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Transcription response in the TGF-beta pathway Francisco Manuel Sánchez de Oria Degree project in biology, Master of science (2 years), 2008 Examensarbete i biologi 30 hp till masterexamen, 2008 Biology Education Centre and Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University Supervisors: Claes Wadelius and Ola Wallerman Table of Contents List of abbreviations...................................................................................................................................2 Abstract......................................................................................................................................................3 Introduction................................................................................................................................................3 The TGFβ superfamily.........................................................................................................................3 Role of TGF β in tumor pathogenesis..................................................................................................4 TGFβ signal transduction and the Smad proteins................................................................................5 Studying transcription factors binding: Chromatin ImmunoPrecipitation (ChIP) assays.....................7 ChIPseq: next generation ChIP assays................................................................................................10 Results......................................................................................................................................................13 In vivo mapping of binding sites for Smad4 transcription factor........................................................13 Verification of known binding sites.....................................................................................................13 Analysis of Smad4 target genes...........................................................................................................17 Smad4 enriched regions contained an over representation of Smad binding sites..............................19 Discussion................................................................................................................................................20 Global mapping of Transcription Factor Binding Sites in the postgenomic era.................................20 Unraveling the secrets of the complex network of TFs in the TGFβ pathway...................................21 Materials and methods.............................................................................................................................23 Experimental procedures.....................................................................................................................23 Cell cultures....................................................................................................................................23 Antibodies.......................................................................................................................................23 ChIP and DNA template preparation for sequence analysis...........................................................23 PCR confirmation...........................................................................................................................25 Data analysis........................................................................................................................................25 Acknowledgments....................................................................................................................................25 References................................................................................................................................................26 1 List of abbreviations BMPs Bone MorphogeneticProteins CDK CyclinDependent Kinases ChIP Chromatin Immunoprecipitation CLB Cell Lysis Buffer FBS Fetal Bovine Serum FoxG1 Forkhead Box G1B ISmads Inhibitory Smads MAPK Mitogen Activated Protein Kinases PAI1 Plasminogen Activator Inhibitor1 RSmads ReceptorRegulated Smads RIPA RadioImmunoPrecipitation Assay SARA Smad Anchor for Receptor Activation SBE Smad Binding Element Smurf Smad ubiquitination regulatory factor TF Transcription Factor TFBS Transcription Factor Binding Site TGFβ Transforming Growth Factor beta TSS Transcription Start Site 2 Summary Transforming growth factor beta (TGFβ) is a multifunctional cytokine involved in the regulation of numerous cellular responses including cell proliferation, differentiation and apoptosis. Escaping from TGFβinduced apoptosis is one of the hallmarks that characterizes cancer cells. The aim of this project was to identify genes bound and regulated by Smad2 and Smad4 transcription factors, which directly mediate TGFβ signaling. In this project I used ChIPseq, a stateoftheart method used to analyze protein interactions with DNA. ChIPseq combines Chromatin Immunoprecipitation, a powerful method employed to selectively enrich for DNA sequences bound by a particular protein in vivo, with massively parallel DNA sequencing of the ChIPenriched DNA. Therefore the DNA bound to it is identified and mapped to the human reference genome to locate visually the position of every DNA fragment. Using HepG2 cells (human hepatocellular liver carcinoma cell line) as a model system, I identified well known target genes of the Smad4 protein, such as PAI1 and JUNB, as well as some candidate genes that could potentially be targets for therapeutic intervention, like FoxG1 and HNF4 genes. 