ChIP-seq Experiment and Data Analysis in the Cyanobacterium Synechocystis sp. PCC 6803
蓝藻集胞藻PCC 6803的ChIP-seq实验和数据分析   

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Nucleic Acids Research
Oct 2017

 

Abstract

Nitrogen is an essential nutrient for all living organisms. In cyanobacteria, a group of oxygenic photosynthetic bacteria, nitrogen homeostasis is maintained by an intricate regulatory network around the transcription factor NtcA. Although mechanisms controlling NtcA activity appear to be well understood, the sets of genes under its control (i.e., its regulon) remain poorly defined. In this protocol, we describe the procedure for chromatin immunoprecipitation using NtcA antibodies, followed by DNA sequencing analysis (ChIP-seq) during early acclimation to nitrogen starvation in the cyanobacterium Synechocystis sp. PCC 6803 (hereafter Synechocystis). This protocol can be extended to analyze any DNA-binding protein in cyanobacteria for which suitable antibodies exist.

Keywords: ChIP-seq (ChIP-seq), Cyanobacteria (蓝藻), Synechocystis (集胞藻), Nitrogen (氮), NtcA (NtcA)

Background

To maintain homeostasis, bacteria frequently need to adjust gene expression in response to environmental changes. Many of these adjustments are controlled by transcriptional factors (TF) that sense metabolic signals and activate or repress target genes. However, reflecting the traditionally laborious tasks necessary to characterize the activity and scope of TFs in vivo, our knowledge of their binding sites in bacteria is still limited. Only recently, the combination of chromatin immunoprecipitation with high-throughput sequencing analysis has opened the door to rapid determination of genome-level regulons. In particular, ChIP-seq uses the capacity of next-generation sequencing (NGS) to identify numerous DNA sequences in parallel. An attractive feature of ChIP-seq, compared to microarrays, is that there is no restriction to certain regions, such as promoter sequences, and the whole genome can be investigated for TF binding sites.

In cyanobacteria, the global regulator for nitrogen assimilation and metabolism is NtcA, a TF belonging to the CRP (cAMP receptor protein) family (Herrero et al., 2001). In Synechocystis, NtcA controls the cellular response to nitrogen availability by binding as a dimer to the promotor or intragenic regions of its target genes containing the consensus sequence GTAN8TAC (Herrero et al., 2001; Giner-Lamia et al., 2017). In the absence of ammonium, NtcA activates the expression of genes for nitrogen assimilation pathways but also acts as a transcriptional repressor of other genes, such as gifA and gifB, which encode for the glutamine synthetase inactivating factors IF7 and IF17 (García-Domínguez et al., 2000).

The protocol detailed herein has been optimized for immunoprecipitation of DNA from Synechocystis cells using antibodies against NtcA, followed by NGS to identify the specific binding sites of NtcA during early acclimation to nitrogen depletion. Following this protocol, we identified 192 genomic regions bound by NtcA (51 in ammonium-replete conditions and 141 after 4 h of nitrogen starvation) (Giner-Lamia et al., 2017). This protocol can be extended to study other TFs in cyanobacteria. Although the bioinformatic component is applicable to any sequenced prokaryote, the wet-lab component needs to be optimized to ensure efficient DNA extraction.

Materials and Reagents

  1. 2 ml screw-cap conical tubes (Thermo Fisher Scientific, catalog number: 3462 )
  2. Glass beads, acids-washed 425-600 µm (Sigma-Aldrich, catalog number: G8772-10G )
  3. 0.5 ml PCR tubes (Eppendorf, catalog number: 0030124537 )
  4. 1.5 ml tubes (Eppendorf, catalog number: 022363204 )
  5. 15 and 50 ml FalconTM tubes (Corning, catalog numbers: 352070 )
  6. DynaMagTM-2 Magnet (Thermo Fisher Scientific, catalog number: 12321D )
  7. Synechocystis sp. PCC 6803 cells grown on a plate of BG110C-agar (Stanier et al., 1971)
  8. NH4Cl (Sigma-Aldrich, catalog number: 254134 )
  9. TES (Sigma-Aldrich, catalog number: T1375 )
  10. 37% Formaldehyde (Sigma-Aldrich, catalog number: F8775 )
  11. Glycine (Sigma-Aldrich, catalog number: 50046 )
  12. NaCl (Sigma-Aldrich, catalog number: S7653-250G )
  13. EDTA (Sigma-Aldrich, catalog number: E9884 )
  14. Agarose (NZYTech, catalog number: MB02702 )
  15. Triton X-100 (Sigma-Aldrich, catalog number: T8787 )
  16. Sodium deoxycholate (Sigma-Aldrich, catalog number: 30970 )
  17. Protease inhibitor cocktail tablets SIGMAFAST (Sigma-Aldrich, catalog number: S8820-2TAB )
  18. NP-40 (Sigma-Aldrich, catalog number: 74385 )
  19. LiCl (Sigma-Aldrich, catalog number: L9650 )
  20. Anti-NtcA antibody (Giner-Lamia et al., 2017)
  21. SDS (Sigma-Aldrich, catalog number: L3771 )
  22. BSA (Sigma-Aldrich, catalog number: B4287 )
  23. DNase-free RNase A solution (Thermo Fisher Scientific, catalog number: EN0531 )
  24. Proteinase K (Thermo Fisher Scientific, catalog number: 25530049 )
  25. Phenol:Chloroform:Isoamyl alcohol (25:24:1) (Sigma-Aldrich, catalog number: P2069 )
  26. CaCl2 (Sigma-Aldrich, catalog number: 449709 )
  27. Glycerol (Sigma-Aldrich, catalog number: G5516 )
  28. MnCl2·4H2O (Sigma-Aldrich, catalog number: 221279 )
  29. ZnSO4·7H2O (Sigma-Aldrich, catalog number: Z1001 )
  30. Na2MoO4·2H2O (Sigma-Aldrich, catalog number: 331058 )
  31. CuSO4 (PubChem, catalog number: 24462 )
  32. Co(NO3)2·6H2O (Sigma-Aldrich, catalog number: 239267 )
  33. MgSO4·7H2O (Sigma-Aldrich, catalog number: 63138 )
  34. CaCl2·2H2O (Sigma-Aldrich, catalog number: 223506 )
  35. Citric acid (Sigma-Aldrich, catalog number: 251275 )
  36. Na2-EDTA (Sigma-Aldrich, catalog number: 27285 )
  37. Na2CO3 (Sigma-Aldrich, catalog number: S1641 )
  38. Fe-NH4 citrate (Sigma-Aldrich, catalog number: F5879 )
  39. Boric acid, H3BO3 (Sigma-Aldrich, catalog number: B6768 )
  40. 100% freezer-cold ethanol
  41. MiniElute PCR purification kit (QIAGEN, catalog number: 28004 )
  42. dsDNA assay kit (Thermo Fisher Scientific, catalog number: Q32851 )
  43. SsoFastTM EvaGreen® Supermix (Bio-Rad Laboratories, catalog number: 172-5200 )
  44. PearceTM Protein G Magnetic Beads (Thermo Fisher Scientific, catalog number: 88847 )
  45. Bradford Protein Assay (Bio-Rad Laboratories, catalog number: 5000001 )
  46. 5x Tris-buffered saline (TBS) buffer (see Recipes)
  47. Lysis buffer (see Recipes)
  48. Block solution (see Recipes)
  49. Wash buffer 1 (see Recipes)
  50. Wash buffer 2 (see Recipes)
  51. 5x IP solution (see Recipes)
  52. Tris-EDTA (TE) + NaCl Solution (see Recipes)
  53. Proteinase K solution (see Recipes)
  54. Trace metal mix A5 (see Recipes)
  55. Autoclaved BG110C medium liquid (see Recipes) (Stanier et al., 1971)
  56. Autoclaved BG110C+NH4 medium liquid (see Recipes) (Stanier et al., 1971)

Equipment

  1. Micropipettes (1,000, 100, 20 and 10 µl)
  2. 2 L flask and 2 x 1 L flask
  3. Orbital Shaker (VWR, model: 3600 )
  4. FastPrep-24 instrument (MP Biomedicals, catalog number: 116004500 )
  5. Eppendorf Thermomixer R Mixer, 1.5 ml Block (Eppendorf, model: ThermoMixer® R , catalog number: 5355)
  6. Eppendorf MiniSpin plus® (Eppendorf, model: MiniSpin plus® )
  7. Eppendorf centrifuge Falcon (Eppendorf, model: 5810R )
  8. MyCyclerTM Thermal Cycler System (Bio-Rad Laboratories, catalog number: 1709703 )
  9. Sonicator ultrasonic Processor XL (QSonica, model: XL-2020 )
  10. Quibit® 2.0 Fluorometer (Thermo Fisher Scientific, model: Quibit® 2.0 )
  11. CFX Connect Real-Time PCR Detection System (Bio-Rad Laboratories, catalog number: 1855201 )
  12. HiSeqTM 2000 Sequencing System (Illumina)
  13. Personal computer with a minimum of 2 GB of RAM and 2 GHz dual-core processor, a minimum of 25-50 GB of hard-drive space

Software

  1. FastQC (v0.11.5)
    (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  2. Bowtie2 (v2.3.4.1)
    (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml [Langmead and Salzberg, 2012])
  3. Samtools (v1.7)
    (http://samtools.sourceforge.net [Li et al., 2009])
  4. DeepTools (v2.0)
    (http://deeptools.readthedocs.io/en/latest/content/changelog.html [Ramírez et al., 2016]) 
  5. Model-based Analysis of ChIP-Seq (MACS) (v1.4.1)
    (http://liµlµLab.dfci.harvard.edu/MACS/ [Zhang et al., 2008])
  6. BayesPeak (v1.22.0)
    (http://bioconductor.org/packages/release/bioc/html/BayesPeak.html [Spyrou et al., 2009])
  7. Integrative Genomics Viewer (IGV) (v2.3)
    (http://software.broadinstitute.org/software/igv/ [Robinson et al., 2011])
  8. ChIPseeker (v1.6.7)
    (https://bioconductor.org/packages/release/bioc/html/ChIPseeker.html [Yu et al., 2015])

Procedure

  1. Preparation of whole-cell extracts for ChIP analysis (Figure 1)


    Figure 1. Flowchart showing steps in the ChIP experiment. Transcriptional factor (TF), Antibody (AB).

