Chapter 20 - Springer Link

Chapter 20 Probing the Epigenetic Status at Notch Target Genes Robert Liefke and Tilman Borggrefe Abstract Chromatin-based mechanisms significantly contribute to the regulation of many developmentally regulated genes, including Notch target genes. After specific ligand binding, the intracellular part of the Notch receptor is cleaved off and translocates to the nucleus, where it binds to the transcription factor CSL (encoded by the RBPJ gene in mammals), in order to activate transcription. In the absence of a Notch signal, CSL represses Notch target genes by recruiting a co-repressor complex. Both NICD co-activator and CSL co-repressor complexes contain chromatin modifiers such as histone acetyltransferases and methyltransferases, which dynamically regulate chromatin marks at Notch target genes. Here we provide protocols for ChIP (chromatin immunoprecipitation) to analyze the chromatin status of dynamically regulated Notch target genes. Furthermore, an example is presented how to perform a primary analysis of ChIP-Seq data at Notch target genes using the Cistrome platform. Key words Notch, Transcription, Epigenetics, ChIP, ChIP-Seq, Histone modifications, Histone methyltransferase, Histone deacetylases, Cistrome

1

Introduction Though the Notch signaling cascade appears remarkably simple with no second messengers involved [1], the activation of downstream genes in a given tissue often remains complex and poorly understood. Specificity of a given Notch target gene is often set up long before the actual Notch signal is received. This is due to chromatin-based mechanisms that shape the specific epigenetic state of Notch-responsive genes. Notch target genes can be kept in a permissive (or “poised”) state reflected by a combination of positive and negative chromatin marks being able to respond to a Notch stimulus at the right time. Alternatively, certain Notch target genes can be fully shut off by the presence of multiple negative histone marks, DNA methylation, and eventually chromatin compaction. Since Notch target genes are themselves often master regulators, gene regulation of Notch target genes is of major importance to understand molecular control of cell differentiation and carcinogenesis.

Hugo J. Bellen and Shinya Yamamoto (eds.), Notch Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 1187, DOI 10.1007/978-1-4939-1139-4_20, © Springer Science+Business Media New York 2014

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Robert Liefke and Tilman Borggrefe

Histone modifications affect chromatin structure either directly, by altering the electrostatic interactions between histones and DNA, or indirectly, by providing a specific platform recognized by chromatin-binding proteins which promote distinct cellular events. Histone acetylation leads to opening of the chromatin fiber, which allows binding of transcription factors and is generally associated with transcriptional activation. Histone methylation can occur at different lysine or arginine residues and correlates either with activation or repression. Well-known methylation marks are H3K4 methylation for gene activation, while H3K27 methylation is associated with gene repression, as reviewed in [2]. The transcription factor CSL plays a central role in transducing Notch signals into changes in gene expression [3–5]. Following activation, the formation of a ternary complex containing CSL, NICD, and Mastermind is essential for the upregulation of Notch target genes. Upon binding of NICD to CSL, CSL switches from repression into activation mode and promotes gene expression. The interaction of NICD with CSL creates an interface that is recognized by the essential co-activator Mastermind [6]. The CSL/ Notch/Mastermind co-activator can subsequently recruit the histone acetyltransferase p300 [7]. Interestingly, CSL was originally identified as a repressor of transcription [8]. The CSL activator/ repressor paradox was resolved with the finding that repression and activation via CSL involve recruitment of distinct protein complexes. So far, a model has emerged in which NICD displaces corepressors to convert DNA-bound CSL to an activator, as reviewed in [3, 4, 9, 10]. Notch target genes are regulated by a plethora of chromatin modifiers (reviewed in [10]). A role for histone acetyltransferase p300 [7] as well as histone deacetylase HDAC1 [11] and SIRT1 [12, 13] in Notch signaling has been proposed early on. More recently, dynamic regulation of H3K4 methylation has been demonstrated, regulated by histone demethylases KDM5A [14] and LSD1 [15]. The Polycomb complex, which regulates H3K27 methylation, has been implicated in repression of Notch target gene expression. Genetically, Drosophila polyhomeotic, a polycombcomplex component, suppresses Notch signaling [16] and mutations in polycomb complex PRC2 are found in chronic T-ALL [17]. A direct interactor of CSL, FHL1 (also known as KyoT2), may form the bridge between CSL and Polycomb [18]. The epigenetic status at Notch target genes is commonly addressed via chromatin immunoprecipitation (ChIP) experiments. Here, we provide protocols for cross-linking ChIP (X-ChIP) and native ChIP (N-ChIP). The major difference between those two approaches is that during the X-ChIP the chromatin-bound proteins are chemically cross-linked to the chromatin, whereas for native ChIP no such reaction is performed. Cross-linking ChIP is especially suitable for DNA-bound proteins, like CSL, and proteins