3 Introduction Transforming growth factor beta (TGFβ) is a multifunctional cytokine involved in the regulation of numerous cellular responses, such as cell growth and proliferation, differentiation, cellular matrix production, migration and apoptosis (Jennings & Pietenpol, 1998; Verrecchia & Mauviel, 2002). TGFβ is a secreted homodimeric protein member of the TGFβ superfamily. Deregulation of TGFβ expression or signaling is involved in a variety of diseases, including cancer and fibrosis (Blobe et al., 2000). The TGFβ superfamily The TGFβ superfamily includes more than 30 pleiotropic cytokines with similar structure, involved in key roles in development and tissue homeostasis. The first member of the TGFβ superfamily, TGFβ1, was discovered in the late 1970s. Its name stands for the ability to induce growth and morfological transformation of rat kidney fibroblasts (DeLarco and Todaro, 1976). However, shortly after its discovery it was shown that TGFβ1 also acts as an inhibitor of cell proliferation (Tucker et al., 1984). This duality in cell growth regulation is celltype dependent and imprinted during embryonic development (Sporn and Roberts, 1990). Other members of the TGFβ superfamily are TGFβ2 and TGFβ3, bone morphogenetic proteins (BMPs), antimüllerian hormone (AMH), activins and nodal (Piek et al., 1999). Role of TGF β in tumor pathogenesis TGFβ plays important roles in tumor pathogenesis, contributing to cell growth, invasion and metastasis, angiogenesis and also decreasing host tumorspecific immune responses (Jennings & Pietenpol, 1998). Although originally TGFβ acts as a tumor suppressor inhibiting cell growth in most cell types via the Smads pathway, once the tumor has been established most cells become resistant to TGFβ and TGFβ turns prooncogenic (Elliott and Blobe, 2005) (Figure 1). Escaping from TGFβ growth inhibition is the identifying characteristic of many cancer cells (Massagué et al., 2000). 4 Figure 1. The dual role of TGFβ in tumor pathogenesis. TGFβ arrests the cell cycle progression at early G1 through controlling a number of important cell cycle regulators (Hanahan and Weinberg, 2000). Cyclindependent kinases (CDK) regulation is essential for cell growth inhibition mediated by TGFβ. This regulation can be either direct downregulation of CDK levels (Zhang et al., 2001) or by upregulation of CDK inhibitors (Alexandrow and Moses, 1995) Alterations of the TGFβ pathway can increase cancer risk. A common example is TGFBR1*6A , a variant of the TGFBR1 gene with a 9bp inframe deletion. This modification is present in approximately 14% of the general population and results in decreased TGFβmediated growth inhibition. Population studies have shown that this allele is related to increased breast cancer risk by 31% for heterozygotes and 169% for homozygotes, respectively (Zhang et al., 2005). Nonetheless, the most common cause of TGFβ signalling alteration is the mutational inactivation of the TGFBR2, present in about 2030% of all colon cancers (Biswas et al., 2004). TGFβ signal transduction and the Smad proteins The Smads proteins directly mediate the biological effects of TGFβ. The Smads proteins are homolog of both the Drosophila mothers against decapentaplegic (MAD) protein and the Caenorhabditis elegans SMA protein, their name is a combination of the two. TGFβ binds to and activates type I and type II serine/threonine kinase receptors present in the surface of the cell. The receptorregulated Smads (RSmads) directly mediate TGFβ signalling upon receptor activation, and those are Smad1, Smad2, Smad3, Smad5 and Smad8. The Smad anchor for receptor 5 activation (SARA) or endofin mediates Smad activation delivering the RSmads to the receptor, which result in phosphorylation of the RSmads (Tsukazaki et al., 1998; Shi et al., 2007). Once RSmads are activated they form heterodimeric complexes together with Smad4. These complexes are translocated to the nucleus, where they recruit other transcription factors to regulate the expression of target genes through the interaction with other transcription factors, coactivators and corepressors (Massagué et al., 2005). Such genes mediate the biological effects of TGFβ. Some of the activated target genes stimulate