took images at the centre of the wound every 2 hours on an IncuCyte automated microscope (Essen Bioscience) for up to 16 hours and analyzed the images for wound confluence using IncuCyte software (Method: Section 2.18). I assayed a small aliquot of siRNA treated samples by western blot (knockdown: 36% in 184-hTERT-L9, 70% in MCF10A) to confirm the protein level reduction of PPP2R2A before plating them for the wound-healing assay (Figure 5.7A). For 184-hTERT cells, the wounds were almost all healed (confluent) after 7 hours for chambers with siPPP2R2A cells, while the control chambers (siNT) still had an obvious gap between the two sides of the wound (Figure 5.7B). The wounds in 184-hTERT cells all healed after 16 hours (Figure 5.7C, n=6).  In this assay, MCF10A cells treated with siPPP2R2A also migrated and healed the wound faster than in the control conditions. Thus cell migration and wound healing capacities were enhanced in the condition with reduced PPP2R2A expression in these two cell lines. This result was consistent with transcriptome microarray analysis, in which deregulated genes after PPP2R2A knockdown were enriched in TGF-beta related pathways and supported our hypothesis on cell motility.    160 5.2.6 Reduced PPP2R2A expression leads to enhanced cell invasiveness in breast epithelial cells The above experiments also point to invasion as a phenotype of interest in the context of PPP2R2A loss. Additionally in the transcriptome analysis of 184-hTERT (Section 5.2.1), I noted that MMP1 was significantly up regulated in 184-hTERT after PPP2R2A knockdown (Fold change=8.7, ANOVA p=0.0027, Appendix-A). MMP1 (Matrix Metalloproteinase 1) is a cell-secreted proteinase that functions as an autocrine-signaling factor (151). It digests extracellular matrix (specifically interstitial collagen type I, II and III) and allow cells to breakdown extracellular matrix during tumour metastasis, reproduction, and tissue remodeling, etc. (153,154). In breast tumours, up regulation of this gene is a signature for cell invasion and metastasis (151). Therefore, the next question I asked was whether the invasion ability in these cells increased in the condition of reduced PPP2R2A expression.  To test this question, I first validated transcript levels of MMP1 with QPCR (Figure 5.1G) in 184-hTERT and MCF10A after treatment with siRNA targeting PPP2R2A (Method: Section 2.6). After siRNA treatments, the transcript levels of PPP2R2A, as assayed by QPCR, in both 184-hTERT and MCF10A cell lines appeared to be significantly reduced (Figure 5.8A, n=3). The analysis also revealed significant up regulation of MMP1 by 7.4 fold (Mean fold change =7.4, 6.08 to 8.92 fold with 95% confidence interval, n=3) in 184-hTERT (Figure 5.8A, n=3), but with a smaller magnitude of 20% in MCF10A (Figure 5.8A, mean fold change=1.2, 1.07 to 1.35 folds with 95% confidence interval, n=3). Second, I analyzed cell invasion potential in these cell models using a trans-well chamber assay (Method: Section 2.19). I treated cells with siRNA and plated them in chambers of 96-well invasion assay plate with Matrigel diluted (1:20) in serum   161 free medium 2 days after siRNA treatments. The cells were starved for 6 hours before collection. I analyzed a fraction of each sample for PPP2R2A knock down by western blot, which revealed significant reduction of PPP2R2A protein (PPP2R2A level: 38% in 184-hTERT siPPP2R2A vs siNT, 68% in MCF10A siPPP2R2A vs siNT) in both cell models treated with siRNA targeting PPP2R2A (Figure 5.8B). The serum in the medium added to the bottom chamber was a source of chemo attractant to induce cell invasion. Due to different concentrations of serum between the top and bottom chambers, cells were attracted to move through Matrigel and membrane toward the bottom chamber. After overnight incubation in a normal tissue culture incubator, the cells migrated cross the membrane through pores of 8-micron diameter and attached on the membrane facing the bottom chamber.  These cells were washed, disassociated from the membrane of the chamber and stained with live cell stain Calcein-AM for cell counting (n=6). The fluorescent plate reader value is directly proportional to the number of live cells. The results were presented as the percentage of the cells plated that migrated across the membrane. The calculation was based on a calibration curve constructed with cells directly plated on the plate without membrane or Matrigel. Based on my analysis, the cells with invasive potential in the control condition (Figure 5.8C, the black bar) were << 1% of total plated cells in 184hTERT-L9. The breast cancer cell MCF10A appeared to be more naturally invasive than 184hTERT-L9, even in the control condition, showing ~1% of cells undergoing transwell migration. There was a significant increase in live cells migrating across the membrane in 184-hTERT cells (Figure 5.8C, 184-hTERT) when PPP2R2A was knocked down (mean=2.8%) compared to the controls (mean=0.05%) (p-value=8.09x10-6, two paired t-test). For MCF10A cells (Figure 5.8C, MCF10A), the number of live cells migrated across the membrane after PPP2R2A knockdown (mean=2.4%) was also significantly higher than in the control condition (mean=1.3%) (p-  162 value=3.03x10-4, two paired t-test). These data demonstrated that mammary epithelial cells also gain enhanced invasion capacity when there is reduced expression of PPP2R2A and the results support our hypothesis about the functional roles of PPP2R2A on cell motility and invasion.  5.3 Discussion This chapter focuses on determining the functional impact of PPP2R2A deletion on phenotypes commonly observed in cancer cells, such as cellular structure and motility and thus extends the functional significance beyond the ER signaling and holoenzyme mechanisms highlighted in Chapters 3 and 4. Various studies have reported the functional roles of PP2A complexes in cell adhesion and transformation (45). In this chapter, I demonstrated that reduced expression of PPP2R2A, the regulatory subunit of PP2A, led to deregulation of gene expression, decreased 3D hollow structure formation, altered surface adhesion receptor protein localization, enhanced stress induced wound healing, and increased cell migration and invasiveness in mammary breast epithelial cells.  Transcriptome analysis of PPP2R2A knockdown indicated in 3 out of 4 pathway databases that deregulated genes are enriched in TGF-beta and beta-catenin signaling pathways. Many of the PPP2R2A knockdown associated pathways have also been previously found associated with breast cancer or other types of cancer development (151). For example, ALK4 functions as a type I receptor of TGF-beta signaling pathway that recruits SMAD related factors to regulate downstream expressions (147), including E-cadherin (CDH1) (155). Beta-catenin is a downstream factor of E-Cadherin signaling (155). In addition, enrichment map analysis of transcriptome data indicated differential regulation of several genes involved in cell motility and   163 stress responses (Appendix-D4). MMP1, which appeared to be up regulated by about 7 fold in 184-hTERT cells, is also involved in several cancer associated signaling pathways, including cell surface tight junction regulation, insulin growth factor regulation, and extracellular matrix regulation (156). The subsequent experiments extended and functionally validated these observations.  In 3D culture with Matrigel, single non-transformed mammary epithelial cells will divide and grow into colonies composed of multi-layered epithelium with a hollow lumen. Reduced expression of PPP2R2A during the early phase of acini formation led to fewer hollow lumen structures and more cell-filled colonies. Therefore, PPP2R2A could be potentially playing a positive role in the lumen forming mechanism. Since there were still some colonies with hollow centres after PPP2R2A knockdown, this “hollow out” mechanism was probably attenuated, but not completely disrupted in these mammary gland cell lines. On the other hand, these colonies with hollow centres in PPP2R2A knockdown condition might be developed from cells that were not effectively transfected with siRNA targeting PPP2R2A. Even in cells with effective transfection, the level of PPP2R2A knockdown may not reach 100% with siRNA and the duration of the RNAi effect may not have been sufficient for full penetrance of the phenotype. In future studies, apoptosis assay in the centres of these colonies could provide more information on the characteristics of their phenotypic changes. Nevertheless, PPP2R2A knockdown showed a distinct and robust differentiation phenotype.  As lumen-forming apoptosis is generally induced through loss of signaling from surface receptors, I checked sub-cellular localization of several mammary epithelial surface-signaling   164 receptors using immunofluorescence. Surface proteins coded by CDH1, ALK4, and ITG5A, all appeared to be mis-localized after PPP2R2A knockdown. The sub-cellular location of these receptors appeared to be phenotype reminiscent of protein internalization into cytoplasm granules, a known intracellular trafficking pathway for surface receptors (157). PPP2R2A (B55a) has been reported to be involved in regulating phosphorylation of Tau, a protein that modulates microtubule assembly and stability, and which ultimately regulates cytoplasmic protein translocation (158). In addition to internalization of the surface receptors, PPP2R2A knockdown also resulted in cell morphological changes, which could also be due to deregulation of cytoskeleton proteins. . Under control conditions both 184-hTERT and MCF10A cells were flat and spread out, whereas after PPP2R2A knockdown, the cells rounded up into oval shapes. Loss of cell surface receptors and adhesion is an early sign of anoikis, a mechanism and characteristic of increased cell motility and metastasis in cancer (159,160). On the other hand, enriched receptors in sub-cellular granules is a phenotype reminiscent of ubiquitination (161). Therefore, the observed receptor mis-localization could also be due to increased ubiquitination; delineation of the mechanism will require further investigation.  In addition, reduced PPP2R2A expression resulted in enhanced cell motility and invasion capacity. Analysis of transcriptome data identified differentially regulated genes involved in pathways including oxidative stress response and TGF-beta signaling (Appendix-D4). Studies have reported that the level of reactive oxygen species increases in wounded areas (162) and up regulation of genes involved in oxidative stress response has been found associated with enhanced wound healing ability in 2D culture (163). Activated TGF-beta signaling pathway has also been reported to be involved in regulating cell migration through SMAD mediated   165 mechanisms (164). ALK4 functions as an inhibitory receptor for TGF-beta signaling (165). Therefore, mis-localization of ALK4 (Figure 5.6) could possibly alleviate the inhibitory effects on TGF-beta signaling, which leads to activation of TGF-beta and enhanced wound healing. Cell invasion, an acquired function, enables cells to digest and migrate through the surrounding matrix (166). Transcriptome data identified increased expression of metalloprotease MMP1, a secreted protease able to breakdown extra-cellular matrix proteins. Overexpression of MMP1 is reported to be associated with enhanced metastasis in breast and colon cancers (151). Pathway analysis also identified several genes involved in assembly of cell tight junctions, which could lead to decreased cell-cell attachment and increased cell motility (167). Overall, transcript analysis and cell based experiments supported the functional roles of PPP2R2A in cancer metastasis associated mechanisms, such as cell motility and invasion.  There are several pitfalls in this study. First of all, the observations were all based on short duration knockdown of PPP2R2A with siRNA. Assays where the phenotype took more than a week to develop, such as 3D colony formation assay, may under-represent the consequences of complete PPP2R2A deletion. This could be an explanation for the attenuated, but not completely disrupted, acini lumen formation in PPP2R2A knockdown samples. Although the level of knockdown by siRNA had been confirmed by either western blot or QPCR, the results implied that PPP2R2A is an abundant protein and that transcriptional knockdown does not lead to a complete loss of the protein. This technical obstacle prevents us from studying the phenotype of complete PPP2R2A deletion as it appeared in some clinical samples. A conditional, complete gene knockout model would be worth creating for future studies on this gene.     166 Although functional studies provided evidence of PPP2R2A in cellular regulation, there are still not enough details yet to explain the mechanisms leading to these phenotypes. Although the results of mis-localization of surface receptors from 2D fluorescent imaging support the hypothesis of attenuated apoptosis mechanism in the acini in 3D culture, they still only associate the two phenotypes and do not provide direct mechanistic evidence. Loss of contact mediated growth inhibition is a possible explanation for the morphological change of 184-hTERT after PPP2R2A knockdown, but determination that this is the mechanism requires further investigation that links biophysically and genetically the proteins involved with the phenotypes observed.     167  Figure 5.1 Flow chart for studying functional roles of PPP2R2A in cancer associated mechanisms I first investigated transcriptome expression of 184-hTERT in the condition with reduced expression of PPP2R2A (I.). Guided by gene enrichment and signaling network  analysis of the deregulated genes, I performed assays on cell proliferation (II.), morphologies (III.), and mobilities (IV.). I. Transcriptome expression analysis  II. Cell structure and proliferation (D.) 3D colony formation assay Hollow sphere image and quantification  (184-hTERT,n=3; MCF10A, n=3) (E.) 2D cell surface protein expression imaging (184-hTERT, MCF10A) CHD1, ALK4, ITGA5 (F.) Wound healing assay assay 184-hTERT, n=6 (H.) Transwell Invasion assay 184-hTERT, MCF10A, n=4 (G.) QPCR validation of MMP1 184-hTERT, MCF10A, n=3 (B.) Cell growth morphology (184-hTERT) (C.) WST-1 proliferation assay  (184-hTERT, n=6) Cell invasiveness Cell stress response and migration IV. Cell mobility and invasiveness (A.) Affymetrix Transcriptome Microarray v2.0 (184-hTERT, n=3) (Pathway analysis) III. Cell structural changes   168    169 Figure 5.2 Reduced PPP2R2A expression changes the expression profile in 184-hTERT cells Cells were treated with 30nM of siRNA (siNT or siPPP2R2A) and collected after 4 days. Platform: Affymetrix Human transcriptome array based on triplicates samples. Filter: |Linear expression fold change| >=1.8,  ANOVA p<0.05, n=3. Method: 2.4. (A) QPCR analysis of 3 pairs of samples, control: siNT, experimental: siPPP2R2A. (B) Volcano plot of Affymetrix GeneChip Human Transcriptome Microarray v2.0 analysis with Transcriptome console software developed specifically for this type of microarray. Red: 100 up regulated genes, Green: 33 down regulated genes. Refer to Appendix-A.6 for the list of deregulated genes.     170   Salmonella Infection (K) IL1-mediated signaling events (N) Legionellosis (K) Interleukin-1 processing (R) Pertussis (K) NOD-like receptor signaling (K) Cytosolic DNA-sensing pathway (K) Cellular roles of Anthrax toxin (N) Herpes simplex infection (K) RIG-1-like receptor signaling (K) RIG-1/MDA5 mediated induction of IFN-alpha/beta pathway (R) Chemical carcinogenesis (K) Metabolism of xenotiotics by cytochrome p450 (K) Steroid hormone biosynthesis (K) Rheumatoid arthritis (K) Arachidonic acid metabolism (K) Classical complement pathway (B) Alternative complement pathway (B) Lectin induced complement pathway (B) TGF-beta signaling pathway (K) Leishmaniasis (K) Inflammatory bowel disease (K) Influenza A (K) Cell surface interactions at the vascular wall (R) Plasminogen activating cascade (P) Mitotic G1-G1/S phases (R) Cytosolic Iron-sulfur cluster assembly (R) Phagosome (K) TGF-beta-receptor signaling (N) Interleukin signaling pathway (P) Signaling by TGF-beta-receptor complex (R) Fatty acid, triacylglycerol, and ketone body metabolism (R) Regulation of nuclear beta catenin signaling and target gene transcription (N) Bile acid and bile salt metabolism (R) Oxidative stress induced senescence (R) PPAR signaling pathway (K) Staphylococcus aureus infection (K) Arachidonic acid metabolism (R) *"  171 Figure 5.3 Deregulated genes in 184-hTERT cells after PPP2R2A knockdown are enriched in TGF-beta and stress response pathways The pathway enrichment analysis was done with the Reactome-FI plugin (132), and the analysis results were presented as enrichment maps with Cytoscape enrichment map plug in (133). The gene lists in each condition were analyzed with four databases: KEGG (K), Biocarta (B), Reactome (R), and NCI (N). The list of differentially regulated genes after PPP2R2A knockdown in 184-hTERT were identified based on GeneChip Human Transcriptome microarray analysis, n=3, ANOVA p-value<0.05, |Linear Fold Change|>=1.8.  Red circles: Types of signaling pathways. Sizes of red circles: number of genes from the list involved in the pathway. Darkness of red circles: significance of the pathway based on pathway enrichment analysis (lighter pink: low significance; dark red: high significance). Green lines: connections between pathways. Blue box with “*”: signaling pathways associated with cell motility. Refer to Appendix-D.4 for gene hits for each pathways.   172 Ratio of Bend Intensity (PPP2R2A/Actin)PPP2R2A/ActinDay2 Day2 Day5 Day7 Day5 Day7 siNT siPPP2R2A PPP2R2A Actin siNT (Day7) siPPP2R2A (Day2)  (Day5) (Day7) A B C PPP2R2A Actin shNS   shPPP2R2A WST-1 Proliferation Assay0.8 0.9 1.0 1.1shPPP2R2A vs shNSRelative Fold ChangeD E Ratio of Bend Intensity (PPP2R2A/Actin)PPP2R2A/ActinsiNTsiPPP2R2APPP2R2A/Actin 1.03 0.177Normalization 100% 17%Day7 Day2 Day5 Day7PPP2R2A/Actin 1.02 0.582 0.426 0.296Normalization 100% 57% 42% 29%Relative Ratio of  Band Intensity (PPP2R2A/Actin) Relative Ratio of  Band Intensity (PPP2R2A/Actin)   173 Figure 5.4 Reduced PPP2R2A expression changes the morphologies, but not proliferation rate in 184-hTERT cells Proliferation assay of 184-hTERT with reduced PPP2R2A expression. Cells were treated with 30nM siRNA and images were taken on Day 2, Day 5, and Day 7. Cells with stable shRNA were assayed with WST-1 in 6 replicates on Day2 and Day7 after plating. Reading on Day 1 was used as reference for the cell growth starting point. Method: Section 2.20. (A) Western blot analysis of PPP2R2A knockdown with siRNA over 6 days after treatment of siRNA. (B) Analysis of the protein levels of PPP2R2A after siRNA knockdown with densitometry. (C) Images of 184-hTERT Day 2, Day 5 and Day 7 after plating (siRNA treatments were done on Day 1). Top: control condition (siNT), cells stop growing after reaching 70% confluence. Bottom: experimental condition (siPPP2R2A): cell grew to full confluence. 10x magnification scale bar: 100µm. (D) Western blot analysis of protein level knockdown of PPP2R2A in 184-hTERT stable cell line with shRNA. (E) WST-1 proliferation assay of 184-hTERT cells with reduced PPP2R2A expression by shRNA. Data were analyzed with ANCOVA based on absorbance reading (490nm) on Day 2 and Day 7. Refer to Appendix-Q.4 for absorbance reading.     174  A B  Non silencing siRNA PPP2R2A siRNA Non silencing siRNA PPP2R2A siRNA   175  Figure 5.5 Reduced PPP2R2A expression attenuates 184-hTERT and MCF10A hollow structure formations in 3D culture The cells were collected 2 days after 30nM siRNA knockdown in 2D culture and plated with density of 2000 cells/well in 8 slide chamber well bedded with pre-solidified matrigel. Assay medium containing 2% of matrigel was changed every four days. 184-hTERT and MCF10A cells were fixed after 16 days and 21 days of incubation, respectively. Method: Section 2.16. (A) Representative images of 3D colonies for 184-hTERT. (B) Representative images of 3D colonies for MCF10A. For both (A) and (B): Top:  control condition (siNT), cells form hollow spherical 184-hTERT Hollow Non Hollow020406080100% of type of colonies/total coloniesCtl KD (PPP2R2A)****184-hTERT0. Ration of bend intensity (PPP2R2A/GAPDH)MCF10A Ration of bend intensity (PPP2R2A/GAPDH)C D MCF10AHollow Non hollow020406080100% of type of colonies/total coloniesCtl KD (PPP2R2A)****PPP2R2A GAPDA PPP2R2A GAPDA Ctl KD Ctl KDMean 70% 44% 30% 56%Hollow Non Hollow Ctl KD Ctl KDMean 61% 19% 39% 81%Hollow Non HollowsiNT siPPP2R2APPP2R2A/GAPDH 1.00 0.452Normalization 100% 45% siNT siPPP2R2APPP2R2A/GAPDH 0.909 0.376Normalization 100% 41%Relative Ratio of  Band Intensity (PPP2R2A/GAPDH) Relative Ratio of  Band Intensity (PPP2R2A/GAPDH) P2R2A GAPDH P2R2A GAPDH   176 structures. Bottom: experimental condition (siPPP2R2A), form colonies with centre filled. Scale bar: 25µm. (C) Western blot anlaysis of protein level of PPP2R2A 2 days after PPP2R2A siRNA knockdown in 184-hTERT and MCF10A cells. (D) Quantitative analysis of non-hollow and hollow structured colonies in each chamber for 184-hTERT and MCF10A cells after PPP2R2A knockdown with siRNA. Results are presented as percentages of each type of colonies out of total colony numbers in each chamber well. For 184-hTERT, n=4, for MCF10A, n=3. Refer to Appendix-Q.1 for colony counts.    177    178   Figure 5.6 Cell surface receptors are mis-localizations in 184-hTERT and MCF10A after PPP2R2A knockdown Immunofluorescent imaging of cell surface protein E-cadherin (CDH1) and Activin receptor 1B (ALK4), and Integrin α5 (ITGA5) in 184-hTERT and MCF10A. Cells were fixed with ice cold ethanol 4 days after 30nM siRNA knockdown. Method: Section 2.17. (A) Images of E-Cadherin in 184-hTERT, (B) Images of ALK4 in 184-hTERT, (C) Images of ITGA5 in 184-hTERT, (D) Images for E-cadherin and ITGA5 in MCF10A. Green: surface receptors. Blue: DNA stain. A: E-cadherin. B: ITGA5, C. ALK4. Top row for each panel: control condition, siNT. Bottom row for each panel: expeirmental condition: siPPP2R2A. Scale bar: 100µm.   179  0 2 4 6 8 10 12 14 16050100Time (Hour)Wound Confluenc (%)184-hTERT184-hTERT Control184-hTERT KD (PPP2R2A) Ration of bend intensity (PPP2R2A/Actin)184-hTERT0.00.51.0Relative Ration of bend intensity (PPP2R2A/Actin)MCF10ACtl   KD Ctl   KD MCF10A 0 1 2 4 5 6 8 9 10050100Time (Hour)Wound Confluence (%)MCF10A ControlMCF10A KD(PPP2R2A)A B C 184-hTERT MCF10A siNT (Ctl) siPPP2R2A (KD) 0 Hour 7 Hours 0 Hour 10 Hours PPP2R2A Actin PPP2R2A Actin siNT (Ctl) siPPP2R2A (KD) PPP2R2A/Actin 1.28 0.457Normalization 100% 36% PPP2R2A/Actin 0.901 0.624Normalization 100% 69%Rela%ve'Ra%o'of''Band'Intensity'(PPP2R2A/Ac%n)'Rela%ve'Ra%o'of''Band'Intensity'(PPP2R2A/Ac%n)'  180 Figure 5.7 Reduced expression of PPP2R2A enhances the wound healing capacity in 184-hTERT and MCF10A Wound healing cell migration assay in 184-hTERT and MCF10A. Cells were treated with 30nM siRNA and collected 2 days after to be plated in 96 well plates. A 0.8mm wide wound was made in each well using a Essen wound maker. Cell debris was washed off with PBS. Images were taken every 2 hours for up to 16 hours, n=6. Method: Section 2.18. (A) Western blot validation of PPP2R2A knockdown. (B) Representative images of wound healing assay. (C) Time course of wound healing analysis with wound confluence in 184-hTERT and MCF10A after PPP2R2A knockdown. The wound confluences were analyzed with IncuCyte software by measuring the percentage of the cell occupied spaces to empty spaces. Refer to Appendix-Q.2 for detailed data of cell confluence analysis (168).              181  A Ration of bend intensity (PPP2R2A/Actin)184-hTERT0.00.51.0Relative Ration of bend intensity (PPP2R2A/Actin)MCF10ACtl   KD Ctl   KD B MCF10A0.0 0.5 1.0 1.5MMP1PPP2R2ALinear Fold Change184-hTERT0 5 10MMP1PPP2R2ALinear Fold ChangeC PPP2R2A Actin PPP2R2A Actin 184-hTERT MCF10A01234% of invaded cellsCtlKD (PPP2R2A)****PPP2R2A/Actin 0.938 0.353Normalization 100% 38% PPP2R2A/Actin 0.930 0.631Normalization 100% 68%Condition Ctl KD Ctl KDMean 0.47% 2.8% 1.3% 2.4%Relative Ratio of  Band Intensity (PPP2R2A/Actin) Relative Ratio of  Band Intensity (PPP2R2A/Actin)   182 Figure 5.8 Reduced PPP2R2A enhances cell invasion ability in 184-hTERT and MCF10A cells Trans-well cell invasion assay of 184-hTERT and MCF10A cells with PPP2R2A knockdown. Cells were treated with 30nM siRNA and collected 3 days after siRNA transfection. Cells were starved for 6 hours before collection with cell disassociation buffer and plated in cell invasion assay plates with non-serum medium in the invasion chamber. Full medium was added in the bottom chamber to provide chemo-attractants. The cells were incubated at 37°C overnight and cells that migrated across the membrane were disassociated and labeled with CalcineAM. Cells were counted with a Victor 3x fluorescent plate reader (excitation: 400nm, emission: 550nm). Method: Section 2.19. (A) QPCR analysis of MMP1 expression in 184-hTERT and MCF10A, n=3, ANOVA analysis with GraphPad. Refer to Appendix-Q.5 for QPCR analysis data. (B) Western blot analysis of PPP2R2A protein level in 184-hTERT and MCF10A cells after siRNA knockdown and before the cells were plated for the invasion assay. (C) Analysis comparing invaded cells in each cell line with PPP2R2A knockdown versus their controls. Control (Ctl): siNT (non-targeting), Knockdown (KD): siPPP2R2A. The number of cells invaded was calculated based on a calibration curve established with CalcineAM readings for 2000, 5000, 10000, 20000, 50000, 100000 cells per well. The percentage of invaded cells was calculated by dividing the number of cells that crossed membrane to the total number of cells plated in each well, n=8. Refer to Appendix-Q.3 for raw data.     183 Chapter 6: Conclusions and Future Directions  6.1 Major contributions  The aim of this thesis is to investigate the functional implications of PPP2R2A deletion or loss of function in breast cancers, a feature originally identified in a landscape genomic study of 2000 breast cancers conducted by the Aparicio and Caldas labs (31). In the past decade, a large number of mutations due to somatic segmental copy number alterations (CNA) have been discovered through genomic studies of cancers, but the functional consequences of many of these CNAs and their relevance to the life histories of cancers remain to be investigated. In the METABRIC study, the informatics approach reduced the genomic complexity by identifying cis-regulated CNAs (i.e., genes whose transcription was significantly correlated with the degree of copy number gain or loss in the cancer genome.) This reduced the landscape to around 45 hotspots, including 3 major regions of deletion, of which PPP2R2A was a novel finding at the time (31). The strong enrichment of PPP2R2A deletions in mitotic ER+ breast cancers provided the initial motivation for my thesis work (31). Ultimately to determine whether a gene mutation identified in silico is truly a “driver” (i.e., functionally implicated in the ontogeny of a cancer type) or a “passenger” requires experimental investigation.  