257

Epigenetic Status at Notch Target Genes

Table 1 Suitable antibodies to probe the epigenetic status at Notch target genes Location

Recommended antibodies

X-ChIP

N-ChIP

H3

–

Abcam, ab1791

+

+

H3K4me1

Enhancers

Millipore, 07-436

+

+

+

+

+

+

+/−

+

+

+

+

+

Abcam, ab8895 H3K4me2

Enhancers/promoters

Millipore, 05-790 Millipore, 05-1338

H3K4me3

Active promoters, CpG islands

Millipore, 04–745 Millipore, 05-1339 Diagenode, pAb-003-050

H3K27me3

Repressed regions

Millipore, 07–449 Abcam, ab6002

H3K27ac

Enhancers

Active Motif, 39135 Diagenode, pAb-174-050

H3K9me3

Heterochromatin

Millipore, 07-523 Millipore, 05-1250

that are only weakly bound to chromatin. Cross-linking ChIP also works for most histone modifications. Native ChIP is mostly used for histone modifications but is also suitable for some strong histone-binding proteins. In Table 1 we summarize histone modifications, their occurrence, as well as the recommended ChIP antibody and approach. In both ChIP approaches the chromatin is cut in smaller pieces. For X-ChIP this is done physically using ultrasound sonication, while in the case of native ChIP, digestion with the enzyme micrococcal nuclease (MNase) is performed (Fig. 1). Subsequently, the DNA fragments are enriched by immunoprecipitation using a specific antibody. The fragments are isolated and further analyzed either by real-time PCR of selected Notch target genes or by high-throughput sequencing revealing genome-wide binding sites (ChIP-Seq). The obtained ChIP-seq data are usually further processed by a computational biologist. We provide here an introductory guide for a primary analysis of these data using the Cistrome platform [19]. This approach also allows an analysis of individual promoters using publically available datasets. To date, there are a few genome reports describing CSL- and NICD-binding sites in T-cells [20], in B-cells [21], and most recently in muscle cells [22]. These studies can be partially taken as reference points for further

258

Robert Liefke and Tilman Borggrefe Nucleosome

H4 Me

H3

Ac

K4

K27

DNA Transcription factor

H4

K27

H3

Formaldehyde crosslinking/ Sonication

MNase Treatment

Immunoprecipitation

Antibody

DNA Purification DNA

qPCR, ChIP-Seq

Fig. 1 Schematic outline of a chromatin immunoprecipitation (ChIP) experiment

studies looking at dynamic histone modifications in different systems and setups. To decipher the function of the CSL co-repressor complex as well as the Notch co-activator complex, the chromatin status at the target genes are ideally investigated in a dynamic system, where Notch signaling is either turned off or turned on. In chapters 11 (Ilagan and Kopan 2014a), 12 (Ilagan and Kopan 2014b), 19 (Bailis et al. 2014), 23 (De Kloe and De Strooper 2014), 24 (Gordon and Aster 2014), and 25 (Koga and Aikawa 2014), methods and tools for manipulating Notch signaling in mammalian cells are described.


2

259

Materials

2.1 Chemicals and Reagents

1. Glycine. 2. Sucrose. 3. Tris base. 4. EGTA. 5. EDTA. 6. Nonidet P-40 (NP-40) or IGEPAL CA-630. 7. Glycerol. 8. DTT. 9. NaCl. 10. LiCl. 11. MgCl2. 12. CaCl2. 13. KCl. 14. SDS. 15. Ethanol (100 %). 16. Phenol/chloroform/isoamyl alcohol (25:24:1). 17. Chloroform. 18. Formaldehyde (37 %). 19. Sodium butyrate. 20. Proteinase K (10 mg/ml). 21. RNase A (10 mg/ml). 22. Protein A/G Sepharose Beads. 23. Glycogen (20 mg/ml). 24. Protease inhibitors (e.g., cOmplete Protease Inhibitor Cocktail Tablets from Roche). 25. MNase (Micrococcal nuclease, Sigma-Aldrich). 26. MilliQ water.

2.2

Equipment

1. Sonicator (for X-ChIP only). 2. Heat block or water bath at 37 °C, 45 °C, and 67 °C. 3. Ultracentrifuge with swing-out rotor (for N-ChIP only). 4. Refrigerated tabletop centrifuge. 5. Agarose gel electrophoresis apparatus.

2.3

X-ChIP Buffers

X-ChIP Lysis Buffer 1 50 mM Tris–HCl, pH 8.0. 2 mM EGTA.