tumorigenesis, while others suppress it. Although Smad4 is not required for translocation into the nucleus, it seems to be needed for the Smad complex to act as a transcription factor (Liu et al.,1997). Besides the TGFβ superfamily receptors there are other kinases such as cyclin dependent kinases (CDK) and mitogen activated protein kinases (MAPK) that can phosphorylate Smad proteins thus regulating their capacity of controlling transcription of their target genes (Matsuura et al., 2004 ; Kamaraju and Roberts, 2005) (Figure 2). Figure 2. The TGFβSmad pathway. TGFβ binds to and activates type I and type II serine/threonine kinase receptors present in the surface of the cell . The receptorregulated Smads (RSmads) directly mediates TGFβ signalling upon receptor activation. SARA or endofin mediates Smad activation delivering the RSmads to the receptor, which result in phosphorilation. Once RSmads are activated they form heterodimeric complexes together with Smad4 and are translocated to the nucleus, where they control target genes. Smad7/Smurf12 represents the negative loop of the cycle, ending signaling. Modified with permission from ten Dijke and Hill, 2004. 6 Smad6 and Smad7, the ISmads, constitute a subclass of inhibitory Smads that acts in direct opposition to RSmads signalling, forming a negative feedback loop. Originally this subclass was shown to compete with RSmads for activated type I receptor binding (Moustakas et al., 2001). Later on they were found to produce ubiquitination and degradation of the activated type I receptor by recruiting of E3ubiquitin ligases, also known as Smad ubiquitination regulatory factor 1 (Smurf1) and Smurf2, thus ending signalling (Shi and Massagué, 2003). Shortly after this discovery it was demonstrated that I Smads associate with phosphatases, dephosphorylating and therefore inactivating type I receptors (Shi et al., 2004). A possible role for ISmads in transcriptional regulation has also been postulated as Smad6 has been shown to repress BMPinduced transcription by recruiting corepressor CtBP (C terminal binding protein) and Smad7 disrupts Smad2/Smad3 complexes in the nucleus (Lin et al., 2003). Although there are numerous members in the TGFβ superfamily that produce a vast diversity of cellular responses there are only two different Smad pathways known, raising many questions about how signaling specificity and diversity are produced (Attisano and Wrana, 2002; Miyazawa et al., 2002). Studying transcription factors binding: Chromatin ImmunoPrecipitation (ChIP) assays Transcription is controlled by the association of transcription factors (TFs) with their target DNA sequences in gene regulatory regions and additional recruitment of activators of the transcription machinery, hence it is of great importance to be able to study in vivo proteinDNA associations. Those associations are fine tuned by epigenetic modifications including methylation of CpG dinucleotides (Antequera, 2003), posttranslational modifications of histones (Strahl and Allis, 2000 ; Jenuwein and Allis, 2001) and incorporation of histone variants (Mito et al., 2007). Such modifications are used by the transcription factors to modulate transcription and constitute the epigenetic code (Cosgrove and Wolberger, 2005). Chromatin Immunoprecipitation (ChIP) assays are the cuttingedge techniques to study large scale proteinDNA interactions in vivo. The ChIP technique involves reversible crosslinking of proteins with DNA, a procedure by which the proteinDNA interaction is covalently linked using formaldehyde. The purpose of the crosslinking is to ensure that the DNAprotein link is maintained during the ChIP procedure. The chromatin is fragmented into smaller pieces, usually in the range of 200 base pairs length, using either enzymatic digestion or sonication of the nuclei. The sheared chromatin is then immunoprecipitated with an antibody recognizing the protein of interest. In the last steps the crosslink is reversed, proteins are digested and the enriched ChIPDNA is purified (Figure 3). For a recent review of the ChIP current state and applications see Collas and Dahl, 2008. 7 Figure 3. ChIP assay experimental outline. Modified with permission from Collas and Dahl, 2008 For several years a strong limitation of the ChIP technology was the restriction of analysis of the ChIP selected DNA material to a set of predetermined target sequences using PCR with chosen primers. This method introduces a strong bias towards the sequence of interest. Array technology extended the power of ChIP, enabling the discovery of novel target sites for TFs and build the map of posttranslationally modified histones across the genome. This approach is known as ChIPonchip or ChIPchip, and was first successfully applied on yeast in three papers published in 2000 and 2001 (Ren et al.,2000; Iyer et al.,2001; Lieb et al.,2001). Recent advances in microarray technology have made it possible to study TFs