    1. Start a 50-ml preculture of Synechocystis cells at 0.5 µg Chl/ml from a fresh plate (less than 2 weeks old) in liquid BG110C-NH4 medium at 30 °C under constant illumination (45 µmol photons/m2 sec) on a rotatory shaker.
    2. Using the preculture (2-3 µg Chl/ml) inoculate a 2 L flask with 500 ml of BG110C-NH4 and continue its incubation under the same conditions until cells reach a chlorophyll concentration of 3-4 µg/ml.
    3. Split the culture between two autoclaved centrifuge bottles, each containing 250 ml of the original culture for ammonium (NH4+) and nitrogen depletion (-N) treatments. Spin down the cells at 5,000 x g at room temperature for 5 min and discard the supernatants. Wash the pellets twice with 250 ml of BG110C-NH4+ for NH4+, and BG110C for -N treatments. Resuspend the pellets in 250 ml of the corresponding media and transfer the cultures to two 1 L flasks. The cultures were grown as above for 4 h.
    4. Add 6.75 ml of 37% formaldehyde to both cultures (NH4+ and -N) to reach a final concentration of 1% formaldehyde (for cross-linking). Incubate for 15 min at room temperature with occasional gentle shaking.
    5. Stop the cross-linking reaction by adding 12.5 ml of 2.5 M glycine to obtain a final concentration of 125 mM and incubate at room temperature for 5 min with occasional gentle shaking.
    6. Pass the cultures to two autoclaved centrifuge bottles and spin down the cells at 4 °C for 5 min at 5,000 x g. Discard the supernatant (containing formaldehyde) into a suitable waste container. Wash the pellet twice with 10 ml of cold TBS buffer.
    7. Spin down samples at 4 °C for 5 min at 5,000 x g and discard the supernatant. For each centrifuge bottle, resuspend the cell pellets in 2 ml of cold TBS buffer and distribute the suspension into two screw-cap tubes of 2 ml. Finally, spin down samples again and remove the remaining supernatant with a micropipette. You should have two screw-cap tubes for each treatment (NH4+ and -N)
    8. Optional: cell pellets can be snap-frozen in liquid nitrogen at this point and stored at -80 °C.

  2. Cell Lysis
    Note: If tubes with cross-linked cells were stored at -80 °C, it is important to thaw the cell pellets on ice before continuing.
    1. Put the tubes containing cell pellets on ice and resuspend the cells in 500 µl of Lysis buffer (pre-cooled at 4 °C).
    2. Add 0.5 g of acid-washed glass beads and break cells using 10 bead-beating cycles of 1 min in a FastPrep-24, with 1 min on ice between cycles.
    3. Spin the tubes at 4,000 x g for 2 min and, carefully collect 90% of the supernatant (lysate) using a micropipette. Mix all collected lysate (approx. 2.7 ml) and divide it into 2 tubes of 2 ml (approx. 1.35 ml per tube).
      Note: To avoid contamination of the samples with unbroken cells and glass beads, leave behind 10% of the supernatant.
    4. Sonicate the lysate, 15 cycles (10 sec at 10% amplitude, with 40 sec on ice between cycles) to fragment chromosomal DNA into sequences of sizes between 200 and 400 bp.
      Note: This step is critical to retrieve good quality DNA fragments. Duration of sonication and signal amplitude must be adjusted for each apparatus to avoid low or excessive DNA shearing. To optimize this step, we recommend replicating our settings to shear freshly extracted genomic DNA (to avoid wasting valuable sample). Then, load part of the sheared DNA (5-20 µl) on an agarose gel and check whether the DNA fragments are concentrated around 400 bp. Adjust the number of cycles and intensity to compensate for excessive or insufficient shearing. If fragments are mainly > 400 bp, then incrementally increase either amplitude or cycle number, while if they are around < 150 bp, then reduce the number of cycles used.
    5. Centrifuge the sonicated samples at 10,000 x g at 4 °C for 15 min to eliminate cell debris and transfer the supernatant to a clean 1.5 ml microtube.
    6. To check the length distribution of DNA fragments after shearing, load 20 µl of your sonicated samples in a 1% agarose gel. Your sheared DNA must be concentrated around 200-400 bp.
    7. Collect 10-20 µl to measure protein concentration of the whole-cell extract, using the Bradford protein assay.
    8. Whole-cell extracts can be either stored at -20 °C or immediately used for immunoprecipitation.

  3. Prepare magnetic beads for chromatin immunoprecipitation
    1. Prepare 20 µl of Protein G magnetic beads per reaction in 1.5 ml tubes (minimum of 2 tubes: one with beads for the immunoprecipitated (IP) sample, and another with beads for the first wash step).
    2. Add 480 µl of Blocking buffer to each tube to reach a final volume of 500 µl.
    3. Wash the beads with 500 µl of blocking solution (always use fresh solution) by centrifugation at 1,500 x g for 1 min and discard the supernatant. Repeat the wash twice. Resuspend the beads in 20 µl of Lysis buffer.

  4. Chromatin immunoprecipitation and reversion of cross-linking
    1. Prepare 500 µl of whole-cell extract with a concentration of 4 mg/ml of total protein in Lysis buffer. Transfer 50 µl of the supernatant to a 1.5 ml tube and store at -20 °C. This is the 10% total Input DNA (Figure 1) sample for each ChIP sample.
      Note: Input DNA sample control contains cross-linked and sonicated DNA that will not be immunoprecipitated. Input DNA is a very important control in ChIP-seq experiments because it will be used to normalize the signal from ChIP enrichment. It also helps to control for biases in the experimental method by comparing read count enrichment between ChIP and input samples.
    2. Pre-treat cell extracts with 20 µl of magnetic beads washed to reduce unspecific binding of DNA or proteins to magnetic beads. Incubate for 1 h at 4 °C with rotation.
    3. Collect the beads with the DynaMagTM magnetic stand and pass the supernatant (500 µl) to a clean 1.5 ml tube.
    4. Add 2-5 µg of antibody to IP samples (depending on the antibody; for commercial antibodies refer to the manufacture’s ChIP-seq protocols).
    5. Incubate IP samples at 4 °C with rotation overnight (at least 16 h).
    6. Add 20 µl of pre-washed magnetic beads to each IP sample and incubate for 2 h at 4 °C with rotation.
    7. After incubation, discard the supernatant carefully using the DynaMagTM magnetic stand and wash the magnetic beads twice with 1.5 ml of lysis buffer with 5 min rotation at room temperature.
    8. Repeat washing step using Wash buffer 1, Wash buffer 2, and TE buffer.
    9. Resuspend the magnetic beads in 100 µl of TE buffer containing 20 µg of DNAse-free RNase A, incubate at 37 °C for 30 min. Wash the beads with 1.5 ml of TE buffer.
    10. To elute the immunoprecipitated material, resuspend the magnetic beads in 100 µl of Elution buffer and incubate at 65 °C for 30 min with occasional vortex rotation (gently, under medium speed).
    11. Repeat the elution step and combine the two eluates.
    12. Thaw the input sample on ice. Add 20 µl of 5x elution buffer plus 30 µl of MilliQ water to reach the same buffer concentration as the eluted sample.
    13. To reverse the cross-linking, incubate the ChIP samples (Antibody-IP and Input) at 65 °C for 5 h.

  5. DNA purification
    1. Add 100 µl of MilliQ water to the input sample (to reach 200 µl of volume, as for the IP samples). Add 2 µl of proteinase K to a final concentration of 0.4 µg/µl to all ChIP samples and incubate at 37 °C for 1.5 h.
    2. Extract DNA with 200 µl phenol:chloroform:isoamyl alcohol (25:24:1) by vortexing for 1 min and centrifuging at 10,000 x g for 10 min at 4 °C. Transfer the upper phase from each extraction to a clean 1.5 ml tube.
    3. Repeat extractions twice with 200 µl chloroform:isoamyl alcohol (24:1).
    4. Add 53 µl of 7.5 M NH4AcO and 2 volumes (500 µl) of freezer-cold ethanol. Incubate for at least 2 h at -20 °C (best results are achieved, when stored overnight).
    5. Centrifuge at 10,000 x g for 30 min at 4 °C.
    6. Remove the supernatant and wash twice with 1,000 µl of freezer-cold 70% ethanol.
    7. Air-dry the samples and resuspend the pellet in a convenient volume of nuclease-free MilliQ water (25-50 µl).
    8. Measure the quantity and quality of the ChIP DNA samples using the Quibit® 2.0 and the Quibit dsDNA assay kit following the instructions provided by the manufacturer.
    9. To check whether enrichment of the potential or well-known TF binding regions was achieved during the immunoprecipitation, DNA of specific genomic regions can be amplified by quantitative Real-Time PCR (qRT-PCR). To carry out this assessment, add 2.5 pg of IP and Input DNA samples to a 0.5 ml tube per genomic region (locus to study) and perform qRT-PCR using a CFX connect RT-PCR machine and ssoFast EvaGreen Supermix kit.
      Note: This step is optional. If information about well-known targets of the TF analyzed is available, then we encourage researchers to analyze the IP DNA by qRT-PCR prior to library construction. In our study, two well-known NtcA binding promoters (glnA and glnB) were analyzed (Giner-Lamia et al., 2017).
    10. Use a minimum of 10 ng of IP and Input DNA samples for library preparation, using the Illumina TruSeq ChIP-seq DNA sample preparation kit v.2, as recommended in the kit manual.
      Note: If the yield of IP DNA recovered was low, then the resulting IP DNA samples from different experiments can be pooled using a DNA purification column (miniElute kit, QIAGEN) to obtain > 10 ng IP DNA samples.

Data analysis

In this section, we provide an example of ChIP-seq analysis prepared as a tutorial, using a subset of the NtcA original data. The files contain only 1% of total reads obtained from NtcA ChIP-seq experiments (Giner-Lamia et al., 2017) to ensure faster computational time. All material necessary for this tutorial can be found on the GitHub website (https://github.com/ginerorama/NtcA_bio-protocols_tutorial). Although only the command lines for nitrogen depletion ChIP-seq files are described in this tutorial, the intermediate files for both nitrogen depletion (-N) and nitrogen replete (NH4+) conditions are available on the GitHub tutorial page. A flowchart outlining bioinformatic ChIP-seq analysis, as described in this tutorial, is given in Figure 2.



