Download Transcription response in the TGF-beta pathway Francisco Manuel

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.
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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).
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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.
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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.
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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.
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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.
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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.
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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.
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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'.
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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.
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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. I am really grateful to Mehdi Motallebipour for his unconditional patience answering my questions, to Mahdu Bysani and Kalicharan Patra for their generosity lending reagents, to Patricia Respuela for her "ChIP tips and tricks", to Katerina Pardali for useful comments on the manuscript and TGF­beta wisdom and to Aris Moustakas for providing home­made Smad2 antibody.
Lastly, this work is dedicated to the amazing friends I made along the way: Ammar, Gucci, Jelena, Jessica, Millaray and Sara. The moments we shared together made my experience at Rudbeck unforgettable.
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