genomewide in human cells (RadaIglesias et al., 2008). Microarray hybridization overcomes the limitations of regular ChIPPCR analysis and have permitted genomewide scope analysis. Nonetheless, the advent of nextgeneration sequencing technologies have lead ChIP assays to the next frontier. ChIPseq: next generation ChIP assays The so called nextgeneration sequencing machines are machines capable of producing tens to hundreds of millions of short sequence reads during a single instrument run (Shendure and Ji, 2008). This unprecedented sequencing capacity is being applied in many fields of biology enabling striking scientific advances at dizzying speed. ChIPsequencing, also know as ChIPseq, uses this novel technology to sequence ChIPDNA fragments in massively parallel manner. Some of the advantages of ChIPseq over ChIPchip assays are lower cost, less input DNA or less amplification requirements, not limited by microarray content and more accurate mapping (Barski et al., 2007; Johnson et al., 2007; Mikkelsen et al., 2007; Robertson et al., 2007). ChIPseq has been recently used to study epigenetic changes in the DNA and target sites for TFs 8 and other related chromosomeassociated proteins across the entire genome, enabling the possibility to build a high resolution genomewide map for gene expression and genome function (Barski et al., 2007; Johnson et al., 2007; Robertson et al., 2007; Wederell et al., 2008). The Illumina sequencing technology (Figure 5), which relies on proprietary reversible terminatorbased sequencing chemistry. The first step prior to sequencing is the library preparation. Adaptor sequences are ligated to the DNA fragments. The ligated fragments are then amplified and immobilized in a flow cell surface, where they are directly amplified (solid phase amplification) to create up to 1000 clones of each single molecule in very close proximity. Then the clusters of clones are sequenced using fluorescentlylabeled modified nucleotides (sequencingbysynthesis). One important property of those nucleotides is reversible termination, allowing the presence of the 4 nucleotides (A, C, T, G) simultaneously during sequencing, which results in higher accuracy than methods where only one nucleotide is present at the time. For a cycle of sequencing, a laser excites the fluorescentlylabeled nucleotides and the image is captured determining the identity of the base for each cluster. Each cycle is repeated to obtain the sequence of bases in a given fragment. In the last steps the Illumina Pipeline software maps the sequence reads to a reference genome in order to obtain the genomic coordinates of every ChIPDNA fragment (aligned reads). The resulting file contains the sequence of every DNA fragment and its location in the genome, and it can be formatted and uploaded to the University of California Santa Cruz (UCSC) genome browser (http://genome.ucsc.edu/) genome browser to locate visually the position of every DNA fragment in the genome and compare the different samples. Those regions of the genome where several aligned ChIPDNA overlaps form peaks. Each step in the peak represents the position of an aligned ChIPDNA read in the human reference genome. 9 Figure 5. Scheme of the Illumina sequencing technology. Modified with permission from Illumina Inc., www.illumina.com (2008) Aim The aim of this project was to identify genes bound and regulated by Smad2 and Smad4 transcription factors, which directly mediate TGFβ signaling, in HepG2 cells. For that purpose I used chromatin immunoprecipitation and high throughput parallel sequencing (ChIPseq), a method employed to determine the in vivo genomic localization of transcription factors and other chromatin related proteins. 10 Results In vivo mapping of binding sites for Smad4 transcription factor Chromatin immunoprecipitation coupled to highthoughput sequencing technology (ChIPseq) can be used to profile wholegenome binding sites for a chosen transcription factor (Barski et al., 2007; Johnson et al., 2007; Robertson et al., 2007; Wederell et al., 2008). In this study I used chromatin immunoprecipitation to isolate DNA bound by Smad4 in TGFβtreated and control HepG2 cells. All ChIP samples were confirmed for the presence of known binding sites using semiquantitative or quantitative PCR. This step is required to evaluate the efficiency of the ChIP procedure before further analysis using the Illumina genome analyzer. Samples in which the result of the PCR showed low or no enrichment in known binding sites were discarded. Then the inmunoprecipitated Smadbound DNA samples were sequenced using Illumina 1G genome analyzer and mapped with respect to the human genome using the Illumina Analysis Pipeline, thus identifying target genes of the TGFβ pathway. The output text files were converted to browser extensible data (BED) format in order to visualize the data in the University of California Santa Cruz (UCSC) genome browser (http://genome.ucsc.edu/). Table 1 summarizes the statistical information in the output files obtained: Table 1. Sequencing statistics obtained for each sequenced ChIPDNA sample. Name Antibody TGFbeta treated #Aligned reads Peaksa Smad2 antiSmad2 Yes 3176304 586 Smad4 antiSmad4 Yes 2851319 667 Smad4C_Last antiSmad4 No 4846500 17330 Smad4T_Last antiSmad4 Yes 4707083 3117 Since Smad2 and Smad4 samples had less sequences, peaks above 6 overlapping reads are counted, while in Smad4C_Last and Smad4T_Last only peaks above 8 overlapping reads are counted. a Verification of known binding sites In order to validate my data in silico, I looked for peaks located in well known and characterized promoters of the Smads target genes. I extracted 3 known target genes from the literature and analyzed them in the UCSC Genome Browser (Mar. 2006 Assembly): plasminogen activator inhibitor1 or PAI1 (SERPINE1), JUNB protooncogene (JUNB) and SMAD family member 7 (Smad7). For the PAI1 gene, a Smad binding region has been located −586 to −551 upstream of the gene. This 11 region contains 3 Smad Binding Elements (SBE) and an Ebox, and the 3 bp spacer between the Ebox and an SBE has been shown to be essential to mediate TGFβinduced transcription (Hua et al.,1999). An Ebox is a small DNA sequence located typically upstream of a gene promoter and contains a palindromic canonical sequence CACGTG. Transcription factors containing the basichelixloophelix protein structural motif typically bind to Eboxes or related variant sequences and enhance transcription of the downstream gene. TGFβ activates JUNB by binding of a nuclear factor to a promoter distal element, a 22 bp sequence located between nucleotides 2813 and 2792 relative to the JunB gene, where a SBE for this gene has been characterized (Jonk et al., 1998). The Smad7 gene has been shown to be regulated by the Smad3Smad4 complex in the presence of TGFβ treatment. The Smad7 promoter is located 471 to 275, and it contains a perfect 8 bp SBE (GTCTAGAC) (Nagarajan et al., 1999 , Stopa et al., 2000). The first ChIP carried out included Smad2 and Smad4 transcription factors in TGFβtreated HepG2 cells. The samples were sequenced and the sequencing data was analyzed in the UCSC genome browser. The number of peaks in those samples were similar and in many cases overlapping in the same position. However, I did not find peaks at known binding sites. The second set of samples were Smad4 control and TGFβtreated (Smad4C_Last and Smad4T_Last). Those samples confirmed known binding sites for PAI1 (Figure 6A and 6B) and JUNB (Figure 7). Nevertheless, although my data did not support Smad4 binding at 471 to 275 for the Smad7 gene, there was a peak at around +750 bp (Figure 8). Further studies are necessary to determine whether the 471 to 275 region of the Smad7 gene is in fact negative for Smad4 binding in vivo. 12 A B Figure 6. (A) The PAI1 promoter in the UCSC genome browser showing the genome localization of the sequences precipitated with antiSmad4 antibody. The upper panel (Smad4T_Last) represents the sequence tags (black) from the TGFβtreated sample, while the lower panel (Smad4C_Last) represents the sequence tags (black) for the control sample. The Y axis represents the peak height, whereas the X axis represent the localization in the genome. Peaks are scaled according to the tallest peak of each panel, so that different scaling is used in each panel. (B) A closer look at the PAI1 binding site. SBEs are shown in red, the Ebox sequence in black and the the 3 bp spacer between the Ebox and a SBE shadowed in green. 13 Figure 7. The JUNB distal element in the UCSC genome browser showing the genome localization of the sequences precipitated with antiSmad4 antibody. The upper panel (Smad4T_Last) represents the sequence tags (black) from the TGF βtreated sample, while the lower panel (Smad4C_Last) represents the sequence tags (black) for the control sample. The Y axis represents the peak height, whereas the X axis represent the localization in the genome. Peaks are scaled according to the tallest peak of each panel, so that different scaling is used in each panel. Figure 8. The Smad7 promoter in the UCSC genome browser showing the genome localization of the sequences precipitated with antiSmad4 antibody. The upper panel (Smad4T_Last) represents the sequence tags (black) from the TGFβtreated sample, while the lower panel (Smad4C_Last) represents the sequence tags (black) for the control sample. The Y axis represents the peak height, whereas the X axis represent the localization in the genome. Peaks are scaled according to the tallest peak of each panel, so that different scaling is used in each panel The overall success at identifying known targets genes suggests that my data have a good coverage of 14 known Smads binding sites across the genome. Analysis of Smad4 target genes In an attempt to extract relevant biological information from the enormous amount of data, the