Figure 2. Flowchart of the ChIP-seq bioinformatic analysis pipeline

Note: In this tutorial, we assume familiarity with basic shell commands required to work with a terminal interface.

  1. Quality control analysis of the sequencing reads using FastQC. Before analyzing your sequences, you should always carry out quality control of the raw sequence data to identify potential artifacts. The FastQC (Figure 3) software contains different analysis modules including: (i) Per base sequencing quality (the higher the score the better the base call; in any case the lower quartile for any base should be higher than 10); (ii) Per base sequence content (this should show a non-random distribution of the nucleotide at each base; differences between A and T, or G and C should not be greater than 10% for any position); and (iii) Duplicate sequences (non-unique sequences should not constitute more than 20% of the total sequences). More information on FastQC modules is available at https://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/.
    Note: Although FastQC can be run using command line execution, it also has a graphical user interface that facilitates analysis for researchers not familiar with command line programs.


    Figure 3. Quality control analysis using FastQC. The modular set of analyses carried out by FastQC in both N_input.fastq (A) and N_ChIP.fastq (B) files are marked in green, indicating that sequencing data are correct.

  2. Alignment of the reads to the genome. The reference genome for Synechocystis sp. PCC 6803 can be downloaded from the National Center for Biotechnology Information (NCBI) Genomes Database, available at https://www.ncbi.nlm.nih.gov/assembly. It has GenBank assembly accession number: GCA_000009725.1; RefSeq: NC_009911.1. In our case, the genome file is NC_009911.1.fasta. This genome file is also available on the GitHub tutorial page. For each sample, we map the FastQ files containing the sequence reads to the reference genome using the bowtie2 program. To do this, we need to create an index of our reference genome using the bowtie2-build function of Bowtie2; bowtie2-build outputs a set of six files with the suffixes (.1.bt2, .2.bt2, .3.bt2, .4.bt2, .rev.1.bt2, and .rev.2.bt2). These files constitute the index. The original genome sequence Fasta file is no longer used by Bowtie2, once this index is built. Now, we can run Bowtie2 using the default parameters. The output file from Bowtie2 is in a Sequence Alignment Map format (.SAM) and contains all the alignment information. The generic command lines for index generation and mapping in bowtie2 are as follows:

    Genome index generation:
    :$ bowtie2-build path_to_genome_reference genome_index

    Arguments:
    path_to_genome_reference: the system path to the file containing the reference genome downloaded from NCBI in fasta format.
    Genome_index: The basename of the index files. (Name.1.bt2, Name.2.bt2, Name.3.bt2, Name.4.bt2, Name.rev.1.bt2, and Name.rev.2.bt2)

    Thus, the index for Synechocystis genome is generated by the command:

    :$ bowtie2-build NC_009911.1.fasta Synechocystis

    Genome alignment:
    :$ Bowtie2 -x basename_genome_index -U sequence_reads.fastQ
    -S alignment_file.sam

    Arguments:
    -x basename_genome_index: The basename of the index for the reference genome. In this case, the basename is the name of any of the index files, not including the final 1.bt2 /. or rev.1.bt2 /.
    -U sequence_reads.fastQ: File containing the unpaired reads to be aligned.
    -S alignment_file.sam: File to write and save SAM alignments to.

    In our example, we would use the following command line to align two ChIP-samples using bowtie2:

    :$ Bowtie2 -x Synechocystis -U N_ChIP.fq -S N_ChIP.sam
    :$ Bowtie2 -x Synechocystis -U N_Input.fq -S N_Input.sam

  3. SAM to BAM. To analyze our alignment reads, we need to transform the format of the SAM file obtained from Bowtie2 to work more efficiently with the aligned reads. SAM format files are very large files and have to be converted into a Binary Alignment Map (.BAM) format. A BAM file is a binary encoded version of the SAM file that contains the same information, but is typically of smaller size. It is accepted by most programs to analyze the alignment data, once it has been sorted and indexed.
    To convert the SAM format into BAM format, we use Samtools.
    The generic command lines to transform a SAM file into a sorted BAM file in Samtools are:

    :$ samtools view -bS alignment_file.sam > alignment_file.bam
    :$ samtools sort alignment_file.bam > alignment_file_sorted
    :$ samtools index alignment_file_sorted.bam

    Arguments:
    - alignment_file.sam: name of the alignment SAM file generated by bowtie2.
    - alignment_file.bam: name of the BAM file generated.
    - alignment_file_sorted: name of the final sorted BAM file generated.

    Thus, the command lines to convert both N_ChIP.sam and N_Input.sam into N_ChIP_sorted.bam and N_Input_sorted.bam, respectively, are:

    SAM to BAM conversion:
    :$ samtools view -bS N_Input.sam > N_Input.bam
    :$ samtools view -bS N_ChIP.sam > N_ChIP.bam

    Sorting BAM files:

    :$ samtools sort N_Input.bam > N_Input_sorted.bam
    :$ samtools sort N_ChIP.bam > N_ChIP_sorted.bam

    Finally, the index files are generated using samtools index:

    :$ samtools index N_Input_sorted.bam
    :$ samtools index N_ChIP_sorted.bam

    Note: Samtools index will generate .bai files that must be placed in the same folder as the sorted bam files. Otherwise, programs like Bamcoverage or IGV will not load the sorted bam files.

  4. BAM file normalization. BAM files are still large files and inspection of these files using a genome browser like IGV demands high memory usage on a personal computer. To solve this problem, we used the Bamcoverage utility from the Deeptools2 (v2.0) suite. This tool takes an alignment of reads or fragments as input (BAM file) and generates a coverage track (bigWig or bedGraph) as output. The bigWig files are smaller than BAM files, facilitating the simultaneous loading of multiple ChIP-seq tracks in IGV (Figure 4). In addition, Bamcoverage normalizes all the ChIP-seq files (using different methods, i.e., Reads Per Kilobase per Million mapped reads; RPKM) necessary to compare the enriched peaks from samples with different sequencing depths (i.e., different numbers of reads). The bigWig normalized files generated by Bamcoverage can be loaded into IGV to inspect and analyze the NtcA binding peaks (Figure 4). IGV requires to load the genomes in a special format file. For this tutorial, the Synechocystis genomes files in IGV format (pcc6803.genome.fasta and pcc6803.genome.fasta.fai) are available on the GitHub website. Please refer to the user guide on the IGV webpage to know how create a genome file for IGV.


    Figure 4. NtcA ChIP-seq data visualization generated using Integrated Genomics Viewer. The two IP samples (NH4+ and -N) and their respective input samples are represented by four separate tracks. The y-axis of each track represents the normalized coverage of sequenced DNA fragments. A. Complete genome coverage of the NtcA ChIP-seq. B. Zoomed chromosomal regions around an NtcA peak located within the glnA promoter region for -N treatment, which is absent for NH4+ treatment, and in both input samples.

    An example of Bamcoverage usage:

    :$ bamCoverage -b alignment_file.bam -o coverage_file.bw 
    -normalizeUsing

    -b alignment_file.bam: BAM file to process (sorted)
    -o coverage_file.bwouput file in bigWig format.
    -normalizeUsing: It is possible to normalize the number of reads per bin using four different methods: CPM = Counts Per Million mapper reads, BPM = Bin Per Million mapped reads, RPGC = reads per genomic content, and RPKM.

    The command line to normalize our ChIP-seq data using the RPKM method is given by:

    :$ bamCoverage -b N_Input_sorted.bam -o N_Input.bw
    -normalizeUsingRPKM
    :$ bamCoverage -b N_ChIP_sorted.bam -o N_ChIP.bw
    -normalizeUsingRPKM

  5. Peak calling. Peak calling is carried out using two programs, MACS and the Bioconductor R package BayesPeak. Both programs work without sequence files for Input DNA, using regional counts from IP as a background. When Input DNA sample is available, they compare IP with input sample to identify enrichment. This procedure leads to better sensitivity and specificity than using IP sample alone. For both programs, the previously generated BAM files from IP and Input libraries are used as input files. MACS is a very popular peak finder that can be run using a command line interface in Linux or on a MAC computer. Here, we show a standard analysis with MACS, using a command line. To use the BayesPeak package, please refer to user information available on the Bioconductor webpage (https://bioconductor.org/packages/release/bioc/html/BayesPeak.html).

    An example of peak calling using MACS:

    :$ macs14 –t ChIP_alignment_file.bam -c Input_alignment_file.bam
    -g genome_size -n outputfile_name --bw –-nomodel --shiftsize

    Arguments:
    –t ChIP_alignment_file.bam: ChIP-seq treatment BAM file
    -c Input_alignment_file.bam: The control or input BAM file
    -g genome_size: genome size of your sequenced organism
    -n outputfile_name: The name of any of the MACS files generated during the analysis
    --bw: band width used to scan the genome for model building. This parameter can be set to the sonication fragment size expected (see Step B4)
    --nomodel: This setting is optional. It skips the model building step. This is recommended when applying MACS to ChIP-seq data with broad peaks.
    --shiftsize: The shift size in bp.

    The command line to analyze our ChIP-seq data with MACS is given by:

    :$ macs14 –t N_ChIP_sorted.bam -c N_Input_sorted.bam
    -g 3.5e6 -n NtcA_N --bw 200 –-nomodel –-shiftsize 50

    MACS will generate four files, including the NtcA_N_peaks.xls file. This file comprises a table in Excel format containing information about the detected peaks, including chromosome name, start position of peak, end position of peak, length of peak region, peak summit position related to the start position of peak region, number of tags in peak region, -10 x log10 (P-value) for the peak region, fold enrichment for this region against a random Poisson distribution with local lambda, and the false discovery rate (FDR) as a percentage. In our case, a total of 95 binding peaks were detected. The other three files generated by MACS are: NtcA_N_peaks.bed (BED format file containing the peak locations), NtcA_N_summits.bed (BED format file containing the summit locations for called peaks), and NtcA_N_negative_peaks.xls (a tabular file containing information about negative peaks). Negative peaks are called by swapping the ChIP-seq and control channel. In our case study, zero negative peaks were called. A detailed explanation of all setting options available for MACS can be found at https://github.com/taoliu/MACS/blob/macs_v1/README.rst).
  6. Peak annotation. To retrieve the nearest genes around the binding peaks obtained with MACS and to annotate the genomic region of each peak, we used the Bioconductor R package ChIPseeker. It supports annotation of ChIP peaks and provides tools to visualize ChIP peak coverage as well as profiles of peaks binding to transcriptional start site (TSS) regions. To use ChIPseeker, it is necessary to install the Bioconductor package GenomicFeatures, which uses TxDb objects to store transcript metadata. These objects include the maps of 5’ and 3’ untranslated regions (UTRs) and protein coding sequences (CDS) for a set of mRNA or DNA sequences associated with the genome. Here, we will create a TxDb object, based on Synechocystis GTF (General Feature Format, which consists of one line per feature, each containing nine columns of data, plus optional track definition lines). This genomic feature file is available on the GitHub tutorial page.