260


0.1 % NP-40 (v/v). 10 % Glycerol (v/v). 1 mM DTT (freshly added prior to use). Protease inhibitors (freshly added prior to use). X-ChIP Lysis Buffer 2 50 mM Tris–HCl, pH 8.0. 5 mM EGTA. 1 % SDS (w/v). 1 mM DTT (freshly added prior to use). Protease inhibitors (freshly added prior to use). X-ChIP Dilution Buffer 50 mM Tris–HCl, pH 8.0. 5 mM EGTA. 200 mM NaCl. 0.5 % NP-40 (v/v). Protease inhibitors (freshly added prior to use). NaCl Washing Buffer 20 mM Tris–HCl, pH 8.0. 500 mM NaCl. 2 mM EGTA. 0.1 % SDS (w/v). 1 % NP-40 (v/v). LiCl Washing Buffer 20 mM Tris–HCl, pH 8.0. 500 mM LiCl. 2 mM EGTA. 0.1 % SDS (w/v). 1 % NP-40 (v/v). X-ChIP Elution Buffer 10 mM Tris–HCl, pH 7.9. 1 mM EGTA. 2 % SDS (w/v). TE 10 mM Tris–HCl, pH 8.0. 1 mM EDTA.


Proteinase K Buffer (5×). 50 mM Tris–HCl, pH 7.5. 25 mM EGTA. 1.25 % SDS (w/v). 2.4

N-ChIP Buffers

N-ChIP Lysis Buffer 1 15 mM Tris–HCl, pH 7.5. 0.3 M Sucrose. 60 mM KCl. 5 mM MgCl2. 0.1 mM EGTA. 0.5 mM DTT. Protease inhibitors. N-ChIP Lysis Buffer 2 15 mM Tris–HCl, pH 7.5. 0.3 M Sucrose. 60 mM KCl. 5 mM MgCl2. 0.1 mM EGTA. 0.5 mM DTT. 0.4 % NP-40 (v/v). N-ChIP Lysis Buffer 3 15 mM Tris–HCl, pH 7.5. 1.2 M Sucrose. 60 mM KCl. 5 mM MgCl2. 0.1 mM EGTA. 0.5 mM DTT. MNase Digestion Buffer 50 mM Tris–HCl, pH 7.5. 0.32 M sucrose. 4 mM MgCl2. 1 mM CaCl2. Protease inhibitors. Stop Solution 20 mM EDTA pH 8.0.

261

262


Resuspension Buffer 1 mM Tris–HCl, pH 7.5. 0.2 mM EDTA. Protease inhibitors. N-ChIP Dilution Buffer 50 mM Tris–HCl, pH 7.5. 50 mM NaCl. 5 mM EDTA. Protease inhibitors. N-ChIP Washing Buffer A 50 mM Tris–HCl, pH 7.5. 10 mM EDTA. 75 mM NaCl. N-ChIP Washing Buffer B 50 mM Tris–HCl, pH 7.5. 10 mM EDTA. 125 mM NaCl. N-ChIP Washing Buffer C 50 mM Tris–HCl, pH 7.5. 10 mM EDTA. 175 mM NaCl. N-ChIP Elution Buffer 50 mM Tris–HCl, pH 7.5. 50 mM NaCl. 5 mM EDTA. 1 % SDS.

3

Methods

3.1 CrossLinking ChIP

Day 1 1. To the media, containing about 2 × 107 cells (see Note 1), directly add formaldehyde (37 %) to a final concentration of 1 %. 2. Incubate cells for 10 min at room temperature (RT), shaking. 3. Add 1 M glycine pH 7.5 to a final concentration 0.125 M, and shake cells for 5 min at room temperature. 4. Collect cells, and centrifuge cells at 400 × g at 4 °C.


263

5. Resuspend cell pellet with ice-cold PBS, and centrifuge cells at 400 × g at 4 °C. 6. Resuspend the cell pellet in 1 ml ice-cold X-ChIP lysis buffer 1 for 10 min on ice. 7. Centrifuge at 400 × g at 4 °C for 5 min. 8. Resuspend the pellet in 600 μl ice-cold X-ChIP lysis buffer 2 for 10 min on ice (see Note 2). 9. Sonicate cells 9 × 10 s with low energy (65 mA) on ice, wait for 30 s between each sonication step, to cool down. Upon sonication the cell suspension should turn clearer. Do not keep the sample too long on ice (>30 min), since the SDS will start to precipitate. 10. Centrifuge at 20,000 × g for 5 min; the pellet should be very small and is often blackish (see Note 3). 11. Transfer supernatant into a 15 ml Falcon containing 5,400 μl X-ChIP dilution buffer (see Note 6). 12. To reduce nonspecific binding to Sepharose beads, preclear the solution by adding 100 μl of washed (with X-ChIP dilution buffer) Protein G Sepharose Beads (see Note 7). 13. Rotate for 1 h at 4 °C. 14. Centrifuge at 400 × g at 4 °C for 5 min. 15. Transfer supernatants into new tubes (prevent taking any beads). Here split up the sample into several tubes (e.g., 1 ml)—for each antibody one tube. Save 10–50 μl as an “Input” sample, and keep it at −20 °C. 16. Add antibody to be tested (2–50 μg), and incubate rotating overnight at 4 °C. Day 2 17. Add 20 μl of washed (in dilution buffer) Protein A/G beads to each tube, and rotate for another hour at 4 °C. 18. Centrifuge at 400 × g at 4 °C for 5 min. 19. Wash beads twice with 1,000 μl NaCl washing buffer. 20. Wash beads twice with 1,000 μl LiCl washing buffer (see Note 9). 21. Wash beads 1× with ice-cold TE. 22. Add 150 μl X-ChIP elution buffer to beads. 23. Leave the tubes for 15 min at RT, vortex occasionally, centrifuge (400 × g, 5 min), and take the supernatant into a new tube. 24. Repeat previous step, and combine the supernatants of both steps (300 μl together).