treated sample was filtered according the following criteria (see methods section): peaks below 8 hits and peaks located at the same position as peaks above 5 hits in the control sample were removed. Regions were extended +/ 250 bp from center and only those that were within 10 kb of a transcription start site (TSS) were saved. In this way, both weak peaks and peaks located in the same position in the control and treated sample were filtered away, leaving only strong peaks in the proximity of a TSS, which could potentially be gene promoters. Out of the 590 regions analyzed, table 2 shows the top 20 most enriched regions, description of the closest gene and distance from the peak to the TSS. Table 2. Top 20 enriched regions within 10 kb of a TSS. Peak Height Gene id 17 NM_002985 15 NM_032514 14 BX538238 14 AF346307 14 NM_005484 14 NM_080833 14 BC050331 14 AK123337 14 AK125239 13 X87871 13 AK094414 13 NM_148961 13 NM_152837 13 BX161415 13 NM_198441 13 NM_198951 12 NM_203448 12 NM_000088 12 AK131425 12 NM_004455 12 NM_001054 Description CHEMOKINE (C-C MOTIF) LIGAND 5 MICROTUBULE-ASSOCIATED PROTEIN 1 LIGHT CHAIN 3 ALPHA HYPOTHETICAL PROTEIN DKFZP686B0790 CHROMOSOME 19 F379 RETINA SPECIFIC PROTEIN POLY (ADP-RIBOSE) POLYMERASE FAMILY, MEMBER 2 CHROMOSOME 20 OPEN READING FRAME 151 HYPOTHETICAL PROTEIN DKFZP434K191 HYPOTHETICAL PROTEIN MGC12760 SIMILAR TO RIKEN CDNA 4632412N22 GENE HEPATOCYTE NUCLEAR FACTOR 4, ALPHA ACYL-COA SYNTHETASE SHORT-CHAIN FAMILY MEMBER 1 OTOSPIRALIN SORTING NEXIN 16 TETRATRICOPEPTIDE REPEAT DOMAIN 6 FLJ40296 PROTEIN TRANSGLUTAMINASE 2 (C POLYPEPTIDE, PROTEIN-GLUTAMINE-GAMMA-GLUTAMYLTRA... HYPOTHETICAL PROTEIN LOC286286 COLLAGEN, TYPE I, ALPHA 1 CDNA FLJ16545 FIS, CLONE OCBBF3004972 EXOSTOSES (MULTIPLE)-LIKE 1 SULFOTRANSFERASE FAMILY, CYTOSOLIC, 1A, PHENOL-PREFERRING, MEMBER 2 Distance from TSS 9322 7289 30 -4555 -356 -6130 -50 -95 -8043 3317 8863 7343 434 5781 -419 759 -653 -1895 4979 532 2228 It is important to mention that amongst the most enriched regions appears the Forkhead Box G1B (FoxG1) gene with peak height 9. FoxG1 has been shown to regulate p21 expression (Seoane et al., 2004), a gene whose regulation determines TGFβmediated growth inhibition. A histogram of distance from all 590 peaks to the TTS reveal that most were immediately downstream 15 of the TTS. More than 50 peaks were located in the 0 +500 bp region of a TSS, suggesting that the peaks were preferably situated close to TSSs and not randomly distributed (Figure 9). Figure 9. Histogram of distance of peaks to TSSs. Smad4 enriched regions contained an overrepresentation of Smad binding sites To determine whether the regions occupied by the filtered peaks contained Smad binding motif sequences the data was analyzed using RegionMiner software (Genomatix, www.genomatix.de). The software engine searched for all known TF binding motifs potentially contained in the data submitted and the result was sorted by overrepresentation of those motifs compared to a set of background promoters (Table 3). The overrepresentation reflects the fold factor of match numbers in regions compared to an equally sized sample of the background (i. e. found versus expected). The Smad family of transcription factors were reported among the top, with an overrepresentation of 1.95, suggesting that the Smads binding motifs in my data occur almost twice as often as expected by chance. 16 Table 3. Overrepresentation of TF motifs contained in the sequenced samples TF Number of Families Matches Expected V$HAML V$GABF V$GZF1 V$HIFF V$SMAD V$BNCF V$RBP2 V$MEF3 V$AHRR V$MITF V$OAZF V$CHRE V$MTF1 V$HESF V$SREB V$RREB V$SP1F V$P53F V$EBOX 535 502 254 528 416 183 167 102 625 190 278 194 151 692 252 411 1703 604 814 201.13 214.32 109.85 256.55 212.82 97.28 89.09 55.22 339.47 104.09 153.3 109.15 85.63 408.61 154.11 257.71 1075.08 390.16 536.33 Std. Over Dev. representation 14.18 14.63 10.48 16.01 14.58 9.86 9.44 7.43 18.41 10.2 12.38 10.45 9.25 20.2 12.41 16.05 32.73 19.74 23.14 2.66 2.34 2.31 2.06 1.95 1.88 1.87 1.85 1.84 1.83 1.81 1.78 1.76 1.69 1.64 1.59 1.58 1.55 1.52 Z-Score a 23.51 19.62 13.71 16.92 13.9 8.64 8.2 6.23 15.48 8.37 10.03 8.08 7.01 14 7.85 9.52 19.17 10.81 11.98 a Zscores measures the distance from the population mean in units of the population standard deviation. Zscores bellow 2 or above 2 are considered statistically significant, corresponding to a pvalue of about 0.05 17 Discussion Global mapping of transcription factor binding sites in the postgenomic era Global mapping of transcription factor binding sites (TFBSs) and histone modifications has become widely available thanks to ultrahighthroughput sequencing technology. This innovative technology is quickly being used to decipher the complex network of TFBSs that regulate the mammalian genome (Barski et al., 2007; Johnson et al., 2007; Robertson et al., 2007; Wederell et al., 2008). However, not all TFBSs identified in a wholegenome study (using