    The following commands are executed in R to create a genomic feature file using GenomicFeatures:

    #Install GenomicFeatures and ChIPseeker
    source("https://bioconductor.org/biocLite.R")
    biocLite("GenomicFeatures")
    biocLite("ChIPseeker")

    #Creating a TxDb using makeTranscriptDbFromGFF function from GenomicFeatures
    library(GenomicFeatures)
    setwd(path to tutorial files in your computer)
    txdb <- makeTxDbFromGFF(‘NC_000911.1.gff’, format=’gff’)
    genes <- genes(txdb)

    Now, we can annotate the peaks using the annotatePeak function in ChIPseeker. We will use the BED file generated by MACS in the peak calling analysis (see above). The function annotatePeak requires a peak-containing object (peaks in bed format), a TSS range region (in our case: −300 bp and +300 bp from the TSS) and the Synechocystis TxDb object created above. The command lines to annotate the peaks are given by:

    #peak annotation using ChIPseeker.
    library(ChIPseeker)
    peakfile = ‘NtcA_N_peaks.bed’ #bed file generated by MACS and located in the same directory as the R working directory
    peakAnno <- annotatePeak(peakfile, tssRegion=c(-300,300), TxDb=txdb)
    write.table(peakAnno,file = ‘N_annotated_peaks.txt’, sep=’\t’)

    The output file from ChIPseeker contains the position, the strand, and the distance from peak to the TSS of the nearest genes. The genomic region of the peaks is also reported in the annotation column (Promoter, 5’ UTR, 3’ UTR, exon, intron, downstream, intergenic).

Notes

All software mentioned in this article can be readily installed and run on computers with Linux (Ubuntu version 14 or higher) or Mac OS (Mac OSX 10.6 or higher). They require a minimum of 2 GB of RAM and 2 GHz dual-core processor for genomes of the size of Synechocystis (i.e., 3.6 Mb). To store all files generated during the analysis, depending of the number of experimental samples, a minimum of 25-50 GB of hard-drive space is essential. However, to perform the tutorial described in our data analysis section, only 1 GB of hard-drive space is required. Most of the software can also be executed within Windows, but require a lengthier installation process. Alternatively, some of the bioinformatic tools listed above are offered by online platforms, such as Galaxy (https://usegalaxy.org/).

Recipes

  1. 5x TBS buffer (for 1 L)
    100 ml of 1 M Tris-HCl (pH 7.5)
    150 ml of 5 M NaCl
    ddH2O to 1 L
    Filter sterile
    Store at 4 °C
  2. 1x Lysis buffer (for 100 ml)
    10 ml 0.5 M HEPES/KOH, (pH 7.5) (50 mM final)
    28 ml 5 M NaCl (140 mM final)
    200 µl 0.5 M EDTA (1mM final)
    5 ml 20% Triton X-100 (1% final)
    2 ml 5% sodium deoxycholate (0.1% final)
    EDTA-free protease inhibitor cocktail
    MilliQ H2O to 100 ml
    Filter sterilize
    Store at 4 °C
  3. Block solution (20 ml)
    20 ml 1x Phosphate-buffered saline (PBS)
    0.1 g Bovine serum albumin (BSA)
    Always use fresh solution
  4. 1x Wash buffer 1 (for 100 ml)
    10 ml 0.5 M HEPES/KOH, (pH 7.5) (50 mM final)
    10 ml 5 M NaCl (500 mM final)
    200 µl 0.5 M EDTA (1mM final)
    5 ml 20% Triton X-100 (1% final)
    2 ml 5% sodium deoxycholate (0.1% final)
    EDTA-free protease inhibitor cocktail
    MilliQ H2O to 100 ml
    Filter sterilize
    Store at 4 °C
  5. 1x Wash buffer 2 (for 100 ml)
    2.5 ml 1 M Tris-HCl, (pH 8) (10mM final)
    2.5 ml 10 M LiCl (250 mM final)
    5 ml 10% NP-40 (0.5% final)
    10 ml 5% sodium deoxycholate (0.5% final)
    MilliQ H2O to 100 ml
    Filter sterilize
    Store at 4 °C
  6. 5x IP elution solution (2 ml)
    500 µl 1 M Tris-HCl (pH 7.5) (250 mM final)
    200 µl 0.5 M EDTA (50 mM final)
    1 ml 10% SDS (5% final)
    MilliQ H2O to 2 ml
  7. TE + NaCl Solution (25 ml)
    250 µl 1 M Tris-HCl (pH 7.5) (10 mM final)
    50 µl 0.5 M EDTA (1 mM final)
    2.5 ml 5 M NaCl (50 mM final)
  8. Proteinase K solution (1 ml)
    20 mg Proteinase K (20 µg/µl final)
    20 µl 1 M Tris-HCl, pH 7.4 (20 mM final)
    1 µl 1 M CaCl2 (1 M final)
    625 µl 80% glycerol (50 % final)
    Store at -20 °C
    Stable only for 6 months
  9. Trace metal mix A5 (1L)
    2.86 g H3BO3
    0.22 g ZnSO4·7H2O
    1.81 g MnCl2·4H2O
    0.31 g Na2MoO4·2H2O
    0.08 g CuSO4.5H2O
    0.05 g Co(NO3)2.6H2O
  10. 100x BG11 (1 L)
    7.5 g MgSO4·7H2O
    3.6 g CaCl2·2H2O
    0.6 g citric acid
    0.6 g Fe-NH4 citrate
    0.1 g Na2-EDTA
    2.0 g Na2CO3
    100 ml Trace metal mix A5
    ddH2O to 1 L
  11. BG110C (1L)
    1 g NaHCO3
    0.2 ml 1M K2HPO4
    10 ml 100x BG11
    ddH2O to 1 L
    Autoclave before use
  12. BG110C-NH4 (1 L)
    970 ml autoclaved BG110C
    10 ml of pre-filtered 1 M NH4Cl (10 mM final)
    20 ml of pre-filtered 1 M TES pH 7.5 (20 mM final)

Acknowledgments

This protocol was adapted from Picossi et al. (2014). A sincere thank you to Trudi A. Semeniuk for her diligent proofreading of this protocol. This work was supported by grants from National Portuguese Funding through FCT (Fundaçao para a Ciência e a Tecnologia) projects [PTDC/BIA-MIC/4418/2012, IF/00881/2013, UID/BIM/04773/2013–CBMR, UID/Multi/04326/2013–CCMAR]. MAHP current position is funded by the Australian Government through the Australian Research Council Centre of Excellence for Translational Photosynthesis (CE1401000015). JGL current position is funded by the Spanish Ministry of Economy and Economy and European Regional Development Funds(FEDER) BIO2016-77639-P (MINECO/FEDER), respectively. None of the authors have any conflicts of interest or competing interests to declare.

References

  1. García-Domínguez, M., Reyes, J. C. and Florencio, F. J. (2000). NtcA represses transcription of gifA and gifB, genes that encode inhibitors of glutamine synthetase type I from Synechocystis sp. PCC 6803. Mol Microbiol 35(5): 1192-1201.
  2. Giner-Lamia, J., Robles-Rengel, R., Hernandez-Prieto, M. A., Muro-Pastor, M. I., Florencio, F. J. and Futschik, M. E. (2017). Identification of the direct regulon of NtcA during early acclimation to nitrogen starvation in the cyanobacterium Synechocystis sp. PCC 6803. Nucleic Acids Res 45(20): 11800-11820.
  3. Herrero, A., Muro-Pastor, A. M. and Flores, E. (2001). Nitrogen control in cyanobacteria. J Bacteriol 183(2): 411-425.
  4. Langmead, B. and Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nat Methods 9(4): 357-359.
  5. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R. and Genome Project Data Processing, S. (2009). The sequence alignment/map format and SAMtools. Bioinformatics 25(16): 2078-2079.
  6. Picossi, S., Flores, E. and Herrero, A. (2014). ChIP analysis unravels an exceptionally wide distribution of DNA binding sites for the NtcA transcription factor in a heterocyst-forming cyanobacterium. BMC Genomics 15: 22.
  7. Ramírez, F., Ryan, D. P., Gruning, B., Bhardwaj, V., Kilpert, F., Richter, A. S., Heyne, S., Dundar, F. and Manke, T. (2016). deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 44(W1): W160-165.
  8. Robinson, J. T., Thorvaldsdottir, H., Winckler, W., Guttman, M., Lander, E. S., Getz, G. and Mesirov, J. P. (2011). Integrative genomics viewer. Nat Biotechnol 29(1): 24-26.
  9. Spyrou, C., Stark, R., Lynch, A. G. and Tavare, S. (2009). BayesPeak: Bayesian analysis of ChIP-seq data. BMC Bioinformatics 10: 299.
  10. Stanier, R. Y., Kunisawa, R., Mandel, M. and Cohen-Bazire, G. (1971). Purification and properties of unicellular blue-green algae (order Chroococcales). Bacteriol Rev 35(2): 171-205.
  11. Yu, G., Wang, L. G. and He, Q. Y. (2015). ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31(14): 2382-2383.
  12. Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W. and Liu, X. S. (2008). Model-based analysis of ChIP-Seq (MACS). Genome Biol 9(9): R137.