Based on the patterns of PPP2R2A deletion in different breast cancer subtypes, I divided my work into two main areas, covered by the three data chapters in this thesis. Detailed discussion of the findings is presented at the end of each chapter and the overall synthesis is discussed here. In Chapters 3 and 4, I investigated molecular mechanisms by which PPP2R2A deletion or loss of expression might modulate PP2A holoenzyme complex function. In Chapter 3, due to the   184 predominant occurrence of PPP2R2A deletion in ER positive breast cancers, I studied the effects of PPP2R2A deletion on ER signaling pathway in ER positive breast cancer cell model T47D. The key discovery in Chapter 3 demonstrated for the first time was that there is indeed a functional relationship between ER signaling and PP2A function. This work identified SPDEF as an important transcription factor co-associated with the modification of ER signaling by PPP2R2A containing PP2A complexes. These studies were carried out from the perspective of ER signaling, especially the regulation of ER phosphorylation, binding specificity and regulated downstream gene expression. In Chapter 4, I quantitatively investigated PP2A holoenzyme composition changes due to reduced expression of PPP2R2A from the perspective of PP2A complex composition. I was able to develop a quantitative mass spectrometry assay for PP2A subunits and to demonstrate the relationship between free and complexed PP2A subunits in the cell. The assay allowed me to investigate relative PP2A subunits changes when PPP2R2A is depleted; this identified the divergent subunit STRN as a major target. In Chapter 5, I set out to understand whether PPP2R2A deletion has functional consequences in terms cell structure, morphology, differentiation capacity and cellular behaviours that are often associated with cancer development as “hallmarks”(3). I was able to show that loss of PPP2R2A modifies the behaviour of non-transformed epithelial cells in terms of differentiation, wound repair and invasiveness. Taken together, the three data chapters provided experimental evidence that loss of PPP2R2A is indeed a modifier of mammary epithelial cell behaviours that could reasonably be associated with cancer ontogeny.    185 6.2 Significance of research, pitfalls, and future studies Genes encoding PP2A subunits, especially the regulatory subunits, are considered to be tumour suppressors due to the fact that the carcinogenic drug okadaic acid and the onco-protein simian virus 40 small antigen (SV-40) cause tumour formation by displacing regulatory subunits in the PP2A complexes (169). Cancer associated genetic aberrations can be generally classified as either oncogenes or tumour suppressors based on their mutation characteristics (eg. point mutation or copy number changes), genomic prevalence, and functional consequences (170). Oncogenes are genetic variants that contribute to the proliferation and growth of tumours (170). They include genomic amplifications leading to transcriptional overexpression, such as HER2 in breast cancer, and genetic mutation of one or a few coding nucleotides that lead to constant activation of their coded proteins, such as H-Ras. Tumour suppressors, on the other hand, are usually genetically deleted or functionally inactivated due to single nucleotide mutations, such as deletion of PTEN or inactivating point mutations in p53 (170). In my study, reduced expression of B55α (PPP2R2A) led to several cancer associated phenotypes, such as enhanced ER signaling activities in ER positive breast cancer cell model T47D (Chapter 3), as well as surface protein mis-localization and enhanced cell motilities in primary immortalized breast cell models (Chapter 5). Therefore, as a frequently deleted gene in breast cancer genomes, I hypothesize that PPP2R2A is a tumour suppressor gene.  Classification of a gene as an oncogene or a tumour suppressor also requires experimental evidence of enhanced tumour formation in cell line or animal models caused by activation/amplification of putative oncogenes or inactivation/deletion of putative tumour suppressors (170). The study of Sabina et al (2010), using an anchorage-independent colony   186 formation assay to identify which PP2A subunits are involved in tumour formation, found various efficiencies of PP2A regulatory subunits deletion during tumour formation in the HEK (human embryonic kidney) cell model (169). In this study, overexpression of SV40 small antigen led to an increased rate of anchorage-independent colony formation (200 colonies per 105 cells plated compared to no colony in the control) and complete loss of B55α (PPP2R2A) subunit in PP2A complexes, suggesting that SV40ST imposes strong effects on the function of PPP2R2A. However, silencing of PPP2R2A alone with shRNA only produced ~2.5% (~5 colonies out of 105 cells plated) the number of anchorage-independent colonies as did SV40ST overexpression (169). This suggested that silencing of PPP2R2A could not recapitulate the full phenotype of SV40ST oncoprotein overexpression, which is similar to the results in my study where cells with reduced PPP2R2A expression by siRNA appeared to have attenuated, but not completely disrupted hollow structure formation (Chapter 5). Therefore, PPP2R2A could be an important tumour suppressor, but may not be the only critical member among all PP2A regulatory subunits. In other words, deletion of PPP2R2A may have more profound phenotype in other genomic backgrounds, such as in ER+ cells. On the other hand, based on western blot and QPCR analysis of experimental samples, PPP2R2A is an abundantly expressed protein that cannot be completely silenced by RNAi. Hence, to fully understand the functional consequences of PPP2R2A deletion, cell models with PPP2R2A genomic knockout and assays of the tumour formation capacity of this cell model in mice xenografts are needed. In addition, the study of Reid et al (2013) reported that PPP2R2A is also positively involved in p53-dependent metabolic adaptation to glutamine-deprivation, and a xenograft model with HT1080 cells expressing shB55α resulted in decreased tumour formation due to their reduced levels of metabolism in the centre of the tumour (171). Since PP2A can potentially interact with a number of signaling pathways, it would be   187 worthwhile to generate several PPP2R2A knockout cell models with different background expression profiles (eg. ER positive cell model and ER negative cell model).  In Chapter 3, my findings demonstrated that PPP2R2A deletion not only altered, but also enhanced ER signaling activity in ER positive breast cancers cell lines. The ER signaling pathway is regulated at multiple different levels. My results showed that after PPP2R2A knockdown, the binding specificity of ER (~1000 sites, Section was affected and led to altered expression of downstream ER response genes. Knockdown of PPP2R2A transcripts also changed phosphorylation status of ER in a ligand-depend at manner, which could also contribute to the altered mechanisms of ER binding specificity (Section To understand further the functional correlations between ER and PP2A, it would be worth investigating if PPP2R2A deletion is involved in activating ER signaling by assaying ER signaling activity in primary cell models. Moreover, I also identified SPDEF as a key mediator in the co-factor regulation mechanism of ER signaling. My results showed that SPDEF was not only transcriptionally up regulated in response to beta-estradiol ligand activation after PPP2R2A knockdown, but its encoded protein was also increasingly recruited to the ER transcriptional complexes and to its own regulatory sequence. RNA sequencing data indicated that SPDEF was up regulated after PPP2R2A knockdown even without β-estradiol treatment in T47D (Appendix-A.2), suggesting that PPP2R2A could be the starting key of this positive feedback mechanism, in which SPDEF up regulation, caused by PPP2R2A deletion, alters ER binding specificity through its increased relative abundance in ER complexes, which further up regulates its own transcripts. In future studies, assays on ER signaling in cells with overexpressed SPDEF can be used to test this hypothesis. Furthermore, expression studies of ER response genes in METABRIC clinical   188 samples suggested that the background expression in T47D after PPP2R2A knockdown is more reminiscent of Luminal A breast cancers, which is more responsive to ER targeting therapies (Section Although it is still unclear whether PPP2R2A deletion could result in altered epithelial differentiation by affecting the transcriptionally determined molecular subtype of ER+ breast cancers (Luminal A vs Luminal B), or the background gene expression of ER positive breast cancers selects for PPPP2R2A deletion, further clinical studies on PPP2R2A copy number status and ER targeting therapies may provide more information on whether PPP2R2A could be used as a prognostic marker for this type of therapy.   In Chapter 4, I provided experimental data supporting the hypothesis that PP2A subunits exist in equlibrium between tri-subunit holocomplexes and free monomers in cells. I established a method for studying PP2A complex composition using combined size-exclusion chromatography and quantitative mass spectrometry (Section 4.2.1). I fractionated the cell lysates based on protein sizes to separate PP2A complexes from the free monomers and found that the majority of PP2A subunits (~70% of PPP2CA and ~90% of PPP2R1A) are in the complexes. I also discovered increased relative abundance of STRN in the PP2A complexes after reduced expression of PPP2R2A in two ER positive cell models, which supported my hypothesis that the reduced abundance of one subunit could lead to compositional changes amongst the holocomplexes. Based on RNA-sequencing and microarray analysis, there was not significant deregulation of any PP2A subunits after PPP2R2A knockdown in T47D (Appendix-A.1, A.2), suggesting it was unlikely that increased recruitment of STRN in PP2A complexes is due to transcript changes of PP2A subunits. There are many holoenzyme complexes in cells and the relative composition of subunits in these holoenzymes plays an important role in their ultimate   189 function during intracellular signaling. For example, recruitment of histone deacetylase (HDAC) cofactors to the ER transcription holocomplex results in increased transcription, whereas histone acetylase recruitment leads to transcription suppression (172). Therefore, the consequences of PPP2R2A deletion were not limited to the loss of phosphatase activity on its own targets, but also increased dephosphorylation of the targets recruited by STRN. In future studies, comprehensive investigation on overall phospho-signaling pathways using phospho protein enrichment and quantitative mass spectrometry would provide more information. In addition, STRN functions as a modulator scaffold for ER-involved signaling pathways in a ligand-independent manner (137,173). Hence, the increased recruitment of STRN in a PP2A holocomplex could potentially result in enhanced regulation of non-genomic ER activities, which needs further detailed investigation to confirm. Moreover, to answer the question as to whether differential background expression of ER positive cells, compared to basal cells, is involved in STRN, a future study on PP2A complex composition in ER positive cell lines with/without ligand activation might be helpful.  Finally, cancer cells differ from surrounding normal cells by their distinct cellular phenotypes, including loss of cell-cell contact, enhanced cell motility, and invasion capacity through the extra-cellular matrix. In Chapter 5, I investigated these cancer-associated phenotypes in immortalized breast mammary epithelial cells, and my results demonstrated that reduced PPP2R2A indeed contributed to the development of these phenotypes in addition to ER signaling. These phenotypes supported the hypothesis that PPP2R2A functions as tumour suppressor in cancer development. Microarray analysis provided some preliminary information on the signaling pathways that could be potentially involved in the mechanisms leading to these   190 phenotypes (Section 5.2.1). There have been a number of studies reporting cancer associated genomic aberrations in other PP2A subunits, but mutations on different subunits may lead to distinct consequences on downstream signaling and ultimate phenotypic changes. Reduced expression of PPP2R2A has been reported to result in delayed mitotic exit during cell cycle (59). Point mutations on the scaffold subunit PPP2R1A may alter its binding to regulatory subunits, such as PPP2R5A, and thereby result in a phenotype reminiscent of PPP2R5A deletion and increased phosphorylation level on the downstream target AKT (45). Therefore, to find out which pathways are specifically affected and ultimately contributing to each phenotype, further investigations of downstream signaling pathways may provide more information. Also, since PPP2R2A deletion has been found to enhance ER signaling based on my results in Chapter 3, it is also worth finding out if these cancer associated phenotypes are ER dependent and if ER signaling can be activated in these immortalized primary breast cell models by PPP2R2A deletion.   6.3 Concluding comments Taken together, the results in this thesis provided in vitro evidence of PPP2R2A deletion contributing to the ontogeny of breast cancer. This research linked ER signaling pathway to PP2A phosphatase regulation. The results detailed transcriptional alterations and mechanistic changes of ER binding specificity in ER positive breast cancer cell model due to PPP2R2A deletion. The study also provided us with functional insights of this mutation on cell morphology and mobility. Furthermore, structural studies with quantitative mass spectrometry uncovered the elasticity of PP2A complex and further raised the hypothesis that functional effects of PPP2R2A   191 deletion could be due to a combination of the mutation itself and its resultant secondary effects on PP2A complex composition.    192 Bibliography 1.  Release P. Latest world cancer statistics. 2013;(December):2012–4.  2.  Boveri T. The Nature of Cancer. In: Robert Allan Weinberg, editor. Biology of Cancer. 1st ed. Garland Science; 2007. p. 25–56.  3.  Hanahan D, Weinberg R a. Hallmarks of cancer: the next generation. Cell [Internet]. Elsevier Inc.; 2011 Mar 4 [cited 2014 Jul 9];144(5):646–74. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21376230 4.  Baan R, Grosse Y, Straif K, Secretan B, El Ghissassi F, Bouvard V, et al. A review of human carcinogens—Part F: Chemical agents and related occupations. Lancet Oncol [Internet]. 2009 Dec [cited 2014 Jul 24];10(12):1143–4. 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Gene$Symbol Chr Genomic$Position Public$Gene$IDs Fold$Change p$(ANOVA)$LRRC31 3 169556967*+*169587718 NM_024727 5.7 7.1E+04SNORA14B 1 235291118*+*235291252 NR_002956 4.7 5.8E+03STEAP1 7 89783689*+*89794143 NM_012449 3.4 1.0E+06ABCC11 16 48200821*+*48281479 NM_033151 3.4 3.0E+04PIP 7 142829170*+*142836839 NM_002652 3.4 6.0E+06ABCC12 16 48116884*+*48189929 NM_033226 3.1 3.1E+04MIR4500 13 88270920*+*88270995 NR_039722 2.8 7.6E+04GNG11 7 93551011*+*93555831 NM_004126 2.7 3.4E+02SKAP2 7 26706681*+*27034858 NM_003930 2.6 5.2E+05LOC100506827 4 44018878*+*44024080 ENST00000512678 2.5 9.6E+04TNFSF10 3 172223298*+*172241297 NM_003810 2.5 2.3E+04GABRA3 X 151334706*+*151620337 NM_000808 2.4 4.8E+04CYP4Z2P 1 47308767*+*47366147 NR_002788 2.4 5.9E+04UBL3 13 30338508*+*30424821 NM_007106 2.4 4.3E+05CYP4Z1 1 47533160*+*47583992 NM_178134 2.3 8.0E+04HMGCS2 1 120290619*+*120311555 NM_001166107 2.3 4.3E+04STEAP2 7 89796904*+*89870091 NM_001040665 2.2 2.0E+06FAM125A 19 17516503*+*17526545 ENST00000500836 2.2 1.2E+02PRICKLE2+AS3 3 64173220*+*64186641 ENST00000473434 2.2 2.9E+02GNMT 6 42928496*+*42931618 NM_018960 2.2 1.1E+02TFF3 21 43731777*+*43735761 NM_003226 2.2 1.3E+02CLDN16 3 190040330*+*190129932 NM_006580 2.1 2.7E+04ARHGEF6 X 135747706*+*135864247 NM_004840 2.1 6.7E+04GRIK3 1 37261128*+*37499844 NM_000831 2.1 2.1E+02HLA+DQA2 6 3991698*+*3997550 NM_020056 2.1 1.2E+03SOD3 4 24797085*+*24802467 NM_003102 2.1 2.6E+04LOC643401 5 27472399*+*27496508 NR_038848 2.1 6.3E+04BST2 19 17502082*+*17516457 NM_004335 2.1 4.8E+02HLA+DQA2 6 4155020*+*4161095 NM_020056 2.1 3.7E+04HLA+DQA2 6 4166277*+*4171838 NM_020056 2.0 1.4E+03RERG 12 15260716*+*15374411 NM_001190726 2.0 2.3E+03POU6F2+AS2 7 39023843*+*39053161 ENST00000420243 2.0 4.9E+04KDELR3 22 38864067*+*38879452 NM_006855 2.0 3.2E+05CTSO 4 156845270*+*156875069 NM_001334 1.9 2.0E+04HLA+DQA2 6 4046301*+*4052169 NM_020056 1.9 1.4E+03GRIK1+AS1 21 31120494*+*31136325 NR_027021 1.9 2.8E+03GALNT7 4 174089904*+*174245118 NM_017423 1.9 1.1E+02LOC728323 2 243030784*+*243102469 NR_024437 1.9 9.9E+04FLJ23152 6 17102489*+*17131603 NM_001190766 1.9 1.6E+03HLA+DQA2 6 3997436*+*4003267 NM_020056 1.9 1.1E+03LOC729013 11 10879764*+*10900823 NR_03413 1.9 1.8E+04  212  Gene$Symbol Chr Genomic$Position Public$Gene$IDs Fold$Change p$(ANOVA)$HPX 11 6452268*+*6463847 NM_000613 1.9 8.0E+06DERA 12 16064106*+*16190315 NM_015954 1.9 8.5E+03ALDH1L2 12 105413562*+*105478355 NM_001034173 1.8 2.2E+02CLDN8 21 31586324*+*31588469 NM_199328 1.8 1.7E+02HLA+DQA2 6 32709119*+*32714992 NM_020056 1.8 8.4E+04NEB 2 152341850*+*152591001 NM_001164508 1.8 2.4E+03C5 9 123714614*+*123837452 NM_001735 1.8 2.3E+03HERPUD1 16 56965748*+*56977793 NM_001010990 1.8 2.1E+04AKAP11+IT1 13 42848464*+*42851124 ENST00000432967 1.8 1.0E+02SPDEF 6 34505579*+*34524110 NM_001252294 1.8 1.2E+02LYPLA1 8 54958938*+*55014577 NM_006330 1.8 1.4E+03FAM129A 1 184759858*+*184943682 NM_052966 1.8 1.6E+04TMEM106A 17 41363894*+*41371957 NM_145041 1.7 8.1E+05MGP 12 15034115*+*15038860 NM_000900 1.7 1.2E+03STXBP5L 3 120626919*+*121143608 NM_014980 1.7 1.0E+02SERPINI1 3 167453031*+*167543357 NM_001122752 1.7 1.8E+02STEAP1B 7 22459063*+*22672544 NM_001164460 1.7 3.0E+06SLC46A3 13 29274201*+*29293150 NM_001135919 1.7 1.6E+05HLA+DQA2 6 3941202*+*3947061 NM_020056 1.7 6.6E+04HLA+DQA2 4140787*+*4146657 NM_020056 1.7 5.0E+04RAPGEF6 5 130759614*+*130970929 NM_016340 1.7 2.9E+04PI15 8 75736772*+*75767264 NM_015886 1.7 6.1E+03TYRP1 9 12685439*+*12710274 NM_000550 1.7 1.2E+03LOC100506676 17 5015227*+*5017672 ENST00000413077 1.7 4.6E+04TGFB3 14 76424442*+*76449334 NM_00323 1.7 3.6E+03CACNA1D 3 53528683*+*53846492 NM_000720 1.7 1.6E+04LOC729852 7 7680342*+*7918854 NR_034084 1.7 1.1E+04LOC440173 9 89623366*+*89657041 NR_027471 1.7 2.5E+03PLCB4 20 9049410*+*9461889 NM_000933 1.7 5.8E+03C9orf152 9 112952328*+*112970469 NM_001012993 1.7 2.0E+03RABL3 3 120405528*+*120461840 NM_173825 1.7 3.2E+03STYX 14 53196883*+*53241716 NM_001130701 1.7 2.7E+04RN5S299 10 8698679*+*8698794 ENST00000391203 1.7 2.7E+03LOC100505648 15 40978922*+*40987303 NR_040058 1.7 5.2E+04MDGA2 14 47308826*+*48144157 NM_001113498 1.7 1.6E+04MT2A 16 56642111*+*56643409 NM_005953 1.7 3.0E+03CYB5R4 6 84569362*+*84670146 NM_016230 1.7 7.9E+04LRRC58 3 120043356*+*120068186 NM_001099678 1.7 6.0E+05LOC100272217 9 133452737*+*133454881 NR_027440 1.7 1.8E+02LOC170425 10 86039736*+*86054415 NR_038220 1.7 3.6E+02LINC00173 12 116971227*+*116974323 NR_027346 1.7 2.9E+02NEK11 3 130745694*+*131069309 NM_001146003 1.6 3.1E+03SCAMP1 5 77656339*+*77776562 NM_004866 1.6 3.6E+05CDNF 10 14861249*+*14880574 NM_001029954 1.6 9.1E+04CATSPERB 14 92047118*+*92198430 NM_024764 1.6 6.0E+04  213  Gene$Symbol Chr Genomic$Position Public$Gene$IDs Fold$Change p$(ANOVA)$FLJ27352 15 55700723*+*55710910 NM_001198784 1.6 1.3E+02MIR548W 17 60798858*+*61268734 NR_036146 1.6 2.5E+02TIAM2 6 155411423*+*155582018 NM_012454 1.6 2.4E+03CD44 11 35160417*+*35253949 NM_001001391 1.6 1.2E+03ARHGAP15 2 143848931*+*144525921 NM_018460 1.6 1.9E+03IER3IP1 18 44681390*+*44702745 NM_016097 1.6 3.2E+04FMO4 1 171283347*+*171311223 NM_002022 1.6 1.3E+03ATP10B 5 159990127*+*160279221 NM_025153 1.6 4.2E+04CDR1 X 139864570*+*139867036 NM_004065 1.6 1.7E+02MPHOSPH6 16 82181767*+*82203831 NM_005792 1.6 2.8E+03TMEM238 19 55890612*+*55895627 NM_001190764 1.6 1.2E+03OPN3 1 241753404*+*241840678 NM_014322 1.6 1.8E+03ZNF323 6 28292470*+*28324048 NM_001135216 1.6 1.2E+03GOLM1 9 88641058*+*88715116 NM_016548 1.6 2.4E+02AGA 4 178351924*+*178363657 NM_000027 1.6 1.9E+05KRT23 17 39078948*+*39093836 NM_015515 1.6 4.6E+02VAV3 1 108113782*+*108507858 NM_001079874 1.6 2.7E+03ARHGEF37 5 148931510*+*149014531 NM_001001669 1.6 1.5E+03KIAA0825 5 93486556*+*93954309 NM_001145678 1.6 2.5E+04WWP1 8 87354967*+*87490649 NM_007013 1.6 1.6E+04IQCK 16 19727778*+*19868907 NM_153208 1.6 3.6E+05NQO1 16 69743304*+*69760707 NM_000903 1.6 8.6E+05C17orf28 17 72946837*+*72968900 NM_030630 1.6 1.1E+04KIAA1377 11 101785746*+*101871793 NM_02080 1.6 1.6E+03SLC38A1 12 46576841*+*46663208 NM_001077484 1.6 4.1E+04ST6GALNAC2 17 74561461*+*74582145 NM_006456 1.6 1.9E+03SRRM5 19 44116253*+*44118650 NM_001145641 1.6 1.9E+03KYNU 2 143635067*+*143799890 NM_001032998 1.6 1.3E+03MLPH 2 238394071*+*238463961 NM_001042467 1.6 1.4E+03SERP1 3 150259780*+*150321015 NM_014445 1.6 8.6E+04AGXT2 5 34998206*+*35048240 NM_031900 1.6 6.3E+03C1GALT1C1 X 119759529*+*119764005 NM_001011551 1.6 6.9E+04TLE1P1 X 64628131*+*64628565 ENST00000426165 1.6 3.3E+05ERP27 12 15066969*+*15092016 NM_152321 1.6 1.6E+02RNU5E+4P 1 11969865*+*11969984 ENST00000364931 1.6 1.8E+02TMEM87B 2 112812800*+*112876895 NM_032824 1.6 6.3E+04CAPN2 1 223889295*+*223963720 NM_001146068 1.6 6.4E+05MAP2 2 210288771*+*210598842 NM_001039538 1.6 1.7E+04CDH19 18 64168320*+*64271409 NM_021153 1.6 1.0E+02PALMD 1 100111431*+*100178513 NM_017734 1.6 3.5E+02ZNF860 3 32023263*+*32033228 NM_001137674 1.6 1.3E+03ADHFE1 8 67344718*+*67381142 NM_144650 1.6 6.4E+04SGCE 7 94214536*+*94285521 NM_001099400 1.6 4.2E+05PTER 10 16478963*+*16555736 NM_001001484 1.6 2.3E+04CASP1 11 104896235*+*104972158 NM_001223 1.6 3.2E+02  214  Gene$Symbol Chr Genomic$Position Public$Gene$IDs Fold$Change p$(ANOVA)$FNTB 14 65453438+,+65529373 NM_002028 1.6 5.0E,03LINC00266,1 20 62921738+,+62944485 NR_040415 1.6 1.5E,03CREB3L4 1 153940010+,+153946840 NM_001255978 1.5 8.5E,04MAP3K1 5 56110900+,+56191979 NM_005921 1.5 2.3E,02SNRNP48 6 7590432+,+7612200 NM_152551 1.5 1.9E,02LGI1 10 95517566+,+95557916 NM_005097 1.5 1.8E,02LRRC6 8 133584320+,+133687838 NM_012472 1.5 2.9E,04FLRT3 20 14303634+,+14318313 NM_013281 1.5 5.6E,03CYP4X1 1 47427036+,+47516423 NM_178033 1.5 2.4E,03LRRC39 1 100614004+,+100643829 NM_001256385 1.5 2.9E,02MATN3 2 20191813+,+20212455 NM_002381 1.5 4.7E,04EGF 4 110834040+,+110934118 NM_001178130 1.5 4.4E,02OGFRL1 6 71998477+,+72158906 NM_024576 1.5 8.0E,04LHFPL3,AS2 7 104535075+,+104567092 NR_027374 1.5 2.5E,02SAT1 X 23801275+,+23804343 NM_002970 1.5 3.0E,03SCYL3 1 169821804+,+169863408 NM_181093 1.5 4.8E,03AFF3 2 100163716+,+100759201 NM_001025108 1.5 1.2E,02LAMTOR3 4 100799493+,+100815703 NM_001243736 1.5 1.1E,03SPEF2 5 35617946+,+35814713 NM_024867 1.5 7.8E,04DDIT3 12 57910371+,+57914300 NM_001195053 1.5 1.6E,02DCBLD2 3 98514785+,+98620533 NM_080927 1.5 8.2E,05CYP39A1 6 46517445+,+46620523 NM_016593 1.5 4.0E,05HCAR1 12 123104824+,+123215390 NM_032554 1.5 1.7E,02ACOT6 14 74083548+,+74086592 NM_001037162 1.5 3.2E,02PPT1 1 40538377+,+40563375 NM_000310 1.5 1.7E,03ERBB4 2 212240442+,+213403565 NM_001042599 1.5 1.9E,03CLGN 4 141309607+,+141349122 NM_001130675 1.5 4.9E,02OR2A7 7 143955700+,+143956815 ENST00000493325 1.5 1.5E,03ZMAT1 X 101137260+,+101187039 NM_001011657 1.5 1.2E,04LOC100506394 13 114567150+,+114569805 NR_044993 1.5 7.3E,03DNAJC3,AS1 13 96325089+,+96329179 ENST00000499499 1.5 1.9E,02LOC728755 14 27728348+,+28142436 ENST00000553392 1.5 6.7E,04GNG12 1 68167149+,+68299155 NM_018841 1.5 3.7E,03ZBTB41 1 197122814+,+197169672 NM_194314 1.5 1.1E,02NOSTRIN 2 169643049+,+169722024 NM_001039724 1.5 2.1E,03KCNMA1 10 78629359+,+79398353 NM_001014797 1.5 3.6E,02SLC5A12 11 26688566+,+26744974 NM_178498 1.5 4.1E,03AVIL 12 58191160+,+58210254 NM_006576 1.5 1.2E,03LRRC37A4P 17 43578684+,+43597889 NR_002940 1.5 3.1E,04EXO1 1 242011269+,+242058450 NM_003686 ,1.5 4.6E,03YEATS2,AS1 3 183525552+,+183526729 ENST00000425008 ,1.5 2.9E,02KLHL5 4 39046451+,+39128477 NM_001007075 ,1.5 6.6E,03KCNQ5,IT1 6 73340223+,+73388282 ENST00000445310 ,1.5 2.4E,02NRIP3 11 9002123+,+9025596 NM_020645 ,1.5 5.7E,04WNK1 12 976960+,+978360 BC130469 ,1.5 3.0E,03  215  Gene$Symbol Chr Genomic$Position Public$Gene$IDs Fold$Change p$(ANOVA)$CASC5 15 40886218+,+40956540 NM_144508 ,1.5 1.1E,03RTF1 15 41709302+,+41775761 NM_015138 ,1.5 1.8E,03VASN 16 4421849+,+4433529 NM_138440 ,1.5 4.9E,02CRIM1 2 36583069+,+36778278 NM_016441 ,1.5 1.2E,02LYN 8 56792372+,+56925006 NM_001111097 ,1.5 1.3E,02KIAA1161 9 34366668+,+34376894 NM_020702 ,1.5 7.9E,03C9orf41 9 77595936+,+77643339 NM_152420 ,1.5 8.9E,03UNC13D 17 73823308+,+73840851 NM_199242 ,1.5 5.0E,06RPS5 19 58898636+,+58906171 NM_001009 ,1.5 3.1E,05XAGE2B,+XAGE2 X 52380351+,+52387096 NM_001079538 ,1.5 5.3E,04NECAP1 12 7926148+,+8250373 NM_015509 ,1.5 4.5E,03HIVEP2 6 143072604+,+143266338 NM_006734 ,1.5 2.4E,03GEM 8 95261481+,+95274578 NM_181702 ,1.5 6.4E,03ACER3 11 76571917+,+76737841 NM_018367 ,1.5 6.6E,03MIR614,+GPRC5A 12 13043716+,+13070871 NR_030345 ,1.5 1.6E,03KIF21A 12 39687030+,+39837192 NM_001173463 ,1.5 1.6E,03EIF2B2 14 75469612+,+75476294 NM_014239 ,1.5 1.8E,03RDH11 14 68143518+,+68162531 NM_001252650 ,1.5 2.1E,04SMG8 17 57287371+,+57292611 NM_018149 ,1.5 4.1E,03MAN2B1 19 12757322+,+12777591 NM_000528 ,1.5 5.8E,04KLHL15 X 24001833+,+24045303 NM_030624 ,1.5 3.4E,03SPRED1 15 38544527+,+38649450 NM_152594 ,1.5 1.6E,03FURIN 15 91411822+,+91426688 NM_002569 ,1.5 5.7E,04CLN6 15 68499330+,+68549444 NM_017882 ,1.5 1.5E,03BLMH 17 28575213+,+28619184 NM_000386 ,1.5 7.6E,04PDE12 3 57541981+,+57547768 NM_177966 ,1.6 1.3E,02SKIL 3 170075466+,+170114637 NM_001145097 ,1.6 4.4E,03NPNT 4 106815932+,+106925184 NM_001033047 ,1.6 2.4E,02SLC9A7 X 46464753+,+46618490 NM_032591 ,1.6 1.5E,03TUFT1 1 151512781+,+151556059 NM_020127 ,1.6 9.0E,06SMG9 19 44235301+,+44259142 NM_019108 ,1.6 2.4E,04PLEKHH2 2 43864412+,+43995126 NM_172069 ,1.6 3.5E,03TRPC1 3 142442916+,+142526730 NM_001251845 ,1.6 1.1E,03DCDC2 6 24171983+,+24383520 NM_001195610 ,1.6 3.0E,03BTG1 12 92534054+,+92539673 NM_001731 ,1.6 9.7E,03WDHD1 14 55405656+,+55493823 NM_001008396 ,1.6 7.7E,04LGMN 14 93170152+,+93215047 NM_001008530 ,1.6 7.4E,03KIF23 15 69706585+,+69740764 NM_004856 ,1.6 1.3E,03SGK196 8 42948657+,+42978577 NM_032237 ,1.6 3.5E,04SCCPDH 1 246887349+,+246931440 NM_016002 ,1.6 1.9E,02NABP1 2 192542794+,+192553251 NM_001031716 ,1.6 1.7E,02SLC4A7 3 27414214+,+27525911 NM_003615 ,1.6 2.1E,02C6orf7 6 80513300+,+80580137 NM_001243308 ,1.6 6.4E,03CEP55 10 95256369+,+95288849 NM_018131 ,1.6 6.1E,04PPP2R2A 8 26149007+,+26230196 NM_001177591 ,1.6 3.5E,04  216  Gene$Symbol Chr Genomic$Position Public$Gene$IDs Fold$Change p$(ANOVA)$ZWINT 10 58116989,-,58121036 NM_032997 -1.6 6.8E-04FANCI 15 89787180,-,89860492 NM_001113378 -1.6 2.1E-03TGFBR1 9 101867371,-,101916585 NM_001130916 -1.6 4.0E-03CHORDC1 11 89933597,-,89956532 NM_001144073 -1.6 4.6E-02OIP5 15 41601466,-,41624819 NM_007280 -1.6 4.7E-03RPS3AP47 15 43407897,-,43408565 ENST00000382175 -1.6 1.2E-02C12orf49 12 117153593,-,117175874 NM_02473 -1.6 8.4E-05DOCK10 2 225629807,-,225907330 NM_014689 -1.6 8.4E-04DTL 1 212208919,-,212280742 NM_016448 -1.6 6.7E-03HIST1H3 6 26031817,-,26032289 NM_003537 -1.6 1.1E-03CREBL2 12 12764761,-,12798042 NM_001310 -1.6 4.5E-04SEC61A2 10 12171636,-,12211960 NM_001142628 -1.6 2.5E-03BRIP1 17 59756547,-,59940920 NM_032043 -1.6 2.7E-03LOC100499467 17 70380443,-,70588943 NR_036488 -1.6 1.9E-02ERVV-2 19 53547991,-,53554380 NM_001191055 -1.6 1.1E-02ABCC13 21 15608527,-,15710338 NR_003087 -1.6 1.3E-02SLC12A2 5 127419458,-,127525380 NM_001046 -1.6 2.2E-04PHTF2 7 77428109,-,77586821 NM_001127357 -1.6 1.8E-04SLITRK6 13 86366922,-,86373623 NM_032229 -1.6 1.8E-04POLA1 X 24712036,-,25015103 NM_016937 -1.7 5.3E-04GUCY1A2 11 106544738,-,106889250 NM_000855 -1.7 2.4E-02IRS1 2 227596033,-,227664475 NM_005544 -1.7 1.6E-02IGFBP7 4 57896939,-,57976551 NM_001253835 -1.7 2.7E-04LPPR4 1 99729509,-,99775146 NM_001166252 -1.7 7.5E-03FN1 2 216225163,-,216300895 NM_002026 -1.7 5.3E-04MIR221 X 45605585,-,45605694 NR_029635 -1.7 2.3E-03GPR137C 14 53019865,-,53104431 NM_001099652 -1.7 4.4E-03AMOTL1 11 94501508,-,94609918 NM_130847 -1.7 1.7E-04ACER2 9 19397807,-,19452500 NM_001010887 -1.7 4.0E-04MPZL3 11 118097409,-,118123065 NM_198275 -1.7 6.7E-04FLJ42627 16 2688983,-,2696130 NR_024492 -1.7 5.4E-03BCAR4 16 11913692,-,11922689 NR_024049 -1.7 5.4E-05LPHN2 1 81771845,-,82458107 NM_012302 -1.7 2.9E-03C12orf75 12 105720744,-,105789875 NM_001145199 -1.7 6.1E-03AASS 7 121713598,-,121784344 NM_005763 -1.7 4.7E-04LOC100131096 17 76103480,-,76107880 NR_040071 -1.7 3.8E-03GJA1 6 121756745,-,121770873 NM_000165 -1.7 2.9E-02ID1 20 30193086,-,30194318 NM_002165 -1.7 2.8E-03SNORD93 7 22896232,-,22896305 NR_003075 -1.7 1.2E-02CDKL5 X 18443703,-,18671749 NM_001037343 -1.7 5.2E-03C14orf132 14 96505661,-,96560417 NM_001252507 -1.7 7.6E-03DNAH10OS 12 124410008,-,124419531 ENST00000514254 -1.7 2.0E-03PFKFB2 1 207207761,-,207254369 NM_001018053 -1.7 1.3E-05MIR3975 18 33171701,-,33171770 NR_039771 -1.8 4.4E-02SLC6A4 17 28521337,-,28562954 NM_001045 -1.8 1.9E-04  217   Gene$Symbol Chr Genomic$Position Public$Gene$IDs Fold$Change p$(ANOVA)$STARD7 2 96850597-.