264


25. Include the input sample to each of the following steps (see Note 10). 26. Add 1 μl of RNase A (10 mg/ml). 27. Add 5 M NaCl to a final concentration of 0.3 M (18 μl). 28. Incubate for 4–5 h or overnight at 67 °C (reverse cross-linking). 29. Add 750 μl ethanol, mix, and let precipitate at −20 °C overnight. Day 3 30. Centrifuge at 20,000 × g for 15–20 min at 4 °C. 31. Remove supernatant, and let the pellet air-dry completely. 32. Dissolve pellet in 100 μl TE. Add 25 μl 5 × Proteinase K buffer and 1.5 μl Proteinase K (10 mg/ml), and incubate for 1 h at 45 °C, shaking. 33. Add 175 μl TE. 34. Add 300 μl phenol/chloroform/isoamyl alcohol (25:24:1), and vortex the tubes vigorously. 35. Centrifuge at 20,000 × g for 5 min, and take upper phase into a new tube, without touching the lower phase. Discard the lower phase. 36. Add 300 μl chloroform, and vortex the tubes vigorously. 37. Centrifuge at 20,000 × g for 5 min, and take upper phase into a new tube. Discard the lower phase. 38. Add 18 μl of 5 M NaCl and 5 μg glycogen (helps to precipitate small amounts of DNA), and mix. 39. Add 750 μl 100 % ethanol, and precipitate overnight at −20 °C. Day 4 40. Centrifuge at 20,000 × g for 15–20 min at 4 °C, remove supernatant, and let pellet air-dry completely. 41. Add 20–30 μl TE. Optional steps: 42. Quantify DNA (see Note 11). 43. Analyze DNA of suitable target genes with quantitative PCR. 44. Create DNA library for deep sequencing using Library Preparation kits (see Notes 12 and 13). 3.2

Native ChIP

Day 1 1. Harvest 1–5 million cells, and wash twice with PBS (see Note 1). 2. Resuspend cell pellet in 2 ml N-ChIP lysis buffer 1.


265

3. Add 2 ml of N-ChIP lysis buffer 2 (total 4 ml), and keep it on ice for exactly 10 min (see Note 8). 4. Prepare two polypropylene tubes containing 8 ml N-ChIP lysis buffer 3. Layer on each of them 2 ml of the suspension. 5. Centrifuge at 10,000 × g in a swing-out rotor at 4 °C for 20 min; the nuclei form a pellet at the bottom, while the cytoplasmic fraction remains in the top layer. 6. Remove the supernatant using vacuum. The NP-40 containing top layer should not get into contact with the nuclear pellet (see Note 8). 7. Resuspend the pellet in 1 ml MNase digestion buffer. The DNA content of the resuspended nuclei may be quantified at 260 nm. The ratio OD260/OD280 should be around 1.1, due to the high protein proportion. 8. Add 2 U/ml MNase and incubate for 10 min in a 37 °C water bath (see Note 4). 9. Stop digestion by adding EDTA to a final concentration of 5 mM, and put samples on ice. 10. Centrifuge at 9,000 × g at 4 °C. 11. Save the supernatant (S1). 12. Resuspend pellet in 1 ml resuspension buffer, and incubate it at 4 °C overnight. The nucleosomes diffuse out of the nuclei into the solution. Day 2 13. Centrifuge at 9,000 × g at 4 °C. 14. Save the supernatant (S2). 15. Check the S1 and S2 on via agarose gel electrophoresis. Typically the S1 contains only mono- and di-nucleosomes, while S2 also has longer chains (see Note 3). 16. Dependent on the results from step 15, either use S1 or S2 or merge S1 and S2. 17. Dilute solution 1:10 in N-ChIP dilution buffer. 18. Divide the sample into several microcentrifuge tubes (e.g., 1 ml)—for each antibody one tube. Save 10–50 μl as an “Input” sample. 19. Add the antibody (2–50 μg) of interest, and incubate rotating for 4 h or overnight at 4 °C, dependent on the antibody. Day 3 20. Add 20 μl of washed (in N-ChIP dilution buffer) Protein A/G beads to each tube, rotate for another hour at 4 °C (see Note 7). 21. Centrifuge at 400 × g at 4 °C for 5 min.