简介

氮是所有生物体的必需营养素。 在蓝细菌中,一组含氧光合细菌通过围绕转录因子NtcA的错综复杂的调节网络维持氮稳态。 尽管控制NtcA活性的机制似乎已被很好地理解,但其控制下的基因集(即它的调节子)仍然没有很好的定义。 在该协议中,我们描述了使用NtcA抗体进行染色质免疫沉淀的过程,随后在蓝藻Synechocystis sp。早期适应氮饥饿期间进行DNA测序分析(ChIP-seq)。 PCC 6803(以下简称<集气囊)。 该协议可以扩展到分析蓝细菌中存在合适抗体的任何DNA结合蛋白。

【背景】为了维持体内平衡,细菌经常需要响应环境变化来调整基因表达。许多这些调整是由转录因子(TF)控制的,这些转录因子可以感知代谢信号并激活或抑制目标基因。然而,反映传统上费力的任务来表征TFs在体内的活性和范围,我们对它们在细菌中的结合位点的了解仍然有限。直到最近,染色质免疫沉淀与高通量测序分析的结合为快速确定基因组水平调节子打开了大门。特别是,ChIP-seq使用下一代测序(NGS)的能力来并行识别大量DNA序列。与微阵列相比,ChIP-seq的一个有吸引力的特征是对某些区域如启动子序列没有限制,并且可以研究整个基因组的TF结合位点。

在蓝细菌中,氮同化和代谢的全球调节剂是NtcA,属于CRP(cAMP受体蛋白)家族的TF(Herrero等人,2001)。在集胞蓝细菌中,NtcA通过将二聚体结合至包含共有序列GTAN 8 TAC的靶基因的启动子或基因内区域而控制对氮可用性的细胞应答(Herrero等, et al。,2001; Giner-Lamia et al。,2017)。在没有铵的情况下,NtcA激活氮同化途径的基因表达,但也作为其他基因的转录抑制子,如gifA和emifB,它们编码谷氨酰胺合成酶失活因子IF7和IF17(García-Domínguez等人,2000)。

本文详述的方案已经针对使用针对NtcA的抗体免疫沉淀来自集胞蓝细胞的DNA进行了优化,随后NGS在早期适应氮缺乏期间鉴定NtcA的特异性结合位点。遵循该协议,我们鉴定了192个与NtcA结合的基因组区域(在铵饱和条件下为51,在氮饥饿4小时后为141)(Giner-Lamia等人,2017)。该协议可以扩展到研究蓝藻中的其他TFs。尽管生物信息学成分适用于任何测序的原核生物,但湿实验室成分需要进行优化以确保高效提取DNA。

关键字:ChIP-seq, 蓝藻, 集胞藻, 氮, NtcA

材料和试剂

  1. 2毫升螺旋盖锥形管(Thermo Fisher Scientific,目录号:3462)
  2. 玻璃珠,酸洗425-600微米(Sigma-Aldrich,目录号:G8772-10G)
  3. 0.5 ml PCR管(Eppendorf,目录号:0030124537)
  4. 1.5毫升试管(Eppendorf,目录号:022363204)
  5. 15和50ml Falcon TM管(Corning,目录号:352070)
  6. DynaMag TM TM-2 Magnet(Thermo Fisher Scientific,目录号:12321D)
  7. Synechocystis sp。在BG11 0C琼脂(Stanier等人,1971)的平板上生长的PCC 6803细胞。
  8. NH 4 Cl(Sigma-Aldrich,目录号:254134)
  9. TES(Sigma-Aldrich,目录号:T1375)
  10. 37%甲醛(Sigma-Aldrich,目录号:F8775)
  11. 甘氨酸(Sigma-Aldrich,目录号:50046)
  12. NaCl(Sigma-Aldrich,目录号:S7653-250G)
  13. EDTA(Sigma-Aldrich,目录号:E9884)
  14. 琼脂糖(NZYTech,目录号:MB02702)
  15. Triton X-100(Sigma-Aldrich,目录号:T8787)
  16. 脱氧胆酸钠(Sigma-Aldrich,目录号:30970)
  17. 蛋白酶抑制剂鸡尾酒片SIGMAFAST(西格玛奥德里奇,目录号:S8820-2TAB)
  18. NP-40(Sigma-Aldrich,目录号:74385)
  19. LiCl(Sigma-Aldrich,目录号:L9650)
  20. 抗NtcA抗体(Giner-Lamia等人,2017)
  21. SDS(Sigma-Aldrich,目录号:L3771)
  22. BSA(Sigma-Aldrich,目录号:B4287)
  23. 无DNA酶的RNA酶A溶液(赛默飞世尔科技,产品目录号:EN0531)
  24. 蛋白酶K(Thermo Fisher Scientific,目录号:25530049)
  25. 苯酚:氯仿:异戊醇(25:24:1)(Sigma-Aldrich,目录号:P2069)
  26. CaCl 2(Sigma-Aldrich,目录号:449709)
  27. 甘油(Sigma-Aldrich,目录号:G5516)
  28. MnCl 2·4H 2 O(Sigma-Aldrich,目录号:221279)
  29. ZnSO 4·7H 2 O(Sigma-Aldrich,目录号:Z1001)
  30. Na 2 MoO 4·2H 2 O(Sigma-Aldrich,目录号:331058)。
  31. CuSO 4(PubChem,目录号:24462)
  32. Co(NO 3)2·6H 2 O(Sigma-Aldrich,目录号:239267)
  33. MgSO 4·7H 2 O(Sigma-Aldrich,目录号:63138)
  34. CaCl 2·2H 2 O(Sigma-Aldrich,目录号:223506)
  35. 柠檬酸(Sigma-Aldrich,目录号:251275)
  36. Na 2 -EDTA(Sigma-Aldrich,目录号:27285)
  37. Na 2 CO 3(Sigma-Aldrich,目录号:S1641)
  38. Fe-NH 4柠檬酸盐(Sigma-Aldrich,目录号:F5879)
  39. 硼酸,H 3 BO 3(Sigma-Aldrich,目录号:B6768)
  40. 100%冷冻冷藏乙醇
  41. MiniElute PCR纯化试剂盒(QIAGEN,目录号:28004)
  42. dsDNA检测试剂盒(Thermo Fisher Scientific,目录号:Q32851)
  43. SsoFast TM EvaGreen超级混合物(Bio-Rad Laboratories,目录号:172-5200)
  44. Pearce TM蛋白G磁珠(Thermo Fisher Scientific,目录号:88847)
  45. Bradford Protein Assay(Bio-Rad Laboratories,目录号:5000001)
  46. 5x Tris缓冲盐水(TBS)缓冲液(见食谱)
  47. 裂解缓冲液(见食谱)
  48. 块解决方案(请参阅食谱)
  49. 清洗缓冲液1(见食谱)
  50. 清洗缓冲液2(见食谱)
  51. 5倍IP解决方案(请参阅食谱)
  52. Tris-EDTA(TE)+ NaCl溶液(见食谱)
  53. 蛋白酶K溶液(见食谱)
  54. 痕量金属混合物A5(见食谱)
  55. 高压灭菌的BG11 0C中等液体(参见食谱)(Stanier等人,1971)
  56. 高压灭菌的BG11 0C + NH 4 4中等液体(参见食谱)(Stanier等人,1971)

设备

  1. 微量移液器(1,000,100,20和10μl)
  2. 2升烧瓶和2×1升烧瓶
  3. 轨道摇床(VWR,型号:3600)
  4. FastPrep-24仪器(MP Biomedicals,产品目录号:116004500)
  5. Eppendorf Thermomixer R混合器,1.5ml块(Eppendorf,型号:ThermoMixer R,目录号:5355)
  6. Eppendorf MiniSpin plus (Eppendorf,型号:MiniSpin plus )
  7. Eppendorf离心机Falcon(Eppendorf,型号:5810R)
  8. MyCycler TM热循环仪系统(Bio-Rad Laboratories,目录号:1709703)
  9. Sonicator超声波处理器XL(QSonica,型号:XL-2020)
  10. Quibit <2.0>荧光计(Thermo Fisher Scientific,型号: Quibit ® 2.0
  11. CFX Connect实时PCR检测系统(Bio-Rad Laboratories,目录号:1855201)
  12. HiSeq TM 2000 Sequencing System(Illumina)
  13. 个人电脑至少具有2 GB的RAM和2 GHz双核处理器,硬盘空间不低于25-50 GB

软件

  1. FastQC(v0.11.5)
    https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ < / a>)
  2. Bowtie2(v2.3.4.1)
    http://bowtie-bio.sourceforge.net/bowtie2/index.shtml [Langmead and Salzberg,2012])
  3. Samtools(v1.7)
    http://samtools.sourceforge.net [Li et。,2009年])
  4. DeepTools(v2.0)
    http://deeptools.readthedocs.io/en/latest/content/changelog。 html [拉米雷斯等人,2016年])&nbsp;
  5. ChIP-Seq(MACS)的基于模型的分析(v1.4.1)
    http://liμlμLab.dfci.harvard.edu/MACS/ [Zhang et al。,2008])
  6. BayesPeak(v1.22.0)
    http://bioconductor.org/packages/release/bioc/html/BayesPeak。 html [Spyrou et。,2009])
  7. 整合基因组查看器(IGV)(v2.3)
    http://software.broadinstitute.org/software/igv/ [Robinson ,2011])
  8. ChIPseeker(v1.6.7)
    https://bioconductor.org/packages/release/bioc/html/ChIPseeker。 html [Yu et。,2015])

程序

  1. 用于ChIP分析的全细胞提取物的制备(图1)