either ChIPchip or ChIPseq) are functional TFBSs. Although ChIPseq allow for a more accurate and unbiased mapping of TFBSs, there is an increased need for tools that are able to differentiate true regulatory elements from those that are the consequence of biological noise (Struhl, 2007) or simply indirect TFDNA interaction crosslinked through proteinprotein interaction during the ChIP process. Certain factors like the high interdependency among transcriptional networks or functional redundancy make it difficult to recognize true binding sites. Moreover, it is important to note that binding sites that are located far from TSS are difficult to attribute any transcriptional regulatory function (Carroll et al., 2005), thus a given TF with a large number of binding sites located in such areas may act on enhancers, silencers or other distal regulatory elements, hence leading to a high false discovery rate of target genes. The data presented here show somewhat low peak height. This could be caused by technical factors related to the efficiency of the antibody recognizing the Smad4 protein or the ChIP protocol itself, or due to biological factors such as the way transcription factors form complexes with Smad proteins. Nonetheless, this study attempted to overcome those limitations by restricting the analysis to proximal regions of TSSs. Functional studies of the potential candidate genes are necessary in order to validate their implication in the TGFβ pathway. Unraveling the secrets of the complex network of TFs in the TGFβ pathway In this study I used HepG2 cells to identify targets of Smad4 in the TGFβ pathway. The number of genes detected may be underestimated due to the filter process. Along with known target genes my data suggest interesting genes that potentially could be good candidates for follow up studies. Interestingly, FoxG1 gene appears as a candidate gene with peak height 9. FoxG1 binds to FoxOSmad complexes and blocks p21 expression (Seoane et al., 2004). p21 is thought to be one of the most 18 important genes whose regulation determines TGFβmediated growth inhibition. It has been shown previously that p21 is constitutively expressed in HepG2 cells through Smad4 and Sp1 and does not get upregulated further by treating the cells with TGFβ (Moustakas and Kardassis, 1998). These findings are supported by the fact that in my data the control sample showed a peak for Smad4 at about +175 bp of p21 and there was no signal in the treated sample, suggesting a basal state of p21 expression. Nonetheless, there was no binding for the FoxG1 gene in the control sample, but there was binding in the treated sample. Taken together these results indicate that, in contrast to other cancer cell lines, p21 has no further upregulation during TGFβ treatment in HepG2 cells. Another interesting candidate gene is the hepatocyte nuclear factor 4 (HNF4). HNF4 belongs to the nuclear hormone receptor family of transcription factors. This gene is recognized as a key regulator of hepatocyte differentiation and function (Watt et al., 2003). Alterations of the HNF4 activity have very serious consequences, such as maturityonset diabetes of the young 1 (Fajans et al., 2001). It has been shown recently that TGFβ represses HNF4 expression via ALK5/Smad3/HMGA2 signaling pathway in mouse epithelial cells (Ishikawa et al., 2008). Those findings are supported by my data since there was a peak covering the TSS of the HNF4 gene, suggesting that the same mechanism may act in human cells. The detailed knowledge database of the molecular basis of cancer provides many unexploited targets for therapeutic intervention. This study expands that knowledge a little, allowing for future projects derived from these candidate genes presented. Emerging genebased therapies, such as gene regulation by enhancing or suppressing expression or gene insertion (tumor suppressors, apoptosisinducing genes, etc.) targeting cancer cells are in a growing number of clinical trials worldwide. Those innovative approaches combined with conventional therapies such as chemotherapy, radiotherapy and surgery can lead to a more effective and less invasive ways of cancer treatment. I sincerely hope that these findings will one day contribute to winning the race against cancer. 