266


22. Wash beads 1× with 10 ml N-ChIP washing buffer A. 23. Wash beads 1× with 10 ml N-ChIP washing buffer B. 24. Wash beads 1× with 10 ml N-ChIP washing buffer C. 25. Resuspend beads in 300 μl N-ChIP elution buffer, and incubate for 30 min at room temperature. 26. Centrifuge (400 × g, 5 min, 25 °C), and transfer supernatants into new tube. 27. Proceed as described in X-ChIP protocol, step 34.

4

Analysis of ChIP-Seq Results Using Cistrome Analysis of ChIP-Seq data is often a challenge for non-computational biologists. Here we provide a step-by-step guide for how to extract the most crucial information from ChIP-Seq data. All steps presented here do not require any deep bioinformatics knowledge, special software, or strong computer power. It only requires an Internet connection and a free account at the Cistrome project (http://cistrome.org/ap/) [19]. We will use the publically available data from [20] as example. For advanced analyses, collaboration with a bioinformatics lab or usage of Bioconductor and R (http://www.bioconductor.org/) is recommended. Throughout this guide, standard settings are used, if not otherwise mentioned. 1. As first step we need to upload the data into Cistrome (if not yet done, first create an account). As starting material we use publically available Bed files (Table 2), which already contain the mapped reads of a ChIP-Seq experiment (see Notes 1–3). (a) Go to Import Data/Upload File. (b) Insert the URL to the BED files containing the mapped reads, into the field “URL/Text:” To obtain this URL go to the desired dataset in the GEO depository. Right click on the http link to the Bed file, and copy the address of the link. Paste this URL into the text field using right click, or CTRL-V. (c) Set the field “File Format” to “bed”. (d) Set the field “Genome” to “Human Mar 2006 (NCBI36/ hg18) (hg18)”. (e) Press “execute”. (f) Upload the bed files of the datasets presented in Table 2 into Cistrome. (g) For simplicity reasons we merge duplicate samples. Use Text Manipulation/Concatenate two datasets to merge


267

Table 2 Datasets used for bioinformatic analysis GEO ID

Sample

GSM732903

Notch1-1

GSM732904

Notch1-2

GSM732905

CSL-1

GSM732906

CSL-2

GSM732907

ZNF143

GSM732908

Input-1

GSM732909

Input-2

GSM732910

H3K4me1

GSM732911

H3K4me3

GSM732912

H3K27me3

Input-1 and Input-2, CSL-1 and CSL-2, as well as Notch1-1 and Notch1-2, respectively. Afterwards, the unmerged files of CSL, Notch1, and Input can be deleted. We recommend renaming datasets in order to prevent later confusion. This can be done using the pen tool. 2. MACS (Model-based Analysis of ChIP-Seq): Next we perform MACS analysis to identify genomic regions where ChIP-Seq tags are enriched. Under Data Preprocessing/MACS use the dataset for Notch1 (and subsequently CSL, ZNF143, H3K4me3, H3K4me1 and H3K27me3) under “Treatment file:” and Input under “Input file:”. As settings, use Effective Genome Size: Human (hg18); File format: Bed; P-value: 1e–06. 3. Visualization in UCSC browser: The MACS analysis creates also a Wiggle file which can be used to visualize the ChIP-Seq data at the UCSC genome browser (Fig. 2). For practical reasons, we reduce the file size of the wiggle by using Liftover/Others/Standardize wig file with a span of 64 or 128 bps. For visualization in the UCSC browser perform the following steps: (a) Download standardized Wiggle. (b) Download Bed file containing called peaks (has been created by MACS). (c) If possible compress the file using gzip.

268


Fig. 2 Visualization of ChIP-Seq data in the UCSC browser allows one to get a general idea about the data. For example here it can be seen that Notch1 and CSL occupancy positively correlate with H3K4me3, while Notch1 and CSL peaks are hardly present at H3K27me3-enriched regions

(d) Go to the UCSC browser (http://genome.ucsc.edu/). (e) Click on “Genome” and Select Group: Mammalian, Genome: Human; assembly: “Mar 2006 (NCBI36/hg18)”. (f) Upload the downloaded (and compressed) files using “add custom track”. (g) If you want to upload multiple files, please see Note 4. 4. Venn diagrams: Venn diagrams are a simple but efficient way to show the overlap between certain ChIP-Seq datasets. To create a Venn diagram of, e.g., Notch1, CSL, and ZNF143 Peaks use the Integrative Analysis/Venn Diagram tool. Use the MACS peaks results created by MACS (Fig. 3a). 5. Genomic distribution: To find out how a certain factor is distributed in the genome, we perform Integrative Analysis/CEAS: Enrichment on chromosome and annotation. As wiggle, use the wiggle made by MACS, and as Bed file use MACS Peaks or MACS Summits of the same factor. Transcription factors like CSL are often found enriched at promoters (Fig. 3b) (see Note 5). 6. Motif search: To search for enriched motifs at specific regions use Integrative Analysis/SeqPos motif tool. Use either the MACS peaks or the MACS summits as input Bed file. We generally recommend to search for known motifs but to also perform a de novo search. A maximum of 5,000 regions can be analyzed by SeqPos. If your Bed file contains more than 5,000 regions, you can