    图1. ChIP实验中的步骤流程图。转录因子(TF),抗体(AB)。

    1. 在新鲜平板(小于2周龄)中在液体BG11 0C-NH4中开始从0.5μgChl / ml开始50ml集胞蓝细胞前期培养物,在恒定光照下(45μmol光子/米2秒),在30℃下,在旋转振荡器上进行4次培养。
    2. 使用前培养物(2-3μgChl / ml)接种具有500ml BG11C-NH4的2L烧瓶并在相同条件下继续孵育直至细胞达到3-4μg/ ml的叶绿素浓度。
    3. 将培养物在两个高压灭菌的离心瓶之间分开,每个瓶含有250ml用于铵(NH 4 +)+和氮耗竭(-N)处理的原始培养物。在室温下将细胞以5,000xg离心5分钟并弃去上清液。对于NH 4 4 + 2,用250ml的BG 1 0 -CO 4 -NH 4 +洗涤沉淀2次,对于-N处理,BG11 <0>和C.在250毫升相应的培养基中重悬沉淀,并将培养物转移到两个1L烧瓶中。
      培养如上培养4小时。
    4. 向两种培养物(NH 4 +和-N)中加入6.75ml的37%甲醛以达到1%甲醛的最终浓度(用于交联)。在室温下孵育15分钟,偶尔轻轻摇动。
    5. 通过加入12.5ml 2.5M甘氨酸终止交联反应以获得125mM的最终浓度,并在室温下孵育5分钟,偶尔轻微摇动。
    6. 将培养物传至两个高压灭菌的离心瓶中,并在4℃下以5,000μg×g的速度离心细胞5分钟。丢弃上清液(含甲醛)到合适的废物容器中。
      用10 ml冷却的TBS缓冲液洗涤沉淀两次
    7. 在5000℃下将样品在4℃下旋转5分钟并弃去上清液。对于每个离心瓶,将细胞沉淀重悬于2ml冷的TBS缓冲液中,并将悬浮液分配到两个2ml螺旋盖管中。最后,再次旋下样品并用微量移液器除去剩余的上清液。












    8. 可选:此时细胞团块可在液氮中快速冷冻并储存在-80°C。

  2. 细胞裂解
    注意:如果交联的细胞管储存在-80°C,在继续之前在冰上融化细胞团很重要。
    1. 将含有细胞沉淀的试管置于冰上,并将细胞重悬于500μl裂解缓冲液(在4℃预冷)。
    2. 加入0.5g酸洗玻璃珠,并在FastPrep-24中使用10次珠磨循环1分钟,在循环之间冰上1分钟。
    3. 将试管以4,000×g g离心2分钟,并使用微量移液管小心收集90%的上清液(裂解液)。将所有收集的溶胞产物(大约2.7ml)混合并将其分成2个2ml管(每管约1.35ml)。
      注意:为避免样品被细胞和玻璃珠污染,留下10%的上清液。
    4. 超声裂解物,15个循环(10%振幅10秒,循环之间冰上40秒)以将染色体DNA片段化成大小在200和400bp之间的序列。
      注意:这一步对于检索高质量的DNA片段至关重要。超声处理和信号幅度的持续时间必须针对每个设备进行调整,以避免低剪切或过量的DNA剪切。为了优化这一步,我们建议复制我们的设置以剪切新提取的基因组DNA(以避免浪费有价值的样本)。然后,将部分剪切的DNA(5-20μl)加载到琼脂糖凝胶上并检查DNA片段是否集中在400bp左右。调整循环次数和强度以补偿过度或不足的剪切。如果片段主要是&gt; 400bp,然后逐渐增加振幅或周期数,而如果它们大约< 150 bp,然后减少使用的循环次数。
    5. 将超声处理的样品在4℃下以10000×g离心15分钟以除去细胞碎片并将上清液转移到干净的1.5ml微管中。
    6. 为了检查剪切后DNA片段的长度分布,将20μl超声处理的样品加入1%琼脂糖凝胶中。您剪切的DNA必须浓缩在200-400 bp左右。
    7. 使用Bradford蛋白质测定法收集10-20μl以测量全细胞提取物的蛋白质浓度。
    8. 全细胞提取物可以储存在-20°C或立即用于免疫沉淀。

  3. 准备用于染色质免疫沉淀的磁珠
    1. 每个反应在1.5 ml试管中准备20μlProtein G磁珠(最少2个试管:一个含免疫沉淀(IP)样品珠,另一个含第一次洗涤步骤珠)。

    2. 每个试管加入480μl封闭缓冲液至终体积为500μl。
    3. 用500μl封闭溶液(通常使用新鲜溶液)通过在1,500gxg离心1分钟来洗涤珠粒并丢弃上清液。重复洗涤两次。在20μl裂解缓冲液中重悬珠。

  4. 染色质免疫沉淀和交联反转
    1. 在裂解缓冲液中制备500μl浓度为4mg / ml总蛋白的全细胞提取物。将50μl上清液转移到1.5ml管中并储存在-20℃。这是每个ChIP样本的10%总输入DNA(图1)样本。
      注意:输入DNA样本对照包含不会被免疫沉淀的交联和超声处理的DNA。输入DNA在ChIP-seq实验中是一个非常重要的控制因素,因为它将用于标准化来自ChIP富集的信号。通过比较芯片和输入样品之间的读数增加,它还有助于控制实验方法中的偏差。
    2. 用20μl磁珠预处理细胞提取物,洗涤以减少DNA或蛋白质与磁珠的非特异性结合。
      在4°C下旋转孵育1小时
    3. 用DynaMag™磁力支架收集珠子,并将上清液(500μl)转移至干净的1.5ml管中。
    4. 向IP样品中加入2-5μg抗体(取决于抗体;商业抗体参考制造商的ChIP-seq方案)。

    5. 在4°C孵育IP样品并旋转过夜(至少16小时)。
    6. 向每个IP样品中加入20μl预洗磁珠,并在4°C下旋转孵育2 h。
    7. 温育后,使用DynaMag TM磁力架小心弃去上清液,并在室温下用1.5ml裂解缓冲液洗涤磁珠两次,旋转5分钟。
    8. 使用清洗缓冲液1,清洗缓冲液2和TE缓冲液重复清洗步骤。
    9. 在含有20μg不含DNAse的RNA酶A的100μlTE缓冲液中重悬磁珠,37°C孵育30 min。用1.5毫升TE缓冲液洗珠。
    10. 为了洗脱免疫沉淀物质,将磁珠重悬于100μl洗脱缓冲液中,并在65℃孵育30分钟,偶尔涡旋(轻轻地,中等速度)。
    11. 重复洗脱步骤并合并两种洗脱液。
    12. 在冰上解冻输入样本。加入20μl5x洗脱缓冲液加30μlMilliQ水以达到与洗脱样品相同的缓冲液浓度。
    13. 为了逆转交联,在65℃孵育ChIP样品(抗体-IP和输入)5小时。

  5. DNA纯化
    1. 向输入样品中加入100μlMilliQ水(达到200μl体积,与IP样品一样)。向所有ChIP样品中加入2μl蛋白酶K至终浓度为0.4μg/μl,并在37°C孵育1.5 h。
    2. 用200μl苯酚:氯仿:异戊醇(25:24:1)通过涡旋1分钟提取DNA并在4℃下以10000×g离心10分钟。将每次提取的上层相转移到干净的1.5毫升管中。
    3. 用200μl氯仿:异戊醇(24:1)重复提取两次。
    4. 加入53μl的7.5M NH 4 4 AcO和2体积(500μl)冷冻 - 冷乙醇。
      在-20°C孵育至少2小时(当过夜储存时可达到最佳效果)。

    5. 在10000×g离心30分钟,4℃。
    6. 取出上清液,用1000μl冷冻冷却的70%乙醇洗涤两次。
    7. 空气干燥样品,并将颗粒悬浮在方便量的不含核酸酶的MilliQ水(25-50μl)中。
    8. 按照制造商提供的说明,使用Quibit 2.0和Quibit dsDNA测定试剂盒测量ChIP DNA样品的数量和质量。
    9. 为了检查在免疫沉淀期间是否实现了潜在的或已知的TF结合区域的富集,可以通过定量实时PCR(qRT-PCR)扩增特定基因组区域的DNA。为了进行这种评估,将2.5μgIP和输入DNA样品加入到每个基因组区域(要研究的基因座)的0.5ml管中,并使用CFX连接RT-PCR仪和ssoFast EvaGreen Supermix试剂盒进行qRT-PCR。 > 注意:这一步是可选的。如果有关已知TF分析目标的信息可用,那么我们鼓励研究人员在构建文库之前通过qRT-PCR分析IP DNA。在我们的研究中,分析了两个众所周知的NtcA结合启动子(glnA和glnB)(Giner-Lamia等,2017)。
    10. 按照试剂盒手册中的建议,使用Illumina TruSeq ChIP-seq DNA样品制备试剂盒v.2,使用最少10 ng的IP和Input DNA样品进行文库制备。
      注:如果回收的IP DNA产量低,则可以使用DNA纯化柱(miniElute试剂盒,QIAGEN)汇集来自不同实验的所得IP DNA样品以获得&gt; 10 ng IP DNA样品。

数据分析

在本节中,我们提供了一个使用NtcA原始数据的子集作为教程准备的ChIP-seq分析示例。这些文件仅包含从NtcA ChIP-seq实验(Giner-Lamia等人,2017)获得的总读数的1%,以确保更快的计算时间。本教程所需的所有材料可在GitHub网站上找到( https://github.com/ginerorama/NtcA_bio -protocols_tutorial )。虽然在本教程中仅描述了氮耗尽ChIP-seq文件的命令行,但氮耗尽(-N)和充氮(NH4 +)的中间文件)条件可在GitHub教程页面上找到。如本教程中所述,概述生物信息学ChIP-seq分析的流程图如图2所示。



图2.ChIP-seq生物信息学分析流程的流程图

注意:在本教程中,我们假设您熟悉使用终端界面所需的基本shell命令。

  1. 使用FastQC对测序读数进行质量控制分析。在分析序列之前,您应始终对原始序列数据进行质量控制,以识别潜在的伪影。 FastQC(图3)软件包含不同的分析模块,包括:(i)每个碱基的测序质量(分数越高,碱基调用越好;在任何情况下,任何碱基的四分位数应低于10)。 (ii)每个碱基序列含量(这应显示每个碱基处核苷酸的非随机分布; A与T之间或G与C之间的差异对于任何位置应不大于10%);和(iii)重复序列(非独特序列不应占总序列的20%以上)。有关FastQC模块的更多信息,请访问 https://www.bioinformatics.babraham。 ac.uk/projects/fastqc/Help/
    注意:虽然FastQC可以使用命令行执行,但它也有一个图形用户界面,便于不熟悉命令行程序的研究人员进行分析。


    图3.使用FastQC的质量控制分析。 由FastQC在N_input.fastq(A)和N_ChIP.fastq(B)文件中执行的模块化分析组标记为绿色,表示测序数据正确。