-96874573 NM_020151 .1.8 4.1E.04MCM10 10 13203554-.-13253104 NM_018518 .1.8 7.1E.03SOX13 1 204042227-.-204096871 NM_005686 .1.8 6.9E.04SPRR3 1 152974223-.-152976332 NM_001097589 .1.8 2.5E.02ACO1 9 32384601-.-32450834 NM_002197 .1.8 6.1E.03FREM2 13 39261173-.-39461267 NM_207361 .1.8 5.5E.04TCTN3 10 97423153-.-97454712 NM_015631 .1.8 4.0E.05TCHHL1 1 152056618-.-152061541 NM_001008536 .1.8 2.7E.02ABCC5 3 183637722-.-183735803 NM_001023587 .1.8 5.9E.04DEPDC1B 5 59892739-.-59996017 NM_001145208 .1.8 1.4E.03SHISA2 13 26618735-.-26625198 NM_001007538 .1.8 1.7E.03TMEM2 9 74298282-.-74383800 NM_013390 .1.9 3.4E.03ATP2B1 12 89981826-.-90102608 NM_001001323 .1.9 1.3E.05HELLS 10 96305543-.-96373662 NM_018063 .1.9 5.6E.04GULP1 2 189156396-.-189460653 NM_001252668 .2.0 1.9E.03HEXA 15 72635775-.-72668817 NM_000520 .2.0 8.8E.04ROBO2 3 75955846-.-77699115 NM_001128929 .2.0 4.1E.03MIR449C 5 54468090-.-54468181 NR_031572 .2.0 6.6E.03COL12A1 6 75794042-.-75915767 NM_004370 .2.0 8.8E.04LOC100289488 1 201969222-.-201979481 ENST00000415582 .2.0 3.0E.03FAM5C 1 190066792-.-190446759 NM_199051 .2.1 3.8E.04GRHL3 1 24645812-.-24690972 NM_001195010 .2.1 1.9E.03EPHA4 2 222282747-.-222438922 NM_004438 .2.1 6.9E.04SULF1 8 70378859-.-70573150 NM_015170 .2.2 3.4E.03PCP4 21 41239243-.-41301322 NM_006198 .2.2 1.6E.03GALC 14 88304164-.-88460009 NM_000153 .2.2 6.1E.05SCUBE2 11 9041047-.-9159661 NM_001170690 .2.2 9.3E.04FBLN5 14 92335755-.-92414167 NM_006329 .2.4 3.5E.04TGFB2 1 218518676-.-218617961 NM_001135599 .2.4 5.8E.05LUM 12 91496406-.-91505608 NM_00234 .2.5 1.7E.03SEMA6A 5 115779251-.-115910630 NM_020796 .2.6 2.6E.03VGLL1 X 135614311-.-135638966 NM_016267 .2.6 1.6E.04GPR158 10 25463991-.-25891157 NM_020752 .2.8 1.3E.03LOC100132735 6 140092210-.-140181608 NR_038399 .3.0 7.1E.04UPK2 11 118795873-.-118829269 NM_006760 .3.0 1.9E.03HMCN1 1 185703683-.-186160085 NM_031935 .3.8 1.5E.04CCL2 17 32582296-.-32584222 NM_002982 .4.7 8.8E.03SEMA3D 7 84624869-.-84816171 NM_152754 .6.4 2.0E.07ANKRD1 10 92671853-.-92681033 NM_014391 .7.1 5.4E.03  218 A.2 Strand-Specific RNA-sequencing of T47D cells.  Condition: siPPP2R2A+E2 vs siNT+E2, 6 hours of β-estradiol treatment, n=2.   Gene$name logFC FDR Gene$name logFC FDRLOC1001280544.09 1.9E-97 ASB9P1 2.52 2.2E-47CYP1A2 3.69 3.2E-144 LOC1005056482.52 3.7E-66GDAP1L1 3.57 2.0E-50 KCNC1 2.47 8.7E-71CACNG1 3.48 3.9E-51 FTX 2.45 5.1E-29ARHGAP15 3.37 8.7E-232 KCNB1 2.44 2.4E-64CEACAM20 3.36 2.2E-52 PAR-SN 2.41 1.3E-50KIAA1456 3.35 1.3E-62 CYP1A1 2.41 1.4E-196ACSL6 3.33 9.1E-89 ALOX12P2 2.41 1.4E-18CDHR5 3.32 1.8E-31 LOC1005071172.39 3.3E-28CEACAM22P 3.29 1.4E-43 NRP1 2.37 7.2E-40LOC1005054783.29 2.2E-37 TPO 2.37 2.5E-40PARK2 3.20 1.8E-39 SLC9A10 2.35 9.7E-28EPS8L3 3.06 1.3E-43 MAP2 2.35 4.0E-157PAR5 3.03 1.4E-64 DCN 2.34 2.3E-77IGSF23 3.03 9.4E-36 TIAM2 2.34 1.6E-62C9orf131 3.02 4.8E-36 SPATC1 2.33 2.1E-26TRPM8 2.97 9.0E-182 ZNF460 2.32 1.5E-59FLJ40292 2.88 2.7E-21 LOC1001304512.32 1.1E-27MGC70870 2.86 1.4E-41 GUCY1B2 2.31 9.0E-46SLC47A2 2.85 8.5E-32 ANKRD36BP1 2.30 1.7E-52C10orf108 2.84 4.7E-19 LOC730091 2.27 7.1E-51LGALS2 2.83 1.5E-50 AHR 2.26 1.1E-107PCDHA9 2.80 8.7E-37 GPR179 2.25 2.4E-24FAM106CP 2.77 1.7E-25 C5orf65 2.24 4.8E-49C17orf78 2.76 8.6E-37 POU5F1P4 2.23 5.4E-20RPL23AP64 2.76 1.2E-70 FKBP1AP1 2.23 3.4E-39SARM1 2.73 2.5E-67 KIAA1210 2.23 1.4E-29LOC1001299172.71 2.2E-93 CLDN16 2.21 2.1E-72VTN 2.65 3.9E-36 CDH19 2.19 4.2E-80CCDC144B 2.65 5.5E-70 LRRTM2 2.18 6.9E-17MUC6 2.64 1.0E-17 ZNF642 2.17 1.9E-23THSD7A 2.64 1.5E-84 PAMR1 2.15 1.9E-60LRRC31 2.64 7.8E-203 C17orf66 2.15 4.9E-14STXBP5L 2.63 3.5E-112 C8orf39 2.11 7.3E-25CREB5 2.62 1.0E-105 SPDYE6 2.11 9.6E-24CD5 2.62 8.5E-50 BDNF-AS1 2.10 9.4E-29LGI1 2.60 9.1E-69 DPRXP4 2.10 2.2E-28CCDC144A 2.54 9.6E-21 PCDHGA12 2.09 4.1E-16  219  Gene$name logFC FDR Gene$name logFC FDRPCDHGA12 2.09 4.1E.16 PCDHGB3 1.85 1.4E.16CRYM 2.09 3.6E.29 SFT2D2 1.85 2.5E.61MYO3B 2.08 1.0E.63 CYP1B1.AS1 1.84 4.4E.38PLAC4 2.08 1.9E.21 C21orf49 1.83 9.4E.22CYP4Z2P 2.05 1.4E.168 PCDHGA9 1.83 4.2E.40SLC9A7P1 2.05 7.9E.22 EFCAB6 1.82 1.8E.36KRT81 2.04 2.6E.34 LOC728323 1.82 6.3E.56GNAT2 2.04 8.6E.25 WDR78 1.82 9.5E.33KSR2 2.03 1.0E.165 KCNMA1 1.82 2.2E.165LOC641515 2.03 2.4E.25 ZNF154 1.82 7.0E.20SPDYE1 2.02 4.5E.43 MEGF11 1.82 4.4E.18SLC1A2 2.01 9.5E.108 WDR31 1.82 1.0E.57ZNF573 2.01 2.3E.33 NLGN1 1.82 9.1E.27PCDHB19P 2.00 6.2E.29 UBAP1L 1.81 5.8E.19PLCE1 1.99 7.7E.151 TRIM9 1.81 3.3E.66SPDYE5 1.98 1.4E.30 MCM3AP.AS1 1.80 5.9E.22L1CAM 1.97 6.1E.135 PCDHB18 1.79 5.6E.18FGF12 1.97 1.1E.107 CLPS 1.78 5.3E.25NPR3 1.96 1.3E.54 LOC441204 1.78 1.1E.31CLEC18B 1.96 1.0E.23 EYS 1.78 7.8E.12C2orf88 1.95 8.4E.27 TET1 1.78 9.0E.67EPHA8 1.95 2.7E.72 TMPRSS9 1.78 9.8E.14EID3 1.95 8.9E.16 GCNT7 1.77 4.5E.17DPY19L2 1.95 4.5E.28 LOC641298 1.77 1.2E.23S100A8 1.94 1.2E.22 LOC646862 1.77 3.6E.20LINC00472 1.93 1.5E.54 PFN1P2 1.76 1.1E.22NBEAL1 1.92 4.8E.38 ZNF483 1.75 3.7E.25SSPN 1.91 1.5E.42 FSIP2 1.75 1.3E.59BDKRB2 1.91 4.4E.38 LOC1005066551.75 9.6E.15DUOX1 1.90 5.6E.29 OCLM 1.75 1.8E.27CACNA2D1 1.90 2.3E.25 ZNF221 1.74 1.0E.39C10orf68 1.89 8.0E.27 SLC7A5P2 1.74 3.1E.21ZNF716 1.89 2.1E.32 CCDC146 1.74 7.7E.50UBL3 1.89 1.1E.152 PNPLA7 1.74 3.8E.50CDH4 1.88 4.8E.29 HIPK3 1.73 3.3E.37LOC1005075571.88 1.6E.24 ZNF808 1.73 2.0E.86SLC38A1 1.88 4.8E.136 HDAC9 1.73 1.1E.77MDGA2 1.87 7.6E.129 ATP2A1 1.73 1.5E.32LOC1001328321.87 2.5E.28 SPATA24 1.73 2.0E.73FAT1 1.86 8.3E.44 PP2D1 1.72 3.0E.18SAMD12 1.86 1.3E.63 C16orf89 1.72 1.1E.20FAM49A 1.86 4.3E.52 COL4A6 1.72 8.4E.60IGSF9B 1.86 4.8E.44 RAB27B 1.71 3.5E.112  220  Gene$name logFC FDR Gene$name logFC FDRNIPAL1 1.71 6.0E,35 MIR186 1.61 2.2E,14SLC13A4 1.71 4.9E,29 KNDC1 1.61 3.4E,24NEB 1.70 4.9E,84 TRIM2 1.60 2.7E,100HSPG2 1.70 5.0E,55 ACP5 1.60 7.4E,15LINC00476 1.69 7.7E,31 PLXDC1 1.60 4.2E,22LOC1002718361.69 6.7E,23 GPR157 1.60 1.9E,21STEAP2 1.68 1.5E,125 HCAR1 1.60 3.0E,41RAPGEF6 1.68 3.3E,76 LMBRD2 1.60 1.0E,51MRPL23,AS1 1.67 3.3E,28 VTCN1 1.60 1.8E,127HGD 1.67 3.8E,20 FSD1L 1.60 7.1E,43KIAA1683 1.67 1.7E,72 ZNF587 1.59 4.4E,59LOC1002165461.67 2.9E,53 ZNF699 1.59 7.5E,14S1PR3 1.67 7.6E,32 TRIM29 1.59 5.4E,99DNAJC27,AS1 1.67 1.3E,35 PLA2G4C 1.59 2.0E,52KLF5 1.67 9.2E,59 SRRM5 1.58 5.2E,23GABRA3 1.66 1.7E,22 SEC14L5 1.58 6.4E,110GPR135 1.66 3.5E,25 SEMA6D 1.58 3.4E,16NDST4 1.65 2.0E,36 WDR88 1.58 1.1E,24GP6 1.65 8.8E,26 PARD3B 1.58 9.7E,55FAM73A 1.65 7.9E,67 DNAH1 1.58 9.0E,46SYNPO2 1.65 6.5E,17 ZBED6 1.58 1.4E,16POLN 1.64 4.3E,20 AP1S3 1.57 4.7E,58LRRIQ4 1.64 6.0E,35 LHFPL5 1.57 1.2E,23C9orf106 1.64 6.7E,47 LOC400084 1.57 3.0E,23CACNB2 1.64 3.9E,20 ATP10B 1.57 3.0E,50ATF7IP2 1.63 4.1E,39 KRBA2 1.57 1.8E,29DAPP1 1.63 5.5E,31 PCDHGA11 1.56 7.9E,17KIAA2022 1.63 7.5E,29 DNAH6 1.56 2.0E,25SLC34A3 1.63 3.4E,17 SLC30A4 1.56 1.2E,37DRD4 1.63 3.3E,19 PCDHGC4 1.56 1.2E,13TMEM106A 1.63 6.7E,109 AKAP11 1.56 7.1E,65BRWD1,IT2 1.63 4.9E,20 STOX2 1.56 6.0E,21LOC399715 1.62 1.0E,16 NBEA 1.56 2.8E,57CACNA1D 1.62 4.2E,114 C5orf4 1.56 5.6E,19FP588 1.62 2.9E,25 CHRM1 1.56 1.2E,47PCDHGC5 1.62 5.8E,20 REL 1.55 4.6E,17FILIP1L 1.62 3.2E,19 OPN3 1.55 2.2E,55CSRNP3 1.62 8.2E,45 NEK5 1.55 3.3E,20KLHL11 1.61 1.0E,20 UCKL1,AS1 1.55 4.2E,11GNG12 1.61 7.8E,143 DNAJC18 1.54 1.9E,53MYH3 1.61 1.3E,29 PTPRQ 1.54 1.5E,28ZNF121 1.61 4.8E,28 CYP8B1 1.54 5.6E,22LRRC37A3 1.61 1.5E,44 FUT9 1.53 4.2E,45  221  Gene$name logFC FDR Gene$name logFC FDRNEK11 1.53 2.9E*95 PCDHGA2 1.45 3.0E*20LTBP1 1.53 5.1E*80 MYCBPAP 1.45 7.2E*17DGKH 1.53 6.1E*23 BCO2 1.45 2.6E*12SLC44A5 1.52 4.5E*20 NAALADL2 1.45 1.4E*60LOC650623 1.52 2.0E*34 INTS4L2 1.45 2.0E*18CLMP 1.52 2.3E*16 LOC283922 1.44 1.9E*40COL5A1 1.52 8.9E*23 CEACAM19 1.44 4.1E*20CDH10 1.51 5.0E*60 KIF5A 1.44 5.4E*20LRRC37A4 1.51 1.2E*63 KLHL7*AS1 1.44 9.8E*20SLC2A13 1.51 1.1E*101 ITGB8 1.44 1.1E*32LINC00478 1.51 5.6E*22 LATS1 1.44 3.9E*36IGSF22 1.51 1.9E*16 GUCY1A2 1.44 8.3E*29EPAS1 1.50 1.8E*88 GPD1 1.44 1.3E*28NRK 1.50 2.3E*17 ZNF736 1.44 9.8E*19HUNK 1.50 9.2E*102 ZNF81 1.43 4.1E*38LOC1005064971.50 2.0E*44 ANO5 1.43 2.3E*80FLJ45513 1.50 4.0E*19 GHRLOS 1.43 1.9E*21ZKSCAN1 1.49 9.7E*46 XYLB 1.42 3.9E*55SHANK2 1.49 8.6E*75 SH3D21 1.42 1.2E*13LOC440173 1.49 8.3E*57 CDC42BPA 1.42 1.9E*56CPAMD8 1.49 1.4E*82 CNTN5 1.42 7.1E*14HS6ST3 1.49 1.1E*72 ANKRD31 1.42 2.9E*19SLC22A20 1.49 6.1E*22 ZC3HAV1L 1.42 3.4E*27ZBTB41 1.48 1.2E*91 AGXT2 1.42 1.2E*57DNAH2 1.48 2.1E*25 WDR96 1.41 6.9E*24FAM22A 1.48 9.6E*18 LOC1005075891.41 4.5E*15EXOC6B 1.47 5.5E*17 NLRC3 1.41 2.4E*22KRT86 1.47 2.2E*38 RAET1E 1.41 8.4E*13C5 1.47 1.3E*84 GPR155 1.41 1.7E*34TEP1 1.47 3.1E*74 LOC1005060461.41 3.0E*12VPS13B 1.47 7.2E*58 SYNM 1.41 1.6E*18CYP39A1 1.47 2.2E*34 ITPR1 1.41 2.7E*90PCDHA11 1.46 1.2E*24 PVRIG 1.40 3.1E*20LOC553103 1.46 6.3E*23 HOOK3 1.40 6.2E*60KLHL31 1.46 7.5E*23 LOC148696 1.40 4.2E*09ZDBF2 1.46 6.2E*35 CLUL1 1.39 1.8E*13TTN 1.46 4.5E*65 SYCP3 1.39 1.3E*10LOC338651 1.46 7.5E*18 SAMD4A 1.39 3.9E*75ID2 1.46 6.4E*77 GRIK1 1.39 3.1E*28PHEX 1.46 4.8E*25 STAG3L1 1.39 1.5E*17HCLS1 1.46 7.8E*17 ALS2CR8 1.39 5.3E*68BCAS1 1.45 1.0E*117 GUSBP2 1.39 7.1E*17ASPG 1.45 1.9E*15 TPM4 1.39 8.4E*50  222  Gene$name logFC FDR Gene$name logFC FDRCYP2E1 1.39 3.7E+16 SLC16A4 1.34 4.9E+27HIST1H2AI 1.39 2.7E+27 GALNS 1.34 1.2E+92HIVEP1 1.39 6.6E+56 ANKAR 1.34 2.3E+14ZNF323 1.39 3.9E+48 NXNL2 1.34 1.8E+25GNAQ 1.38 2.6E+40 NIPAL2 1.34 1.0E+64PCDHA2 1.38 3.2E+27 MYO5A 1.34 2.2E+66ZNF727 1.38 8.5E+18 MZB1 1.34 9.8E+44PAG1 1.38 4.9E+84 FAM186B 1.34 1.4E+14ZNF846 1.38 7.3E+25 ERN1 1.34 1.1E+31CDHR3 1.38 8.2E+22 TRPC6 1.34 5.6E+19LOC1001313471.38 5.9E+15 FANCC 1.33 5.0E+42TSC22D1+AS1 1.38 9.8E+16 MMP16 1.33 1.1E+28SPNS2 1.38 8.9E+76 ATXN1 1.33 1.3E+72CERS6 1.38 1.5E+51 MIR4664 1.33 1.1E+09TCF4 1.38 1.0E+35 PADI2 1.33 8.7E+71IKZF2 1.38 3.5E+50 ZNF70 1.33 8.2E+40KRTAP5+1 1.37 1.0E+19 RPS6KA2 1.32 6.1E+25PCDHGA6 1.37 6.6E+37 GPR132 1.32 1.9E+22ODZ2 1.37 1.7E+50 FNIP1 1.32 5.9E+75CYP2W1 1.37 1.6E+13 CTNS 1.32 5.1E+33ALDH1L2 1.37 6.7E+51 KIF27 1.32 1.4E+27GP1BA 1.37 4.3E+34 PAQR8 1.32 5.2E+21DLX2 1.37 1.4E+35 TCP11L2 1.32 6.0E+16DNAH12 1.37 6.8E+18 HLF 1.32 6.9E+35P2RX7 1.37 5.4E+24 APOOL 1.31 2.1E+70ZNF541 1.36 2.7E+13 CSMD1 1.31 6.9E+46C16orf3 1.36 4.6E+11 CCDC7 1.31 1.7E+16DLX1 1.36 1.7E+63 CLOCK 1.31 8.0E+25NCOA2 1.36 1.7E+22 FLJ34208 1.31 8.2E+41PAH 1.36 3.1E+13 CNTN3 1.31 7.6E+33FLRT3 1.36 6.9E+68 PANK3 1.31 7.8E+65CUX2 1.36 2.6E+81 GPT 1.31 4.5E+12SLC16A8 1.36 3.5E+18 LOC644961 1.31 4.4E+10TTBK2 1.35 2.6E+26 ATF7IP 1.31 7.7E+54SH3GL1P1 1.35 1.6E+16 PAPPA 1.31 2.5E+11PALM2 1.35 8.5E+28 DIXDC1 1.31 2.1E+67GAL3ST2 1.35 1.6E+14 KIAA0889 1.31 4.2E+46ATP2B4 1.35 2.3E+94 PRICKLE2 1.31 4.8E+66LUST 1.35 7.3E+13 PRLR 1.31 8.8E+46LOC1005063341.35 3.1E+21 KIAA0825 1.30 3.4E+27ANKK1 1.34 6.3E+22 NRIP2 1.30 5.3E+17  223  Gene$name logFC FDR Gene$name logFC FDRZNF91 1.30 4.8E,44 CBX5 1.26 1.4E,55STL 1.30 1.0E,20 WDFY3,AS2 1.26 7.4E,24PTPN14 1.30 1.3E,62 ZNF417 1.26 5.6E,30C8orf44 1.30 3.6E,28 EPPK1 1.26 6.9E,26MAFF 1.30 4.9E,18 F8 1.26 1.2E,27SH3RF2 1.30 2.4E,71 C17orf57 1.26 2.6E,23CD24 1.29 2.3E,85 FSTL1 1.25 1.4E,80SIAE 1.29 7.8E,35 CSAD 1.25 2.3E,61CAPS2 1.29 5.9E,15 PCDH7 1.25 8.5E,31NDNF 1.29 2.2E,10 CYFIP2 1.25 8.5E,75PIK3C2G 1.29 2.8E,24 LOC286437 1.25 1.4E,24ANKRD36 1.29 2.3E,25 C6orf164 1.25 3.5E,10ZBTB37 1.29 8.8E,15 SRCRB4D 1.25 2.9E,16DTX2P1,UPK3BP1,PMS2P111.29 1.6E,20 LOC284412 1.25 3.3E,12PPM1L 1.29 1.7E,26 AVIL 1.25 4.3E,39MATN3 1.29 5.7E,84 C8orf77 1.25 9.6E,16EVC 1.29 1.9E,49 SLC13A3 1.25 1.5E,38SYDE2 1.28 1.3E,13 ABCC2 1.25 3.5E,16ATP13A4 1.28 2.5E,39 GTF2A1 1.24 1.7E,16RFTN1 1.28 2.9E,09 NCALD 1.24 3.7E,42NNAT 1.28 2.1E,13 KCNIP4 1.24 3.2E,12SLC7A5 1.28 2.1E,73 CYP1B1 1.24 9.3E,63SPDYE3 1.28 4.8E,18 CDC42EP3 1.24 1.1E,52PTPRJ 1.27 3.7E,60 MYH11 1.24 1.5E,28CSPG4 1.27 5.5E,18 FAM71F2 1.24 2.7E,15TNFRSF21 1.27 7.3E,91 USP54 1.24 4.1E,59MACROD2 1.27 2.5E,16 STARD4 1.23 2.8E,49LRRC15 1.27 4.3E,35 C10orf103 1.23 3.7E,24MAML2 1.27 6.1E,40 GAN 1.23 3.1E,45PHF7 1.27 2.2E,20 LOC1005072661.23 1.8E,19SNED1 1.27 1.9E,21 LOC441242 1.23 6.7E,18RIN2 1.27 1.8E,47 ZNF518A 1.23 1.9E,44PCDHAC1 1.27 3.3E,13 SGK196 1.23 5.2E,21FAM40B 1.27 3.4E,42 DICER1 1.23 2.4E,43GPR75 1.27 2.2E,17 DPY19L2P2 1.23 2.5E,19FRMD3 1.26 3.7E,26 ZNF708 1.23 2.3E,30ZNF551 1.26 9.4E,30 KIAA1024 1.22 2.2E,18F2RL1 1.26 5.2E,41 ZNF587B 1.22 5.4E,32EDN1 1.26 1.7E,66 MLLT4,AS1 1.22 5.1E,18PPIL6 1.26 1.3E,10 CEP97 1.22 7.9E,19ZNF141 1.26 3.3E,14 KIAA0240 1.22 1.1E,64  224  Gene$name logFC FDR Gene$name logFC FDRST3GAL5 1.22 3.5E,12 SLC2A12 1.20 2.1E,28ZDHHC8P1 1.22 2.7E,44 ZSCAN12P1 1.20 1.4E,22PLCB4 1.22 5.4E,71 PCDHA5 1.20 1.2E,28ZNF493 1.22 9.9E,51 PLIN1 1.20 3.5E,11LOC254100 1.22 2.6E,18 MICAL2 1.20 2.7E,37HPD 1.22 2.6E,19 LOC1001323561.20 3.7E,26LRP2BP 1.22 7.1E,23 CEACAM16 1.19 2.1E,09CDKL5 1.22 2.4E,11 LNPEP 1.19 2.3E,22NFAT5 1.22 4.7E,23 PRKAR2A 1.19 9.0E,36FAM63B 1.21 4.9E,18 SYT15 1.19 2.1E,28RASGRF1 1.21 2.7E,33 CHIC1 1.19 1.4E,47ZNF192 1.21 1.4E,25 CYP4V2 1.19 2.3E,39FLJ45340 1.21 9.3E,34 CATSPERB 1.19 6.9E,13PUS10 1.21 2.5E,38 LOC1001293871.19 5.6E,27GNG7 1.21 3.2E,13 C5AR1 1.19 2.5E,16IYD 1.21 3.6E,18 VASH2 1.19 6.1E,36YPEL4 1.21 2.5E,13 HPX 1.19 3.9E,61CMYA5 1.21 8.4E,32 MYO15B 1.19 8.0E,24MYO9A 1.21 3.3E,28 GABRB3 1.19 5.7E,23UBXN7 1.21 3.0E,36 KCNH5 1.19 1.3E,17PCDH18 1.21 2.1E,20 KLHL28 1.19 1.1E,61KIAA1841 1.21 9.9E,38 LOC339535 1.19 6.0E,81RORA 1.21 1.1E,15 HMBOX1 1.19 2.0E,25CKMT2 1.21 2.3E,24 ARL5B 1.19 9.9E,26ZNF611 1.21 1.5E,33 GAB1 1.18 4.8E,49DOCK5 1.21 5.1E,33 GNB1L 1.18 1.2E,16YPEL1 1.21 9.8E,38 AKAP6 1.18 2.3E,45RC3H2 1.21 1.1E,25 TAG 1.18 1.2E,10PPM1E 1.21 2.1E,11 STARD13 1.18 2.2E,75LOC1001316911.21 8.1E,28 JAKMIP3 1.18 8.7E,35HIPK2 1.20 8.1E,50 PCDHGA10 1.18 1.6E,43LOC642236 1.20 2.0E,22 PPP1R12B 1.18 2.6E,57TFAP2B 1.20 2.3E,14 LOC643401 1.18 1.8E,11FRRS1 1.20 1.7E,19 PCDHA13 1.18 1.3E,10RGL4 1.20 4.4E,13 TBC1D8B 1.18 5.4E,32LOC150622 1.20 1.3E,06 ARHGAP35 1.18 1.2E,51ALDH1A3 1.20 1.1E,45 RYR3 1.18 2.4E,14TXNRD1 1.20 2.3E,69 ZBTB32 1.17 1.2E,08TGFBR3 1.20 7.1E,14 01,Mar 1.17 1.4E,35ENTPD5 1.20 1.1E,30 LOC90246 1.17 2.1E,27LOC728537 1.20 6.0E,23 LOC92249 1.17 2.3E,70LOC1001327741.20 4.9E,11 MLL3 1.17 1.9E,37  225  Gene$name logFC FDR Gene$name logFC FDRLOC1002165451.17 5.2E-30 ASXL2 1.14 4.6E-23MIR570 1.17 7.8E-10 MBD5 1.14 5.9E-41GRK4 1.17 1.5E-24 CHRNB2 1.14 2.3E-11CAMKK1 1.17 7.3E-38 EPS15 1.14 3.7E-75HIST1H4H 1.16 3.1E-44 ZNF469 1.14 4.0E-14ARHGEF26-AS11.16 6.2E-21 SUMO1P3 1.14 2.3E-08ASH1L 1.16 6.0E-30 RGS5 1.13 2.3E-19GALNT4 1.16 1.0E-08 RASEF 1.13 5.4E-20DGCR9 1.16 6.6E-21 ZNF780B 1.13 1.5E-32FLJ39639 1.16 1.5E-12 TMED8 1.13 1.1E-12ZNF354C 1.16 2.9E-10 ATP6AP1L 1.13 1.6E-13TLN2 1.16 2.0E-50 CCDC122 1.13 4.5E-11LOC646719 1.16 2.4E-56 TACR2 1.13 4.6E-11PCDHGA4 1.16 4.5E-10 FSCN2 1.13 4.9E-11ZNF749 1.16 2.3E-17 ZNF726 1.13 1.4E-13RASA2 1.16 6.6E-21 ZFP106 1.12 2.5E-45SLC26A1 1.16 2.3E-14 RNF24 1.12 2.6E-49ARHGEF38 1.16 1.3E-65 TNS3 1.12 4.1E-45SRGAP2P2 1.16 1.1E-17 PLEKHG4B 1.12 1.1E-41COL4A5 1.16 1.7E-54 KLF7 1.12 4.5E-24SMG1 1.16 9.1E-37 ADD3 1.12 3.5E-64LOC1003291091.15 4.1E-30 DST 1.12 2.0E-36RBM26-AS1 1.15 1.6E-21 PCDHGB7 1.12 2.8E-10KIAA1033 1.15 3.5E-56 LOC1001333151.12 1.2E-14GARNL3 1.15 8.4E-25 RGS6 1.12 2.3E-13ZNF397 1.15 2.0E-29 TP53INP1 1.12 3.7E-67BTBD11 1.15 3.9E-24 ANKRD36B 1.12 1.2E-21LOC1005058151.15 6.7E-15 LOC1001308901.12 2.2E-21TULP1 1.15 8.8E-17 CHST1 1.12 1.5E-42C5orf42 1.15 2.8E-33 CAPN9 1.12 3.8E-28TMEM140 1.15 5.4E-23 DENND4A 1.12 4.4E-36FABP3 1.14 1.2E-09 BAZ2B 1.12 4.0E-53SPATA13 1.14 2.2E-41 C17orf109 1.11 5.8E-14TMEM154 1.14 1.0E-25 MBNL1 1.11 2.1E-64MANEA 1.14 1.9E-62 ZNF665 1.11 2.5E-13ODZ1 1.14 2.3E-14 DNAH3 1.11 2.3E-12SERPINA10 1.14 4.4E-29 ID4 1.11 1.0E-16LOC1005074951.14 2.1E-36 CNTNAP2 1.11 4.7E-36SLC6A17 1.14 2.1E-37 PLD1 1.11 4.2E-59SRGAP1 1.14 9.8E-30 CTNND1 1.11 1.9E-15MAPK10 1.14 2.5E-27 PEX5L 1.11 1.1E-09KCNAB1 1.14 9.6E-25 ANO6 1.11 2.2E-42  226  Gene$name logFC FDR Gene$name logFC FDRATL3 1.11 1.8E)30 MAK 1.08 6.7E)16NEDD9 1.11 2.8E)35 CARNS1 1.08 9.5E)08ALDH6A1 1.11 6.3E)23 GPX2 1.08 4.4E)09LNX2 1.10 1.1E)57 FBXO15 1.08 2.8E)12R3HDM2 1.10 2.1E)65 MFAP3L 1.08 5.6E)27PHC3 1.10 4.5E)45 LOC1005081201.08 2.0E)15KIAA0430 1.10 1.9E)42 CYP2D7P1 1.08 6.9E)08MAP1LC3B2 1.10 1.8E)10 GAS2L3 1.08 1.7E)15ZNF586 1.10 7.1E)45 PLA2R1 1.08 4.4E)42STOM 1.10 3.6E)69 POF1B 1.08 2.4E)23PHACTR3 1.10 1.4E)11 DMXL2 1.08 1.2E)39WDR66 1.10 1.5E)30 HEG1 1.08 1.4E)40CCDC125 1.10 4.0E)48 TGFB2 1.08 1.7E)30SPOPL 1.10 8.2E)54 LOC158257 1.08 3.2E)12TMCO3 1.10 9.9E)55 C9orf5 1.07 3.9E)46ZNF284 1.10 2.9E)14 CG030 1.07 3.4E)12XIAP 1.10 3.9E)42 ITSN1 1.07 1.3E)62HIST1H2BF 1.09 5.6E)14 UNC5A 1.07 2.2E)22GPR110 1.09 2.8E)27 ANKRD20A5P 1.07 2.6E)23HINT3 1.09 3.2E)04 PTER 1.07 9.0E)47CYP2D6 1.09 1.1E)11 HEATR4 1.07 1.0E)20LOC1001309501.09 2.7E)16 ADAM9 1.07 3.9E)52PLEKHA7 1.09 1.3E)34 CDK6 1.07 1.2E)25LRRC58 1.09 5.8E)50 PMS2P5 1.07 3.5E)46CCL28 1.09 2.8E)39 DSCR9 1.07 1.0E)10MLLT10P1 1.09 4.4E)13 KIAA1199 1.07 5.3E)12BRSK2 1.09 7.0E)07 LOC1003350301.07 2.9E)10C5orf41 1.09 4.1E)41 C10orf118 1.07 3.6E)22GPR64 1.09 4.0E)09 SYTL2 1.07 5.3E)19SHPK 1.09 1.5E)29 DISP1 1.07 1.3E)45GPNMB 1.09 6.4E)57 GLIPR1L2 1.07 8.2E)10AFF4 1.09 2.9E)31 STRN 1.07 2.1E)39ZNF790 1.09 1.1E)21 AR 1.07 2.1E)45PDLIM5 1.09 1.9E)59 PCLO 1.07 1.4E)38BMS1P4 1.09 1.5E)18 GHRLOS2 1.07 2.3E)10ATG14 1.09 5.2E)60 MUM1L1 1.07 2.1E)32LYPLA1 1.09 5.8E)63 LRGUK 1.06 1.1E)09TTC18 1.09 1.9E)44 LOC646214 1.06 4.9E)30APAF1 1.09 4.5E)27 PUS7L 1.06 8.5E)29SYCP2L 1.08 9.1E)13 ENPP3 1.06 2.4E)14NHLRC2 1.08 1.6E)35 ZNF137P 1.06 1.8E)26  227  Gene$name logFC FDR Gene$name logFC FDRMIR3916 1.06 7.4E-08 LOC1002722281.04 2.0E-15LOC1002722161.06 3.4E-11 CLEC3A 1.04 1.0E-07SPTBN4 1.06 1.5E-24 PLEKHM1P 1.04 1.3E-16ATP11B 1.06 2.5E-50 RNF152 1.04 2.3E-13PHKG1 1.06 1.1E-11 UNC5B 1.04 1.3E-19C7orf41 1.06 9.1E-62 PDZD2 1.04 1.1E-34KIF5C 1.06 1.3E-17 ITGA2 1.04 1.1E-39TMED10P1 1.06 2.6E-14 ANKRD62P1-PARP4P31.0 4.3E-10TCP11L1 1.06 2.5E-33 POLH 1.04 3.5E-48PIK3IP1 1.06 6.5E-37 LOC652276 1.04 2.2E-23DKFZP586I14201.06 2.9E-36 FOSL2 1.04 1.6E-32SOD3 1.06 2.4E-38 EXPH5 1.04 5.8E-28SLC22A23 1.05 6.4E-49 C2CD3 1.04 8.5E-30ZNF28 1.05 3.4E-45 DGCR10 1.04 3.4E-11PER3 1.05 1.4E-57 MAP3K1 1.04 1.4E-35ADAMTS15 1.05 8.5E-39 FAM154B 1.04 2.1E-20C12orf51 1.05 6.7E-32 C19orf18 1.04 7.9E-09GNL3L 1.05 5.8E-43 ATP7A 1.04 2.3E-34JMJD1C 1.05 1.4E-31 MORN1 1.04 2.5E-23ALOX12 1.05 1.1E-15 N4BP2 1.03 1.2E-23C15orf62 1.05 5.2E-18 AHNAK 1.03 1.1E-21C6orf163 1.05 2.7E-06 SYNC 1.03 4.5E-14BACE1-AS 1.05 1.3E-13 TMEM106B 1.03 2.3E-51VPS13C 1.05 1.2E-32 PPARA 1.03 5.3E-29PAQR5 1.05 1.2E-12 ZNF562 1.03 2.9E-26GK5 1.05 1.3E-55 FAM135A 1.03 8.2E-22NTN4 1.05 1.2E-34 STAC3 1.03 1.2E-07FAR2 1.05 3.2E-13 DTWD2 1.03 5.9E-37ZNF224 1.05 2.5E-11 FOXO3 1.03 1.1E-40ATL1 1.05 1.2E-11 PRDM6 1.03 9.8E-17KATNAL1 1.05 1.2E-35 ATM 1.03 3.9E-30DLG2 1.05 6.9E-67 LOC1001327351.03 2.2E-16FAM198B 1.05 3.1E-56 GPR98 1.03 3.1E-35FLJ37201 1.04 1.2E-28 PCDHA3 1.03 1.2E-25FAM22G 1.04 3.6E-08 PCDHGB2 1.03 1.4E-24CPEB2 1.04 3.7E-29 STYX 1.03 9.0E-63JAG1 1.04 1.9E-17 GPR68 1.03 3.2E-18SLC11A2 1.04 6.1E-62 FMN1 1.03 9.0E-29ZMAT1 1.04 9.8E-50 PALLD 1.03 1.8E-48SECISBP2L 1.04 2.4E-33 FAT2 1.02 1.8E-11KIAA1109 1.04 6.0E-33 GNAS-AS1 1.02 1.4E-11  228  Gene$name logFC FDR Gene$name logFC FDRANK2 1.02 6.3E+11 LOC1002880691.00 1.0E+11C3orf35 1.02 5.6E+14 SGK494 1.00 1.1E+20RFX3 1.02 3.3E+16 EHD2 1.00 1.0E+60TRPS1 1.02 2.0E+36 ZNF132 1.00 1.6E+09ZRANB2+AS1 1.02 1.7E+12 ZNF780A 1.00 2.1E+46LOC1001303571.02 9.2E+16 NQO1 1.00 4.9E+42SH3TC2 1.02 7.6E+16 LOC285359 1.00 2.5E+16CCDC30 1.02 2.7E+12 C7orf63 1.00 4.8E+22PARP11 1.02 1.2E+18 MCM3 +1.00 2.7E+53ENKUR 1.02 9.1E+09 GFM1 +1.00 1.9E+52VMAC 1.02 5.1E+24 ANKFN1 +1.00 2.7E+08ADAMTS20 1.02 1.1E+08 UBA52 +1.00 1.2E+20F3 1.02 1.1E+09 RAD51 +1.00 1.2E+30ENPP5 1.02 2.3E+58 EMD +1.00 6.7E+45RHOBTB3 1.02 2.6E+50 LIPT2 +1.00 2.0E+17CROCCP3 1.02 3.5E+20 PSMB7 +1.00 6.5E+31CASC2 1.02 9.2E+17 ARL6IP4 +1.00 5.5E+36C1GALT1C1 1.02 1.9E+41 PSMB4 +1.01 8.3E+34HIATL2 1.02 3.6E+15 LTB4R +1.01 1.1E+39AHCYL2 1.02 8.0E+46 LOC100506343+1.01 3.8E+11ZNF813 1.01 1.2E+20 CDC34 +1.01 9.2E+32MAGI3 1.01 3.1E+40 TSPAN4 +1.01 4.9E+48ZNF557 1.01 1.0E+27 NENF +1.01 7.2E+24AGAP11 1.01 3.0E+19 C12orf57 +1.01 1.6E+18STAG3L2 1.01 1.5E+41 HMGB3 +1.01 7.6E+46WNK3 1.01 2.9E+11 MRPL2 +1.01 1.8E+36FGD5+AS1 1.01 6.7E+51 RPS5 +1.01 3.9E+32ARID5B 1.01 3.0E+39 YDJC +1.01 2.3E+39ZNF462 1.01 2.7E+28 RPL28 +1.01 1.0E+20SPEF2 1.01 1.2E+45 MOCS1 +1.01 7.4E+19LOC1001290341.01 9.2E+39 PIM1 +1.01 3.2E+08ARHGAP31 1.01 6.5E+17 ICAM1 +1.01 1.1E+08HEATR5A 1.01 1.4E+43 NDUFS3 +1.01 3.7E+26STX1B 1.01 2.1E+22 PFDN2 +1.01 7.2E+25WDR27 1.01 1.1E+31 MRPL27 +1.01 4.5E+31CRISPLD1 1.01 3.9E+51 IFI35 +1.01 2.8E+28USP53 1.01 8.2E+30 NDUFB9 +1.01 3.2E+28EGR1 1.01 3.7E+07 ACAT2 +1.01 2.2E+41GRM4 1.00 2.6E+34 PSMC3 +1.02 1.4E+36LANCL1 1.00 1.9E+57 UBXN1 +1.02 4.4E+31KIAA1549 1.00 1.9E+42 NASP +1.02 1.1E+56  229  Gene$name logFC FDR Gene$name logFC FDRC17orf61 (1.02 1.2E(10 MTHFD1L (1.05 3.6E(30MRPS23 (1.02 1.0E(46 PDLIM1 (1.05 6.3E(42MZT2B (1.02 1.1E(12 CCNB2 (1.05 1.9E(44HNRNPAB (1.02 3.3E(56 AAGAB (1.05 2.1E(55SLC38A3 (1.02 1.1E(13 NOP16 (1.05 8.2E(23C19orf43 (1.02 1.8E(30 METTL12 (1.05 9.5E(23C10orf114 (1.03 4.3E(16 RPL23A (1.05 4.8E(32SNHG8 (1.03 5.4E(33 PCNA (1.05 3.5E(45FAM65C (1.03 1.1E(33 C11orf84 (1.05 1.0E(35TIMM10 (1.03 9.1E(27 OIP5 (1.05 7.6E(31CHCHD10 (1.03 6.2E(21 GSTT2 (1.06 9.9E(27MRPL28 (1.03 1.2E(30 CENPA (1.06 1.9E(32NFKBIL1 (1.03 3.1E(09 CNTFR (1.06 5.7E(07RGS19 (1.03 5.0E(20 VEGFB (1.06 1.2E(47SCARNA7 (1.03 6.7E(04 DLL1 (1.06 1.9E(35FAM173A (1.03 1.4E(23 ATP6V0B (1.06 1.8E(48SEC61B (1.03 5.2E(21 RGS14 (1.06 1.4E(25MRPS18C (1.03 1.1E(24 TK1 (1.06 2.7E(46RPL7A (1.03 3.2E(31 PSD3 (1.06 1.1E(09CCNO (1.03 2.6E(51 FBXL15 (1.06 2.1E(32SNRPB (1.03 1.1E(47 BCAR4 (1.06 1.2E(15AURKB (1.03 7.6E(28 PPIB (1.06 6.1E(42TSTA3 (1.04 4.9E(34 HOXB6 (1.06 5.0E(08LOC388796 (1.04 1.1E(29 CCDC85B (1.06 1.4E(23ALOXE3 (1.04 3.4E(39 RHOD (1.06 5.2E(40CD63 (1.04 1.7E(28 TRAPPC5 (1.06 2.8E(30MESDC1 (1.04 2.7E(43 MRPL11 (1.06 5.6E(29P4HA1 (1.04 4.4E(36 SDF2L1 (1.06 1.5E(18PTGES (1.04 3.5E(46 DONSON (1.06 3.9E(49DTD1 (1.04 2.2E(35 IGFBP4 (1.07 4.5E(42RPL36AL (1.04 7.4E(26 FBXO2 (1.07 3.0E(30TIMM8B (1.04 1.2E(21 ARPC4 (1.07 7.8E(33GLIS2 (1.04 1.2E(49 ANGPT1 (1.07 1.7E(39CD74 (1.04 1.6E(24 PDF (1.07 4.2E(24SPC25 (1.04 2.1E(29 PHLDA2 (1.07 1.7E(18PPAP2C (1.05 7.4E(43 PHF5A (1.07 2.3E(21TMEM189 (1.05 4.8E(30 TMEM98 (1.07 8.6E(28COX8A (1.05 6.2E(26 DIABLO (1.07 2.6E(42NUAK1 (1.05 5.9E(17 CDC25A (1.07 2.7E(26MAFG(AS1 (1.05 4.7E(27 TRMT112 (1.07 1.1E(28SCAND1 (1.05 6.7E(32 TOMM7 (1.07 3.0E(24  230 A.3 Strand-Specific RNA-sequencing of T47D cells  Condition: siPPP2R2A vs siNT   without any β-estradiol treatment, n=2.  