269


b

Genome

Notch1 ChIP

CSL ChIP

a Notch1

CSL 6070

4377

3393 2994 4917

ZNF143

c

z-Score

ETS family

CTCF

-35.2255

-24.1474

d

CSL

-23.775

-18.6977

H3K27me3 2.4 2.2 1.4

1.6

20

1.8

30

2.0

40

50

CREB

-21.8593

H3K4me3 CSL only sites Notch1/CSL sites

1.2

10

Average Profile

ZNF143

−2000

−1000

0

1000

2000

Relative Distance from the Center (bp)

CSL only sites Notch1/CSL sites

−2000

−1000

0

1000

2000

Relative Distance from the Center (bp)

Fig. 3 Using Cistrome, crucial information can be extracted from ChIP-Seq data. (a) Overlap of CSL, Notch1, and ZNF143 in CUTLL1 cells. (b) Genomic distribution of CSL and Notch1. (c) Enriched transcription factor motifs at CSL/Notch1-occupied sites. (d) Enrichment of histone modifications at CSL-only and CSL/Notch1occupied sites

randomly select 5,000 regions by using Text Manipulation/Select random lines. If you want to analyze specifically regions that are co-occupied by two factors use the Operate on Genomic Intervals/Intersect tool. For example, when using regions that are occupied by CSL and Notch1, many different transcription factor-binding motifs are found enriched, suggesting that formation of a CSL/Notch1-activating complex binding could be dependent on the presence of other transcription factors, beyond CSL. Some selected ones are shown in Fig. 3c (see Note 6).

270


7. Profile of histone marks at CSL-binding sites: To address how histone marks correlate with the binding of Notch1 to CSL, we use Integrative Analysis/SitePro: Aggregation plot tool for signal profiling. For this purpose, we create summit datasets that contain regions that are bound by CSL, but not by Notch1 or regions that are bound by CSL and Notch1, respectively. To do this, we use the Operate on Genomic Intervals/Intersect or Subtract tool. For CSL-only regions subtract Notch1 MACS Peaks from CSL MACS Summits. For CSL- and Notch1-bound sites Intersect CSL MACS Summits (first dataset) and Notch1 MACS Peaks (second dataset). Afterwards set in SitePro the “SitePro behavior mode” to “multiple Bed vs 1 wiggle”. Use the MACS-created wiggle for H3K4me3 or H3K27me3 as wiggle and both Bed files created above as Bed. Use 2,500 bps as span. The outcome shows that Notch1 binding to CSL positively correlates with the presence of H3K4me3. The opposite is the case for H3K27me3 (Fig. 3d). 8. Heatmap: The heatmap tool under Integrative Analysis/Heatmap is useful to visualize ChIP-Seq data in a highly condensed way. When we use CSL MACS Peaks as Bed, and the wiggles for CSL, Notch1, H3K4me1, and H3K4me3, clustering by kmeans (with kmeans = 3), and an upstream and downstream span (under advanced option) of 2,500 bps, a heatmap can be obtained, as shown in Fig. 4a. A subset of CSL-bound regions are specifically enriched for H3K4me1, suggesting that these are enhancer sites. 9. Combining ChIP-Seq with microarray results: Lastly, we want to elucidate genes that are occupied by Notch1/CSL and their transcription is activated by Notch and hence are direct Notch1 target genes in CUTLL1 cells (see Note 7). (a) As first step we need to upload microarray data into Cistrome. For this purpose, we use Import data/Expression CEL file packager. Use as Control dataset GSM731503, GSM731504 and GSM731505 (GSI-treated cells) and as Sample Dataset GSM731515, GSM731516, GSM731517 (GSI washed off in the presence of cycloheximide). (b) Then perform Gene Expression/Gene expression index with standard setting on the uploaded dataset. Here the data will be normalized. (c) Subsequently, identify genes differently expressed in both dataset by using Gene Expression/Calculate differential expression on the normalized refseq value dataset, with a twofold cutoff.


a

b Gene

CSL

-2.5

0

Notch1

2.5 -2.5

0

H3K4me1

2.5 -2.5

0

H3K4me3

2.5 -2.5

0

HES1 HEY1 NRARP JUN APCDD1 SLC30A1 FOS BHLHE23 BMP4 DTX1 ZRANB3 C11orf96 PRR5 CPA4 LRP4 DDB2 RUNX3 GADD45A CD244 PFKFB2 NR4A3 ARHGEF3 COQ2 ICOS HES5