  2. 读数与基因组的比对。 Synechocystis sp的参考基因组。 PCC 6803可以从国家生物技术信息中心(NCBI)基因组数据库下载,可在 https:/ /www.ncbi.nlm.nih.gov/assembly 。它具有GenBank组装登录号:GCA_000009725.1; RefSeq:NC_009911.1。在我们的例子中,基因组文件是 NC_009911.1.fasta 。这个基因组文件也可以在GitHub教程页面上找到。对于每个样本,我们使用bowtie2程序将包含序列读数的FastQ文件映射到参考基因组。为此,我们需要使用Bowtie2的bowtie2-build功能创建参考基因组的索引; bowtie2-build输出一组包含后缀(.1.bt2,.2.bt2,.3.bt2,.4.bt2,.rev.1.bt2和.rev.2.bt2)的六个文件。这些文件构成索引。一旦建立了这个指数,原始的基因组序列Fasta文件就不再被Bowtie2使用。现在,我们可以使用默认参数运行Bowtie2。 Bowtie2的输出文件采用序列对齐映射格式(.SAM),并包含所有对齐信息。
    在bowtie2中用于索引生成和映射的通用命令行如下所示:

    基因组索引生成:
    :$ bowtie2-build path_to_genome_reference genome_index

    参数:
    path_to_genome_reference: 包含从NCBI以fasta格式下载的参考基因组的文件的系统路径。
    Genome_index: 索引文件的基本名称。 (Name.1.bt2,Name.2.bt2,Name.3.bt2,Name.4.bt2,Name.rev.1.bt2和Name.rev.2.bt2)

    因此,集胞藻基因组的指数由命令生成:

    :$ bowtie2-build NC_009911.1.fasta Synechocystis

    基因组比对:
    :$ Bowtie2 -x basename_genome_index -U sequence_reads.fastQ
    -S alignment_file.sam

    参数:
    - x basename_genome_index: 参考基因组索引的基名。在这种情况下,基本名称是任何索引文件的名称,不包括最终的1.bt2 /。或rev.1.bt2 /。
    - U sequence_reads.fastQ: 包含未配对阅读对象的文件。
    - S alignment_file.sam :用于写入和保存SAM对齐的文件。

    在我们的例子中,我们将使用以下命令行来使用bowtie2对齐两个ChIP样本:

    $ Bowtie2 -x Synechocystis -U N_ChIP.fq -S N_ChIP.sam
    Bo wtie2 -x Synechocystis -U N_Input.fq -S N_Input.sam

  3. SAM到BAM。为了分析我们的对齐读取,我们需要转换从Bowtie2获得的SAM文件的格式,以更高效地使用对齐的读取。 SAM格式文件是非常大的文件,必须转换为二进制对齐映射(.BAM)格式。 BAM文件是包含相同信息的SAM文件的二进制编码版本,但通常较小。它被大多数程序接受,一旦它被排序和索引,就分析对齐数据。
    为了将SAM格式转换成BAM格式,我们使用Samtools。
    在Samtools中将SAM文件转换为分类的BAM文件的通用命令行为:

    :$ samtools view -bS alignment_file.sam&gt; alignment_file.bam
    :$ samtools sort alignment_file.bam&gt; alignment_file_sorted
    :$ samtools index alignment_file_sorted.bam
    参数:
    - alignment_file.sam: 由bowtie2生成的对齐SAM文件的名称。
    - alignment_file.bam: 生成的BAM文件的名称。
    - alignment_file_sorted: 生成的最终排序后的BAM文件的名称。

    因此,命令行将 N_ChIP.sam 和 N_Input.sam 转换为 N_ChIP_sorted.bam 和 N_Input_sorted.bam ,分别是:

    SAM到BAM的转换:
    :$ samtools view -bS N_Input.sam&gt; N_Input.bam
    :$ samtools view -bS N_ChIP.sam&gt; N_ChIP.bam

    排序BAM文件:

    :$ samtools sort N_Input.bam&gt; N_Input_sorted.bam
    :$ samtools sort N_ChIP.bam&gt; N_ChIP_sorted.bam

    最后,索引文件是使用 samtools index 生成的:

    :$ samtools index N_Input_sorted.bam
    :$ samtools index N_ChIP_sorted.bam

    注意:Samtools索引会生成.bai文件,这些文件必须与排序后的bam文件放在同一个文件夹中。否则,像Bamcoverage或IGV这样的程序将不会加载排序后的bam文件。

  4. BAM文件标准化。 BAM文件仍然是大文件,使用IGV等基因组浏览器检查这些文件需要在个人计算机上使用高内存。为了解决这个问题,我们使用了Deeptools2(v2.0)套件中的Bamcoverage实用程序。该工具将读取或片段对齐为输入(BAM文件)并生成覆盖轨道(bigwig或bedGraph)作为输出。 bigwig文件比BAM文件小,便于在IGV中同时加载多个ChIP-seq轨道(图4)。此外,Bamcoverage将所有ChIP-seq文件(使用不同的方法,即,读取Per Kilobase per Million映射读数; RPKM)对所有ChIP-seq文件进行归一化处理,以比较具有不同测序深度>即,读取次数不同)。 Bamcoverage生成的大规格文件可以加载到IGV中检查和分析NtcA结合峰(图4)。 IGV需要以特殊格式文件加载基因组。对于本教程,可以在GitHub网站上找到IGV格式的 Synechocystis 基因组文件(pcc6803.genome.fasta和pcc6803.genome.fasta.fai)。请参阅IGV网页上的用户指南,了解如何为IGV创建基因组文件。


    图4.使用Integrated Genomics Viewer生成的NtcA ChIP-seq数据可视化两个IP样本(NH + 4 + sup和-N)及其它各个输入采样由四个独立的轨道表示。每条轨道的y轴表示测序DNA片段的标准化覆盖率。 A. NtcA ChIP-seq的全基因组覆盖。 B.位于用于-N处理的glnA启动子区域内的NtcA峰周围的变焦染色体区域,其对于NH 4 4 +处理不存在,和两个输入样本。

    一个Bamcoverage用法的例子:

    :$ bamCoverage -b alignment_file.bam -o coverage_file.bw
    -normalizeUsing

    - b alignment_file.bam 文件来处理(排序)
    - o coverage_file.bw 输出文件格式为大写。
    - normalizeUsing 使用四种不同的方法规范每个bin的读取次数:CPM =每百万次mapper读数,BPM =每百万次映射读取次数,RPGC =每个基因组内容的读取次数,以及RPKM。


    使用RPKM方法规范化ChIP-seq数据的命令行如下:

    :$ bamCoverage -b N_Input_sorted.bam -o N_Input.bw
    -normalizeUsingRPKM
    :$ bamCoverage -b N_ChIP_sorted.bam -o N_ChIP.bw
    -normalizeUsingRPKM

  5. 呼叫高峰。呼叫峰值使用两个程序进行,MACS和Bioconductor R软件包BayesPeak。这两个程序都没有输入DNA的序列文件,使用来自IP的区域计数作为背景。当输入DNA样本可用时,他们将IP与输入样本进行比较以确定富集。与单独使用IP样本相比,该程序导致更好的灵敏度和特异性。对于这两个程序,先前从IP和输入库生成的BAM文件都用作输入文件。 MACS是一种非常流行的峰值搜索器,可以在Linux或MAC计算机上使用命令行界面运行。在这里,我们使用命令行显示一个标准的MACS分析。要使用BayesPeak软件包,请参阅Bioconductor网页上提供的用户信息( https ://bioconductor.org/packages/release/bioc/html/BayesPeak.html )。

    一个使用MACS进行呼叫峰值的例子:

    :$ macs14 -t ChIP_alignment_file.bam -c Input_alignment_file.bam
    -g genome_size -n outputfile_name --bw --nomodel --shiftsize

    参数:
    - t ChIP_alignment_file.bam: ChIP-seq处理BAM文件
    - c Input_alignment_file.bam: 控制或输入BAM文件
    - g genome_size: 序列生物体的基因组大小
    - n outputfile_name: 分析过程中生成的任何MACS文件的名称
    - bw: 用于扫描基因组进行模型构建的带宽。该参数可以设置为预期的超声处理片段大小(见步骤B4)
    - nomodel:此设置是可选的。它跳过模型构建步骤。当将MACS应用于具有宽峰的ChIP-seq数据时,建议使用这种方法。
    - shiftingize: 转换大小以bp为单位。

    用MACS分析我们的ChIP-seq数据的命令行如下:

    :$ macs14 -t N_ChIP_sorted.bam -c N_Input_sorted.bam
    -g 3.5e6 -n NtcA_N --bw 200 --nomodel --shiftize 50

    MACS将生成四个文件,包括NtcA_N_peaks.xls文件。该文件包含Excel格式的表格,其包含关于检测到的峰值的信息,包括染色体名称,峰的起始位置,峰的末端位置,峰区的长度,与峰区的起始位置相关的峰顶位置,峰值区域,峰值区域的-10×log 10( P 值),针对具有局部lambda的随机泊松分布折叠富集该区域,以及错误发现率(FDR)百分比。在我们的例子中,共检测到95个结合峰。 MACS生成的其他三个文件为:NtcA_N_peaks.bed(包含峰值位置的BED格式文件),NtcA_N_summits.bed(包含峰峰位置的BED格式文件)和NtcA_N_negative_peaks.xls(包含负值信息的表格文件峰)。通过交换ChIP-seq和控制通道调用负峰。在我们的案例研究中,调用了零负峰值。有关可用于MACS的所有设置选项的详细说明,请参阅 https:// github .com / taoliu / MACS / blob / macs_v1 / README.rst )。
  6. 峰值注释。为了检索MACS获得的结合峰附近最近的基因并注释每个峰的基因组区域,我们使用了Bioconductor R软件包ChIPseeker。它支持ChIP峰的注释,并提供可视化ChIP峰覆盖率的工具以及与转录起始位点(TSS)区域结合的峰分布图。要使用ChIPseeker,必须安装Bioconductor软件包GenomicFeatures,它使用TxDb对象存储转录本元数据。这些对象包括与基因组相关的一组mRNA或DNA序列的5'和3'非翻译区(UTR)和蛋白质编码序列(CDS)的图谱。在这里,我们将基于Synechocystis GTF(通用特征格式,每个特征包含一行,每个包含九列数据以及可选的轨道定义行)创建一个TxDb对象。这个基因组功能文件可以在GitHub教程页面上找到。