Gene$name logFC FDR Gene$name logFC FDR Gene$name logFC FDRARHGAP15 3.00 1.2E-212 CREB5 1.58 2.6E-39 SLC38A1 1.35 7.6E-72PARK2 2.99 3.0E-33 BCO2 1.56 6.2E-13 KCNC1 1.35 9.2E-29ACSL6 2.36 6.8E-44 BDNF-AS1 1.55 1.4E-17 CNTN5 1.34 7.5E-11EPHA8 2.25 4.9E-60 IGSF23 1.53 1.4E-07 MGC70870 1.33 3.1E-07CACNG1 2.19 1.0E-15 ASB9P1 1.53 9.2E-13 SLC13A3 1.32 3.4E-41CEACAM20 2.18 1.1E-21 FLJ40292 1.52 2.7E-05 SKAP2 1.30 6.5E-86LOC100128054 2.11 3.0E-18 TIAM2 1.51 7.4E-27 S1PR3 1.30 3.9E-20GDAP1L1 2.09 2.0E-07 TRPM8 1.51 1.0E-53 ZNF812 1.29 2.3E-23LOC100505478 2.02 2.4E-09 TXNDC5 1.50 1.3E-12 ABCC2 1.28 4.1E-16CEACAM22P 1.95 2.8E-15 AHR 1.49 4.0E-48 STEAP2 1.28 3.4E-74HSPA6 1.95 5.5E-22 KIAA1456 1.49 2.1E-12 SSPN 1.28 2.1E-17CRYM 1.93 7.9E-27 UBL3 1.46 1.5E-93 GPR75 1.28 3.6E-16SLC47A2 1.83 6.7E-09 LGALS2 1.46 2.6E-12 LOC440173 1.26 1.5E-43VTN 1.81 3.2E-14 GNB1L 1.45 1.2E-25 KIAA1024 1.26 1.6E-18SEMA3A 1.81 1.6E-16 THSD7A 1.45 3.7E-28 DNAJC27-AS1 1.26 2.6E-21LRRC31 1.80 6.8E-96 CLDN16 1.43 8.5E-34 LOC338651 1.25 1.9E-15SARM1 1.75 6.8E-28 COL4A6 1.43 6.6E-40 HMGCS2 1.24 6.4E-68C17orf78 1.74 6.1E-10 ABCC12 1.41 8.1E-73 FKBP1AP1 1.24 1.0E-09BDKRB2 1.74 7.4E-43 CCDC146 1.40 7.7E-33 RGS6 1.24 1.7E-17STXBP5L 1.73 4.5E-42 OPN3 1.40 2.2E-44 BMPER 1.24 4.4E-18SYNPO2 1.70 1.9E-21 CLMP 1.39 2.5E-11 MAP2 1.22 1.1E-44GUCY1B2 1.70 9.7E-24 CYP4Z2P 1.39 1.1E-80 KIAA1210 1.21 2.0E-11SLC9A10 1.65 2.2E-13 STEAP1 1.37 1.0E-57 WDFY3-AS2 1.20 2.1E-21LINC00476 1.59 4.5E-29 HGD 1.35 1.0E-11 CALCR 1.20 1.7E-12CATSPERB 1.58 2.1E-26 TFF1 1.35 8.4E-20 ANKRD36BP1 1.19 7.9E-15AGXT2 1.19 9.3E-39 EPAS1 1.07 1.4E-45 TLE6 -1.03 8.6E-13IFI27 1.19 1.4E-03 LEPR 1.07 4.9E-39 C20orf160 -1.03 1.9E-11PHLDA1 1.19 1.7E-86 EGLN3 1.07 1.0E-27 GLIS2 -1.03 2.3E-51RND3 1.19 1.7E-41 ZNF121 1.06 6.3E-11 RTBDN -1.04 1.9E-08AVIL 1.17 3.9E-34 AP1S3 1.05 7.0E-24 KDM5B-AS1 -1.04 2.0E-14SRRM5 1.17 2.2E-11 CLEC18B 1.05 3.5E-06 KIAA0319 -1.04 6.5E-08KCNB1 1.17 2.1E-15 LOC1005075571.05 1.2E-06 CPVL -1.05 5.6E-16TP53AIP1 1.17 8.7E-19 PTPRQ 1.04 4.3E-13 ZSWIM4 -1.06 7.1E-20CHST1 1.16 1.1E-53 LOC728323 1.04 4.9E-20 WLS -1.06 1.7E-09CD99L2 1.15 1.0E-64 MAFF 1.04 4.4E-09 PANX2 -1.07 5.8E-08VTCN1 1.15 1.4E-68 EID3 1.03 1.9E-04 PAK6 -1.07 6.4E-14NDNF 1.14 3.1E-11 RAD9B 1.03 4.9E-08 C7orf31 -1.07 4.4E-14RAPGEF6 1.14 2.5E-35 CCL28 1.03 7.8E-33 HEY2 -1.08 1.3E-14CNTNAP2 1.12 1.5E-32 C1GALT1C1 1.02 4.9E-42 DRD2 -1.08 9.0E-06NEK11 1.11 1.0E-51 CLGN 1.02 2.5E-14 RCOR2 -1.08 1.8E-10C14orf162 1.11 2.2E-14 UBAP1L 1.02 2.3E-06 TFCP2L1 -1.08 7.4E-07  231                 Gene$name logFC FDR Gene$name logFC FDR Gene$name logFC FDRSPATA13 1.11 1.2E*38 SAMD4A 1.02 3.4E*40 SEMA3D *1.09 9.7E*21LOC100130451.11 3.1E*07 NOSTRIN 1.01 2.4E*15 DOCK10 *1.09 6.5E*33IFIT2 1.11 4.2E*13 LOC399715 1.01 7.3E*04 NGFR *1.09 3.4E*14CDH4 1.11 7.2E*09 CDH10 1.01 1.0E*28 FAM167A *1.10 1.6E*32ATP10B 1.10 6.1E*27 VAV3 1.01 1.0E*51 TPM2 *1.11 2.1E*21POU5F1P4 1.10 1.4E*03 PAPSS2 1.00 1.5E*46 ITGA7 *1.11 7.1E*16FBXO32 1.09 3.3E*67 ZNF238 *1.00 9.1E*10 CD83 *1.12 6.4E*13LGI1 1.09 7.3E*10 LINC00256B *1.01 9.2E*11 KREMEN2 *1.12 1.8E*29MDGA2 1.08 5.3E*45 CIB2 *1.01 1.0E*08 TMCC2 *1.12 9.3E*10KCNMA1 1.08 3.5E*59 ODAM *1.01 5.9E*11 SLC47A1 *1.13 1.3E*14FSD1L 1.07 3.7E*19 TMEM51 *1.01 5.4E*08 ADAMTS7 *1.13 2.1E*32LOC1005063341.07 2.2E*13 MEIS3 *1.02 5.7E*20 SLITRK6 *1.14 2.0E*20DSE 1.07 2.6E*10 BFSP2 *1.02 3.1E*09 PRKCG *1.14 6.1E*13  232 A.4 Strand-Specific RNA-sequencing of T47D cells.  Condition: siPPP2R2A+E2 vs siPPP2R2A, 6 hours of β-estradiol treatment, n=2.    Gene$name logFC FDR Gene$name logFC FDR Gene$name logFC FDRIFI27 %4.53 8.0E%29 HOPX %1.43 3.2E%14 GRB7 %1.17 1.0E%56IFITM1 %3.18 3.7E%34 STEAP4 %1.42 2.3E%32 HMGB3 %1.16 4.4E%60OAS1 %3.15 1.2E%22 BIK %1.42 2.6E%27 GDF15 %1.16 2.4E%22TFF1 %3.06 4.8E%67 AQP3 %1.41 1.4E%24 TSC22D3 %1.16 2.0E%34C14orf162 %2.95 2.3E%55 IGFBP6 %1.40 4.0E%08 MVD %1.16 2.5E%61IFIT1 %2.87 1.0E%17 PLEKHD1 %1.37 1.5E%28 ISG20 %1.15 1.5E%18HSPA6 %2.71 5.4E%31 CRISP3 %1.36 2.3E%52 SELENBP1 %1.14 2.1E%51ISG15 %2.70 2.4E%17 ADM %1.35 4.1E%67 KMO %1.14 7.5E%44IFI6 %2.67 7.0E%30 S100P %1.33 2.9E%31 UGT3A2 %1.13 6.3E%24XAF1 %2.32 2.2E%09 TLE6 %1.32 1.0E%12 CLU %1.13 8.9E%42DUSP4 %2.28 1.1E%112 PALMD %1.30 3.9E%33 DUSP6 %1.13 4.7E%21MX1 %2.23 3.1E%26 IER5 %1.29 6.9E%46 BMP4 %1.12 1.3E%63IFITM3 %2.16 4.6E%21 DDX60 %1.29 5.4E%07 EGLN3 %1.12 4.8E%29IFIT3 %2.12 1.0E%13 RTBDN %1.28 4.5E%07 IFITM2 %1.12 1.1E%17RND1 %2.06 2.7E%138 RMRP %1.28 1.4E%06 SNORD104 %1.12 1.3E%09OASL %2.03 7.8E%14 MANF %1.27 5.4E%48 SNCB %1.11 1.9E%08AZGP1 %1.91 5.5E%122 DHRS2 %1.27 5.7E%14 S100A1 %1.10 1.8E%23CXCR4 %1.86 4.2E%33 RARRES3 %1.26 2.1E%17 TRAPPC3 %1.10 1.3E%30TFF3 %1.84 1.1E%72 HMGCS2 %1.26 4.5E%69 TTC36 %1.10 4.2E%22RPPH1 %1.83 1.1E%11 EVPLL %1.25 4.7E%29 S100A13 %1.09 2.4E%24DDIT4 %1.76 3.0E%100 SRGN %1.25 1.1E%09 IVL %1.09 5.7E%24APOD %1.75 5.4E%91 TRAF4 %1.25 7.2E%86 AMTN %1.09 1.3E%07FAM83A %1.69 3.6E%29 HLA%A %1.23 3.1E%38 CEACAM6 %1.08 1.9E%34CREB3L1 %1.67 1.9E%15 NR2F1 %1.23 2.0E%35 CYR61 %1.08 4.6E%42NDRG1 %1.65 1.4E%129 SOX2 %1.23 8.0E%30 TIMP1 %1.08 2.1E%15DUSP5 %1.65 8.0E%64 COX16 %1.22 5.7E%18 TMEM92 %1.08 3.4E%07IRF9 %1.64 4.2E%28 C21orf63 %1.21 1.8E%16 PPAP2C %1.08 2.9E%45CHRD %1.64 6.2E%22 IFIT2 %1.21 6.2E%15 LGALS3BP %1.08 2.7E%26TP53I3 %1.61 4.2E%77 DLL1 %1.21 1.5E%46 C4BPB %1.08 2.4E%09TGM3 %1.60 4.9E%15 C11orf51 %1.20 8.9E%20 EFNA1 %1.08 3.4E%37SOCS2 %1.57 8.0E%12 PIP %1.20 3.2E%45 ANGPTL4 %1.07 4.6E%43TCL1B %1.57 1.1E%14 PACSIN3 %1.20 9.8E%71 U2AF1 %1.07 1.6E%39ADCY2 %1.56 8.8E%21 SNORD36C %1.19 2.4E%07 PLSCR1 %1.06 1.4E%25SNCG %1.55 1.4E%42 MGP %1.19 3.3E%38 PTGER2 %1.06 2.4E%03C2orf54 %1.53 3.6E%20 CNFN %1.18 3.5E%12 RAB20 %1.05 1.1E%40C1orf64 %1.52 1.2E%63 VASN %1.18 7.8E%55 RPS19 %1.05 1.7E%26RGS16 %1.46 2.8E%19 TRPV4 %1.18 2.0E%09 RHOBTB1 %1.05 1.1E%36BAMBI %1.46 7.8E%88 PPIB %1.18 2.1E%51 LIN28A %1.05 4.8E%30  233    Gene$name logFC FDR Gene$name logFC FDR Gene$name logFC FDRLOC150622 )1.05 1.1E)08 LNPEP 1.26 5.7E)24 KAZN 1.45 6.6E)33GREM2 )1.04 1.3E)22 LOC284412 1.26 2.8E)12 COL5A1 1.45 4.0E)21FBXO32 )1.04 5.9E)61 GRIK3 1.26 4.5E)59 SPDYE6 1.45 2.8E)13DERA )1.03 3.8E)52 FAT1 1.26 3.1E)22 C8orf39 1.46 5.0E)14LMCD1 )1.03 2.3E)52 CYP2W1 1.26 1.7E)11 RAB6C 1.46 1.2E)27ELF3 )1.03 6.0E)61 FLJ34208 1.26 5.6E)38 NPY1R 1.46 7.8E)64RPS11 )1.02 5.3E)25 PVRIG 1.27 9.6E)17 LOC641298 1.46 7.1E)17CPNE7 )1.02 2.2E)12 SGK494 1.27 4.0E)31 GNAT2 1.46 5.1E)15CA11 )1.02 1.9E)10 CSRNP3 1.27 1.4E)29 PGLYRP2 1.46 2.4E)17EPB41L2 )1.02 4.0E)23 CACNB2 1.27 1.8E)13 ASB9P1 1.46 5.9E)21FRAT1 )1.02 3.7E)28 GPR37L1 1.27 1.1E)30 AMZ1 1.47 6.4E)65PPP2R4 )1.02 7.0E)31 CACNA2D1 1.27 1.7E)13 SEC14L2 1.47 6.6E)51CLDN7 )1.01 2.7E)24 STXBP5L 1.27 1.1E)34 CDKL5 1.47 2.9E)15RPL12 )1.01 1.3E)21 VTN 1.28 1.8E)11 NRCAM 1.47 2.8E)27SCARA3 )1.01 6.7E)52 PLXDC1 1.28 4.0E)15 HMCN1 1.47 3.7E)32CD40 )1.01 6.9E)09 IL16 1.28 4.3E)14 EPHA8 1.48 1.7E)45BIRC7 )1.01 1.9E)07 TMPRSS9 1.28 4.8E)08 SULF2 1.49 6.8E)21UQCRHL )1.01 3.8E)10 ATF7IP2 1.28 1.4E)24 THBS1 1.49 1.8E)89JUNB )1.00 3.3E)35 C1orf226 1.28 5.6E)75 C17orf78 1.49 1.3E)15ODAM )1.00 1.0E)06 KRBA2 1.28 9.2E)21 GPR77 1.49 2.1E)38CEACAM5 )1.00 2.8E)08 C1orf130 1.28 1.9E)13 ZNF699 1.49 2.0E)12C2orf72 )1.00 8.1E)43 DSCAM 1.29 3.1E)56 C8orf45 1.50 2.1E)17MIR570 1.00 1.2E)07 DNAH10 1.29 2.4E)17 PPM1K 1.50 2.5E)99MYO15B 1.00 4.5E)17 ASPG 1.29 1.9E)12 RUNX1 1.50 7.2E)76CYP1A1 1.00 7.9E)38 KIAA1456 1.29 1.1E)16 CEACAM19 1.50 3.9E)21CYP2D6 1.00 6.2E)10 RASGRP1 1.29 2.1E)14 PCDHGA9 1.50 1.3E)29TRIM2 1.00 1.9E)41 SYT12 1.30 5.0E)53 MYC 1.52 1.9E)102FAM40B 1.00 2.0E)28 PLK5 1.30 1.3E)11 NBEAL1 1.52 2.4E)24ANKAR 1.00 7.6E)09 NLRC3 1.31 2.8E)19 CADM1 1.52 1.2E)133LOC441204 1.01 5.8E)13 KSR2 1.31 2.6E)73 C14orf182 1.54 1.5E)12LOC283174 1.01 7.7E)16 LGALS2 1.31 2.4E)15 MAP6D1 1.54 6.8E)132LOC1001313471.01 4.9E)09 SLC1A2 1.31 3.1E)49 SLC47A2 1.54 2.2E)13ADCY9 1.01 1.8E)40 WDR31 1.31 6.9E)33 CCDC144A 1.54 5.6E)09ZBTB32 1.01 9.4E)07 CYP1B1 1.31 3.2E)69 ZNF154 1.54 5.4E)15LOC650623 1.01 3.2E)16 MAN1A1 1.32 8.7E)50 ZNF573 1.55 2.4E)21FAM186B 1.02 4.3E)09 UGCG 1.32 1.5E)43 SERPINA10 1.56 4.5E)51TTLL6 1.02 1.5E)08 CLPSL2 1.32 3.2E)12 NOS1AP 1.56 6.3E)33SNED1 1.02 1.9E)14 CYP2S1 1.32 4.3E)41 CACNA1H 1.57 3.4E)59PCDHB18 1.58 1.8E)14 ZNF518A 1.02 1.2E)30 UBXN7 1.02 4.6E)26SLC34A3 1.58 5.9E)16 CPAMD8 1.02 4.6E)40 SSPO 1.02 4.1E)10LGI1 1.58 3.8E)33 NRP1 1.02 5.2E)11 BRWD3 1.02 7.3E)17  234   Gene$name logFC FDR Gene$name logFC FDR Gene$name logFC FDRLOC1005060461.02 2.0E,07 PXK 1.07 8.8E,64 ZBED6 1.12 6.6E,09FAM22A 1.03 3.5E,10 SACS 1.07 6.6E,29 DNAH12 1.12 4.5E,13RGL4 1.03 7.5E,10 NXNL2 1.08 2.2E,17 GP6 1.12 5.3E,14NRIP1 1.03 1.5E,37 SHC2 1.08 7.1E,24 LOC283922 1.12 3.8E,25OSGIN1 1.03 6.4E,47 TEX14 1.08 9.1E,24 HEG1 1.12 1.4E,43MIR635 1.03 7.3E,09 RYR3 1.08 2.7E,12 SH2B3 1.12 6.7E,35CACNG4 1.03 1.1E,44 LOC1005063941.08 1.4E,24 TET1 1.12 4.2E,30KRT15 1.03 1.6E,34 DNAH3 1.08 1.2E,11 FOSL2 1.13 2.6E,37L1CAM 1.03 1.6E,39 XYLT1 1.08 9.0E,35 ZNF808 1.13 3.8E,39PRTG 1.03 1.3E,09 ZNF121 1.08 4.4E,14 KLHL11 1.13 7.6E,11CDHR3 1.03 1.4E,13 FANCC 1.08 5.0E,28 CEP135 1.13 9.7E,54SPDYE3 1.04 2.1E,12 PPM1E 1.08 1.7E,09 PDLIM3 1.13 1.8E,16DNAJC18 1.04 1.1E,25 KATNAL1 1.08 1.5E,37 NEU3 1.13 8.1E,15AKAP12 1.04 3.2E,24 GUCY1A2 1.08 6.2E,17 ATP11A 1.13 4.6E,28CARNS1 1.04 3.4E,07 VWA3A 1.08 3.8E,09 PCDHGB3 1.13 2.2E,07PCDHGC4 1.04 3.2E,07 MGC45800 1.08 1.9E,16 LOC1001313201.14 1.8E,08GAS2L3 1.04 3.1E,14 PCDHB3 1.08 1.0E,19 LOC1006166681.14 1.2E,13SPATA24 1.04 1.6E,31 ARHGAP26 1.08 3.1E,35 REL 1.14 6.7E,10CEACAM22P 1.05 4.0E,08 MAN1C1 1.08 4.9E,34 HSPB8 1.14 2.6E,57ATP13A4 1.05 2.2E,27 TIPARP 1.09 6.9E,56 CYP2E1 1.14 9.5E,12CLEC18B 1.05 2.8E,09 SLC2A12 1.09 7.1E,24 GAL3ST2 1.14 5.4E,11PCDHA2 1.05 1.2E,16 GPR157 1.09 6.7E,11 FRRS1 1.14 1.2E,17NRIP2 1.05 1.7E,11 GAN 1.09 1.3E,35 DCAF16 1.15 1.1E,42ZDBF2 1.05 4.5E,20 TNFRSF25 1.09 8.1E,20 PCDHGA4 1.15 1.1E,09MACC1 1.06 1.6E,38 SLC7A5 1.09 2.2E,53 EID3 1.15 5.8E,07PDZK1 1.06 8.2E,15 MIR647 1.09 3.3E,10 SLC16A8 1.15 3.7E,13PHKG1 1.06 1.5E,11 SFXN2 1.09 1.3E,56 ZNF727 1.15 1.5E,12LOC730091 1.06 2.9E,15 NPR3 1.09 3.2E,20 CHST3 1.15 9.6E,17STOX2 1.06 1.3E,11 BRSK2 1.09 1.0E,06 CPLX3 1.15 1.4E,08LOC1001293871.06 9.3E,22 ZNF81 1.09 6.9E,23 DNAH6 1.15 2.6E,15PLEKHG5 1.06 2.0E,25 WDR88 1.10 9.2E,14 KIRREL 1.15 2.9E,17SRPK3 1.06 4.2E,08 SPINK4 1.10 1.6E,05 LOC1005071781.15 3.4E,10FRK 1.07 2.4E,11 MDGA1 1.10 1.0E,19 DPY19L2 1.16 8.7E,13PPP4R4 1.07 4.9E,64 HOOK3 1.10 2.1E,37 LOC1002165461.16 7.9E,27CROCCP3 1.07 1.7E,21 KIAA0889 1.10 9.4E,33 DCN 1.17 1.1E,25ZBTB40 1.07 8.2E,60 INE1 1.10 4.1E,12 DGKH 1.17 4.0E,14LOC1001327741.07 3.7E,09 CBX5 1.10 1.4E,42 FHDC1 1.17 2.9E,19ZFPM2 1.07 2.0E,23 NAT8L 1.10 1.3E,04 ZC3HAV1L 1.17 6.8E,19TMED10P1 1.07 2.5E,14 ZNF665 1.11 6.1E,13 C5AR1 1.17 9.2E,16CSPG4 1.07 2.1E,13 CYP2D7P1 1.11 3.4E,08 ZNF221 1.17 5.0E,21CLIC6 1.07 2.9E,31 FLJ45513 1.11 1.5E,11 SLC4A7 1.17 1.1E,49  235   Gene$name logFC FDR Gene$name logFC FDR Gene$name logFC FDRINTS4L2 1.18 3.7E.13 PCDHGA2 1.33 2.0E.17 AMOTL1 1.58 1.9E.80LOC641515 1.18 2.8E.11 CACNG1 1.34 1.7E.14 S100A8 1.59 2.0E.16RNF223 1.18 1.1E.37 SLC22A4 1.34 1.8E.18 ZNF716 1.60 2.9E.24KCP 1.18 1.8E.06 C12orf69 1.34 4.6E.10 RET 1.60 3.5E.36NNAT 1.19 9.4E.12 SFT2D2 1.34 7.8E.36 PFN1P2 1.60 1.9E.19CYP8B1 1.19 2.6E.14 GPR179 1.35 3.9E.11 MGC70870 1.61 4.0E.19ZNF587B 1.19 7.0E.30 DUOX1 1.35 1.4E.16 PGR 1.61 2.2E.72GALNT4 1.19 6.4E.09 CD5 1.35 2.4E.18 LOC1005054781.62 1.3E.14MIR4664 1.19 3.1E.08 KDM4B 1.36 2.1E.69 FKBP1AP1 1.64 8.0E.24MIR1914 1.19 1.7E.06 HSPG2 1.36 7.0E.36 OLFM1 1.64 4.6E.121SAMD12 1.19 1.1E.30 PLAC4 1.36 8.3E.11 RAPGEFL1 1.64 2.6E.150KCNC4 1.20 4.1E.26 ACP5 1.36 2.4E.11 ADRA2A 1.65 1.6E.24SH3GL1P1 1.20 2.8E.13 NR2E3 1.37 3.8E.18 NECAB2 1.65 6.3E.57RUNX2 1.20 3.9E.44 ATP2A1 1.37 2.2E.21 IL6ST 1.65 7.7E.113GPR155 1.20 5.8E.26 C17orf67 1.37 7.8E.14 LOC1005066551.66 1.7E.13LOC1005073731.20 3.9E.10 PCDHGA12 1.37 3.0E.08 ELOVL2 1.66 4.8E.46C5orf4 1.20 2.5E.12 CDK6 1.37 9.9E.40 FTX 1.66 2.7E.15GCNT7 1.20 2.8E.09 TPO 1.38 1.0E.15 ZNF642 1.68 3.3E.16LOC1001328321.20 1.8E.13 UCKL1.AS1 1.38 6.1E.09 NKAIN1 1.68 5.7E.69CG030 1.21 2.1E.14 FAM22G 1.38 1.9E.12 LOC1003350301.68 4.2E.20RFTN1 1.21 1.9E.08 ANKRD62P1.PARP4P31.38 1.6E.15 EGR1 1.69 3.3E.16SGK196 1.21 3.6E.20 EYS 1.38 5.4E.08 CYP1A2 1.70 1.3E.47RBM24 1.21 5.2E.35 ZNF587 1.39 5.6E.45 IGSF23 1.70 1.2E.15MICAL2 1.22 4.3E.38 TGFBR3 1.40 2.6E.17 SPDYE5 1.70 2.3E.23SLC26A1 1.22 2.3E.15 KIAA1211 1.40 1.1E.50 DRD4 1.72 1.5E.20B4GALT1 1.22 4.8E.73 NEDD9 1.40 9.2E.52 ST8SIA6 1.72 3.0E.28C2orf88 1.22 8.9E.13 LOC100505648 1.40 4.3E.25 LOC440905 1.72 5.4E.27MEGF11 1.22 8.7E.10 SNHG4 1.40 7.1E.18 SPDYE1 1.73 1.8E.32BAIAP2L2 1.22 1.6E.18 FLT4 1.40 2.2E.74 STC1 1.73 1.0E.57POLN 1.22 9.2E.13 IGSF9B 1.41 6.3E.27 CCDC144B 1.75 1.3E.34B7H6 1.23 5.1E.12 SLC7A5P2 1.41 1.3E.14 LOC399715 1.76 1.1E.18CTNS 1.23 2.0E.28 FLJ10661 1.41 6.8E.21 TRPC6 1.76 4.2E.30MYB 1.23 4.8E.65 CELSR1 1.42 2.9E.66 SLC13A4 1.77 1.3E.30SRCRB4D 1.23 1.1E.15 RPS6KA2 1.42 7.8E.28 LOC1005071171.77 2.9E.18ZNF483 1.24 4.1E.14 LOC100271836 1.42 1.0E.16 DPRXP4 1.78 2.9E.22PLCE1 1.24 4.5E.62 MAPT 1.43 3.8E.66 OCLM 1.79 2.6E.28HEATR4 1.24 3.1E.26 PCDHB19P 1.44 3.2E.17 ALOX12P2 1.79 1.3E.11RORA 1.25 3.8E.16 C5orf65 1.44 1.2E.21 MYO3B 1.80 4.5E.51PCDHGC5 1.25 3.1E.13 ST3GAL5 1.44 2.7E.15 SGK3 1.81 8.5E.14FABP3 1.25 6.9E.11 ANKRD36BP1 1.44 2.5E.22 MCM3AP.AS11.81 6.9E.22WNK3 1.26 1.9E.15 KAZN 1.45 6.6E.33 LRRTM2 1.82 1.6E.12  236               Gene$name logFC FDR Gene$name logFC FDR Gene$name logFC FDRNEK10 1.85 1.9E*26 GATA4 3.65 2.2E*35 C9orf131 2.19 1.1E*22FMN1 1.87 5.1E*88 LOC1005075845.21 2.0E*119 ADAMTSL5 2.20 5.0E*187GJA1 1.88 6.8E*06 KCNF1 1.33 3.8E*27 LHFPL5 2.21 8.8E*39ZNF460 1.88 6.0E*42 C16orf89 1.33 7.2E*14 LOC1001280542.22 1.3E*44KCNQ5 1.88 9.1E*39 SH3D21 1.33 4.6E*12 KRT13 2.24 4.2E*28TGM2 1.93 1.2E*09 EPS8L3 2.42 3.7E*30 GREB1 2.25 4.1E*186C17orf66 1.93 8.1E*12 CLPS 2.44 8.9E*40 CXCL12 2.26 4.2E*267LOC1001299171.93 1.1E*54 PCDHA9 2.47 5.0E*31 SEMA3G 2.34 2.1E*68SYCP3 1.94 1.9E*17 MUC6 2.49 8.8E*16 RPL23AP64 2.14 1.9E*47KCNH1 1.97 2.0E*139 GDAP1L1 2.50 7.8E*34 MYBL1 2.16 2.8E*212POU5F1P4 2.00 6.3E*17 PAR5 2.53 5.3E*49 FAM106CP 2.17 1.9E*17FLJ40292 2.02 1.8E*12 SLC47A1 2.59 2.5E*94 LRRC15 2.96 7.3E*139CITED1 2.06 1.7E*17 NXPH3 2.59 1.1E*40 GPR132 3.23 7.1E*110C10orf108 2.07 1.0E*11 CDHR5 2.77 3.5E*24 KCNK5 3.25 1.8E*91PAR*SN 2.10 2.0E*41 COL27A1 2.93 1.0E*35 SPATC1 2.12 2.1E*22PLCL1 2.94 7.7E*74  237 A.5 Strand-Specific RNA-sequencing of T47D cells  Condition: siNT+E2 vs siNT, 6 hours of β-estradiol treatment, n=2.  Gene$name logFC FDR Gene$name logFC FDR Gene$name logFC FDRCYP1A2 '1.69 1.7E'23 FKBP4 1.09 7.1E'52 SERPINA10 1.37 3.1E'32C2orf54 '1.60 4.1E'32 JAK1 1.09 3.1E'62 PDLIM3 1.39 1.2E'20LOC150622 '1.52 4.0E'10 C1orf168 1.09 1.1E'21 GPR132 1.39 2.7E'22KCNC1 '1.44 5.1E'22 NOS1AP 1.09 4.5E'16 CYP2S1 1.40 1.3E'48CXCR7 '1.30 2.0E'16 CT62 1.10 1.0E'20 RAB37 1.41 7.4E'23CXCR4 '1.29 2.8E'15 RBBP8 1.11 6.7E'64 TNNI2 1.41 4.4E'24GRM4 '1.18 1.5E'48 NRCAM 1.11 3.7E'14 SSTR2 1.42 9.0E'41PTHLH '1.17 2.4E'10 XYLT1 1.11 5.2E'37 KLK10 1.42 1.8E'51SOCS2 '1.15 8.4E'09 LOC100131320 1.11 1.0E'07 CLPSL2 1.42 9.8E'15AMIGO2 '1.14 1.0E'62 PFKFB3 1.12 9.2E'69 PDZK1 1.43 2.5E'28DDIT4 '1.13 2.7E'47 KCNK15 1.14 7.1E'24 WFIKKN2 1.44 1.7E'30CYP1A1 '1.12 1.4E'44 C8orf46 1.14 5.9E'31 BFSP2 1.45 2.6E'25ADAMTS15 '1.12 8.9E'44 RLN2 1.14 4.4E'20 CLPS 1.46 2.5E'09ARL4D '1.10 2.7E'41 LOC399715 1.15 3.0E'04 SUSD3 1.46 5.0E'33FAM113B '1.10 1.1E'24 TXNDC5 1.15 8.1E'07 MYB 1.47 4.7E'90TWIST1 '1.10 9.3E'13 GRIK4 1.17 5.7E'48 SFXN2 1.47 1.3E'102FAM83A '1.08 3.1E'15 TFF1 1.17 2.2E'14 LRRC15 1.49 2.0E'34ID2 '1.04 2.0E'38 RANBP3L 1.17 9.7E'24 STC2 1.49 2.2E'106CCDC154 '1.03 6.0E'29 PCP4 1.18 6.9E'18 NEK10 1.50 2.4E'15FAM171B '1.02 2.1E'08 IGFBP4 1.18 1.6E'50 ACOX2 1.51 2.6E'25BAMBI '1.01 5.4E'43 SULF2 1.20 1.8E'17 KCTD6 1.51 9.5E'98EGLN3 '1.01 1.9E'16 RUNX2 1.20 2.1E'40 PTGES 1.56 4.3E'100NDP '1.00 5.7E'11 ANKFN1 1.21 1.5E'10 IFITM10 1.59 6.2E'72FGD3 1.02 5.0E'45 PGR 1.23 6.7E'43 FMN1 1.60 5.0E'64BMPER 1.03 1.2E'11 GRIK3 1.23 1.2E'54 CLIC6 1.62 2.1E'79GNG11 1.03 1.4E'32 FLT4 1.24 2.1E'60 MYBL1 1.64 1.7E'123SIAH2 1.03 9.0E'53 C1orf226 1.25 1.9E'74 SEC14L2 1.65 1.4E'65CMTM7 1.03 3.1E'18 RARA 1.27 2.0E'70 RNF223 1.69 7.8E'83C12orf60 1.05 3.1E'11 HSPB8 1.28 9.9E'72 MAP6D1 1.69 4.4E'159KDM4B 1.05 2.7E'41 MAPT 1.28 2.1E'54 RASGRP1 1.70 2.1E'25PXK 1.05 2.8E'59 GPR77 1.28 1.3E'24 RUNX1 1.72 3.3E'96ZNF669 1.05 1.1E'20 IL6ST 1.30 1.1E'70 CPLX3 1.74 3.4E'19AMZ1 1.06 2.2E'34 ST8SIA6 1.30 1.9E'14 CALCR 1.75 4.3E'27NRIP1 1.06 1.6E'39 CADM1 1.32 2.1E'101 HEY2 1.76 8.1E'51NOD2 1.06 1.1E'27 B4GALT1 1.33 3.2E'87 TEX14 1.78 2.8E'59SYN1 1.07 2.8E'14 LHFPL5 1.34 1.5E'08 ADAMTSL5 1.78 4.3E'125PPM1K 1.07 2.5E'51 MAN1A1 1.36 1.8E'51 EPHA8 1.79 7.8E'35CACNA1H 1.08 3.7E'29 OSGIN1 1.36 2.6E'81 NECAB2 1.81 7.1E'68  238                  Gene$name logFC FDR Gene$name logFC FDR Gene$name logFC FDRRAPGEFL1 1.81 1.3E,192 RET 2.20 4.4E,92 PLCL1 2.78 5.1E,76KCNH1 1.83 3.5E,110 SEMA3G 2.23 8.0E,75 NXPH3 2.88 7.4E,60ELOVL2 1.84 1.9E,63 ADRA2A 2.25 4.7E,50 KCNK5 3.20 3.8E,118NPY1R 1.85 6.7E,94 COL27A1 2.26 2.7E,28 KRT13 3.25 6.3E,119THBS1 1.86 5.0E,137 C12orf69 2.27 8.9E,50 CXCL12 3.37 0.0E+00RBM24 1.87 2.6E,86 NKAIN1 2.41 1.4E,204 C14orf182 3.46 6.3E,64CHST8 1.89 8.6E,19 SGK3 2.46 9.6E,38 CITED1 3.53 9.6E,76OLFM1 1.96 4.7E,174 KCNQ5 2.56 2.0E,80 TGM2 3.93 2.2E,82STC1 1.98 1.1E,83 PGLYRP2 2.57 9.7E,77 GATA4 4.44 2.4E,100GREB1 2.01 1.1E,150 SLC47A1 2.59 1.5E,147 LOC100507584 4.98 9.3E,124SEMA3A 2.12 1.1E,23 MYC 2.67 1.6E,297 SPINK4 2.75 2.4E,34GJA1 2.16 1.1E,29  239 A.6 Transcriptome microarray analysis of 184-hTERT cells  Condition: siPPP2R2A vs siNT, n=3.   Gene$Symbol Fold$Change$(linear) p$(ANOVA)$ Gene$Symbol Fold$Change$(linear) p$(ANOVA)$SCEL 10.3 4.5E+01 ZPLD1 2.3 5.8E+01MMP1 8.7 4.5E+01 GPR115 2.3 5.6E+01SPRR2A 6.3 4.8E+01 FLG 2.3 5.9E+01CARD17 4.9 4.5E+01 TLN2 2.3 5.9E+01SLITRK6 4.6 5.3E+01 CDKN2B 2.3 5.5E+01SULT1E1 4.2 5.1E+01 APOBEC3B 2.3 4.5E+01AGR2 4.1 4.8E+01 LOC100506676 2.3 4.5E+01ABCC2 4.1 5.4E+01 LOC100505633 2.3 4.9E+01FLJ35282 4.0 5.3E+01 MAL2 2.2 4.5E+01SPRR1A 3.9 5.7E+01 AKR1C2 2.2 5.4E+01SPRR1B 3.8 4.1E+01 KRT16 2.2 5.6E+01LINC00520 3.8 5.7E+01 HLA+DOB 2.2 4.8E+01LOC100506377 3.7 4.5E+01 CARD16 2.2 5.5E+01IL18 3.5 4.2E+01 IL13RA2 2.2 5.8E+01SERPINB7 3.3 4.5E+01 HSD17B3 2.1 5.9E+01FAM25A 3.3 5.2E+01 CSTA 2.1 4.8E+01OLR1 3.3 5.6E+01 MIR584 2.1 5.0E+01ADAM32 3.2 4.5E+01 LOC730091 2.1 5.7E+01FAM155B 3.2 6.0E+01 KRT24 2.1 4.1E+01LOC100506267 3.1 6.1E+01 SPRR2B 2.1 5.1E+01OLAH 2.9 5.7E+01 IL20RB 2.0 5.2E+01SESN3 2.8 5.1E+01 AKR1C3 2.0 4.5E+01NAV3 2.7 4.5E+01 SOX15 2.0 4.1E+01IL1RN 2.7 6.1E+01 TNFAIP6 2.0 5.9E+01FAM25C 2.6 5.0E+01 PKIB 2.0 4.5E+01SLAMF7 2.6 5.7E+01 ARHGAP42 2.0 5.2E+01SEMA3D 2.6 5.7E+01 C13orf44+AS1 2.0 5.2E+01VWA5A 2.6 5.9E+01 HLA+DOB 2.0 4.5E+01CYP39A1 2.6 5.7E+01 PUS10 2.0 5.6E+01NCF2 2.5 5.8E+01 LOC100507127 2.0 6.1E+01GSTA4 2.5 4.5E+01 MRGPRX3 2.0 6.1E+01FAM25C 2.5 5.1E+01 CASP1 2.0 5.7E+01FAM25C 2.4 5.0E+01 CERS3 2.0 5.7E+01LCE1F 2.4 5.8E+01 LOC100507564 1.9 4.5E+01LOC729013 2.4 5.9E+01 ABI3BP 1.9 6.0E+01SPRR2D 2.4 4.5E+01 AKAP6 1.9 5.6E+01HRASLS2 2.4 5.9E+01 HLA+DOB 1.9 4.5E+01  240  Gene$Symbol Fold$Change$(linear) p$(ANOVA)$ Gene$Symbol Fold$Change$(linear) p$(ANOVA)$HLA$DOB 1.9 4.5E$01 ADAMTS6 $1.8 5.2E$01HLA$DOB 1.9 4.5E$01 CHPT1 $1.8 5.6E$01HLA$DOB 1.9 4.5E$01 E2F5 $1.8 5.4E$01GRHL3 1.9 5.3E$01 MCAM $1.9 6.1E$01CAPNS2 1.9 4.5E$01 MYL9 $1.9 4.5E$01LINC00113 1.9 5.7E$01 TRPC1 $1.9 6.1E$01C5 1.9 5.2E$01 MMP14 $1.9 5.5E$01SDCBP2 1.9 4.6E$01 SCAPER $1.9 5.9E$01DDX58 1.9 5.5E$01 LOC100130992 $1.9 5.7E$01FAM213A 1.9 5.5E$01 LINC00342 $1.9 4.8E$01KITLG 1.9 5.3E$01 MCOLN3 $1.9 4.5E$01SPRR2E 1.9 4.5E$01 CTGF $1.9 6.1E$01LOC255187 1.9 4.5E$01 SNORD11 $1.9 5.7E$01TPMT 1.9 4.5E$01 CPT1A $2.0 5.5E$01NTNG1 1.9 5.1E$01 CDCA7L $2.0 6.0E$01GLTP 1.9 4.5E$01 VCAN $2.0 6.0E$01PTGES 1.8 4.5E$01 LOC389831 $2.0 5.9E$01TNIK 1.8 5.7E$01 SMARCA1 $2.1 4.1E$01LOC100506963 1.8 6.1E$01 SNORD61 $2.1 5.3E$01GJB5 1.8 4.6E$01 RNF125 $2.1 5.6E$01APOM 1.8 4.1E$01 MIR2355 $2.1 5.6E$01APOM 1.8 4.1E$01 SNORD75 $2.2 5.2E$01APOM 1.8 4.1E$01 LOC100129034 $2.2 5.8E$01APOM 1.8 4.1E$01 SCCPDH $2.2 6.1E$01APOM 1.8 4.1E$01 PRTG $2.2 4.9E$01APOM 1.8 4.1E$01 FLJ42627 $2.7 5.5E$01PPP2R2A $1.8 4.5E$01 LOC730755 $3.3 5.3E$01EPCAM $1.8 4.5E$01 BRIP1 $3.4 6.0E$01ERMP1 $1.8 5.5E$01 PM20D2 $3.8 6.0E$01MYADM $1.8 5.0E$01  241 Appendix B  - Unique ER binding sites and associated genes identified by ChIP-Sequencing in T47D after PPP2R2A knockdown  B.1 Unique ER binding sequences treated with of siNT in T47D cells.  1 hour of β-estradiol treatment, n=2 Chr Coordinate MASC.score Associated.Genes Chr Coordinate MASC.score Associated.Geneschr1 1005446 1005653 9.9 (L:)AL390719.4721,(R:)C1orf159 chr1 151750136 151750409 29.0 (L:)SRP_euk_arch,(R:)S100A6chr1 2447398 2447675 53.6 PANK4 chr1 157053623 157053823 7.6 (L:)AL513205.8,(R:)MNDAchr1 8008902 8009148 19.9 ERRFI1 chr1 160566260 160566578 11.9 NOS1APchr1 8300764 8301069 30.2 SLC45A1 chr1 177241404 177241640 12.7 (L:)RALGPS2,(R:)FAM20Bchr1 15247664 15247873 4.9 RP1221O18.1 chr1 178390267 178390480 10.6 (L:)CEP350,(R:)QSOX1chr1 22672300 22672508 9.7 ZBTB40 chr1 184388621 184389170 58.7 HMCN1chr1 23729995 23730304 10.3 E2F2 chr1 199589147 199589347 11.4 (L:)PKP1,(R:)TNNT2chr1 37275854 37276124 23.9 (L:)GRIK3,(R:)U6 chr1 200571075 200571434 33.5 UBE2Tchr1 43230799 43231041 8.3 (L:)U6,(R:)FAM183A chr1 205149640 205149853 6.5 IL24,FAIM3chr1 45755147 45755358 8.5 PRDX1 chr1 209257488 209257775 16.5 KCNH1chr1 51805921 51806283 29.2 (L:)AC104170.2,(R:)OSBPL9 chr1 209761468 209761666 12.2 (L:)RD3,(R:)SLC30A1chr1 51817075 51817270 6.9 OSBPL9 chr1 213338244 213338439 6.5 KCNK2chr1 56793736 56793989 20.7 PPAP2B chr1 213715692 213715970 4.9 (L:)RP112199H2.1,(R:)KCTD3chr1 61980579 61980900 21.4 INADL chr1 213780254 213780460 12.7 (L:)RP112199H2.1,(R:)KCTD3chr1 65235170 65235560 34.6 (L:)AL606517.14,(R:)U6 chr1 216720765 216721109 44.0 (L:)TGFB2,(R:)AC092015.222chr1 71113124 71113395 16.8 PTGER3 chr1 233209096 233209312 16.2 (L:)AL391832.1021,(R:)RP42597N16.1chr1 71686926 71687209 19.0 NEGR1 chr1 238665901 238666178 10.2 FMN2chr1 77182481 77182709 11.2 ST6GALNAC5 chr1 238705834 238706179 41.2 FMN2chr1 79599647 79599842 6.9 (L:)RP112548C21.1,(R:)ADH5P2 chr1 238917832 238918078 17.8 (L:)RP112467I20.5,(R:)Y_RNAchr1 81152076 81152357 11.2 (L:)RP112115A15.2,(R:)RP1122B19.1 chr1 239296532 239296850 29.2 RGS7chr1 93876552 93876764 11.2 BCAR3 chr1 239621962 239622242 12.6 (L:)RGS7,(R:)FHchr1 94146886 94147176 16.8 GCLM chr1 243566276 243566471 5.2 KIF26Bchr1 94661285 94661480 9.7 ABCD3 chr10 126195 126423 13.9 (L:)IL9RP2,(R:)ZMYND11chr1 107764670 107764905 9.7 NTNG1 chr10 162442 162670 9.6 (L:)IL9RP2,(R:)ZMYND11chr1 108082725 108083008 20.7 VAV3 chr10 654262 654457 8.6 DIP2Cchr1 108111373 108111734 15.8 VAV3 chr10 4160304 4160517 11.7 (L:)COPEB,(R:)U6chr1 117223884 117224200 14.1 (L:)RP42655N15.3,(R:)AL157904.24 chr10 6447505 6447725 11.4 (L:)AL137145.1322,(R:)PRKCQchr1 120135004 120135267 12.7 (L:)HMGCS2,(R:)REG4 chr10 8808008 8808270 11.9 (L:)5S_rRNA,(R:)RP521051H14.1chr1 143957709 143957910 8.6 NOTCH2NL chr10 9270124 9270427 25.6 (L:)5S_rRNA,(R:)RP521051H14.1chr1 150094891 150095103 13.8 (L:)THEM5,(R:)THEM4 chr10 9270489 9270931 18.3 (L:)5S_rRNA,(R:)RP521051H14.1  242  Chr Coordinate MASC.score Associated.Genes Chr Coordinate MASC.score Associated.Geneschr10 9270124 9270427 25.6 (L:)5S_rRNA,(R:)RP581051H14.1 chr10 115989025 115989325 42.5 VWA2chr10 9270489 9270931 18.3 (L:)5S_rRNA,(R:)RP581051H14.1 chr10 121292263 121292566 23.6 (L:)RGS10,(R:)TIAL1chr10 19830002 19830317 16.8 C10orf112 chr10 122699657 122699918 14.2 (L:)WDR11,(R:)RPL19P16chr10 25663275 25663784 38.3 GPR158 chr10 125843009 125843204 6.6 (L:)RP11847G11.1,(R:)OATchr10 25713545 25713952 51.6 GPR158 chr10 126064764 126065029 7.3 (L:)RP11847G11.1,(R:)OATchr10 26544938 26545156 11.4 (L:)MYO3A,(R:)GAD2 chr10 129883133 129883328 10.0 (L:)MKI67,(R:)MGMTchr10 29254386 29254640 5.8 (L:)RP118492M23.2,(R:)LYZL1 chr10 133828214 133828419 10.2 JAKMIP3chr10 29436893 29437125 7.3 (L:)RP118492M23.2,(R:)LYZL1 chr11 1346196 1346391 6.9 (L:)TOLLIP,(R:)BRSK2chr10 43991839 43992142 11.2 (L:)C10orf136,(R:)CXCL12 chr11 1811932 1812150 7.1 SYT8chr10 47102947 47103269 8.6 (L:)AL450388.13,(R:)ANTXRL chr11 2146904 2147236 10.8 THchr10 59498721 59498920 9.7 (L:)RP118448K10.1,(R:)RP118123G9.1chr11 10030099 10030510 15.3 SBF2chr10 59531518 59531869 22.0 (L:)RP118448K10.1,(R:)RP118123G9.1chr11 13313428 13313805 27.4 ARNTLchr10 59730575 59731025 30.2 (L:)CISD1,(R:)UBE2D1 chr11 16967650 16967871 12.7 PLEKHA7chr10 62701309 62701614 31.7 (L:)RHOBTB1,(R:)TMEM26 chr11 17383921 17384127 15.8 ABCC8chr10 63347938 63348155 7.1 ARID5B chr11 20010030 20010250 10.0 NAV2chr10 73498762 73498957 10.6 SPOCK2 chr11 34609519 34609714 8.0 EHFchr10 75249237 75249619 67.4 CAMK2G chr11 34610309 34610504 6.1 EHFchr10 76256479 76256837 64.6 MYST4 chr11 34643763 34643972 12.6 (L:)EHF,(R:)APIPchr10 78740416 78740611 8.0 KCNMA1 chr11 34646379 34646715 24.5 (L:)EHF,(R:)APIPchr10 78847262 78847470 10.1 KCNMA1 chr11 35929768 35929963 7.2 LDLRAD3chr10 80556925 80557120 9.9 ZMIZ1 chr11 35994844 35995094 28.0 LDLRAD3chr10 80592582 80592974 22.1 ZMIZ1 chr11 37066570 37066970 30.3 (L:)C11orf74,(R:)U6chr10 82040801 82041033 15.8 (L:)MAT1A,(R:)RP11836D19.4 chr11 38769411 38769665 12.0 (L:)C11orf74,(R:)U6chr10 86861010 86861255 15.3 (L:)AL356115.9,(R:)U6 chr11 44297186 44297389 11.4 (L:)ALX4,(R:)CD82chr10 88285055 88285350 30.1 (L:)WAPAL,(R:)RPL7AP8 chr11 44543137 44543390 23.6 (L:)ALX4,(R:)CD82chr10 88718021 88718254 11.9 C10orf116 chr11 44555415 44555623 12.6 CD82chr10 90291352 90291557 9.9 C10orf59 chr11 59856533 59856908 36.5 (L:)MS4A4A,(R:)MS4A6Echr10 93110679 93111029 13.5 (L:)PCGF5,(R:)HECTD2 chr11 59894940 59895401 22.6 (L:)MS4A6E,(R:)MS4A7chr10 95491153 95491353 8.0 (L:)C10orf4,(R:)LGI1 chr11 59900782 59901001 6.4 (L:)MS4A6E,(R:)MS4A7chr10 95785995 95786372 97.1 PLCE1 chr11 59948617 59948898 27.3 (L:)MS4A14,(R:)MS4A5chr10 104457362 104457627 28.7 ARL3 chr11 67542518 67542768 14.1 ALDH3B1chr10 104519250 104519570 10.2 (L:)SFXN2,(R:)C10orf26 chr11 68470865 68471060 7.4 (L:)IGHMBP2,(R:)MRGPRDchr10 104918179 104918513 40.6 NT5C2 chr11 68646752 68647081 26.5 (L:)TPCN2,(R:)MYEOVchr10 112540486 112540790 12.7 (L:)AL136368.2482,(R:)RBM20 chr11 69178394 69178627 26.3 CCND1,AP001888.4chr10 112540969 112541247 8.3 (L:)AL136368.2482,(R:)RBM20 chr11 69727237 69727479 15.5 FADD  243  Chr Coordinate MASC.score Associated.Genes Chr Coordinate MASC.score Associated.Geneschr11 74739902 74740111 10.7 ARRB1 chr12 52629790 52629986 7.5 (L:)HOXC13,(R:)HOXC12chr11 76424120 76424375 13.6 B3GNT6 chr12 56598204 56598422 10.1 (L:)AC083805.17,(R:)XRCC6BP1chr11 76796437 76796731 11.3 PAK1 chr12 67484375 67484572 10.5 (L:)SLC35E3,(R:)MDM2chr11 84077114 84077350 11.1 DLG2 chr12 67645289 67645484 9.0 (L:)CPM,(R:)AC127894.6chr11 86060037 86060232 6.6 ME3 chr12 68097815 68098156 18.2 (L:)SRP_euk_arch,(R:)FRS2chr11 87468872 87469201 55.0 (L:)AP000676.6J4,(R:)Y_RNA chr12 74624577 74624772 8.3 (L:)KRR1,(R:)AC011611.26J1chr11 90682965 90683271 32.5 (L:)hsaJmirJ1261,(R:)FAT3 chr12 78937693 78937942 8.6 (L:)PPP1R12A,(R:)C12orf64chr11 90856713 90857075 8.3 (L:)hsaJmirJ1261,(R:)FAT3 chr12 86358151 86358374 5.8 (L:)MGAT4C,(R:)AC079598.13J1chr11 91379747 91379942 7.3 (L:)hsaJmirJ1261,(R:)FAT3 chr12 90305524 