271

Fold Change upon Notch1 activation (log2) 5.02057501 4.60188118 4.57030821 3.9203584 3.70484386 3.52980369 3.00227784 2.90971876 2.84473854 2.80876668 2.77441408 2.73170015 2.69301734 2.60365478 2.57451069 2.55962378 2.510723 2.45534732 2.37930761 2.29667305 2.26805165 2.20189313 2.17405869 2.14497144 2.14252305

2.5 kbs

Fig. 4 (a) Heatmap of CSL, Notch1, H3K4me1, and H3K4me3 clustered by kmeans. (b) The 25 most Notch1 upregulated genes, which are occupied by CSL and Notch1, and are therefore likely direct Notch1 target genes in CUTTL1 cells

(d) Two files are created, a txt file and an HTML file. We continue to work with the txt file. It contains a table of the Refseq ID (e.g., NM_000043) and the log2 fold change. Next we want to convert the Refseq IDs into Gene Symbols. For this, we first need to remove the first line, by using Text Manipulation/Remove beginning. (e) Afterwards use the file created in (d) in Liftover/ Others/Convert between RefSeq, Gene Symbols to Entrez IDs with Conversion “Refseq IDs to Gene Symbol”. A new file containing the original RefSeq ID and the corresponding Gene Symbol is made. For further processing this file must be converted to a table. Use Text Manipulation/Convert delimiters to TAB and “whitespaces” for conversion. (f) The created file from (e) also lacks the expression data. Therefore we need to merge this file with the file obtained from (d) (after removal of the first line). For merging use Text Manipulation/Paste two files side by side. Use file from (e) as first file and the file created in (d) as second file. Now we have a file containing genes that are affected by Notch1 activation, with their respective fold expression change. (You can sort this with the Filter and Sort/Sort tool.)

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(g) Next we want to identify genes that are occupied by Notch1 and CSL. We use here a Peak dataset of overlapping peaks of CSL and Notch1, created by Operate on Genomic Intervals/Intersect. To find nearby genes for those peaks, we use Integrative Analysis/peak2gene: Peak Center Annotation, with standard settings. (h) Two files are created: one file containing annotation for each peak, and a second one containing the annotations for each gene. We continue to work with the latter one. First we remove the first eight lines using Text Manipulation/Remove beginning. (i) Then we want to join the results from gene expression analysis with the genes that are occupied by CSL/Notch1. To do this we use Join, Subtract and Group/Join two Datasets. Join the dataset from (f) (using “c2” as column) and the data from (h) (with “c4” as column). The outcome contains genes that are occupied by CSL/Notch1 and their expression is at least twofold affected upon activation of Notch. We recommend to download this file and further process this data with Excel (remove duplicate Columns/Genes, and Sort the data according to their expression change). Individual validation of each gene of interest is crucial. The top 25 upregulated genes, occupied by CSL/Notch1, are shown in Fig. 4b.

5

Notes

5.1 Notes for ChIP Experiments

1. Typically 20 million cells are suitable for most cell types and antibodies when doing cross-linking ChIP. However, the optimal amount of cells has to be individually determined for each cell type and antibody. A range from 500,000 to 50 million cells is recommended for testing. For native ChIP, best results are typically obtained with about 1–5 million cells, since too many cells increase the background. 2. If using a sonication tip, in our hands 600 μl volume works best, in most cases. Too large volume reduces the shearing efficiency, while lower volume can lead to formation of foam. 3. For X-ChIP the sonication should lead to DNA fragments of 300–800 bps (Fig. 5). To check and optimize the sonication we recommend analyzing the material before and after sonication via agarose gel electrophoresis. However, since proteins are still cross-linked to the DNA the results may not reflect the true size of the DNA. Therefore, analyzing the DNA size after decross-linking (4 h at 67 °C) is more reliable.

Epigenetic Status at Notch Target Genes X-ChIP

bps 1000 500 200

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N-ChIP

Multi-nucleosomes

sheared DNA

Di-nucleosomes Mono-nucleosomes 3 6

9 min

Fig. 5 DNA after sonication or MNase treatment. Optimal are DNA fragments from 300 to 800 bps for X-ChIP or mainly mono- and di-nucleosomes for N-ChIP (shown are samples after 3, 6, and 9 min of MNase digestion)