    在R中执行以下命令以使用GenomicFeatures创建基因组特征文件:

    #安装GenomicFeatures和ChIPseeker
    source(“https://bioconductor.org/biocLite.R”)
    biocLite(“GenomicFeatures”)
    biocLite(“ChIPseeker”)

    #使用GenomicFeatures中的makeTranscriptDbFromGFF函数创建TxDb
    library(GenomicFeatures)
    setwd(指向计算机中教程文件的路径)
    txdb&lt; - makeTxDbFromGFF('NC_000911.1.gff',format ='gff')
    基因&lt; - 基因(txdb)

    现在,我们可以使用ChIPseeker中的annotatePeak函数对峰进行注释。我们将在峰呼叫分析中使用由MACS生成的BED文件(参见上文)。函数annotatePeak需要一个包含峰的对象(床格式的峰),一个TSS范围区域(在我们的例子中:距离TSS约-300 bp和+300 bp)以及上面创建的Synechocystis TxDb对象。
    注释峰的命令行如下:

    #使用ChIPseeker进行峰值标注。
    图书馆(ChIPseeker)
    peakfile ='NtcA_N_peaks.bed'由MACS生成并位于与R工作目录相同的目录中的#bed文件
    peakAnno&lt; - annotatePeak(peakfile,tssRegion = c(-300,300),TxDb = txdb)
    write.table(peakAnno,file ='N_annotated_peaks.txt',sep ='\ t')
    来自ChIPseeker的输出文件包含位置,链,以及从最近基因的峰到TSS的距离。在注释栏(启动子,5'UTR,3'UTR,外显子,内含子,下游,基因间)中也报道了峰的基因组区域。

笔记

本文中提到的所有软件都可以在装有Linux(Ubuntu 14或更高版本)或Mac OS(Mac OSX 10.6或更高版本)的计算机上轻松安装和运行。它们需要至少2 GB的RAM和2 GHz的双核处理器才能处理大小为3.6×106的基因组(如 )。要存储分析过程中生成的所有文件,根据实验样本的数量,至少需要25-50 GB的硬盘空间。但是,要执行我们的数据分析部分中介绍的教程,只需要1 GB的硬盘空间。大多数软件也可以在Windows中执行,但需要更长的安装过程。或者,上面列出的一些生物信息工具由在线平台提供,例如Galaxy( https://usegalaxy.org/)。

食谱

  1. 5倍TBS缓冲液(1升)
    100毫升1M Tris-HCl(pH 7.5)
    150毫升5M NaCl
    ddH 2 O到1L
    过滤无菌
    在4°C储存
  2. 1x裂解液(100 ml)
    10ml 0.5M HEPES / KOH(pH7.5)(最终50mM)

    28毫升5 M NaCl(终浓度140毫摩尔) 200μl0.5M EDTA(1mM终浓度)
    5毫升20%Triton X-100(1%最终)
    2毫升5%脱氧胆酸钠(终浓度0.1%)
    无EDTA蛋白酶抑制剂鸡尾酒
    MilliQ H 2 O至100毫升
    过滤消毒
    在4°C储存
  3. 块溶液(20毫升)
    20毫升1x磷酸盐缓冲盐水(PBS)
    0.1克牛血清白蛋白(BSA)
    始终使用新的解决方案
  4. 1次清洗缓冲液1(100毫升)
    10ml 0.5M HEPES / KOH(pH7.5)(最终50mM)
    10毫升5M NaCl(终浓度500毫摩尔)
    200μl0.5M EDTA(1mM终浓度)
    5毫升20%Triton X-100(1%最终)
    2毫升5%脱氧胆酸钠(终浓度0.1%)
    无EDTA蛋白酶抑制剂鸡尾酒
    MilliQ H 2 O至100毫升
    过滤消毒
    在4°C储存
  5. 1倍清洗缓冲液2(100毫升)
    2.5ml 1M Tris-HCl(pH 8)(终浓度10mM)

    2.5毫升10毫升LiCl(最终250毫米) 5毫升10%NP-40(最终0.5%)
    10毫升5%脱氧胆酸钠(终浓度0.5%)
    MilliQ H 2 O至100毫升
    过滤消毒
    在4°C储存
  6. 5倍IP洗脱液(2毫升)
    500μl1M Tris-HCl(pH 7.5)(终浓度250 mM)
    200μl0.5M EDTA(最终50mM)
    1毫升10%SDS(最终5%)
    MilliQ H 2 O 2至2ml
  7. TE + NaCl溶液(25毫升)
    250μl1M Tris-HCl(pH7.5)(终浓度10mM)
    50μl0.5M EDTA(终浓度1mM)
    2.5毫升5M NaCl(最终50毫米)
  8. 蛋白酶K溶液(1毫升)
    20 mg蛋白酶K(终浓度20μg/μl)
    20μl1M Tris-HCl,pH7.4(终浓度20mM)
    1μl1M CaCl2(最终1M)
    625μl80%甘油(50%最终)
    在-20°C储存
    只能稳定6个月
  9. 痕量金属混合物A5(1L)
    2.86克H 3 BO 3 3 0.22克ZnSO 4·7H 2 O 1.81克MnCl 2·4H 2 O
    0.31克Na 2 MoO 4·2H 2 O
    0.08克CuSO 4·5H 2 O
    0.05克Co(NO 3)2:0.6H 2 O
  10. 100x BG11(1 L)
    7.5克MgSO 4·7H 2 O
    3.6克CaCl 2·2H 2 O 0 0.6克柠檬酸
    0.6克Fe-NH 4柠檬酸盐
    0.1克Na 2 -EDTA
    2.0克Na 2 CO 3 3/2 100毫升痕量金属混合物A5
    ddH 2 O到1L
  11. BG11 0 C(1L)
    1克NaHCO 3
    0.2ml 1M K 2 HPO 4 4 10毫升100x BG11
    ddH 2 O到1L
    使用前高压灭菌器
  12. BG11 0C-NH4(1L)
    970 ml高压灭菌的BG11 0 C C 10ml预过滤的1M NH 4 Cl(10mM终浓度)
    20 ml预过滤的1M TES pH 7.5(20 mM终浓度)

致谢

该协议改编自Picossi 等人(2014)。衷心感谢Trudi A. Semeniuk对本协议的认真校对。这项工作得到了国家葡萄牙基金通过FCT项目[PTDC / BIA-MIC / 4418/2012,IF / 00881/2013,UID / BIM / 04773/2013-CBMR,UID /多/ 04326/2013-CCMAR]。 MAHP目前的职位由澳大利亚政府通过澳大利亚研究委员会翻译光合作用卓越中心(CE1401000015)资助。 JGL目前的职位分别由西班牙经济和经济部和欧洲区域发展基金(FEDER)BIO2016-77639-P(MINECO / FEDER)资助。没有任何作者有任何利益冲突或竞争利益需要申报。

参考

  1. García-Domínguez,M.,Reyes,J.C。和Florencio,F.J。(2000)。 NtcA抑制gifA和gifB的转录,编码谷氨酰胺合成酶I型抑制剂的基因来自集胞藻 sp。 PCC 6803. Mol Microbiol 35(5):1192-1201。
  2. Giner-Lamia,J.,Robles-Rengel,R.,Hernandez-Prieto,M.A.,Muro-Pastor,M.I.,Florencio,F.J。和Futschik,M.E。(2017)。 在蓝藻集体早期适应氮饥饿期间鉴定NtcA的直接调节因子< sp。 PCC 6803. Nucleic Acids Res 45(20):11800-11820。
  3. Herrero,A.,Muro-Pastor,A.M和Flores,E。(2001)。 蓝藻中的氮素控制。
  4. Langmead,B。和Salzberg,S.L。(2012)。 快速阅读与Bowtie 2对齐。 Nat Methods 9(4):357-359。
  5. Li,H.,Handsaker,B.,Wysoker,A.,Fennell,T.,Ruan,J.,Homer,N.,Marth,G.,Abecasis,G.,Durbin,R.and Genome Project Data Processing, S.(2009)。 序列比对/地图格式和SAMtools。 生物信息学 25(16):2078-2079。
  6. Picossi,S.,Flores,E.和Herrero,A。(2014)。 ChIP分析揭示了形成异形体中NtcA转录因子的DNA结合位点的特别广泛的分布蓝细菌。
  7. Ramírez,F.,Ryan,D. P.,Gruning,B.,Bhardwaj,V.,Kilpert,F.,Richter,A. S.,Heyne,S.,Dundar,F.和Manke,T.(2016)。 deepTools2:深度排序数据分析的下一代网络服务器。 Nucleic Acids Res 44(W1):W160-165。
  8. Robinson,J.T.,Thorvaldsdottir,H.,Winckler,W.,Guttman,M.,Lander,E.S。,Getz,G。和Mesirov,J.P。(2011)。 整合基因组学查看器 Biotechnol 29(1) :24-26。
  9. Spyrou,C.,Stark,R.,Lynch,A.G。和Tavare,S。(2009)。 BayesPeak:ChIP-seq数据的贝叶斯分析 BMC Bioinformatics 10:299.
  10. Stanier,R.Y.,Kunisawa,R.,Mandel,M.和Cohen-Bazire,G.(1971)。 单细胞蓝藻的纯化和性质(订购球菌)。 Bacteriol Rev 35(2):171-205。
  11. Yu,G.,Wang,L.G.and He,Q.Y.(2015)。 ChIPseeker:用于ChIP峰值注释,比较和可视化的R / Bioconductor软件包。 <生物信息学 31(14):2382-2383。
  12. Zhang,Y.,Liu,T.,Meyer,CA,Eeckhoute,J.,Johnson,DS,Bernstein,BE,Nusbaum,C.,Myers,RM,Brown,M.,Li,W.and Liu,XS 2008)。 基于模型的ChIP-Seq(MACS)分析 Genome Biol 9(9):R137。
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Copyright: © 2018 The Authors; exclusive licensee Bio-protocol LLC.
引用:Giner-Lamia, J., Hernández-Prieto, M. A. and Futschik, M. E. (2018). ChIP-seq Experiment and Data Analysis in the Cyanobacterium Synechocystis sp. PCC 6803. Bio-protocol 8(12): e2895. DOI: 10.21769/BioProtoc.2895.
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