19 Materials and methods Cell cultures HepG2 cells were grown at 37°C and 5% CO2 in 175 cm2 cell culture plates with 50 ml RPMI 1640 medium (SigmaAldrich, cat# R0883) supplemented with 10% heatinactivated fetal bovine serum (FBS) (SigmaAldrich, cat# F7524). Before each ChIP assay, cells were incubated overnight with starvation medium (RPMI 1640, 1% FBS) at 37°C and 5% CO2. Antibodies The antibodies used for ChIP assays are presented in table 4. Table 4. Antibodies used in the ChIP assay. Antibody Type Company Catalog number AntiSmad2 Monoclonal Santa Cruz Biotech SC6200 AntiSmad3 Monoclonal Zymed 511500 AntiSmad4 Monoclonal Santa Cruz Biotech SC7154 AntiPhosphorylated Smad2 Polyclonal Kindly provided by Aris N/A Moustakas, Ludvig Institute, Uppsala, Sweden AntiImmunoglobulin G Monoclonal Upstate 12370 ChIP and DNA template preparation for sequence analysis Around 108 cells in starvation medium were used per ChIP experiment. TGFβ1 (PeproTech, cat# 100 21R) was added to a final concentration of 2.5 ng/ml during 1 hour. The nuclear proteins were cross linked to the DNA using 0.37% formaldehyde for 10 minutes on a rocking bed at room temperature. The crosslinking was stopped by adding glycine to a final concentration of 0.125 M and incubating another 5 minutes on a rocking bed at room temperature. The cells were then resuspended in cell lysis buffer (CLB) (0.01 M TrisHCl pH 8, 0.01 M NaCl, 0.20% Nonidet P40 [NP40]) with protease inhibitors (PIs) added (10 µl/ml sodium butyrate, 1 µl/ml Leupeptin, 5 µl/ml 100x phenylmethylsulfonyl 20 fluoride). The cellCLB+PIs mix was incubated on ice for 10 minutes and centrifuged at 600 g at 4°C to collect the nuclei. The supernatant was discarded and the nuclei pellet was resuspended in RadioImmunoPrecipitation Assay (RIPA) (1xPhosphate Buffered Saline [PBS] [SigmaAldrich, cat# 2318345], 1% NP40, 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate [SDS], 0.004% sodium azide) buffer containing PIs. The nuclei were incubated 10 minutes on ice and transferred to a 15 ml Falcon tube for sonication. Chromatin was fragmented using Bioruptor (Diagenode, cat# UCD200) to approximately 200 base pairs length. The sheared chromatin was precleared by adding 75 µl of protein Gagarose (Roche Diagnostics, cat# 11 243 233 001) and incubated for 1 hour at 4°C on a rotating wheel. The agarose beads were centrifuged at 16000 g and the supernatant (contains precleared chromatin) was used to set up the ImmunoPrecipitation (IP) reactions, aiming to use DNA from around 5x106 cells per reaction. A small fraction of precleared chromatin was kept as input DNA. Every IP reaction was incubated at 4°C overnight with 10 µg of antibody recognizing the protein of interest. The following day 100 µl of protein G agarose were added to every IP and incubated for 2 hours at 4°C with rotation to allow the antibodyproteinDNA complex to bind the agarose beads. Then the beads were collected by centrifugation at 16000 g for 2 minutes at 4°C and washed 4 times with RIPA buffer, one time with ImmunoPrecipitation Washing Buffer 2 (IPWB2) (0.01 M Tris HCl [pH 8],0.25 M LiCl, 0.001 M EDTA, 1% NP40, 1% Na deoxycholate) and one time with TE buffer (0.01 M Tris HCl [pH 8], 0.001 M EDTA). The immunocomplexes were diluted from the beads with 450 µl of freshly prepared IPElution Buffer (IPEB) (0.1 M NaHCO3, 1% sodium dodecyl sulfate [SDS]) to every IP reaction and incubated 30 minutes with gentle agitation at room temperature. Then the enriched DNA was transferred to a new tube and incubated with 15 μg of RNase A (Amersham Biosciences, cat# E70194Y), and NaCl to a final concentration of 0.3 M at 65°C for 6 hours to reverse crosslinking. Proteins were degraded by adding 90 μg of proteinase K (Amersham Biosciences, cat# E76230Y) and incubating at 45°C overnight. DNA was extracted by standard phenol/chloroform/isoamyl alcohol extraction (Sambrook et al., 1989), ethanol precipitated (Sambrook et al., 1989) and resuspended in water. Polymerase chain reaction confirmation Polymerase chain reaction (PCR) was performed on each sample to confirm the presence of known binding sites in the immunoprecipitated material, as a quality control before sending the samples for sequencing. 35 to 40 cycles of PCR were used to amplify the enriched DNA using primers for promoters of known early target genes. The PCR mix was composed of 2 µl 10x PCR buffer (Invitrogen, cat# Y02028), 0.6 µl MgCl2 (Invitrogen, cat# Y02016), 0.2 µl dNTP (10 mM) (Promega, cat# C1141), 0.2 µl Taq Platinum DNA polymerase (5U/ml) (Invitrogen, cat# Y02016), 1 µl Reverse+Forward primer mix (Table 5) 10 µM, 0.5 µl DNA sample and 15.5 µl water, to a total volume of 10 µl per sample. The program used was as follows: 95°C 3 (95°C 30”, 59°C 30”, 72°C 45”)x35 or x40, 72°C 7'. 21 Table 5. Oligonucleotides used in the PCR reactions Gene name Forward primer Reverse primer PAI1 CAGAGGGCAGAAAGGTCAAG CTCTGGGAGTCCGTCTGAAC JunB GTTAGCTTCCCAAGGTGCTG GGTCCCTGTGACCCCTAAAT Smad7 TCGGACAGCTCAATTCGGAC GGTAACTGCTGCGGTTGTAA Data analysis A perl script made by Ola Wallerman was used to filter peaks in the treated sample that were present either both in control and treated samples or not located within 10 kb of a TSS. Briefly, the script compares the positions of the peaks to all TSSs downloaded from UCSC genome browser and reports the distance to the nearest one. 22 Acknowledgments I would like to thank Claes Wadelius for giving me the chance to work in his group. Many thanks to Olla Wallerman for teaching me the deep secrets of ChIP and for his support. 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