90305720 12.6 (L:)DCN,(R:)BTG1chr11 94192452 94192669 13.8 AMOTL1 chr12 90306638 90306961 18.7 (L:)DCN,(R:)BTG1chr11 94203628 94203978 84.5 AMOTL1 chr12 90342406 90342648 14.8 (L:)DCN,(R:)BTG1chr11 94204393 94204635 6.9 AMOTL1 chr12 96485696 96486319 12.4 (L:)hsaJmirJ135aJ2,(R:)AC018659.35chr11 100554504 100554907 36.2 (L:)PGR,(R:)TRPC6 chr12 96905608 96905926 45.8 (L:)U6,(R:)U4chr11 100676470 100676704 13.9 (L:)PGR,(R:)TRPC6 chr12 98353014 98353222 8.3 ANKS1Bchr11 100722612 100722809 10.7 (L:)PGR,(R:)TRPC6 chr12 108996753 108996962 9.3 (L:)AC007546.6J2,(R:)IFT81chr11 100765567 100765905 35.0 (L:)PGR,(R:)TRPC6 chr12 114937898 114938163 22.7 MED13Lchr11 108043244 108043558 27.7 DDX10 chr12 115221302 115221565 8.2 (L:)MED13L,(R:)NCRNA00173chr11 113155185 113155488 55.2 (L:)ZW10,(R:)USP28 chr12 122113491 122113699 11.4 PITPNM2chr11 116447337 116447567 15.2 AP006216.9 chr12 123268490 123268777 40.7 (L:)ZNF664,(R:)FAM101Achr11 120002821 120003033 8.1 (L:)AP002348.3,(R:)GRIK4 chr12 123420501 123420697 10.3 NCOR2chr11 130048079 130048316 19.1 C11orf44 chr12 123810396 123810614 17.5 (L:)NCOR2,(R:)SCARB1chr12 576725 577040 51.6 NINJ2 chr12 123978831 123979057 19.1 (L:)UBC,(R:)DHX37chr12 1914290 1914569 25.3 (L:)CACNA2D4,(R:)DCP1B chr12 132042798 132043104 18.7 ZNF605chr12 6513060 6513426 35.3 (L:)NCAPD2,(R:)GAPDH chr13 42705097 42705427 19.0 ENOX1chr12 12311162 12311548 18.2 (L:)LRP6,(R:)U6 chr13 44890568 44890797 10.0 (L:)SLC25A30,(R:)RP11J38J6.1chr12 31504261 31504522 15.9 DENND5B chr13 47234882 47235205 22.4 (L:)SRP_euk_arch,(R:)RP11J214O11.1chr12 31703234 31703491 28.8 C12orf72 chr13 53028710 53028970 27.5 (L:)AL450423.9,(R:)RPL13AP20chr12 31769370 31769643 25.3 AMN1 chr13 53682713 53682909 11.2 (L:)AL450423.9,(R:)RPL13AP20chr12 41297944 41298139 4.0 (L:)PRICKLE1,(R:)AC079600.19 chr13 71185217 71185447 9.9 DACH1chr12 44881402 44881937 18.9 SLC38A1 chr13 79181437 79181847 69.5 (L:)NDFIP2,(R:)RP11J276G3.2chr12 45174023 45174218 5.6 (L:)SLC38A2,(R:)SLC38A4 chr13 80614945 80615219 14.2 (L:)RP11J325L2.1,(R:)U6chr12 47532139 47532414 40.2 DDX23 chr13 88270545 88270950 9.7 (L:)RP11J545P6.1,(R:)RP11J370B1.1chr12 47868049 47868284 16.2 TUBA1A chr13 90288825 90289161 69.2 (L:)AL355799.12,(R:)RP11J319L6.1chr12 50828791 50829040 17.6 (L:)AC078864.20,(R:)KRT80 chr13 90305803 90306023 12.7 (L:)AL355799.12,(R:)RP11J319L6.1chr12 51625861 51626116 6.9 (L:)KRT8P9,(R:)KRT18P19 chr13 92776719 92777059 20.7 GPC6  244  Chr Coordinate MASC.score Associated.Genes Chr Coordinate MASC.score Associated.Geneschr13 96730148 96730510 20.7 MBNL2 chr15 36788140 36788453 14.2 (L:)C15orf53,(R:)AC087878.7chr13 108369714 108369941 9.4 MYO16 chr15 37868581 37869034 15.8 (L:)FSIP1,(R:)GPR176chr13 109588509 109588704 8.6 (L:)SRP_euk_arch,(R:)COL4A1 chr15 40007593 40007871 19.0 EHD4chr13 113815913 113816201 25.9 RASA3 chr15 50735894 50736236 37.2 (L:)KIAA1370,(R:)ONECUT1chr14 25820733 25820984 14.8 (L:)AL049831.2N1,(R:)NOVA1 chr15 59829935 59830147 9.9 (L:)RORA,(R:)VPS13Cchr14 34942884 34943159 16.2 NFKBIA chr15 62703070 62703408 10.5 ZNF609chr14 36240040 36240344 19.0 SLC25A21 chr15 67087491 67087813 29.2 NOX5chr14 37085860 37086055 6.2 MIPOL1 chr15 68464430 68464689 17.4 (L:)U6,(R:)UACAchr14 37126994 37127259 25.3 (L:)MIPOL1,(R:)FOXA1 chr15 68554204 68554418 11.9 (L:)U6,(R:)UACAchr14 40355407 40355845 32.1 (L:)FBXO33,(R:)AL390335.3 chr15 69158556 69158835 31.0 (L:)AC013752.10,(R:)THSD4chr14 43437131 43437506 52.9 (L:)AL445074.4,(R:)AL109766.5 chr15 69173129 69173529 8.6 (L:)AC013752.10,(R:)THSD4chr14 50408397 50408610 14.2 C14orf29 chr15 70196898 70197152 8.8 MYO9Achr14 53483426 53483622 8.4 (L:)AL138479.3,(R:)BMP4 chr15 70310587 70310938 23.0 PKM2chr14 53500048 53500263 6.6 (L:)BMP4,(R:)AL138479.2 chr15 73761765 73762096 45.3 CSPG4chr14 61183123 61183388 15.0 (L:)PRKCH,(R:)HIF1A chr15 74390487 74390714 8.3 ETFAchr14 61250529 61250803 16.3 HIF1A chr15 81959394 81959672 5.6 SH3GL3chr14 63084351 63084674 10.0 (L:)PPP2R5E,(R:)RPL31P5 chr15 88037936 88038149 17.3 WDR93chr14 66848483 66848740 13.6 MPP5 chr15 91090206 91090499 16.2 (L:)AC091544.11N1,(R:)CHD2chr14 67709740 67709962 9.7 RAD51L1 chr15 94239875 94240070 11.2 (L:)AC015574.8N1,(R:)AC016251.9chr14 74788878 74789079 10.6 (L:)AL691403.2,(R:)FOS chr15 94255929 94256177 11.2 (L:)AC015574.8N1,(R:)AC016251.9chr14 74796519 74796867 88.9 (L:)AL691403.2,(R:)FOS chr15 94447460 94447788 40.1 (L:)AC015574.8N1,(R:)AC016251.9chr14 74929175 74929443 22.2 (L:)AF111167.2,(R:)JDP2 chr15 97922806 97923015 11.1 (L:)LRRC28,(R:)MEF2Achr14 76454204 76454418 16.8 (L:)7SK,(R:)AC007686.5 chr15 99525076 99525322 17.3 (L:)LRRK1,(R:)CHSY1chr14 87546771 87547152 32.4 GPR65 chr16 631600 631867 14.1 C16orf14chr14 90663582 90663808 15.3 C14orf159 chr16 1183244 1183441 5.3 CACNA1Hchr14 92510078 92510278 8.5 ITPK1 chr16 2329752 2330013 19.1 ABCA3chr14 92582144 92582362 5.4 ITPK1 chr16 2709336 2709547 3.2 PRSS27chr14 93876643 93877110 20.7 (L:)SERPINA6,(R:)SERPINA2 chr16 4361599 4361813 11.9 CORO7chr14 94865332 94865567 7.3 (L:)CLMN,(R:)C14orf49 chr16 11613757 11613996 28.8 (L:)LITAF,(R:)SNNchr14 95052199 95052456 21.9 (L:)C14orf49,(R:)SCARNA13 chr16 25038434 25038629 8.3 LCMT1chr14 99728796 99729039 14.9 (L:)DEGS2,(R:)YY1 chr16 27282758 27282991 12.6 IL4Rchr14 100762963 100763158 8.8 (L:)U3,(R:)AL049836.3 chr16 29984135 29984476 53.8 ALDOAchr14 100944476 100944865 32.4 (L:)U3,(R:)AL049836.3 chr16 30597255 30597495 17.8 (L:)AC093249.4N1,(R:)SRCAPchr14 103621065 103621290 8.7 (L:)TDRD9,(R:)ASPG chr16 30705257 30705514 13.6 ZNF629chr15 23658784 23659006 11.1 ATP10A chr16 51646095 51646344 29.3 (L:)AC007346.6,(R:)CHD9  245  Chr Coordinate MASC.score Associated.Genes Chr Coordinate MASC.score Associated.Geneschr16 52676868 52677150 16.6 FTO chr17 69119863 69120165 26.1 (L:)SDK2,(R:)C17orf54chr16 56975569 56975853 27.5 (L:)U6,(R:)GINS3 chr17 71292328 71292523 6.8 (L:)H3F3B,(R:)UNKchr16 69030580 69030869 60.6 (L:)ST3GAL2,(R:)U6 chr17 74034455 74034731 21.9 DNAH17chr16 71520849 71521182 96.2 ATBF1 chr17 77009165 77009382 12.3 AC139149.5E1chr16 71607078 71607285 9.8 ATBF1 chr17 77462038 77462325 13.0 PCYT2chr16 79268039 79268234 6.1 CDYL2 chr17 77655026 77655320 52.4 CCDC57chr16 79429001 79429389 81.5 (L:)CDYL2,(R:)C16orf61 chr17 78651128 78651383 14.2 (L:)METRNL,(R:)NULLchr16 83903006 83903271 37.4 (L:)TMEM148,(R:)AC092275.3 chr18 19556632 19556827 9.7 LAMA3chr16 83976039 83976388 30.0 (L:)AC092275.3,(R:)KIAA0182 chr18 19826526 19826750 11.2 TTC39Cchr16 84048627 84048848 9.7 (L:)AC092275.3,(R:)KIAA0182 chr18 19988779 19988974 6.9 CABYRchr16 84062183 84062378 9.7 (L:)AC092275.3,(R:)KIAA0182 chr18 41636678 41636951 12.7 (L:)SLC14A1,(R:)SIGLEC15chr16 84063673 84064039 29.0 (L:)AC092275.3,(R:)KIAA0182 chr18 41672674 41673043 23.9 SIGLEC15chr16 84561881 84562167 17.0 (L:)IRF8,(R:)Y_RNA chr18 44777319 44777579 22.7 (L:)SMAD7,(R:)DYMchr16 84806437 84806652 10.6 (L:)IRF8,(R:)Y_RNA chr18 44801998 44802242 11.6 (L:)SMAD7,(R:)DYMchr16 86542001 86542282 14.2 (L:)CA5A,(R:)BANP chr18 54871587 54871952 49.6 (L:)ZNF532,(R:)SEC11L3chr16 87523084 87523319 12.1 CBFA2T3 chr19 1291759 1291954 8.5 (L:)EFNA2,(R:)MUM1chr17 937697 938018 37.0 ABR chr19 2440274 2440611 42.5 (L:)GADD45B,(R:)AC005512.1E1chr17 2561895 2562243 26.6 (L:)KIAA0664,(R:)hsaEmirE1253 chr19 2530084 2530555 90.0 AC005512.1E2chr17 3826245 3826550 25.1 (L:)ATP2A3,(R:)ZZEF1 chr19 2621250 2621502 10.3 AC005512.1E2chr17 4426019 4426214 9.3 (L:)GGT6,(R:)SMTNL2 chr19 3287731 3288155 15.3 (L:)BRUNOL5,(R:)NFICchr17 4443889 4444153 29.0 SMTNL2 chr19 3335947 3336178 16.2 NFICchr17 7083564 7083785 15.1 (L:)PHF23,(R:)GABARAP chr19 3338713 3338980 13.9 NFICchr17 19783989 19784263 25.9 AKAP10 chr19 7369688 7369887 9.3 ARHGEF18chr17 19784989 19785296 26.2 AKAP10 chr19 7589985 7590398 47.8 (L:)KIAA1543,(R:)XAB2chr17 23985334 23985529 7.0 KIAA0100 chr19 11500627 11500965 16.4 ECSITchr17 36028569 36028936 7.4 (L:)AC004585.1E1,(R:)AC004585.1E2 chr19 23375019 23375235 15.8 (L:)ZNF91,(R:)ZNF725chr17 36320198 36320404 6.8 (L:)KRT20,(R:)KRT23 chr19 39268262 39268564 9.9 AC016587.9chr17 38268126 38268377 18.9 (L:)AOC3,(R:)AC016889.28E2 chr19 40223741 40224095 23.0 HPNchr17 45062548 45062893 66.5 SPOP chr19 43918878 43919111 13.8 CAPN12chr17 46288422 46288689 13.0 (L:)WFIKKN2,(R:)TOB1 chr19 45663765 45663982 7.3 (L:)BLVRB,(R:)SPTBN4chr17 46290915 46291316 24.7 (L:)WFIKKN2,(R:)TOB1 chr19 46095005 46095208 11.2 AC008537.5E1chr17 46379233 46379492 22.6 (L:)TOB1,(R:)SPAG9 chr19 46988753 46988960 14.2 (L:)CEACAM6,(R:)CEACAM3chr17 63799832 63800055 5.1 (L:)SLC16A6,(R:)ARSG chr19 47080137 47080359 11.1 ARHGEF1chr17 65779280 65779617 20.7 (L:)KCNJ2,(R:)AC005181.1 chr19 48461639 48461834 5.6 PSG9chr17 68148293 68148524 14.2 (L:)AC005243.1,(R:)SLC39A11 chr19 48629327 48629522 9.7 (L:)TEX101,(R:)LYPD3  246  Chr Coordinate MASC.score Associated.Genes Chr Coordinate MASC.score Associated.Geneschr19 49943536 49943795 22.3 BCL3 chr2 165977975 165978237 8.3 (L:)SCN2A2,(R:)CSRNP3chr19 50266267 50266628 8.4 ZNF296 chr2 176648700 176648895 9.7 RPLP1P4chr19 51979814 51980095 44.2 SLC1A5 chr2 190060260 190060571 14.2 (L:)WDR75,(R:)SLC40A1chr19 52098549 52098914 30.6 (L:)AP2S1,(R:)GRLF1 chr2 190060735 190060960 8.3 (L:)WDR75,(R:)SLC40A1chr19 52104554 52104808 14.2 (L:)AP2S1,(R:)GRLF1 chr2 190101803 190102021 9.7 (L:)WDR75,(R:)SLC40A1chr19 54838196 54838400 8.4 SCAF1 chr2 198415681 198416168 44.6 (L:)BOLL,(R:)PLCL1chr19 56265997 56266301 12.7 (L:)KLK13,(R:)KLK14 chr2 204687938 204688149 12.7 (L:)ICOS,(R:)PARD3Bchr19 57002614 57002809 8.3 FPR3 chr2 215789804 215790227 61.4 (L:)ABCA12,(R:)AC073284.5chr2 10009241 10009520 29.2 GRHL1 chr2 217330658 217330866 9.7 (L:)IGFBP5,(R:)5S_rRNAchr2 18874210 18874445 8.3 (L:)RDH14,(R:)OSR1 chr2 219973203 219973494 22.7 (L:)DNPEP,(R:)DESchr2 19201658 19201941 19.0 (L:)RDH14,(R:)OSR1 chr2 226256967 226257162 8.3 (L:)AC019231.1,(R:)AC093784.3chr2 20438149 20438424 13.6 (L:)PUM2,(R:)5S_rRNA chr2 232092455 232092661 11.2 (L:)C2orf52,(R:)NMUR1chr2 20532446 20532740 25.8 (L:)RHOB,(R:)Y_RNA chr2 232280776 232280982 15.0 PTMA,PTMAchr2 35377161 35377413 11.2 (L:)AC013442.1,(R:)U6 chr2 232898785 232899076 21.2 DIS3L2,DIS3L2chr2 43536973 43537299 24.0 THADA chr2 233015626 233015989 9.2 (L:)ALPPL2,(R:)ALPIchr2 43822108 43822331 12.7 PLEKHH2 chr2 239854836 239855217 20.7 HDAC4chr2 46176470 46176753 11.2 PRKCE chr2 241481413 241481710 8.8 C2orf54chr2 55472791 55473027 11.2 CCDC88A chr20 2697003 2697222 9.2 (L:)EBF4,(R:)RPL19P1chr2 66293492 66293709 17.4 (L:)AC074391.5,(R:)AC118345.1 chr20 9460313 9460588 24.3 (L:)C20orf103,(R:)PAK7chr2 72655562 72656002 29.2 EXOC6B chr20 19712784 19713097 12.5 (L:)SLC24A3,(R:)AL132821.17chr2 74978536 74978836 32.8 (L:)HK2,(R:)AC104135.5 chr20 22608950 22609202 15.8 (L:)FOXA2,(R:)AL158175.7chr2 85214481 85214699 8.0 TCF7L1 chr20 25552290 25552528 12.4 NANPchr2 98069896 98070236 44.1 VWA3B chr20 26136884 26137085 6.5 (L:)C20orf191,(R:)RP4N610C12.2chr2 100049737 100050132 20.7 AFF3 chr20 32045107 32045306 7.5 RALYchr2 100870669 100870864 9.7 NPAS2 chr20 32401768 32401965 8.8 (L:)CTDN3216D2.4,(R:)ITCHchr2 101235411 101235631 12.7 (L:)TBC1D8,(R:)C2orf29 chr20 34116053 34116577 58.0 (L:)C20orf152,(R:)HMGB3L2chr2 105381992 105382187 11.6 FHL2 chr20 36170466 36170703 8.5 (L:)SRP_euk_arch,(R:)TGM2chr2 106130143 106130342 11.2 UXS1 chr20 42240697 42240967 24.4 JPH2chr2 132890175 132890398 9.9 (L:)U6,(R:)GPR39 chr20 43885986 43886333 43.3 TNNC2chr2 143637444 143637843 38.3 ARHGAP15 chr20 48377325 48377559 16.9 (L:)AL354889.14,(R:)AL034429.1N1chr2 143808919 143809148 9.7 ARHGAP15 chr20 48445243 48445594 16.2 (L:)AL354889.14,(R:)AL034429.1N1chr2 146801701 146801927 14.2 (L:)AC064865.6,(R:)PABPCP2 chr20 48464756 48465036 19.9 (L:)AL354889.14,(R:)AL034429.1N1chr2 156637505 156637824 38.8 (L:)U6,(R:)NR4A2 chr20 55388409 55388615 6.9 (L:)RAE1,(R:)RBM38chr2 158696883 158697192 25.8 UPP2 chr20 61134947 61135175 16.4 AL121673.41chr2 163054028 163054381 22.4 KCNH7 chr20 62058176 62058516 51.9 UCKL1,ZNF512B  247  Chr Coordinate MASC.score Associated.Genes Chr Coordinate MASC.score Associated.Geneschr20 62164642 62165024 67.4 TCEA2 chr22 45240363 45240608 14.3 CELSR1chr21 13983291 13983500 9.7 (L:)AL050303.5,(R:)FEM1DP chr22 45240730 45240971 20.2 CELSR1chr21 14644570 14644768 8.8 (L:)ABCC13,(R:)STCH chr22 48732812 48733090 12.4 (L:)CRELD2,(R:)PIM3chr21 15157531 15157927 23.5 (L:)AF127936.8,(R:)NRIP1 chr3 1247956 1248166 8.3 CNTN6chr21 15636736 15637163 44.7 (L:)NRIP1,(R:)CYCSP42 chr3 4705124 4705492 5.6 ITPR1chr21 17414686 17414884 11.2 (L:)C21orf34,(R:)AP000457.1 chr3 4994896 4995321 7.5 (L:)AC018816.6,(R:)BHLHE40chr21 21323316 21323582 20.8 NCAM2 chr3 8493544 8493829 19.0 (L:)AC023481.6,(R:)LMCD1chr21 21533007 21533357 11.2 NCAM2 chr3 17984752 17985014 13.9 (L:)AC104297.1,(R:)SATB1chr21 22183099 22183441 57.8 (L:)AF241725.1,(R:)C21orf74 chr3 17990199 17990569 27.5 (L:)AC104297.1,(R:)SATB1chr21 22191582 22192174 46.2 (L:)AF241725.1,(R:)C21orf74 chr3 18522473 18522843 20.7 (L:)SATB1,(R:)AC099053.2chr21 22215592 22216004 29.2 (L:)AF241725.1,(R:)C21orf74 chr3 18787722 18787955 9.7 (L:)SATB1,(R:)AC099053.2chr21 26648068 26648467 11.2 (L:)C21orf118,(R:)AP001597.1 chr3 18857045 18857333 17.4 (L:)SATB1,(R:)AC099053.2chr21 26684461 26684727 9.7 (L:)C21orf118,(R:)AP001597.1 chr3 18859696 18860114 10.8 (L:)SATB1,(R:)AC099053.2chr21 29152030 29152297 13.4 (L:)C21orf94,(R:)HEMK2 chr3 20202705 20202987 15.3 (L:)SGOL1,(R:)U6chr21 36252792 36253015 11.2 (L:)AF020802.2I4,(R:)AP000688.1I1 chr3 24285335 24285698 26.8 THRBchr21 39739094 39739340 21.2 C21orf13 chr3 27468459 27468670 17.4 SLC4A7chr21 39739528 39739817 13.6 C21orf13,SH3BGR chr3 32126829 32127053 16.8 GPD1Lchr21 39888489 39888693 9.7 B3GALT5 chr3 35502893 35503155 6.8 (L:)U6,(R:)ARPPI21chr21 40604046 40604466 24.0 DSCAM chr3 39999277 39999722 21.8 MYRIPchr21 40608149 40608483 17.4 DSCAM chr3 40541206 40541473 18.0 ZNF621chr21 46536719 46537051 30.2 C21orf57 chr3 46012274 46012520 5.4 FYCO1chr21 46537939 46538308 27.2 C21orf57 chr3 57007329 57007582 27.4 ARHGEF3chr22 22131054 22131260 6.5 (L:)AP000344.1I1,(R:)AP000344.1I3 chr3 58388783 58388978 5.6 PDHBchr22 22193981 22194215 6.2 (L:)AP000344.1I3,(R:)IGLL1 chr3 58482795 58483117 15.3 ACOX2chr22 23268797 23269041 24.8 C22orf13 chr3 61100726 61100938 14.2 (L:)5S_rRNA,(R:)PTPRGchr22 24126808 24127060 22.6 LRP5L chr3 64452194 64452461 9.7 (L:)PRICKLE2,(R:)U6chr22 25503465 25503741 17.4 (L:)Z99774.1,(R:)RP1I40G4P.1 chr3 77470094 77470346 9.9 ROBO2chr22 27544599 27544953 16.8 (L:)XBP1,(R:)CTAI292E10.7 chr3 102076374 102076672 18.9 ABI3BPchr22 29118982 29119197 16.6 (L:)RNF215,(R:)SEC14L2 chr3 121862816 121863170 49.8 HGDchr22 35915182 35915475 20.7 (L:)C1QTNF6,(R:)SSTR3 chr3 127126411 127126626 16.2 AC092903.11I1chr22 36944643 36945150 11.6 (L:)MAFF,(R:)TMEM184B chr3 127468843 127469162 56.7 (L:)ALDH1L1,(R:)KLF15chr22 38009635 38009945 36.7 (L:)5S_rRNA,(R:)RPL3 chr3 130687423 130687649 9.2 IFT122chr22 38389691 38389978 26.5 CACNA1I chr3 131109978 131110194 8.1 (L:)TMCC1,(R:)TRHchr22 40143946 40144207 21.2 (L:)TEF,(R:)TOB2 chr3 132563201 132563450 7.3 (L:)NEK11,(R:)AC010210.24chr22 42125954 42126192 9.2 (L:)SCUBE1,(R:)MPPED1 chr3 135565704 135565957 15.5 AMOTL2  248  Chr Coordinate MASC.score Associated.Genes Chr Coordinate MASC.score Associated.Geneschr3 142603619 142603916 13.6 ZBTB38 chr4 3432895 3433393 28.2 (L:)HGFAC,(R:)DOK7chr3 142628467 142628685 13.8 ZBTB38 chr4 3438489 3438780 26.8 DOK7chr3 145363727 145364099 22.7 (L:)C3orf58,(R:)AC022495.17B1 chr4 6332969 6333164 7.4 WFS1chr3 147316112 147316351 10.9 PLOD2 chr4 27924708 27924994 22.4 (L:)AC097380.2,(R:)AC091602.6chr3 150425679 150425921 8.7 (L:)CP,(R:)TM4SF18 chr4 27925123 27925331 9.9 (L:)AC097380.2,(R:)AC091602.6chr3 151185770 151186118 15.7 (L:)AC117395.5B3,(R:)AC117395.5B4 chr4 37150211 37150542 21.4 C4orf19chr3 158700743 158701064 22.0 VEPH1 chr4 68477027 68477247 15.8 TMPRSS11Achr3 160366674 160366904 11.1 IQCJ chr4 85722616 85722851 8.3 (L:)NKX6B1,(R:)CDS1chr3 162231648 162231918 13.4 PPM1L chr4 89382774 89383007 17.4 (L:)U6,(R:)AC097484.3B2chr3 162473162 162473358 8.8 (L:)NMD3,(R:)C3orf57 chr4 101920367 101920737 55.5 (L:)EMCN,(R:)PPP3CAchr3 170157907 170158163 11.4 (L:)C3orf50,(R:)EVI1 chr4 103056342 103056668 12.7 BANK1chr3 170629764 170630113 8.4 (L:)MDS1,(R:)AC074033.18 chr4 113100803 113101037 11.2 (L:)U6,(R:)C4orf32chr3 171535363 171535562 5.7 (L:)PRKCI,(R:)SKIL chr4 115466960 115467372 22.4 (L:)ARSJ,(R:)UGT8chr3 172818709 172819300 36.2 PLD1 chr4 116910883 116911100 8.3 (L:)NDST4,(R:)AC024248.7B1chr3 175681247 175681648 11.1 (L:)AC069218.13,(R:)NAALADL2 chr4 141291769 141291998 16.3 (L:)MAML3,(R:)SCOCchr3 176327594 176328050 6.6 NAALADL2 chr4 145877434 145877658 14.2 HHIPchr3 176337148 176337439 25.9 NAALADL2 chr4 154076640 154076851 7.1 (L:)AC099339.6,(R:)FHDC1chr3 178621249 178621451 6.5 (L:)TBL1XR1,(R:)AC026325.17 chr4 158504008 158504350 20.7 GRIA2chr3 183900294 183900732 113.5 (L:)AC007547.26B2,(R:)ATP11B chr4 159235323 159235657 20.7 (L:)Y_RNA,(R:)C4orf18chr3 183909193 183909571 12.5 (L:)AC007547.26B2,(R:)ATP11B chr4 164526166 164526361 4.9 (L:)NPY5R,(R:)TKTL2chr3 183945927 183946279 38.2 (L:)AC007547.26B2,(R:)ATP11B chr4 164589112 164589307 7.6 (L:)NPY5R,(R:)TKTL2chr3 185798429 185798649 15.8 (L:)EPHB3,(R:)MAGEF1 chr4 168092605 168092812 7.3 SPOCK3chr3 189236931 189237126 10.6 (L:)BCL6,(R:)AC022498.18 chr4 169974592 169974806 8.3 PALLDchr3 190303600 190303919 16.9 (L:)LPP,(R:)TPRG1 chr4 185489743 185490034 17.4 (L:)AC079080.5B2,(R:)AC103540.5chr3 190374264 190374462 9.8 TPRG1 chr4 186795781 186795998 11.2 SORBS2chr3 190764579 190764823 9.3 (L:)TPRG1,(R:)AC078809.9 chr4 187835355 187835558 9.7 FAT1chr3 193495693 193495961 12.5 FGF12 chr4 187920972 187921466 15.8 (L:)FAT1,(R:)AC097521.2B1chr3 194804054 194804301 10.1 OPA1 chr5 32599513 32599732 8.3 (L:)ZFR,(R:)SUB1chr3 194935251 194935498 16.1 (L:)OPA1,(R:)AC069421.9 chr5 32746305 32746576 17.5 (L:)SUB1,(R:)NPR3chr3 195070967 195071233 20.6 (L:)AC069421.9,(R:)AC024559.15 chr5 35345167 35345605 70.9 (L:)PRLR,(R:)U3chr3 195335406 195335737 14.1 (L:)AC024559.15,(R:)HES1 chr5 38867009 38867306 20.8 (L:)AC091435.3B1,(R:)OSMRchr3 195342169 195342432 16.9 (L:)HES1,(R:)SRP_euk_arch chr5 39748562 39749025 145.8 (L:)DAB2,(R:)U1chr3 195516061 195516299 14.6 (L:)AC117469.3,(R:)CPN2 chr5 40811000 40811564 144.6 PRKAA1chr4 977573 977836 28.5 IDUA chr5 43449888 43450088 9.5 (L:)CCL28,(R:)C5orf28chr4 1227919 1228228 32.1 CTBP1 chr5 43505398 43505992 107.2 C5orf28  249  Chr Coordinate MASC.score Associated.Genes Chr Coordinate MASC.score Associated.Geneschr5 45906151 45906436 9.7 (L:)HCN1,(R:)EMB chr6 1985785 1985980 6.9 GMDSchr5 52811489 52811731 16.9 (L:)AC026477.3,(R:)FST chr6 3402625 3402885 17.4 (L:)SLC22A23,(R:)C6orf145chr5 68664328 68664579 18.0 CCDC125 chr6 7007661 7007965 8.5 (L:)AL158817.11C1,(R:)AL355336.15C1chr5 76504693 76505016 19.7 (L:)AC008581.11,(R:)PDE8B chr6 10491779 10492039 18.1 (L:)RP1C290I10.2,(R:)TFAP2Achr5 82654105 82654398 19.0 XRCC4 chr6 11154991 11155343 65.5 (L:)ELOVL2,(R:)AL121955.20chr5 82804637 82804877 14.1 CSPG2 chr6 12600665 12600955 18.8 (L:)AL355137.23C1,(R:)RPL15P3chr5 82895224 82895592 12.7 CSPG2 chr6 12951027 12951335 48.6 PHACTR1chr5 96319950 96320206 18.4 LNPEP chr6 17143380 17143644 29.2 (L:)AL137003.12,(R:)AL136305.14chr5 100006147 100006453 11.2 (L:)FAM174A,(R:)7SK chr6 18973375 18973636 23.2 (L:)hsaCmirC548aC1,(R:)RPL21P61chr5 102370595 102370904 37.2 PAM chr6 21846871 21847327 32.5 (L:)RP3C322L4.2,(R:)7SKchr5 104752183 104752501 25.0 (L:)AC099520.2,(R:)5S_rRNA chr6 26100151 26100367 13.4 (L:)TRIM38,(R:)HIST1H1PS2chr5 104980292 104980664 34.6 (L:)AC099520.2,(R:)5S_rRNA chr6 33807801 33808045 20.3 IP6K3chr5 104983500 104983779 14.2 (L:)AC099520.2,(R:)5S_rRNA chr6 38868199 38868469 11.1 DNAH8chr5 105314064 105314375 22.7 (L:)5S_rRNA,(R:)AC114940.2C1 chr6 43410915 43411161 6.9 ZNF318chr5 109729533 109729730 11.0 (L:)MAN2A1,(R:)hsaCmirC548fC3 chr6 47050653 47050869 9.4 (L:)GPR116,(R:)GPR110chr5 122232909 122233223 22.4 SNX24 chr6 51444037 51444330 11.2 (L:)SNORD66,(R:)AL445529.14chr5 131591670 131591931 10.5 (L:)P4HA2,(R:)PDLIM4 chr6 72134902 72135193 9.7 (L:)OGFRL1,(R:)AL136164.8chr5 133667968 133668272 24.0 CDKL3 chr6 73387950 73388207 17.6 (L:)AL132673.17,(R:)KCNQ5chr5 134470843 134471088 7.6 (L:)PITX1,(R:)H2AFY chr6 73798387 73798689 21.6 KCNQ5chr5 138997353 138997548 5.3 (L:)UBE2D2,(R:)CXXC5 chr6 73800588 73800801 13.5 KCNQ5chr5 138997949 138998347 11.6 (L:)UBE2D2,(R:)CXXC5 chr6 75735726 75736090 71.5 (L:)RP11C560O20.1,(R:)COL12A1chr5 139015487 139015714 6.8 CXXC5 chr6 86360079 86360340 23.4 SNX14chr5 139020634 139020963 20.0 CXXC5 chr6 88174422 88174679 22.6 C6orf165chr5 145341775 145342026 6.1 SH3RF2 chr6 88340620 88340901 30.6 RARS2chr5 147290199 147290405 13.0 (L:)C5orf46,(R:)SPINK5 chr6 91436304 91436576 38.7 (L:)MAP3K7,(R:)AL132766.13C1chr5 147620015 147620212 9.9 (L:)SPINK6,(R:)AC011346.5 chr6 110937743 110937938 6.2 (L:)SLC22A16,(R:)RP11C346C16.4chr5 147620906 147621264 30.2 (L:)SPINK6,(R:)AC011346.5 chr6 113861223 113861486 8.3 (L:)RP1C132N8.1,(R:)AL513123.10chr5 150517501 150517773 8.6 ANXA6 chr6 117910542 117910788 18.4 DCBLD1chr5 153615256 153615451 7.7 GALNT10 chr6 121550540 121551051 63.5 C6orf170chr5 161646422 161646704 34.8 (L:)GABRG2,(R:)U6 chr6 122180162 122180455 11.2 (L:)RP11C129H15.4,(R:)RP3C438G17.1chr5 171595224 171595497 37.2 UBTD2 chr6 139796480 139796685 8.3 (L:)CITED2,(R:)RP11C15H7.1chr5 172859168 172859400 11.4 (L:)U6,(R:)FAM44B chr6 140860222 140860437 10.6 (L:)RP11C15H7.2,(R:)AL356137.10chr5 174111236 174111535 11.9 (L:)MSX2,(R:)AC008413.7 chr6 149439148 149439582 38.3 USTchr5 176206319 176206643 22.8 UNC5A chr6 152027269 152027503 12.7 (L:)C6orf97,(R:)ESR1chr6 1778028 1778223 8.3 GMDS chr6 166961360 166961774 32.1 RPS6KA2  250  Chr Coordinate MASC.score Associated.Genes Chr Coordinate MASC.score Associated.Geneschr7 1257896 1258139 5.6 (L:)UNCX,(R:)MICALL2 chr8 28803649 28803846 7.4 HMBOX1chr7 1565093 1565459 65.9 (L:)TMEM184A,(R:)PSMG3 chr8 29474538 29474803 17.4 (L:)DUSP4,(R:)AC131254.5chr7 2693656 2693859 14.6 AMZ1 chr8 30508502 30508776 20.7 RBPMSchr7 7360866 7361076 8.6 (L:)AC079448.1,(R:)COL28A1 chr8 35511883 35512122 19.0 UNC5Dchr7 7402007 7402218 12.6 COL28A1 chr8 35516505 35516772 13.4 UNC5Dchr7 8002734 8003041 14.8 GLCCI1 chr8 37113898 37114145 8.6 (L:)AC090453.4,(R:)AC137579.3F1chr7 16427670 16427869 11.5 (L:)AC004741.2,(R:)SOSTDC1 chr8 37115028 37115282 12.6 (L:)AC090453.4,(R:)AC137579.3F1chr7 17624704 17624901 4.3 (L:)SNORA63,(R:)SNX13 chr8 40322695 40322981 18.3 (L:)C8orf4,(R:)ZMAT4chr7 20227112 20227319 8.6 (L:)MACC1,(R:)ITGB8 chr8 40337021 40337370 11.0 (L:)C8orf4,(R:)ZMAT4chr7 26370008 26370276 12.7 SNX10 chr8 40350060 40350340 7.4 (L:)C8orf4,(R:)ZMAT4chr7 34037329 34037605 16.0 BMPER chr8 40367577 40367772 6.4 (L:)C8orf4,(R:)ZMAT4chr7 34075759 34076062 15.3 BMPER chr8 40573658 40573954 47.3 ZMAT4chr7 43688406 43688847 61.4 C7orf44 chr8 42868508 42868863 30.2 RNF170chr7 50806044 50806256 7.3 GRB10 chr8 52926173 52926387 9.9 PCMTD1chr7 55777053 55777295 16.8 (L:)FKBP9L,(R:)U6 chr8 67587102 67587327 12.1 C8orf46chr7 55999976 56000297 5.7 GBAS chr8 67600853 67601048 6.0 (L:)C8orf46,(R:)MYBL1chr7 63024006 63024318 59.0 (L:)AC079355.6F1,(R:)AC092634.3 chr8 69833089 69833306 6.9 C8orf34chr7 69830860 69831067 5.4 AUTS2 chr8 75710401 75710692 17.0 (L:)Y_RNA,(R:)PI15chr7 80031320 80031530 9.5 (L:)GNAT3,(R:)CD36 chr8 79605234 79605569 13.1 PKIAchr7 84003400 84003687 22.7 (L:)SEMA3A,(R:)HMGN2L11 chr8 87281303 87281642 50.1 (L:)ATP6V0D2,(R:)SLC7A13chr7 91602043 91602285 21.6 (L:)CYP51A1,(R:)AC000120.1F1 chr8 98395777 98395986 7.3 (L:)TSPYL5,(R:)U3chr7 97719340 97719640 33.0 TECPR1 chr8 102593814 102594042 17.4 GRHL2chr7 105500196 105500410 11.2 (L:)AC004836.2,(R:)SYPL1 chr8 102595873 102596114 26.1 GRHL2chr7 108243498 108243768 12.7 (L:)DNAJB9,(R:)C7orf66 chr8 107705419 107705744 49.0 (L:)AC027031.5,(R:)OXR1chr7 109710512 109710712 6.9 (L:)C7orf66,(R:)IMMP2L chr8 108688068 108688305 12.6 (L:)ANGPT1,(R:)5S_rRNAchr7 115767000 115767225 12.7 (L:)TES,(R:)AC002066.2 chr8 108689701 108689898 8.8 (L:)ANGPT1,(R:)5S_rRNAchr7 129739903 129740233 18.7 CPA4 chr8 108690429 108690864 9.3 (L:)ANGPT1,(R:)5S_rRNAchr7 135108807 135109002 8.3 (L:)FAM180A,(R:)AC091736.2F1 chr8 116862250 116862516 16.5 (L:)TRPS1,(R:)5S_rRNAchr7 135271932 135272205 11.2 MTPN chr8 116919773 116920120 16.2 (L:)TRPS1,(R:)5S_rRNAchr7 139661911 139662118 9.6 (L:)AC093087.3F1,(R:)SLC37A3 chr8 116920233 116920932 108.3 (L:)TRPS1,(R:)5S_rRNAchr7 154216650 154216930 16.3 DPP6 chr8 117051064 117051426 5.1 (L:)TRPS1,(R:)5S_rRNAchr8 303933 304128 4.3 (L:)ZNF596,(R:)FAM87A chr8 117167443 117167653 8.6 (L:)5S_rRNA,(R:)AC103863.2F1chr8 8194516 8194734 9.9 (L:)AC068020.7F2,(R:)AC068353.34 chr8 117507666 117507938 24.3 (L:)AC103863.2F1,(R:)EIF3Hchr8 22043334 22043570 23.9 HR chr8 120736519 120736909 61.7 (L:)ENPP2,(R:)7SKchr8 27607089 27607561 12.7 (L:)U6,(R:)CCDC25 chr8 128992699 128992923 24.0 (L:)AC084123.9,(R:)TMEM75  251     Chr Coordinate MASC.score Associated.Genes Chr Coordinate MASC.score Associated.Geneschr8 129215421 129215661 10.1 (L:)hsa3mir31207,(R:)hsa3mir31208 chr9 110281173 110281479 30.5 (L:)AL137019.6,(R:)RPL36P14chr8 130532363 130532662 16.2 (L:)7SK,(R:)GSDMC chr9 112378126 112378352 11.1 SVEP1chr8 134297870 134298111 10.5 WISP1 chr9 113719331 113719712 13.0 UGCGchr8 143666428 143666834 22.6 (L:)BAI1,(R:)ARC chr9 114941654 114941919 15.8 (L:)ZFP37,(R:)SLC31A2chr8 143676412 143676607 8.7 (L:)BAI1,(R:)ARC chr9 116710274 116710573 27.3 TNFSF8chr9 12768500 12768716 6.9 C9orf150 chr9 118489229 118489429 10.2 ASTN2chr9 13066411 13067030 44.2 (L:)RP113382H24.2,(R:)MPDZ chr9 126551985 126552196 5.4 NR6A1chr9 14442696 14442891 8.3 (L:)NFIB,(R:)RP113408A13.2 chr9 128417465 128417750 17.4 LMX1Bchr9 15367949 15368198 12.7 (L:)AL390246.17,(R:)SNAPC3 chr9 128445079 128445356 11.9 LMX1Bchr9 21440991 21441304 19.0 (L:)IFNA1,(R:)AL353732.14 chr9 130978412 130978727 42.7 IER5Lchr9 23426250 23426454 9.7 (L:)RP113370B11.1,(R:)RP113315I14.1 chr9 131208579 131208780 8.4 (L:)RP11365J3.6,(R:)AL391056.2531chr9 31535462 31535657 11.2 (L:)RP113291J9.2,(R:)RP113402B2.1 chr9 136703078 136703276 8.2 COL5A1chr9 33319215 33319477 20.7 NFX1 chr9 139008507 139008810 48.4 (L:)C9orf142,(R:)CLIC3chr9 35655059 35655267 9.2 C9orf100 chrX 9933070 9933381 12.7 (L:)SHROOM2,(R:)WWC3chr9 37789850 37790076 8.6 SHB chrX 10828595 10828968 22.4 (L:)MID1,(R:)HCCSchr9 79468604 79468822 11.2 (L:)GNA14,(R:)GNAQ chrX 12902572 12902861 18.2 (L:)TLR8,(R:)TMSB4Xchr9 79845306 79845720 36.4 (L:)GNAQ,(R:)RP113336N8.1 chrX 16239803 16240056 20.7 (L:)AC078993.20,(R:)RP113759L5.2chr9 84867545 84867776 8.9 RASEF chrX 16508484 16508722 9.7 (L:)SRP_euk_arch,(R:)CTPS2chr9 85023915 85024191 17.5 (L:)RASEF,(R:)FRMD3 chrX 21532920 21533149 15.8 CNKSR2chr9 92761022 92761276 6.9 (L:)SYK,(R:)RP113305L7.5 chrX 22045217 22045646 43.8 PHEXchr9 96621448 96621655 7.6 C9orf3 chrX 40263342 40263537 5.6 (L:)RP113126D17.1,(R:)ATP6AP2chr9 96806149 96806391 26.6 C9orf3 chrX 43761817 43762110 25.8 (L:)NDP,(R:)AL034370.1chr9 101621852 101622142 29.0 (L:)AL137067.731,(R:)NR4A3 chrX 66832599 66832876 49.6 ARchr9 107172309 107172527 13.9 SLC44A1 chrX 99887576 99887836 12.7 (L:)SYTL4,(R:)RP33347M6.1chr9 107756214 107756543 62.7 (L:)RP113219P18.3,(R:)7SK chrX 100593417 100593651 17.4 (L:)RP13164F3.8,(R:)ARMCX4chr9 109045960 109046231 10.4 (L:)AL445487.8,(R:)RAD23B chrX 149212378 149212596 12.7 (L:)RP133507I23.2,(R:)MAMLD1chr9 109430796 109431032 7.0 (L:)RP113438P9.2,(R:)U6 chrX 153869519 153869912 59.4 F8  252 B.2 Unique ER binding sequences treated with of siPPP2R2A in T47D cells.  