4. For native ChIP the MNase digestion is crucial for a successful experiment. Optimal are mainly mono- and di-nucleosomes, and to a lower extent larger fragments. For optimization, we recommend to split the sample into three fractions and perform the MNase digest for, e.g., 3, 6, and 9 min. Afterwards choose the condition with the best ratio between lower and larger nucleosomal fragments (Fig. 5). 5. When performing ChIP against histone-acetylation marks, sodium butyrate (an HDAC inhibitor) should be added to all buffers (final concentration: 5 mM). 6. Do not use DTT during the immunoprecipitation step, since DTT can destroy the disulfide bonds of the antibody and impair the immunoprecipitation efficiency. 7. Do not use Protein A/G beads saturated with salmon sperm DNA for ChIP experiments that will be analyzed by deep sequencing. Since this DNA will be amplified together with the precipitated DNA during the library preparation, it will decrease the quality of the ChIP-Seq results. 8. The cell lysis at this step is achieved by a combination of a hypotonic buffer and NP-40. It is critical not to incubate the cells too long with this buffer, since NP-40 will also start to lyse the nuclei, which would reduce the quality of the ChIP experiment. 9. The buffers used here are relatively stringent. If the ChIP does not lead to enrichment on target genes, the buffer stringency should be reduced. Alternatively, ChIP protocols from antibody vendors like abcam and Upstate may be tested, as well. 10. During the DNA purification steps, we strongly recommend to always keep the input sample separate from the immunoprecipitation samples. Since the DNA content in the input sample

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is a thousandfold higher, any cross-contamination would impair the quality of the experimental results. Usually at each step we first process the immunoprecipitation samples, and afterwards the input sample. 11. To quantify the DNA, a fluorescence-based method is recommended, since this is more sensitive for small concentrations of DNA (e.g., Qubit). 12. If performing a ChIP experiment on an uncharacterized protein, the expected results are open. Therefore, it can be useful to create a cell line stably expressing a Flag- or a GFP-tagged version of the protein of interest. The ChIP should be performed with Flag-M2 beads (Sigma) or GFP antibody. A ChIP-Seq analysis will allow one to judge the general binding pattern of the protein and to select the best qPCR targets for optimization of the ChIP on the endogenous protein. 13. Multiplex ChIP-Seq allows merging several separate samples together. It saves money but reduces the number of reads per sample. 5.2 Notes for Bioinformatic Analysis

1. Most ChIP-Seq raw data are in a fastq format. This file format contains the sequence information of each read, but it does not contain the information, at which place in the genome a specific read maps. For mapping the reads we recommend using Bowtie (http://bowtie-bio.sourceforge.net/index.shtml) [23]. The output can be uploaded to Cistrome. See also Notes 2 and 3. 2. For uploading a large file to Cistrome and UCSC genome browser we recommend to compress the file beforehand using gzip (.gz extension). 3. In the GEO database many ChIP-Seq data are deposited as SRA files and not as fastq. However, most of these data are also available as fastq at the DRASearch database (http://trace. ddbj.nig.ac.jp/DRASearch/); for example the ChIP-Seq of ZNF143 in CUTLL1 cells (GSM732907/SRX070885) can be found as fastq, when searching for “SRX070885”. Alternatively, SRA files can be converted to fastq using the sratoolkit from NCBI (http://www.ncbi.nlm.nih.gov/Traces/ sra/?view=software). 4. MACS does not create an appropriate header for the UCSC track information, which leads to problems when uploading more than one dataset, since the Track names must be unique for each dataset. To correct the header we propose two ways:


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(a) Load the Wiggle/Bed locally into a suitable text editor, which is able to handle larger files (e.g., Ultraedit). In the wiggle change the header to, e.g., track type = wiggle_0 “Notch1”

name = “Notch1”

description =

In the Bed insert a first line as header: track name = “Notch1 Peaks”

Peaks”

description = “Notch1

(b) If a suitable text editor is not available, the headers can be directly changed in Cistrome: For the wiggle, the inappropriate header must first be removed using the Text Manipulation/Remove beginning (1 line). Afterwards use Graph/Display Data/Build custom track to add a new header to the wiggle or the Bed file. This program automatically creates a suitable header. Use for each dataset a unique track name. 5. A genomic distribution of ChIP-Seq peaks that is similar to the normal genomic distribution suggests that the ChIP did not work well, and many of the called peaks could be false positives. In such cases, a careful evaluation of the data is recommended. 6. Results from motif search should generally be handled very cautiously, because some motifs might get significantly enriched due to bioinformatic artifacts. Specifically, repeat sequences like GCGCGCG or ATATATAT are in most cases artifacts. 7. During preparation of this manuscript a new feature has been implemented into Cistrome, which allows performing this analysis directly. It can be found under Integrative Analysis/BETA. However, currently this feature is only available for the human genome hg19 and therefore cannot be applied for the example dataset used here.

Acknowledgments We thank Drs. K. Hein and B.D. Giaimo for critical reading of the manuscript and testing the bioinformatics guide. This work was supported by the Heisenberg program (BO 1639/5-1) of the DFG, the Max-Planck society, and the Excellence Cluster CardioPulmonary System (ECCPS) to T.B. R.L. has been supported by a DFG postdoctoral fellowship (LI 2057/1-1).

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