1 hour of β-estradiol treatment, n=2   Chr MASC'score Associated'Genes Chr MASC'score Associated'Geneschr11 69693280 69693918 421.0 ANO1 chr5 16641925 16642297 135.0 FAM134Bchr17 72796428 72796802 309.1 (L:)SEC14L1,(R:)SEPT9 chr19 38812260 38812710 130.5 (L:)PEPD,(R:)CHST8chr5 141640743 141641152 307.6 (L:)NDFIP1,(R:)SPRY4 chr11 116212041 116212559 129.6 APOA1chr6 34626572 34627123 298.9 SPDEF chr9 33224033 33224458 127.3 SPINK4chr7 75777375 75777751 277.6 (L:)HSPB1,(R:)YWHAG chr20 46747799 46748491 127.0 PREX1chr14 92541373 92541732 253.8 ITPK1 chr19 19521450 19521939 124.7 (L:)CILP2,(R:)PBX4chr3 50139258 50140040 246.5 (L:)RBM5,(R:)SEMA3F chr7 72943526 72944144 122.2 (L:)WBSCR28,(R:)ELNchrX 40160040 40160406 233.0 (L:)AC091807.4H1,(R:)Y_RNA chr11 1805882 1806275 121.4 (L:)AC139143.2,(R:)SYT8chr9 135542096 135542557 224.8 SARDH chr19 52531944 52532432 121.2 (L:)C5AR1,(R:)GPR77chr2 128755819 128756252 223.9 HS6ST1 chr22 40144763 40145107 119.9 (L:)TEF,(R:)TOB2chr15 38134331 38135177 220.2 AC021755.9H4 chr15 68457464 68458031 114.9 (L:)U6,(R:)UACAchr3 130768850 130769208 217.1 PLXND1 chr3 195544996 195545372 112.0 CPN2chr17 23849029 23849483 214.1 (L:)SLC13A2,(R:)AC005726.1H1 chr19 4927315 4928371 112.0 JMJD2Bchr10 78845062 78845396 202.3 KCNMA1 chr21 32800752 32801172 111.5 C21orf63chr1 223704839 223705165 200.1 (L:)LBR,(R:)ENAH chr5 66568366 66568677 110.1 (L:)CD180,(R:)AC008459.7chr5 134411037 134411429 200.0 (L:)PITX1,(R:)H2AFY chr17 4383524 4384126 109.1 SPNS2chr22 31451442 31451854 195.4 SYN3 chr17 60621261 60621649 108.9 RGS9chr17 27006094 27006524 195.2 (L:)hsaHmirH365H2,(R:)U6 chr3 22880486 22880852 105.3 (L:)AC104740.2,(R:)AC099544.2chr15 62024963 62025437 194.6 DAPK2 chr20 46308552 46308862 102.6 (L:)AL121777.39,(R:)PREX1chr20 48118300 48118759 193.8 (L:)SNAI1,(R:)UBE2V1 chr16 578144 578538 101.6 (L:)PIGQ,(R:)RAB40Cchr12 52663330 52663770 191.4 (L:)HOXC11,(R:)HOXC10 chr17 46088114 46088448 101.4 ABCC3chr8 128882185 128882588 187.3 AC084123.9 chr3 140502585 140502945 101.0 (L:)AC069525.16,(R:)MRPS22chr22 38379324 38379858 185.6 CACNA1I chr3 152507034 152507368 100.3 MED12L,GPR87chr9 129757612 129758164 181.2 FAM102A chr1 15176378 15176832 99.8 RP1H21O18.1chr4 141776266 141776638 165.7 TBC1D9 chr8 11684544 11684986 98.0 (L:)NEIL2,(R:)FDFT1chr18 44771190 44771643 159.8 (L:)SMAD7,(R:)DYM chr14 74791431 74791815 94.8 (L:)AL691403.2,(R:)FOSchr7 101403074 101403525 157.1 CUX1 chr22 38208687 38209121 93.8 MGAT3chr8 128948410 128948875 156.9 (L:)AC084123.9,(R:)TMEM75 chr16 87357348 87357962 91.4 (L:)AC138028.1H3,(R:)CDT1chr17 78437050 78437483 151.1 TBCD chr22 25283000 25283347 90.7 TPST2chr8 99248247 99248698 135.7 (L:)POP1,(R:)NPAL2 chr3 58062742 58063149 90.7 FLNBCoordinate Coordinate  253 Chr MASC'score Associated'Genes Chr MASC'score Associated'Geneschr9 35507569 35508028 90.7 RUSC2 chr10 95320808 95321113 57.8 GPR120chr11 72582510 72582876 89.0 (L:)FCHSD2,(R:)P2RY2 chr2 20538100 20538492 56.2 (L:)RHOB,(R:)Y_RNAchr11 60281320 60281917 88.8 MS4A15 chr5 122487514 122487930 55.6 PRDM6chr6 44185889 44186400 88.5 (L:)AL109615.42D2,(R:)MRPL14 chr16 83051827 83052470 55.0 ATP2C2chr1 181515601 181515956 85.8 NMNAT2 chr1 202376879 202377260 52.9 ETNK2chr2 159292027 159292426 85.3 AC005042.2 chr6 33815951 33816316 52.1 IP6K3chr4 77751691 77752107 85.3 SHROOM3 chr5 145272405 145272699 51.9 (L:)GRXCR2,(R:)SH3RF2chr9 124007146 124007555 83.8 LHX6 chr11 46880946 46881339 51.9 LRP4chr19 54765547 54765974 83.6 NOSIP chr22 38018767 38019199 51.3 (L:)5S_rRNA,(R:)RPL3chr16 47601660 47602147 82.7 (L:)U6,(R:)CBLN1 chr6 3693525 3693882 51.3 C6orf145chr3 8340741 8341159 81.8 (L:)GRM7,(R:)AC023481.6 chr1 41137645 41138007 51.2 (L:)CITED4,(R:)RP11D348A7.1chr6 36197805 36198527 81.1 (L:)MAPK14,(R:)MAPK13 chr11 66551613 66552083 50.0 SYT12chr20 2997626 2998035 80.2 (L:)MRPS26,(R:)OXT chr10 32087990 32088413 49.7 (L:)Y_RNA,(R:)ARHGAP12chr2 18169170 18169515 78.3 (L:)SNORA40,(R:)RDH14 chr8 49471250 49471506 48.1 (L:)AC041040.6,(R:)AC026904.7chr17 52323061 52323465 75.6 TRIM25 chr1 18365330 18365647 48.0 IGSF21chr6 119839064 119839470 74.8 (L:)MAN1A1,(R:)AL606830.4 chr22 32586334 32586739 47.3 LARGEchr11 78448705 78449019 73.3 ODZ4 chr2 240293508 240294469 46.5 AC093802.3chr16 8787637 8788084 72.9 (L:)ABAT,(R:)C16orf51 chr1 109495412 109495741 44.9 KIAA1324chr11 72616327 72617003 72.4 P2RY2 chr3 135241128 135241446 44.9 (L:)SLCO2A1,(R:)RYKchr1 231284018 231284365 72.2 PCNXL2 chr20 52173615 52173968 43.4 (L:)BCAS1,(R:)CYP24A1chr15 87794697 87795005 71.5 (L:)hsaDmirD9D3,(R:)RHCG chr19 18971274 18971659 43.3 SFRS14chr7 62139106 62139597 71.5 (L:)U6,(R:)snoU2_19 chrX 109303096 109303435 43.3 TMEM164chr3 53560266 53560683 71.3 CACNA1D chr11 46467519 46467849 43.2 AMBRA1chr11 1806344 1806749 71.1 (L:)AC139143.2,(R:)SYT8 chr1 17436436 17436825 41.7 PADI1chr19 43896880 43897317 70.3 ACTN4 chr6 133798002 133798586 41.7 EYA4chr3 43013209 43013678 70.3 C3orf41 chr14 105279036 105279293 40.8 (L:)IGHEP1,(R:)IGHG3chr17 68025539 68025873 69.3 (L:)AC005243.1,(R:)SLC39A11 chr18 47272072 47272348 40.2 (L:)U1,(R:)AC011260.8chr11 44567537 44567843 68.7 CD82 chr2 238069773 238070140 40.2 MLPHchr8 29632632 29633031 67.9 (L:)DUSP4,(R:)AC131254.5 chr1 153180552 153181012 39.4 (L:)PMVK,(R:)PBXIP1chr17 63949194 63949569 64.2 WIPI1 chr20 45414709 45415083 38.6 ZMYND8chr3 14451495 14451814 63.8 SLC6A6 chr22 23949416 23949826 37.2 CRYBB2chr1 167344703 167345058 63.0 ATP1B1 chr2 42091414 42091708 37.1 (L:)AC013480.1,(R:)SGK493chr5 134410349 134410639 61.6 (L:)PITX1,(R:)H2AFY chr20 57762307 57762594 37.0 PHACTR3chr5 96423492 96423873 61.1 (L:)AC008865.3D2,(R:)LIX1 chr7 1942479 1943211 36.0 MAD1L1chr14 25263418 25263733 59.5 (L:)AL163052.4,(R:)SNORD37 chr6 118552320 118552583 35.6 SLC35F1Coordinate Coordinate  254  Chr MASC'score Associated'Genes Chr MASC'score Associated'Geneschr9 33364820 33365149 35.6 (L:)NFX1,(R:)AQP7 chr6 159046296 159046964 27.7 SYTL3chr21 36482315 36482743 35.5 DOPEY2 chr6 3657897 3658337 27.5 (L:)SLC22A23,(R:)C6orf145chr13 98508887 98509163 35.5 DOCK9 chr8 37829862 37830123 27.1 (L:)BRF2,(R:)RAB11FIP1chr19 3664074 3664632 35.0 RAX2 chr20 49437535 49437789 26.8 (L:)RPSAP1,(R:)NFATC2chr3 13492621 13492988 34.8 (L:)NUP210,(R:)HDAC11 chr13 97743918 97744140 26.8 FARP1chr21 41950141 41950444 34.6 (L:)SNORA32,(R:)RIPK4 chr2 20134669 20135389 26.8 (L:)LAPTM4A,(R:)AC098828.3chr1 21440727 21441098 34.1 ECE1 chr7 127411262 127411578 26.8 SND1chr3 66562027 66562376 34.1 LRIG1 chr19 2480536 2480794 26.4 AC005512.1K2,GNG7chr10 51790870 51791141 33.1 SGMS1 chr15 96852785 96853133 26.1 (L:)FAM169B,(R:)IGF1Rchr20 48616326 48616579 32.7 PTPN1 chr8 22026028 22026425 26.0 (L:)NUDT18,(R:)HRchr17 1626944 1627234 32.6 SERPINF1 chr6 15330128 15330394 25.9 (L:)AL050335.32K2,(R:)JARID2chr6 42302480 42302873 32.6 TRERF1 chr19 15451963 15452278 25.6 (L:)PGLYRP2,(R:)CYP4F22chr20 29760275 29760622 32.4 BCL2L1 chr1 178464340 178464796 25.3 (L:)QSOX1,(R:)LHX4chr10 79319137 79319450 31.3 DLG5 chr10 6446919 6447233 25.2 (L:)AL137145.13K2,(R:)PRKCQchr12 23279646 23279959 31.3 (L:)ETNK1,(R:)SOX5 chr5 16562385 16562649 24.9 FAM134Bchr10 60752965 60753265 31.1 FAM13C chr15 68387701 68387972 24.6 (L:)U6,(R:)UACAchr2 126727970 126728266 31.1 (L:)snosnR60_Z15,(R:)AC013474.10K1 chr3 58057662 58057942 24.0 FLNBchr1 109590317 109590774 30.8 (L:)SARS,(R:)CELSR2 chr6 19023000 19023309 23.7 (L:)hsaKmirK548aK1,(R:)RPL21P61chr15 38190198 38190759 30.1 (L:)BMF,(R:)AC021755.9K1 chr19 56288048 56288523 23.7 (L:)KLK14,(R:)ATPBD3chr17 43979119 43979623 30.0 (L:)HOXB2,(R:)HOXB3 chr1 205481541 205481821 23.4 (L:)RP11K6J21.1,(R:)CD55chr7 43814625 43814914 30.0 (L:)BLVRA,(R:)MRPS24 chr1 11549523 11549755 22.6 (L:)PTCHD2,(R:)FBXO2chr1 17728571 17728838 29.7 (L:)RCC2,(R:)ARHGEF10L chr1 31504173 31504478 22.6 (L:)NKAIN1,(R:)SNRNP40chr11 38439068 38439516 29.7 (L:)C11orf74,(R:)U6 chr8 8586904 8587212 22.6 (L:)SRP_euk_arch,(R:)AC087269.5chrX 151834567 151834947 29.7 ZNF185 chr9 33226027 33226338 22.6 SPINK4chr2 99592755 99593038 29.6 AFF3 chrX 71505455 71505707 22.6 HDAC8chr18 9747722 9748278 29.4 (L:)U6,(R:)RAB31 chr17 63943951 63944216 22.6 WIPI1chr11 82799368 82799836 29.3 (L:)AP000446.5,(R:)DLG2 chr2 159676226 159676583 22.3 TANC1chr10 134663416 134663929 29.0 (L:)AL691429.17K2,(R:)GPR123 chr10 82717165 82717411 22.3 (L:)RP11K315E23.1,(R:)RP11K102H24.1chr8 128941016 128941596 28.8 (L:)AC084123.9,(R:)TMEM75 chr20 46995917 46996153 22.0 ARFGEF2chr5 122216233 122216559 28.5 SNX24 chr5 159529526 159529754 21.7 (L:)PWWP2A,(R:)FABP6chr10 27316844 27317087 28.2 (L:)AL139404.9K2,(R:)ANKRD26 chr10 100064150 100064628 21.2 (L:)LOXL4,(R:)C10orf33chr11 35075979 35076296 28.2 (L:)PDHX,(R:)CD44 chr22 22103487 22103819 21.2 (L:)AP000344.1K1,(R:)AP000344.1K3chr10 99321410 99321745 28.1 (L:)UBTD1,(R:)ANKRD2 chr3 66616409 66616666 21.2 LRIG1chr11 128244030 128244316 27.9 (L:)KCNJ1,(R:)KCNJ5 chr3 101305713 101305961 21.2 C3orf26,FILIP1Lchr20 19510209 19510600 27.9 SLC24A3 chr4 15613229 15613531 21.2 PROM1Coordinate Coordinate  255  Chr MASC'score Associated'Genes Chr MASC'score Associated'Geneschr6 135617279 135617575 21.2 (L:)hsa2mir2548a22,(R:)AHI1 chr2 152184338 152184556 16.0 NEBchr20 48515087 48515369 21.1 (L:)AL034429.121,(R:)COX6CP2 chr5 73300114 73300379 16.0 (L:)AC093283.322,(R:)AC091899.2chr14 45430415 45430866 20.8 (L:)U6,(R:)RPL10L chr9 120806866 120807148 15.7 (L:)RP112127L21.2,(R:)DBC1chr15 23657450 23657736 20.6 ATP10A chr20 45563880 45564142 15.4 NCOA3chr11 131272433 131272695 20.4 HNT chr20 55253192 55253378 15.1 BMP7chr10 24197173 24197436 19.9 KIAA1217 chr11 93569227 93569560 15.0 (L:)PANX1,(R:)FOLR4chr11 6293618 6293889 19.9 (L:)CCKBR,(R:)PRKCDBP chr1 147476134 147476433 14.8 (L:)U1,(R:)AL356957.2725chr18 59043874 59044229 19.9 BCL2 chr9 126064636 126065167 14.8 NEK6chr19 53257833 53258079 19.9 PLA2G4C chr1 15604316 15604683 14.7 (L:)FHAD1,(R:)EFHD2chr19 63017111 63017438 19.9 ZNF552 chr13 27915129 27915338 14.7 FLT1chr11 82028785 82029016 19.6 (L:)AP002783.326,(R:)FAM181B chr2 235778356 235778571 14.7 (L:)SH3BP4,(R:)AC092576.3chr6 42151178 42151405 19.6 TAF8 chr4 104919276 104919604 14.7 (L:)TACR3,(R:)CXXC4chr16 79213020 79213316 19.5 CDYL2 chr21 40612274 40612763 14.6 DSCAMchr20 62340134 62340511 19.2 MYT1 chr14 90662465 90662722 14.5 C14orf159chr16 66853062 66853363 19.1 (L:)LYPLA3,(R:)SLC7A6 chr3 176886360 176886627 14.5 NAALADL2chr12 51649955 51650219 18.7 (L:)KRT18P19,(R:)EIF4B chr20 46622205 46622456 14.2 (L:)AL121777.39,(R:)PREX1chr11 94218408 94218784 18.7 AMOTL1 chr16 67008085 67008336 14.0 SMPD3chr10 22961966 22962218 18.5 PIP5K2A chr16 27816838 27817038 13.8 GSG1Lchr22 37528768 37529053 18.5 (L:)DNAL4,(R:)NPTXR chr3 64216621 64216831 13.8 (L:)PRICKLE2,(R:)U6chr6 97005245 97005463 18.5 (L:)SRP_euk_arch,(R:)KIAA0776 chr17 56831562 56831843 13.7 (L:)BCAS3,(R:)TBX2chr10 78946953 78947217 18.5 KCNMA1 chr10 120375060 120375302 13.4 (L:)PRLHR,(R:)AL356865.1922chr20 52245342 52245581 18.5 (L:)CYP24A1,(R:)PFDN4 chr5 173183626 173184089 13.4 (L:)FAM44B,(R:)CPEB4chr9 115886351 115886710 18.3 (L:)AMBP,(R:)KIF12 chr7 30166063 30166322 13.1 C7orf41chr13 105357289 105357577 17.5 (L:)DAOA,(R:)AL603632.3 chr11 36127739 36128029 13.1 LDLRAD3chr12 2756335 2756625 17.5 (L:)CACNA1C,(R:)FKBP4 chr8 22648299 22648644 13.1 AC105046.10chr2 64354346 64354634 17.4 (L:)PELI1,(R:)AC008074.1 chr2 105434510 105434803 13.0 (L:)FHL2,(R:)NCK2chr11 61570980 61571286 17.2 (L:)FTHL16,(R:)INCENP chr4 77709466 77709675 13.0 SHROOM3chr2 57173649 57173928 17.2 (L:)AC008173.1,(R:)SNORD78 chr20 29755942 29756191 12.3 BCL2L1chr8 67195150 67195440 17.2 (L:)AC084082.5,(R:)TRIM55 chr1 48132891 48133212 12.2 (L:)AL691459.25,(R:)RP42683M8.1chr14 94876341 94876744 16.8 (L:)CLMN,(R:)C14orf49 chr11 13907702 13907896 12.2 (L:)5S_rRNA,(R:)SPON1chr7 22395807 22396076 16.7 (L:)RAPGEF5,(R:)AC099759.1 chr12 15323910 15324159 12.2 (L:)RERG,(R:)PTPROchr21 40279109 40279344 16.4 (L:)PCP4,(R:)DSCAM chr4 149208213 149208405 12.2 ARHGAP10chr11 59870113 59870375 16.2 (L:)MS4A6E,(R:)MS4A7 chr12 68614689 68614915 12.2 (L:)RAB3IP,(R:)C12orf28chr8 101459976 101460202 16.0 (L:)RNF19A,(R:)ANKRD46 chr1 201295018 201295378 12.0 PPFIA4chr2 20444549 20444855 16.0 (L:)PUM2,(R:)5S_rRNA chr1 165076636 165076877 11.8 POGKCoordinateCoordinate  256       Chr MASC'score Associated'Genes Chr MASC'score Associated'Geneschr3 103525990 103526299 11.6 ZPLD1 chr11 34801470 34801645 7.7 (L:)EHF,(R:)APIPchr12 67473233 67473466 11.2 (L:)SLC35E3,(R:)MDM2 chr20 52117750 52117931 7.7 BCAS1chr2 44184378 44184563 11.0 (L:)AC019129.1,(R:)AC019129.8 chr5 161851767 161851966 7.7 (L:)U6,(R:)AC010602.7B1chr17 36815414 36815751 10.6 (L:)KRT31,(R:)AC003958.1 chr20 55166059 55166234 7.6 (L:)U6,(R:)BMP7chr17 55396647 55396861 10.4 RNFT1 chr10 70762716 70762913 7.6 HK1chr1 93838537 93838870 10.3 BCAR3 chr8 130541268 130541450 7.6 (L:)7SK,(R:)GSDMCchr3 98399138 98399344 10.1 EPHA6 chr11 129979826 129980225 7.1 (L:)AP004371.2,(R:)C11orf44chr6 30866822 30867037 10.1 (L:)IER3,(R:)AL662797.7 chr2 99645688 99645874 6.7 AFF3chr2 238075940 238076183 9.9 MLPH chr5 177356559 177356845 6.7 (L:)PROP1,(R:)FAM153Cchr4 185487506 185487746 9.9 (L:)AC079080.5B2,(R:)AC103540.5 chr22 19754884 19755118 6.3 (L:)AC002472.14,(R:)AP000550.1B2chr9 86794906 86795087 9.9 NTRK2 chr9 118279365 118279554 6.1 ASTN2chr11 60225420 60225688 9.2 MS4A8B chr2 157690053 157690228 5.7 (L:)GPD2,(R:)AC016732.7B1chr17 55824530 55824773 9.2 (L:)USP32,(R:)C17orf64 chr3 59622729 59622967 5.7 (L:)C3orf67,(R:)FHITchr6 53438580 53438771 9.0 (L:)AL591034.5B2,(R:)GCLC chr15 68095644 68095820 5.6 (L:)AC021818.9,(R:)TLE3chr20 48812927 48813167 8.9 (L:)PARD6B,(R:)BCAS4 chr12 25562932 25563107 5.1 IFLTD1chr9 114566948 114567179 8.8 SNX30 chr20 48774774 48774949 4.8 (L:)C20orf175,(R:)PARD6Bchr4 52619300 52619498 8.8 SPATA18 chr15 97229319 97229494 4.7 IGF1Rchr6 169080905 169081142 8.8 (L:)SMOC2,(R:)THBS2 chr17 52404664 52404883 4.6 (L:)COIL,(R:)SCPEP1chr7 96338496 96338708 8.8 (L:)SHFM1,(R:)AC004774.1B1 chr17 56895003 56895210 4.5 TBX4chr3 46179567 46179806 8.7 (L:)XCR1,(R:)CCR1 chr20 45381229 45381404 4.3 ZMYND8chr5 122518969 122519253 8.7 PRDM6 chr20 52250661 52250836 4.1 (L:)CYP24A1,(R:)PFDN4chr20 45420949 45421170 8.4 (L:)ZMYND8,(R:)RPL35AP chr20 48781610 48781787 3.5 PARD6Bchr20 55144391 55144592 8.1 (L:)U6,(R:)BMP7 chr20 55224640 55224815 3.5 BMP7chr7 148532389 148532603 8.1 ZNF282 chr20 51965480 51965683 3.2 (L:)AC005220.1,(R:)BCAS1Coordinate Coordinate  257 Appendix C  -  RNA sequencing identified differentially regulated genes in T47D cells after PPP2R2A knockdown.  C.1 Up regulated genes in T47D cells after PPP2R2A knockdown  With/without 6 hours of β-estradiol treatment, n=2.  A:#siPPP2R2A+E2#vs#siNT+E2#(Yellow);#B:#siPPP2R2A#vs#siNT#(Green);#C:#siNT+E2#vs#siNT#(Blue);#D:#siPPP2R2A+E2#vs#siPPP2R2A#(Red)A.BCD:#!GNG12!BCAS1!CACNA1D!RAB27B!SEC14L5!TMEM106A!FGF12!HUNK!SLC2A13!TRIM29!ATP2B4!GALNS!ZBTB41!TNFRSF21!ITPR1!CD24!C5!PAG1!NEB!MATN3!CUX2!LOC339535!FSTL1!ANO5!CDH19!LTBP1!HDAC9!ID2!SPNS2!STARD13!EPS15!FNIP1!CYFIP2!SHANK2!TEP1!HS6ST3!ATXN1!KIAA1683!SH3RF2!PLCB4!PADI2!APOOL!LOC92249!TXNRD1!STOM!ALS2CR8!FLRT3!DIXDC1!TP53INP1!DLG2!FAM73A!EDN1!MYO5A!TRIM9!PRICKLE2!ARHGEF38!R3HDM2!TTN!AKAP11!PANK3!NIPAL2!KIAA0240!MBNL1!ADD3!LRRC37A4!DLX1!LYPLA1!STYX!ITSN1!PTPN14!MANEA!SLC11A2!C7orf41!KLHL28!CSAD!HPX!EHD2!NAALADL2!PAMR1!PTPRJ!ATG14!FSIP2!PDLIM5!USP54!PLD1!KLF5!ENPP5!VPS13B!LNX2!PER3!LANCL1!PPP1R12B!NBEA!GPNMB!CDC42BPA!LOC646719!FAM198B!KIAA1033!HIVEP1!GK5!XYLB!PARD3B!TMCO3!LINC00472!COL4A5!ATF7IP!SPOPL!BAZ2B!CDC42EP3!PLA2G4C!ADAM9!FAM49A!LMBRD2!ARHGAP35!CERS6!TMEM106B!CRISPLD1!FGD5GAS1!ALDH1L2!ZNF493!ODZ2!TLN2!ATP11B!RHOBTB3!IKZF2!PNPLA7!LRRC58!HIPK2!TPM4!ZMAT1!EVC!RNF24!STARD4!GAB1!SLC22A23!PALLD!POLH!ZNF323!CCDC125!CHRM1!CHIC1!RIN2!C9orf106!PTER!ZNF780A!PMS2P5!C9orf5!CSMD1!AHCYL2!PRLR!DNAH1!ZKSCAN1!ALDH1A3!SPEF2!DISP1!AR!AKAP6!ZFP106!ZNF28!TNS3!FUT9!PHC3!ZNF586!LRRC37A3!TTC18!LOC100506497!ZDHHC8P1!HIST1H4H!ZNF91!MZB1!HEATR5A!PCDHGA10!DICER1!GNL3L!KIAA1549!KIAA0430!ANO6!NCALD!XIAP!PLA2R1!NQO1!PLEKHG4B!STAG3L2!HCAR1!C5orf41!F2RL1!MBD5!FOXO3!GNAQ!MAGI3!MAML2!ZNF70!ITGA2!DMXL2!STRN!CYP4V2!ARID5B!ADAMTS15!LOC100129034!PCLO!KRT86!SOD3!PUS10!CYP1B1GAS1!CAMKK1!YPEL1!KIAA1841!SLC30A4!MLL3!SLC6A17!HIPK3!DTWD2!PIK3IP1!PCDHGA6!SMG1!EFCAB6!TRPS1!DST!NDST4!LOC100507495!DKFZP586I1420!LATS1!DENND4A!VASH2!PRKAR2A!TCF4!MARCH1!MAP3K1!DLX2!NHLRC2!GPR98!NIPAL1!LRRIQ4!HLF!SIAE!JAKMIP3!PDZD2!NTN4!PLEKHA7!CYP39A1!ATP7A!GRM4!KRT81!GP1BA!FLJ45340!ZNF611!SECISBP2L!TCP11L1!RASGRF1!C5orf42!DOCK5!KIAA1109!CNTN3!WDR78!ITGB8!VPS13C!ZNF780B!MUM1L1!TBC1D8B!C12orf51!CMYA5!ERN1!WDR27!JMJD1C!AFF4!DAPP1!PCDH7!ENTPD5!WDR66!TGFB2!ATL3!ZNF708!ATM!LOC100329109!LOC646214!LOC100216545!ZNF417!ASH1L!USP53!C2CD3!ZNF551!SRGAP1!MYH3!SHPK!ZNF397!CPEB2!PPARA!KIAA2022!PUS7L!MMP16!PCDHA5!FLJ37201!GPD1!MYH11!SYT15!ZNF462!GRIK1!MYO9A!MRPL23GAS1!C8orf44!CAPN9!EXPH5!LOC100131691!PALM2!ZNF557!F8!KIF27!LOC90246!MAPK10!HIST1H2AI!GPR110!KIAA0825!APAF1!SLC16A4!MFAP3L!C10orf68!NLGN1!PPM1L!ZNF137P!TTBK2!ZNF562!FRMD3!LOC100132356!EPPK1!ARL5B!TMEM154!RC3H2!PCDHA3!ZNF192!HMBOX1!DNAH2!ANKRD36!FP588!GPR135!PHEX!ZNF846!CLOCK!GARNL3!KCNAB1!PCDHA11!PCDHGB2!LOC286437!GRK4!SPTBN4!CKMT2!PIK3C2G!KNDC1!C10orf103!BTBD11!KLF7!VMAC!P2RX7!WDR96!N4BP2!LOC652276!POF1B!MORN1!ANKRD20A5P!C17orf57!LOC400084!ASXL2!NFAT5!TMEM140!GABRB3!LOC728537!ALDH6A1!LOC553103!LRP2BP!KLHL31!ZSCAN12P1!GABRA3!NCOA2!LOC642236!STX1B!UNC5A!C10orf118!C7orf63!LINC00478!SLC22A20!ANKK1!SLC9A7P1!FAM135A!C21orf49!AHNAK!ZNF790!ANKRD36B!RBM26GAS1!GHRLOS!LOC100130890!PAQR8!ARHGEF26GAS1!DGCR9!RASA2!STL!ZNF813!DTX2P1GUPK3BP1GPMS2P11!FAM154B!PCDH18!PHF7!NEK5!LOC646862!SLC44A5!BRWD1GIT2!KIF5A!RASEF!KLHL7GAS1!KRTAP5G1!UNC5B!LOC100507266!RGS5!DPY19L2P2!HPD!ANKRD31!AGAP11!FILIP1L!SYTL2!CEP97!ZNF736!PARP11!BMS1P4!SYNM!LOC254100!PP2D1!GPR68!IYD!FAM63B!MLLT4GAS1!C15orf62!LOC441242!SRGAP2P2!KIF5C!KCNH5!STAG3L1!JAG1!NRK!ZNF749!EXOC6B!ARHGAP31!GUSBP2!MYCBPAP!HCLS1!PCDHGA11!TULP1!CASC2!PRDM6!ID4!PLEKHM1P!CCDC7!GTF2A1!IGSF22!LOC100132735!LOC285359!MACROD2!LOC100130950!RFX3!SEMA6D!TCP11L2!MAK!SH3TC2!LOC100130357!C8orf77!TSC22D1GAS1!ALOX12!CTNND1!LOC100508120!LOC100272228!FAM71F2!HIATL2!LOC100507589!CAPS2!LOC100505815!ZBTB37!LOC100133315!MIR186!TFAP2B!ODZ1!ENPP3!ZNF284!ZNF141!ZNF469!SYNC!HIST1H2BF!C3orf35!C17orf109!SYDE2!BACE1GAS!ZNF726!ATP6AP1L!CLUL1!RNF152!YPEL4!ZNF541!PAH!FAR2!GNG7!PCDHAC1!MLLT10P1!LUST!RAET1E!SYCP2L!TMED8!PAQR5!FLJ39639!ZRANB2GAS1!CCDC30!FBXO15!KCNIP4!LOC158257!GPT!KIAA1199!LOC100288069!ATL1!PHACTR3!GNASGAS1!FAT2!LOC643401!CHRNB2!ZNF224!PAPPA!LOC100272216!DGCR10!PLIN1!CCDC122!C16orf3!TACR2!FSCN2!ANK2!DSCR9!TAG!PCDHA13!PPIL6!MAP1LC3B2!GHRLOS2!PCDHGB7!ZNF354C!C6orf164!LOC644961!GLIPR1L2!PEX5L!LRGUK!F3!ZNF132!CEACAM16!GPR64!LOC148696!GPX2!C19orf18!ENKUR!ADAMTS20!SUMO1P3!MIR3916!CLEC3A!STAC3!LOC150622!C6orf163!HINT3!B.ACD:!PHLDA1!SKAP2!ABCC12!HMGCS2!FBXO32!CD99L2!STEAP1!VAV3!PAPSS2!RND3!LEPR!EGLN3!ZNF812!HSPA6!TP53AIP1!NOSTRIN!C14orf162!CLGN!IFIT2!DSE!RAD9B!C.ABD:#STC2!PTGES!KCTD6!IFITM10!RARA!PFKFB3!RBBP8!JAK1!SIAH2!FKBP4!KLK10!HEY2!IGFBP4!GRIK4!FGD3!SSTR2!SUSD3!GNG11!C8orf46!WFIKKN2!NOD2!ACOX2!BFSP2!TNNI2!KCNK15!RANBP3L!RAB37!C1orf168!CT62!ZNF669!RLN2!CHST8!CMTM7!PCP4!SYN1!C12orf60!ANKFN1!D.ABC:!AMOTL1!CELSR1!PPP4R4!ZBTB40!DSCAM!TIPARP!CEP135!SYT12!KIAA1211!SLC4A7!CACNG4!UGCG!DCAF16!ADCY9!MACC1!ARHGAP26!SH2B3!KRT15!MAN1C1!KAZN!HMCN1!GPR37L1!SACS!ATP11A!RAB6C!KCNF1!LOC440905!KCNC4!PLEKHG5!LOC100506394!AKAP12!SHC2!ZFPM2!FLJ10661!TNFRSF25!PCDHB3!MDGA1!FHDC1!BAIAP2L2!SLC22A4!NR2E3!SNHG4!C8orf45!DNAH10!KIRREL!BRWD3!CHST3!MGC45800!LOC283174!NEU3!IL16!C17orf67!LOC100616668!C1orf130!INE1!B7H6!PLK5!FRK!MIR647!LOC100507178!LOC100507373!SSPO!PRTG!VWA3A!MIR635!TTLL6!SRPK3!MIR1914!KCP!NAT8L!AB.CD:!ARHGAP15!LRRC31!TRPM8!CYP4Z2P!KCNMA1!MAP2!UBL3!SLC38A1!MDGA2!VTCN1!STEAP2!AHR!CREB5!NEK11!ACSL6!EPAS1!THSD7A!RAPGEF6!SAMD4A!CLDN16!KCNC1!SARM1!KCNB1!TIAM2!CDH10!COL4A6!AP1S3!AGXT2!LOC440173!LOC728323!OPN3!CEACAM20!ATP10B!CCDC146!GUCY1B2!FSD1L!CHST1!SSPN!C1GALT1C1!SPATA13!PARK2!CCL28!AVIL!SLC13A3!BDKRB2!CNTNAP2!DNAJC27GAS1!S1PR3!LINC00476!KIAA1210!CRYM!CDH4!BDNFGAS1!PTPRQ!SLC9A10!LOC100130451!LOC100507557!WDFY3GAS2!SRRM5!LOC100506334!HGD!UBAP1L!KIAA1024!MAFF!LOC338651!GPR75!SYNPO2!GNB1L!CLMP!ABCC2!CNTN5!RGS6!CATSPERB!BCO2!NDNF!AD.BC:!CYP1A1!KSR2!PLCE1!CYP1A2!L1CAM!SLC1A2!TRIM2!LOC100129917!ZNF808!CPAMD8!DCN!SPATA24!SLC7A5!RPL23AP64!CCDC144B!TET1!LOC100505648!PAR5!MYO3B!SAMD12!CYP1B1!SFT2D2!HOOK3!ZNF460!ZNF587!WDR31!CBX5!HSPG2!NPR3!DNAJC18!LOC100216546!LOC730091!PARGSN!CD5!C5orf65!KIAA0889!GAN!CSRNP3!ZNF518A!IGSF9B!FAT1!EPS8L3!SPDYE1!FAM40B!FANCC!FLJ34208!HEG1!LOC283922!TPO!PCDHGA9!NRP1!ZNF221!ATP13A4!ATF7IP2!ZNF81!NBEAL1!MICAL2!PCDHA9!UBXN7!C9orf131!KATNAL1!NEDD9!ZDBF2!GPR155!LOC650623!ZNF573!CTNS!ATP2A1!FOSL2!ZNF716!ZNF587B!LOC441204!CDHR5!SPDYE5!KRBA2!SLC13A4!FTX!DUOX1!PCDHB19P!GUCY1A2!SLC2A12!DPRXP4!LOC100132832!LOC100507117!DPY19L2!OCLM!PCDHA2!ZC3HAV1L!LOC100129387!C2orf88!SPATC1!GP6!CDK6!FAM106CP!NXNL2!DNAH6!CACNA2D1!LOC641515!ZNF483!RPS6KA2!C8orf39!GNAT2!WDR88!GPR179!MYO15B!SPDYE6!LOC641298!ZNF642!DGKH!LOC100271836!COL5A1!PFN1P2!S100A8!LNPEP!NLRC3!PLXDC1!CYP8B1!MCM3APGAS1!CDHR3!SNED1!PLAC4!GPR157!SLC7A5P2!SGK196!STOX2!CCDC144A!KLHL11!HEATR4!SGK494!C16orf89!PCDHGA2!PVRIG!CROCCP3!CACNB2!CEACAM19!POLN!POU5F1P4!PCDHGC5!ZNF154!FRRS1!DRD4!FLJ45513!C10orf108!C5orf4!TRPC6!ALOX12P2!INTS4L2!SLC16A8!MEGF11!SPDYE3!CSPG4!PCDHB18!DNAH12!ZNF727!FAM22A!MUC6!SLC34A3!GCNT7!REL!NRIP2!LRRTM2!PCDHGB3!ZBED6!SH3GL1P1!C5AR1!SRCRB4D!CYP2E1!PCDHGA12!RORA!GAS2L3!ASPG!LOC100131347!ACP5!LOC100506655!FAM186B!GAL3ST2!SLC26A1!ANKAR!RYR3!TMED10P1!C17orf66!TGFBR3!ZNF699!TMPRSS9!SH3D21!PCDHGC4!CYP2W1!NNAT!ZNF665!RGL4!DNAH3!LOC100506046!LOC284412!CG030!ST3GAL5!EYS!PHKG1!CYP2D6!PPM1E!CDKL5!WNK3!UCKL1GAS1!LOC100132774!SYCP3!LOC100335030!ANKRD62P1GPARP4P3!PCDHGA4!MIR570!MIR4664!FABP3!RFTN1!GALNT4!ZBTB32!FAM22G!CYP2D7P1!CARNS1!EGR1!BRSK2!BC.AD:!TFF1!BMPER!SEMA3A!TXNDC5!CALCR!ABD.C:#STXBP5L!LOC100128054!LGI1!KIAA1456!ANKRD36BP1!CACNG1!LGALS2!GDAP1L1!ASB9P1!CEACAM22P!MGC70870!FKBP1AP1!LOC100505478!C17orf78!VTN!IGSF23!SLC47A2!ZNF121!CLEC18B!FLJ40292!EID3!ACD.B:!LRRC15!SERPINA10!FMN1!CLPS!LHFPL5!GPR132!ABCD:#!EPHA8!LOC399715!  258 C.2 Down regulated genes in T47D cells after PPP2R2A knockdown,  With/without 6 hours of β-estradiol treatment, n=2.      A:#siPPP2R2A+E2#vs#siNT+E2#(Yellow);#B:#siPPP2R2A#vs#siNT#(Green);#C:#siNT+E2#vs#siNT#(Blue);#D:#siPPP2R2A+E2#vs#siPPP2R2A#(Red)A.BCD:#CLN6%STARD7%PPP2R2A%EPB41L4A2AS1%SLC29A1%E2F1%TSPAN15%NKAIN1%CDT1%TMEM66%EI24%FEN1%DEPDC1B%RTF1%TCTN3%RPS2%BZW1%TSPAN3%GLTSCR2%MAEA%HMGA1%MCM5%CHORDC1%LGMN%DHX58%SNHG1%SYT8%NOLC1%SRSF3%GAS5%SNHG7%GINS2%CDC6%CDCA5%SNHG5%STC2%PRKCD%IFITM10%C19orf48%BIRC5%RPS6%FUS%KCNK15%KIAA0101%PRMT1%ITM2C%H2AFX%PPP1R14B%RPS27A%ZNFX12AS1%EEF1G%TMEM206%RPS3%MED19%HEXA%NASP%CDC45%RPS9%TUBG1%HNRNPAB%UBE2S%MCM10%KRT13%AAGAB%PDRG1%PA2G4%RPL8%RPL3%MCM3%C20orf20%EIF3D%APOE%PPM1G%RPL7%GFM1%NFATC4%STAP2%CCNO%ZMYND19%RBM38%MB%HSPA1A%PSME2%RPL11%C10orf81%RPL13A%DONSON%RPS14%ORC6%RPS25%PKMYT1%RPL35A%C17orf96%RPL9%RPS23%HERC6%RPS3A%ATP6V0B%ASF1B%TSPAN4%MRPS28%SNRPB%VEGFB%RPL23%RPL18A%CMTM7%RPL4%PSMG1%AGR3%FBL%MRPS23%RPL13%NOP56%CNIH2%TK1%PTGES%C12orf75%RPL27A%CDC20%GAR1%RPL22L1%PCNA%RCC1%EMD%HRAS%CCNB2%RPL38%TGM2%JAGN1%NACA%RPL37A%RPS13%PSMG3%MESDC1%RPL27%RPL6%C17orf762AS1%RPL36%ISOC2%SNHG6%RPL34%DIABLO%IGFBP4%RPL18%PDLIM1%RPS16%RPL39%RPS21%ACAT2%RPS24%MDK%RPL37%CENPM%ACOX2%RPLP0%RPL31%RPL10A%NT5C%SIVA1%RPL24%SNRPG%RHOD%RPL5%RPS7%LTB4R%ANGPT1%TNFAIP2%RPL19%YDJC%PSMB1%ALOXE3%SNHG12%RPSA%SPINK5%TMEM208%ENO3%RPL26L1%RAB34%TLCD1%GNB2L1%RNASEH2A%FCGRT%RPL35%RMI2%TSPAN1%RPIA%CREBL2%RPL32%SUSD3%RPS15%GMNN%PSMC3%MRPL2%ATP5D%PRDX5%P4HA1%ARL6IP4%SLIRP%MTFP1%RPS12%C11orf84%MFGE8%SSTR2%MRPL14%FAU%DTD1%RBP7%RPS4X%RPLP2%FOS%UBE2C%RPL29%RPS27%TSTA3%LSM7%PSMB4%RPS8%FAM65C%RPS26%PCP4%CTSL2%SNHG8%CITED1%PGLYRP2%ARPC4%CENPA%FBXL15%RPS20%RPS5%NUDT1%DMKN%RPL23A%RPS29%WDR54%SCAND1%CDC34%EBP%MRPL12%SH3BP1%RPL26%RPL7A%UBXN1%MRPL27%C19orf66%CKS2%PSMB7%OIP5%C16orf59%C16orf48%MRPL28%RAD51%SURF2%C19orf43%C12orf69%NRGN%TRAPPC5%FBXO2%MTHFD1L%TMEM189%EGFL7%RPS15A%LOC388796%METTL11A%SPC25%CENPH%PTTG1%FIBCD1%MRPL11%SNHG15%TRMT112%SNRPF%CD63%HERC5%LGALS1%IFI35%NDUFB9%HLA2DRB1%LOC100288911%AURKB%TMEM98%NDUFS5%TSSC4%FAM57A%METTL1%SNRPD2%FBLN5%MAFG2AS1%SAT2%TIMM10%GSTT2%CDC25A%HLA2DQB1%NDUFS3%CHCHD5%MYO7A%COX8A%HSD11B2%RPL36AL%ARTN%RGS14%LINC00116%MZT2A%TMEM177%PFDN2%MRPS18C%GATA4%CD74%TOMM7%TNNC1%PDF%TMEM121%NENF%RPS28%NUDT8%RP9%TNNI2%FAM173A%CCDC85B%MRPL54%CHD5%DOK7%GLYCTK%MRPL23%PTRHD1%NOP16%RIMS4%CCDC80%METTL12%CENPW%CCNE1%MSX1%CRIP1%ORC1%POLE4%VEGFC%IGF2BP2%TIMM8B%WNK2%PHF5A%SEC61B%CHCHD10%SENP3%RPL28%UBA52%LHX2%RGS19%CCDC74B%C1orf135%ADORA1%HSH2D%MOCS1%SDF2L1%HSPA2%C12orf57%PHLDA2%MGST2%LOC400236%PLEK2%RPLP1%SOCS3%SCN1B%C11orf75%LIPT2%RND2%NUAK1%HAUS7%MND1%RAB3IL1%HOXB7%C4orf48%WT1%HYAL3%BCAR4%C19orf40%C14orf182%F12%SNORA57%SLC38A3%HHEX%CORO7%SNORD31%ARNTL2%EFEMP2%MZT2B%RAC3%CYB5R2%ZNRD1%LOC100131138%PDK4%RASL10B%IGFBP7%SLC43A1%SNORA61%LOC100506343%SGK3%HLA2F%SPINK4%C17orf61%FBLN1%RPL17%ZNF6252ZNF20%STMN3%METTL7B%NFKBIL1%ICAM1%ANKFN1%PIM1%HOXB6%LINGO1%CNTFR%SCARNA7%#B.ACD:#CBLN2%FAM167A%DCLK2%SPEG%SEMA3D%SLITRK6%ZSWIM4%ITGA7%NGFR%C7orf31%PRKCG%HIST1H1E%C20orf160%ZNF618%LINC00256B%RCOR2%ZNF238%KCP%WLS%CIB2%PANX2%KIAA0319%MIR1914%#C.ABD:%AMIGO2%GRM4%CYP1A1%ADAMTS15%ARL4D%ID2%CCDC154%FAM113B%CYP1A2%KCNC1%CXCR7%TWIST1%NDP%PTHLH%FAM171B%D.ABC:#%HMGCS2%DUSP5%C1orf64%BMP4%FBXO32%ELF3%GRB7%LMCD1%DERA%SCARA3%SELENBP1%PIP%KMO%ANGPTL4%C2orf72%CYR61%CLU%RAB20%HLA2A%MGP%EFNA1%RHOBTB1%JUNB%CEACAM6%TSC22D3%PALMD%STEAP4%S100P%HSPA6%PPP2R4%LIN28A%PLEKHD1%FRAT1%LGALS3BP%AQP3%IVL%GREM2%GDF15%TTC36%ADCY2%RGS16%ISG20%C21orf63%CREB3L1%TGM3%IFIT2%HOPX%UQCRHL%C4BPB%CEACAM5%AB.CD:%LUM%UPK2%PROCR%ABCC13%LYN%PLEKHB1%KREMEN2%HEY2%DOCK10%GLIS2%PALM3%A4GALT%IRS1%BFSP2%SLC47A1%TPM2%GJA1%SPHK1%PTGER2%DUSP15%ADAMTS7%FAM105A%C11orf93%CPVL%SH3KBP1%MEIS3%PMP22%GJB2%KDM5B2AS1%CD83%SERPING1%TGFB1%NMB%DRD2%ITGB6%C10orf114%HSPA1B%TMEM88%KRTAP321%PAK6%TMEM51%NAT8L%SLC26A10%SEMA6B%TFCP2L1%PSD3%TMCC2%AD.BC:#ADM%CRISP3%APOD%TP53I3%MVD%RND1%DUSP4%PACSIN3%U2AF1%TRAF4%PLSCR1%AZGP1%RPS19%TFF3%VASN%TFF1%RPS11%NDRG1%IER5%RPL12%IFITM2%UGT3A2%S100A13%HMGB3%PPAP2C%EVPLL%PPIB%SNCG%IFITM1%SOX2%IFITM3%CHRD%BIK%MANF%DUSP6%DLL1%TRAPPC3%SNORD104%CLDN7%NR2F1%IRF9%S100A1%C11orf51%CPNE7%COX16%EPB41L2%MX1%OAS1%IFI6%TIMP1%RARRES3%ISG15%RPPH1%IFIT1%IGFBP6%RMRP%IFI27%SNORD36C%CD40%OASL%BIRC7%CNFN%IFIT3%DHRS2%TRPV4%SNCB%TMEM92%XAF1%TCL1B%C14orf162%DDX60%ABD.C:%CA11%SRGN%TLE6%RTBDN%AMTN%ODAM%  259 Appendix D  - Signaling pathways and network enrichment analysis of differentially regulated genes.  D.1 Signaling pathway analysis in T47D cells after PPP2R2A knockdown with 6 hours -estradiol treatment.  Based on transcriptome analysis, condition: siPPP2R2A+E2 vs siNT+E2, with 3 hours of β-estradiol treatment. Pathway databases: Reactome (R), Biocarta (B), KEGG (K), NCI (N).  GeneSet # GeneSet #Interferon(Signaling(R) 32 EPHA(forward(signaling(N) 3Herpes(simplex(infection(K) 20 CytokineDcytokine(R(interaction(K) 8Influenza(A(K) 15 EPO(signaling(((N) 3Antigen(process/(presentation(K) 11 Signaling(events(((by(TCPTP(N) 3Class(I(MHC(presentation(R) 15 RIGDIDlike(receptor(signaling(((K) 4Allograft(rejection(K) 7 Leishmaniasis(K) 4GraftDversusDhost(disease(K) 7 Mitotic(MDM/G1(phases(R) 7Type(I(diabetes(mellitus(K) 7 GMCSFD((signaling(events(N) 3Endocytosis(K) 12 ECM(organization(R) 6Autoimmune(thyroid(disease(K) 7 Signaling(by(FGFR(R) 6EpsteinDBarr(virus(infection(K) 11 (BCR)(R) 9Hepatitis(C(K) 9 Signaling(by(ERBB2(R) 6Viral(myocarditis(K) 7 Glutamatergic(synapse(K) 5Measles(K) 9 TGFDbeta(signaling(((P) 4Phagosome(K) 9 Dopaminergic(synapse(K) 5HTLVDI(infection(K) 11 TGFDbeta(signaling(((K) 4Pancreatic(cancer(K) 6 ErbB(receptor(signaling((N) 2Chagas(disease((K) 7 Mitotic(G1DG1/S(phases(R) 5Chemokine(signaling(((K) 9 Signaling(by(EGFR(R) 6Growth(Hormone(signaling(R) 4 Integrins(in(angiogenesis(N) 3Signaling(by(SCFDKIT(R) 8 Signaling(by(EGFR(in(Cancer(R) 6Cell(adhesion(molecules((CAMs)(K) 7 Type(II(diabetes(mellitus(K) 3Natural(killer(cell(((cytotoxicity(K) 7 Ceramide(signaling(((N) 3Signaling(by(PDGF(R) 8 Beta5(beta6(beta7integrin((N) 2RIGDI/MDA5(IFNDalpha/beta((s(R) 5 map(kinase(inactivation(of(smrt((B) 2PIP3(activates(AKT(signaling(R) 6 Signaling(events(((by(PTP1B(N) 3PI3K/AKT(Signaling(in(Cancer(R) 6 Malaria(K) 3TGFDbeta(receptor(signaling(N) 4 SHP2(signaling(N) 3Signaling(by(ERBB4(R) 7 MAPK(signaling(((K) 7egf(signaling(((B) 3 IL2D((signaling(events(N) 3Platelet(regulation((R) 9 ErbB1(downstream(signaling(N) 4Rheumatoid(arthritis(K) 5 Retrograde(endocannabinoid((K) 4Immunoregulatory(interactions((R) 8 Staphylococcus(aureus(infection(K) 35HT2(type(receptor(signaling(((P) 3 NonsenseD((Decay(R) 4ALK1(signaling(events(N) 3 TRAIL(signaling(((N) 2Signaling(by(FGFR(in(disease(R) 7 NGF(signalling(via(TRKA(R) 6OsteopontinD((events(N) 3 Amoebiasis(K) 4MHC(class(II(presentation(R) 5 IL12D((signaling(events(N) 3  260  GeneSet # GeneSet #Colorectal)cancer(K) 3 Aquaporin4))transport(R) 2Cytosolic)DNA4sensing))(K) 3 GnRH)signaling))(K) 3Alzheimer)disease4presenilin))(P) 4 Toll4like)receptor)signaling))(K) 3inhibition)of)cellular)proliferation(B) 2 Chromosome)Maintenance(R) 3Cholinergic)synapse(K) 4 keratinocyte)differentiation(B) 2Glypican)1)network(N) 2 DNA)replication(P) 1Beta1)adrenergic)receptor)signaling)(P) 2 Signaling(c4Kit)(N) 2pdgf)signaling))(B) 2 )s)in)cancer(K) 6AP41)transcription)factor)network(N) 3 il22)soluble)receptor)signaling))(B) 1Serotonergic)synapse(K) 4 alternative)complement))(B) 1ErbB4)signaling)events(N) 2 TCR)signaling)CD8+)T)cells(N) 2EGF)receptor)(ErbB1))signaling))(N) 2 S)Phase(R) 3Chronic)myeloid)leukemia(K) 3 Tuberculosis(K) 4Host)Interactions)of)HIV)factors(R) 4 Endothelin)signaling))(P) 2IGF1))(N) 2 mapkinase)signaling))(B) 2Regulation)of)CDC42)activity(N) 2 Thromboxane)A2)receptor)signaling(N) 2Regulation)of)DNA)replication(R) 3 GPCR)ligand)binding(R) 7Mucin)type)O4Glycan)biosynthesis(K) 2 lectin)induced)complement))(B) 1p38)mapk)signaling))(B) 2 regulation)of)eif2(B) 1B)cell)receptor)signaling))(K) 3 NOD4like)receptor)signaling))(K) 2Syndecan444))signaling)events(N) 2 Cell)Cycle)Checkpoints(R) 3Toxoplasmosis(K) 4 PDGFR4beta)signaling))(N) 3toll4like)receptor))(B) 2 insulin)signaling))(B) 1Opioid)Signalling(R) 3 B)cell)activation(P) 2Ubiquitin)))proteolysis(K) 4 p53)tumor)suppressor))s(R) 2T)cell)activation(P) 3 Histamine)H2)receptor)signaling)(P) 1signal)transduction)through)il1r(B) 2 classical)complement))(B) 1Prion)diseases(K) 2 Thyrotropin4releasing)signaling))(P) 1Hypertrophic)cardiomyopathy)(K) 3 Histamine)H1)receptor)signaling(P) 1IL234))signaling)events(N) 2 RNA)induced)gene)expression(B) 1Regulation)of)RAC1)activity(N) 2 Cytomegalovirus)and)map)kinase)(B) 1Regulation)of)actin)cytoskeleton(K) 5 antiapoptotic))bad)phosphorylation(B) 1GABAergic)synapse(K) 3 Wnt)signaling))(P) 5Signaling))by)Ret)tyrosine)kinase(N) 2 Fc4epsilon)receptor)I)signaling(N) 2IFN4gamma))(N) 2 Integration)of)energy)metabolism(R) 3Dilated)cardiomyopathy(K) 3 Neurotrophin)signaling))(K) 3Sodium)reabsorption(K) 2 Downstream)signaling)in)CD8+)(N) 2Synthesis)of)DNA(R) 3 IL44))signaling)events(N) 2Proteasome(K) 2 Ras))(P) 2TNF)receptor)signaling))(N) 2 IL54))signaling)events(N) 1JNK)signaling)in)the)CD4+)TCR))(N) 1 ECM4receptor)interaction(K) 2BCR)signaling))(N) 2 5HT4)type)receptor)))signaling))(P) 1TGF4beta)Receptor)Complex(R) 2 Hedgehog)family(N) 1  261     GeneSet # GeneSet #Osteoclast)differentiation(K) 3 Protein)export(K) 1Interferon8gamma)signaling))(P) 1 alk)in)cardiac)myocytes(B) 1Platelet)Adhesion)to)exposed)collagen(R) 1 Regulation)of)mRNA)Stability)(R) 2Epithelial)cell)signaling)infection(K) 2 p75)NTR)receptor8))signalling® 2Complement)and)coagulation)cascades(K) 2 ErbB)signaling))(K) 2Adipocytokine)signaling))(K) 2 mtor)signaling))(B) 1the)igf81)receptor)and)longevity(B) 1 p53)))feedback)loops)2(P) 1Renal)cell)carcinoma(K) 2 Ephrin)B)reverse)signaling(N) 1Long8term)depression(K) 2 Alpha9)beta1)integrin)signaling)events(N) 1CDC42)signaling)events(N) 2 nfkb)activation)hemophilus)Influenzae(B) 1Signaling)by)Interleukins(R) 4 Gap)junction(K) 2Syndecan818))signaling)events(N) 1 tpo)signaling))(B) 1role)of)mal)in)rho)activation)of)srf(B) 1 ras8independent))in)nk)cell8)cytotoxicity(B) 1Costimulation)by)the)CD28)family(R) 2 mcalpain)and)friends)in)cell)motility(B) 1SMAD2/3)signaling(N) 1 links)between)pyk2)and)map)kinases(B) 1EPHA2)forward)signaling(N) 1 VEGFR3)signaling(N) 1Signaling)by)NODAL(R) 1 Cortocotropin)releasing)factor)receptor)(P) 1Tyrosine)kinase)signals(B) 1 Morphine)addiction(K) 25HT1)type)receptor)))signaling)(P) 1 NF8kappa)B)signaling))(K) 2tgf)beta)signaling))(B) 1 Apoptosis)signaling))(P) 1fas)signaling)))(cd95)(B) 1 IL278))signaling)events(N) 1CXCR48))signaling)events(N) 2 bcr)signaling))(B) 1Platelet)homeostasis(R) 2 S1P2))(N) 1Fc)epsilon)RI)signaling))(K) 2 PI3)kinase))(P) 1Beta2)adrenergic)receptor)signaling))(P) 1 p38)MAPK)signaling))(N) 1tnf/stress)related)signaling(B) 1 IL38))signaling)events(N) 1Induction)of)apoptosis)(B) 1 Cell)surface)interactions)(R) 2Oxytocin)receptor)))signaling))(P) 1 Fc)gamma)R8))phagocytosis(K) 2Beta3)adrenergic)receptor)signaling))(P) 1 fc)epsilon)receptor)i)in)mast)cells(B) 1nf8kb)signaling))(B) 1 G8protein)signaling)(P) 1ctcf:)(B) 1 IL88)and)CXCR18))signaling)(N) 1EGF)receptor)signaling))(P) 2 Peptide)induced)signaling))(B) 1Apoptosis(R) 3 CD40/CD40L)signaling(N) 1Regulation)of)mitotic)cell)cycle(R) 2 Cadherin)signaling))(P) 2igf81)signaling))(B) 1 Alpha8synuclein)signaling(N) 1growth)hormone)signaling))(B) 1 erbb2)in)oncology(B) 1regulation)of)eif84e)and)p70s6)kinase(B) 1 Transmission)across)Chemical)Synapses(R) 3  262 D.2 Signaling pathway analysis in T47D cells after PPP2R2A knockdown with 3 hours β-estradiol treatment Condition: siPPP2R2A+E2 vs siNT+E2, with 3 hours of β-estradiol treatment. Pathway databases: Reactome (R), Biocarta (B), KEGG (K), NCI (N).  GeneSet # GeneSetInfluenza)Infection(R) 71 BARD1)signaling)events(N)Nonsense:Mediated)Decay(R) 71 DNA)Repair(R)Translation(R) 72 HIF:1:a)transcription)network(N)Ribosome(K) 70 Focal)adhesion(K)Mitotic)G1:G1/S)phases(R) 22 Axon)guidance(R)Regulation)of)DNA)replication(R) 16 p53)signaling)pathway(B)Cell)Cycle)Checkpoints(R) 20 Signaling)by)VEGF(R)S)Phase(R) 19 Circadian)Clock(R)Synthesis)of)DNA(R) 17 Syndecan:2)signaling)(N)Cadherin)signaling)pathway(P) 17 Formation)of)Fibrin)Clot(R)Mitotic)M:M/G1)phases(R) 27 ion)channels)in)VE)(B)Cell)cycle(K) 18 Renal)cell)carcinoma(K)G:protein)signaling)(P) 16 Viral)myocarditis(K)Interferon)Signaling(R) 20 Axon)guidance(K)Wnt)signaling)pathway(P) 28 Targets)of)deltaNp63)isoforms(N)Hypertrophic)cardiomyopathy)(K) 13 Regulation)of)mRNA)Stability)(R)cdk)regulation)of)replication(B) 6 il:2)receptor)b)chain)in)t)cell)(B)Dilated)cardiomyopathy(K) 13 Mitochondria)in)apoptotic)(B)Platelet)activation(R) 27 (ARVC)(K)HTLV:I)infection(K) 25 Parkinson)disease(P)E2F)transcription)network(N) 10 Growth)hormone)receptor)(R)Dopaminergic)synapse(K) 15 Antigen)processing)and)(K)Pancreatic)cancer(K) 10 DNA)replication(K)Gastrin:CREB)signalling)(R) 20 Trk)R)by)PI3K)and)PLC:gamma(N)Regulation)of)mitotic)cell)cycle(R) 11 MAPK)signaling)pathway(K)p53)tumor)suppressor)(R) 9 Cardiac)muscle)contraction(K)Muscle)contraction(R) 8 Class)I)MHC)processing(R)EMC)organization(R) 17 Apoptosis(R)ErbB1)downstream)signaling(N) 12 SHP2)signaling(N)FOXM1)transcription)network(N) 7 Signaling)by(c:Met)(N)Platelet)Adhesion)to)collagen(R) 4 Angiogenesis(P)Signaling)by)Rho)GTPases(R) 13 Beta1)integrin)interactions(N)Small)cell)lung)cancer(K) 10 Cholinergic)synapse(K)B)cell)activation(P) 8 Complement)and)coagulation)(K)ECM:receptor)interaction(K) 10 AP:1)transcriptionnetwork(N)Actions)of)nitric)oxide(B) 6 G:protein)signaling)pathway(P)VEGF)and)VEGFR)signaling)(N) 3 Integrin)surface)interaction)(N)PDGFR:beta)signaling)pathway(N) 12 GnRH)signaling)pathway(K)  263  GeneSet # GeneSet #Retrograde)endocannabinoid)(K) 9 African)trypanosomiasis(K) 4Beta3)integrin)interactions(N) 5 ccr3)signaling)in)eosinophils(B) 3p53)pathway(N) 6 Capped)Intronless)PreDmRNA(R) 3Herpes)simplex)infection(K) 14 Pkc)through)gDprotein(B) 2IGF1)pathway(N) 4 gDprotein)by)tubby)proteins(B) 2AlphaDsynuclein)signaling(N) 4 Metabotropic)glutamate)(P) 2Pathways)in)cancer(K) 22 cxcr4)signaling)pathway(B) 2Proteasome(K) 5 Gene)regulation)peroxisome)(B) 4CDMYC)transcription)(N) 7 GMCSFDmediated)signaling)(N) 4Signaling)by)NOTCH1)in)Cancer(R) 7 ErbB2/ErbB3)signaling)events(N) 4Sphingosine)1Dphosphate)(N) 3 Vascular)muscle)contraction(K) 9Androgen)receptor)activity(N) 6 Alzheimer's)disease(K) 12Rheumatoid)arthritis(K) 8 Toxoplasmosis(K) 10Signaling)by)EGFR(R) 14 p53)pathway)feedback)loops)2(P) 3Trk)signaling)by)the)MAPK)(N) 4 Allograft)rejection(K) 4EPO)signaling)pathway(N) 4 Class)I)PI3K)signaling)events(N) 4NotchDmediated)HES/HEY)(N) 5 Autoimmune)thyroid)disease(K) 5IL6Dmediated)signaling)events(N) 5 Endothelin)signaling)pathway(P) 5B)cell)receptor)signaling)(K) 7 Ubiquitin)mediated)proteolysis(K) 10Colorectal)cancer(K) 6 LongDterm)depression(K) 6FcDepsilon)receptor)I)signaling)(N) 6 CDC42)signaling)events(N) 6Endothelins(N) 6 Nongenotropic)Androgen)(N) 3Glutamatergic)synapse(K) 10 Cellular)responses)to)stress(R) 3Signaling)by)EGFR)in)Cancer(R) 14 Cortocotropin)releasing)factor)(P) 3Type)II)diabetes)mellitus(K) 5 Regulation)of)Hypoxia)(R) 3Ras)Pathway(P) 6 MHC)class)II)Presentation(R) 8LPA)receptor)mediated)events(N) 6 Toll)Receptor)Cascades(R) 9Spliceosome(K) 10 Potassium)Channels(R) 7Platelet)homeostasis(R) 7 Chagas)disease)(K) 8Transcription)Regulation)of)iPS)(R) 5 Aurora)B)signaling(N) 4Androgen)receptor)activity(N) 5 Camp)kinase)inhibition)(B) 4Network)for)IgA)production(K) 5 Chromosome)Maintenance(R) 8Internalization)of)ErbB1(N) 4 arrestins)in)gpcr)desensitization(B) 3Opioid)Signalling(R) 7 activation)of)pka(B) 3intrinsic)prothrombin)pathway(B) 3 5HT2)type)receptor)pathway(P) 3Circadian)rhythm)D)mammal(K) 3 S1P2)pathway(N) 3cell)cycle:)g1/s)check)point(B) 3 Histamine)H2)receptorpathway(P) 2BCR)signaling)pathway(N) 6 Thyrotropin)signaling)pathway(P) 2Targets)of)TAp63)isoforms(N) 5 Histamine)H1)pathway(P) 2oxidative)stress)via)nrf2(B) 2 ATM)pathway(N) 3repression)of)pain)sensation(B) 2 p53)signaling)pathway(K) 5  264   GeneSet # GeneSet #Chronic(myeloid(leukemia(K) 5 Signaling(by(VEGFR(N) 4Signaling(by(Wnt(R) 7 NGF(signalling(via(TRKA((R) 12Staphylococcus(infection(K) 5 Regulation(of(Telomerase(N) 5Gap(junction(K) 7 Serotonergic(synapse(K) 8Integrin(surface(interactions(R) 7 Signaling(by(PTP1B(N) 4Beta1(adrenergic(pathway(P) 3 Malaria(K) 4FOXA1(transcription(network(N) 4 GPCR(ligand(binding(R) 23Bladder(cancer(K) 4 cyclins(and(cell(cycle(regulation(B) 2Signaling(by(NOTCH(R) 8 Bile(acid(bile(salt(metabolism(R) 2IL5Vmediated(signaling(events(N) 2 5HT1(type(receptor(pathway(P) 2Ras(signaling(in(the(CD4+((N) 2 (cftr)(and(((b2ar)(pathway(B) 2Chromatin(remodeling(B) 2 Adipocytokine(signaling((K) 5Energy(metabolism(R) 9 p75(NTR)Vmediated(signaling(N) 5p73(transcription(network(N) 6 signal(transduction(through(il1r(B) 3Neuroactive(ligand(interaction(K) 17 Prion(diseases(K) 3Host(Interactions(of(HIV(factors(R) 9 IL2(signaling((by(PI3K(N) 3RIGVI/MDA5(induction(of(IFN((R) 6 Caspase(cascade(in(apoptosis(N) 4Transmission(Chemical((R) 12 mTOR(signaling(pathway(K) 4CD40/CD40L(signaling(N) 3 Signaling((cVKit)(N) 4Oocyte(meiosis(K) 8 Notch(signaling(pathway(N) 4IL12(signaling(by(STAT4(N) 3 Mucopolysaccharidoses(R) 7bone(remodeling(B) 2 LongVterm(potentiation(K) 5Aurora(A(signaling(N) 3 Amoebiasis(K) 7Initiation(of(mucosal(healing(B) 3 Regulation(by(carbohydrates((B) 3Asthma(K) 3 PPAR(signaling(pathway(K) 5DAG(and(IP3(signaling(R) 3 Neurotrophin(signaling((K) 8T(cell(activation(P) 6 IL2Vmediated(signaling(events(N) 4SyndecanV1(signaling(events(N) 2 NonVsmall(cell(lung(cancer(K) 4Primary(bile(acid(biosynthesis(K) 2 Thromboxane(A2(receptor((N) 4Signaling(by(FGFR(R) 11 Targets(of(AP1(members(N) 3Regulation(of(cytoskeleton(K) 13 ATR(signaling(pathway(N) 3Metabolism(of(nonVcoding(RNA(R) 4 Legionellosis(K) 4Signaling(by(ERBB2(R) 11 EVcadherin((in(keratinocytes(N) 2tollVlike(receptor(pathway(B) 3 caspase(cascade(in(apoptosis(B) 2Base(excision(repair(K) 3 Beta3(adrenergic(receptor((P) 2Macrophage(differentiation(B) 2 Beta2(adrenergic(receptor((P) 2Integrin(signalling(pathway(P) 10 Oxytocin(receptor(mediated((P) 2Meiosis(R) 6 pkcVcatalyzed(phosphorylation(B) 2angiotensin(ii(activation((B) 3 Leishmaniasis(K) 5IL8V(and(CXCR1signaling(events(N) 2 Role(of(NFAT(in(lymphocytes(N) 3GVprotein(signaling(pathway((P) 2 phospholipase(cVepsilon((B) 1Notch(signaling(pathway(K) 3 attenuation(of(gpcr(signaling(B) 1FGF(signaling(pathway(N) 3 akap95(role(in(mitosis((B) 1  265   GeneSet # GeneSet #Morphine)addiction(K) 6 Apoptosis)signaling)pathway(P) 2Signaling)by)FGFR)in)disease(R) 11 bcr)signaling)pathway(B) 2igfB1)signaling)pathway(B) 2 PI3)kinase)pathway(P) 2Nicotinic)acetylcholine)(P) 2 RXR)and)RAR)(N) 2regulation)of)ck1/cdk5)(B) 2 DNA)replication(P) 1Ret)tyrosine)kinase)pathway)(N) 3 Ttert)transcriptional)regulation(B) 1(CAMs)(K) 8 Signaling)by)PDGF(R) 11IFNBgamma)pathway(N) 3 mTOR)signaling)pathway(N) 4Oxidative)stress)response(P) 3 Fanconi)anemia)pathway(N) 3signaling)from)gBprotein)(B) 2 Insulin)Pathway(N) 3ras/)rho))in)cell)cycle)transition(B) 2 Calcium)signaling)pathway(K) 10Reproduction(R) 2 TGFBbeta)signaling)pathway(K) 55HT4)signaling)pathway(P) 2 Huntington's)disease(K) 10Signaling)mediated)by)PRL(N) 2 VEGFR1)specific)signals(N) 2Glucocorticoid)network(N) 5 Glycosaminoglycan)synthesis)(K) 2)Adipocyte)Differentiation(R) 5 p38)MAPK)signaling)pathway(N) 2Fc)gamma)R)in)phagocytosis(K) 6 pdgf)signaling)pathway(B) 2Targets)of)CBMYC)repression(N) 4 Megakaryocyte)development(R) 7GraftBversusBhost)disease(K) 3 Metabolism)of)steroid)(R) 3Trk)receptor)signaling(N) 4 TNF)receptor)pathway(N) 3Proliferation)by)gleevec(B) 2 NFATBdependent)transcription)(N) 3mtor)signaling)pathway(B) 2 AquaporinBmediated)transport(R) 3corticosteroids)(B) 2 Sphingolipid)metabolism(K) 3EBcadherin)adherens)junction(N) 3 rb))response)to)dna)damage(B) 1Shigellosis(K) 4 Hedgehog)signaling)pathway(P) 1Processing)of)Capped)IntronB(R) 8 Stress)fiber)formation(B) 1Fc)epsilon)RI)signaling)pathway(K) 5 Bone)mineralization(B) 1PAR1Bmediated)thrombin)(N) 3 il22)soluble)receptor)signaling)(B) 1Type)I)diabetes)mellitus(K) 3 RAF/MAP)kinase)cascade(R) 1pyk2)and)map)kinases(B) 2 initiation)of)tcr)activation(B) 1S1P3)pathway(N) 2 alternative)complement)(B) 1Nephrin/Neph1)signaling(N) 2 Ubiquinone)biosynthesis(K) 1VEGFR3)signaling)(N) 2 Oocyte)maturation(K) 5Phosphatidylinositol)signaling)(K) 5 p75)NTR)receptorsignalling(R) 5RNA)Pol)II)Transcription(R) 6 fc)epsilon)receptor)i)signaling(B) 2IL4Bmediated)signaling)events(N) 4 arrestins)in)map)kinases(B) 2Regulation)of)retinoblastoma)(N) 4 thrombin)signaling)(B) 2Integrins)in)angiogenesis(N) 3 carm1)and)regulation)of)ER))(B) 1TGFBbeta)receptor)signaling(N) 3 insulin)signaling)pathway(B) 1Signaling)by)ERBB4(R) 9 NFBkappa)B)signaling)pathway(K) 5Apoptosis(K) 5 Direct)p53)effectors(N) 7Phospholipid)metabolism(R) 10 Fanconi)anemia)pathway(K) 3Signaling)by)(BCR)(R) 18 FAS)(CD95))signaling)pathway(N) 2  266  GeneSet # GeneSet #sumoylation+by+ranbp2+(B) 1 Pathway+(ngf)(B) 1apoptotic+signaling+(B) 1 Cholesterol+biosynthesis(R) 1il+4+signaling+pathway(B) 1 Rho>mediated+activation+of+srf(B) 1JNK+signaling+in+the+CD4++(N) 1 Renin>angiotensin+system(K) 1stress+induction+of+hsp+(B) 1 Signaling+by+Interleukins(R) 10Atypical+NF>kappaB+pathway(N) 1 Nicotine+addiction(K) 2Nuclear+b>catenin+regulation++(N) 4 Downstream+signaling+in+CD8+(N) 3SMAD2/3+signaling(N) 4 Metabolism+of+amino+acids(R) 7Protein+folding(R) 3 SMAD2/3+signaling(N) 1Ether+lipid+metabolism(K) 2 VEGF+signaling+pathway(P) 1Regulation+of+adherens(N) 3 +src+kinases+in+gpcr+signaling(B) 2Chemokine+signaling+pathway(K) 10 Syndecan>4+signaling+events(N) 2Osteopontin>mediated+events(N) 2 Phospholipids+in+signaling+(B) 2Beta2+integrin+interactions(N) 2 Measles(K) 7EGF+receptor+(ErbB1)+signaling+(N) 2 Gastric+acid+secretion(K) 4Myogenesis(R) 2 Pertussis(K) 4Cholesterol+regulation+by+SREB(R) 3 methyltransferase+of+carm1(B) 1Endocrine+calcium+absorption(K) 3 Map+kinase+pathways(B) 1Amyloids(R) 3 Bad+phosphorylation(B) 1keratinocyte+differentiation(B) 3 il+3+signaling+pathway(B) 1t+cell+receptor+signaling+(B) 3 cell+cycle+and+breast+tumors+(B) 1lectin+induced+complement+(B) 1 classical+complement+pathway(B) 1keratinocyte+differentiation(B) 1 Calcium+signaling+in+hepatitis+b+(B) 1cardiac+protection+against+ros(B) 1 Non>homologous+end>joining(K) 1protein+kinase+a+centrisome+(B) 1 phosphorylation+of+mek1+(B) 1salmonella+hijack+a+cell(B) 1 eicosanoid+metabolism(B) 1Hematopoietic+cell+lineage(K) 5 Synaptic_vesicle_trafficking(P) 1Salivary+secretion(K) 5 Actin+assembly(R) 1Prostate+cancer(K) 5 N>cadherin+signaling+events(N) 2GABAergic+synapse(K) 5 Transcriptional+misregulation+(K) 9Fatty+acid+metabolism(R) 8 RAC1+signaling+pathway(N) 3Hepatitis+C(K) 7 Tuberculosis(K) 9Parkinson's+disease(K) 7 Cell+surface+interactions+(R) 5Osteoclast+differentiation(K) 7 Generic+Transcription+Pathway(R) 19RIG>I>like+receptor+signaling+(K) 4 Endocytosis(K) 10Mucin+type+O>Glycan+synthesis(K) 2 Epstein>Barr+virus+infection(K) 10EPHA+forward+signaling(N) 2 Arf6+trafficking+events(N) 2EGFR+in+cardiac+hypertrophy(B) 2 Interleukin+signaling+pathway(P) 3Influenza+A(K) 9 VEGF+signaling+pathway(K) 4Presenilin+action+in+Notch+(N) 2 Costimulation+by+the+CD28+(R) 3Insulin+signaling+pathway(K) 6 Alpha9+beta1+integrin(N) 1segmentation+clock(B) 1 transcription+factor+creb+(B) 1nf>kb+signaling+pathway(B) 1 skeletal+muscle+hypertrophy+(B) 1  267  GeneSet # GeneSet #E"cadherin+signaling+(N) 2 regulation+of+pgc"1a(B) 1Arf6+downstream+pathway(N) 1 MAP+inactivation+of+smrt+(B) 1Interferon"gamma+signaling+(P) 1 S1P1+pathway(N) 1Prolactin+receptor+signaling(R) 1 EPHA2+forward+signaling(N) 1Cadmium+in+dna+synthesis+(B) 1 gata3+th2+cytokine+expression(B) 1Regulation+of+cell+cycle+(B) 1 Sodium+reabsorption(K) 2Glycosaminoglycan+synthesis+(K) 1 Mitotic+G2"G2/M+phases(R) 4PAR4"mediated+thrombin+(N) 1 ErbB+signaling+pathway(K) 4Estrogen+receptor"b+network(N) 1 Tight+junction(K) 6CXCR4"mediated+signaling+(N) 4 +(uPA)+and+uPAR+signaling(N) 2ATF"2+transcription+network(N) 3 Estrogen+receptor+signaling(N) 2Pancreatic+secretion(K) 5 TCR+signaling(R) 3nfat+and+hypertrophy(B) 2 Glioma(K) 3rac1+cell+motility+signaling+(B) 2 Signaling+by+NODAL(R) 1Alzheimer+disease+pathway(P) 3 arf+in+ribosomal+biogenesis(B) 1C"MYB+transcriptionnetwork(N) 4 Regulation+of+HIF"1"alpha(N) 1Actin+organization+(B) 1 Botulinum+neurotoxicity(R) 1hypoxia+in+the+cardivascular+(B) 1 stimulatory+in+t"cell+activation(B) 1Peroxisomal+lipid+metabolism(R) 1 tgf+beta+signaling+pathway(B) 1the+igf"1+receptor+and+longevity(B) 1 TCR+signaling+inCD4++T+cells(N) 3y+branching+of+actin+filaments(B) 1 TGF"beta+Receptor+Complex(R) 3hswi/snf+atp+complexes(B) 1 LKB1+signaling+events(N) 2Circadian+rhythm+pathway(N) 1 Signaling+by+SCF"KIT(R) 7EPHB+forward+signaling(N) 2 Signaling+by+Hippo(R) 1Regulation+of+RAC1+activity(N) 2 fas+signaling+pathway+(cd95)(B) 1EGF+receptor+signaling+pathway(P) 4 Bad+phosphorylation(B) 1Inositol+phosphate+metabolism(K) 3 p53+pathway(P) 2estrogen+receptor"a+network(N) 3 Systemic+lupus+erythematosus(K) 6nitric+oxide+signaling+pathway(B) 1 RhoA+signaling+pathway(N) 2ctcf:(B) 1 Ephrin+B+reverse+signaling(N) 1Visual+signal+transduction+(N) 1 nfkb+activation+by+influenzae(B) 1Terpenoidbiosynthesis(K) 1 Cocaine+addiction(K) 2PDGFR"alpha+signaling(N) 1 Organelle+biogenesis(R) 2Notch+signaling+pathway(P) 1 fmlp+induced+chemokine+(B) 1tnf/stress+related+signaling(B) 1 mcalpain+in+cell+motility(B) 1HIV+Life+Cycle(R) 5 tpo+signaling+pathway(B) 1Regulation+of+RhoA+activity(N) 2 Maturity+onset+diabetes+(K) 1Postsynaptic+differentiation(B) 2 Glypican+1+network(N) 1Nucleotide+excision+repair(K) 2 Melanogenesis(K) 4Amphetamine+addiction(K) 3 Endometrial+cancer(K) 2Bacterial+invasion+(K) 3 Taste+transduction(K) 2egf+signaling+pathway(B) 1 ALK1+signaling+events(N) 1growth+hormone+signaling+(B) 1 Signaling++by+HDAC+Class+III(N) 1  268  GeneSet # GeneSet #Phototransduction(K) 1 Diabetes4pathways(R) 4vegf4hypoxia4and4angiogenesis(B) 1 Lysosome(K) 4Thyroid4cancer(K) 1 Primary4immunodeficiency(K) 1T4cell4receptor4signaling4(K) 4 Helicobacter4pylori4infection(K) 2Oxidative4phosphorylation(K) 5 Signaling4by4HDAC4Class4II(N) 1Metabolism4of4nucleotides(R) 2 Tyrosine4metabolism(K) 1Acute4myeloid4leukemia(K) 2 Tryptophan4metabolism(K) 1Signaling4by4Insulin4receptor(R) 4 BMP4receptor4signaling(N) 1Retinoic4acid4receptors(N) 1 Pyrimidine4metabolism(K) 34ifn4beta4enhancer(B) 1 TollOlike4receptor4signaling4(K) 3Regulation4of4CDC424activity(N) 1 CytokineOcytokine4receptor4(K) 9Glycerophospholipid4metab(K) 3 Carbohydrate4digestion(K) 1amb24Integrin4signaling(N) 1 Fatty4acid4metabolism(K) 1p384mapk4signaling4pathway(B) 1 Vasopressinwater4reabsorption(K) 1Regulation4of4p38(N) 1 PLK14signaling4events(N) 1Salmonella4infection(K) 3 IntegrinOlinked4kinase4signaling(N) 1IL12Omediated4signaling4(N) 2 Basal4transcription4factors(K) 1NetrinOmediated4signaling4(N) 1 Peroxisome(K) 2eifO4e4and4p70s64kinase(B) 1 IL27Omediated4signaling4events(N) 1stathmin4in4antimicrotubule4(B) 1 Calcium4signaling4in4the4CD4+(N) 1Oocyte4maturation(B) 1 TCR4signaling4in4CD8+4T4cells(N) 2TRAIL4signaling4pathway(N) 1 (ALS)(K) 2Inflammation4signaling4(P) 3 The4citric4acid4(TCA)4cycle(R) 5Melanoma(K) 3 Insulin4glucose4transport(N) 1PIP34activates4AKT4signaling(R) 5 IL3Omediated4signaling4events(N) 1PI3K/AKT4Signaling4in4Cancer(R) 5 Glycerolipid4metabolism(K) 2Membrane4Trafficking(R) 6 mapkinase4signaling4pathway(B) 2Ceramide4signaling4pathway(N) 2 Phagosome(K) 6Lipid4regulation4(R) 2 TGFObeta4signaling4pathway(P) 3FoxO4family4signaling(N) 2 Escherichia4coli4infection(K) 2Protein4processing4in4ER4(K) 7 Protein4digestion(K) 3mef2d4in4tOcell4apoptosis(B) 1 Wnt4signaling4network(N) 1Canonical4NFOkappaB4pathway(N) 1 Homologous4recombination(K) 1Mismatch4repair(K) 1 ErbB44signaling4events(N) 1Signaling44by4the4Hedgehog4(N) 1 IL24signalingby4STAT5(N) 1Protein4export(K) 1 JakOSTAT4signaling4pathway(K) 6alk4in4cardiac4myocytes(B) 1 Alcoholism(K) 7Adherens4junction(K) 3 Steroid4hormone4biosynthesis(K) 2Huntington4disease(P) 5 Ion4channel4transport(R) 2gsk34by4akt4in4macrophages(B) 1 Synaptic4vesicle4cycle(K) 1IL23Omediated4signaling4events(N) 1 Retinol4metabolism(K) 1Epigenetic4pathways(R) 3 PostOtranslational4modification(R) 5RNA4degradation(K) 1 RNA4transport(K) 2Alzheimer4diseaseOpresenilin4(P) 2 Immunoregulatory4interaction4(R) 4Metabolism4of4xenobiotics4(K) 1 SLC4transmembrane4transport(R) 1Purine4metabolism(K) 3 Complement4cascade(R) 1Mitochondrial4Transcription(R) 1 Metabolic4pathways(K) 24  269               GeneSet # GeneSet #hiv$1&nef&(B) 1 Fat&digestion&and&absorption(K) 1ceramide&signaling&pathway(B) 1 Ribosome&biogenesis&(K) 2rho&cell&motility&signaling&(B) 1 Natural&killer&cell&cytotoxicity(K) 4Noncanonical&Wnt&signaling&(N) 1 Hedgehog&signaling&(N) 1Arachidonic&acid&metabolism(R) 1 Metabolism&of&carbohydrates(R) 7FAS&signaling&pathway(P) 1 GPCR&downstream&signaling(R) 33Alpha4&B1&integrin&signaling&(N) 1 mRNA&decay(R) 1HIV$1&Nef:&(N) 1 Tie2$mediated&signaling(N) 1integrin&signaling&pathway(B) 1 Transendothelial&migration(K) 3HIF$2$alphanetwork(N) 1 Drug&metabolism(K) 1IL8$&and&CXCR2&signaling&(N) 1 Biological&oxidations(R) 2FGF&signaling&pathway(P) 3 Vibrio&cholerae&infection(K) 1Class&I&PI3K&signaling&&by&Akt(N) 1 FCGR&dependent&phagocytosis(R) 7a6b1&and&a6b4&Integrin&(N) 1 Wnt&signaling&pathway(K) 4Arf6&signaling&events(N) 1 mRNA&surveillance&pathway(K) 2Signaling&&by&TCPTP(N) 1 NOD$like&receptor&signaling&(K) 1CXCR3&signaling&events(N) 1 Cell$Cell&communication(R) 3SNARE&in&vesicular&transport(K) 1 Focal&adhesion&kinase(N) 1  270 D.3 Signaling pathway analysis in T47D cells with genes deregulated after 3 hours and 6 hours of β-estradiol treatment.  Conditions: siPPP2R2A+E2 vs siNT+E2, genes differentially regulated both after 3 hours and 6 hours of β-estradiol treatment. Pathway databases: Reactome (R), Biocarta (B), KEGG (K), NCI (N).   GeneSet # GeneSet #Ribosome(K) 9 Oncogene0Induced0Senescence(R) 1Eukaryotic0Translation0Initiation(R) 9 Signaling0by0EGFR(R) 1SRP0protein0targeting0(R) 9 Tuberculosis(K) 1Eukaryotic0Translation0Elongation(R) 9 Alzheimer's0disease(K) 1Eukaryotic0Translation0(R) 9 mRNA0surveillance0pathway(K) 1NonsenseIMediated0Decay(R) 10 Glycerophospholipid0metabolism(K) 1GnRH0signaling0pathway(K) 2 Hepatitis0B(K) 1Detoxification0of0ROS0(R) 1 Cell0cycle(K) 1alphaILinolenic0acid0metabolism(K) 1 Signaling0by0ERBB2(R) 1Regulation0of00(HIF)0by0Oxygen(R) 1 Cell0adhesion0molecules0(CAMs)(K) 1Glutamatergic0synapse(K) 2 Ubiquinone0biosynthesis(K) 1Mitotic0G2IG2/M0phases(R) 2 Taurine0metabolism(K) 1Hippo0signaling0pathway(K) 2 S0Phase(R) 1Beta10adrenergic0pathway(P) 1 TollILike0Receptors0Cascades(R) 15HT20type00pathway(P) 1 Amoebiasis(K) 1Metabolic0pathways(K) 2 Signaling0by0FGFR(R) 1Pathways0in0cancer(K) 1 betaIcatenin0signaling(R) 1Generic0Transcription0Pathway(R) 1 Toxoplasmosis(K) 1B0Cell0Receptor0(BCR)0signaling0(R) 1 Circadian0entrainment(K) 1ErbB40signaling0events(N) 1 Wnt0signaling0pathway(K) 1MicroRNAs0in0cancer(K) 1 HIFI2Ialpha0network(N) 1Vascular0muscle0contraction(K) 2 Lysosome(K) 1Signalling0by0NGF(R) 1 Ion0channel0transport(R) 1Metabolism0of0carbohydrates(R) 1 Direct0p530effectors(N) 1Class0I0MHC0processing0(R) 1 Cholinergic0synapse(K) 1Neuroactive0interaction(K) 1 ErbB10downstream0signaling(N) 1Ras0signaling0pathway(K) 1 Synthesis0of0DNA(R) 1Calcium0signaling0pathway(K) 1 Rheumatoid0arthritis(K) 1Rap10signaling0pathway(K) 1 GABAergic0synapse(K) 1Linoleic0acid0metabolism(K) 1 PI3KIAkt0signaling0pathway(K) 2Proteoglycans0in0cancer(K) 1 Cell0Cycle0Checkpoints(R) 1Fatty0acid0metabolism(R) 1 Leukocyte0migration(K) 1JakISTAT0signaling0pathway(K) 1 Tight0junction(K) 2Uptake0by0Scavenger0Receptors(R) 1 Mitotic0G1IG1/S0phases(R) 2Endocytosis(K) 1 Cell0junction0organization(R) 1Membrane0Trafficking(R) 1 Response0to0cytosolic0Ca2+(R) 1Dopaminergic0synapse(K) 2 Inflammatory0bowel0disease0(IBD)(K) 1Amphetamine0addiction(K) 1 Extracellular0matrix0organization(R) 2  271      GeneSet # GeneSet #M/G1%Transition(R) 1 Degradation%of%beta7catenin%(R) 1Degradation%of%cell%cycle%(R) 1 p53%signaling%pathway(K) 1Integration%energy%metabolism(R) 1 Cytokine7cytokine%interaction(K) 3Osteoclast%differentiation(K) 1 MAPK%signaling%pathway(K) 3Mitotic%Metaphase%and%Anaphase(R) 2 (American%trypanosomiasis)(K) 2Retrograde%endocannabinoid%(K) 1 Signaling%by%the%Hedgehog%(N) 1Serotonergic%synapse(K) 2 (HCM)(K) 2Insulin%secretion(K) 1 Dilated%cardiomyopathy(K) 2Oxidative%Stress%by%Senescence(R) 1 Ether%lipid%metabolism(K) 1Regulation%of%mRNA%Stability%(R) 1 LKB1%signaling%events(N) 1Signaling%by%NOTCH2(R) 1 Carbohydrate%digestion(K) 1Adrenergic%signaling%(K) 2 TGF7beta%receptor%signaling(N) 1Iron%uptake%and%transport(R) 1 Type%II%diabetes%mellitus(K) 1TGF7beta%signaling%pathway(K) 1 Targets%of%TAp63%isoforms(N) 1TGF7beta%signaling%pathway(P) 1 Malaria(K) 1Transport%of%glucose%(R) 1 Mineral%absorption(K) 1Metabolism%of%amino%acids%(R) 2 Ovarian%steroidogenesis(K) 1Signaling%by%ERBB4(R) 2 Signaling%by%PTP1B(N) 1Regulation%of%DNA%replication(R) 1 Circadian%Clock(R) 1%(ARVC)(K) 1 Glycerophospholipid%biosynthesis(R) 1Leishmaniasis(K) 1 p53%pathway(N) 1Chronic%myeloid%leukemia(K) 1 ATF72%transcription%factor%network(N) 1Induction%of%IFN7alpha/beta%(R) 1 Coregulation%of%Androgen%(N) 1Prolactin%receptor%signaling(R) 1 Regulation%of%Apoptosis(R) 1Cardiac%muscle%contraction(K) 1 Long7term%depression(K) 1(SASP)(R) 2 VEGF%signaling%pathway(K) 1Signaling%by%NOTCH1(R) 1 Regulation%of%retinoblastoma%(N) 1Prolactin%signaling%pathway(K) 1 Colorectal%cancer(K) 1Hepatitis%C(K) 2 NCAM%signaling%for%neurite%(R) 1ISG15%antiviral%mechanism(R) 1 Arachidonic%acid%metabolism(K) 1Growth%hormone%signaling(R) 1 Cytosolic%sensors%of%pathogen%(R) 1Fanconi%Anemia%pathway(R) 1 HTLV7I%infection(K) 2Signaling%by%TGF7beta(R) 1 Pancreatic%cancer(K) 1Fc%epsilon%RI%signaling%pathway(K) 1 Renal%cell%carcinoma(K) 1  272 D.4 Signaling pathway analysis of differentially regulated genes in 184-hTERT after PPP2R2A knockdown.  Pathway databases: Reactome (R), Biocarta (B), KEGG (K), NCI (N).   Pathways # HitsInfluenza)A(K) 4 IL18,CASP1,HLA7DOB,DDX58PPAR)signaling)pathway(K) 3 MMP1,OLR1,CPT1ACytosolic)DNA7sensing)pathway(K) 3 IL18,CASP1,DDX58Steroid)hormone)biosynthesis(K) 3 AKR1C3,AKR1C2,HSD17B3Rheumatoid)arthritis(K) 3 IL18,MMP1,HLA7DOBFatty)acid,)triacylglycerol,)and)ketone)body)metabolism(R) 3 CTGF,HSD17B3,CPT1AMitotic)G17G1/S)phases(R) 3 E2F5,CDKN2B,PPP2R2APhagosome(K) 3 NCF2,HLA7DOB,OLR1Herpes)simplex)infection(K) 3 C5,HLA7DOB,DDX58Cytokine7cytokine)receptor)interaction(K) 3 IL18,IL20RB,KITLGHTLV7I)infection(K) 3 TLN2,CDKN2B,HLA7DOBExtracellular)matrix)organization(R) 3 MMP1,MMP14,VCANPathways)in)cancer(K) 3 MMP1,CDKN2B,KITLGGeneric)Transcription)Pathway(R) 3 E2F5,CDKN2B,CTGFMetabolic)pathways(K) 3 AKR1C3,PTGES,HSD17B3Interleukin71)processing(R) 2 IL18,CASP1Cellular)roles)of)Anthrax)toxin(N) 2 IL18,CASP1Bile)acid)and)bile)salt)metabolism(R) 2 AKR1C3,AKR1C2IL17mediated)signaling)events(N) 2 CASP1,IL1RNArachidonic)acid)metabolism(R) 2 AKR1C3,PTGESNOD7like)receptor)signaling)pathway(K) 2 IL18,CASP1TGF7beta)receptor)signaling(N) 2 CTGF,PPP2R2AStaphylococcus)aureus)infection(K) 2 C5,HLA7DOBTGF7beta)signaling)pathway(K) 2 E2F5,CDKN2BChemical)carcinogenesis(K) 2 AKR1C2,GSTA4Arachidonic)acid)metabolism(K) 2 AKR1C3,PTGESInterleukin)signaling)pathway(P) 2 IL18,IL13RA2Legionellosis(K) 2 IL18,CASP1Inflammatory)bowel)disease)(IBD)(K) 2 IL18,HLA7DOBPertussis(K) 2 CASP1,C5Metabolism)of)xenobiotics)by)cytochrome)P450(K) 2 AKR1C2,GSTA4Regulation)of)nuclear)beta)catenin)signaling)and)target)gene)transcription(N) 2 TNIK,VCAN  273           Pathways # HitsSignaling'by'TGF-beta'Receptor'Complex(R) 2 E2F5,CDKN2BRIG-I-like'receptor'signaling'pathway(K) 2 RNF125,DDX58Leishmaniasis(K) 2 NCF2,HLA-DOBSalmonella'infection(K) 2 IL18,CASP1RIG-I/MDA5'mediated'induction'of'IFN-alpha/beta'pathways(R) 2 RNF125,DDX58Oxidative'Stress'Induced'Senescence(R) 2 CDKN2B,TNIKCell'surface'interactions'at'the'vascular'wall(R) 2 MMP1,OLR1Leukocyte'transendothelial'migration(K) 2 NCF2,MYL9Alzheimer'disease-presenilin'pathway(P) 2 MMP1,MMP14Cell'cycle(K) 2 E2F5,CDKN2BSystemic'lupus'erythematosus(K) 2 C5,HLA-DOBTight'junction(K) 2 PPP2R2A,MYL9Hepatitis'C(K) 2 PPP2R2A,DDX58Direct'p53'effectors(N) 2 CASP1,VCANCell'adhesion'molecules'(CAMs)(K) 2 VCAN,HLA-DOBJak-STAT'signaling'pathway(K) 2 IL13RA2,IL20RBHippo'signaling'pathway(K) 2 CTGF,PPP2R2ATuberculosis(K) 2 IL18,HLA-DOBRap1'signaling'pathway(K) 2 TLN2,KITLGFocal'adhesion(K) 2 TLN2,MYL9Signaling'by'the'B'Cell'Receptor'(BCR)(R) 2 KITLG,TRPC1PI3K-Akt'signaling'pathway(K) 2 PPP2R2A,KITLG  274 Appendix E  - Unique ER binding sequences associated genes and their expression analysis in T47D cells after PPP2R2A knockdown E.1    Expression of genes associated with unique ER binding sequences (ChIP-Seq) in T47D cells treated with siNT. Expression of genes after PPP2R2A knockdown and β-estradiol treatment, based on RNA-Sequencing (6 hours β-estradiol) and transcriptome microarray (3 hours β-estradiol)    ConditionsiNT%Unique Fold%Change FDR Fold%Change p6value%(ANOVA)ARHGAP15 10.4 8.7E6232 1.6 1.9E603LGI1 6.1 9.1E669 1.5 1.8E602HGD 3.2 3.8E620 1.3 5.7E603NEK11 2.9 2.9E695 1.6 3.1E603GNAQ 2.6 2.6E640 1.1 1.4E602PRLR 2.5 8.8E646 1.2 3.2E604PALLD 2.0 1.8E648 1.2 1.4E603ZBTB38 1.9 1.7E628 1.1 9.8E603VAV3 1.9 4.5E646 1.6 2.7E603OGFRL1 1.8 4.4E633 1.5 8.0E604PCMTD1 1.8 1.3E640 1.4 6.6E603ZMIZ1 1.8 2.9E629 1.3 3.6E602PRKCI 1.8 2.7E637 1.1 3.0E602ZMYND11 1.7 2.9E632 1.1 1.1E603SPAG9 1.7 3.5E634 1.3 4.8E603SLC40A1 1.6 2.6E629 1.3 5.6E603ZBTB40 1.5 5.2E621 1.2 1.5E602NFIB 1.5 1.5E616 1.1 2.0E602DNAJB9 1.5 4.4E618 1.2 6.8E603TMEM184B 1.5 4.5E617 1.2 6.5E604TFAP2A 1.4 7.2E617 1.2 3.2E602ABR 1.4 4.3E615 1.2 4.4E603CTPS2 1.4 4.3E613 1.2 8.2E603NCAM2 1.4 1.3E611 1.1 1.8E602OPA1 1.4 8.2E612 1.2 1.2E602ABCA3 1.3 1.0E609 1.1 2.1E602MEF2A 1.2 3.4E604 1.1 1.6E602KRT23 1.2 1.6E603 1.6 4.6E602RAD23B 1.1 3.2E602 1.1 3.4E602RNA*Sequencing Transcriptome7Microarray  275 E.2    Expression of genes associated with unique ER binding sequences (ChIP-Seq) in T47D cells treated with siPPP2R2A.  Expression of genes after PPP2R2A knockdown and β-estradiol treatment, based on RNA-Sequencing (6 hours β-estradiol) and transcriptome microarray (3 hours β-estradiol)         ConditionsiPPP2R2A'Unique Fold'Change FDR Fold'Change p7value'(ANOVA)NEB 3.3 4.9E784 1.8 2.4E703CACNA1D 3.1 4.2E7114 1.7 1.6E704PLA2G4C 3.0 2.0E752 1.3 1.3E704BCAS1 2.7 1.0E7117 1.2 5.0E702SPNS2 2.6 8.9E776 1.4 1.9E704PRDM6 2.0 9.8E717 1.1 2.2E702FLNB 2.0 1.0E735 1.2 2.0E702FLNB 2.0 1.0E735 1.2 2.0E702GPR77 2.0 5.6E718 1.4 2.7E704SHROOM3 2.0 1.3E748 1.2 9.4E704ABCC3 1.9 2.4E730 1.4 1.0E703NEK6 1.9 2.2E742 1.3 3.2E703SH3BP4 1.9 7.4E744 1.1 1.7E702RAB11FIP1 1.7 2.8E729 1.1 2.1E702SYTL3 1.7 6.2E715 1.1 7.1E704ETNK1 1.6 1.2E719 1.2 5.2E703CD44 1.6 2.1E724 1.6 1.2E703AHI1 1.5 4.6E714 1.2 6.1E703ABAT 1.5 1.2E716 1.3 4.7E703MLPH 1.4 9.6E713 1.6 1.4E703LBR 1.3 3.1E710 1.2 3.2E702NFX1 1.3 4.9E708 1.2 3.8E702ATP2C2 1.3 7.1E706 1.3 2.9E702KIAA1324 1.2 1.6E705 1.3 1.4E704SPDEF 1.2 2.1E705 1.8 1.2E702TOB2 1.2 1.4E704 1.1 1.4E702RNA*Sequencing Transcriptome7Microarray  276 Appendix F  - QPCR validation results of randomly selected ER targets.  The ER targets are associated with unique ER binding sites and appear up regulated after PPP2R2A knockdown in T47D cells. Grey: QPCR validated genes that appear further up regulated with 6 hours of 100nM β-estradiol treatment in addition to PPP2R2A knockdown in T47D, n=3. PPP2R2A is control for confirming siRNA knockdown.          Gene Mean 95% upper 95% lowerSPNS2 1.84 2.12 1.60SPDEF 1.34 1.56 1.15SH3RF2 1.30 1.43 1.19NEB 1.89 2.09 1.70MLPH 1.92 2.14 1.73KCNMA1 1.63 1.74 1.52CD44 1.17 1.29 1.06CACNA1D 1.19 1.34 1.05AFF3 1.33 1.70 1.04ABCC3 1.59 1.82 1.39PPP2R2A 0.54 0.63 0.47Gene Mean 95% upper 95% lowerSPNS2 2.20 2.82 1.72SPDEF 2.34 2.87 1.91SH3RF2 2.13 4.30 1.06MLPH 2.08 2.58 1.68KCNMA1 1.80 2.41 1.35NEB 1.57 2.10 1.18CD44 1.37 1.81 1.05CACNA1D 2.04 2.57 1.63AFF3 1.38 1.83 1.04ABCC3 1.87 2.48 1.41PPP2R2A 0.52 0.70 0.39T47D siPPP2R2A E2 VS siNT E2T47D siPPP2R2A vs siNT  277 Appendix G  - Differentially regulated genes in T47D cells after PPP2R2A knockdown that common in MCF7 cells.  T47D%siNT+E2%vs%siNT%(E2%6%hours) T47D%siPPP2R2A%vs%siNT%(E2%6%hours)BAMBI AP1S3 ADD3 LYPLA1 UBE2CCXCL12 C20orf160 AHNAK MB VEGFBCYP1A1 CD99L2 AP1S3 MCM3 WDR54DDIT4 EPAS1 ASF1B MGST2 WNK2FKBP4 GLIS2 BAZ2B NACA ZFP106HEY2 GNB1L BCAS1 NDRG1 ZKSCAN1IGFBP4 HEY2 C5orf4 NEBJAK1 IFI27 CBX5 NEDD9MYC NMB CCNB2 NMBNPY1R PHLDA1 CD63 NOLC1NRCAM TFF1 CDC6 NQO1NRIP1 CDCA5 OAS1PCP4 CENPH OASLPFKFB3 CLDN7 PCP4RARA COX8A PDLIM1RET CYP1A1 PFDN2RLN2 DDIT4 PIM1SFXN2 EPAS1 PKMYT1SIAH2 FABP3 PPP2R2ASTC2 FEN1 PRDX5TFF1 FOS RAC3GALNT4 RHOBTB3GLIS2 RND1GMNN RPL36ALGNB1L SAT2GPNMB SCN1BHEXA STARD13HEY2 STC2HSPG2 TFF1IFI27 TIMP1IFI35 TNFAIP2IFITM1 TOMM7IFITM3 TRAF4IGFBP4 TRPS1KIAA1033 TSPAN1KLF7 TSPAN15L1CAM TXNRD1T47D%siPPP2R2A+E2%vs%siNT+E2%%%%%%%%%%%%%%%%%%%(E2%6%hours)  278 Appendix H  - List of differentially regulated genes in T47D cells with reduced PPP2R2A expression after 3 hours and 6 hours of β-estradiol treatment.        ABCC13 HCAR1 OPN3AGXT2 HEXA PCP4ALDH1L2 HPX PLCB4ARHGAP15 IGFBP7 PPP2R2AATP10B IRS1 PTERAVIL KCNMA1 RAPGEF6BCAR4 KIAA0825 RPS5C12orf75 LGI1 RTF1C1GALT1C1 LGMN SGK196C5 LOC100132735 SLC38A1CACNA1D LOC100505648 SOD3CATSPERB LOC440173 SPEF2CDH19 LOC643401 SRRM5CDKL5 LOC728323 STARD7CHORDC1 LRRC31 STEAP2CLDN16 LRRC58 STXBP5LCLN6 LUM STYXCREBL2 LYN TCTN3CYP39A1 LYPLA1 TFF3CYP4Z2P MAP2 TGFB2DEPDC1B MAP3K1 TMEM106ADOCK10 MATN3 UBL3FBLN5 MCM10 UPK2FLRT3 MDGA2 VASNGABRA3 NEB ZBTB41GJA1 NEK11 ZMAT1GNG12 NQO1 ZNF323GUCY1A2 OIP5Gene$deregulated$after$PPP2R2A$knockdown$after$3$hours$and$6$hours$of$E2$treatment  279 Appendix I  Amplicons used in ChIP-QPCR  ER-Chromatin-Immuno-precipitation and sequencing identified ER binding sites and amplicons used for ChIP-QPCR validation (underlined).      >ABCC3 CAGCTAAGCCATCTTCCTCAAGGCAGCCCAATTCTCTGTGGGGCTGCCACAGCACTAAACTGTTCTCTGTGTCCCCAGGACTCCAACCTGTCTGTGCACACAGAAAACCCGGACCTCACTCCCTGCTTCCAGAACTCCCTGCTGGCCTGGGTGCCCTGCATCTACCTGTGGGTCGCCCTGCCCTGCTACTTGCTCTACCTGCGGCACCATTGTCGTGGCTACATCATCCTCTCCCACCTGTCCAAGCTCAAGATGGTCAGTGGCTCAGGGATCTCCTACCGATGGGGCTGGGCCCTGGGGATTCTGCTTTTATTTTTTAATTTAATTAAATTAAT  >CACNA1D CTCTCTGTTTAGTGCTAATGTGCACTTAAATAGCACAGGTGTGAGTGCTTTTCCCTGGCCACGGTCACACATGACTTTCCCAAGGGCTGATTGGTTATGGGGTTGGCAGGTTTAAGGTCAGGTTCCCCGGCTTGCCCTCCACCCAGAGGAGCCAGGACTCTCAGGCTCCCTGTAAGGGAAGAAGTTACCCAGATCATGAGCCTGTTTTGTCTGGGTCAGACTGACTTGAGCCACAGGGAGTGGGTACAGAAGCCTGGGTTGCTGCCGGGTATGACCCGACCTTCGTCTTGTACCCACTGTCTCCTTCCTTTTGTGAGTCACCAGCTCACACATCTACATGCCTGTAAATGAACTATGAGGTATCGAGTATGTGCTACCTACCGTGGTGGATGCTGGGAAGGATGAACCATGCATTTTC  >SPDEF AAGTGGAGCGCCAGGGGAACCCAGTGGTTTTTCTCTTCCCACAGTTCCCACGGGCAGCAGGAACACATTGCATCAGTGAAGCCTCCTCTGTGCCCTGCTGCATGGGCAGCCTGGGTGTCCACACACACCCTCACTGGCACTACTTCCAGCGCTTTCTGGGACAGGAGGAAGGTCAAACTGAATAAACTGCTTTGCCGGGCCCTCCCAGCTGGGGCAGCCAGCATTATTCCCACTTGACGGACAAGGGCGCTGTGACCTGCTACGGGGCACCTGTCCATGCGTGAGTGAGCAGGGACTCGAACTCAAGACTCGGCTCCACCCTCCTTTCTGCTGGTGCTGGCAGACCCGGCATCTTCTAGCTCCTAAGGTTGCCCCTGTTGACCATCCCCTTTGCCTCTGCTCAGGCCACCTGCCAACCCAGCCCCACAGCAGTTGCCTTAGTGGGGTGTGGGTAGCACAGGTGCCCGCAGCCTGGAGGTACAGAGGCCCACCGCAGCCCCAAGTGGACCATCGTGGCTGAGTTTACTCCAAAAGGGGATGTGGACTGGAAAG  >SPNS2 GCGGGAGGGTCTGGAACAGGACATTCGGTCTGACTTACTGGATGTTCCCTTCCCTCGGGGAGGCTCCTATTCTCTGGCTGCCCCTGCTCATCCCCAGCCTGGCCACACACAGCCTGTCCAGGGCCTCTGGGTGCCTCAGGGCCGTGGGCATGGCTTGAAGTCTCTGGATAGCCCATGAATGGATGTGGGCCCGGGAGGGGTGTGAGGTTTTGTGGGCTCCCTGCCAGCAGCCAGTGTGTAGGGAACAGAGACTGTGGTTGCTGCGGATGGAGGGACCGCTGATGGTGGCTTTGCCTTCTCCCCGGCAGATCTGTATCTTCGTCGGGGAGACGCTGCTGTTTTCTAACTGGGCCATCACTGCAGACATCCTCATGGTGAGCCAGGCAGGCCGAGGTCACCTTGTGCTGCTGACCCAGGCCTCTTGACCTCACAGGGGTGCCTGGGGAGGGCGGGTGAAGGGGCGGGAGAGCTGGTAGGAAGGCAGGCCTGCCAGGAACGTGAACCCAGAGGCAGGGAGGGGATCAGAGAGTTTGGGGTCTGCCTCTGGCCAGGGCTGGAAAACAGGGTTGGGCAGGCAGACAGAGCACCATGTGGAGACTGAGG   280 Appendix J  - Expression correlations of SPDEF and PPP2R2A in METABRIC cases Each dot represents one case based on Illuminal HT-12 v3. The trend lines connect averaged value SPDEF expression. Dashed line: medium measurement of PPP2R2A copy number. x-axis: SPDEF expression; y-axis: PPP2R2A copy number         281 Appendix K  - Spectrum of size-exclusion chromatography of samples used in mass spectrometry  T47D-shPPP2R2A-1 T47D-shNS-1 T47D-shPPP2R2A-2 T47D-shNS-2 T47D-shPPP2R2A-3 T47D-shNS-3 A   282  MCF7-shPPP2R2A-1 MCF7-shNS-1 MCF7-shPPP2R2A-2 MCF7-shNS-2 MCF7-shPPP2R2A-3 MCF7-shNS-3 B   283     184-hTERT-shPPP2R2A-1 184-hTERT-shPPP2R2A-2 184-hTERT-shPPP2R2A-3 184-hTERT-shNS-1 184-hTERT-shNS-2 184-hTERT-shNS-3 C   284 Appendix L  - Expression of PP2A subunits  L.1 Expression of PP2A subunits in primary samples Presented as Z score= (Measured expression- medium score (expression for the given probe ste))/Standard deviation among research centres            Ensemble ID Gene name A01029 HS1187 HS1823 A01030 HS1188 HS1824ENSG00000105568 PPP2R1A 1.75 0.99 2.82 1.88 1.75 3.78ENSG00000137713 PPP2R1B 0.59 1.05 0.54 0.63 0.62 0.59ENSG00000113575 PPP2CA 4.39 2.80 1.64 4.18 3.23 1.21ENSG00000104695 PPP2CB 1.89 2.02 1.19 2.58 2.54 1.18ENSG00000221914 PPP2R2A 1.29 1.53 0.44 1.36 1.55 0.56ENSG00000156475 PPP2R2B 2.62E-04 2.32E-04 3.87E-03 3.57E-03 1.16E-02 1.61E-02ENSG00000074211 PPP2R2C 9.48E-04 9.38E-03 9.50E-03 1.39E-03 3.68E-03 1.75E-03ENSG00000175470 PPP2R2D 0.47 0.29 0.28 0.41 0.30 0.28ENSG00000073711 PPP2R3A 0.55 0.79 0.26 0.51 0.48 0.54ENSG00000167393 PPP2R3B 0.05 0.05 0.10 0.02 0.02 0.01ENSG00000092020 PPP2R3C 0.27 0.46 0.28 0.17 0.28 0.32ENSG00000119383 PPP2R4 0.38 1.01 2.06 0.33 0.46 0.67ENSG00000066027 PPP2R5A 0.49 0.94 0.43 0.21 0.44 0.30ENSG00000068971 PPP2R5B 1.41 0.43 0.65 0.61 0.21 0.59ENSG00000078304 PPP2R5C 0.40 0.45 0.30 0.22 0.26 0.18ENSG00000112640 PPP2R5D 0.40 0.38 0.64 0.45 0.32 0.61ENSG00000154001 PPP2R5E 0.27 0.51 0.18 0.30 0.47 0.24ENSG00000115808 STRN 0.89 1.41 0.27 0.21 0.37 0.40ENSG00000196792 STRN3 0.60 0.84 0.32 0.69 0.77 0.89ENSG00000090372 STRN4 1.52 0.83 1.28 1.48 1.11 2.38Luminal Myoepitheial  285 L.2 Expression of PP2A subunits in breast cancer cell lines Unit: counts per molecule          Ensemble ID Gene name T47D (CPM) MCF7 (CPM) 184--hTERT (CPM)ENSG00000105568 PPP2R1A 423 485 404ENSG00000137713 PPP2R1B 43 19 22ENSG00000113575 PPP2CA 202 223 89ENSG00000104695 PPP2CB 36 29 72ENSG00000221914 PPP2R2A 66 48 133ENSG00000156475 PPP2R2B 0 0 0ENSG00000074211 PPP2R2C 88 74ENSG00000175470 PPP2R2D 33 61 32ENSG00000073711 PPP2R3A 26 23 13ENSG00000167393 PPP2R3B 0 0 12ENSG00000092020 PPP2R3C 11 17 9ENSG00000119383 PPP2R4 360 381 40ENSG00000066027 PPP2R5A 72 42 37ENSG00000068971 PPP2R5B 27 16 27ENSG00000078304 PPP2R5C 142 110 57ENSG00000112640 PPP2R5D 151 109 56ENSG00000154001 PPP2R5E 83 95 54ENSG00000115808 STRN 45 40 72ENSG00000196792 STRN3 48 42 26ENSG00000090372 STRN4 138 204 184  286 L.3 Expression of PP2A subunits in METABRIC breast cancer samples Units: arbitrary fluorescent reading from Illumina HT-12 v3.            Genes%Name PPP2R2A%copy%number%loss%(N%=%48) Others%(N%=%1470) PPP2R2A%copy%number%loss%%(N%=%28) Others%(N%=%446)PPP2R1A_rawexp.ILMN_1810467 10.9 10.9 10.7 10.8PPP2CA_rawexp.ILMN_1722858 11.3 11.2 10.8 10.9PPP2CB_rawexp.ILMN_1712659 8.2 8.8 8.4 8.7PPP2R1B_rawexp.ILMN_1704656 6.0 6.1 6.2 6.1PPP2R2A_rawexp.ILMN_1788961 7.8 9.0 8.0 8.8PPP2R2B_rawexp.ILMN_1660732 6.1 6.3 6.2 6.4PPP2R2C_rawexp.ILMN_2401344 7.3 6.6 6.3 6.3PPP2R2D_rawexp.ILMN_1778587 8.4 8.3 8.5 8.3PPP2R3A_rawexp.ILMN_1656393 6.9 6.9 7.1 7.0PPP2R3B_rawexp.ILMN_1712257 6.2 6.0 6.0 6.1PPP2R3C_rawexp.ILMN_2214603 7.8 7.7 7.7 7.9PPP2R4_rawexp.ILMN_1729123 10.1 10.0 10.0 9.8PPP2R5A_rawexp.ILMN_1738784 8.6 8.9 8.5 8.5PPP2R5B_rawexp.ILMN_2124082 6.3 6.2 6.3 6.3PPP2R5C_rawexp.ILMN_2364971 8.5 8.5 8.6 8.6PPP2R5D_rawexp.ILMN_1780940 7.8 7.6 7.9 7.9PPP2R5E_rawexp.ILMN_1666761 9.4 9.4 9.3 9.4STRN3_rawexp.ILMN_1772946 8.2 8.0 7.8 7.7STRN4_rawexp.ILMN_1696190 6.9 6.8 6.9 6.8STRN_rawexp.ILMN_1749882 6.8 6.8 6.8 6.9ER%positive ER%negative  287 Appendix M  Densitometry analysis data  M.1 T47D sample fractions Presented as pixel measurements by ImageJ.   Densitometry of PP2A subunit in each T47D fractionFraction PPP2CA PPP2R1A PPP2R2A#27 1.39E+04 7.23E+03 2.40E+03#28 2.06E+04 1.12E+04 9.91E+03#29 2.23E+04 1.00E+04 1.09E+04#30 2.26E+04 6.44E+03 1.08E+04#31 2.34E+04 7.30E+03 1.17E+04#32 2.54E+04 8.42E+03 9.91E+03#33 2.91E+04 1.16E+04 1.56E+04#34 2.94E+04 1.70E+04 1.71E+04#35 2.77E+04 1.62E+04 1.58E+04#36 2.02E+04 1.86E+03 2.05E+03#37 1.44E+04 5.55E+02 0#38 1.20E+04 1.39E+03 0#39 2.12E+04 2.08E+03 6.79E+03#40 2.03E+04 1.95E+03 5.06E+03#41 1.32E+04 1.61E+03 7.95E+02#42 8.17E+03 1.04E+03 0#43 5.87E+03 1.20E+03 0#44 6.30E+03 1.37E+03 0#45 4.36E+03 1.52E+03 0#46 4.52E+03 2.08E+03 0#47 6.26E+03 2.44E+03 0#48 3.47E+03 3.53E+03 0Sum of intensitiesTotal PP2A 354587.338 117984.722 118837.236Complex (<=#37) 2.49E+05 9.78E+04 1.06E+05Free monomer (>=#38) 1.06E+05 2.02E+04 1.26E+04Percentage of total PP2A protein (%)Complex (<=#37) 0.702 0.829 0.894Free monomer (>=#38) 0.298 0.171 0.106  288 M.2 Densitometry of fractions around gap regions of each mass spectrometry analyzed sample Presented as pixel measurements by ImageJ.  T47D shNSFractions PPP2R1A PPP2CA PPP2R1A PPP2CA PPP2R1A PPP2CA35 1.80E+04 1.30E+04 1.60E+04 1.33E+04 1.09E+04 6.11E+0336 7.97E+03 1.73E+04 1.44E+04 1.69E+04 1.11E+04 1.04E+0437 1.89E+03 8.97E+03 2.92E+03 1.50E+04 1.96E+03 1.01E+0438 3.46E+03 1.05E+04 1.15E+03 9.88E+03 2.10E+03 8.15E+0339 4.69E+03 7.00E+03 7.21E+02 5.28E+03 1.53E+03 7.53E+0340 2.23E+03 1.25E+04 4.55E+02 9.27E+03 1.17E+03 9.29E+03T47D shPPP2R2AFractions PPP2R1A PPP2CA PPP2R1A PPP2CA PPP2R1A PPP2CA35 6.43E+03 6.81E+03 7.39E+03 1.31E+04 1.60E+04 2.23E+0436 6.31E+03 1.60E+04 6.42E+03 1.24E+04 1.35E+04 1.65E+0437 3.63E+03 9.44E+03 1.67E+03 7.28E+03 3.89E+03 9.42E+0338 2.85E+03 5.18E+03 1.05E+03 7.22E+03 2.86E+03 9.18E+0339 4.18E+03 7.91E+03 7.44E+02 8.97E+03 5.08E+03 9.84E+0340 5.32E+03 6.71E+03 2.74E+02 1.64E+04 3.44E+03 8.15E+03MCF shNSFractions PPP2R1A PPP2CA PPP2R1A PPP2CA PPP2R1A PPP2CA35 1.93E+04 1.60E+04 2.01E+04 1.26E+04 5.82E+03 1.19E+0436 1.86E+04 2.04E+04 1.72E+04 1.45E+04 9.31E+03 7.89E+0337 1.03E+04 1.01E+04 9.90E+03 1.23E+04 8.98E+03 4.24E+0338 6.20E+03 3.83E+03 2.95E+03 7.20E+03 6.61E+03 1.65E+0339 5.75E+03 2.48E+03 3.74E+03 2.73E+03 6.63E+03 2.76E+0340 6.02E+03 7.14E+03 1.07E+03 6.96E+03 8.48E+03 2.06E+03MCF7 shPPP2R2AFractions PPP2R1A PPP2CA PPP2R1A PPP2CA PPP2R1A PPP2CA35 9.29E+03 1.64E+04 1.47E+04 9.35E+03 1.90E+04 8.35E+0336 1.14E+04 1.27E+04 1.55E+04 7.44E+03 1.54E+04 7.15E+0337 9.55E+03 1.12E+04 1.15E+04 3.76E+03 7.92E+03 4.82E+0338 6.78E+03 7.32E+03 8.20E+03 4.32E+03 9.43E+03 4.46E+0339 6.96E+03 8.52E+03 7.53E+03 5.93E+03 8.46E+03 3.63E+0340 7.63E+03 9.05E+03 2.98E+04 1.31E+04 6.95E+03 2.66E+03#1 #2 #3#1 #2 #3#1 #2 #3#1 #2 #3  289               184-hTERT shNSFractions PPP2R1A PPP2CA PPP2R1A PPP2CA PPP2R1A PPP2CA35 1.28E+04 1.59E+04 1.32E+04 5.41E+01 9.47E+02 2.60E+0336 5.32E+03 1.43E+04 1.87E+04 1.76E+03 5.01E+02 2.28E+0337 1.70E+03 5.45E+03 2.33E+04 5.16E+03 3.62E+03 2.66E+0338 1.74E+03 3.12E+03 9.59E+03 5.95E+03 1.19E+04 7.88E+0339 1.49E+03 8.46E+02 7.49E+03 4.18E+03 1.32E+04 1.51E+0440 2.01E+03 1.01E+03 8.40E+03 2.41E+03 3.81E+03 1.54E+04184-hTERT shPPP2R2AFractions PPP2R1A PPP2CA PPP2R1A PPP2CA PPP2R1A PPP2CA35 1.57E+04 6.72E+03 9.23E+03 1.15E+04 2.00E+04 2.11E+0436 9.60E+03 8.55E+03 6.17E+03 8.20E+03 1.83E+04 1.83E+0437 3.74E+03 5.51E+03 5.12E+03 4.98E+03 6.85E+03 1.14E+0438 3.62E+03 2.76E+03 6.37E+03 2.69E+03 2.07E+03 5.28E+0339 2.26E+03 1.79E+03 4.28E+03 1.13E+03 1.24E+03 3.47E+0340 2.59E+03 4.52E+02 2.73E+03 6.90E+02 1.75E+03 1.42E+03#1 #2 #3#1 #2 #3  290 Appendix N  One-way ANCOVA analysis results of PP2A subunits relative abundance Cells: T47D, MCF7, and 184-hTERT with respect to PPP2R1A based on quantitative mass spectrometry. Refer to Appendix-R, K, L for original data.     T47D Mean 95% upper 95% lower Mean 95% upper 95% lower p-valuePPP2R2A -1.69 0.07 -3.44 0.31 1.05 0.09 0.05PPP2R1B -0.34 3.65 -4.32 0.79 12.57 0.05 0.81PPP2CA 0.11 2.25 -2.04 1.08 4.74 0.24 0.89PPP2R2B 0.96 6.54 -4.62 1.94 93.25 0.04 0.62PPP2R2C 0.14 1.65 -1.37 1.10 3.13 0.39 0.79PPP2R2D -0.38 0.81 -1.56 0.77 1.75 0.34 0.39PPP2R3B 0.13 2.39 -2.14 1.09 5.22 0.23 0.87PPP2R4 -0.90 1.61 -3.42 0.53 3.05 0.09 0.34PPP2R5A 0.75 1.93 -0.43 1.68 3.82 0.74 0.14PPP2R5B 0.43 2.57 -1.72 1.34 5.93 0.30 0.57PPP2R5C -0.02 2.15 -2.19 0.99 4.45 0.22 0.98PPP2R5D 0.78 2.19 -0.64 1.71 4.57 0.64 0.18PPP2R5E 0.81 2.26 -0.65 1.75 4.80 0.64 0.18STRN 0.76 1.44 0.08 1.69 2.71 1.06 0.04STRN3 0.33 2.40 -1.73 1.26 5.27 0.30 0.64STRN4 -1.13 0.63 -2.89 0.46 1.55 0.13 0.13MCF7 Mean 95% upper 95% lower Mean 95% upper 95% lower p-valuePPP2R2A -2.54 -0.46 -4.63 0.17 0.73 0.04 0.03PPP2CA 0.48 1.43 -0.47 1.39 2.69 0.72 0.16PPP2R2B -0.55 1.35 -2.46 0.68 2.55 0.18 0.34PPP2R2D -0.66 -0.12 -1.19 0.63 0.92 0.44 0.03PPP2R3B -1.60 0.34 -3.53 0.33 1.26 0.09 0.07PPP2R3C 0.29 3.03 -2.45 1.22 8.16 0.18 0.70PPP2R4 0.58 3.51 -2.36 1.49 11.41 0.20 0.49PPP2R5A -0.08 1.41 -1.56 0.95 2.66 0.34 0.85PPP2R5B -1.50 1.73 -4.73 0.35 3.32 0.04 0.18PPP2R5C 0.28 1.49 -0.94 1.21 2.82 0.52 0.43PPP2R5D -0.25 0.30 -0.80 0.84 1.23 0.57 0.19PPP2R5E -0.48 0.30 -1.25 0.72 1.23 0.42 0.12STRN 0.81 1.51 0.10 1.75 2.86 1.07 0.04STRN3 -0.80 0.23 -1.84 0.57 1.17 0.28 0.08STRN4 -1.03 0.48 -2.55 0.49 1.39 0.17 0.10ANCOVA-PPP2R1A( Log2)ANCOVA-PPP2R1A( Log2)ANCOVA-PPP2R1A (Linear)ANCOVA-PPP2R1A (Linear)  291             184-hTERT Mean 95% upper 95% lower Mean 95% upper 95% lower p-valuePPP2R2A -2.30 -4.10 -0.50 0.20 0.06 0.71 0.03PPP2CA -0.06 -2.93 2.80 0.96 0.13 6.98 0.93PPP2R2B 0.77 -0.74 2.28 1.70 0.60 4.84 0.16PPP2R2C -0.21 -4.58 4.16 0.86 0.04 17.94 0.86PPP2R2D 1.10 -1.31 3.52 2.15 0.40 11.45 0.19PPP2R4 -0.81 -2.36 0.75 0.57 0.19 1.68 0.16PPP2R5A 0.95 -0.20 2.10 1.94 0.87 4.30 0.07PPP2R5B -1.09 -4.75 2.57 0.47 0.04 5.93 0.33PPP2R5C 0.09 -1.21 1.39 1.06 0.43 2.62 0.80PPP2R5D 0.70 -5.77 7.17 1.63 0.02 144.41 0.69PPP2R5E -0.60 -6.32 5.11 0.66 0.01 34.55 0.69STRN 0.94 -4.45 6.32 1.91 0.05 80.10 0.53STRN3 0.07 -1.41 1.55 1.05 0.38 2.92 0.86STRN4 -0.33 -3.25 2.60 0.80 0.11 6.05 0.68ANCOVA-PPP2R1A( Log2) ANCOVA-PPP2R1A (Linear)  292 Appendix O  - The averaged MRM output data. Data was based on blue color-coded raw data in Appendix-P. The measurements of averaged highest values from reliable peptides were selected for each protein. O.1 Averaged MRM raw data of T47D Averaged(MRM(outputSample PPP2R1A PPP2R1B PPP2CA PPP2R2A PPP2R2B PPP2R2C PPP2R2D PPP2R3B PPP2R4 PPP2R5A PPP2R5BT47D%shNS*1 1.24E+07 1.03E+06 1.29E+07 4.67E+05 4.23E+05 1.69E+06 1.91E+05 4.58E+05 2.12E+05 3.45E+05 4.91E+06T47D%shNS*2 2.25E+07 2.68E+06 5.00E+06 7.93E+05 2.65E+05 7.39E+05 4.00E+05 2.47E+06 1.64E+05 6.62E+05 4.77E+06T47D%shNS*3 1.04E+07 1.11E+05 5.97E+06 2.18E+05 1.38E+04 7.88E+05 1.06E+05 4.01E+05 5.39E+04 4.70E+05 1.15E+06T47D%shPPP2R2A*1 9.52E+06 3.09E+05 8.56E+06 1.59E+05 2.80E+05 8.97E+05 7.57E+04 7.56E+05 5.03E+04 5.99E+05 2.24E+06T47D%shPPP2R2A*2 3.67E+07 1.14E+06 1.35E+07 1.69E+05 1.98E+05 1.25E+06 4.20E+05 1.34E+06 5.58E+04 8.68E+05 8.76E+06T47D%shPPP2R2A*3 8.58E+06 4.50E+05 4.20E+06 9.14E+04 2.08E+05 1.17E+06 1.21E+05 5.96E+05 1.02E+05 9.88E+05 3.43E+06Sample PPP2R5C PPP2R5D PPP2R5E STRN STRN3 STRN4T47D%shNS*1 3.96E+08 1.66E+07 3.48E+05 2.57E+05 1.33E+05 2.39E+05T47D%shNS*2 2.10E+08 1.10E+07 6.58E+05 4.04E+05 3.05E+05 1.99E+05T47D%shNS*3 1.25E+08 9.85E+06 2.46E+05 3.53E+05 5.85E+04 1.41E+05T47D%shPPP2R2A*1 1.26E+08 1.28E+07 7.55E+05 5.75E+05 1.60E+05 5.63E+04T47D%shPPP2R2A*2 2.76E+08 2.89E+07 6.35E+05 6.74E+05 2.05E+05 7.37E+04T47D%shPPP2R2A*3 2.86E+08 2.46E+07 6.34E+05 4.62E+05 1.47E+05 1.54E+05Log(value(of(MultiAquant(outputSample PPP2R1A PPP2R1B PPP2CA PPP2R2A PPP2R2B PPP2R2C PPP2R2D PPP2R3B PPP2R4 PPP2R5A PPP2R5BT47D%shNS*1 23.57 19.97 23.62 18.83 18.69 20.69 17.55 18.80 17.69 18.40 22.23T47D%shNS*2 24.43 21.35 22.25 19.60 18.02 19.50 18.61 21.24 17.33 19.34 22.19T47D%shNS*3 23.31 16.76 22.51 17.73 13.75 19.59 16.70 18.61 15.72 18.84 20.14T47D%shPPP2R2A*1 23.18 18.24 23.03 17.28 18.09 19.77 16.21 19.53 15.62 19.19 21.09T47D%shPPP2R2A*2. 25.13 20.12 23.69 17.36 17.59 20.25 18.68 20.35 15.77 19.73 23.06T47D%shPPP2R2A*3 23.03 18.78 22.00 16.48 17.67 20.16 16.89 19.19 16.64 19.91 21.71Sample PPP2R5C PPP2R5D PPP2R5E STRN STRN3 STRN4T47D%shNS*1 28.56 23.98 18.41 17.97 17.02 17.87T47D%shNS*2 27.64 23.39 19.33 18.62 18.22 17.60T47D%shNS*3 26.89 23.23 17.91 18.43 15.84 17.11T47D%shPPP2R2A*1 26.91 23.60 19.53 19.13 17.29 15.78T47D%shPPP2R2A*2. 28.04 24.79 19.28 19.36 17.65 16.17T47D%shPPP2R2A*3 28.09 24.55 19.27 18.82 17.17 17.23


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